From de7c545b1ee7493d6c95a8f736831d6e2d696a12 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Dec 2023 18:28:32 +0100
Subject: [PATCH 001/240] add new "overlay_layer" keypress callback and improve
 "show_layer" callback

---
 docs/api_callbacks.rst  |   1 +
 eomaps/callbacks.py     | 106 +++++++++++++++++++++++++++++++++++++---
 tests/test_callbacks.py |  41 +++++++++++++++-
 3 files changed, 138 insertions(+), 10 deletions(-)

diff --git a/docs/api_callbacks.rst b/docs/api_callbacks.rst
index f3c1f654d..c8a42ced2 100644
--- a/docs/api_callbacks.rst
+++ b/docs/api_callbacks.rst
@@ -199,6 +199,7 @@ Callbacks that can be used with ``m.cb.keypress``
     :nosignatures:
 
     switch_layer
+    overlay_layer
     fetch_layers
 
 
diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 15eb1129c..0ac66b292 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -1232,7 +1232,7 @@ def __init__(self, *args, **kwargs):
 class KeypressCallbacks:
     """Collection of callbacks that are executed if you press a key on the keyboard."""
 
-    _cb_list = ["switch_layer", "fetch_layers"]
+    _cb_list = ["switch_layer", "fetch_layers", "overlay_layer"]
 
     def __init__(self, m, temp_artists):
         self._temporary_artists = temp_artists
@@ -1240,16 +1240,62 @@ def __init__(self, m, temp_artists):
 
     def switch_layer(self, layer, key="x"):
         """
-        Change the default layer of the map.
+        Set the currently visible layer of the map.
 
-        Use the keyboard events to set the default layer (e.g. the visible layer)
-        displayed in the plot.
+        Parameters
+        ----------
+        layer : str or list
+            The layer-name to use (or a list of layer-names to combine).
+
+            For details on how to specify layer-names, see :py:meth:`Maps.show_layer`
+
+        Additional Parameters
+        ---------------------
+        key : str, optional
+            The key to use for triggering the callback.
+            Modifiers are indicated with a "+", e.g. "alt+x".
+            The default is "x".
+
+        Examples
+        --------
+        Show layer A:
+
+        >>> m.cb.keypress.attach.overlay_layer(layer="A", key="x")
+
+        Show layer B with 50% transparency on top of layer A
+
+        >>> m.cb.keypress.attach.overlay_layer(layer="A|B{0.5}", key="x")
+
+        Show layer B on top of layer A:
+
+        >>> m.cb.keypress.attach.overlay_layer(layer=["A", "B"], key="x")
+
+        Show layer B with 50% transparency on top of layer A
+
+        >>> m.cb.keypress.attach.overlay_layer(layer=["A", ("B", 0.5)], key="x")
+
+
+        """
+        if isinstance(layer, (list, tuple)):
+            self._m.show_layer(*layer)
+        elif isinstance(layer, str):
+            self._m.show_layer(layer)
+
+    def overlay_layer(self, layer, key="x"):
+        """
+        Toggle displaying a layer on top of the currently visible layers.
+
+        - If the layer is not part of the currently visible layers, it will be
+          added on top.
+        - If the layer is part of the currently visible layers, it will be removed.
 
         Parameters
         ----------
-        layer : str
-            The layer-name to use.
-            If a non-string value is provided, it will be converted to string!
+        layer : str, tuple or list
+            The layer-name to use, a tuple (layer, transparency) or a list of
+            the aforementioned types to combine.
+
+            For details on how to specify layer-names, see :py:meth:`Maps.show_layer`
 
         Additional Parameters
         ---------------------
@@ -1258,8 +1304,52 @@ def switch_layer(self, layer, key="x"):
             Modifiers are indicated with a "+", e.g. "alt+x".
             The default is "x".
 
+        Note
+        ----
+        If the visible layer changes **while the overlay-layer is active**,
+        triggering the callback again might not properly remove the previous overlay!
+        (e.g. the overlay is only removed if the top-layer corresponds exactly to
+         the overlay-layer specifications)
+
+        Examples
+        --------
+        Toggle overlaying layer A:
+
+        >>> m.cb.keypress.attach.overlay_layer(layer="A", key="x")
+
+        Toggle overlaying layer A with 50% transparency:
+
+        >>> m.cb.keypress.attach.overlay_layer(layer=("A", 0.5), key="x")
+
+        Toggle overlaying a combined layer (showing layer B with 50% transparency
+        on top of layer A)
+
+        >>> m.cb.keypress.attach.overlay_layer(layer="A|B{0.5}", key="x")
+
+        Toggle overlaying a combined layer (showing layer B on top of layer A)
+
+        >>> m.cb.keypress.attach.overlay_layer(layer=["A", "B"], key="x")
+
+        Toggle overlaying a combined layer (showing layer B with 50% transparency
+        on top of layer A)
+
+        >>> m.cb.keypress.attach.overlay_layer(layer=["A", ("B", 0.5)], key="x")
+
         """
-        self._m.show_layer(layer)
+
+        if isinstance(layer, list):
+            layer = self._m._get_combined_layer_name(*layer)
+        elif isinstance(layer, tuple):
+            # e.g. (layer-name, layer-transparency)
+            layer = self._m._get_combined_layer_name(layer)
+
+        # in case the layer is currently on top, remove it
+        if not self._m.BM.bg_layer.endswith(f"|{layer}"):
+            self._m.show_layer(self._m.BM.bg_layer, layer)
+        else:
+            newlayer = self._m.BM.bg_layer.removesuffix(f"|{layer}")
+            if len(newlayer) > 0:
+                self._m.show_layer(newlayer)
 
     def fetch_layers(self, layers=None, verbose=True, key="x"):
         """
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index b98d577ce..12248d16a 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -654,6 +654,43 @@ def loadmethod(db, ID):
                 m.cb.pick.remove(cid)
                 plt.close("all")
 
+    def test_overlay_layer(self):
+        # ---------- test as CLICK callback
+        m = self.create_basic_map()
+        m_a = m.new_layer("A")
+        m_b = m.new_layer("B")
+
+        cid0 = m.all.cb.keypress.attach.overlay_layer(layer="A", key="0")
+        cid1 = m.all.cb.keypress.attach.overlay_layer(layer=("B", 0.5), key="1")
+        cid2 = m.all.cb.keypress.attach.overlay_layer(layer=["A", ("B", 0.5)], key="2")
+
+        init_layer = m.layer
+
+        key_press_event(m.f.canvas, "0")
+        key_release_event(m.f.canvas, "0")
+        self.assertTrue(m.BM._bg_layer == m._get_combined_layer_name(m.layer, "A"))
+        key_press_event(m.f.canvas, "0")
+        key_release_event(m.f.canvas, "0")
+        self.assertTrue(m.BM._bg_layer == m.layer)
+
+        key_press_event(m.f.canvas, "1")
+        key_release_event(m.f.canvas, "1")
+        self.assertTrue(
+            m.BM._bg_layer == m._get_combined_layer_name(m.layer, ("B", 0.5))
+        )
+        key_press_event(m.f.canvas, "1")
+        key_release_event(m.f.canvas, "1")
+        self.assertTrue(m.BM._bg_layer == m.layer)
+
+        key_press_event(m.f.canvas, "2")
+        key_release_event(m.f.canvas, "2")
+        self.assertTrue(
+            m.BM._bg_layer == m._get_combined_layer_name(m.layer, "A", ("B", 0.5))
+        )
+        key_press_event(m.f.canvas, "2")
+        key_release_event(m.f.canvas, "2")
+        self.assertTrue(m.BM._bg_layer == m.layer)
+
     def test_switch_layer(self):
         # ---------- test as CLICK callback
         m = self.create_basic_map()
@@ -666,7 +703,7 @@ def test_switch_layer(self):
         cid1 = m.all.cb.keypress.attach.switch_layer(layer="2", key="2")
 
         # a callback only active on the "base" layer
-        cid3 = m.cb.keypress.attach.switch_layer(layer="3", key="3")
+        cid3 = m.cb.keypress.attach.switch_layer(layer=["2", ("3", 0.5)], key="3")
 
         # switch to layer 2
         key_press_event(m.f.canvas, "2")
@@ -686,7 +723,7 @@ def test_switch_layer(self):
         # now the 3rd callback should trigger
         key_press_event(m.f.canvas, "3")
         key_release_event(m.f.canvas, "3")
-        self.assertTrue(m.BM._bg_layer == "3")
+        self.assertTrue(m.BM._bg_layer == m._get_combined_layer_name("2", ("3", 0.5)))
 
         m.all.cb.keypress.remove(cid0)
         m.all.cb.keypress.remove(cid1)

From 5062bdc18592e511bf566528b533cf62b84329c0 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Dec 2023 19:35:31 +0100
Subject: [PATCH 002/240] fix removesuffix requires python >= 3.9

---
 eomaps/callbacks.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 0ac66b292..95334ace6 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -6,10 +6,18 @@
 from matplotlib.transforms import TransformedPath
 import warnings
 import logging
+import sys
 
 _log = logging.getLogger(__name__)
 
 
+def _removesuffix(s, suffix):
+    if s.endswith(suffix):
+        return s[: -len(suffix)]
+    else:
+        return s[:]
+
+
 class _ClickCallbacks(object):
     """
     A collection of callback-functions.
@@ -1347,7 +1355,11 @@ def overlay_layer(self, layer, key="x"):
         if not self._m.BM.bg_layer.endswith(f"|{layer}"):
             self._m.show_layer(self._m.BM.bg_layer, layer)
         else:
-            newlayer = self._m.BM.bg_layer.removesuffix(f"|{layer}")
+            if sys.version_info >= (3, 9):
+                newlayer = self._m.BM.bg_layer.removesuffix(f"|{layer}")
+            else:
+                newlayer = _removesuffix(self._m.BM.bg_layer, f"|{layer}")
+
             if len(newlayer) > 0:
                 self._m.show_layer(newlayer)
 

From 9e7bb899eee4c47d40e86a0aafa7a71085ab3b6c Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 18:42:22 +0100
Subject: [PATCH 003/240] add nbformat to test-env

---
 tests/test_env.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_env.yml b/tests/test_env.yml
index 09ef72b21..56025a54d 100644
--- a/tests/test_env.yml
+++ b/tests/test_env.yml
@@ -26,6 +26,7 @@ dependencies:
   - xmltodict
   - cairosvg
   # --------------for testing
+  - nbformat     # to parse Jupyter Notebooks
   - coveralls
   - pytest
   - pytest-cov

From 6b867b771c7266a038c1978450576e9e17db162c Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 14:55:59 +0100
Subject: [PATCH 004/240] support multiple Qt versions

---
 docs/contribute_env.yml                       |  2 +
 eomaps/eomaps.py                              | 10 ++--
 eomaps/qtcompanion/app.py                     | 14 ++---
 eomaps/qtcompanion/base.py                    | 24 ++++----
 eomaps/qtcompanion/signal_container.py        | 26 ++++----
 eomaps/qtcompanion/widgets/annotate.py        | 18 +++---
 eomaps/qtcompanion/widgets/click_callbacks.py | 16 ++---
 eomaps/qtcompanion/widgets/draw.py            | 31 +++++-----
 eomaps/qtcompanion/widgets/editor.py          | 59 ++++++++++---------
 eomaps/qtcompanion/widgets/extent.py          |  6 +-
 eomaps/qtcompanion/widgets/files.py           |  6 +-
 eomaps/qtcompanion/widgets/layer.py           | 12 ++--
 eomaps/qtcompanion/widgets/peek.py            | 36 +++++------
 eomaps/qtcompanion/widgets/save.py            | 14 ++---
 eomaps/qtcompanion/widgets/utils.py           | 15 ++---
 eomaps/qtcompanion/widgets/wms.py             | 20 +++----
 setup.py                                      |  1 +
 tests/test_env.yml                            |  2 +
 18 files changed, 162 insertions(+), 150 deletions(-)

diff --git a/docs/contribute_env.yml b/docs/contribute_env.yml
index 4f4ac1cb2..a379885e3 100644
--- a/docs/contribute_env.yml
+++ b/docs/contribute_env.yml
@@ -25,6 +25,8 @@ dependencies:
   - requests
   - xmltodict
   - cairosvg
+  # --------------to support multiple Qt versions
+  - qtpy
   # --------------for testing
   - coveralls
   - pytest
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index d7b9d86cb..d9b3cbedd 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -1177,7 +1177,7 @@ def new_inset_map(
 
     def _get_always_on_top(self):
         if "qt" in plt.get_backend().lower():
-            from PyQt5 import QtCore
+            from qtpy import QtCore
 
             w = self.f.canvas.window()
             return bool(w.windowFlags() & QtCore.Qt.WindowStaysOnTopHint)
@@ -1187,7 +1187,7 @@ def _get_always_on_top(self):
     def _set_always_on_top(self, q):
         # keep pyqt window on top
         try:
-            from PyQt5 import QtCore
+            from qtpy import QtCore
 
             if q:
                 # only do this if necessary to avoid flickering
@@ -4016,9 +4016,9 @@ def _save_to_clipboard(self, **kwargs):
         """
         import io
         import mimetypes
-        from PyQt5.QtCore import QMimeData
-        from PyQt5.QtWidgets import QApplication
-        from PyQt5.QtGui import QImage
+        from qtpy.QtCore import QMimeData
+        from qtpy.QtWidgets import QApplication
+        from qtpy.QtGui import QImage
 
         # guess the MIME type from the provided file-extension
         fmt = kwargs.get("format", "png")
diff --git a/eomaps/qtcompanion/app.py b/eomaps/qtcompanion/app.py
index 1491cc048..8080c7661 100644
--- a/eomaps/qtcompanion/app.py
+++ b/eomaps/qtcompanion/app.py
@@ -1,6 +1,6 @@
-from PyQt5 import QtWidgets
-from PyQt5.QtCore import Qt, pyqtSignal, pyqtSlot, QSize, QObject
-from PyQt5.QtGui import QKeySequence
+from qtpy import QtWidgets
+from qtpy.QtCore import Qt, Signal, Slot, QSize, QObject
+from qtpy.QtGui import QKeySequence
 
 from .base import AlwaysOnTopWindow
 from .widgets.peek import PeekTabs
@@ -128,7 +128,7 @@ def __init__(self, *args, m=None, **kwargs):
             """
         )
 
-    @pyqtSlot()
+    @Slot()
     def tabchanged(self):
 
         if self.currentWidget() == self.tab_compare:
@@ -183,7 +183,7 @@ def __init__(self, *args, m=None, **kwargs):
         # self.resize(int(sh.width() * 1.35), sh.height())
 
         # clear the colormaps-dropdown pixmap cache if the colormaps have changed
-        # (the pyqtSignal is emmited by Maps-objects if a new colormap is registered)
+        # (the Signal is emmited by Maps-objects if a new colormap is registered)
         self.m._connect_signal("cmapsChanged", self.clear_pixmap_cache)
 
     def show(self):
@@ -196,7 +196,7 @@ def show(self):
         self.shortcut.activated.connect(self.toggle_show)
         self.shortcut.activatedAmbiguously.connect(self.toggle_show)
 
-    @pyqtSlot()
+    @Slot()
     def toggle_show(self):
         if self.isVisible():
             self.hide()
@@ -206,6 +206,6 @@ def toggle_show(self):
             self.activateWindow()
             self.m._indicate_companion_map(True)
 
-    @pyqtSlot()
+    @Slot()
     def clear_pixmap_cache(self):
         get_cmap_pixmaps.cache_clear()
diff --git a/eomaps/qtcompanion/base.py b/eomaps/qtcompanion/base.py
index 2f4c0a8a7..6adf924ed 100644
--- a/eomaps/qtcompanion/base.py
+++ b/eomaps/qtcompanion/base.py
@@ -1,8 +1,8 @@
 import logging
 from weakref import WeakSet
 
-from PyQt5 import QtWidgets, QtCore, QtGui
-from PyQt5.QtCore import Qt, pyqtSlot
+from qtpy import QtWidgets, QtCore, QtGui
+from qtpy.QtCore import Qt, Slot
 
 from .common import iconpath
 
@@ -257,7 +257,7 @@ def enterEvent(self, e):
                 "</ul>",
             )
 
-    @pyqtSlot()
+    @Slot()
     def callback(self):
         if not self.m.parent._layout_editor._modifier_pressed:
             self.m.parent.edit_layout()
@@ -362,7 +362,7 @@ def __init__(
 
         self.press_pos = None
 
-    @pyqtSlot()
+    @Slot()
     def toggle_show_help(self):
         if self.b_showhelp.isChecked():
             self.window().showhelp = True
@@ -371,7 +371,7 @@ def toggle_show_help(self):
             self.window().showhelp = False
             # self.b_showhelp.setText("?")
 
-    @pyqtSlot()
+    @Slot()
     def close_button_callback(self):
         self.window().close()
         if self.m is not None:
@@ -380,11 +380,11 @@ def close_button_callback(self):
         if self._on_close is not None:
             self._on_close()
 
-    @pyqtSlot()
+    @Slot()
     def open_file_button_callback(self):
         self.window().tabs.tab_open.openNewFile.emit()
 
-    @pyqtSlot()
+    @Slot()
     def maximize_button_callback(self):
         if not self.window().isMaximized():
             self.window().showMaximized()
@@ -395,7 +395,11 @@ def maximize_button_callback(self):
 
     def mousePressEvent(self, event):
         if event.button() == QtCore.Qt.LeftButton:
-            self.press_pos = event.windowPos().toPoint()
+            try:
+                self.press_pos = event.windowPos().toPoint()
+            except Exception:
+                # for PyQt6 compatibility
+                self.press_pos = event.scenePosition().toPoint()
 
     def mouseReleaseEvent(self, event):
         if event.button() == QtCore.Qt.LeftButton:
@@ -452,7 +456,7 @@ def __init__(self, *args, m=None, title=None, on_close=None, **kwargs):
 
         _windows_to_close.add(self)
 
-    @pyqtSlot()
+    @Slot()
     def on_close(self, e):
         self.close()
 
@@ -483,7 +487,7 @@ def __init__(self, *args, m=None, **kwargs):
 
         self.addToolBar(self.toolbar)
 
-    @pyqtSlot()
+    @Slot()
     def toggle_always_on_top(self, *args, **kwargs):
         q = self.m._get_always_on_top()
 
diff --git a/eomaps/qtcompanion/signal_container.py b/eomaps/qtcompanion/signal_container.py
index cbb305f66..c8cf6131c 100644
--- a/eomaps/qtcompanion/signal_container.py
+++ b/eomaps/qtcompanion/signal_container.py
@@ -1,27 +1,27 @@
 """A container class for signals sent to the CompanionWidget"""
 
 
-from PyQt5.QtCore import QObject, pyqtSignal
+from qtpy.QtCore import QObject, Signal
 
 
 class _SignalContainer(QObject):
-    cmapsChanged = pyqtSignal()
+    cmapsChanged = Signal()
 
-    clipboardKwargsChanged = pyqtSignal()
+    clipboardKwargsChanged = Signal()
 
-    dataPlotted = pyqtSignal()
+    dataPlotted = Signal()
 
     # -------- shape drawer
-    drawFinished = pyqtSignal()
-    drawAborted = pyqtSignal()
-    drawStarted = pyqtSignal(str)
+    drawFinished = Signal()
+    drawAborted = Signal()
+    drawStarted = Signal(str)
 
     # -------- annotation editor
-    annotationEditorActivated = pyqtSignal()
-    annotationEditorDeactivated = pyqtSignal()
-    annotationSelected = pyqtSignal()
-    annotationEdited = pyqtSignal()
+    annotationEditorActivated = Signal()
+    annotationEditorDeactivated = Signal()
+    annotationSelected = Signal()
+    annotationEdited = Signal()
 
     # -------- layout editor
-    layoutEditorActivated = pyqtSignal()
-    layoutEditorDeactivated = pyqtSignal()
+    layoutEditorActivated = Signal()
+    layoutEditorDeactivated = Signal()
diff --git a/eomaps/qtcompanion/widgets/annotate.py b/eomaps/qtcompanion/widgets/annotate.py
index 11d09d617..21b050caf 100644
--- a/eomaps/qtcompanion/widgets/annotate.py
+++ b/eomaps/qtcompanion/widgets/annotate.py
@@ -1,5 +1,5 @@
-from PyQt5 import QtWidgets
-from PyQt5.QtCore import Qt, pyqtSlot, pyqtSignal
+from qtpy import QtWidgets
+from qtpy.QtCore import Qt, Slot, Signal
 from .utils import GetColorWidget
 
 from matplotlib.patches import BoxStyle, ArrowStyle
@@ -148,7 +148,7 @@ def enterEvent(self, e):
 
 
 class AddAnnotationWidget(QtWidgets.QWidget):
-    widgetShown = pyqtSignal()
+    widgetShown = Signal()
 
     def __init__(self, *args, m=None, **kwargs):
         super().__init__(*args, **kwargs)
@@ -291,7 +291,7 @@ def __init__(self, *args, m=None, **kwargs):
         self.m._connect_signal("annotationEditorDeactivated", self.update_buttons)
 
     def eventFilter(self, widget, event):
-        from PyQt5 import QtCore
+        from qtpy import QtCore
 
         if (
             event.type() == QtCore.QEvent.KeyPress
@@ -485,7 +485,7 @@ def enterEvent(self, e):
                 "in the list of background-artists!",
             )
 
-    @pyqtSlot(int)
+    @Slot(int)
     def dial_value_changed(self, i):
         self.annotate_props["rotation"] = int(180 - i)
         self.annotate_props["horizontalalignment"] = "center"
@@ -497,14 +497,14 @@ def dial_value_changed(self, i):
         if self.m._edit_annotations._drag_active:
             self.update_selected_rotation(self.annotate_props["rotation"])
 
-    @pyqtSlot()
+    @Slot()
     def do_add_annotation(self):
         if self._annotation_active is True:
             self.stop()
         else:
             self.add_annotation(text=self.text_inp.toPlainText())
 
-    @pyqtSlot()
+    @Slot()
     def remove_selected_annotation(self):
         ann = self.selected_annotation
         if ann:
@@ -643,7 +643,7 @@ def showEvent(self, event):
     def _annotations_editable(self):
         return self.m._edit_annotations._drag_active
 
-    @pyqtSlot()
+    @Slot()
     def toggle_annotations_editable(self):
         if not self._annotations_editable:
             self.m._edit_annotations(True)
@@ -654,7 +654,7 @@ def toggle_annotations_editable(self):
 
         self.update_buttons()
 
-    @pyqtSlot()
+    @Slot()
     def update_buttons(self):
         if self._annotations_editable:
             self.edit_annotations.setText("Annotations editable!")
diff --git a/eomaps/qtcompanion/widgets/click_callbacks.py b/eomaps/qtcompanion/widgets/click_callbacks.py
index 7e465f20b..7cc354ff6 100644
--- a/eomaps/qtcompanion/widgets/click_callbacks.py
+++ b/eomaps/qtcompanion/widgets/click_callbacks.py
@@ -1,5 +1,5 @@
-from PyQt5 import QtWidgets, QtGui
-from PyQt5.QtCore import Qt, pyqtSignal, pyqtSlot
+from qtpy import QtWidgets, QtGui
+from qtpy.QtCore import Qt, Signal, Slot
 
 
 class AnnotateButton(QtWidgets.QPushButton):
@@ -152,7 +152,7 @@ def enterEvent(self, e):
 
 
 class ClickCallbacks(QtWidgets.QFrame):
-    widgetShown = pyqtSignal()
+    widgetShown = Signal()
 
     def __init__(self, *args, m=None, **kwargs):
         super().__init__(*args, **kwargs)
@@ -322,7 +322,7 @@ def identify_pick_map(self):
 
         return pickm
 
-    @pyqtSlot()
+    @Slot()
     def clear_annotations_and_markers(self):
         # clear all annotations and markers from this axis
         # (irrespective of the visible layer!)
@@ -401,7 +401,7 @@ def on_layer_change(self, *args, **kwargs):
         self.populate_dropdown()
         self.update_buttons()
 
-    @pyqtSlot()
+    @Slot()
     def update_buttons(self):
         if self._pick_map is None or self._pick_map.coll is None:
             self.t_pick.setEnabled(False)
@@ -494,7 +494,7 @@ def attach_callback(self, key):
 
             self.cids[key] = (self.m.all, method(**self._kwargs.get(key, dict())))
 
-    @pyqtSlot()
+    @Slot()
     def radius_changed(self):
         try:
             radius = float(self.radius_inp.text())
@@ -504,7 +504,7 @@ def radius_changed(self):
         self.attach_callback("mark")
         self.update_buttons()
 
-    @pyqtSlot()
+    @Slot()
     def n_points_changed(self):
         try:
             n = int(self.n_points_inp.text())
@@ -516,7 +516,7 @@ def n_points_changed(self):
         self.update_buttons()
 
     def button_clicked(self, key):
-        @pyqtSlot()
+        @Slot()
         def cb():
             if self.cids.get(key, None) is not None:
                 self.remove_callback(key)
diff --git a/eomaps/qtcompanion/widgets/draw.py b/eomaps/qtcompanion/widgets/draw.py
index 11b1fbc0e..586c1d987 100644
--- a/eomaps/qtcompanion/widgets/draw.py
+++ b/eomaps/qtcompanion/widgets/draw.py
@@ -1,7 +1,7 @@
 import logging
 
-from PyQt5 import QtWidgets
-from PyQt5.QtCore import Qt, pyqtSlot, pyqtSignal
+from qtpy import QtWidgets
+from qtpy.QtCore import Qt, Slot, Signal
 
 from .utils import ColorWithSlidersWidget
 
@@ -157,14 +157,14 @@ def enterEvent(self, e):
                 "are removed from the map.",
             )
 
-    @pyqtSlot(int)
+    @Slot(int)
     def tabbar_clicked(self, index):
         if self.tabText(index) == "+":
             w = self._get_new_drawer()
             self.insertTab(self.count() - 1, w, "0")
             self.update_tab_icon(w=w)
 
-    @pyqtSlot(int)
+    @Slot(int)
     def close_handler(self, index):
         curridx = self.currentIndex()
         drawerwidget = self.widget(index)
@@ -202,7 +202,7 @@ def set_layer(self, layer):
             if self.tabText(i) != "+":
                 self.widget(i).set_layer(layer)
 
-    @pyqtSlot()
+    @Slot()
     def update_tab_icon(self, w=None):
         if w is None:
             w = self.sender()
@@ -211,7 +211,7 @@ def update_tab_icon(self, w=None):
 
 class DrawerWidget(QtWidgets.QWidget):
 
-    colorSelected = pyqtSignal()
+    colorSelected = Signal()
 
     _polynames = {
         "Polygon": "polygon",
@@ -356,7 +356,7 @@ def enterEvent(self, e):
             )
 
     def draw_shape_callback(self, poly):
-        @pyqtSlot()
+        @Slot()
         def cb():
             s = self.sender()
             for b in self.polybuttons:
@@ -373,7 +373,7 @@ def cb():
 
         return cb
 
-    @pyqtSlot()
+    @Slot()
     def _new_poly_cb(self):
         # callback executed on creation of a new polygon
         npoly = len(self.drawer._artists)
@@ -394,7 +394,7 @@ def _new_poly_cb(self):
         self.save_button.setText(txt)
         self.save_button.setFixedSize(self.save_button.sizeHint())
 
-    @pyqtSlot()
+    @Slot()
     def save_shapes(self):
         try:
             save_path, widget = QtWidgets.QFileDialog.getSaveFileName(
@@ -412,7 +412,7 @@ def save_shapes(self):
                 exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
             )
 
-    @pyqtSlot()
+    @Slot()
     def remove_last_shape(self):
         try:
             self.drawer.remove_last_shape()
@@ -425,7 +425,7 @@ def remove_last_shape(self):
                 exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
             )
 
-    @pyqtSlot()
+    @Slot()
     def cancel_draw(self):
         self.drawer._finish_drawing()
 
@@ -440,16 +440,17 @@ def _new_drawer(self):
     def set_layer(self, layer):
         self.drawer.set_layer(layer)
 
-    @pyqtSlot()
+    @Slot()
     def get_tab_icon(self):
-        from PyQt5 import QtGui
-        from PyQt5.QtCore import QRectF
+        from qtpy import QtGui
+        from qtpy.QtCore import QRectF
 
         canvas = QtGui.QPixmap(20, 20)
         canvas.fill(Qt.transparent)
 
         painter = QtGui.QPainter(canvas)
-        painter.setRenderHints(QtGui.QPainter.HighQualityAntialiasing)
+        painter.setRenderHint(QtGui.QPainter.Antialiasing, True)
+        painter.setRenderHint(QtGui.QPainter.SmoothPixmapTransform, True)
 
         painter.setBrush(QtGui.QBrush(self.colorselector.facecolor, Qt.SolidPattern))
 
diff --git a/eomaps/qtcompanion/widgets/editor.py b/eomaps/qtcompanion/widgets/editor.py
index b6a441899..5734bce76 100644
--- a/eomaps/qtcompanion/widgets/editor.py
+++ b/eomaps/qtcompanion/widgets/editor.py
@@ -1,9 +1,9 @@
 import logging
 from textwrap import dedent
 
-from PyQt5 import QtCore, QtWidgets, QtGui
-from PyQt5.QtCore import Qt, pyqtSignal, pyqtSlot, QPointF
-from PyQt5.QtGui import QFont
+from qtpy import QtCore, QtWidgets, QtGui
+from qtpy.QtCore import Qt, Signal, Slot, QPointF
+from qtpy.QtGui import QFont
 
 from matplotlib.colors import to_rgba_array
 
@@ -22,7 +22,7 @@
 
 
 class AddFeaturesMenuButton(QtWidgets.QPushButton):
-    FeatureAdded = pyqtSignal(str)
+    FeatureAdded = Signal(str)
 
     def __init__(self, *args, m=None, sub_menu="preset", **kwargs):
         super().__init__(*args, **kwargs)
@@ -134,7 +134,7 @@ def enterEvent(self, e):
 
         super().enterEvent(e)
 
-    @pyqtSlot()
+    @Slot()
     def show_menu(self):
         self.feature_menu.popup(self.mapToGlobal(self.menu_button.pos()))
 
@@ -142,7 +142,7 @@ def set_layer(self, layer):
         self.layer = layer
 
     def menu_callback_factory(self, featuretype, feature):
-        @pyqtSlot()
+        @Slot()
         def cb():
             # TODO set the layer !!!!
             if self.layer is None:
@@ -284,7 +284,7 @@ def __init__(self, m=None):
 
         self.setLayout(layout_tight)
 
-    @pyqtSlot()
+    @Slot()
     def update_props(self):
         # don't specify alpha! it interferes with the alpha of the colors!
 
@@ -477,7 +477,7 @@ def enterEvent(self, e):
                 "</ul>",
             )
 
-    @pyqtSlot()
+    @Slot()
     def on_click(self, *args, **kwargs):
         global _last_info_button
         global _init_size
@@ -523,7 +523,7 @@ def on_click(self, *args, **kwargs):
 
 
 class LayerArtistTabs(QtWidgets.QTabWidget):
-    plusClicked = pyqtSignal()
+    plusClicked = Signal()
 
     def __init__(self, *args, m=None, **kwargs):
         super().__init__(*args, **kwargs)
@@ -733,7 +733,7 @@ def mousePressEvent(self, event):
         else:
             super().mousePressEvent(event)
 
-    @pyqtSlot()
+    @Slot()
     def get_tab_icon(self, color="red"):
         if isinstance(color, str):
             color = QtGui.QColor(color)
@@ -744,7 +744,8 @@ def get_tab_icon(self, color="red"):
         canvas.fill(Qt.transparent)
 
         painter = QtGui.QPainter(canvas)
-        painter.setRenderHints(QtGui.QPainter.HighQualityAntialiasing)
+        painter.setRenderHint(QtGui.QPainter.Antialiasing, True)
+        painter.setRenderHint(QtGui.QPainter.SmoothPixmapTransform, True)
 
         pencolor = QtGui.QColor(color)
         pencolor.setAlpha(100)
@@ -799,7 +800,7 @@ def repopulate_and_activate_current(self, *args, **kwargs):
         except StopIteration:
             pass
 
-    @pyqtSlot()
+    @Slot()
     def tab_moved(self):
         # get currently active layers
         active_layers, alphas = self.m.BM._get_layers_alphas()
@@ -822,7 +823,7 @@ def tab_moved(self):
             alpha_order = [alphas[active_layers.index(i)] for i in layer_order]
             self.m.show_layer(*zip(layer_order, alpha_order))
 
-    @pyqtSlot(int)
+    @Slot(int)
     def close_handler(self, index):
         layer = self.tabText(index)
 
@@ -842,7 +843,7 @@ def close_handler(self, index):
         _ = self._msg.show()
 
     def get_close_tab_cb(self, index):
-        @pyqtSlot()
+        @Slot()
         def cb():
             self._do_close_tab(index)
 
@@ -959,7 +960,7 @@ def color_active_tab(self, m=None, layer=None, adjust_order=True):
             # re-connect tab_moved callback
             self.tabMoved.connect(self.tab_moved)
 
-    @pyqtSlot()
+    @Slot()
     def populate_on_layer(self, *args, **kwargs):
         lastlayer = getattr(self, "_last_populated_layer", "")
         currlayer = self.m.BM.bg_layer
@@ -972,7 +973,7 @@ def populate_on_layer(self, *args, **kwargs):
             # still update tab colors  (e.g. if layers are removed from multi)
             self.color_active_tab()
 
-    @pyqtSlot()
+    @Slot()
     def populate(self, *args, **kwargs):
         if not self.isVisible():
             return
@@ -1034,7 +1035,7 @@ def populate(self, *args, **kwargs):
         # try to restore the previously opened tab
         self.set_current_tab_by_name(self._current_tab_name)
 
-    @pyqtSlot(str)
+    @Slot(str)
     def set_current_tab_by_name(self, layer):
         if layer is None:
             layer = self.m.BM.bg_layer
@@ -1051,7 +1052,7 @@ def set_current_tab_by_name(self, layer):
             if found is False:
                 self.setCurrentIndex(0)
 
-    @pyqtSlot(int)
+    @Slot(int)
     def tabchanged(self, index):
         # TODO
         # modifiers are only released if the canvas has focus while the event happens!!
@@ -1362,7 +1363,7 @@ def _get_artist_layout(self, a, layer):
 
         return layout
 
-    @pyqtSlot()
+    @Slot()
     def populate_on_layer(self, *args, **kwargs):
         lastlayer = getattr(self, "_last_populated_layer", "")
         currlayer = self.m.BM.bg_layer
@@ -1383,7 +1384,7 @@ def populate_on_layer(self, *args, **kwargs):
             # layers are selected (and the transparency of a sub-layer is changed)
             self.tabBar().color_active_tab(adjust_order=False)
 
-    @pyqtSlot()
+    @Slot()
     def populate(self, *args, **kwargs):
         if not self.isVisible():
             return
@@ -1463,7 +1464,7 @@ def get_layer_alpha(self, layer):
             alpha = 1
         return alpha
 
-    @pyqtSlot()
+    @Slot()
     def populate_layer(self, layer=None):
         if not self.isVisible():
             return
@@ -1599,7 +1600,7 @@ def _do_remove(self, artist, layer):
         self.m.redraw(layer)
 
     def remove(self, artist, layer):
-        @pyqtSlot()
+        @Slot()
         def cb():
             self._msg = QtWidgets.QMessageBox(self)
 
@@ -1620,7 +1621,7 @@ def cb():
         return cb
 
     def show_hide(self, artist, layer):
-        @pyqtSlot()
+        @Slot()
         def cb():
             if artist in self.m.BM._hidden_artists:
                 self.m.BM._hidden_artists.remove(artist)
@@ -1635,7 +1636,7 @@ def cb():
         return cb
 
     def set_zorder(self, artist, layer, widget):
-        @pyqtSlot()
+        @Slot()
         def cb():
             val = widget.text()
             if len(val) > 0:
@@ -1646,7 +1647,7 @@ def cb():
         return cb
 
     def set_alpha(self, artist, layer, widget):
-        @pyqtSlot()
+        @Slot()
         def cb():
             val = widget.text()
             if len(val) > 0:
@@ -1657,7 +1658,7 @@ def cb():
         return cb
 
     def set_linewidth(self, artist, layer, widget):
-        @pyqtSlot()
+        @Slot()
         def cb():
             val = widget.text()
             if len(val) > 0:
@@ -1668,7 +1669,7 @@ def cb():
         return cb
 
     def set_cmap(self, artist, layer, widget):
-        @pyqtSlot()
+        @Slot()
         def cb():
             val = widget.currentText()
             if len(val) > 0:
@@ -1678,7 +1679,7 @@ def cb():
 
         return cb
 
-    @pyqtSlot()
+    @Slot()
     def set_layer_alpha(self, layer, alpha):
         layers, alphas = self.m.BM._get_layers_alphas()
         if layer in layers:
@@ -1811,7 +1812,7 @@ def __init__(self, *args, m=None, show_editor=False, **kwargs):
         # with respect to the currently selected layer-tab
         self.artist_tabs.tabBar().currentChanged.connect(self.set_layer)
 
-    @pyqtSlot()
+    @Slot()
     def set_layer(self):
         layer = self.artist_tabs.tabText(self.artist_tabs.currentIndex())
         for s in self.addfeature.selectors:
diff --git a/eomaps/qtcompanion/widgets/extent.py b/eomaps/qtcompanion/widgets/extent.py
index 27c259b82..1bdaed73e 100644
--- a/eomaps/qtcompanion/widgets/extent.py
+++ b/eomaps/qtcompanion/widgets/extent.py
@@ -1,8 +1,8 @@
 import logging
 from datetime import datetime
 
-from PyQt5 import QtWidgets
-from PyQt5.QtCore import pyqtSlot
+from qtpy import QtWidgets
+from qtpy.QtCore import Slot
 
 _log = logging.getLogger(__name__)
 
@@ -34,7 +34,7 @@ def enterEvent(self, e):
                 "'location' can be a country-name, a city-name an address etc.",
             )
 
-    @pyqtSlot()
+    @Slot()
     def set_extent(self):
         # make sure that queries have a couple of seconds delay
         # to comply to OpenStreetMap Nominatim Usage Policy
diff --git a/eomaps/qtcompanion/widgets/files.py b/eomaps/qtcompanion/widgets/files.py
index cb14c56ef..116d7b6a0 100644
--- a/eomaps/qtcompanion/widgets/files.py
+++ b/eomaps/qtcompanion/widgets/files.py
@@ -1,7 +1,7 @@
 import logging
 
-from PyQt5 import QtWidgets, QtGui
-from PyQt5.QtCore import Qt, QLocale, pyqtSignal
+from qtpy import QtWidgets, QtGui
+from qtpy.QtCore import Qt, QLocale, Signal
 from pathlib import Path
 import io
 import numpy as np
@@ -1338,7 +1338,7 @@ def enterEvent(self, e):
 
 
 class OpenFileTabs(QtWidgets.QTabWidget):
-    openNewFile = pyqtSignal()
+    openNewFile = Signal()
 
     def __init__(self, *args, m=None, **kwargs):
         super().__init__(*args, **kwargs)
diff --git a/eomaps/qtcompanion/widgets/layer.py b/eomaps/qtcompanion/widgets/layer.py
index 90d84dc65..170c42365 100644
--- a/eomaps/qtcompanion/widgets/layer.py
+++ b/eomaps/qtcompanion/widgets/layer.py
@@ -1,7 +1,7 @@
-from PyQt5 import QtWidgets
-from PyQt5.QtCore import Qt, pyqtSlot
+from qtpy import QtWidgets
+from qtpy.QtCore import Qt, Slot
 from ..common import iconpath
-from PyQt5 import QtGui
+from qtpy import QtGui
 
 
 class AutoUpdatePeekLayerDropdown(QtWidgets.QComboBox):
@@ -45,7 +45,7 @@ def enterEvent(self, e):
                 "select the peek-method as well as the transparency of the overlay.",
             )
 
-    @pyqtSlot()
+    @Slot()
     def set_last_active(self):
         self._last_active = self.currentText()
 
@@ -315,7 +315,7 @@ def update_visible_layer(self, *args, **kwargs):
         self.update_layers()
         self.update_display_text(self.m.BM._bg_layer)
 
-    @pyqtSlot()
+    @Slot()
     def actionClicked(self):
         action = self.sender()
         if not isinstance(action, QtWidgets.QWidgetAction):
@@ -392,7 +392,7 @@ def update_checkstatus(self):
                 # re connect action trigger
                 w.clicked.connect(action.trigger)
 
-    @pyqtSlot()
+    @Slot()
     def update_layers(self):
         layers = self.layers
         if layers == self._last_layers:
diff --git a/eomaps/qtcompanion/widgets/peek.py b/eomaps/qtcompanion/widgets/peek.py
index 6147866d2..b71a8eb7c 100644
--- a/eomaps/qtcompanion/widgets/peek.py
+++ b/eomaps/qtcompanion/widgets/peek.py
@@ -1,5 +1,5 @@
-from PyQt5 import QtWidgets, QtGui
-from PyQt5.QtCore import Qt, pyqtSignal, QSize, pyqtSlot
+from qtpy import QtWidgets, QtGui
+from qtpy.QtCore import Qt, Signal, QSize, Slot
 
 from .layer import AutoUpdatePeekLayerDropdown, AutoUpdateLayerMenuButton
 from ..common import iconpath
@@ -59,7 +59,7 @@ def enterEvent(self, e):
 
 
 class PeekMethodButtons(QtWidgets.QWidget):
-    methodChanged = pyqtSignal(str)
+    methodChanged = Signal(str)
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -141,7 +141,7 @@ def __init__(self, *args, **kwargs):
         self.round_button.setCurrentWidget(self.buttons["circle"])
 
     def button_clicked(self, method):
-        @pyqtSlot()
+        @Slot()
         def cb():
             if method == "square":
                 if self._method == "square":
@@ -178,7 +178,7 @@ def cb():
 
         return cb
 
-    @pyqtSlot(int)
+    @Slot(int)
     def rectangle_sider_value_changed(self, i):
         self.rectangle_size = i / 100
         if self._method in ["rectangle", "square", "circle", "ellipse"]:
@@ -244,13 +244,13 @@ def set_alpha_slider_stylesheet(self):
             """
         )
 
-    @pyqtSlot(int)
+    @Slot(int)
     def alpha_changed(self, i):
         self.alpha = i / 100
         self.methodChanged.emit(self._method)
         self.set_alpha_slider_stylesheet()
 
-    @pyqtSlot(str)
+    @Slot(str)
     def method_changed(self, method):
         self._method = method
 
@@ -338,7 +338,7 @@ def __init__(self, *args, m=None, layers=None, exclude=None, **kwargs):
             m=self.m, layers=self._layers, exclude=exclude
         )
         self.layerselector.update_layers()  # do this before attaching the callback!
-        self.layerselector.currentIndexChanged[str].connect(self.set_layer_callback)
+        self.layerselector.currentTextChanged.connect(self.set_layer_callback)
         self.layerselector.setMinimumWidth(100)
 
         self.buttons = PeekMethodButtons()
@@ -376,7 +376,7 @@ def __init__(self, *args, m=None, layers=None, exclude=None, **kwargs):
 
         self.setLayout(layout)
 
-    @pyqtSlot(str)
+    @Slot(str)
     def set_layer_callback(self, l):
         self.remove_peek_cb()
         if self.cid is not None:
@@ -399,7 +399,7 @@ def set_layer_callback(self, l):
         )
         self.current_layer = l
 
-    @pyqtSlot(str)
+    @Slot(str)
     def method_changed(self, method):
         self.add_peek_cb()
 
@@ -482,9 +482,9 @@ def __init__(self, *args, m=None, **kwargs):
         cb = self.settxt_factory(w)
         w.modifier.textChanged.connect(cb)
         w.buttons.methodChanged.connect(cb)
-        w.layerselector.currentIndexChanged[str].connect(cb)
+        w.layerselector.currentTextChanged.connect(cb)
 
-        # emit pyqtSignal to set text
+        # emit Signal to set text
         w.buttons.methodChanged.emit(w.buttons._method)
 
         # a tab that is used to create new tabs
@@ -501,7 +501,7 @@ def __init__(self, *args, m=None, **kwargs):
         self.tabBarClicked.connect(self.tabbar_clicked)
         self.setCurrentIndex(0)
 
-        self.setTabPosition(0)
+        # self.setTabPosition(0)
         self.setStyleSheet(
             """
             QTabWidget::pane {
@@ -557,18 +557,18 @@ def make_new_tab(self):
         cb = self.settxt_factory(w)
         w.modifier.textChanged.connect(cb)
         w.buttons.methodChanged.connect(cb)
-        w.layerselector.currentIndexChanged[str].connect(cb)
-        # emit pyqtSignal to set text
+        w.layerselector.currentTextChanged.connect(cb)
+        # emit Signal to set text
         w.buttons.methodChanged.emit(w.buttons._method)
 
         return w
 
-    @pyqtSlot(int)
+    @Slot(int)
     def tabbar_clicked(self, index):
         if self.tabText(index) == "+":
             self.make_new_tab()
 
-    @pyqtSlot(int)
+    @Slot(int)
     def close_handler(self, index):
         curridx = self.currentIndex()
         self.widget(index).remove_peek_cb()
@@ -577,7 +577,7 @@ def close_handler(self, index):
             self.setCurrentIndex(index - 1)
 
     def settxt_factory(self, w):
-        @pyqtSlot()
+        @Slot()
         def settxt():
             self.setTabIcon(self.indexOf(w), peek_icons[w.buttons._method])
             mod = w.modifier.text().strip()
diff --git a/eomaps/qtcompanion/widgets/save.py b/eomaps/qtcompanion/widgets/save.py
index eaa100d04..bab0a6322 100644
--- a/eomaps/qtcompanion/widgets/save.py
+++ b/eomaps/qtcompanion/widgets/save.py
@@ -1,5 +1,5 @@
-from PyQt5 import QtWidgets, QtGui
-from PyQt5.QtCore import Qt, pyqtSlot
+from qtpy import QtWidgets, QtGui
+from qtpy.QtCore import Qt, Slot
 
 
 class FiletypeComboBox(QtWidgets.QComboBox):
@@ -245,14 +245,14 @@ def __init__(self, *args, m=None, **kwargs):
         # set export props to current state of Maps._clipboard_kwargs
         self.set_export_props()
 
-    @pyqtSlot()
+    @Slot()
     def tight_cb_callback(self):
         if self.tightbbox_cb.isChecked():  # e.g. checked
             self.tightbbox_input.setVisible(True)
         else:
             self.tightbbox_input.setVisible(False)
 
-    @pyqtSlot()
+    @Slot()
     def rasterize_cb_callback(self, *args, **kwargs):
         if self.filetype_dropdown.currentText() in ["svg", "pdf", "eps"]:
             self.rasterize_cb.setVisible(True)
@@ -261,7 +261,7 @@ def rasterize_cb_callback(self, *args, **kwargs):
             self.rasterize_cb.setVisible(False)
             self.rasterize_label.setVisible(False)
 
-    @pyqtSlot()
+    @Slot()
     def save_file(self):
         selected_filetype = self.filetype_dropdown.currentText()
 
@@ -295,7 +295,7 @@ def save_file(self):
                 **kwargs,
             )
 
-    @pyqtSlot()
+    @Slot()
     def update_clipboard_kwargs(self, *args, **kwargs):
         clipboard_kwargs = dict(
             format=self.filetype_dropdown.currentText(),
@@ -312,7 +312,7 @@ def update_clipboard_kwargs(self, *args, **kwargs):
         # use private setter to avoid triggering callbacks on set
         self.m._set_clipboard_kwargs(**clipboard_kwargs)
 
-    @pyqtSlot()
+    @Slot()
     def set_export_props(self, *args, **kwargs):
         # callback that is triggerd on Maps.set_clipboard_kwargs
 
diff --git a/eomaps/qtcompanion/widgets/utils.py b/eomaps/qtcompanion/widgets/utils.py
index 90efef2aa..13479937b 100644
--- a/eomaps/qtcompanion/widgets/utils.py
+++ b/eomaps/qtcompanion/widgets/utils.py
@@ -1,5 +1,5 @@
-from PyQt5 import QtWidgets, QtGui
-from PyQt5.QtCore import Qt, QRectF, QSize, pyqtSlot, pyqtSignal
+from qtpy import QtWidgets, QtGui
+from qtpy.QtCore import Qt, QRectF, QSize, Slot, Signal
 from eomaps import Maps
 from functools import lru_cache
 
@@ -264,8 +264,9 @@ def resizeEvent(self, e):
     def paintEvent(self, e):
         super().paintEvent(e)
         painter = QtGui.QPainter(self)
+        painter.setRenderHint(QtGui.QPainter.Antialiasing, True)
+        painter.setRenderHint(QtGui.QPainter.SmoothPixmapTransform, True)
 
-        painter.setRenderHints(QtGui.QPainter.HighQualityAntialiasing)
         size = self.size()
 
         if self.linewidth > 0.01:
@@ -424,7 +425,7 @@ def set_stylesheet(self):
         elif self._style == "alpha":
             self.set_alpha_stylesheet()
 
-    @pyqtSlot(int)
+    @Slot(int)
     def value_changed(self, i):
         self.alpha = i / 100
         self.set_stylesheet()
@@ -484,7 +485,7 @@ def set_alpha_stylesheet(self):
 
 
 class ColorWithSlidersWidget(QtWidgets.QWidget):
-    colorSelected = pyqtSignal()
+    colorSelected = Signal()
     alpha_slider_scale = 100
     linewidth_slider_scale = 10
 
@@ -541,12 +542,12 @@ def linewidth(self):
     def alpha(self):
         return self.color.alpha
 
-    @pyqtSlot(int)
+    @Slot(int)
     def set_alpha_with_slider(self, i):
         self.color.set_alpha(i / self.alpha_slider_scale)
         self.colorSelected.emit()
 
-    @pyqtSlot(int)
+    @Slot(int)
     def set_linewidth_with_slider(self, i):
         self.color.set_linewidth(i / self.linewidth_slider_scale)
         self.colorSelected.emit()
diff --git a/eomaps/qtcompanion/widgets/wms.py b/eomaps/qtcompanion/widgets/wms.py
index 096c0fc2d..d9dac08e6 100644
--- a/eomaps/qtcompanion/widgets/wms.py
+++ b/eomaps/qtcompanion/widgets/wms.py
@@ -1,8 +1,8 @@
 import logging
 
-from PyQt5 import QtWidgets
-from PyQt5.QtCore import Qt, QThread, QObject, pyqtSignal, pyqtSlot, QTimer
-from PyQt5.QtGui import QStatusTipEvent
+from qtpy import QtWidgets
+from qtpy.QtCore import Qt, QThread, QObject, Signal, Slot, QTimer
+from qtpy.QtGui import QStatusTipEvent
 
 from ... import Maps, _data_dir
 from pathlib import Path
@@ -12,7 +12,7 @@
 _log = logging.getLogger(__name__)
 
 # the path to which already fetched WebMap layers are stored
-# (to avoid fetching available layers on menu-population)
+# (to avoid fetching available layers on menu-population)W
 wms_layers_dumppath = Path(_data_dir) / "_companion_wms_layers.json"
 
 
@@ -597,7 +597,7 @@ def eventFilter(self, watched, event):
 
 
 class AddWMSMenuButton(QtWidgets.QPushButton):
-    wmsLayerCreated = pyqtSignal(str)
+    wmsLayerCreated = Signal(str)
 
     def __init__(
         self, *args, m=None, new_layer=False, show_layer=False, layer=None, **kwargs
@@ -698,11 +698,11 @@ def enterEvent(self, e):
                     "NOTE: This is not necessarily the currently visible layer!",
                 )
 
-    @pyqtSlot()
+    @Slot()
     def show_menu(self):
         self.feature_menu.popup(self.mapToGlobal(self.menu_button.pos()))
 
-    @pyqtSlot()
+    @Slot()
     def populate_menu(self):
 
         self.sub_menus = dict()
@@ -711,7 +711,7 @@ def populate_menu(self):
             self.sub_menus[wmsname].aboutToShow.connect(self._populate_submenu_cb)
         self.feature_menu.aboutToShow.disconnect()
 
-    @pyqtSlot()
+    @Slot()
     def _populate_submenu_cb(self):
         wmsname = self.sender().title()
         self.fetch_submenu(wmsname=wmsname)
@@ -862,9 +862,9 @@ def populate_submenu(self, wmsname=None):
         except Exception:
             _log.error(f"There was a problem with the WMS: {wmsname}")
 
-    @pyqtSlot()
+    @Slot()
     def menu_callback_factory(self, wmsname, wmslayer):
-        @pyqtSlot()
+        @Slot()
         def wms_cb():
 
             self.window().statusBar().showMessage(
diff --git a/setup.py b/setup.py
index 7a50b5189..a1cbf5775 100644
--- a/setup.py
+++ b/setup.py
@@ -55,6 +55,7 @@
         "cairosvg",
         "packaging",
         "click",
+        "qtpy",
     ],
     entry_points={"console_scripts": ["eomaps = eomaps.scripts.open:cli"]},
     keywords=["visualization", "plotting", "maps", "geographical data"],
diff --git a/tests/test_env.yml b/tests/test_env.yml
index 56025a54d..805b76630 100644
--- a/tests/test_env.yml
+++ b/tests/test_env.yml
@@ -30,6 +30,8 @@ dependencies:
   - coveralls
   - pytest
   - pytest-cov
+  # --------------to support multiple Qt versions
+  - qtpy
   # --------------for building the docs
   - sphinx-copybutton
   - sphinx

From 39dc0fa6376444af985ef5380462d68e9247772f Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:22:27 +0100
Subject: [PATCH 005/240] update gitignore

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index 9702458f1..3f2defa0b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,3 +26,7 @@ tests/.ipynb_checkpoints
 tests/.pylint.d
 
 _exclude*
+
+docs/jupyter_execute/*
+docs/.jupyter_cache/*
+docs/.virtual_documents/*

From 8cec5e9f8cb2d1ccb945fb8ff83d7e3ba675cf55 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:23:02 +0100
Subject: [PATCH 006/240] update contribute_env

---
 docs/contribute_env.yml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/docs/contribute_env.yml b/docs/contribute_env.yml
index a379885e3..2f037a0ff 100644
--- a/docs/contribute_env.yml
+++ b/docs/contribute_env.yml
@@ -38,6 +38,4 @@ dependencies:
   - sphinx-copybutton
   - sphinx
   - docutils
-  - pip
-  - pip:
-    - sphinx_rtd_theme
+  - sphinx_rtd_theme

From 9fc25ecb08e39ea2d442fe954988d6ac29890848 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:25:16 +0100
Subject: [PATCH 007/240] update readthedocs to install from source

---
 .readthedocs.yaml |  7 ++++++-
 docs/docs_env.yml | 37 ++++++++++++++++++++++++++++++-------
 2 files changed, 36 insertions(+), 8 deletions(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index f90e5639c..05593cd47 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -6,4 +6,9 @@ build:
     python: "mambaforge-4.10"
 
 conda:
-  environment: tests/test_env.yml
+  environment: tests/docs_env.yml
+
+python:
+  install:
+    - method: pip
+      path: .
diff --git a/docs/docs_env.yml b/docs/docs_env.yml
index fd17ec292..029052838 100644
--- a/docs/docs_env.yml
+++ b/docs/docs_env.yml
@@ -3,11 +3,34 @@ channels:
   - conda-forge
 
 dependencies:
-  - eomaps
+
+  - python = 3.10
+  - numpy
+  - scipy
+  - pandas
+  - geopandas
+  - matplotlib >=3.4
+  - cartopy >=0.20.0
+  - descartes
+  - mapclassify
+  - pyproj
+  - pyepsg
+  # --------------for data-shading
+  - datashader
+  # --------------for GeoTIFF and NetCDF files
+  - netcdf4
+  - xarray
+  - rioxarray
+  # --------------for WebMaps
+  - owslib
+  - requests
+  - xmltodict
+  - cairosvg
+
   # --------------for building the docs
-  - sphinx-copybutton
-  - sphinx
-  - docutils
-  - pip
-  - pip:
-    - sphinx_rtd_theme
+  - docutils<0.19
+  - sphinx=7.2.6
+  - sphinx-copybutton=0.5.2
+  - myst_nb=1.0.0
+  - sphinx-design=0.5.0
+  - sphinx_rtd_theme=1.3

From ca8c2448afffa3e93615c7ff42434de516f85f83 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:25:48 +0100
Subject: [PATCH 008/240] update docs conf.py

---
 docs/conf.py | 23 ++++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index b7c44f598..fb88f677c 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -26,6 +26,8 @@ def setup(app):
     "sphinx.ext.napoleon",
     "sphinx_copybutton",
     "sphinx_rtd_theme",
+    "myst_nb",
+    "sphinx_design",
 ]
 
 
@@ -61,10 +63,29 @@ def setup(app):
 napoleon_type_aliases = None
 napoleon_attr_annotations = True
 
+myst_update_mathjax = False  # to use single $x^2$ for equations
+myst_render_markdown_format = "myst"  # to parse markdown output with MyST parser
+myst_enable_extensions = ["dollarmath", "colon_fence"]
+myst_title_to_header = True
+
+nb_execution_mode = "cache"
+nb_execution_timeout = 120
+
 # handle compiler warnings for duplicate labels due to documents
 # included via the  ..include:: directive
-exclude_patterns = ["introduction.rst"]
+exclude_patterns = [
+    "introduction.rst",
+    "build",
+    "jupyter_execute/*",
+    ".jupyter_cache/*",
+    ".virtual_documents/*",
+]
 
+source_suffix = {
+    ".rst": "restructuredtext",
+    ".ipynb": "myst-nb",
+    ".myst": "myst-nb",
+}
 
 # a global substitution used to fix issues with images in tables
 # in the mobile-theme (without a span they get resized to 0. This forces a size

From c8f79ef832c2824c7911f732bce1dbb1e7d14e9f Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:26:12 +0100
Subject: [PATCH 009/240] re-write feature section as notebook

---
 docs/index.rst                      |    2 +-
 docs/notebooks/basic_features.ipynb | 2061 +++++++++++++++++++++++++++
 2 files changed, 2062 insertions(+), 1 deletion(-)
 create mode 100644 docs/notebooks/basic_features.ipynb

diff --git a/docs/index.rst b/docs/index.rst
index 700a6a1e0..6de7b81e6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -189,7 +189,7 @@ Make sure to check out the :doc:`EOmaps_examples` for an overview of the capabil
     :caption: Map Features
 
     api_inset_maps
-    api_naturalearth_features
+    notebooks/basic_features.ipynb
     api_webmaps
     api_vector_data
 
diff --git a/docs/notebooks/basic_features.ipynb b/docs/notebooks/basic_features.ipynb
new file mode 100644
index 000000000..009c1d5a3
--- /dev/null
+++ b/docs/notebooks/basic_features.ipynb
@@ -0,0 +1,2061 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "de7394e2-8772-41bc-8d4a-8fb4e6c75cdd",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27c9d610-126b-4dc6-bc4f-b149c8f3cb92",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# 🌵 NaturalEarth features\n",
+    "<br>\n",
+    "\n",
+    "EOmaps provies access to a large amount of basic map features provided by [NaturalEarth](https://www.naturalearthdata.com/) via [`Maps.add_feature`](eomaps.Maps.add_feature).\n",
+    "\n",
+    ":::{note}\n",
+    "The first time a feature is added to a map, the corresponding dataset is downloaded and stored locally for subsequent use.\n",
+    ":::\n",
+    "\n",
+    ":::{dropdown} <small>Where is the data stored?</small>\n",
+    "EOmaps uses `cartopy's` API to download and cache the features.\n",
+    "Run the following lines to get the used data-cache directory:\n",
+    "\n",
+    "```python\n",
+    "from cartopy import config\n",
+    "print(config[\"data_dir\"])\n",
+    "```\n",
+    ":::\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f111eac-4f63-49db-82ab-0adf504fc307",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Preset Features\n",
+    "For the most commonly used features, style-presets are available:\n",
+    "\n",
+    "\n",
+    "```{eval-rst}\n",
+    ".. currentmodule:: eomaps.ne_features.NaturalEarth_presets\n",
+    "\n",
+    ".. autosummary::\n",
+    "    :nosignatures:\n",
+    "\n",
+    "    coastline\n",
+    "    ocean\n",
+    "    land\n",
+    "    countries\n",
+    "    urban_areas\n",
+    "    lakes\n",
+    "    rivers_lake_centerlines\n",
+    "\n",
+    "```\n",
+    "\n",
+    "To add individual preset features (and optionally override style properties), use:\n",
+    "```python\n",
+    "m.add_feature.preset.<FEATURE-NAME>(**STYLE-KWARGS)\n",
+    "```\n",
+    "\n",
+    ":::{tip}\n",
+    "The native projection of the provided feature shapes is `epsg 4326` (e.g. PlateCarree or lon/lat projection).  \n",
+    "If you create a map in a different projection, the features have to be re-projected which might take some time.  \n",
+    "Re-projected features are cached until the kernel is restarted, so creating the same figure again will be much faster!\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "08af1506-87d3-466d-8a7e-342a66ffa882",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps(facecolor=\"none\", figsize=(6, 3.5))\n",
+    "m.add_feature.preset.coastline()\n",
+    "m.add_feature.preset.land()\n",
+    "m.add_feature.preset.ocean()\n",
+    "m.add_feature.preset.urban_areas()\n",
+    "m.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f027b9b-8216-4e22-9329-43ef8f2d9ea1",
+   "metadata": {},
+   "source": [
+    "You can override the feature-styles of the presets to quickly adjust the look of a map!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7c1eb8eb-9a8a-4dfa-ad14-7168fdcd0218",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps(facecolor=\"none\", figsize=(6, 3.5))\n",
+    "m.set_frame(ec=\"none\")\n",
+    "m.add_feature.preset.coastline()\n",
+    "m.add_feature.preset.land(fc=\"darkkhaki\")\n",
+    "m.add_feature.preset.ocean(hatch=\"////\", ec=\"w\")\n",
+    "m.add_feature.preset.urban_areas(ec=\"r\", lw=0.25)\n",
+    "m.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fae57bc2-aee0-4f12-94a2-e737bd44cace",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    ":::{tip}\n",
+    "It is also possible to add multiple features in one go with:  \n",
+    "(In this case, the provided style arguments are applied to **all** added features!)\n",
+    "```python\n",
+    "m.add_feature.preset(*FEATURE-NAMES, **STYLE-KWARGS)\n",
+    "```\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d1a440ff-1dac-42e9-8bcc-81a74a945fd6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps(facecolor=\"none\", figsize=(6, 3.5))\n",
+    "m.add_feature.preset.coastline(lw=0.4)\n",
+    "m.add_feature.preset(\"ocean\", \"land\", \"lakes\", \"rivers_lake_centerlines\", \"urban_areas\", lw=0.2, alpha=0.5)\n",
+    "m.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2518589-7e39-4a05-bc39-aef32b90894d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## General Features\n",
+    "\n",
+    "[NaturalEarth](https://www.naturalearthdata.com/)  provides features in 2 categories: **physical** and **cultural**. \n",
+    "\n",
+    "You can access all available featues of a corresponding category with:\n",
+    "```python\n",
+    "m.add_feature.cultural.<FEATURE-NAME>(**STYLE-KWARGS)\n",
+    "```\n",
+    "```python\n",
+    "m.add_feature.physical.<FEATURE-NAME>(**STYLE-KWARGS)\n",
+    "```\n",
+    "\n",
+    ":::{note}\n",
+    "NaturalEarth provies features in 3 different scales: 1/10, 1/50 and 1/110.  \n",
+    "By default, an appropriate scale is selected based on the visible extent.  \n",
+    "You can manually select the scale to use with the ``scale`` argument (e.g. ``m.add_feature.physical.coastline(scale=10)``)\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "49f9a813-b4d6-44d7-9b8a-b50b4eadd188",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps(facecolor=\"none\")\n",
+    "m.set_frame(rounded=0.2, ec=\"darkred\", lw=3)\n",
+    "m.set_extent_to_location(\"europe\")\n",
+    "\n",
+    "m.add_feature.preset(\"land\", \"ocean\", \"coastline\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0744f40e-6dc7-4075-99c2-a75b7ad40e8b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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upycgp4hzLLm0v8CpJZTF1c6JoNAX3YProhIB4p1QbLwKPat85Zn2Wl4RgrkTLvo2vQYNAK3zxhXiTbJxLdRknSu/yVWdCtuNpbPG4eLpMKQkxfMPbi7e/2ENAoPLq5neqN4Tk06ug4DApv6VUM0r+CbPoLtrCWgJ3KkSkL8XAs6OJV3C0ogDiM5IMl6qLcGcBAznUXUouWr9nNxR0T1IAS4/R3c4UAOXkpWOuKwUNaa+T3nYpaRi68o5OLhjFVzcPFG6QjX4BASjVuMOcKFm63jiJRzlJtrCQwkX1WY4oZwv0NkTtb3Loo5PWTgnJCMp5jI8/IOQ4emBsMQIJBKgyqIycrIQk5GMSAJGmU82GV/a1RdV+Tcu0MkToR5BxWoaDee1tm8bV6GwLVRc2ZJcqFU3/KcS0ADwPxV/kSd3LHTEwalQ0+3YkMe/xlERZ3Fw52psW/kHLp45UugyYi5fMALA8u4B8HBwQQzNKd4O8jdEFy0BLYG7WQJLI/bjCrV5om1Ly8k0ikK0dzVonm0dWJVm2tKwN7MkGDrKi+aav35BXFQEGre7B7WrNYCjkzOOHdiKr0cMNnSDi4sLwnavV/u/T3wPPkx19s6EFahbvhwGo6nSKC4n6PRz8kBDvxAEOHsgKzMd6xZOw5TFLyGKf8euFRvYO9jD0dEJAcEhqNeyOwZ0GQx3Tx+CwjSsvHwQh2k2vpAag/PcDEWuyYt//wQUdipREyWcvQpZSAx9rebbofDji2uz2Gg1a75LF6IBoHXe+II2UK7Rxq4AuaZ1rrqYVSXEXsFv49/G/q0rLPayp0+Mf4kyqNagLSrXucYm/+HBebicloAqHiVR0tXb4ljdqCWgJXDnS8DZ1kFd5PaYU4RFgCeBUQW3UihBf7wgAqPaToHw9PQuEiBd4Yvnqrk/UsM3m5aGHDg5OSptn2gHy1SsjpbdH1TaPvlblZuTjbS0tAJCjYu+rPblb9jimV/jSvhpVGbmi/vf+9l4zgkfPo6jezepfh99NAJdu7bHyZNn1BYbG4+oqGhs3boL8yd/hoizx/Do8K/hTKt09egMVEx3QZ6NK3I9PJAbGIgzadE0K0chkn//tkWfVJtMLJpCO3oJiQ+hr5MbKnmUQIcSNVi3qHlTa/m/fhQkgjac+vZ+gBmu4g771gDwDruh1no58gZtCv6q1m2BJu37IaBkOfgywtnLN8j4R9T0GgT8yRvwc1U6mzbrupaAlsBdJoEOJWswaMMRpZx94Jdto/5enDt5EIvGf4iN50/gt6xMVGcu2udGTkU4LQviclamYk0lpe1r/sSUMS+p+uDB92DkyDfg5+eDw4ePYdWq9fjpp+mY+c0Idbxatco4cuS4qtvyxfud8cvh6ROAU2G7MOqZToiLjkSZMqXQoH5NbNmyBhdOHUa5SrV4vjy4efiocfLx7rufoX79WgoEiqk6LOw4VqxYi4MH860eJcpUxNZVc/D7hPeQkZ5qHCcVL98A3P/cp+hSuTE8vAOUVnBX7BnE0mycQI2h+DSmZGUgnEErF1JjsSYyDN6OrhhUtilN0GUs/i0tcIJ/cUcAtS63hwQ0ALw97tNtv8qHXh5Dc0tfePoGonylOswWdOMvhFU9g4s059z2gtEXoCWgJWBRAoumj8XpI7vh6OyKtJREnDu+T/XLzEhXYMvSoDKhNbHkt2+waPpX6rC9gwNqNmyPKvVaGrs///xQBAT4qf1atapBtpdeegqnTp3FhQvhGD36O3XMzcMLHfs9CQ8vP0z54kUc2rUOlStXxORJn1M7mI777nuKgM+bILOG6r945jjs3rBQzedJnz97ezvVX8BflSrNEB0da1xD/ZY9COz8MO2r19CkSQN89dVIBAX5Izs7B0ePnsCLL76DiSOHqv42NGWHVK0PdwZXnDq8Q117y65D0O2+5+FE2ZxMisSay4exL+4cfji5RmlHA+hDWJE+hPV9y6Omd2njeXVFS8BUAhoAmkpD1/81CTjTMbhu867/2vx6Yi0BLYE7RwK5OTnKzGp6RWXLlkJiYjK1ZWnU3vkiJuYaoJJgDXv6ni3/fbzx5bJFi8bYvHkH9m1djgPbV8LDwx2PPnofypcvxFOngkQWLFiOjz8eq/wB73/+E+UfuIzzvflgY2rUgM8/fw9Dhz6g+s6Y8YdaWpMO/ZWmce2CaVg8YyxEuzhx4uemy1aArUGDOli+fC3c3d2QnJyCsqG1MPO7t9GxY2vMnj1JzWkYFBQUgL17V+PYsZM4ffocduzYg0mTZhBQ2qNDu+ZKBsvnTMDKeT+iHv+mBlGT2JGaz3vr3IutNBXn09nE4kp0IvdP0FRsh76lG6JdieqGU+hvLQElARtRW+tiXRJgyHwAV3TFdFVuv+2BDd9E77YybMdkNPGriEcqtr7bLl1f71UJHIq/iPHHV+Leck3QLkg/xO6WH0Z6ajJW/PE9dqyZi5grEcbL9gsspYItsrOzkJqcaGwXrV6pUiWxb98hODg6qz6Ggx06tMYvv3xNH0EPQ1OBbwFngwc/iQate2LIC59hFzV5f076GOlpKejbtxu1gu8ZtYYycMmSVRgyZBiDOHyRkZaMLJqfO3dui1mzfiwA5gqchDtvv/0JJkyYDBe+EGdmpBHkbVFg1rxfcfuZmZno0GEAIiIu8Vy2iI8n9QwBs4eXDxq3H4Du1Ay6unup6Oh1kUewPvIoMnKzGCBTmq40nYqb+h85lpcQg5T76pvPFUgamCjzRr3/30pAawD/W/nrs2sJaAloCdz2EhAftqhL51CybCXYkT/vVooAPkcnF6XBS4yLwvGD25R2q16rnvCmj/CqeT8gMT6GYDCcAM+RAC/TeJqGDetg1679DLLIj6DNJc1Ku3Yt0KNHZ5pfK6JlyybGvuaV9PQMfPTRl/AhzcvDr36FT57tgssXz6Bx4/r47rtPUalSiPkQZd4VXzdfb1d0HdwD/fv3gqzhev5v69dvVXOVCg7AlCnf3jT4k8GOvPaNGxcY1yQmZpl37NiJWP3nTwwuCcMLH8+EDwmu7ynTEL1K1cXow4sYZXyR3IipylfQOFhX7moJ3Nq/1LtaZPritQS0BLQE7l4JiFZszKt9cen8STzzwWTUaNgW4997BCcObYezixv326HDPUNRvkpdBYgEzM2dNApnj+2BgLxaTTrjgRdHGwUolCxzfvjQuC9+baZBEXZ2ttRw5cLf3xevvPyU8qvbuXMffv11Nn3mcrllKfAnE9jRt/iFF4Zi+PDnSOPibJyzqEpqahqBW2dcvhxJzdkL+PadhxT4mzdvigKQRY3r3LkdYmOPF3W4yHbR3kmZO/cXlC17zTcvOjoGDvRXdHV1Ud9FTmBy4OzZ8wSRvxMMbsO5cxe4nnh1NC+vYBCGPaOFharmYlocfj2zES52jvQTtIErs6AEkbdQNIPFZTYxOaWu3mES0ADwDruh+nK0BLQEtAT+LQmIb97Mb99E+Nl88LP6z0kILFUB9Vv1UABQwOHujYvUVrF6A7w2Zh4mfPAozp04aFzS5uWzMGjYh8pMK435iY+MhxX4e+SRwXj11WcUVYuPj5eK1q1SJRTOzk6q48CBvWlybYOBA4eiy73PYu/GhchIjVe+cz4+3tcmu07txInTuHQpn95Fgjjc3FzxySdvFwv+rjOl8fDqsxH4ef955CbGIdHWA2+0roVly2bh/PnwAuBvyZKVNCc/YxxnqLjRX9CRoNDJyYlgVjZn+hMCcXEJ9CNMVr6EtvTvCyodgvLVW6Bd1XpwKlUW7uUr4khCBFKzM7CbkcPHSDKdepUvMYzt5mUOdjDziZ0KGglxD0SLgErWQyljvli9/49KQAPAf1ScejItAS0BLYE7UwInD+/Ej6OeRFJCLIGYMzp1aoOFC5fjvcfz/XOr1WtF8+MRZvRJUFq5ijUaI2zPBlSp05Kky+FISU5CDrV1TcgGkJIYr6hV1i+ehr+mfG4UmJhQJZhjypRZygR73333qGN16uRH2ho7siJ+fb16dcHC2eONzevWbcE993Q37l+vUrt2dYwa9SYECIqZuFevzgpwXW/c1vAr2HU5DumM2k3PyUN5Lyc0pVk3Pi0Tj/1+EPHH42ATz+ul/6qNkyty4i7i3p9+Q6/HO+PHIb0KTN+wYT3lm5iYmFSgPYXBIilXW0SbmprNHSr3AklEXZm54SswMrhei64qEli6HSO4+/rYcuBs+NVR+V+u1Pg1JlG1ZCiRdHNRJ8MQefE0gkJrINnTFYeZoWQ/M5sc5bdsSyL2wZ6+hc4cJxRcJV28UZk8rI38KjCHsWOBufXO7S0BHQRihfdPB4Fcuyk6COSaLO7Wmg4C+W/vvGS3EEqSI3s2kv8uGOPHj1bRrBKV+sor7xrNr30fHYFO/Z+CZPL59evXcIoULqIxlNL7oeGkZ/ka2VlZxotxINCT4IlmzRoqsDdz5lzlwycaQeHwE3Po+fN7lVnXOMhC5eDBMAz/dCZy6OM2fepYBPn7WOj1zzQlpGei5XdbkbD3LHKT6W9IDZyYU0EzqyoEsBnhu5DlmY57GDE8plc7eNJn71RcEjq/OhKJq9eAqjx4d+qAkU8/iAdrVS6wsBSmojtz5jyJo09Tm3kI27btYhTwXrTsdh+DYf5k4Eg6RM4RZ48qDWrPB15RUcsSzPnb+W3YnRyBHqXq0czrADuuraFvBbg75JvCMxk9fWjXWvz0yTDjOYUmJ6hUCJozK0mdpp0RRXy3Lfa04h1MZpo80Rya5jau4BagOFldiwGCOgjEKF6rr2gAaIW3SAPAazdFA8BrsrhbaxoA/v/vvAC3v6Z+jrULfqHWLlvl6Pb19caCBdPJmfcOQd8+46JEa2dgk5DMGMLX52Bvi8ceu19x3A0Z8jSc6Bvo4mTPCNjRED84GbNlyy4EBvrj+ecfV35vEsxw+PBRzJmzEN9+O0m1nTixHV5eHsZzFVVJybKhRs4Fkan2yMyxIejKRbcKSXCwK2rEjbdn8vqnHj6HJftSsHfrOaTtWwDHRqUw7bUnqRGz52aH47EJ2HUpCv4ErQ1L+qNdmRIWyZjH7jyMn3+fj/ClK8DwXfAi4Vi2LEo1qo/m7VqifeXyaF82GN7O+Zq28+cvok6ddgUWKxrSbAJn8XeU6F/z4uziSu1qIMpVroOq5D8MLlsZMZEX8cePHyKeWU6kiL/h9OkTsGHDFqxevUlxD0q7cA56km8wuEJ1PPLaWHh6+1PLmYm95BjccOUYzqZEqcwr79fqJ90tFg0ALYrFKhs1ALTC26IB4LWbogHgNVncrTUNAP+9O3/h1CE4u3oy80QgxMHM0dkFi8hnt3jGOJ40D61bN0OFCmW4lVPZMWbPXgAv9ovnUcmkISTKNWpURbVqlfDcc2+qhQrVys6dKxS4E/664cM/UGAk7vIpcuHNRtWqlVS/4j6yqCkUUuQbCeSwNM/ZBAdsjnDFkGoJlg7fVFvdH88iZuVSpAvJcqgfzeBvoG+VCjc1h3nnozHx+H7fMewMO4Yz6zcj7fgJce4DkTZz3Hli6twp6F29ohr2+effMRr5ZwWcJQI4mSTUed7e8GlYH1lbt+HD915VATLzwk7jr6++Q9mmjVCCmkLhEUxISDSeukSJIHIO/gj5/uabn9SccjAgwJ/aVmdINHRmZhaXEa/GSCDPgCfeVXXDx4i9s5CQlaZyLmcxnV4GU+ZJcWTkdwADTaozF3NtWwbxDGlsGGL41jQwBklY0bf2AbSim6GXoiWgJaAl8G9LQLR7U796ReWsTYyPLnC6UhWqIiriHNvy+WE3bNhKLVE+dYl0tKfpL54aocGD+1Kb97nS5Em7aO4MRahTRLN38eIljBmT75937vh+dbhZs+7k4xt3XT890VDJVlRZetodLUunwoOaPktl/UU39Awp6FNnqd/12r7acQTxNPem5DLTxvrJKOvpfr0hhY5HRKQRHO9hlG4mJk9uRDDtjqp+3vi6QxM6MnJ7/iE15kJiClbQf+/1Nz/Gwz0G44eFM3Bvzcp4/fXn1Cad4uIyUePl2Uj76wvE7dwNZGXj1Y/HwdbPL1/jSA1ecJ/eWPp4fzWncASeO3eRWUYCCPwI8q8WW1sbQ1XlJzbumFQatumj9gxmfMne1NQ/lFlHwkgpEy6Gb9jSXC8llyb7E0mXsYXE0+4pKXhfteoPa5eABoDWfof0+rQEtAS0BP4BCYiZds38n7Ft1R+4yFy5Ury8PKmhe1YFdaSmpqpMGJkZmQR6DiqQQ0y1EmwgKdmk2Nvb4OWXn6Xv3zAj+JN2oSKR0rx5I/Ts2RlLl67G/fc/rdrko2HDuirV2dmzF1C6dElj+61Utkbkm3odbPNBqvkcJ+IYvOCYAz+XwuZR876G/UvJ9lh1zo1+cyrOAgIr7Yhwvtlsg+QN32Hc7HE3Df4OH05gYMlawynU96xZF/Dmm9UKtBl2cuOA19qEwd65HwHVfrw85kfcO2UMUjOz8cvB41gzJxxrf0hhd4LQcr0x769hWE7AuHrbXsRRg5hFjWnZQf0wZXA3w5Tw9vZSm7HhauXDD99Q9/Dnn2cwCGasMuEbfC8NfUe/1IsmYF9yL8YSXNrhlc/noG/1huhLbkFLJZE5infRf/DguQOWDus2K5SABoBWeFP0krQEtAS0BG5UAqKhWTRzLLYT2Dk4OmHgUx8oLj7z8Uf3bsIfP32kNEGSkiyDQG/y5G+MlCdXrkQTAH6thgntyOzZv+Lrr3+k2XaNcSoxE4aFHTfSsRgO9OvXU2mgHn98iAKG4icoRdKvTZnyDdq3b2XoavxecSYc367ejP1bdyE3IwOO9KXzKV0Kox4dhG4VSxv7mVaSM22w45IrXmwQY9pcoL7hoiserxVfoM3SThbx4bwTnsjOZeAG/QYHVU2Am8M1UPnN1n1IGPsYHDp0wIM1Qi1NUWybi4sdSpZ0Js1MOrkLPVCzpif9HStZHJPIe/HszxvVsezMg0TSjZB6+jR8KjcHCMxVya1M570u8CX+mvH9C2haNgDtygUDbRpZnLO4RtEkZmU5kjPxKWpa2+O998IIAkWnl4ZHHolW90wocVq1oJmZWsYVK9Zh+ewJ5HLcrQJR8n0+KSsRF18SXNzc4e0frPwNG5SuWNyp9TErkoAGgFZ0M/RStAS0BLQEblQC8hA+zKhOce6PDD+rzHyR4ZewY+38QgBQIkD3bFqspt60aaEKIBgzZgKefvo1ctKVUkTL4jOWQSAmRfz6mjZtwNRmP2DsmjR4R6xAQvRlXLgQQX6+YaqP6YeYfJ98Mt+UKe1Ll85CCk2BNWtWK6ApDE9KhReDQdadj8SD3e4Fnc5gFxgIO0cHpOfmIW7jZtz/22y0evV5zHv6fthLEl6T4u6Yh8o+GTgW64gqvpkmRwhdsm2wm4EgWQR0xZXEDFucTXTAugtueKB6PHyccgXDFCpjfpoO+Pri6MSPCh27kYaQEHeC5a430hXVXvgAqQsWwpmA69FXnsHE51+HrZcX6j31GEqGhuCeaiGoQZ/LMh5ucHX8e4/tjIwchIQsMVtXvgD++KM9wXogs4o8T0qcDVi8eBtl87vqe3DHKkh+5Vq1qhPsS38bRiknkTtR3BbjcerUWRzewbzLq5JQ12x2vWudEvh7vyTrvCa9Ki0BLQEtgTtOAhdPhynqDyH+jScYG/f2/bh84ZTSsvn5+SIyMoqUHhUw4Mn3Cl37F6/dg4un882+Qp589OgJpQGsUK0BDh4OYz7bFOMYe2riJK2ZoQxp6o75Jx7BS3XiDE3X/Q4JKYfLUXFYvfcy3DMjFHHxK9/sxoWNIfljyy0DG4FGjXBx3o8qmlYOxKano82I0dg46nOUWrAUv3//BdqWp5bLpHQLScYfxz1xOt5R+QAmZtriUooDgVwO6geloUWpqxozkzG7LjvjeJyTapEo4YYl0jCsTmyxUcKuvj5IYjCK0LgUVS5cSKUGdR1iYvLBaFRUb5rJC4LWosYa2iXKOHXLVhCVwb9KJUx8+S2lVVv8xyRyCwYauhm/d+6MpWb2OJ55JpSAzN/YfqMVR0fL65s5swm5FYOM02zaNIX1jRIbpMr777/GCPCn8nf4+eqr+7Fnzxm1Hxvbxwj04+n7+Uet1sZ+umK9EtAA0HrvjV6ZloCWgJaAyqKxhdkzNiyZrqTRrNNAJJDOQ8DfF1+8r/y3Xn99JOo274rH3vgmP1CDANHTJ0Dl1ZVBbXo+TA3gEqZvO05+uYOoVKMRHhsxnibhkTgdtotBCo/j3nv7MECBmSRoHjaUTJpJZ4R547FaNw7+DGM7dRyAaNeqCKreEi7UWsXmBMEptAyyo+ORc46asWqX0aBjWyP4k3G+JJg++PX7GN2/Jz579R3c06IbAgf0x+4vRsDdBIgNqJyI89Ti2drk0WybCx/nXMNpi/wWnVU/jnOyu4poLPS8lJyKlczg4ZyXg8gVqyXUGdsirjDgpITqHZNmh3M874UrOdixPwUTXmKfq1QsnToFkTfPDhE8finZAVGpdsiiWbWsRxYyqJ10Z8BK7YB0uNLMfCAqFiNmzseejduRceRofgTwxYu4SGoYt1o1MO6D4YXAX1ZWLqpXX47oaIOW1gujRx8nkM+lub4J/TkdLFxR4Sbx6zx8uAsOHEhAxYpu5GDkNbA0aeJXoPNCgr8vWrfEfff1VqTbCQlOGDfuBDW9IYzOtqOPZ1ll4pZvmdNQnK5S2Bj29bf1SkADQOu9N3plWgJaAnepBM4c2YMF07+Eo6MLDmxfaZRCfwKjuXPnKH42aZw7dxHNtD/xwfwTNXyHsXb+L4rGJSszAy6u7uRy+xq1m3ZCy673qU3GiM9gJsmdV8+fhN0bFuPZZx/BRx+NkEOFygdbAtG7YhLNttcHWOaDD+5fTQ3VLITNc8YTT1TAQ99WR5abI/q+sx+JC3YB585iz29z8FiQPz7q3halPFyNU7zRqgGGrvsTj06eg41jvkGZ9Rvw2oQxeLt5XWOfsp5Zxrp5ZfU5mqqnz0d2UjLqNqiLvrUbISEjBIejnaglTDfvjkUnL+DZLyYgcdUagmYP5KYnwKnlQLS89wmcTyqPmUfyg0N8nHOQGxOHxwdthGOpQDR8ojWOHo5Hx46B3IKw/mIeSrlnoZpfBlqVzsaRGCesOe9GkJunQKAEmlT0zsKXq8Nw+mMGX5BsmfZU0O6KpeM+LAT6TBdqx6iUfv1KMeAmmpHUDRV469Rpk4oMdna2rNUzHW9aDw52gWyZmbnM6BKEKlU8GCxSEEC2jDuBllcHJSRkUlmbbzYODXVDjx7BaNDAB5UquTMifBtdBrz4MlJb9TZoDE3Pp+vWKQHNA2iF90XzAF67KZoH8Jos7tba3cYD+Mvnz2PnugUFiH4XLZqB+vVrK168kSPH0EfrB/VzWL/+L/rZVSVfX2+VL1caS5YMYmTnCFKwTCAX3Gl8OGk9rkScQdjuDUhNilNkwCcObFVRvpI27XuaWYVfzlJJp+ZqJ/3qTtLc2r9SIjxvEgjWqzcLZ886q6ldaoRg/OQW6FEhATsuX8Hiwyfw6/dTkbJjJwNb3VF36KN4tlcHaunKqYASw3oWnDiHR4YMQ15sLGxKloQjI1sDggLhz+u0oYbunpaMTK1cAdMPncDyjTtwmRq1yF274ehRiSTWcci5fJpgrQFyHdPhUcoLpenDVp/p1x6oVYmkzQHqNP69H0POgQOo9dovaFe3EuIz8tCyFDCgimsh/0DxoZs06QxlZkutaZkiNW8ChL7c5YfXGsUYLsX4LZQvE/YeweHIaGx8+0NQ9YrIJdMYCGOdOpkcprv74oujSEvLxYgRVZUGUC5m8uQzjCber64rLq4vaQzz0K31Igw8/IrxWq9WNA+guUSsYF8DQCu4CeZL0ADwmkQ0ALwmi7u1drcBwK/fHIxj++kTZlIkmtbJyYkam0wYcsYKSfNff00jp94jWLdus+rt7++HDz8crtKJ/fjjr6qvmOckYERAnhArOzs7UYPTES+++JQKADE5TYFqOv3xYqjtKlWqpAqwWHHWHSlZtqRHycO9VRILASPTwRIp/Pbbn+D48VOYP38upk6NpKapBKo2LEGuO3dFt9I3NFGZQ389fBIjx09B9PKVjORIg03p0qjQoR3u6dUJbzWtrcDgdNKgvDtuEhxJgpwYE4N08tsxyiQ/QpZjlBZNEJcrAZu/P7wadME9/V9BvUAX+gamYvHRFVjx5xLm6D2J3KiofP9D8tp5dX0duTStij9imR6d8GbLmuhZMdn0Um66LpQyG8NdcYVZSdqXSUZ1/3z/QNOJJOvJ4JmLsPKtD2BXtSrm//KV0cxs2u9W69m8JAmGcaHm8d8sAgzXrbuCevV8GC/jyN9bFlo3mI/nounHWLBoAFhQHlaxpwGgVdyGgovQAPCaPDQAvCaLu7V2twHAHGZXEOLk2KgIePuVUDl1jzIPb1JCDGKZ0istNYkmu1CmTJvE3LylVKqwiIhLipj5o4++YqRuuAJNASXLquhg+d2IlrB8+bL0D7RTmsU0AqmDB48ogPfuu6/gqaceKuDHVYcAKsMjBI7lmiCLPHTZh+fRzy5H5ecVIOrr60XzX0X6j9VD27bNqcBzN/48z527wCCSTmp/+PBrJMbGDqwIncuSMx7IuRqxKy5kbUrF4dvt2zH5p1+Rvo9apaQk2DDjhVPZMnDw80dJZhtpTXqUWoF+uL96iIoQFiA1cst+RPN6+lNz2MYkBZv4CIrJtaRbNiRwxLScT0zG01PnMaVdOEFSAv3c7sFLTeujhLuDabci6zkEWH+ezKeQkU6mZk9f8g+2I/BztCtyOCbtP4bhfR+Ae+tWOPjDZ8bUb0WPuPEjVMLRZO2FZsGpytx84yP/mZ5p0TGYUamp+WQaAJpLxAr2NQC0gptgvgQNAK9JRAPAa7K4W2t3GwA8c2wf/poyGscPbCOwINIwKYb8r0KsLKBl8+YdzM+7nObcbNXLJ6AkmrTvj4P0Gww/e8xkZPHVqKgjBIf55sf4dFs0f3ERZn7UD7X800ElD/487oHz4VcIPlPgkBmHtKiziDq8FieOHkN4+GWVk1Y0jBJEIuD05Mkz2L17P4YOHYL33x9e/Ml5VIJNvtnjh+frxxqDNF5YvhkLV6xD+lEGOtD8KyZgFTkss5F7xLdlc7z67GN4rE7lAoEkhpN9ut1fZQOpFZAfNGFoN/3eF+mIA9Gu1EjmwZ6azWD3bAS6Mvcxr1n8HoXt5EKSA4M2nOAswSPc5/+KN7BbhWSUILi8lRKZkopqHQfBlmD64qpZFtd/M/OKxm87+RFPx0tgDNC2bAp9Ea+/tsjIdOVTKD6M3t6W3QCut44rV9L5ErKSqeRyGJkcgj4d3XGgXxfzYRoAmkvECvY1ALSCm2C+BA0Ar0lEA8Brsrhba3cSABRT7JXwM5j8xYv8PoUMmlkdCJwyyb8XVLoCgzv8cPzgDnWrJatGdHSsMqMWde/dPLwQWqMJSodUw/mTh+DJnL6bl/2msj+IVk8AWRIDIdau3cR5xG/NgVrDYDr011U+hWLelRRhkr/XUAT8VOvyBo6tGG1oMn7LsWhGwop581C0s1Hz5Wyfi5I2lxC2bQ2O7N+rzvHMM48aQaVxgiIqkSl2+HxnAMa0uVysaflMfBLmHz+LmYvX4OT8BWAeMziSqHnbtyNRwfvaNRhO89dJD/hRcylp465X5NouEuydSbCnVpDWZfo/CjlyCbcscg+Sr7CYOAvRAIYx4COSchFS6fIMUJFAkOLKpN2HMbzPEFR6aAi2jHq1EOdhcWNNj8n5vtzph8Eksq7glVWs/EzHHT+ehJb9D8KBwSx1Kzrgh09DUNqTSJxF/BzXrYtSvn7r1+d/P/B0dWyKcEM5XptQ6RjodjZvjkbv3lvo/5f/suKGZHyAD01PJXUNAM0lYgX7GgBawU0wX4IGgNckogHgNVncrbXbHQAK6IuPuYxDu9Zhzvfv06SaDwxEmyfHDA9Oub8eXuSeS4gz3mp7B0dUb9AGrbs/gNTkRPgGBhMoVsSRPRsYHbwah3ZQ85JWGNxUqsSAi/GfEejVM85lqEjk56hRYahWzZYP7spUpuVr/gzHhey5RYseOH9+r6Hput9Cj3KKgSICgJLJyyekzAPpJ1hUrl5LE+6JdFYg6oHqCZYOF2rLJuB4Zt5KzHmBGsbKlRHSrjV2vvtcgQASGSSp447HOqFOIDVVpGERc3NxZSMziQjAFX9HSTdXxTcDNfx5z3ivxKx7OcUel3m8OgGegVLmM2obQ7wy0bp0CpzodyfXfSN0gB3GTcWe0V8ywwc1d/RddK8YgorNGqNZg9p4pFYoKvl4GZeaJHyH9C88T5Aam06uRnIZuvJcvx72RuOSqTR/F/4dGAdbqMyfH47np+ahTNQJnIi0QbmmFRAVnYnyaRHo1DEAX355nCZ/e6Sm58K1YXV88WUddApJwxVev/g1GuTYosVaEl5fu2fl/LO0D6AFeVtjkwaAVnhXNAC8dlM0ALwmi7u1djsDwOSEWPzw0eM4Gban0O0Tk6u9gxOqN2yLtuTpK1e5DjWCKbh07jgcnVzg5unD9FoliA2c1NhL508g8uIp/DDqqQJz/fjjlypQo0aNKqQGaazMsdJBNHtHjmwu0FfIokeOnIiZM2sb2x0doxhIchIDB/ZUY5966lV8+uk7pPe4x9jnZisSPTz5kA/alU1W2jMxS95I2XHJBUcI1hwJvDyYz7c7ffeKGyskynU+/QHxjPpN37kL9qGh6P/i03izYzOU8/JgTEkOlYTpzD/siv3RLooGRpbiS62gmEmvFyQhWsFjXM8h0se4UMuZlGmnTMQlaV7de8UZJWgu7lAuRWlCZf9MggOBo53yv6tEreH1ioD/H/cfx8pDx3Bw03bEhR1BdkQEaE+VRM146ZvP8Hb75phxxFvxHZb3zGRO4ix4UzYbw92opbRF9wpJxWonLa1h/QVXnE0gkTbJsyNX7cVbbx1S3Zyrlic1TTZeGuBGIE0tH4mmf/nlDNq0CaBfpy/316h+v//eFK3al8R3e/1QJu0SMpmTuHlzf2qbs1CldC5mVtY+gJbkbm1tGgBa2x3hejQAvHZTNAC8Jou7tXa7AkDR7r39cFPEkZRZSqkKVZGekoiS5aqg84BhqEgyZluzVGeW7nFc9CVM/fIVRgZvsXQYmzYtIg9bFXVs5MgvSRHzvapv2LCAabuqqfrEiZMxceJUleqtY8fW6Np1KB566LBxvmbNIvnwXoT4+ETy7c2DrW8IQshXZzDzGTveREWCa3cS0ImpuGO5ZDXfTQynOdYeC0554qnasTcEcF5Ytgm/vjUSDIFG8FsjcHj443jnnYPUhJ5Sp121qo3irpMdMTmvveDO1Gp/7xpnHfWk6TWxwGXxtmM1uf/EnCzXXdrj+r54phOkZtngl4PA2FnLkHnqFAIbNcHS5xsi0I0T/0PlLIFqeZqLDeXChRQMGbIDZ86lIZfBPf3uD0Gbln7ozPUbqH927IhFly4b1JAXX6yEDz6ooXwlF592V/6GBn5FHQRikKr1fxfU/Vv/evUKtQS0BLQEbgsJXDh1yAj+WnQdjEFPf6hSud3M4iUS+N3HWiIvNweff/4+qlYNZRqwH6kVtEft2tWpdWnE7BCVjVO+996rivpl2bI11AqWUO2zZv2Jb76ZhN9//0mNMXSOi6vE9HHp5CBej86d2xVI85WRk4iph7z/FgB0sAW61yyNZrHk5yP1y1YGKUipF5BmkRrFsC7DtwCn/pUS8As1iWKGbUyTpxAqF1UaMlvHr1euwLF/P7zdvZ3qNmhQGQUAhfTYNAVakFsOBlVJwI8HfBDqTf9L7t9KSaZGUFLRedLkayhiGu1IraAEZqzkdWfnZhQAW4Z+RX3/fsyLpmYbLHqjB56a8QeOzZ2Mqj8/hw7DX8Ln93a36OtomCsuLhMnTiTT9O9DE23RaldT8Cdjy5Rxw4YN7RSPX0pKNtMLOiA1K4mmfN7Eq6VxY19cudJbpb0rUcJZtYp2Vs4S4HJzINcwp/7+byWgNYD/rfwtnl1rAK+JRWsAr8nibq3drhpAoXM5tm8zQphv15lZOW6lHOX4cW/dr4Z6eXkiISFf2ySgb/PmxdedUvz5WrXqxSCQeQwIKXfd/qYdJIvF1nP2cGJEBPEImpNWxI8PejemMvs7JYWWUSYEuaki0a37rrjQvGqjtHZty6YWGr/+/CX0bd4Nj/w4DmO7tyl03FJDBH3qNpGzT3gNb6WIyVqAXvNSabcy3OIY0SBmkh5nH03K4ve3+cJR/DR1IlKWLQVJHPHwxLH4umNTi2Pff/8wwf4JRoiXJS1QfXI97sW0aefQsqU/5sxtAWfHokGhxQlvoFHM5GLu70BzvwBLrQG8AaFZSRetAbSSG6GXoSWgJXBnScDOjs7yDOD4O8WdfoCGYgB/si8ZF0aP/hY7d+7lw74fJEWcoYhf2f79hzFv3mJMn/4Hff5eV+BPTLJRDF6QYA3hiLtekELy6Ut4tfMGNa29swO+ntcDbv7u9DuzU1ofg4JJAAv/V35wEjQhRQIupPiTE09858TXztD/ZsGfzCPmaNmkTGPQA2N0pVqgJJGvUOhhZkybfcMAUDRXAnSvMG9voOvNaQEFnG0nALyvWkKBdfzdHZGTBJc0KZkPKmv5l0eg+/fY0X0d5j8zBAGu+do3S+eRPL0Ld+chsmxF/EYuwLQq1eHW1BN7yLPz6x76lXo5Xb13eeo3EEQfxug0e0V/U5yvpeFc3j7MksIUcYZy4EA8A1Ls4ePtqUze5ppFQz/9bZ0S0ADQOu+LXpWWgJbAXS6BjUtmYM6PHyhTnvD+SYo34f2LiYnF0aMn8Nln+Q/ihIQkIwDctWufygwiASCVmvXAc1N3wyHAk5GiQDYpTYKF0oTmVNHoXa/Uq+dt7JKdzsjO7vON+2IOXLq0lQoUMDayItowgYCbGEkrIOYMAw2EpiQ+w07tC1gUgCNEyqEMkjCAHNM5rlcXzj5L5a0Fq0AbJbLOnkPZIS/grdeewdP1qlrqamzbHemi1hBAoHqzZTZNtY/XilPZTG527M30j0yNwthxLyPxRAb8nvkDjzGFnaUi0c4n470wdbI3qjJC2cGWBNfVnJA3LETlC46NjUZoaL4mOpuXuymCgTHUMno45OKP3XZ4ukUGiPOLLabgr2evTdifG4xcAu8ds91QyucGflTFzq4P/r8loAHg/1vi+nxaAloCWgLFSCAzIx3fvvMATh7eyYwb3szB+oGK6HVycsSrrz6NQ4eO4vTpc6TpmKhmWbDgV+Ns8UyRJvQyzz77OB3578O8454EAjmkJsn31UrPscUWRo9uukjmEVKaNCphOVvEV18dx3ffnWAEaCOakP2xZMkl/PHHRUUaLCe7ciXDqNEznpwVg1ZRyJcXkIOvim8mGhFwStStQQMomkh7YgXDvun4G6mHeluOrh3bvwueO3cOMcwznLR2Hd51ccXTkz4rdkohT36hfswtrUWIo4X2Rrj3/o0iEc7tx0zC4Yk/qRR3LegD2JqBQyXcC5qrDaD6RJwT+jC9nhBYmxbxBZwy5Rw++ihMNbu62vFFoj3als/vZTATZzDH7xtvFA+YDfNKgNOWnQnw6lIDmZeiUaf+GsydUot0MIlwzkk2dNPfVi4BDQCt/Abp5WkJaAncPRJIT0vBmj8nKfAnVx0bG4/HH3/JogBatmyCRx4ZrPL7Gjp07NiGIG0hunUbTJB4lubfN9Qh0bgVR2RsGG/4Xrv2CrVGWXjssZ2qKS6uL6OGyxsOX/dbTL+P1Yon/54jdpPfL4ZmRskoIqTMncsn3xLgMpy0KK1hh3LBeOnRwRjx9GvKV64Vya6LKxIJbE9toiUgOnfuRQbF7EOTJr6kzGlaIIDEMOdDNeIZpexBQO1KHr40lSpPru8or1mutyjyafFjFGAeQJOz+M0JEDcvy05dwJChryD3+HGEPv4UhvZ/mZlIXGBPrarpesX/TjSR8i3mbNNgFNM5n35aeCFP8veUgNTUXcwhncmAIl8VTFSBGU3atbNhMJCv6ZBi6wIq169syUwvu3DifAZcalfCjz+e5ovCZfg6peHNYkfrg9YiAQ0AreVO6HUUkoCBILe4aLZCg3SDlsBtJoHsrEyaekdi28rZyGJdtCumxdXVhQ99yUohpNH5xNGS0/c1mjjbtGlu2lXVJdPHmjWLULfuE3jwwSQcty0J0fyl0xRryx6OBBF9qSkqChBGR2cw4xr96UzKjBnnSBNSzqTlxqqVqQGsQI2dRARLuZBoj+lh3gp4yfnlUoXXrn5Q2nU5+fJnKP7zRGyiyiHs3bE95j01uNjO4vMnZuip9ClswPPXFLLnq+XYsSQlg9Wrr2Dbthi0bh1gOGT8FiDWJzRJpbGTABXhABR/OvGDFOLoLJpZLYE7CaJ5sEaCIpT+43g+eHPgPRFTbCJzJM8gp+GOSRNh71wK/X+ejjblA9CrYhJlJmZd4+lVRfz2JAPI9YoQOp861R2TJ8/CK68sIIXLgkJD1q4VRaMLAWIaeQFfwvDhzxbqY9pQq5YXlq5qhw/mATW8knFf+/LMT30BS+ccZV460566bq0S0FHAVnhndBRw/k0RAPjsrqlo5h+Kh0JaWeGd0kv6f0jgdo0Cvp5sIkj4vH7RNJW3VzKFDBjQmwktKqJHj07M7ZuF8uXLko4j32eruLnEPPvaa/tx6VI6qV6aYjlz6M6ceQzpmTXgM6AjMk5exMNNMpn9o6by2ZNMFououZKctyEEZ8HuWQp8GMDKqVPJJP1dVeiUogW8lbKOpMPhyQ6MtE0oBGAEAJ5LdFDBFNmMfG1PcmYhWb7VIn8zWo6dgiNjxsKlZQss/e5j1Anyu+50QnkjwSqiyTMEMggdigDhoCDn647/ux3ET1IyqLT9eAzCZ85DQJ/78N2LzzNQIxfujhTS3yhnz6agXr2Vaob169vSh/QQ9u49qNwIVq5cT3N+tDo24In3cHDHKsU3KTRDly4dUu4ERZ1aSLY/XeuGwTWScGzPFeUqUKnSUuhUcEVJzPratQbQ+u6JXpGWgJbAHS6B9NRkfDSsk/EqJW2bRPPm0E5qxwiNm9F6R0SkKfAnkz3yyCxmvyjPWnWCvTwkLN4E5+oVMI0RoAEbXdC8sg157zIxtHYcEjNscZbgawf94BIybMj5ZsfUYrnMwOGO+QfuRxYB2RWaSffujVeBHRsY2FHLP52mzoI+ZnLe4kpbpiiTVHG/HBSqkBSldTP0Fy2aAC5FH8LsIZtpTl17wY3BJDZKK6dSsBk638C3EGtvevkRPBNSBr+/Mwpt2/TBT/OnYUDVkGJHP1wzntcLLDvjwUAKR8XjJynyzNPkFTvJ3zgoATMOTlm4+Mdc2LdqgmNjn+FsNx+YYmkJcg116niT5DsLTk52NPe2VJuh7/33P82AntXYtnoOug5+HiXKhKoXE8kqM2hQH/VCIkFIonUW/1LZpIwceRiT1+ZgCvPjJR08jRH0Ifzkk5r4a+YR4JBhdv1tzRLQGkArvDuWNICjXn8DKaQ4kPL33gf/zgUXPLOZpervTFzk2FxebTP/StQAtiyyjz5wZ0vgTtEA5ubkYP+2FTh7fD+2r5qDhLh8zYvcPT+/IGpm6LvGMmBAafz0U0NVv5GPzMxMPProyzRV7qWWbwQf1PWwfXusmqdpUz9I7t9sCc9lvtlL1P5J5GsGwdaZLafgnxWPBx4oB0/P/PBP8RUUehNJMfbJR4cx40c+zK8We39Gl7atiMeeqwEPFxI6B6ahHM23hsAPQ7+ivuXvhaQgE21gDiOS25VJRhlPy9o+6bvzsouirBHtWD3J43uVWqao+c3be05fgM0vvY5yw1/BvjeepGkzW/k1Bgc7E6gcgb+/E556qmKBYWK2/ZlA9em6cQXa/1871d4YjcvTpsO/dw+s+eJdyscNJ+McUZGgXcCy3EYB05dSmMmDaeGiaHIWWYkmlw9zFajz9tsH6TdaAW++WU0tWzKLLCWwFTlyCmMRDkTxHazsHo/Ta6dhyo+TmP/5Irx9A1G7WRfs2bQYkspQiryQyPltbGyZbrA8+vbtxlZ3LNllgxqMMj992oP8g83oV+ipeQCVxG6PD60BvD3uE4Kcyenk4lHkak3/YRfVqeA/fwu9zCYx27UwQJoK9iq4V8QQs+ZC6zKZxI5/cDqWqGE2Qu9qCdx+Ehj9Ui+cP3VYOd7XrFkNr333kfLrE61VRIQvzbjn1EW1bx94wxe3atV6PP306+jYsRUzQGyi1i/fSWzgwDLGOSQDhpgBe/ZcSW1OSfIH1qavYS5CnwyHQwl/vP/VJjSr7oCpUxvDx8cR3tTweSMXnZq7Y+0iF64tTc2VHR2PQ3/sRtXHXFG9agCDHZyosXODu0OO8icUfzTJitGVQR5+FmhVBEAYCJwFCO6LciHASTKu07QifcUcK5uU1efcFDFy3ZsAgfvn0s+tShUsffYBNUebNutx8mTB81Wv7knT5TX/PjGDy3UcjHKCRDL/v8vhT4fj0TrVsWDE+6h/KRJhsyZi2yUvlRbvIoGz+BdKRHUpZkkRAmsJJBEQN3aaDeb/sA/pR8+qJX/+xXHU7l0L0QRpotU9MHsXvvsqPwpYXjB++KEBM6z4Khqb2HQ7ODo/hTfaP4PDW1dj6qgXsWHxr8ZLf27kVIi7QnZmBl8m0nFox2pGpo9Xv13pZFD2de/ujZUr52DLirXGsbpi3RLQGkArvD+WNIBuv+2BjZefFa5WL0lL4N+VwJ2gARz1TCeEnz1O6pYPGVl7v0WBiYaKLmxwd7/+e/mZM+fw5HMf4sLlWEyd8D6jVetZnNPQuHVrDLp332jYVd8zZjTG2+8dQaRLIOz9fZAdFYt+dcVMaI9nnqlIH0Q3Bf48Pe0ZaWxPreRpBAY6wRRcykTpVOKJRi+Xmx2pUcSEm0DePynEUiqNW3Xy0pmncVt6xl0RRTdiircbKbOOeik/va6MWhWTaVFla/gV3PPpeGTMnQefhx7E6dGvq64+PvMLDBk4sDTGjKlj1H4aDhq0j/ujnFWwjICs/3eZtOsQhg98FHZVKmPKd9/jUEwFxd34dN1Ymuj5IzEr06efwyufXIBTxdLIoxrXzsMVcz8tgYa0fEvwUIsW63DkSIIaVbeuD1atao3Jh33xSM24Qhrco0cT8d7oI7h4bD7H/AE3kpG36nY/ylSsiYrVG8KLGkIpMm92VgauhJ/FqGe7SotqFzvVB6pW4CPwtby8qAIteuc/l4AGgP/5LSi8AEsAcFxAMPwr1cajw8fB0enfd0ouvCrdoiXw30jgdgeAsVfC8fYjzZlvty21bN8xm5dTAUHGxmagQ4e11NKlE3zZUovSGrVqeRfoY77z/PNv4mSiK1764FNmYnBQj14BWzXoo1eVvHtSF9OsaNIMRbR+n356FGPHHjc04cyZ7jT7pTKaeB0cSgfBsXQAbB0dOR//y8iCjRNNw1Qxpe47jpgLnY3jbrQiJssTNGHup7ZPgNUgRqyKhk2KHJOsHuLnVxS1S37Pa5+x6baYddQbj9QomoC59LtjkfLTzyj15FAseW0oo4zd1QSbN0dT+3mUkbCNlKbT1rCQa9MXqMn6xA9Sspj8nfL007sZnHMB335bT5nbb3SuqXvD8FLfB1GyVw8c/Gakkpvp/TTMI0CsfPklzAGdZWhS32vDBmJPtJtafy77RIQzlzLzEgeU8+HvI09l/2hVOrXAGJnrs8+OMu/0Mbbn0AdyEyOD9yCKOaml2FDD/N2CU0ZNs2rkh7g3HNm7EbbMflPGvwTwVAfDIcO3BoAGSVjR9/VfNa1osXfzUuL4D/Bi9CVkpKdoAHg3/xD0tVu9BITI2c6e+XP5MJTiTs29+FAFBPgpZ/uwsGOIjo6h39R5cvZtM5rSgAeQkVGHxzIU3YsAFKF9kdyuP/xwGpMmNaQmx59tuShdOhhbZv+FLhWuPcAFsIQxrdnCU54KEKbR90sAg7dTjqI4KeORhXffraa0exI5fOJEMh/u9qhd2xvff9+ApuTdaF0lj9xuIaR84bq4/pbNvLFxaxzcGt+aG4aA0Gp+mWozv3FyTLgCJfBj/gkP9K2UZN6l0L4vzdMPVY/D7+S+u49g0lyrKAPcCVJSPDxwaORLBcaL7BYsaFmgrbgdWZ/wBP7dIuBPyvPP78U77xzCxIn1ydNYsshpz1+Niq5bqj5qv/45js36Cz2/X4pxg3oJAkMMTbYpWbZKI1ia97Skm/iAlsesWRc4f1Wk5pCKhkEv51Kc8CiDW4ygsbqckheFhCLPLVrefPAnXXielDZ8YTls7J/H396HT7VDqfLVEFqjsdIIlglldDkDQ2o0bKv65SXEIMU4QlesWQJaA2iFd8eSBjB5zFwEMan8zUQHWuGl6SVpCdy0BG4nDeCWFbMxfdzr9PNzxCuf/0FfuwxcvngSM8aNKPa6BewJgXN4uDfuvXer6hsQYE8wmK00Z4bBZcv+SmqS8yhbVsyXH6BBgzqGQ0V+x1NrdjjGWfmR7d2fgF9/Oo53B9gT8FUsMEZoT0RL6O3tWKBdIpP/Ou6GIE+gRalrgLNAp7+xI8EJQowsRNGSxaJnSNI10HIL876/cTe+ufcR2IaEoPGgflj6wsO3MMs/N0S0aq1arcXhw4lqUjGt793bqcgTCJDnEOVjmZCUhWenTEfk6q1wrl8HwzoPwKlNUXj4/tIIqegB8QsUsm0PxxxueTjN1HuXk+2UpjXUp6BG8Mknd2HZskj06RNMDbMXevcOZoaZa9akjIwc8vhdVBlgatb0YlBSJtatE6ttNLfd3Py5xZJSJora6nMMqInlPuDi6obaTbug+/0vIjC4PBQAvK++OmbyoTWAJsKwlqoGgNZyJ0zWYQkAzqzVFA++9xNc3PhXWBctgbtIArcLABTH+d/Gv2O8M6IlCT97xLgvGkDh9hOuv/xCQmZGU7Zo0cSYzWPZssukg9lmHOMzsCOyYxIUl1/WlRiM+9yf5M6NjcdvtpKQkIXq95xExpkIRJ/pcFPD9zFvrARHCIGzWE9LMK9w8+BUEkvf1DTFdpaI122XXBQQlIwhxfn6FTWRaEifm78aC36bi5RNmxH6zFPY+e5zRXX/v7aLf52fnxO1wQXdACwt4ty5FJJ5rzQesgkcD48m3yEv1QM2UdE4t7628ZhEbieQ1qcUORRFfhIoImn4TIuPz1/cJbK8Wlq08MOiRa3UXhKBZvnyi5UPqjSsX99Wgb/337+m/VMd+SEazGHD9rCWxZcIe6YmXIFduzZSe52JNj0fRt/+TyHrkWaG7oZvDQANkrCib20CtqKbUdxSjh/chkUzvsbAJ98rrps+piWgJfAfScDNw4fuGS7IzEhTKzAFf1WrVsLWrUuuu7KuXUuQvqOqMsM5O29Fuzodkb7tIlYRdHm0bUB/vZjrzlFcBy8vB7zazwmrrjTGzaaHkwhcQxSuaKguJDlgrmSy4AklL66kfxMzs2S1CCLJtCszXtxskcwcsp1lVo0/T3gqP8FuDPrwIUdeSkqqIsZOT88g2MiAl5enxeklEnpCv05qa/nJRBwe/z2+69oWzzWqqfoLQBSw4uJyTftlcaK/2XiAYPlgtLMyuoqMeAup2fQijwu9667kISU9F55udiS/TiYpd06hs5Ut68qsHZUxJ6MWapS3Q6JjA+ybMQ112j+Ee+tXLtBfAkMMwSGS1eSnA768FwX9JFu39sOGDdFo2dIP+9MD4NKsFH474onXh+9Xc3k3q4mUqESMGtMQIVXzlGuAAMMxY/J9Rn19HenC0EplRsk/uQNdB0B/yl6Mrv4S7703mv6VU3Fk3Z94rcDq9I61SkBrAK3wzljSAH7AdVZt1RMly1eBLwNCmnUcYIUr10vSEvjnJXC7aADlyqd+9Qq2rZqrnOHF0b5HDwK49EwS6vZGldb9VcDDjUpo2LDh5Krzw0cfjUBsqg3GrHbE+10ZKOJA9ZuFIqbahx/egd2748gD2MEY3SrZLJo1W42LF9No3nUgV1xzuFUIwh7yAQpEq0zAdaORuBZOq5rEbCn8dBcJCjMZOCHZRtLIJWhYqZynik+GApBFpaCzNLeYh/8Ic8Tnj3fB+RNHSGhcA8ePn6JMMxRB8TPPPEpQ6IbZ9Ic8fz6c5vEUJCWlMGghmtlM6sI3KBA/TP1dkBeaNaiNOlUq0p9yGv0zbWluP8Acv46WTvu32tLJsSj5eX2dsyERy+JLaF769NmkwFj79kGYO7eQtkxp8yTHsIDhcuT7C6MJ/1isHd6YOAMZ58OwZtJL9Kt0NZ9W7Qs4PxztSJoYt+vyGYp5+ssvj+MTBgd99U0DavAqKCD5/T5fvN00Clu2RJMrcTdllf9SM316Y8o9WPFKLl58SYHBwYPLKqJpObncm3s6DcTLiUnma9MaQHOJWMG+1gBawU240SXs3rgIkI0lMz0VoTWbwNuvBFzdPbVv4I0KUffTEvgHJJBFPrQZ37yJM8f2oHW3B9Ch3xMqmKNcpTrYvWERgU8ugdYv9P269nCff8JRpV+7UVqRffsOM2DjUbVaX9c8PNc2Gx8utMfyZZdwYcdpvDLYC6++WoX+hvkIQx7mBw4wx+zldPTrt1VRfchg4QEUQug//rioskF07LjeKAGJiD1TtRoqeGUqDZ7xwE1WBOQEueWozdJQASUSoPIHff2ySReTQ5DYs2IiQZLoxoouYgLe+sMruHDyKJoRWBzZvQ6VardEYOmKWLFoKk2YK9Rge3sHuHp48lqdGYDjSOqSACxeupba2HSqJ/mY47Z790Hs3Lhd3afs7ByCxgWktfEjOG50Qyn3il7ltSPCybfmvJsiyS6OR9DDIx94rlkTeW0wa3v3xqHf/btRqkE5dHm6CXypVd100YvZSa4gLmk/vCtXQXxcDoavzUWPUB9FvSNBPhLoIWBbgLZ8iyZWsr0YimhUN0e4Ki2k9BFtYcdyyYwQBvknq6gtv2+WykbTwC8RP+1xhweJqD/6s4KilqnpGovKZfNN1/a84X36lFKb4RzyLSTREgCly+0hAa0BtML7ZEkD2HjxbyhVJQShoU0KrdjJ2QV1m3dTgLBR295wcrb8ZlhooG7QErgNJGCNGsCd6/7CL5+/oKQnL2Cf/7YX377zAPOo0mzLVFvff/8FwYV/AelKRgYxzQkVSgm3bHVMzLDCNyfRrMKVZyiizerS5V5GAM9TWhYBUB06BJLMeZPq4lIjBHa+Xvh4QB6DRsoo+hhDgJhEcgroe/jh8obpjN+TJ5+hWXG/cd+zbihGftsC1f3/vhbQOOkNVCTjxvQj3mhBH8LKZr5qMjw+PkFFTD/zzBtqtrKVauHNcYsKzCzUI+dPHkR8bKSKQHVwyAcnBTpxZ2n4fiwI34PmzCj0IDMKCS3PV68PQMyVCNVV5BYaWoFBEV0JuB+Br6+P+RTX3Rffxd2Rzjif5Ij2zHBSLyjdSHdjGCxaUcnEUsk7A24OubhwIZXncjLyPu7dn4huT52CA3MXZ5wJx4KfqqBpE198snU/vugxkDZkomwvL9pcfTHw4/F4sEZNLN8Qj6Vr4zD5eSemAMxRmjhn54JOmfIbG0+N3vP1YhVQlPUInc7MMG88xChhTxNeQYlYlmhwQ6lay4e/5YYoXcFT8TvaEWK2Ymo/MfdbKuPG/YAvPhiDDwof1BrAwjL5z1s0APzPb0HhBVgCgENObMMl8vP3atMWmXbu+PrLd+Hvyci+ixGkNljObZmaqFHbPnjs9W8KT6pbtARuUwlYIwBMS0nEwl+/QnpaMroOeg7LZ09gBoTfaV4cQ0DWp0hJy8N4/klPxS8noI67qBOQjtPMP5tMag8pwe5Z2LZgGvZuWoUVy7qqNvlo0yZAcb1Jbl4pDiX94VSpLCfJRW5yGsaOq4vG5XJVXl3VwezjgQd2YPHiCJQp40JNU2dEJtti5NYgfNvpMv32zDr/H3bl+r/bm5+NQvwFExISqb28onz9OnToX2AFr33xByrWaFSg7UZ2DsVdwA8n1zLCOAfv1+qHIBcCqKslMz0N8TGXsXfzMuxmBPbFM0fgRJPw1q2LVbCOod+FRHtsoDlVitwhLtsI7kKoOT1O8Ccgqjujl6VYMvlKWjvRwtUkT6NwIoqmUAJpFi26hPUbGGlLYQy+ryymfLQZTUMFkLqj07CmOJPiSpqXPXi8CwFg9eoI//NndQ5Xx3wtm5Bbu7driOS1u1S7ZDX5eVZrlfrNQGEjvzkB2eZci/JCsogp3ITrUDStkud5/6pTyuSrJrv6IbmpT5/urlwKZMw6En3HkYpG5CDX3yw4jfcshb/9qUyxNw6NG7ZF/x1rTKeQugaA5hKxgv38X5EVLEQvoXgJzD3mCd+SXoy42ojfyIjfo1qCGtCkSX1GitU0AsCajdoXP5E+qiWgJfC3JSDR+Pc+/YGa5/zJQwr8vfzy0wXAX2RkFE2071Mj2Jy5WR9QfcX/rX/lxELnr0kyZEM5Rx64z8ISENr+BYx6rj4u0aQbdSWT4C0cttQYuTVNJd2GO/O0ZsDh6BFkMddvk9ZBaFU2AyeTXLGVpj4DpYr49hm0jaVLO6tTXLiQRt/Cv9CpUxCeHtNR8fAZUrQZ1vD/+Baz5WO14hjs4QH7g9OoeRpe4LTlqPV78p0flc9zgQM3sCOBHr+c3oDdsWfIl2eLJ0LbFQB/MoUjLSeBpSqgy73D1LZt9VxM/fIVZJAKR4qktdvAlHXJWXa4n1pbWa9pES2m0K4MqpKgALRQ6Hw7/hSy3T3w8H3BjGTOo5+lM/ZctMOc7w/CMzocv/3WFNWqZhqniVhyEYs3hqF5r1BUreaJMXN6Ke5B+Z2U9klHNOdccTqaPj+Z+O27j2EAfoYJtm3rgK83uSCgZiiJpk/SlFtZRQA/WD2+0HoNYwzfTra5yNlzSIHadt2CcTDRE4dtS8GtSQZyEpJhd+ECKlXy4ObOLDIb8NxzlejLWoZAN9kwBY7EOGImn0ffv/AQDu3aiuoNWmPwsFHI2tHc2EdXrFcCGgBa770psLL+VRJhyzydy866q8Tgpgft7a+9vufkZJke0nUtAS2Bf1kCKYlx6gw//jgNp06dQdeu7TFhwmS+rB1V7ceOnTQCwBtZSvyZfQhb+B0mv9MT7iRRPhHnjPgMF7iW9EFZxyTkurmjBDkCXWk2dnGoAWe7/AjQZJpE9y8+jFpV3dG6tT8BjB197vKDEQRgfvxJbYwcWRPvv39IEUu7uzswAjUFop2aecTLuDRJ5ybZOUxBqfHgP1xxo+ZvcJU4+Le4Bv5G0NTrE1ASnt7+t3S2iymkyzm2AsnZ6Sjp4o2XqnSlhs7lunOJOdnJhTmKc2pjV1h+BpAWpVKK9GsUrakp1YpQ7Hz85Rm4t6oH30rkUyzpCYnITVizFwfm5/8W0tNzjCZfWZBE+comwS6OtskFQJto277cmYkFTz8J2oox58BxBpWULnAdVap4oHaaL4Y9bq/ubYGDRewcOpSg0vr17BlMk/ce1euBB6Ixffp5KhO8kRWWCIdGtZHCvMLTpzdhn93kMExS1C8eHvYqCMQwtYHku9TbT2HAgB24fP4EkhNjYdkYbxilv61FAtoEbC13wmQdlkzA9guPwMXfV6nxy3sVBnnlyzdQJhR7EtB+OfugzhZiIk9dvb0lYI0mYCF4PrZvCzavmIVjTIGVlppiUcjDhj2CUaPeLJQ6SzpL1Oyqc27MpWuLNmVSlF/Y7/ty8N33v2HQPe1RMqSq4nWrxOhZP/K6eZIk2cCLJ1G3wv0m+XcFPCSzfj46Fw8/GwY7NxdkxyWiS8VUTP6lEY/ZKCJo8VOTOa5HtCzmyR3kkhOalwHUVpprvixe6N9s3Lp1J7VM96tZJi45d8uzzb+wC8svHVSBEN2C66JX6XqF5srJycYJ0molJcSSxNhd5biV/LbvPdYSUZcvYOzqM7B3dlMm2nKeWQXIr8VsncYoXzGbinwO0H9TFIMiI9G6njmbhsMEUL++6Mi4E6rxWCTHs6TbE22amFNvpAj4E0tPVPJRjH73JZBnRQWybN66FNUDfIxTbItwVr+B5qXSjG0ShbyLoF7WJyWAlDwtSeBtyJoiROMrV0aSRNwHw4dXwaVL6YoY+vHHd+L116uoCOkXPo2AW2k/HJ0XwmCZCyqLScWKblixog2xqOXI6YMHw+ij+gAcqLUckVbo34M2ARvvkPVUtAbQeu5FsSsZSDODG9/6LZVp036n03f+P/ac7CydLs6SkHSblsBNSEAiaqMizuLQzjUqK0OtJh3gH1QGu9YvwPrF03D2+H6V/1S46Lp2aUNzahvm2f2Gjv3hfPDb0RTcFx98MFylfzOcVh7M8lAX5ymJ7nS2z0VUqj2eqx+ruhw9egJfPXAf53kXpRtXQNOScYp02TDe9FuwhY9ZBG1lX+Zr+LUsqlVbBofgAKxyL6kifwcMKK1eHMUHTPLyTg/zUlGgQtPiSN+v3ozGXb3yMoMuLqlctRERaQRjJeno78ygFR+0JTi1FKhhup6/U9+1ax+1S/nBHoOf+eiWpkrITMVXR5fiSnoiPO2d8WLVrgh2vQaUTCcd/95DzFu72bQJH05aD/+S5RUAtO9QAQ/EnVD3/WisE3497KXug4A+KRJ168AUcWJal3R0BQByNXbolm9qV535Ien2qlb1NOwW+y1m579oEs8hsBfwPfUgfxs0xSqG5ubN4S65mU2K/AZ2kc7nbKKj4nWU34Xy56NfaUvm+RXAGsnf2KSDPniydqwi7f7ii9r4668IkpCXYkYZV+Ns+/Z1NtZ79CiJHEZWOzK7yAMPlLuhHMa1alXH5s2L0ap+B+M8umLdEtAaQCu8P5Y0gBIE4kJOMEulVateytxUt1kXtO7xIKrVb2Wpm27TErgtJfD/1gBKcMDrQxogw0yL4eHlq7RGktFj4MA+uP/+fvTLr0wAcH2tjmj6hCOvHx/qEjCw85IzjjCFVznPbOWDVy1yNp5/9iuMG/ciKVxIYsyoTT+CROGSCyJJcIh3ppHo93o3UUyRo0cfxeTV2bAv6YesS9GYNqYiOrci+DQrEg264qwHFi+5jHXrryDjVDiywq+QJNkOERG9VLCC+BRKpLIAxdIe2WYz3Pqu+OkNG/a64vDzpgbusRHfoRKprW62bIw8hlnntjKgJg9N/CrioQotLWpcDfNOGfMytq+Zp3bFYuLlG4SgUiEI27NetS1bNgtNmjQwdP+/fEughgQCCVVLn9BEI7gftWUfvuz3AB74dgy+Hdj1htayalUkf59b8dVXdVWOYBkk2ubV/A0KR2Mws4WI6f9mSjZVzvv2xZPs+bCKRhf6oaLK5O9+Rs67n5kf1hpAc4lYwb5llZIVLEwvwbIEJFJu3rwl5Fsqx7e7PIwf/4sCf217PYxBw0ZaHqRbtQS0BG5YAuLDZA7+ZLCYDKVERcWQb+8QPv74TbVf3IdEkK4454HGJVLJu3btobv+opvSrEkk6OlF4zGO1C2ZmUMxdGgK6tdPwbP1mGiLoCAxw06RKgu33KVkewypnnBdICjZPj75pBaeJXlvzdorYOfjidXJpdE2J6lQ2jbh4RtMLdbgqi74JCMV30a5wblyQ2RcjFSXJeuT/L/NSNey6JQH1l+0pc9hnlq7ANS/U+bOXaTAnwRivP/9apJn293UdJnZ2fj2+AqcTI6k/xxzG1fqgGpewcXOIfyN4WcO5/chcHf19GE2DlvEhIepth49Ov1fwZ9o6EQjm0dDcohXhpKrqWZ3RNPamNS3D6a/MBzTP/gUtn5+sA8NxVevPokhNUILXOuZM3QjYGaR2NhM1R4fn/8tO3Kv7q2aqNoFDN5sGTPmGF8qjqlhW7fGMJ2hB3r1sizrxo3rYevNnkD3/08koAHgfyL2WzupAL777nuKNAW7jBM402m57yNvoPPAYcY2XdES0BK4dQn4BpbC+EVnkJqcyC0eW1fOxrLfxxeY8J13Ximwb74jplbRnPm5ZOPRmnFG2hBDv1cbxmD5KQeM/HQCojdeJvirZThEHysHVRd+Z3lwu+RlIGztSdQJ9aYWpyRi0u1RkfQb14vcLVXKBXExfdRco7Y6k4g5y5jKzXgyk8pbb1bDiDfyGG18CWGxFTBDAROgErNRCEVI79Ak1Vv80yQYLYXmyp4Vk4zaKpOpbqgqZvM2bZoz7+wWHNixEnWb3ZiGSyY/khCO70+soUYrG5U9SuDZKp0UCCzuxInx0fj8pV6K/08Itl944QnE2JdW96msR2YBgF7cPJaOVay4RAGv554LZeaWmpa6FGqTvMoSgNOKplqRsaViT+6/4999iNd6d8EGkljHMcgocdkyPO/mhpLDPyNozFa0PydOJKFx49VqirCwLvTr6wVzPkDD/NcD7idPJjOgYwuCg10g5uKHHtpJGphkBoh4UQuYoKbZsiWmSAAo+a41ADRI27q/NQC07vtTYHU5jPIzgL8PflxLtxCah0qHFmvuKDCB3tES0BK4IQnERUXgh4+G4sLpI8rEK0ClVasmjPDtUKzZV7jeRLtXilx+xVFxiNX43fvaId4lFPd98RmmTo5D2smLGPlcELl+CzrZz5x5ns76B9S633uvOl5+ubICDlMOeeMREvler2RSUScARwIWDLl8ixpjS5WfaHZ6qQ4JyodMMniI9k/8BSW1mXD29auUBAOZ86PXWYOkqBOKFHNA4u3thT//nMLctL0wlWZZn9GlUS60ePAkZuOpZzZiR8xp0rvY4IHyLdAisHJRl2Nsj7p0DmPfGIj0lAQCzvnMc1tDHQtChiLgPsP7Jv6ZvC3K7C0ceuKDdyNp62LTbFRghGjexKcul1E0AqIqVHAzZmk5cSoZnbtshLOTHeYt74A9ib4q6OexWvHGNRZVcWRmjdHtWuCX3CC8s2OkIoR2r9CNgDdbZZaRcSEh7hgxoqoy3ZcowcCUG3BLKOp8KSnZOHcuVW0tW641dqte3ZPX5I5u3UpQ02jP4KYwPPJIeZQu7WrsIxX5Delye0hAA8Db4z7hi8/HY+7yNWq1g58ZReAXcpusXC9TS+D2kIAEUP019Qv6h/2BxLgYZtdwVBk9Onduy4T33gUuYsNFV4TTp0/Im4WGRZI0SCnBiMv7q8UzSCB/v7jPJUtmKrqYRaMGwC4uHZU7jIR3q7r010pTmj/DWHnIygP3r7/CaZ70Vc3C77dx7WWUe+48xr3kqzj9unXbxEhTN2YPqaf6G8Y70uL3UM18zY2h7Ua/BUcIJYxswk84ndkjyjAyVrJdzCJgEnPw9UqvXpv44hrD3LdtGW3qXaC7AJXZs39CzZqt8dkLPfDxlC0QDaylEpEah6+PLkMS6V1KOHvhJQZ6eDkWBB/m4xKYJURS9h3csZr0K25YvXoeg2QqmXdjKrwsbtdkFEFz+/f7fdG5fDJCmbmjOEC1M9INr0/vh76VSJ/C+z5ixAFFs9O4sS+WL2+tQPR3u32RGRKKbBI4j59P0u5nyB14A78Rw0K/++4ExvxObr6M1sipylRwbduqIA/DcYkufuONqsyFnI3mzdfi6NFERfj90EPlyUV5Y36qMpdEgNfkPXrmhSrYtzsaW6npc3KyxVtvVeOcSQTs4WoznPebb06SVLwTRNusy+0nAR0EYoX3zFIQyEg+YbwrVEXvh4ZDkz1b4U3TS/rXJPD/CAKJvnyeqdwexBVG/nbo0IquFvegY8e21MZ5FHldotmqXn0ZrlzJwLp1bVUariI738CBihUbY+PO9dgdF6hoXQRaySY4wZbcfLUDMlDVV8BI/mRr117BkHcjYcPI0E8fZuTri/vUgVat/AkWWxQLWvJnuLVPSWn210kP5Rd4nqAwliZpH9LU5OeWLQwI33rrICZOPEUKkdZo1CgfwMqZxaVFshh98snXOH78FPOaB2LU5C3MJZtvAjdd3cILe7D00n7V1LVkbfQu08D0sMW6ZPb49u0h9OdMptb0KWXydXEpGKFrceDVRgFDn/+RjgkzohDgY4exH4bgTJKzus5SDKQwLVdS8zkXezAbSFZMAho3WYN+z9RHp0FVkcHAC/GhDGUgz8WLacq0eqN0MKfjHTBmpz8qO8dj8mc74NzJEZd2bEPmpX3IcY9Hx8H98Hmv9ihBcCvFljLt0GE99u+PV/vyIcE8EtRzvSIAf0u4K7IysvHma3tg505QRxJtO1LQnDnYVtHZLF16GX5+jti0KRpTp56j2dkPP/5Yj1PbGF860qJjMKNSU/PT6SAQc4lYwb4GgFZwE8yXYAkAukzfydyfgeZd9b6WwB0vgX8TAGakp2L9ol+xYOrnfIC54Pfff0TTpg1vSKZRURl0hl+q+h492hVBQcWDi6Wn3UkqnI1oOuHHM5WWUMGISVY4/gTUtWt3D95880WIxtG8CBgRbrfdpPwYQg2j8PlJ2bw5GgOGh8Mh0Bfpx8/BPp6mZOaErVvXB2vXtjGf5ob3xYwppkAPj8JgTCaR9Uhk86VkBxWhXN0vHb+G+eDpOrFGvjnDyWSumJhMUuI4GZoIUA7jwQefVbQ5gaXKo/OAZyBpLB2dCsowMZPaMtK7XE5n8MtVepdSRdC7GCZPSojBr2OHK62fi5sH3pq8HI1qlkVl8imaBlgY+hf3fe5cCjVqa+Dk5oh5KzujhL+DiqaVwBlz87AE7QiAik6zV8Dd3SFHRfZK3lyDFrW4c1k6JtRB00hDI7+TxiXTaV7OJTXPccxevRFhi1Yg/RgDMxo2xMTPRiLPpgYzkuSbrmWujIwcZY51uKpqlNzDrq52BHBOJHZOYBDTEWU2rl3bu8CpJeK3QYNVirvQuUJJDP+4MV7pdvWto0BPciLytxYcvFC1LlnSCs2a+UEDQDMhWfGuNgFb8c0xXdrNRsiZjtV1LQEtgcISWLtgCub9PArZWTRptm+FX34ZZ1Hjl5ycQo3HdoKVCIKBRqhRo4qaTADNlSu91UO2OI3OpUuRNIHuRNixc4izCUD5IBcGdAQioHxV7LoUjMupDgg/dQRnY3OpRcz3TTNfrbhVNSaPX1W/DOWPV9ojC60ZPNCihT+2z3LFc8/vwQ4PNwx6qwXOn01CjbxL5lMU2E9MzCK4sy9SSzhhwkm8++5hRRYsmjtzvy5ZT+fyKWpOCXiZf9ILj9SIU7llzzA92hO1YuF9ladQxpqCv4iIy+QZvA8Ozu54ftSvqF6/dYG1GXa2RB3HzLNbyYmXi0Z+IXikQiuuo2i7qYCjY/s2Y/q44UyTF40PPniNNDOPIsfWCRE0jW4mOIshOBNi5PpBNCMTjF+vlCvnhvDwfI/ImJgMzJpyCg27hmLWMS+lmRVfwYHM0iRFcFabMqlmU6ZASLsFuP/GbCsC9O1t2MBvIQAXPkEhm05lXeYStwIp9kzTZs8+4mIwiNG7XlcBv1z/U3Wrqg2vPoFXlq7H5Cdfwl/HT+Dk95G4SEC+o3kewXV5Re5supjOnTcw13I6Vq1qowJWRJt34kQydu7saNpNEVjv39/Z2LbolDumHmIGEAb8CJg1LWJqNpQwEmALANTl9pGABoC3z73SK9US0BL4ByQgQGHflmWY88MHTJnWlD5zn9B5v3ShmQ8fPoYnn3wFR44cV35chg4TJvxOAFNTBWsYtCuGY4bv2Nh4mhzfxLZte+hD5UB/rFJ0lg/mw/UM1u6IweyoaMTFJTD6Nz/608bZE29/+zsWRIagu1uSygBimMv0WzgE72ce8E+2+aMkAYxEj0rgwYK/Whq7xaT5YEuE+NElGdsMFaEK6dZtAyIjM/Dll3Xw2GMVDIcKfPfvX1oBwN2742i6jWTwS4kCx013ZA0HGWASkZKfOeTng95G8GfaT+ppaekK/OXm2eLNb5aQg6+wVUMie8cfW4njSZdVZO8w0rvU8C58fwxziyn52P4tmP71ayrC155BE8uX/046ndpXu+QxfWaW2qQhiubafVecsfi0h+LcMwc1hnnNv2fMOM80eofRe0cszZ+NzQ9b3BdfUWfbHKZVO4c+TX0VKbRpR9HwXaJJ3ZWk4JnM6NKCpmJzzaJpf9P6itMXMZPmcyZ2hreLE1wzkmDrXBJzooLwU+u1hUzuH39cU+UL9vFxUMBefDKLKpGR6SSBtqXvqyMauUdj8FM78bZDMF4ZXg3NqtqpNIHuDrk4eDDBOEWtWl7Guq7cHhLQAPD2uE96lVoCWgI3IIGE2CuIZsRnxRqNLPbeu3kZzYOvMnVbMrVtNTFr1k+MTr1mmjQdNHz4hwgLO27axAwfA5gb9RzbzhPA9S1wzLCzdOlq9nkDgwffg0mTxnL+gmZNQz9L31m5cVhJipW15+3Ql4TA7szEYKm81TQaArQkAEXMyhLoYSjijyd+esRFSuMk2j55oEdEpKvIVAF/UvbsiSsSAJYs6UK+w96YMuWsMfDEML+lbyG4FpPw3khnBbCEQiaUwFB8FkV7df78RQaBbCPYnoTz1KS+/uV8i+DvSAKjr0+spt9cNkLdg/B85c6QKFhLJT4mEgumjcG+zUvU/RQQP3nBrzThN2D0rWXTtcwTQGLtTtRepmWncs3uiiRZfPQEyBYXwPrEEyHw9HSgxdXH0nIKtO267IxjcU7K7y9671kMeXQP3elssGNja4SGuuMoScCFGLxN6RQVgFJg8HV2IlNS8db8lZj39ke8mAA8+sVHGNO+MWJbO2Dienv8+NY6+qZ6ol497wIz9etXmiTj14C0eUCOofORI4nK7C37J05045bEgJAY7sXA66Ij/OuUx9uTU1CyrAdK1K6FYT9VwHPtaQr2LVo7a5hbf1uXBCz/y7KuNerVaAloCWgJWJRAOrN1/DXlcxzYtgKxpG4xlMfe+A6N2uSb7gxtl5io/sePn1K7Y8d+RH6zewuZFEWbtHv3fjq2/6rMtoaxhu8aNSqhXDl30ohY1nY8//yb1D6tYxDGNPapbhhW5LecT4JIAgOdlDlWzIjdQ5IhXHuioRJA0p/gylJ5qEY8NpJyRvo1L5XCIJF8baKMEd6+Occ8Ffnvd9+dJJ8bfcVYpkxpRDNgLxWV27p1gKVpjW2Sy3bo0BDj/vUqpkTXAj4PX7HBt3N2Y+nkr5hlY7sa7htQEsPe+xnlq9QpMF02tbIzzm7GtuiTNK3a4P5ypN0Jyje1m3YUIucr4WexduEUbFnxOxwIDrt0acd7OVCZ8YszEZvOI3UX+zz0ollT/BmFj+9LBltI1G8d+tsZitwf8Z3j8lC+vJuiPTEcK+77ZLwTOpZNJjjPIdWPH3z7t0dWZCye/zkL9w7xVmZfAce/HPIhAMxUmTkkjVtxRTTXpYe8iLQNG1Q37949sXfs+9S2Oqp9L0dmjCnrjnlL2kOixG+12Ivt+Wr57bfzzANciS9KTanNjsEjQ0ojPj4Vv45YrnqIFnFAbx/+Br2ReMGOv8NUVKCsdLk9JKAB4O1xn/QqtQTuWglEpsUXee27NyzEOoIB8zKDfmCr/piICtUaoFP/J3H22H5sJmAwlJdfflf58jVqVI/BDqkkuD2EmTPnkgR5FSTbjmkJCPRH715d8PDDg0hXUpWmXRtG/V4h8NigONGefDKEvG/HmS91mDIlHzq0nuaz/Iey6TyW6vPmhRNk7VKHhG5DOP5eeKESXBm1Kb5lX+/2Uz5klsCBgEVJ6SXbhguu9NNypW9bmgIXkvf3FFOLiX/eo4+WJ9XKBcXrlp2dR5O0Hdq2DbS0nL/ddvr0ObzzzqfULh4gsI1Wkb4eXj6479mPUb9ld7gznZ4UAXxhCRfJ53cKJ5OuICErVbUH0hT+ctVu8LZA77KTeZh/Gf286ic+0R3ufQJvjHgOdUo7M/hBNd/whxAbT5hwCk8+XRFRLoE4Qq7DepSd+Fgaivj8hYYuNewSAHVAlSoexv3iKhIVnM18vlK8vR1JQO2JGm5OCPbIoRYxXrXLx1N14qgJpNbugK/S+EqatqKKArdOTqDfAMhlg+fv7YcKJZeo7j//3FBp9yRTzOyjnvnmZIKxmy1CIxMXl6U0iOLTJ6nfZBMqmfffz/dPlSCSb7+tR1/CSJTly9BUgth7GRQTQP/AvTStT2EwEFepy20gAR0FbIU3yVIUsNtvNCF4+VnhavWStAT+PQn8cX4HVl8+rE7wQpUuKtVXfPRlnD66B2kpzH7Qri9+GPUkDu9ap7R5devWVIEU4ls3Y8bcAgsTB3xfXx8F+NLT8x/0jo4OfJ5mGft58N+YRJFKWbbsd5r76sCOYMO8TJ16Fi+9tE81jx1bl/xynzOowoOgYrR512L3k5KyFH2L8KsZyurVbei/5qN2t0W4KE7AojJFGMbIt2iyxLdNfMqSMu0QTRASysjXswzKEMJoZ2q8/ski2rHZs/+ixnMtM0Wco6m4Pn76aTqcnF1QqVYzZYavUqc5ylWqrbSbu6JPYwmpXGIzkpWJ17AWZzv6SLr4orF/RbQMrGJoNn6fPb5fUfTI/bajxm/hgmnEP5Vh6+JFU6oTwqKdVPRr9zJxpPFJoYbWVYFc4wQWKj4+85kn2R/Olcrip3eC0KWhaGCBY8eSGM3tpECbkFcHBi4wjn7xxUr4bRvw3av+infReMCkMv+EB03Ltgzsscf9VeOVBlAOL2EEeE1yKZZlwIelIrmAf9jvgwcI4AwR3oZ+ArQyM3PpO1lSRQG/v34nxn/0JfLC+JvJGG7oxnvRzLiu+Sfd0ZiBLsE3kbvZ/HqNE7MSGOjMlKOdjcTWckzM2L+TC3JorTi456apNUqwz9awVBxuUcd0uNQ1DYy5RKxgX2sAreAm6CVoCWgJWJbAusgwOtE74J2afUiX4oHZP3yAtX9NNnY+d2I/ecvS1L6YyFxcXPDVVyPVvqurKwHJr4yUbUJi3i9IV1FCARHR+A0d+jIBHlOJmYA/GWQAf1KX/pbAnxx7+OHypG0JoDk1g6Aykem/dmLMmA/lkKJPOX8+FVWreqjzqcYiPoSfLTk5p8BR4XFbtKilivBtQFPeDyQkLuUepzJwFOhotiOmX4luNRTJACImRgkamX+S5uAqCcX6uBnG3ci3gL/hwz/Azz/PNHYXapeQavXw4iezCtC5HIg7j98YzRtPLZ+Yd30c3RBK8FaJKdya+VeCp6OLcQ5LlTGv9kNOTjZKlgzCq5PXITHIi1q0FMqW0cGUj2zx6UxLNzwWpw9GYkCdTHwzrp6lqYxto6Z0wiffXkTllDNIzwjANzSTP/pwOfoPrlZ9GjTwUb6Pb71VFWfPpijgI8E2zvtzMPMg8zh3yI/6NU54tXIu0ZF5nGMYxVvwSDtqaQUsFZW5RQI/5NhCZlyRsQ15TSXdspRv58CBW9Vkv//elBRBJdCralskvdMb2RkHMfv77+Fk1xC5jCR/9LeFaJ/aCPfWaEqgaIO/ONewunEFFiJA01KQSUJCFj5lrl+HsiWQx5ej7CvxHJf/wjB+fD0MHFjGCP7EvH+MmmVJZVeZfpMbF59mtPUe43kGdmfua+OerlizBLQG0ArvjiUNYNRPK5DHxOW5V/9RGv5xyj9GKbmqkr+T74KRd7Xtas+rHQ3j8/sbZ8mfwzD31Tnlj7xUOZM6ruqGY2xV/5num9SvjZIaDxQ4xt2r65Hm/LNIm6Fbfmdpt7exQ+uAqkU6gquF6Y87SgISBbo/9jx2Mt3XwYQL8HZwxaf1BqlrlOjdH0Y9lX+9VNkEliyPuOgIZGXma/RKlSpJTcUGozyySPFSVEDAt99OIsXHJTz99MMqQld+k9HRsWpsIM2+RYE/4+SsCOisUKEhxo4dSRNcTxVs0ajRaohm7/33q1NLWNnYXYIuhBC5YkU3BpOUYWYKe5VC7PHHd5EQOcLYTyqfflqL66qo2hIzbBV3YAgjWW+2CFHz0jPuaEqTcO2AdIKmm53hWv8VK9aSp/Bj5XNn0Pq16/MYmncaCFd3L3j4+DPK2dEIekU2P55ci/3x5xXwa0IN3+DyzYw5e3/6dBj2bFyC4PJVEMqgnfZ9H4cE8Vy5eBpunr6KKuV02G6smvejWsSxY1upifKnadsBy896KNoZSUtnKJs2RWHYO2dw7xst8WaXzEIgzNBPvg9Ra7ifkctS1s0/iRV7s5G4cjtfGBriiSfyTfLq4NUPoU4RUJiQkIk0W2f8ddpL5fCtbmIylq4SfCN0M+Z+m0kZNipzyuBqifBgJHdxJYY8kaLZFGJmubqES4n4Y9JhvP5kCaQFl0Nqji1zAGeqbeNFFwYCHcHEJZuxd9kO5CZl89/CZVTr3w1vD+iGbiEl1KnEnLv0mCN8Snnhscb5/1bk9yj+jcHBzuj51Gm4li+BFKYjtPf2gEuAFwJSLiOdP7mPv2mGysG2Kr3h0jMeippGrruWfxq1ytRuLrmEDz8Mo29gNs3+aXBDMj5A/suQyXVqDaCJMKylqgGgtdwJk3VYAoAfDB+OFCYAvxtLhxI1MKCsfqe8k+/9afqBLbt0gP5gl5GWcw3ouJP8d0DZRqhq64kLpw6pFwchb7aj2XDrytmK7PfBBwcyBVqIolppx5ypXl6e/zdRzZt3Aa+88jU1gK3IvdZXaf/699+K7duZDWFGE2W2k8XExfGBHbKkwLqEY27fvk7GNvG/EuoN2W6miD/iPfdsUUN++aUR66UKDDeYhiXQoQz5A+Mz7NCKQSNlPIv2NzOdYO3aTdR4Pk9Qm2xs9g8qhUFMSVlUViJJ2yYEzsnZGSjj6qfStrkSHJqWr5ib98TBHcYmJ2dXyL01LQ4O9ipvr4D0/v17GsGlALgUcuSJr6N5EdP3mvME2eTPu5EigG/hAVvkURv8y6gyDCYJYsaOVAXWJ08+a5zCNOo7gaB89A5/vNwgRkUVGzqtoy9mBgFaFwaTmJfkTBusIHAVIvBB9JkzcPuZ9zPfF0AoYFCirCXvskQxF1WmHjyOD7/5GXHLV4rdltq5GfB0KYEnHt+O464VUDWA6Q5HByrNopjBpbzzTjWMXeOAlO0HsWp5K/6Gc/D73AjM3cbzUGX4xXsVcDzSFsu2puPLp93QuvG1f19Cfi2+qFKEQDqJANQmJR5/1GqR33jtUwPAa7Kwmpo2AVvNrSh+IZL+KMsjn0jURlhEVcmvGd7qZc94hI3GvauNhv38f6/Xjst4wzFDveC+HDeMEmoJ44Tqzd6wFvk2nN/2ah8x+ajC/YLHDDPmjzL0zx+W3zOLmqBvjq2gQ/M1QJA/mf68UyRwMSUGE0+sQWxm/gPTk4Av1KsEKti6ohQfJhEnD2HDzNcx5ciefBWxhQsXP7Qvv/wQffp0vfbbtNDvn25avz4Kjz++m9O2wuuvr0TLlvWoDSxH03KrQqcSPrWFC1vwIZmngjFk7IMPlivQTzSC5kWyOUhu1zZtAmjarmN+WO0LkPTycqB2KotzpxTqYzAN16IGMI4ZSAKLARCmg2fNmk/TXr6PmZuHN4Y8/xmpP0KLpNgxjF0WsR8LLvJ+sfQsVQ89StVVdfOP50dNx/LfJzBCeL0K1lnz5yTV5QA/1y3ajpRcVwTTT0+iiyVi1/BnRzpJWrVph70tAsBT9HksTw2ZpSJgeAHT2Ik/pKSuE3Or+L5lHL8Il9qVMGGnJyo1c8YfMUFoMLQ0mjzZGs7nzsCDWT1Mi5A6P1M3thAYC2FOYQFqpsDIME4ofYQuJzxJ0sZ54vFa8YZDxX77MbhCtgCXbCw45alyTYs8LJWHa1XGwz+Nxu5Lr6FjrwfRqkUP1HvnL0wbVwXPDtuHLZszUeN8Jl5ksNGTr9ZBajTzFzvybzNTvvXvG6zIo2fNuoC+PYOwZ8cRVKjoivu6+2DcuJPwOHsFNhlVlO9r5cru6P1gVcw84o3XG0erpUjU+E8/ncY3n+7CB5YWp9usTgJaA2h1twSwpAG8G4NAUrMz8eqeGWgVUBn3Vyj0RmmFd04v6WYkcDY5Cp+HLVJDGvhWQD9q+myoZRpLzdCViHOqXV42xBfvscfuox9SH2r+bJmyzZUaNfpLLVzOCMXPC5xy8+bFjGCsXKDt39rp0WMjtmyJUdM7Oe0nWHKl2TcfMG2/5KLMgX1Ck9RxSZmVSac8L6+CWrDrrS0+PpOgconqJrx88pD9t0o0c7ju3x+mciHLOUJDGzOFW5w63cifNyCgZLliT53Of69jjy7D+dQYuNk5UevXBaXd/IodIwfDzxyFaANTkxNpHi+PefMmG4m5JUJWUuBJRgwpAmZlcyT/YTXSqOy47EoQl4f6pG4Jds9SWrUkatqW0P8t9+orZyb94eoFigmcvm3UWG2/5Iq9689jyqJEzB4VSM2sO8+3SGmvZs9phsyQimhMHzzRtAkdz7wTnkpzKplXDCBUfA5F25fHSN97KuffY7VAfojG7k+O8XLKYRYTG1RhIM4+mpsdmaZNXm1l61cpAU7E+5JJ46+/ItCrVzADW67vM5qUaYvZzELSuEQqavF6iitrz0XgiVFfIzu9AZxKnsFXbR5ipHq+xtWWwTTOtUPx+Vf1ceBwEqZ8uQdZZ8IpBzfjS0TTpv6Mbk9iZHxLpjz0oEvCbqZKvKBessRd58yZbtgS64sEapRFs1yNZuENy0gb89haAsAPzZemNYDmErGCfQ0AreAmmC9BA8B8iWgAaP7LuP33Y9KT8Ff4HojJNzYzX1v1Vo1eCiicO34AY98cBBum/nrppSeV03+9erUUXYsAQaFniYyMUvQiFy6EK789AXuzZy/A229/ooQTElKOPH6r/i+CEp/B8PA0rNhwHmO+/ghhO2YUOK8iA6Yv14E9MZg6/iByUzIw4oM6KFGthPIDk4e48MRdr1y6lIbZu+3hF+KvumYwe4TgQCdqgXL4ILa3ySNAsoPknm1bhgELBBq+V9OwXW9u0+PPPPM6fvvtT9MmVa9Azr7Xxy4o1G7acDj+Igmc1yArLwe1vMrgyUrtucZ80Gbaz7Sem8OAivFvYfOyWUxm4Usanu8htDzFFQEeu0k2vfeKC56oHae6ElfTp4/Ey5S1AJGWBGqG8uWXx5jqLA7PjWmnTN89KyarQ2XK8MWjXk24MWXcjpWN4W6fQ/Abr0y/CxdewltvVVO8f4Z5wkgTs5gp0USDJ1lFDEW0fULdYu4LKMcly4cd782YnX4Y0STGCB7l2K5dsYzY3aD874SgW8qvvzZGz57Bql7ch8jg692+8CbA7FeJGsUUR+XvLanlBFxK3mFDxPeSU+cx9IUpcKzVGqc+zk/pN378SYwadUSdQnJYC01NXFwGNdI7mfnDgQFIlwqc/pHv+qJBPS/KJwG/vLaMASf56/3zz+Z0vfBAtliH3N2QFpeCKuUckR4TixmVmhaYgzsaAJpLxAr2+Q6ii5aAloCWwL8rAdEOzadZcP2V/AePk609glw80Te4PoIc3ZEYF4XRr/RBHgMHpHz22TfGBYkfWFaWZX+1NWuWM+vGo2o7dowkwmagY8WKyxg0aJuaq1Ild5UHVbI5/BMll9qdU3kMfKhdAjnBdVQQk0FDJPM3LJGuNo+zMfglmhG4JOxtWNkerRmVKxok8cm7ct4e2UwB1pSZKCSiUnjUapLE2TSzh2TleLKrDX4+YIfHSLlhMP8ZziVAw4mgL43fEoAgAPGlt48h+cxlZJy4AD8/R0Xk27Chb7GXPXToA0YA6O7pjY79nkbLbvdBzL/Fle0kb55yeiPBji0eCWmFJv6hxXU3Hls8c5wCf88/P5R+aC9flzvxOGlHxLdPyIYN4E8mE1lJ9LNsQn4tPnaS3i0yxQ4/rcxG5K4rcFiUigmPsePVcuRIV6xZcwX7bJtg2iEvVKY28WSqCz6deAQ2TiXwR/MNiAm/5lIgAK8yNXki3z28b/Gk2XmkZpwyT/90wEdF7PqYgW4BYaINDHbPKQD+ZAnCtSfFAP6kLqbo4or8NpKpATxJfsdG/G1JUM8GEoELABVNpPwGBPzf+3kadsw9hKG9XPHDVua65m/vkRGf899GslrHc89VgmymxcfHSUWeC93MuHHHVWR6bGym0gb6lvNF+zKJzJ3shs8vduWxLJVTWFwWqldfrqZp3NgHO3bEoXfvYHz9cQXTqXXdiiWgNYBWeHO0BjD/pmgNoBX+OG9gSRL9uSHqGLZFnUQMffvEl1NSe0mRiN5nKndEKRcfTPzgURzevV4FdtzAtMYuQ4cOUb52b799kW01+DDtZzxmXhHt2b33bmVkMB9gJVyUH56k4vonSgaB2wU65/vkXkabz87h0Xs74pWG+ZqeXDqbSfo1AW9du25ksEec4mmzlH1DNDqSM1aiUpsFpyleO3/XbPRgRhDTIuTCuwk+upm1m/Yx1H38/oJX95ZIWLRRNUmmEQE9tmI/vVoyMmgSzc5RJnXhRfz11zn0ZfxQHf3mr+OMnnYydC32e+yRpSpv72d1B8HLAoFzUYNHPduFOXAzCRzyQURR/QztL60pgZ7M3NGCANAAgg3HDN9i4v2dJlKRqR0v1efyGTz84jHYB/gifHUVQ7cC30KNItyJZ/ZfJuH3Jtj5e6P2PXXx8lNl0L0CuQep4DIUMUkL6KJrJoGmPTWO2RDt4FtNooxaN0Nf+Z5HX7/GJVNR2gIfn0Qt9+q12bS7CuCZNKlhgfskHdZfcMFfJ73wEoNOSlPLaXIbC4yXHaG0GfV9JFxq+PKFagM6PPQAvu7sYQw6OZPgoCJ6TV8yCk1i0pDFaxVfv9Mn4hHHGN9W1W0ZG0ItNGl4Fvx5ETXtLlMT6E43jV1q1IjnS8Dv2wdNZlBVrQE0l4gV7GsAaAU3wXwJGgDmS2Rv7DnSSKzRPoDmPxAr3Rct3+7YswwA2I3EbFKO8D93eydqJhwQ6OSJWk5+8KPTuTs1SpKSTXK5mpf16+cjlSam+PgEZeo9evSEkWhY+n777Sf0YxqohoWGLqGPWiYBYF/SseRh48Zo5cTu63tzfnbma7jZfQG8Vau2wLvf/wabEnWUliXq4AV89OpWRpZmw47gNzvPFn2H1cOYt8oRvOQqsFDcQ1yAgwBMKe6kDakbkFYgaldAy5zj+UBH9EaCUSTAQbSCMioiNg97dkXj6/vzKM9sFSBSu7Y3j1wr/X0qYQ13q1QJpa/XGXLt8UnP0vuh19B10HOcK//8qrGYj3f2zVHRvl83fKCYXoUPzZ30MTkdfyYVyT7yNzoX7mDWIgqyWGrUVjBXsmi7XEgB05q5dM2jaRV9FWUhYFfAzsy9znBJjMErva//uxCtlx2RowTVCKXLIvoSCoAT/0Gh1FnAfQn+yCIAcqTvofgXityLAqQCnv5i4EZFBq3UC8w3nZpelgT5fP31CWq8jxqbT5/uTlNswbVO3Ec+xwqRSnMoKemKK2sOXUL/Nlv5g0hHg/eyserlh4zdBRi/sSEIR9edguOZ05j5Y80bijqXl5nGLyciKyIKwXbJuL+HD6PfK2MPzfGSRUXcEfwZwT/rpyMYPiwIa9u2M57zakUDQHOJWMG+BoBWcBPMl3A3A0B5mG6LOYlD9CnaRwJZeQSNoI9YGTqTR106z5ReexFzJRy1GndAcLnKWDD1C+5fxAMvfl6AfNZcpnr/35NAVHoiJpxYhctpCcaTCHVP39IkqM3KxJ+TP8OONXOVk7+xg4WKh4c7Hct33RD/ngyXdF5iShN+tpMnk+k/tkrNOnFifQweXNbCGf75JoMp9/zOdXj7wzhkUQT9e/pj0LN18eCwMNi6u6jo5Y8+rYvGZTJxMZk+UjTRpl4NapDAAHPOOPNViuP/rkvO9PVyUJofeYgLNmtF8CMaKEsljcELEnggWUDY3TjOk35jnRj9GkgC5tJsD+cmwR3lKtdGo7Z9IZk7hI7lZsqLu36Fp4MLPqoz4GaGIfzsMYx6prPSQE6b9p3K5XujExhyJQsANPejfPHFvZg24yJaPt0KzzxWGl0r5Js+b3Ru837fE3y5OeQS6NngLEHg+82jjF3kXswhWO9PXzxTTaGxw9XKJJqJe1VMLLRW836GfTEdLyTYFKBL3Isk0s4c+HEVTfQXFAn511/XNXQ1fmdmZ+PRqX9iyagvmC6uE0pUaoI9y7tDlO+vv36AmWrsMWnSGea5boCXPjwLhwAfbJgaglKl+BstpkgaQfEbPHAwEWVrBiI+OASZZyPw5hB3vMA8wVIS+RsVLbZQwrR2P6d9AIuRpzUd0gDQmu7G1bXcrQBQfIl+PbOZju3yJ49v1dQcvVS1G8oS/Il24qV+VZFNQCGaCXnLl5RT2fyjl5OdhaBS5fHBT+uvSlB//b8kIID9ZUZqC3lzRfdAldmhlkcwwo/ug7OLG36f+C7Ok8qlU6c2isdNgjpWrVrPSM8lzObgzmwXjam1q660UGXLlvpby54167wxI8GKFa0JCH3/1nwyWDQ455McEMXUXk1pnjUvQkXSsWwSdl2ww7CHlsMmoAzSw85gxZ8NVTq3lBQS81J1JY72lspqBhGUcMtWjvuWjv/TbeIXtzGcNCXULq5fthC/j3xcnWLI85/S3+/+WzrdsB2TUdWzJF6s2vWmx186fxxjXhuAyqFlsHbtnzc1XiKthQ5GKFJMy0ejwvDTHg8ERp3Fro2tTA/ddN0muBHGLjytTK8y2BK9y+7LzjjDDCADGCAiJT09hxG9y6jFFhJyGwa3MDNHaIgCRxKkY1CsGoC8GmT2IaZmIbt+qEacegkWbfG2rdHo0WOT6mnKSXgpORWPzlqC7d99D0RFwbdPL6z9bATTzrmrvt99dwLvvnsY9ep5MxIdzOtbCRUblsJWphlsUI5k+9RkJtOnUaKoLZGN9+mzBRs2XCHVUh0MGFAa33xzAt/+kYR7nmuArx52KJCh5gCzgxzmy1hK9+pmV6SDQMwFYg379tawCL0GLQGRwLTT9L+hI3mfMg3RJqCayv4hqbme6VG+gJ/YwIG9VQ7SjPQ0SFJ4KXWbd1ff+uP/K4FpZzYp8HcPtX2dg2upk3/7zoPkdtug6k5MXv/DD1+SwqWXcWFC2jxs2KPG/X+qIlq/6tU9IXl1xSfpnyiLTnuQh9IGBjoX0zlF6+FKzVCwRw56V8/B585f4t2XXmN2jwYkL/ZSXd3crv2JffXV/fjllzOQfKkCUMWU14Ecd5IDdv6GFHw9YjNs4uOp2eyhsoSYnksCPNZfYAoyAghDhKcc37kzlunB6OfVIVAFelyPJka0ZQagMqhKS8z+KP9lys3r1sCyaH+lBNOn81ZKYKkQ+qnlqBzNNzvelAQ6JiaDWqpTzKtcCU+9Ugvpm1zRsnwog0bsSRGTBm+zAI0bPdek5SfwdLVYY3cD6bGxgZUGDMj4Ybsr1ozbQx/CYJWLWMCflKysPP72t+LceT/sj3PH5H1ucGb6DIlcnkvNYQMGrrQuk6qArPJDFI0ttYr8X/3uJKBH+AOlNG/uz6CRXspErRr4MevQCfL7vY7cU6fg06YV3v91Ah6sEUrzt62hC4OgypKYPFZpDtu3DzT6F5alWTsy2Q4pOXYIos/pLKaqe5E+hq405xpAqkzy55/NGFkfh7p1vVU6uHfeqc6gHfEL9MLJOAajBGYYzyWm8tSoTBwytuiKNUvg2l8na16lXttdIQH5M2fHP1z+TpIPM/8PmLOrO9yZAi8p4dofYSH+NZS6zTrj8RETCvzBMxzT3/+uBITwd3vMKQQ6exrBn2hmj+7bzJyqDZR2r2rVSuSTq0BfvVj1kDf4lUlKrRMnkjFlylmatc7Tf6+9Am8REWlKwyD0EkKULFkxztKPa2uEK/2McpUWJZAPK9OHv+lVip+bua+b6fGbrQvgWnve3Qi6hPZl5MjDiibEM4iEwuSjkyKa0IiISGo6hWrj2sPX9HzC8yYlKioDb7xxgJxqzdT+U3UYPfnxXjiU5sO5Ulk0euIy+Q1rqIewgAEpQvciPHTykBYfQgmGcKMPnMF8t3r1FQLLBcxeUdD8LdpHWXNQEP3gmHfYtMyZs1C9WA3/8k/m8K1veuiG6yfo9yWl3A3w/VmaNIxBQGmpyXj22ccsHb7hNkmlN3bsceUDKplQ3uuQrCJwbW1yMJecfDdKumw4oZiYT8Q5kU4nx3jvDccsfS8YtQI25Nb7beY2/pbbqfsggE2IuU+fToGHux3mf7BdvQCIj+HBg11Qv2U6plKDLJlLlJ8sf9eNSyQUAF/m5zLcw/OJyej47hhE/fEnbENCMHHFXAyumW+ONR8jLxx9+5ZiurbjDIZKMKYnFFCcD4zzwapkGVlO/8o00sk4MsCjZ2iy8m0UX0pL2vQOZZOxghpsUwAo55Y0dRoAmt8F69zXANA678tduaq+pRuQKmSXyh8qAnAkVYgEEQz7YSkcYxiBxpyvJcqEYsPi6Vg59wclo9pNuxT5wL0rhfh/uuhd0afxF2ld5P68TDO9lBOHdmDy588RDOVg27bdajNfjpeXB81jNXjMk4fkz48AqEiCxeGqa7lyH/Ch6abqwsv2zTf1mNUhi1uC8p0T5/vTpMGYQe0DrWJKU+LJIIkg1yzSp1D7wIeXLbnXnOk7JXXxpRJuONOcsWryG/wQao+kq/56MuT8+VRqny+qLSamD/wIEKQsWbKKqeiCi/0tDh0aAtkEFBge5GowPyZPrI0PPjiM6dOPYcHqNqhPqhhL5RE+pAWcSHCCkAzn5nnhz72D8O6DS1Wks4BLQxHy6eDghYZdrrEVmjXzM+6PGvUVSleoesvgTyY6lxyt5qtEE/CtFHfm/HVwdMT99z9FE/B8/jYsgxhLc0vqvNGjj+Khh8qp63ruuVAC8CDVVXznxDQsWiqJWL3ZMoeRxPWC0hTQvt7Y7fTPdCgZgJz4JNVVQLfBB7VsWVe0ahWg2p9/PlQBwByi+cuX02iSdcaXgZHYT3qXXQymaEjyaVPNW1Hn/XHnQbzxxMtMEpyAdm+/julDB8HV8dqjPI4UQ5to5m/K4BVD2rjGjX0poxhFOF3UvCVJJ9OvUhLBaTzORmRjclIIOlZMJ0UO7cYWimiTfZ1yqZl2RRtqMQ3Fg2263B4S0D6AVnif7lYfQLkVqcwduj7yKI4mRiA2IxnRpBFpFVCFEXDNjXfqwPbVmPjhNY3BzVBWGCfRlb8lgSmnNijt35s1eisfTZnsG5p+TxzYwrRlzSFZJdLT01Ve3qNHTyIxMf/heL2T/vbbdDqrV+RDKIEPTn+SQHsVO0QiMAUQRVKLsv+KKwmW87MvSKCFOO77M31WWIyzerAKcW73G6BQMT/hvBMeJDdORqXAfHXcxo1ROH48mbQX5TlvPri4555HaIpt86+Yts3XY76/JdxF+aDJSrIJduoTuAgYFv9DyXBB5aQqr71WmYTZ1VWE9d69B+nP9TjqNOuKJ9+eaD7lDe9/RQoYyd88ofGjNzzGvGNyYhw+frYz7EgpvGjRYpIh76TGOIN+os3Rrl2geXfj/ij6+n355XG17+9Pwu0DnY3AWgIShKKnFzOxyAvCzRT57fxOTevDBNvFFcnt+/sxb9RhhHbJnDjyCkYq2p+2bQNuOGPLpWR7HCGB9ck4RwgA60XNblElMSMTPT6biEM//EzuydpY8PNXaFbKsnzkxUd8JOXn2Y1BMMUV0TJLwIm8OMWl2eLdN/chx9aOEb+Mqu9TG1/1SaFm1bvoKUo1ZjTRDuPxNP7b10TQRnFYdeXaa4NVL1Mv7m6RgCs1St1K1VGbgQfQ/NovnDqEwc98pKIIXd29bpivzHwevX/rEqjqGawA4DECdQnSkSLa2aN7N9IHqhRzgn5JKgtv1S4fYho+f/4igd0R+hMd4D3jg+9ILh/4cTzqwvpQ8vRde5i1aOFvHFtcRZzjxUfK3ZFBKN6JFrvWC8rXiglQvJXidPw42n6Xgi3fl+S1kQeNGh2DVscwn3DqTZnyOwJD66J/p3qG5v/Ld/NSaSRHzg9Q2bEjFj1fZAqz/ccxY2woDh/uStPfYeVTKL5gUt57b7Ti/JP63s1LkBB7BV6+12Qv7Tda5CXNydbhRrtb7CcuHi9+8htGPt2RUao/EPzVUP0+/DDMCACFLmXFiki+SGSR17GM8kV77LEKKvdsYmI2XzgyFGgsXdpVjRXwN6R6gvF8AijzAXG+dtl4wEJlG4FTG5r+r1ckr7I3f3dC+C2/4QcfLH+9IYWOiyZ7UJUEtC9b+Hzbt8cof8CmTf3w8+4wvPbkyyrIo9oLw7DqlScKaP3MJxbtpwTICNl4cWXyIW9FZyPXG3GWWU7ss/HZEHuMn3AKaeRDTD9wCqdq8gWoOABoAv6KO5c+Zn0S0ADQ+u6JXtF1JLBo+leqh2hfvpxz8Dq99eF/QwI1vUsrE6yYgcu6+qOKV0n0feQNxEZeIBCaTcfxpdSGPUyn/CdVhge5V+XKlVFbjx6dVCqqRYvytTeyvmrVtpDKpRuzVjj9G8tVc96qGfjwYWbxcHShOfqkioS0tMAlS37DH38sxMdzTuDNZ58hpUkLfPXVSLi65gMSS2P+jbby5V3hfIiR9BUq4n7S0ESdFH+0BsZTZWVlMSp1HjwY9DFo2Eeq3dMn30Rp7HQTlSTyPXqRAubvlmxG8stLws8/TyGQO67AmmkAjfj3jR59TJ1G8tKKT1pwsAvN6T2Vtvjs2RSjP6R0Sr/KoSiZLYKCFhiXFx0t+aSLfhEQfsUj1BhLLuDrFck1vIfm2/5Vkq7XtcDxdeddEZ6cT+nTin6dZTyzCxyXnSVLLmHIkO2qvdS9+xC+cB7satTAzDk/o3NI6UL9LTVUYmaZosy30l9eiGz5LUDZNIK+ShVPumi0V1OK/MQPV5c7UwL6zt6Z9/WOvqoHXxqjrk8eGHs2Lbmjr9VaL87dwRkPVGihKHsM6d0cnZxpbsxWPoBxcfH45JNxpHzZYPESJFLXtAiXn/hGmRchgq5fvyPJcn8wP6T2hQvwyy+PcjumEtUfOBCvwEM2U0IcO5ZkcU6LExXTKP5cWRev4PfDbszoEV9kzwEDemHEK0Px5bwNSHMPQY3aHfhg/bPI/v/GgcBAZxzd3wH9KsajfJsqvBcFZWrPQAUB41XrtkSD1j3VZjBj38p6hP7Hz8n9VoYWGBN58bTaF5ogX7/KKFO2Ov0prwE1SV32/vvVMWdOswIBCRE0ocbw5SMptBrmn/TEmKFL8csBLwSNeAfeJLp2CaptPE+LFn400S4gyDyt/NwkTaD8TkyLkG/b0BRa1MuC/M1p3HgVtdvzsftoBiN4rw8UTedfxHzC4lcqoOs++nkWFcx0JfbavOHrVqHOqy/i2F+Tbxj8yTnF/FtcEfJqiS4XahuJ8O3atQRf0Fzx5ptVjMM0+DOK4o6saA3gHXlb7+yLat55IB3X62HymJfh6uZ5Z1+slV6dRL0eTsgHcUHOXsZVHt27mZqYkvQ1e5k0J6UZDdzQeMxQEcCxZ08nwy592ZPwxRffITY2GIGBlYztKSmpdFzvrvZFc2Ve1q69gn79tpg3F9gfP74eAwzKFWi72R1x5N+3uBbuf/08did6oy4n2LAhioDkMMaMqaOIqA1zDqySSN5AVwS/9gpCuz+PN1/uibSAhqhctQpqMcev5z/oIC9ghP8XAEqGdYwjUfAPD87BnguDsGPpCThTi9O/fyn6ZDoq7sxj9NX8uyXyKvH3rVLAmJ4/oGRZtfv55+8hvmFd00OqLnlnX3qpsqonkBRZsoFIQJADOewk2KFt2VR17LdXB6t8uI7j30NEqeEKyO0+l4bVx+0x4rG1JCbPw2uvHRC6Abi1qIuWtCAMe6MB2oTma+EE+FVkFKtk/fCkP6k5fUx6eq6KXpeTzfkjHB0f9FWBRuZchGoxFj4k1d9sBphIqWMhM4i0Lz12BiMW/A5b7zAE31sb2z9aV6y5V8bcShGAOIC/158O+OLJ2mA0ftNbmUaPuY0loAHgbXzz7uali7/Zm+MW3s0i+M+uPSEzFaPDFiEuk1kYSAHTpWQtXIk4iylfvAAHJxeVwi0s7DgjextRA5ULqe/bd5B+WnGkQIlhBGQkiZG9SP5cUZmERVN48GAYedx+IUeaUFZ0p+nUmSDxoPEapY/42B0+vNHYJinOHEr4o1Hf6uhVn36GMXEMBIhHZkYuAwjCFTdev343Zi4zTlpEpUQJZ6yZVhniM5WUmQiJQBVtYMeO6+nH2JLX6q9GykO1nGeW2pziTmFamSaoX6UEvEiyO4P5VIdUj7+hoIS4uEzKLZHaJgIMprsTYmEpBpoX4f4TDkAp771XHS+/nA+OVMPVjyemDcQDb57Dhn1EiaT1+ItAaNDgAJSpWhfR50+Zdr2luoECprxbwC2NNx104VSY2i1TppRpc6H6OQKzhSc98HjtOIvp10SrJkEc4dQMTgvzVpx2KVk+6NAgBaNndMGiTanYvuosnL3oC3j2PLbYlcehhzfg0ObmjBZWuFDx8i3gOYRvUbgeJdS8LO9pS8lBTCod4eKbM+cCunQJwJIoIIVR4jcKAKXf03VisZem45+YHaR2QDqukGTc1ykZEzcuxIrxk5BFTj/barXw1JTJ+KSNUyEZ/JMN/lyPRNBLxphQmox1ubskoAHg3XW/9dVqCfxtCUw4vkqBv/ZB1TGwXBM13++/fYMzx/Yb5/7uu0mQ7WZLenpGkWbTbt3aF5iuR49gzGIGiabls7E32o1amwpo01YCToC2rzDHLekrnJ2vmdIKDL6FnWhGVgrHmQejSrt3L4nvv2+Ap5/eTZPZQWoE26kZM5lq4fDhY0hKSsaTT76Kzz99B3VKO/BYFh6tGYdfCUqeIHi5XunadRMjjROZwaE6fSkrMhgiQPm6SbSraP4k0tQAAEeODKOWs6zi+jOdVyyoY17wJ73KFUyefBZDG7qgFwHS4hB/bI68aNr1lurnU2LUuEqeJW5pvGFQVMQ5zP7+PfJAVuE1FA8my5LS55l6sSq1nWG8+bcAt4reWSjpFo9TpAy6wEwuuyMZEevjimY+F7DpTDgeuz8I781qRuC8H037BjMThg0Bo4/K8ysUMqLJNS3HYx3xzR4/RZS8aVM0XzQSEFq/JDIJNgUc3kyRl4T6JICuQ/B3NNYWby38BXt+nKhoXZzq1kXfH77B5x2bMe9wEP30EozmaCGPHrfbD6VN0v/JXLaCUPktdDeyL54U1Xwz1DmKW9cJRh5vDndlxHi6Bn/FCeoOPqYB4B18c2//S+NfMl2sTgIJWakqY0uPUnWNa2vSoT9io8Jx7sRBZKSlMM2bRwHql0aN6vEBXxk1a1almdcfp06dVZrB8PDL5EOryWwJvZXpWCKFO3YcoOa9997eTDg/DCEh5Rj1KSCqcGlfRXj4bNCC2pkWZsqjXUzR9fNBH7g75CieMkm59neKaEtM+c4GDSpD6pcglfpL5n3ttQ9UIIiAGNFk9u/fk5HNTZjBYQ2BRiVeYxnFG5hC53shcTaUVasimZ/1NM3gtakRpWaKpXv3EgoAduwYqLRO8+a1UO0C/jp12shI6nztn+SEXbky0qIZWAZIoMSQIeXUpibgh9wD4S2Uuf6O/9+ltHjBHYyE/XuBLhuWTmc2kGzMnz/VsMQivwXgyDlvpIg5txYzU8gmRdwhF7hUw9fNK6NvtVQ4OQITJtRXxy4k2hEwFgRy4kMoJT7uhAqm2HYpB+fjbfHIZ3Gw9fTDxul52PppnOpzsx9nyBn42M+zsf+3OciLiEBwv974acTzyDtnp14oLtK38N5q8SSK9kXn8kn898asL/w9J2XZkZ8wCVVNuPlEcym/Jnvba6sQQGupSN/jBH57CIhFG/lwDd5Dy10tDddtd5gENAC8w26ovpzbTwLrFk7FoZ1rGUX7OkqHVLf6C+geXBe/nduKt/fNwVs1eyOAZuCqdVuo7f2hbVChdhUsW/b7LV1HQIAf6T5m46OPvsKIES+gQoVyheY5ciSRHHw7GWVbtwCxsXlHoeeQTTQnq8+5I4a0Hf0qJRo1KtJf6EFef/2A0rI1bOhrPsV19318iCKuFgGprVs3xbRp4w1N2LQpihQ3idQG7oZkqJCHuZA4D6p6TcMkpmQBcXXrrqSZPD9KVQIeZDMvAtgaN/Zhdokk5nStorKlPPxwefNuFvcl4EFSxTVsWEcdP7B9Jeo07Wyx7400xpKj8+9SwMh5HBhQJMEqvr7eN3La6/YRAuzx40+qzDKiqTUU0Yj2JS+gcO9NC/NFeU/mFefBEuTfOxrjVAgICfAzLfeSrmXTRRf0b++OvMtXkNasLjkmkxTnomm/4uqpmdl4csZfWPzplwCpg9ybN8VL347Gq83y78mEBSeVprd9+/Vqml30lb2UHEB+R6BHuQRsnrgRH5BEevnySHV88+b8DDrm5zSkjzNtF27AOcc80bgkwSWvxcHO9Kiu340S0ADwbrzr+pqtQgIRZ49j5ndv4lTYLrUev6DSuO/ZUVaxtuIW0TqoKqIyErHq8mGVDWRoaFtjdw9Sily8GG7ULokP4PTpfxB42NNM2c/Yr7iKaAsXLPi1UBeJEm7Xbp16QMrB7t03Ms9wA8ULZ945SxSDLPKQc+TWjQTQiQwe+JORopL3tCNz8EoR3jzxF5Ttl18aKZAmmh/zh7/qfJ2PDz8cTl+9lti//zDq1KmherdsGYCzZ7sT2GUq8EXooDQvs496Kl8z0UqK/56bmx03+2IpSgyn/+STWoywrmXYvaHvt98+iIULIynXZtSwtlFjdjDzRu0mnVT9VjSBySRt93L4e9o/OXm1+q2wdNY3vK9HCIJrqvX8nY+jRxPx+efHlDxPny5RSMsphMvP1I1FFMnDBRRepIl4cNWEQgDQfA1iWu5YPhUd3y7NQ6WRkROjglFEsyg5cK9XRq7fhbFv89/36dPw7dkDXw7pi1qlg1CxYnnjUDH3SzpEyWss5bcZ55h3103VJQ5q6tSzqm74EPO+5L++kSJp3h6rFaf+PdxIf93nzpeABoB3/j3WV2hFEog4J6DvLbi6e+IgM5qoQq2Ol48fej7wshWttPilRKUnqQ4DyjYu0LFd70cx6dNn6CB/L5ycnKgB2248PmBAT8UJaGy4wco6ppoSUtuSdFYX/z4pFSq44cyZFKZfc6UTvg2i0+yxPcIF2UyPJkWiQ6PYNrBygjEdlkTgDqEP3CZmiZAUYSkxyQghOvz++/r05dujtIqipbsV8CfndGRKs2+++QSDBj3BtGwbFOiVdom8lc1QBHwmMbhgK9e77Iw7HmBgyLBhoYbDN/U9d+5FBqFEkOy5BrN+5AMFSxMIWbKrqx0q12sEZ/ojVmGnPRsX4xluDg5Mxfb8p7BhDuMzR/eiY78n4F+irKVpjG1C/yJboLOHse1WKgL8Fs/8Rg2dMOEXPPzwYKbZW8AAnpbo3bvrrUxJlwIfbN7cTpFfFwdsDWnSbjSAw3QxL7/8HjXVa/H13DU0zQYoAJidna3yQQcF+avfvqH/b3sP45VR45C+aTPsSpZE/RaNcXTZMjw65w/V5cEHB6rfjezIekeNqqU2Ibf29HQwTKPIr8+e7YHVq0Vb7M3o+Sz1bexQTEUCY8T8Ky9D/2aRc6w/X/Tv8N88t5775iWgAeDNy0yP0BK4JQksmz0Bf00ZbRzr4OiErMwM3PvUB2jT86Fi88gaB1lJxcU+H9AsjziAgQSBtgQPuTk5cHZhpozuQ7BxyQzjSsuWLU2fs5kWwd9lmtJE87N9+26ajdcygOKoGvfUUw/hs8/eVfXqfhkIi3bC+otuGPr9PchISkdIQB5S8hxxmkAq/EweSlCrJ2m/7JiHePbsC6jGB+Q9tb0wiT6AXcsnq3zChgW1JPmulGptdiHFJwjPNc/fl7aFCyMK5EsV02Rqao4CE3JcikTo8nILgLr8IzTT9eiI77fSt+rVCZgx7gVDc6FvCSTpXD5FmSO/3+eL3ly7pK07w2jMVTRXV/NLV9khhJdOsjmID9h5aqralE4xBgG88MJeZvQ4p+bu0qVEsQDw22/rqX6MLUYma+tpfpw/fwn9NFPIizcdU7961bjG3RsW4qVPZ6EU8wQXVXZE52uo6vqUK6rLDbWfOcq0YySBljJnzkK1SX3atN+VFnXIkP7o06eb8huV9hstlSoVBqaSo9fBgTfuJotwOf711zK88MJQclLWxuLFqxiR/pua5cCyqVh7ygYLwzdj4/rNKupdstw0aFBXpUK8FBmFyEv55loZkHPuHA5eiUL1Bm2NHKamL0mmS5OAH/Pi5eVA6iPRQN54kZcmiUAfSLPvFWo9jzGgpT4paNyYQaeoIv0kOlkCYiTjiTt/r+FJ9sp9QvxtTYu8gJ2Ic2I6RnuciXdAZ9+zuGjaQdetVgIaAFrtrdELu9Mk4OHtV+CSBPxJEf85AVC3UxlUtgmOJkRg3ZUjdD4nxxxB4PrFvzKa8304kgpGyhxuLS8dUgERqsHk49df/yB9yWcqL61Js7HapEl9Yz3QNQeBV3nepFGIay/Tj8vOJhWnEpxUVgWJ9BSAdOFcCr79NRO535/H6VXVMZQmrw0EjpsY7Rgdm4Wv3tqG1wa48GFeCQ91dcPE3XY4ejSJ0ad8gEVmUBNzogAAbNJkNTOUJOPChZ5GEBgSskStbcGCFoVSwsmB+zvXIOVIKmLTbeFL0t/iipgjhbokjD5oeyKd4UUt5Qv1Y3CQgPeP416kMcllAEKGojwRGhIqiIylXj1vBQAffricRTO4saOFimhnBw26Rx154IEB1JjtQOXKFRVo6d37QXz8XFfUbNweNQhUGre/By6uBQHVntizamxj34rG2SWo5Lt3H8TRfVvQoE0vlKtUG635MuDg6GzsY1659+kPkBgXhYS4K7jnkRHw8gvCpM+eQ3JCjDKlizn99ddHwpsDnZkqUPwsp0+fgNq1C/tHms9t2H/22T3MfnJe7a5Y0boAkbShj7nZX4KUJCJdtHnDhr2uui1btsbQ3fg96sPRvD4nuLh5oF2fx/lvuTnCdm/Evs2LkUfNarwrtWE9eqBXvS5oULYWPH384UxZiqZvCnlMt6+Zh0sEiL/99ieBXfcCmkPjSf5mZSN/+82DUxX9kAcViuEMinpzYwkGx6SjAqPaDe4Qpqfxc85R/65SSXHjy7pQ3QhlzdlER0wP81IvJAIOU6lZTMu2VUFY9QPT0LlcMtJjsk2n0nUrloAGgFZ8c/TS7hwJ7N2yDNO/zn+QyFX9+ut4lZP1zJnzCCxV4ba7UGdqAF+p1h3vHfgDKdmiU6J5iZlApGRmpKnvmO+/KAT+hB7lxRffZqq4fBClOl79EACycuUcFUFsaBfHdQc+aDypgTAU0Ybti3JBCiMd6/sloUWV1Gu0INWAg9NjscneD2uP2aFbHRjzrCYlZeFzuv1/OjMZzz+fp4iFz3xyEQ26teR556rpJbDCtAj4kyKRun37llJ14fzbvDlaBW6Y5wSWDlM+fhWP9uqGWUe90a5MMrV5+fJRgy18iAlSNgGxfSslqR7iU5ZHc/a+KGfls5iZkIqPvzmtfBRr1PBSfR59tAJku5Uimk0xoUvmhzx7F2RV7E4NYv617tu3lkE4XxKUzFNuCnMnjcLDr32NBi27q1OJX6dwAHrT/8+Rvp1ZWRnYzKCflX9MZCR4hOpzcOsy7KSP4bxJH+ODn9bBQPRsvlYxNY8w4/PsP/RtTP3ylQJd42WP2mIpu3fvpwtAWaU5btasoQJT6kARH+IuYCjmWjUD8Ithh094zdu372F6uYv0Y82/DsM4Ozt7DHjiXdgT1AUEl4ebhzezzOSgVPkqqs3QL5mp8aLLlkSFDq2wJ+4cgZItnq3cCdW8gg1djN+PvDYWpStWx6q5P+CZZ15n0NNH+Pjjt+jb2oEvG27qusLDL5FSKAUSHGWaK9s4yQ1UBPzJvxkp8gJRL4i8kn6XMe2wN+yZ9WTecU/0q5yY3+HqJ3myUc8CUXWQW5rKXiKmXvm9iv9jEP1YDVHtsm9w0ygwod6xSgnYyFubLtYlgTE2NgFcUf5fu6tLc/ttD2y8CmqQrGvV//xqUulk/uqemWgVUAX3V2j+z5/g/zjjmSN78Pmr9xjP6EQOioyMTLToeh8eeOEzY/vtVMmkz9NLe36lmcgBL1ftBtfkVLz7aEt1CT4BwYgjGJAHWXJyCjUbjthIots+fR6mxuOy6iM0KZIvNySkIR+mueRWW0+fvvwHpTxg5CEkRLxC/CvhmvIQE46zTJpFO1HTMG/yEZWN44UXQukDV7OA6CRgZCYB2IM1EoztAgCrDbqgfN0ebZKBke/mgz3JzRoVnog/v91FKpY6BXPKpueovKw9ewYXyIl6/nwK8+nSt48+WiYZy9S5qlZtzlyqy2gi9sRq+kNduJIDB5L7DhkUXCxYES2gAF7JD2soEsE8k+a7VcvCsX59FOlS8vDycxXQsq6L8ou8Ff81mbtbt01cYzRBcCXc/0I9rKTZWQIjzIsAkHr12sPRxQNPvjURFarVx8nUaHxzbAX8HN0ZBd4HB9YtKGBCFgoc0Winpua/CIyavFlpvRydnanBczI/hdqPDD+DkU91oKbRDSnJ+WDkve9XYi4B5OFd6wqM+Z4vFj/++CuJwg/gueceJ1gdUeD49XaOHTuJhU274QA7npXNw13xNopWLqBkObjz72ybng/iNP0hbW35EjH4Od7rgn97N105pjLhRKTGQaKhs/OuvaAYzi85kodX60HqH/5+r1Mizp3A9HHDlQ9mUV0bN66Pn376ikA9/0WkqH432i6PfuGlLEH6G9H09aEbggDE65WzCQ4qm0kj5kt2poZaTMUy3jA2IzoGWb34JlawBL6WlxdVsEnv/dcS0ADwv74DFs6vAWC+UO4kAGi4zQd3rFaULxtoLpXy0S8br+twbxhrjd9rI8Mw59x2xUP2UpWu8CdgOrB9lTIFm6/Xzs5OaU2kXXgCT5/eyahXO0amLmN6shyawHoYh+xnpoRDNIPKQ8XwTCrrwUhaO9AcmotSjqk4sCeaY7aqMaNH1ybVSojKA/zzz2fIlReEbH+SJdN/rh5Jdw3ajNGjj2Lu4ijUfqwtfngoB8fouySm13tID2PQYhgXUURF0oQJ+a8Ek5Tht2T+kCKasZdffpdAbQuzhKw1jv765wh8tZSpv7LOYOFfLYztlipTmGmkT2iiyhcrxyUjRTNqcNxtMlX2iVWrr+D9T+sDri44SO2gBL3Uo+nNj+bww4eYW7aJr4o2tjS3advXXx9nMMEV5lCug2i3QKTn2Bo1pab9pP7qq+8z+vR3de98/IOosVuCaVf2EgBdpAbJDoN8qmHz+FHIykhH+NmjxuHt+jyKzv2fxu8T38W+rStU+5vfLELZ0FrGPobK0X2bMO6tIYZdVKndDC9++pvaFzPpge2rsXfTYrUv2UIuXAiHE/1NhXPS39+PqQdfoum+M8mx9zHfb5ACoNLnwIEw0vAcp0bvkspC4+LiTJP+GcrIgZq8CgrwuXp4oVLNJoxGbg0v30DjGixVziVHYTyJ0JOo6ZPiwOuXXMiBTIcorhCOtvao6VVaafxc7S2DXUvzGtrC9mzAst/Hc5f5iN28UKFqPaVxlJepRdO/4ssGXQReeAKPP36/um7DuFv9lhctKYeinSFZVnqE0I+WL17FFdHyfbQ1AK83jraYjSWNAHBGpabmU2gAaC4RK9jXANAKboL5EjQAzJfInQgA5cq+fnMwju3fiqBS5WkeW29++2+7/Z9PrsOu2DO4l36B7UpUVxqb7957GE899bAiHRYAIRkyDEUoUqZPn4jSpUsamor9lqCOuX9G4IFna6F6TS/lc9R96ElknLsE3/RYppZLhwDAoU+EYPmWZAx9Pxw5NJtNeCOQJtPS2BLhqpLem/o6RabYqQARcWhvQ42bQXthuhCDedC0TbInbCTwe6xWvCKZbhwQi2ObFmHXrn2kklmsslnMmvUDTd/OxmESfFCqzhbYebghYltdnssAaY1djBXxb/xxvy8eIkGvBIqIBvA3ajKFsNdSkeuQwJF7nzlO7WAuauddwOKF+VpYS/0ttW2/5AIB3E/WiSt0OJ2AfvnyywTqwtN3mYD7EQjVz4eTNmIvTZxTTm9Q2i8BPU+GtsUVppjLZSCOq7sXhNZIykqaOOf9/InyBXzr28WQNI6WSiYBpLwQ2BGcmRcJMJJAlcsXT1Gb7IJmnQeiYZve+HrEfTh9ZLfqrgKRKAPTIho8Dy9vavX8Ce6CkJwYx+CWahDfQwlYutky7fQmbI0+wZcSGzxdqT1q+5S92SmM/eV686g5dHJ2VW0XzxzFF6/0oQtFPrg0dmTFhqZkZxf+jlPzzfSiXY5PyNeUSr9bjVw3nEN+Zzsvu+IItdCSsaY4ELibhNQSYd+1Qv5aDHMYvjUANEjC+r/trX+JeoVaAneWBMpQA3LuxAE88+HUO+LCwhLC4WLnqMCfXNDvE96Br58PtVH1aJr9Elk0FZerXAfnju9X1yuO/bVqtVaRnceO5WvwihPEmjVXsGLZJbWdO9cdh5giLWX3Ebg1qo6U3BKo0JiZNVrWxO9H6Y9Uxg0fPhiLd8an4PkJKfDxuULuwEBFuTKDzutVGFAh5NA2BAqVmfu0lVlEo2QiEaf8Jk0aWHyoCnRzd8gHGQ9Xi0KtByYhwNMRtRzTGPixFIdI6Dxq1AmaJWsagZ5EnlYPzsHxPFdqotLoM5dLs7e7xUue+etZ7N62/3/sXQVgFMf7fSHuQnAL7u7ursVaXEuhUFoolEJLW6yUGtAWipUWKMVdi7sGDw7BibsLyf99E/a4XC4hoRb+vxu43MrM7Nzs3u3bT95DYNe6TGAJRYG8VsrCt5K0NXLs0q5xqM7xayWPPbVcGZe1fpI92nY/B5fxTZmRaYWSWdB1jes9FhWTaMU8/LXWre5dCKoHDjyr1letqkN+uq8wdOgYJnocQ3VazCS2be6NP+FFa+CEi2vwLuPdSjjm0bWXhZbd3lGvVBuNrGgxpEZ2IQeB4aDxc1LtWvnjRB34kx1NOg5Ek04DER0RRsqTRLjlKqAsehkB7lQdZmKlr0c9ch/aYo/PFfx8ez+quBTG2yWaZiqJSyzjJxnj2vat9xT4nDGyNb8bCXBydlNhCWEhgchDlZwjRw7BkW7pwMBgEpUHK21t0co+efKcsmaKW/5NPfAX8/k4ChIzbMA+BUhm4mOkqWJFy7o8DBUhOfZvjA2sTbLoALp2HzPzVxJAJDlJg9bFmTiSHvhL07FpQ7aeAZMFMBueHpMFMOWk/H+1AGbDS+4vDel9zxVwsLDBjCo9VD/vdiiqrFH6neYrXBI+D2/rb6K7dDgTYT7UbXv8OBq3b0cqwKbbyAWJUz59OpguyGTKo0VQHi4FSGp1fvmlBi1TKdYmbZsAl0HfRWD86KJ4u2UKfYXsE2m42uYP0LDBAdhVK4txkyqgyNzv0X7jaLrvkmjlKqW6CDFQgdD6FU7CAszcFYA1cOB7tGwmYMjMFbhEd2xFh1AMfO8GzMg+XdnsCTZsqEs6kwfkhoul9F0EjiYWQ8TRC0iKiiE5dHvGCKa2dMnndHPbog5lXaownoVFonEFC9K01FCUMxKzNe+CG0ZVC9aGk+Zd6iz1csUQZj9ntmy67agyPEU/17BIssiuXT4ExU9JfzKP7u3jCth+tmA/8hQspqu+n9neGx95EiQko7474woJlP7pzHYfxs0d2LoUxctWR7kaTeDk4q4bz19ZCPZ/gqcPbqrkjmd8eJHM5Jy5CzDT10nXrfw2CfB9GB2kYiGruBVBITs3BMVFkiQ9QtWr7lYU5Zzy6+bhvc4lkZgQr/aJZdKS18nIkYOVLKJwCJYoUYzJIINeSnnjTKLyML3rUyzVEUzCeaaXOa8b6CsuyPdEtK8n0M2b1WKyAGZ1xv67+iYL4H8396Yjm2bg/8UMFLB1xb2oACZsxDBb1xYeJSrg3q3L6rNJ5mKVKtVw8+YtpfU7ZcpH1MatqRIkhPpCEgymTJlAMuOcjHF7rNqEhHRJNS9iwalTJ6faVquWG7NAQyBWwffeK6kk3FJVfr4iMYC36udERLIVFl92wfvVg9SeDsWY7HGzEOp1K4cT669hxbiHTCZ4T7kt9TOTHz16qktI0e9f3MWLL7tiYOf2cLSzotv3V2r1RqEO4/QkhrCOkz9O+DjgsltB5Cu4i1kr9K2x/PlnQ5wd5Ak0rYGw7UcZmxbKDN6zmDChDN6m61qKfM5Vq2qTPDsQ8+Z7w7F5Tew/ch5lyuwmAOvE/aTV4fGFxLo3qWOMFaljQRJsIf4V5QpjbmzDdlSHo4XUjoH8sTrdXK2OPznrtm+fjTVrtqgEjjZvjlJWNkOw1TxfBVR188DsG7txnC5SsQi+z5jQfHYuWld/+3u+IiXRhwTWr1pCAn3w6O5VHSjTLIXLvvsQcbFRabq1IAWNA7N/HV1zk84lF1zoqrZk5vrdQjmxn4kghuVU4B1ltXW1skclWgodaOkrUpDtGg5HkfCjGP3ekFQqIIbt01vXB39S56+6f40dJysPEMbam7a9HjNgsgBmw/NksgCmnBSTBTAbXpxGhnQzzAdzbu5GRZdCdAG2gN9jb3wxrCkmTfqAVCN1CWoeKmuXWL3EpTVv3q+YPXuBrqfhwwezTiWlbNCtW0HGeOVQyQk2NvRLpVPmz7/DwH8vWsysqBqRmwS9tHoNKUqQQjSjVySGbc1pM9SuYI0yOV9YuPbet8e5u0moYR+AFnUccOHCFTRr1lXXcvv2LSSiDsYXX5Qnqa+rbrss+EZZoO2Qb/DN0GpKVk0sk35+MRy3Oa04BH0sOext4di4GsL3nUEyrYSSbFGjXUkM+9gbbzdMVAoPog8s5cmTDlTpsFAE0xrH4LhxpXDrfhwuupTHp3UC0KNbAVVX/ngyBusKg/YlOaQ4FVIkO1oykcX6JwS+FxjPdyvYGvkdExSXW25SzHCXrpShG1zc2JLAYkuQKEWyjw8ztnFQrbzYTzelgOFDh07g2rWbunaFi1fARMbwvaxsfHgWe329FPjpX7Qh6uQq8bIm/+h+oanxf3wPG5d+ifs3zzPmTpJHIhEdlRa0aQPp3r0jJdjG0HpnrkICHjx4xLm4RQv1XUUTExISprL4A5nwIDKHzsx6T+K7BetbMTHEnDGgsVZ0n9LKF2NLuhRa+Jh2DavChTF12Qm09EiEi82zl/JESmb4okuumN7Aj2TM+meRSUdc5WlXDwbauLPDu8kCmB3OQubGYJG5aqZaphkwzYD+DDz2vo6fPuuHvIVLYfD4uWqX19mDiIoIQYO2vdMQ5+q3/f+2nMfWWX0kyYCUcvbwFuX2mvzlHEyd2JxbbhMYPaIl8DvePB+pOvJHFEK2bl1OLrpCmElDjrgbc+bcovaXLOmgePq0yteDrOhmtVVyVmLl8rKgRaWhpVK12EmgtOWUP2lmApgJXJx8fcdRurQjlS7q08XshV0+ueAeeB+HdtdVrlTps2nhKNwJdUNtpaPKeEBacqQUIsh5dNeLlrkfqUrSim7qi4zJaqr2aX9Ev7dJ596YPHcmKtdrhqU/XiVYvEGeNjt+joqkFEmg7nERVKx5CA4NqsDq0iV0714QGx464NCSIsoyJ268MkfOElzEKvAnfZtrZG1c/vZbSgae64u3+FnbFrXSDq3eJYaxOjObJbnloj/nhEBAg725qIhSxi0e5amecpVZ1GUJ9kRxRCuSZHI31ErR6+zydkQMKXV4CDwlsXaNCSPRnNbcgy17aNV17wKExsxaq1vPaKFr4ZqolbMYvr62A6senEQNLkuG7L9ZIkgkHeDzAA8Za7vm58/Voe0ZI9e+XQuqn0Sg4p8HUWn5TwT3lakZnBI7p1GiCdm0tk0aCjVRrVpVjQ5fMoqXL1+rkoCE1kmK9BPNZI3EwEhYMDbPMjYWcXwI4MWN+IcPMW379yjZfxzigs1wg0B9ZNWgdCXaRAWnT7kwRQruwfg8yfqW2DwhYZZzJt8Foe+Rd1MxzUBWZ8AEALM6Y6b6//MzkBAfi/CwQIQFU8GArwl9aqSaE3tHV9Rr1TPVtv/PK55B3urj1WDMk5Tr5w7TLWqDvasW4vbyRdyyDV5XbVG4ZGUupwDAI0e2MBGknFTXlRw0Y82dW4VE0RfJx3dTkRxrcXJCpqxPqFzLMh4louIw/J1iuHIrBt0+MsM+ryBUIredlJs3I0gVEs2YqhLY3MsLweVLonTLK9j8cwnUqe6MEaMuwt+jPIZVslT1z5w5r95rUMGiesP22EzJvpo1rdGvX0dltXRzc+VN9sVddkZXd1RelU9x9N17Hibl6xuNiROvMMO5luISvHOnHebuBj5hPSsr8qTxCGI99HBOUDFcFbleUf48L6L7GhTUGWvpCvcvUg55HRIUfY1kaHqSzkaoZ8oXLgq/gDsKRAr9S/U8Mcrid5+ZwDI80UQWgEBMCFfrZwS6kVr36l0MpAIQpQiQ1IrQgdxe/xVq1TyBQzEhyEsqlTZMhvn1eYUCHiWZnWuuVX/pe0H7nOhSqDrWPTyDTY/OokeR2i9t81crCPDa+MsMElBv4vw+PynPOxXqlEmT3qeV1vqvHiZV+xIlimLq1AmpthmuzDnjhSnd+ikTrd2IOfi4bVEd5Y5QFG2966Ti7d6iVJuLEeUY4duTlyT3OFnRakiLbmKSmbLeHiTP5GHGpTbRU8oxPL5p3TQD6c2ACQCmNzOm7dlmBl7cdv/7IS2aMRwXju9SdBUilfWQsW7hoYHkE/NQtBdCIFupTqv/fqD/4gjsn/OdPSFwqIIi8CB3mZDoiqSYuMfa9RqNDn3HKt3jsd3LK/oXQ/CnDffTT73UoujvenjsUBa14cNTrHNaHXlfssQbCxd6Y9rUa1TpaITE0AgkJyTScldWvbS60dGJWPxVURVz98Pl82jb4i3Ub1QPx4+0hLlzIDpHVMOst/wpizYMBYuVRfMuQxQNSRjlyQ5uWUpeuR3kXdN6o6bqyp+p1NAC69esR/H48xhRJQh/JFbEjlu2iDxyQVWsUcNNvbvZJaNdXVuc8DVTN2ihcllKnr/uVF2QrEpjRUCwTbVyqJMYhLVzb2JthXLkqMupyKHFWnT0hD+svZMREJqIm9fCGB/ogIYlk5ScnLH+ZJufXywJhL3pao5hZmk8506SSlKAr9ZGaD8EGH58dx9a0x1+gO7wea4uDDALQ4UaTTByym9a1Uy/N8tbHruoFX3I7zo6FqhK0Jrakpnpjp5XFM3gfZuWIMjvMboM/EhRzej3EUu37r6Ni2lRtcX8+bMoF1de8U06OzumUpfRb5PV5V3eDhD5vipGVDKM9SXckF9NnAY+EeDWgU3Y6e2OARXCdFXFmizXg4D8DZT+E7duWVr9RF5NZNry81ha0TK7Y2KeUTHkGurWzUmVufxYesUFTRCtVTO9m2Yg0zNgigHM9FT9exVNMYApc63FADaiEkivbKAEIvxc779ROtWFYEUOr6p126DH8M+VPFSqnf8jK3KTE8WWWFKJSCakh30uVIMz3KzsVAblwunD8OTOFTi65dURBQvps/DmSfHyOqISRGT5+vVwyA3u8uUw5X6dM6cKBgzwkF0KwNy9G4lGjXIxXjCeVC37lX5vz54FsX53COwqlcSjbSl1VQP+cXXdrC0SMNqTm/BdtS4ZqtbWNRAbd5EZy/Fwdc+LyT/vTZXpKZbeQN9HmE66DuGhkyISZOfP70eTJl0Yc9iHFsIeavu1AEtcekwqjZxhdG3bqW3yZwNltuoViEI+0rVISZA3PtGIJU4SNUJizcmpZq5i+nLZJsDdNgmitACv6xg1JxSl3BNwaGNV1Vb7I65yd/ctKuZP4h6//bYyE2muYs6c2/htY1OUqpRLUdxIBZnL7767qfZp7eVdizuUZbGc7dy5j67ukySHPqqyUq2ob2tP9QshIBZ1jKm/HJGqWS4XqBm86M5BVGOCiNClZKVER4bh2J+rkUjN7NAgP/JLHkCw/1PVhdDC5COf4OAJPyF/kVJq26VTe7Fg6lBMnjyWmeIjsnKodOsKL14wz5FQpIRS2/nP+w54s0x4uvX1d9wMCkPbkRMRcuwE3v99MZoUbwwPWnHFAqyVuLhn6N//LK+pEIYs1EPpss7K0mfHhCKx+PnRYizxnc2pfCOa2FJWkCpo9OiLDFtwZHZ8cxUjaIzDUTvG3/0ulERigeSwOKZE1MoXnSqW0RQD+HfP+D/Xn8kC+M/Nrann/2czIDcYwxIfG43TBzcyFqwVqtZva7j7f2JdwNQXlbri51v7SYsRiEfRwbhFZQTZ9oAW0uvnj6IeXWW+YQG6+dDAn8RXCeeZVsqWdVKLVatSEaNzfgK4F1YjATmSKbxiRS2INNuNG20VKLS1Ncf4jxOx7okQS4dpXal3icmbOHETYxKXEvyl3HiLl6tBYPOEhLu30KBJD6W00KBtrzRxm5ZWNhD6mlIV6+DxnYtMitjMpI9uqFy5CV3ctujTp5vuWOVyJcA/1g63knKhMKJ02yMoZeesp2PMnAAIlcwjqi5I1rArEwEcuL93mTBcY5xjMm+rRaMfYTVpb95sQXqWzpXZV2rAIVbCj5Z2wH3fZ6hc2xWRzL5esO8ZnFrXxbuzArDpl9xYR2vSqjWPcO64H2K8HsIyn7six3bj8d56qzDnw0wBvXHjPlf6tzExsYprLz8/78APZ6Nmk84QACbKNdXoEn/VIpnBuawdIUAwND4aLnwoyGw5Qy3hTb98mar6unVLCNytmES0EAcPHsfXYzph9obryj0vlnkpDRvWTdXmVVdEGUPcq6ISI2XTHSclQZiZ/t5cvhl7pswk0rdEuzmz8EWTmqQgslKk44btr10Lp2U2TiUBSUa2FuqgKcxEJZhxHPYIiTNHL14n1au7MS7RDtO/qsIYUBv4ECTKg4VcW/9G6VkqTHcsCWk49theubDLu8fhDkFrQtALq+W/MR7TMV59BkwWwFefu3+spckCmDK12c0CKGDmqw86qsFZke4knjdeKa17jkQnBnX/07xn6mDZ/M+6h6dxwPcaOtDl175AFRzZuRKrfpoE3xsnYBsRiafUBv7111U4fvwM1Tsmo3z5Mpn+RKJI8dZbp1R9X9+OKutWayw360OMhxpQIVTbpN7v33/IWL7WlJpLpGWsDgZ/9INShEhV6SUrNy4ep0xZb455BwmcC6ss0NKlSxhtdcHPmjF5trT4JSqyXLl5b7ztrJQ9tAYhtCS5Gon1kv1ijStYcAeTCFJuoreedEOu55Yfrb3+e2S8Ga1SjvAJSECO6Ci0LRFDK6W9qnLpUiiatTsFmzJFkMPOhi7yJMz9uihCzvyBtWu3kk7nkpJTq1q/Heq06I4S5WuminPUP85fWT4RcAsr7h1HH3IDNsid2oKeUb8yFwJAvamjfevySerknsf48SPp8j+isralrbWNLb5dc1m57R/cvoJZz7+fEmIgcaZZKQKijj8hWXekpYqnzGWbCFGPOUOllJt0v4trthYJkl9WLgcEo3HddsjZpBH+/OZTFHdNeaiRa3QPLYgdKLcmbmStyOeUoh9jqu3TfxcJQlGhEYugxHtake5HiM3lJcDxvyxyHV4PtlGxiiYL4H95JrJ2bJMFMGvzZar9/2AG5AfX99FtxDM7L2/hEkqKSdx9YvGRIi5NoYmIiYpQ8X2+JDAO9HuEyLBgeJSuwrZ3VPyRja0DZaZc0XnA+Jf+eP8/mLZMfYRnEsTE4khiaCn21FmVsodJFh07tkZeLk+c+L7apv/HxyeGVr1jTKCgJutzK6D+fllu3VpapxTDm6VY0uSmeIQ3yEbkygsICMIPPyzBgoW/qYSISrWaY/DHP+lkt7R+MvOeM08hVe3pU19KvZVihrFx8CeVquaJU6+bwVa64Py+5UJVe+1PeuBP9svnGjCgCH7++S4GD6ZWbQbgT+oLNUg3xpBBeUEFBdiTFqcbXYqXZbcq0edT3t3d3TC8RQR57+hqzlsITTsPVrGZryKJ9rzrTL2FPX9Qyvs8WzxTjVhJ5qJS7RbqJTJp343ryuSgeap5noLFkd+jNF33+SAEy5Z0WVep11rXdZcuWbPGC+nx2pvOaFooCo0LpZYGFFdsW4I2zQWrO0g6C7FEkjksHFCvUR0d+JOqYtETfr35JPPuwYSPW9ShrugeqxI/hL4njBa+fPYJaehetMNIEpBkBGfHItehJKqYyus1AyYA+HqdL9No05kB0Qg9d2QbajXtypilwunUStm8dNYoeB7ZrlbMmaTgkjOvCiwXclckPyPoC07TXpIZrKwsydKfC7VqVFTxUnb2jowb25Om7v/yhq4Fa+A0CXCF/sORklnVGrRHsTJLCGpGMdHiDbph+9OFWj4VYBZAvmXLE3h7R3FeA9IFgDKv58+3pKWV1g9m1eoXJ7pRRT/3+zOuOLv2e8wkBY30W75GU7w5YqpOl1a/TWaXRdNWSIAPHTpGF3CjTDWzs0jSxXqJxSYr5csvKzJ7uTjy5bNVdC0PGBMo5M6SpCEg92XFg+DvOeZLVVWkxcS123XwJLgw5vHfKgFx4epQBW3dMnVIibcUzj5RjhHd4Kueh8gteVe1dXR0JM1OBGMBH+q2yY4ExgmePbQVJSrUxN2rnggODsnUsSTe8gAtx5KM0adsqNHkHMmW3n7XkRq5oZma/1r5c8G53XjsXLoY6N811TjkWniTblxPP1tUyBmLA3TtxlBqLZZ0PJVzxZLqyBFRDBvIzwxwsTgWdHxhKUzVkWnFNAN/wwyYAODfMImmLv6bGYiJCsfFE38y828hpZtuq0FcJRffR7O3pDsgAQXnj+1Er15dqcDQl9l0cxTNR9dOTZVbUmgdatasgvz588LFxYnUXW4q8N/JyVHXZ3R0DGNwqqBm0zd020wLKTNgRaD8aYXOmHJlE4P/D6AeZcFGfbUKmxZNY9LHeqxevYl8ebnx008zmcxRlyFSliQdfkKroJfqQIigMyqae9NYnaPMwpj/7psIunuewLMdelMlwp7KDX+1iGvfitZhIbAeNmwAkzwKvLTLQk5/7cZdsKCdSjh44PcMXgfuon6DXCjTgMkhBmUns1LFLelOahCt/EKZsF+4Ite6WEKrVGnKZJBYtbsiLWr/JviTg/rEEDjxX2azgGPIobef2b5SrpFSqFChApz3fkzu+BAODCGIj49X142vr6jBTFSJK1JXLLWjpi7Hr9+8z3O1FC1bNkb9+rVUJrrs37FjL86cuYiGzZvBrXR9nCcI82CmrWRnSxZ0ekWAmA0BvcjwSfavFTn4nkRaIJwWu0a0GBZlUocYvkUzV0i6H4ZboVkD8j6uu4uttx+gU8kiqboWq27bopFqWyGniFT7BGzytKnEoIWX3DC5bkCq/aYV0wz8nTNgAoB/52ya+vrHZyCOSRfytH9kxwpsW/GdOp6HRyF8+eUnBHOzM4zDu3vNE/5P7ysX7717DyhDVhHr18utMnMlJCRUgYDFi1eoBnVb9sxcw/+xWjltHFUCyHfXd+IEZcFO8pWrUXUM7z0cSd7ejAmcSGLkISqYv169WhRIyMkZSqDrsjqzW62zPFuXL18loPyF2rs7YGPniFHTlqN89cZZ7iejBo4uOakcEUFC4FZMnjibiig4o3avuk+yTfPTHXhi53ms+OE2fM7nwZtGkhtaeURi0tE8mNXIT2edmjXrhiKmbto0F3WEq6gh5CNh+ejpK/518CcHf0oA6JqF5I9nifFqzPLn7Nk9JBEvqmI47917SOtfpHrJd3DXrgPqu1yR7v1KdVrS+ldLufgHjpuDacObkxB8QApdU4WyfJBzpXzgUdXvDz8swve/rcKQTjV0c6Y7YDoLAvLerRqMJxEWtNbloDU2Do60Oh9oPRUn536nMmIlmacSrXhNycm36dRV8WETWGaALNM5llh5/cnlaEvQ+SpFAKQkhog+tVjGJYPZVEwzYGwGTADQ2KyYtv3rMxAQG4G7EX4qi9Q3JgxB1NaMSHgeU/Lc7xXEOLzp77ZCbMyLHzQJ9I6MjMLAgaNp5YhDv7HfGx27ZKL+8Glf3b779x9TpjWZXHXHceJEIGOuqtFFWYi/2fz1NSiiHDBz5lzeTP9QN6LSleuh5/ApKkPUoKpp9fkMuFk7YEaVnjhDd/B+JoVIZvAPd/ZDOAMrT/4aVeNtcHnfJpw/tYfnLcXKsX//Tnz00X1abWoyAzj9GC6J0dQSAcSqc+XKdVhZ26AOFSx6DPsc/0RM2+eLDkGSQX74pA9EH7do0dRWnb/zxIssWzBfrQnuin1UGq1a5UGFCs5GD3GPBNDNqGqif9mKTrKUgweZEbzJX+nNenndgNkrgBGjB83CxkjqC8cnJaKYQ6FMtzLX4wts3bonatSooqzzUVTV0IrQ1DTqMAAd+ozRxZlq++T8f7HksMpAF/fxOSrT3H/kh+ZvvI2QwKc4f3QHGlVwTzVnWtuM3sV9a2jZbb7nMzYJS9PsvvcDkXahazlzWc9iQXwUYak0mePpDi5EGb9xNYPS9Gu4QUtc8YlKsQ5b5kjifOdQSUgSk6dvGTZs+6rr69c/pvfEE9OmlceoUSVftRtTu2wwA6Ys4GxwEgyH8P85CzgkPgp7nl4h0AtCCEFeVGIcf7CeGU4BM9zMFVhwtXLAwGINkcvGCaf2rcey7z9URK/NmjVQrkTR5NywYbtqL2S1FWo2TdOXbPh+Qg/cvnKGWp6naHkixxl/mAXstWlzlG6hYNVGNGifPu2oKDJkg+h8zpmziMS5v9PtlMD4wjfQdcgkJQSvGpj+ZHoGwpkIsP7RGXiFPqL8WIJq9wbjBVvlr6g7r/qdSbanN2+icp6Ee0/KpUtXIdJbYgkSMCDyZJKE88aQTyEKHubm/+zzrP+Te/j87SbkJ3xHJbKI+9qw3LsXRV3ivejZsxB5B6sb7n7pugAB0X4dUilEcQVqDeRGH0Q+urz2qb8rIgd28qldSiKIVvn5u7d3pKILqVixASxsc+KTebuNPuAYNMvUqriXpRh7YNLvQMDf+54rsswD+OxZIjb/OovhHYtUd3kLFWcs5zTl0hdCaIkNtLFz0D/US5elz08G1EWJovn4ALFeV180fuU3pEePjrh//xEBd5MMPQm6hhksqEzg5t1FRw5PNv0CO6uMr02hmxEQX4ugzea5PrOx7kWx5Sq5CSUrOYFcfAJK6+ePSgNMDds+CregskxiqmvKsE48Ly3JYBfLYzG6xiWxY/Xqh6qa/sOxkLXPm3cHderkxPbtDdR+fRlDUxaw4cxm3/WMr8rsO27TyF7DGdjv40UQcFaNXMUEmVuSF8weOQnyJEOwsJ07ijvmVmDP2McTN48UicFzpUpBq1ZNMW7cF2qbJH4YA38SUB7o+5BB/CmuxVKl6pL+Yq+Oe+7PPxshNDSesmNnKe6erKyCwpH2ww+L8cWUb9i3GarUbY2e73zxn7jP1If7f/DHibQ5g0mEK8U7wh/zb+/DpseeuB7+FO1r1EOf0V/hxoVjKFSiAo7t/J3kxT/D3sGJ5yMRMaQ3keLg5MpzkB8VardGw3Z9Uaxs9ZcCENXwb/qTu0BRJfEnHHRz5y5mxvJ8tG3bXNd7SEg8M52PqfWcOa1027OyIAkJbRgfJkTR+x7Yqzi18swUfULrUGHHeHLBpfxkW5snEUjnUETBQwkWjZVixRxIqH2NZNkBBMj1/pa5kpjb8NAAbF32DXWvQ9Vhpyw+BJkbY0X0oc3NOE5a9bNSwqnEooE/aSeEz4WKlctKF2nqypjDgv3R6p3eJF8eyYSiK4iNjeNDBhMy6CZu3LgLQxBcuS2evy2NFcl3nTo1XgkMVsrlhjenfoI1Iz5A57W7sat3uwz1kCXzOL0iFuHdDAkQZ7KAviqUAGxbNCLdbGH9flyoOR3KmFDhCNxIUnK5rrpK1rheiSZV0aobLirOUQBoHC2Q359zR6+SwRgx4ryqKQlJjRvnUsvTp1cg8Xh5zguoRHIMJ08GkZexCr0wHnq9mhZfhxkwAcDX4Sz9PxnjlsfnYZ3DEqNLt0IxAr2slhy08AhVi1C2rFixTr2cXd3RiTQsLbq+naY7sRTMHN2e6hM3dfvEcjFu3BQ+8S/VbXNxsaKrrL5uXRZmzfqRChHJqN3sDQwcNzvVPtPKX5sBOfdTK3XD99d34QYBoLzgBli0qA1fWnrL1fiGYveWCIiLQBjJg2nk4EOCPQaXbPrXDvw3tO77/teo3/ot8gL2Um5oAYCXA6wZ+xXHGNTriphaDtOrV4rV0vCQQUFxvPae0IVWFIsvuylrjwP1XbuWjFBV/Rj71YyJBbvuOaC4c5xSWpBkg/aVjYM8w/711+Va79p1kNpUvFzWrZHSMMDnAVbPn0zJw4uIjEgL4oSKxcGZJy+dItb+Z8lJCgSmU8XoZtHTljHHxUShy+CJfxn8qbABysRJQs+ePQeVlW/GjE9ooMuf5vjBwaGMKZ5DS+9kJtEE0wJdiGDwTQKcN9PUTW/DbXom1p334lOLAzzf/xBj8ubEjy3qplc93e1iudtCAurBpI8RdZCsFgF/UiTLWRRMFl92VbJzVgSEUoST8o/rLhhYIUQRk8s2sTYzuBK5LWOV0sjBg/609qU+x5rF7/2Tc3ESfamzfMUEAGXyXrNiAoCv2Ql7nYebg5aAJN4MxL2b2RLLG0Agb0IR5OBLSQBJyWaU9l2HfoIWjOsRN9Q9as8m0VpUjCoPmlvq0LblCvwVLjwQjx9b86m1GrnkPODm5pLh4cXCKLQvUp7cuwqxRty+chrXzh+B0M3Y2NijKPVuHeh+9Hl4R2VbWlhasQ3pOuSdL1EIkZtnQVotGnfon0a3NMMB/A/stGMs4KcVu0BiP0+SKFgSBXxjQ1XGqGgKayUH4V8y/3lHBaA9rUh5ssglp/XzsnfheNy15icc2PyLiu38bME+o03k2hLLo7NbbpXR3GzNHiRX7oZKO8Zh5MgSTFhwYFxpfmaR26ZpHxGRwP271PZu3QpQIi4a5Zhh+geltaSItaZ1rlL4dcMJVKWVZ99DRyo/MIGC4PJVioxV4mOrN+yAJh0HZLmLo7v+UCTeoq3bpk0zSNatEHhLyZ2/KIZ9uhAFCADTKwL+xMorcZ/vl2mdXjWj2yWmc9y3GxHLjGBvEkBvXf4dCaE9uR6BIR/PfynVk2GnwumZxFCTadM+ZtzaEMPdqdbl9+Hbb79Q2yT+9+jR0xgz8VtIlvni78a81CJ4LSAE9b+YQx3ADWAAJtzbtyXAKpXqGJldCY61QDkqbLwK+DN2jO5U8VhyxY18h5EQXsGVBH/DKgUzs/lF7av+Fvj5o4OYfc2btExNFT3TF19cZRx0pReVni+1DlmPdfv8qNttn2afaUP2nwFTDGA2PEf/X2MATwbcpiLAMd7OARtaAq1p0ctJmagSjnlQ1bUIXcA5U/24ylP7x32qK/CnnSYnF3d0e/szumVbKekqAXnXLxxRdBFSp3yNFOF6ufnNGtOZgeCX+Bv8IWPH8qguzpxpjpIlHbXujL6PGDGeN/fNap/EmEWEpQASiUcTehgBiL6+fnRPJuliCZ/R1SwarTJmWTZnALhQxwgfmQSlS/yVRihs9KCmjboZEMmwOMYJyrVhQYvN1kfnsMvnMiZX6IL8dq66en9lQR4mJItcHhzCgn3JK+dDIJ+kuhQLX9/3Z2XY/bHdq7Bq3iew47kOJkiNCrml6ouFz4KyDM7OlmnaC9fhwIFn1fbg4M58F9ebs8rS1GS/9BttI/dc7bxRSmFBXISLNoZgxdxL+O6bSsxGTm2R0W+nv1y+fEM4503JANbfntGyzM28zwbittdpUqk0wW+//aDibnv1egd//nkQ/cd8R+WQbhl1gX0M99jAcA8Bf2LtFcCfmSIPW3vWL1Ca0f582AoO8FHN5Psk3z35Plkywej9r1YjL8mgs1K+HNUWlpTpO3fOOLjX+pI4zAt+NoqoWbbF0+1qySz15YsW4nFwAqa9WRRvdGiivuuenpfIYbmLll8fDB8+kJay6ij48deIWrYCBUe8gyufjdK6feV3UScRvsKcpPqR8IC/WuTzefra4j6VRdqT4FokCfXLhQshaNbssNq0bl1dxkaeVMuBgZ35u2amX1Uty4NNxYp7qM+dQEqexihbOAkrS9YxrJd7XHJygOFG0/p/OwMmC+B/O///U0evm6skswFzY8vjc3gQFaRu8vci/eHN1x6fK8rV50AFiXy2LkpKTIBhyYq1ydu3SzdP5ZnkUatpZ6XMMeuDTkqdQ3ZK4HalSuX45D5fxfyJW+oJ1QPEDXbnzrd8ivWi9SKI7pyXP6lq1j/J9CxVqhjpJNoy/qUeiXnz6MYhQC+BigqSUJJRefzYh7yCrTD/84GYvGB/RlVN+57PQFb0Yl9l0p4+uIU5H7+pYtiKF/dA5fLFUHNgV6xatQl+/sHo/vbkl3bboE0vJiKUxHfju+HtAT3RZPNtxpFeVe26dy+IxYtrpOmjTZu8lGCriyZNcikr9amnjD1jfJ8x8CeNna2fIbd9Es75i5vOGWe9Q3DfuSjGfngJx442TdO/sQ0Sw7Z58y7GUUak0To2Vl+2ndizRoG/H3+cqfSONYv606d+6iGmVtMu6TXVbd/KcA95yMsK+At4+oAPbR2V1a9AgbyoVL4oLY9D0bRpfUUFI+5bT8+LeOONgZgyrBkq1mqmEkOEqDszpQR/S07+uSrDqiLXJu73hrTOvkXCZj5H6krvuX2xeutRzDiQhPEf1OJ2M0UvU6NGZY6vmOIklKS0qLBwIWHE44PMRH5/AIo4Z/zAqTuAwYLE5kkRGpf6BchjSM7H60wAEV7Cv1IkcUS4I9OTtStY8IX1ukEDdwLc+oqeyRj4k3Hs2OGjwJ8sHzsWiLK9M/dwIvVN5b+dARMA/G/n/3/u6OLCG1ayme5zC5CSjOALwQ9wJ9JPBYvfivDF/Fv7MLtGXwz4cA6eMoYvltJsbXuNRsO2fVXby6f26cDf8OED+PQ9gBm+99S+iLAgSMamxAoKcezOnX8wacSaUmNp4310A9FbkBufvDIqcjN6GfiT9qtW0Y1FyTm7v4GQOKPxmPZlbgbu37yoMsLdmES0f+8uWoOL6RoK3UiPHkPw8M4V6gbXVRaemxeP4/G96yrBKF/hkrq6siAxarkoqbZs2RocPuzDLR3V/uHDjVumrK3NaVHLo+r4MHtXMjkNtYvFinzlShgl55yUpJ1U1kiDe5d1xZFObrgSVBarrlvqwAnZjFCJQLKigatYHlDu3r2vwKY5kzHkuyYubuE0rN2sqxqHsT/xcXGqTc+endS71JEHKT8/fz54BWDtoil4a8RUY03Vtj+fXkYCFXXq052dWcvfY+/r+IZSb7Z2dipJq0iRQkb7l3N0+/Zpfj+X4PvvF2Dy4Ab8LAPQb8znqbwHxhoX8CijvotiwRe3tmHxizJXetI18sSgPN2uxspbnRoitLArynzQFk3I96dfPv10jFo95xuEt3cdxb3Pp6JKk65o/M4gzO3VMctAUIilDz2yZwylGfLYJSIwxkJ3Legf9+9etrV9AQt8fWNJ2J4rw0P06FGI85qkrN5t2+ZFcmRohvVNO7PPDLw409lnTKaR/A/NgAApD4dc6qV97LHnfoelWcqlGR7iz7g7b7XrjpcnGrXrp5YbtetDN9F1XDt7gHQbK/Abb8ILF3yj9n0z9g1mjLpQIN5CxUD17j1C3VSsrKy0Q/wr77/8slIFk8vNts/ojAHlvzKg/+GDCPgRqcBljOESJY/9+zeoTHL9KdFAgdCLiBt0bPcKBE3PVJUzBzfhk5926VdX4GjST7uxjoBIKIpu3foauXLlTFVHW9m50wdDh3ryOmyhJN4k27cfFSgMy7p1j/kwc44JCKXw2WfldLuPHUvxnjWiIkgjpAYeUmn5VRcVWiHJKFoZNWqios4Z8fliWNnYYueqH+n2/lbtFvJkOwdnrWqqdz9qXQvgE15N+c7cvHkH8+f/prKJy1VvjMbtU76DqRo9XwmIDaeF/zzszK3QrbBYyTJXfp4yCE7OTpiyYg+KFHkBzpYs8cbVq+H4+utKVP/IoTqzsbGG0ASNGDGQHIETcGr/b3zAciUH5AcZHkziPKVIXKR2rsXKdiXQBmL5i6QEm4BtSZgwLOI2FTesgLA6+WNwli5UicszZkWrzoQPzwGd8H25YopN4PC0WahJEmr/tT8bdpvhuiiG9CgdDskClnE2Jd/jP1XE7XvzZgTeeqswY5xz4IMPSipJxszE9ollUD8DmM/qpvKazIAJAL4mJ+p/ZZhyoxZeQCfrlJtAYkK87qNLfJBWJNGiHzMypcj2r5jtO2DAe9puJJFf8Fliyg/548dP8eDB41TWHl3Ff2hB4gAXjPsCJcrXpBXze50l5R86nKlbzsD1C0chluEkcs/dvnIK4aT8EPJjeRCIjginyz6eHH2V6BJdpqMB0iYuLCyCmbvv0ALlAPe8hVVCj8imXTr5J0Txpcc7n2lVU70LWHxj0ETq0G5mqEB/HOK7IT+gn18sXamnVTuxqAilhqsN9W5vO+GNEuF0JCaTTuM4jwlMmFBG1TOMIezY8bja/vBhe47dMtUYZEV0kFcznvAx6WJEqszBMglnb/hT5zoPKtVuqehPNPBXrlqjdMGf9FWlXmuc3LcOn38+SwHB5cvXqnhbCb0YNP4HqWK0COffV1e38dMkY2SplhnSnuh3EB4aqGL9Fi36Du553LH2hrkCPomJdLWOv6yqDh7swTgzF/1myro/bNhoJnaE4uDWH9GgTTsm8JRKVUdWvK+fx4Yl09S7yA8+s8tL7kRLRaHjy6zrlkUiVdKNfiKEtNt8m7q8iSmgM5bvpV3jkIfgMJExgc0LRyoJOKlnrMiD7bjalRA4rD8WXr2OpJAQY9UytU1i/3K+wMSZaiOAMSuJI7Nn38K2bT7Ik8eGLvfc6uHD0zOEqisJRq+3TA3CVCnbz4AJAGb7U/S/NcDlTBIR2ohKLoUJ4BLgeWS7Il6WrNpiZasZnYy4uBiEBPnBlQL3b1DofunXo1WihnADRlMv2BzPUKxYEaNt/6mN06Z9jzvsfDzHo8VQ/VPH+l/vV6x1ov966eQeWi+slStQkgaiIl+YIvr27Y6+fXugdu0X15DGkSbWrj59htMyFI2pvxzRgaPhkxdlamol3nTE50vx46f9MGTIGLqEf0x1zsPDE9SNdcyYkpQfdFV9tisWiaeR5ire7M6285TDC1Tbe/aMZMZ6ByYXWai4qp49Tyqi8k6d8mHrVh/cuBHBmFI3o+OSmDVxY0oJIXVMp5Y1MPe7k7Rmxqj5ke2SqT7oox9kMd0ifJuShSuubSlt3hyJdr3fJ7C1TreN7Pjm6nZEP4tH90I1s0TztIuWSfmONGhQm+A4CkJ9spBk2LVpaRs3rhS8vMLTVUIZO7YUBg+er/SOpw5viZy5CyiNbnl/cOca7lw5rjwIIgX3weczULzlEBIpJ6GESzwqU7YtI5BUnHUM3eoZToCRnYv+2AyiKHyxeI6Rvf/Mpr337cn3l5zGRZ3R0fr181AAsGvXE0xerseHpSRaA08RZFuQeJ0chkxsMpX/fzNgAoD//87pa/2JboT70P1rjq6Fa5Ji5TZ2rJytPs8YZv1JXJaxItmCUkICfZUeaL8x3+Li8V24cma/2r5t21ZmL/qTtDdvqhuz2qn3Z+nSe7zBhlNHtRJBREoAtt7uTC+ePXuBrp9FjFfsnS5ozXRnpooZzkAUuem+GdtZUe5MmzaBVCxDVNxb/fodlPUrF+lKxBr47ruD6dIqmaovjSNN1EWE3qR97w+Y5FAwVZ3MrpSt2hDdKUO3buEXKhlg5sxPdVZGyTq/caNNmq4u+tuiWp5YWDHeT9xoIk3YuHFuBf6kcnx8kk6lRgDShQstX0q3kee5UkhuxoytWrEK+YuUUm7vm5dOqOO/P3OVItROMxi9DT4PbyE+LoVuqUjJCug84CO9vcYXNz30xGPS91R3K4rm+SoYr2SwVUiZf5/7EbzOHiQvYl9dkpXIrQ2vEqISX/qMqgwP6vBmVFxcnBg3eZjJCHuVXOOf6xaQwzMRZlY5kb9gXqxn4y5BtGZ90JNLERl1lWpfZsDfaR9b3A21QgGHBBiSOQcz9jf53DkQvcLFxiZV3//kykNagYdUDE1zCCF4XnfTCY0LplUOad48d0p94rxu3VKuFdkQGZlIqcygl8YBpjmYacNrMQMmWP9anKb/nUGWdy6gAsg/Or8K+xL90H7qz5i88EC64E9mpnyNpvhswV5Mof5nIgPfnzBoXwN/OXIUpzrDTeWC8/ePIKXFamq5plhb9Gf1998f4ENmVy5efI96vyl0IPr7M7u8adNOAs1e5CkrovSCM9vOVO/VZmDljx/Djwk/u3evUSAvICCIiRxDCZ7imezgjjtXzyjQL9nc6RVNY1bi4v5KadZ5EFr3HIE//tjIe34jRQ2SUX/+dD8m0frYqlVeJUHo5dWKIOgFUMiVy1rJyQ3EWbo5K6jrs1y53Uq5JqN+ZZ+QHct1Ljx6F/gwJMUtVz4VkqBWMvjj7JYHjRjn99a70/EhufheVkTlY6/vFTgxg39wsUYvq672C8D8fGhj3L58nATak0i8ntbFXpRyZAGMuctMcXR0oMXqDezduw6tZlxlkxkYOmAhrnhuQnOSIUc8J0TOTF8vqyOSadeDrPDLFRdSWSWjS/FwUvWktY7+duUOaD4j54on3uvUC58eIRjMZAlk3N93Z3NCpN+yUq6QlLysW5zRJjLW3mXD4BVkg0v+L64zqSwPvLt3N4RdtXKo0pSUXIVtUa+em+onOPhFGI7Rjk0bX9sZyNy367X9eKaBv24z0MejHmOHzHEq8DZO8CUlEbnRRS2l/pOQEIdtJIi9eek4Rk1bjrvXz2Hh1Ld1lRo06EZagspcf4Z33vFFmTLV1T7h8+vRo5OungR3b79jD7sa5ZDDxgrFOnpj1uzq1NmkYB0NgZJl2Zk/8qKNmVHZsWMf3VHvq5us6BJLnKKp/HMzIDJxkcz4ljJgwCiEhIQyG/HFzS8uwo+WoCQV8C8u4fSKRvvzjFajv1q6DPyYsWi98eV77dCwYUcmcwxngPxbOmugfv+leaNe6uWGz+oGMMbODDW63USCXzB27GqIQgXJOUntVtEU7tlzhnLJHTjgr5qfOxeC5s3z6HeValk0lCe89Y7SzQ0OeIry5Uvj+o07eH/m6lT10luR5JBeI6entzvN9h9vkgybW0dR4Udi3zJTHjHTWlz3Bw5spFu8otEmvlEWJCyOMrovo43T37RB92ZDU+Irs2DIj419RiB6Q1m8VqyoxXAC4fK0VAkYx8jFJwkgxOuUVUtWLmSJu5S8lKOPbcnXGJ1mSB9UL4fCx3czwSQS03sNxbzZCzC21o9wy4Q18E9Kv7kx9s88c9OpO7ZYfo88doa4rzVrsG4nF8SxoWWV62+X5dq1c2LCrJIYVTVY1TPcb1r//zcDJgD4/++cvtafSG4gvTzqqpdPdCimem1CTKLxJ1CJ9bvIH1gp6xZOYSD+llSfPSTEizfPBFoFDtOSEqb2lS5dAt27d9TVE5JVcY1MGeSEEcev4f7VCPU0PKr+VVomKtCqVILHN8MvlFBqViRK6bEaAkGxtMybxxgwUlMULVMFY75ao4LmdQcxLfwjM7B2wRdKoUU69/HxS3WMrVuXE4DVpev1tiLmTrXTYMXKylJt+eOHj0lw3J3nTm70yXCj7nCV+m0yDBsw6EqtShLJR99vxoKpQxlMPwt79h3Dti2/UceVMXi8ActDhZQa1F29RmuMGJx/pTXpWWQM7OtUwn5/d4wpkdpVKRmw16+3wcOH0RmSQItahwBPM4I40ert2rU9edx2q0SW3Pk91HH/zj9C+RIUH4n67iVRyD5nprt+5H1N1a1QoUy6bUQWL+NHLuNNS7jG8yEyGetuOavv7gACtcxEdIiu7fnzIarT0qV3K1qT+QfeRCjjKVswUSQ3s3KNlYYFY/D7NWdUzfPi4UPqyW9Z9zJFFf3O5W+mY+vIsSg7dAL2z/4CFXK5GutKbTvy2A62BJktmGiS1SKgT66rhxFWBIAxWWou8aMeVAfJzFxlqWNT5Ww7AyYAmG1PjWlgjtT9zai06jYcBYqUQVO63kSk/vzRHbzZJ1KKqw3jgfaQPuImqTnuoXDJirQOnYOLizO2b/+dVBwhKEgLS968NopkNeUYdOksqs4s0b26Q7ZokWJlsbVIZkxSMF0+rijqRJUH/+PUvpyuxOS1yhKjlUL38pUJ/GmT8g+/X6UKg51dbsZ9TSfI2Um352GlFPHJJ2MU+JPDlylT8qWjKFWqOJYunYvJk79ituiMVPXb9/kAHfqMSbUtMysiGygATMqd+74qnk1urGJBElAjGFAsy12YBbyG2btdSkXCflIBlKrmhCqlwglgpKVIsiZj4sQr5Jw0p5RhOXXNpuwx/nfw4A+U27BZ5wHYvfpHghgnBWY79R9nvMFf2JpI6+rOp5eo722B3rTcZ6VEhocotRzDjGn9PrqWCMUSTyt0KBWLsrllxjJfJG7QwzklKWYFKXJ6lg4jADcju8ALn2oiWQK8HkWjSlEn1bE88Em8m4SA/PbbfXxDxRVJ1slMCYlN38IsQPDXLs1R4vYHCJk9G8O3NsaxIV2Ndisu32skex5eOQWIGq2UzsYLdOte5EtAXJNXsJye8rFjZnPKw/ZOkk5n9rOnMxzT5tdgBkwA8DU4SaYhGp8B0eOVl5So8GAF/mRZ4r/kxi8AsEO/cYwLjMPda+cwderXpII5KlXQv38RzJ1bFfuoY2lra4769d0Z92KHn3+uxoxhB2Zauqay/MgNuTl5uD78aBqOrF+i+tD/I9qwHfqONbl99SflH14ODQpkprgHz2syXf1fK84+PxIVFyiQL8tHFrUXuWY8PArxerChpe0JKlduQom4p1nqS9yax6ihe/7YDgjIyZ3bHVvXz+d1F5ZuP5Ek/N33wIE0NEVw/34UWjQ7wQeMsio2UADj5csRigNPljXrobHOTp48C3n1fu9LiPatWDFXrlyPGo07QSQU/+6y4t5RUjbRTV24trJ2ZaX/GGbnG4I/oYBau3aLkps7d+4yM/mj6Ya1wW91xyLh9M9ArDaHJM55LtsnvIsODg48X4+Vm10e8kTLV6y67u45Kc1WAxXqtscP5/MoQmXh+Av3PotlW8+SXD4GCWQQaNq6NcZ0q8C6Uj/FivnRR+lbJo19zuokj15JK6Ag+57U27U0wIMCAr3GDEChnTtxe/1GIB0AKC7fwRWyDv5kTBLXN6hCqBqeJG8cORKgYkq1zHO1I4M/nZ9bnSUkhh/DVP4HZsAEAP8HTvL/wkcMCfLVfcyTpy/Bxb2AcueJZukXDDavVq0BRo8OZ51QvmZg+XLg44+PMRZwN9edaTlK0bkUItT0iriWfvqsH+bl5BN2k3q0OB1i9uF1JU8VShqaIP/Hr5xFmt4xTdvTnwELiwjYWDuTYsVWVZJYvlcBfwKUOnToQ+vPWWUlFsk/cR1bkzy5Y7+x6Q/AyB4fysytXzxNt0fUYvTVRnQ79BbeobXndogVNt1xxFd99lHpIpKW6qfqoSQxMRrvvx+nyItXrFhL8mpnyo+5kVkkkpbsSwokCciRY4j1TxI96rfuhRsXj6kjJCQkKp5CvcP9LYuPKeV4Jsgbua2d0DRvuSz3mRAfpwON8jmmT59N0movPnx5UIe2AURVo3jxoqpf4bT7834/iDKGSNHWyhfNGLeUzODffmO4BUGThHV4ez8kgH5IGp2nisRa9Lp/+20V7tKyK3bXHCVawszFA07BnpgxphfatW6ER4+eMvO6Mywiv4FHlQaoUcwRQbGWsKHVvyR5/8rmTLGIvewDrvzyOJN/HmL+lvbYbuFGy25EGrAeQ4ujY5VKCNuzj3Q90XTR2hnt1pCPUEIEVl53Qb9yocqEHBBAPkLy9RkWcXvLQ4J8VsnkPXMmWFXx9++kHiAM6xuuR5AIe9S+fHiTdEJZ1RwWSiWvK5cIxqnxbtixaT3bzoAJAGbbU2MaWFZm4M3hUyAvKXJD3076mJ1/zEUoqWFimAlZvXpjumx3ce8BVUdctuXKNeAPY24G2I/nTXUL48g6KlebqvD8z5dfzsGCBcvwxRfjFYmw3NDFSvTOO+MQzuBul5y5IDxwtZt1Q8GiZfWbmpb/8RlIRLt2Nli0qO5fOtJlt1I4wR6avzEU3tc8ERkXr6y5Elpga+eYYd/CVXn6wBZKrC2m/KC3ih+0p+yfuH8bN65LubIXYDCjjkry4WIf+dvGjSuNsWMvYcWKmwR5NxnKMI3Xp3EaFCG4zsGEKVHpkGJjZ4/3v1ylgJXQ0rz9yQLkLVicD0N51f6/88/Pt/er7t4t1eKVupXvytGd0SR3bsT2ZlRJ6YM1axYp5RHDDoWr742SEWqzKHJIjJxYTLvTVT5w4Ju66hJPmFFMoVT8zcsFfQiknouKoFCh/Dh4cCMzrD/H6ZXTsdQ8J5LDHsHcxhFV2vRB/e4jMKrmyxNR9uzxU+OI836C0qUdyTVoTZ3nOLXtkl8QOs/4CWHbj5OragBgF4PW0+fB7+o1mNHC+c67g/B5k1oqVvBGUBgKO9sz4cwqpT/GJ8+/6IY2HoxNJvhdRJaCCRMukzg8H37/vbaqo/0pwKShw4/scNfnmQ781a2bM1PgT/pwtEpCg4LRaO0RmeU4QH+/m6hW8wwsnB1wd2M+bUim92w+AyYAmM1PkGl4mZ8B0f6NCAvBrA86KnUQaSlWOcn6PX9+O9cu6ToTAChAMSHBn9s+5fIYRbybO3dq342X1w1lbZEbhGEpXKIiuQqZuUmX8/5Ni9XLPU8BlKvRTLnditE9bU7iXVP5Z2agSr022LRpqwJZovn8qmV8zapwvvtYgb4fPumDOlT+EEoXY0XURq6dO4yCxcrT2lsA343v8byaB5VAetKCmECr0xq17fThk8oaZYyEPCwsgRmmFqnCDI5tuYl9O3zgYn8QUVG7qViSYn3Klb8IJv2wU4UXxMZE0bUcTKBkA9dc+VV7X/JgPmZSRaVaLZTkmxxcru9q9ds+H9vf+3Yy4DaC46NU4odoe79KKVC0jGo2adIHdH13zXQXAoKaFIpGvfzR+PliTrxbJSiNuzW9znbdc1B8fRr40+oVLlwII75bQ9CVhJpMoJBjBAeHKiWUOWP74mI5Z8z68mNledXaGL7fvt1W/Z7IvNNXrXaLS/uzfScwb+ynjFGJgpPbGP4u0eoXXQcP1s+GTbECiKO+8pz+w/Fr1y4IO3QE8M0N2IfAvnJBVOzUHo3L9uZnDNHFLjZtmothBdZ88EkL6qvmjiHwtMGbVeNh/WNV8lFeUBY5w7GKlVCAtGGG8eMIC8Qy4S2esZK2tCZmpUyffg1WHvkQdfQCm5kAYFbm7r+sawKA/+Xsm479t8zAhRO8Wf7yJQIJ9pIowaYVUQJxdc+HxJhQpWeqbZd3yfY8sWft801x5BDzRqnSFdW6RhAsK3/8sUC5iSTJ4MCBY7hz5z7yPHqCGO57GvQQPXt0VJmWQjNy+/Y9suhvx+l9a3BkxwrGXbnh3SnLUYRJKKby989AkVJVaH3bxNhP+siyWMTF+8YbAxkvmqASR9r1Gs14vye4d+OCegmBcpkq9VP1KpmrP3zSV7etHoFiSmnJt1a0YjVQbtsLBID+hUqoZKCuXQfRgvxNqsxzSUIaOfIKrTjuTDwpp+vv7SbMWF8+Hf4hN3h9dkWTjoMUzY1H6Sq07jmoeg6kFnJwctW1kQWx8snr3yh/Pr2CzY89kYNWu54edV75kGIlleLhUVi9Z/WPgDh7yt0JWMtsCSa3Xp9yYamqy6Xzx3VnlQwmwEeAkfQpcYQ//jiTrvdIuqO/ouZwK4YJtGTc8Ayd61rrKJ6u3c+PXcCVOw9QvEgB5HGwo3SbNfZcu4MDH06ERc2a2L3oG5S0c6K10R8FqXWcu1hLFHKyhw/VZyoOGYew3XvgUKUh4gPrwdIuGlH+q3D94DMEmv2Jj2s3ZKZ4Et5euxueZ86jQF9nfHHiFHYl10Dn6hXQqUQhWNEa7GKTpEtq69u3CLlPC6d6wJDxCgiWeZDM5opUQnEi6BXOwSckjxZ5QqGAURhW+3CZfO/YMT8WH+TDdv0qqNDGCxMz2c5U7b+dARMA/G/n33T0vzgDohW8aPo7ul4kDkyy+3Lmzo+JP+zA9xPeVPxwUsHG1h4VajWnYsNnuEd9UAGA58/vY0ZwfkodWSD05xmQWBZ5SdGAoLiJRo0aql5qRzp/Gjeup1xZogN8lhmqfYdMVNbI9swiFYCRYh1Ip7Fpc5ZnwJu8j66uLsx0dcxy250790MoU6QUL18DTToRDDKBQyuiCCIWZckK9n10h9JotrhB658kdaxevYhxal1xYm/KA8TBg+9SiqyC1hT52jSD98nzmLqU1p/J/alyMZYJRgVJ31JV1cmZ04oWvCSGIDjp2shCVNQ1JIRcxwdUvSmdjupNqgb/4UqPIrVhxezfVy32z0GsKLDUrVsjU92I5Ur0jsU2Jcv1C0SlsWKl11ECnwsfEeQsuOiqgJK0z2GWTJqYHIripaBjYpqmEpd5wS8/5syZjq+//owPie8wDKQFs85nM6SkMjbfesC4xASMnz4HMXvy06xWDsdtvqJp7UFKX/wdsKhVC370DjyNssJ59tekjTWcn2ciC72UG3+Tbv62EJtu28KayGv9hseoXMUVB0Jq4dacj2HToAdyt3wLCffZJ62IzGzBY3nI5e/V9m3bsZ2/dcMaNsTKGR+jbfGCqT6D9nsjMZT3wqwglFd1aDkVHkD5/A/CLVVcZSUCQUlwexXgpx1Q5Am/fzcS7465AtvyebTNpvdsPgOv/g3O5h/MNLzXfwZiGF/1shIeEpCqitDAiHvss4UHaRlKgC/l5LQi7jPPw1sR5PsAHft/pDb37/8eb+gLdckDGujT2hi+S6D03gf2KktO9ERFHN7Vmj/ILJL5FxlvRqF5J9yyaoGdB6rg+88mYM3v3+PMgQ0YOP4HFKU1x1T+nhl4+uCmkg8TV752s3tZz/JwIGowM2bMVlWbdBxAlZl62Ek92nJVG6DzwAm0AJ5XJMqhQf44vH25qpfT3Y3UMrWwZMlsRSwtgE4yT8ePH6kDf/fuPcBGAoSzbCERawe2LMXomX9gdOdSuHjxig4AenjYM0OziepX/0/u3LnU6nW6mLMrAKyZs6iyAD5kEshfKY7OzN51clPn4e23+2UKxAtA6VU2LMuHFQC01MsVHYpHQDJcg2Is0JByaMWeJ5KI1U9oT0Q/uYB9AvI6JCrAFBGXA7n5/V5JC2F+vk+YtxqX/vyDyTYf4dHjN0gyTjes1WrA5SnBn1iBSRc17gsc3BNI678lWox2Q7dyHspiKABT+EYPP7JXoEs+hChzCBUQ/2NAeWZF8/ejX0VnriWh1h0L9F/2DF6fT4VF2TKoOHQgWjesjU/qVVHWQAtmvkgiydA1u3Bs1vfo3aobcndoh4p8EPm9X2cmsby4tT8m8I0m0O3L2EdN+1jm8mUSezLGrJRevQrTnU/9darx/FEqKy1Ndf+rGXhxlfxXIzAd1zQDejMQEBsBLwZhXw55hBvh/GFlcbay06uRetGNou+z11/FSUpAbVxKsfdyNdGRdCwiZr9n/TJaAxP4o/QGVq3apH6IRXN04cLlzJbMjyEfz8OK2R/ShTsIp0/vTt2xkbUgukrEAtGPxLLmtB6IBug5P1uE80YhhTAEVuZ0w/ApuymfqOVHWtx/PXt2YjzOJHzz4Rv4nLJ2eQoUNdK7aVNWZ8DewQnXvM5S6aIhb8q96fLqptOTNdZXTEwsmjbtwnCAuwwNyEsKmQQc2rZMvYSS5NDWX3XNfpk1Cu9NW4G2b72HXeTT68u+v/jiI93+S5cOMmY0nHF8jmqbp+dFtG/ZA8l00eaQm29MNJ7ev4mHt6+o/Y0mTAOG9de1N7ZQs2YVuoVbYuf6hWje9W0ISMpuxc3agWTWOXAvMsV6+qrjUyoujGWUsmTJCiZhvPuqXb20nYCubiXDkY/AThIzxPq1mMTudk+TkIfKGbXyxeA8v8ef1Amgpc4CvpEWqOgeC80qKABRVElWkObl1LHSDBv4gMCP1rhYoErbt7H8+9oY/OZ5sgEwNpNWtZtnuI/lj3l14e5qrZbljxtdrMJFKJQxpdzidduNLXSgW/fO7t+x/74PupUqksrtLL8rUiSLeNvgblhXrxomzl8O//0HsX/VGgxwc8Oazk113b7sWLqKf9PCX7Ek/k1DMHWTyRkwk6dnU8leM/CtmZmYAlL9wtqvOg+zbHhD+CszJ3EtR/1v4FaEL55EByMwjq4J9Tyc0mtuGyd0KVgDeSPjcf3CMVSs1Uxp7Gb2mJ8Oqgd7azMEBQWjYPEqlIo7q7SCpf2kH3cwiL4AprzTFLWZOLI+E1qhYiVoSUUAeVLPaomOjkGJErVQoXZrDP7oh6w2/5+uv/XROezyuYzJFbogv52rbi4kyP7quUPM9p6DB7cuqyD83r27UZXlK10d/QWN269+m144e3ATihTOr1z2rVs3VS7aM2cuqIzbU6fOKcvUpJ92oVCxcviRblzvq6eZx3x41AABAABJREFUJZ4C5vT71JanT/8e3333M62JddB71EyEBfuhZMU6+GXWezh3ZBul2xpSaqu6kmWTcZw+fQ7Xrt1S8aXmjMbPkyeXongRSpR8hUpiwtzt6iFG6z87vX9+aQNCmATyQ82MAa3+mGOp5jPNazMtUfFom78yYvbuxu41P6kqZcqUwiefLEaDBu5MorHSb/aXl+X2tvyaCy1soan6ku1RtAxKLNxOb0eSHkfoAF+qinorUVGJKFv2TyaF0TNhvwJuXWvhztyPdNbnZzTnmZOnJjAwTqmIxCWbK0vipQAbRV9jbZHEbN5InRVOr+u/vChWzi+OeWFx396o+PVMHOnT7i/3adjBdWY2n/KxRcMC0RBKLCkCjP2jzVEpV5yuekxgEFaWrKNbf76Qe1xycmp3jWEN0/q/PgMmC+C/PuWmA8oMXA19jIW3DyAhOcV9KlaFwpSS8rDPhVIOuRHheQpxkYHwv7kTi35NuaGvW/iFIn5u3mUoqjfqoCYyJNAHj+5eVWTPufJ5QGKLJPFDuQT5Ky83W6HKeGvUdFpU3PHkHrPVGM+Vl0H6X45qgwTGfU09viNTJ+WvMOPb2ZFTrmNrbN7yp7I8mbKDMzXlGVYSct2KNZupV3RkGPn3ptPSu45AbAoJhF9YXrROChcuoCyE/syaFY4/CfQfpmeVq127mqoqljih/1n90ycY//0mXk/5VVzpgwePFDGxO93BhtrCH3zwDkHdeRJSn1Ik0GI5lFKlXmvGEN6G54UbOHDwuNImlu2Ozq7ImbcIajTtprb5Pr6DJ/4haNS+P3owRlW7Pk7t30C5w52o1qgjqjdor9suffxXpbhjHpykTvf1sCco61zgpcN4EBmA2Tf+RFxSAizNzLFp9RyQgV21M6O11DvcFu98H4bofmcYr9vlpf1lpYJo+Nagxc2wiJVKuP62X3PEMPIwZiaZxN7eAg8etEOzxetx8eMrCDlIacj+eVCsUUV83aGBAkPR5CqUksTHd6FVKUJVjrfIq6cpuxiO4+9aNzd7hlXrb8GhyWhUy9ecMYWOytqYSIuj6AM3o0ciM58xo/GUzSm8iHFYRmUVDQBK4sj2u45Kt9qd2sWm8nrNgAkAvl7n67Uf7fWwp/jl7iFEJcbxqTgHehSuhSJh8Ti4cYm6UZ58eh+HSRKrXxwdHRh03xBnz6ZkaC75aiRWzBmnMvaEyd+wlGUs18ipyzDxx90qnitf4ZI6guZSDK4Xq/ePn/aFP4+1c+cqBuP/OwEro0e/rZQOzlGyrhZdkaby982AHbVvLeh+lXP79KkvCYWLpOn88OETKiHI3NYN7fuMxer5kxnYP4zu+aEqPk9TppCEIAGXUZGh2EUrVbtetOId3cpYv2aqT3m4kGvmwIGNirdu2rTvmSiwUPG4CbDU1Gmkcg0CN3lJEYtleLA/H1JcYPkSmUOpHx8Xi2XffcilZFw6tQ9//DgRLboOeyVpOunv7ypdC9fAqcA7+P3eccyo0lPX7ZPoEGx4eAZPmXWfQIUQmSfiLETyuy5LvagYcuOXH3DheAr4Q7/+SN63F/H+1xDvc501qb9cvxPKt22NCW91IiddHiZsvLpFUPgCJWyjIbnt9ItY/04+tcX1YBt0Ib9gZoFRaGw8M3F3IjEyAhg+Anb3bZF49xxCK9khl20dupdjmZnMzjMoogd9hC7p5tQVT6/I+C7Salg1N33MmSzWgjATziDy4A5ca+yIyXV7KVobiTN8SLf0r4yBbFIoUkegfZqWPLHoiWVUwHBWiiSNiPaxBmrFCihjNpXXbwZMAPD1O2ev7YjD4qPx0809avx1KR7fuWA1RPk8wawJXfkjlIxSpYqh/cCetMy4MoHjGbU456m61atXUgH3cmPXSudOLZHkkA8uJergg07FlXyXULRILNYnn3xJKpaNqNe6JyrUbKo1Ue9REWHYuuxr5VL+8ccvlVsuVYV/cKVSXBxKEZjs+mO2CQD+jfN8ZOdKkgqvYMLPHdWrqGUYlps375Cnb4DaLPQxx3f/oeJFDxw8oaTHxA0rfHCSUSzJHXL9+T32xo6Vc9D2zVGYPH8vvDwPQST/hHNvOxN7duzYRyqZdgrU5yENi1iabewd1YNFifK10sgCCqjMCilzkN8jjjdZSZqVKVOC1sXTajwtu71DC6ad4Uf819YdLGxQK2cxnA66C89Ab9RwL4Yjfjew6sFJNQY7c2Y5m/PWQlCQxD9i1e9gXRA/DW6rG+O4bzdw/mvgs/2HUaxGJcz8rTGm7zuOC1sLwmv5SvSbvxD8UiNHLnfG2zkiOS4cZpy/ZJJdz50zjckSGT+0iUvUh7F8b5YJ1x1TYnV30N0riiL16MYcUjFEty8zCz0W/gHPGV+DgZ9wbjgeMYHHMX3ee3i7qoyFbmGDIgoeAi41gCmZyD9fckMPElhnVPaQ5LqoU3xGVYzuuz1/GvLR/Xr2x58R37sDk0xSrpHCtEJ2pub0Nlrq8tqH4QrB5V1mBQvnn4A3iY1MrwgjgmFinADT9MCpJMFtuumUXnem7dlsBkwAMJudkP/Pw5GbhNwQBhVriFruJdRH/emb0bSMJNC6tycVJ5hIOmkA8NSp83gSG4u3WjamK+6ZomQoUaKoar/hlhPCbeJQwC2OVhxXZcnZuvVPrPxxAuycnFGlbutUUzp34lsQPjdRDOjTp3uqfcZWRHLrypVrdAnOZSanF5kYXlgTRG/Uz++asWZptlnP/xVWy1bjS/9AdI+Mws1LJ1G6ct009UwbsjYDwQFPseqnSapRzSadcfbQFsXVWLZsCaURq/Um2b/Ozk7q/B3fvUrbDEeXnHy5Kw7AMMZ4xSUnwMY5H5mAQ1WdynVa4fOhjSAavyUq1Ea3oZ+icp2WOLJ9GZOJlikAGEV6jpCQp7o+7149C3kNGj9Xt+1VFiRMYcRnS0gwvgRnzl5UXVSu2/I/BX/a5+hdtB48g+9hzcNTqObmgbV8t85hiU8qdEJigD+WfzcWAU/vCSM1AtnoJz54aSVPgWK46nlYcTgG+T7CW91aomqenNjQpxPAV1B0LBZeuokle48i7MYt5C7eneA6mRZ/c0Q9fIhJ0w9gVoUjOPhhX+Sys9G6TfUu9C5i/dIvTqRfyUoWsVhsB67dhV3LVuHZk6dIDghA7t5v4eKsSVh/xwV+0f0xtIpxS941WtcEbDYpFKXIpWUcYjHjf7jTJWusiBVNqGH8o8ypxpF1ANj3D4aynD0L8urQ8hlHYJcCAOVYOemqrUzL3d77DhBA2JVJMcGx5hmCP2lnCP5kW0Zl/S1ntPO4h60ZVTLtyzYzYAKA2eZU/P8eyJI7B3Eu+L5y+1ZwKaSC5PdvXqqsKaLfakgIK9x8ixd/T4veJRUwn4NB+2uNTJH8kC1kRp/Epmhl48Zf0alTPyycNgy1m3fFwA9na7vUDV9WZsyYhBYkgw7kE3OPHp0o21UPu3btV9nC+fPnURmlDx8+ZYbi78oaZMeMUxt7t1QAsFOnNoozMDM/ks9KFUfsmBHo+NMSVLpxB6vnTSJVzQHlJtMNzrSQ5Rk4uOVXXRsBf1L69XtXzauAPpHua9WqCa1obpQdK8sYrsdMvHiiaxMRGsTEjboKAAoRtMTeSXawFNeceeDvcx8BPg9VHwcO/MmEju1wccul3LkS8/fFF98Q/KWAGzOGNNRlJnAc6YaaUVburxZxoVYi2JSXFAGhf6flL4Ljvn31OqIjH+PpAy/Gx15HRGggYqLC1RyIO13URuq06IE6zbtB3OxaEQ7AxnnK4IDvNRzgQ9Az1q3hVhi5mLj1w8J38eD2ZbrXu/A85FDxk/Xq1VQZ07t3H1ChHPs2zCeVnSVl00rwfPXQulXvOQnqJtWtrF76OwQgbbp5D29/sxzmt8qhzfzDODcu9QOeVl9UPRoTfP3e/RckfvEhfGjpGlcjMEsJXD037cf+cZ/AolRJ5GvZHDXq18LPHZqifRtaKi+E8EVtYTN77ZCp3sX62Ll4eKpsX3G1CgDdcsdJydhJA/lMIt92J9T6ObG1UBql6ipTK54+AdhPOhg+AWPLt5+hQi7XVO2kz2p5YtVL2yHScX9HETLtQ3RrX/C3IadiFBxzcIOpvBYzYAKAr8Vper0HeSfCT4G/vDbO+LBsO5zYtgIbFk9TH6ohua0WLvxOZeqKi0ysNMOGfYjQUN6cbnvzRpuM48e3pzsB8sNmz+w6/SIJF23aNIVkVJ7ev5ExWIwrqtFEgYIB4+Zg1vsd0Llzf9VENEm/n71QZXDK8cswflBIoj/5ZKa6eUmySctuw3hTjyapdE/UqVODOq0/KbecdBBKkJqZktiikaqWQDAyaetuvDVmMkHgp+g04CNyzr24sWamL1OdFzNgCIhEH1eAy6FDm5E3b26VZTtz5lxaBe+RjqezsvyO4dxLBq5o7LYlQXenfh/i4R0v6vn+wvi9ali74DMF8AZP+Albln+j+rty5ToGDepF0GKO9eu3ITQ4RF0fc+cuUqTjQz6eD1HsUMlHL4aXaikmOgKbf52FEFot+74/C06uuVLtf9mK4Wd9WX3D/WLROrV/H/ZtWI1A3/Mkug7RVZGkmbx586BwPnde20VpPRXrkRn1eTczyWoK1i2cogCgZM/nzFNItWuUKwUA7ve9qtatCQrjY2Nwn2oqErM7f/7XarsAhIAKb8Dn0C583a6DjghZ7czCH/mudy1TFDOqloD3jKnI02wdVpGjj91DEhDq5IvW9S3WP3n1XT8EZ57GkKvTkucmCwdj1QOLfoNZkcLwobKPRr0iPXTvXkABwN9/f0CVkHJpOt1HnlA/Ak4BoIbFlr9V+sI1Eut3IcAW71UN0sXUGbbJzHpANB+A7QlGaQHsPvFLjHynPyY3qKZiWY21N+baNVZPf5u4jIW3UJREgmMt6ObnCsthxlpKdrMkmsgcx4jJ11Reixkw0cBkw9P0OtLASHzfnUg/8mjxpsJfAXfyhdkwFkgCgSTx42jATbTOVxFdCtXAUlJjnCUhsxRLulETExLVTVbW7907h5Ilu9NqYIYTJ34i7UJJ2Zxh2c8fXBeSMVfP+yJouhZjV27rtWrGzOEewyarLUIILQBUlB8GkZIlISEO/o/vIXcBDxWcLzdKVQgkclDiTdYnC6WMTQ7+8O9PkwGqd5hML37++Sz88MMSVT83tV4r122Deq16quzkTHfyP1IxPRoY+fhybryveWLJzHeVhazfmO/I7TgWri6OjO1bC1FxMVbEOtiuXW9cvnITczZeT1VF3MqRYUEQreeYqAicObiZr424d/OiLotXGhSgXNyEH7bD0tI6VXtjKwJKZzDr/Mm9G2q3JIuUq9YY/cd+p5N5M9bur2wTWUT5nt3xOgO/J3cZI3lLaWWTuRhFilSmTnF+0q7kp7JFW1rAixg91Lx5SwlyZur2lavemPyIy9W6d4Q/vrm+Qy2b07k5o3Qn/Px5fzwimJaHtjJlUr67QoB8mUDHwfKZsnRFkkxdoIPExoXHmTFhJIeSIdPwmYAIAUkWdPvWZ6yexscnB4rlectXsy0cSpbAvdXzdMAsgFQkv111xfiaqdHH2htOEBqWL+r7w1ADWA1c748+KBJ5tzy12sG9Vg3cXvClXq2UxYCAOKXlbG1trjYIBYyfX6wCQL8x4WJiyxe/RfqNhVNwAeMA2xWN0BExSxhLPOdI5qRYgg+mTDjHpKIqtI466jd96fKTiGi889s6HP9lBfDkCVrNmYU14lb/G0o84xd/OJ8TJUn/Iu5kNwJuAYRSZJtkPGvFRAOjzUT2fzcBwGx4jl4XABjPTL+VzAS8EPxAR+fysun8pHwnFCTdi9B2iDttzc+f0cWa4oqwLdwdyfZvwdzRHvGPfFEhbyIzLZuk26X8KAk5qznvGF0ZWC26llLkh/zG9ePq6bd06RdxdvN33OcPtHabSd2t3KAjeNN3YjyYYRGX2NgeFTFt2seUgxtiuPuV1mWMj9lyxZefKP1giS+UxANJWhk84UfY2mXtx/+VBvGaNMoIABp+BFGG8STvnlismjZtAAkHMFZGjfoYK1duULuKl6uOpp2HKJdk2WqNUvHvfUVr8YPnZM59Rn+lKIZy5fdQrlix4BleT3IdyXUtxOSS8FG4RAUUoJU5MT4ecz/pjY8/Hs3rshJjSt/msZMx7NOFqFqvjbEh/qVtPlTAWTB1CBNSHtCaZ68k7IoX98C77w6loagkKlVyoXs8BbxkdKBkkl338KiOY6RO6jliigKt+oksJwNuY8XVvUg+fQo4elRl/bYY+x4qMKTCke53e0sLAj9LZqSaEyRYPn9ZwYkWuZtBLrgRYoe2xaIUXYrhOOT7vYhgaVS1YN2uPfee4M0G7fDhml/xQa0K7FceMlPKvAtuGEktW63wVGDU/nwKSM5t5qttTvMuoEwsWwIQhRtQJOPeXDgPD+fORdkPP8CJ8UPTtNHfIF6KIkV2UDc45XfMvUQu3D5bX79KqmUBt7+RSqUcw1bq5o/R7ZMM4XeWAlum7MFbXfNg3tzKun3yWWaedsf4WnRjc5xa0Qet2rbqn82G908/o+TUz3FmVB9t80vffzzvho5US9EUQvbct0cAVVM6cZs1Ce5X3XCh/B9jMdlTLGMsCzomMFYxMk2/JgCYZkqy7QaLbDsy08Cy9QyEx8fgiysbEfMsHk6WtqhBPrCi9rlR0NYFQbSmPQl4BAGIVrR0WOXMhRBLymMRSLmacYHFm27WPxi8b8EbRKXy5eHlVRIxT+ogZ8EnMAtlHJZLbtyAlXqqzpPHBk8ZU7P0cADWrN4CKzsHDOrbCXE2+TCYmXyuFEHXLxKTl1d/A5dF+cHwZq1VCfR9iO/GdaVbLwDd3v4ULd6Qm/OLYmvvhGJlqjLe62tUrlyekmB1XuxMZ2k3RddFZJ28sOrmIu/8DUcjSlCJRUPG6MD1EfIaMRBxzBD+9ddVSnh+8qD6eP/LVShUvHw6vZs2G5sBAV3LZ4/T7erYsZVu2XBBpNykyLmU7Nq7186pdTO6MUuUq6ZInIVk+rH3C+ugJI9MmJNiuVaVDf6EkeJFQNd9klKLZvCj26I3vUbVEkuyFFGFadZsN5dkPRGnqCf8dwJAsfqd3L9ecRi60Aq6bdvvJFiuLYd+pWJx/TZ+3rESndv3IfXSR3Am6HWjCzghLhpREaGIpVs7OToqpW8CvWS6IfcuXIq9Py/J+HjMmHasPxqR55Zi4rNYTgfng+51/iDAjMtmBIv25d9EQuAFTIv1Y2ymhXqpekRD3/Uagu8IMG1q1cTY94ehMalpJKM1NDYHHtwMZuJWGON4C2NeCx9MPJrH6FjEMrmR1jf5XloS2Ahnno35M8bokePvsgXshn2PLSMbGW2rvzEHTZmTJpXhy0ttnjulmP7uNMtMOFcaxLWpQKJfBNh924sWNbdmeKux/E7KyFKKZBTbkmLmGccoY9WKfvyxaP2WcInDgLZNMe20J27P/BpfVCiJL5rU0qqneX/C39Wjj+2ZHZygCKotn7t1nzyJwdp9zFoekEQLNwfGok+mLYBU5k+/XA+yojvYHn3zBelvNi1n4xkwWQCz4cl5HSyAc2/splSbD3qS26uucxHMpHvLn8HyQoWRlMRHd70iwEu40e4xNkgsJMKzJi44KfPmzULv3l3V8o0bT/H++2MUj9vmzRvw62FrNGiSF21oIZDiSqvZ+vVL4f3QB7OX/onLBxeqvtROgz8S/zdx4gwVcC67vll1AQ7Obga1gAsndmPZtx+Q4NcSYaGUiyJn4GcL+MtnUCS26ZsPuzBY/pYSh+/btzuf+CX7MxT58+dNdxz63ciPuMQsiUycvltLv4639wOVwPL0qZ+SIRMOOo0UWL/e/9JyZi2AItm2dfm3amrkGrt8+VCG0nDaHMbyutr96yZad8/wZkcxe9uz8PX1R9GihRWRuFj63p2yTHFJ2tjaa8107xJCsHHJlzi2ayXbm+PHH2dSXrC9euCQrPF79x5iaMOOuKlrQcBBt7G0s3d0wbdrLuntydyiJKr4PrqLB3cuM6P8BN2uVyDWz2i6rJPpFq9btwYTmhYpWpvM9Wi8ltljHyQXyIt4xkvu3Ekuwj82qkQaR1rpc5GiRa79smVLkeS8FddzIpLhHGFxCXzFI5rL4fEJfMXTspZInewEAjQz3ArNy33JlFR7Bvu4k+p7FBUZSfW8aMRERiOekn3PXJsT8Xgg7tEemDmUxLPYMCQlxiLq5gaY2eaHhb05ClcshltLf6M5KhY5ihRHhb4j0LJqTwStoyTavmB+TyvApnQRFVsncm9SRMv7nJ8NNYGtYMd4POHjy0l35q+XbuCjL39E0v37SHr6lKbAKFgVr4ekiowjXfRlKm1d1ZHeH2NWOL3dRhfXkirFhmBLFEg0Pj2jFZ9vFGJn+QwZJW4IBcvOe46qTs084cjfsheSQkLwJ7lOa+bPlab7jexTvCaN+VDqE2WpHlirkOJFsoOnz3mI9ScTkcDf2u/H5yOYLpKmvbEN4nJv4PAQOyvWNNxtUgIxnJFssG4CgNngJBgOIbsDwKckfP3y6lbYM8ZvVrVeuHHxOOZO6q37GJs3L6NLpKC6AT58GEp31yo+ke9iJmUjiOyWBORfunQVAnaWLJlNgt0Kuray8PHH07Bly24FEku1HwnrZ5G4c3gtb8zWOMhsYDs7OwLFT+je26HA46xZn6m6hw+fVMeUuK9WjKeLiU1QlpymXQajXNWGqY4hKxILOKZbShC33LwCAoIYk/Ut6jLr0VgRYt4F04bi+vmjKglEMoiliAbtTz99ZaxJmm3i5ll8mSLxJcOo5ZkaKGuVRTbugw8+xbp1W9WmD79ZB+GV+18tmQWAAopEFSbI7zGELHzHjj8g2aeZLY8fM8uWbtFcuawZQuXDa+xT7N9/BI3a9UWvUTN03Yjla/2iqfA6u5+gJZIZsykPM2++2QVTp05Q1j9d5ecLIkf40UdTsWnTTvUQZEe+QAGACQRHohbSmclAQvtirEicox+VQu5Q9/jqucMEe5cREuiri5uVxClx7wqPZvHiRdFqxmxUDL6VrsXb2DH+yrbMACCRC5NMUTFqCehxsHphxTJ2bHHLSqygWPUKkbZEwNJexvq28oiCuCrfe+4WPvnEH7tu3cPyRcsRduQY7Dt+iFOfd8fBff7M7i+Egz7OCtBEkANQtHjFR10zbwyKOjMBaOkGnKKl0owWvOSnPuRKyQmXShURSsoXuiQU358kVvh47s4QABobv2wTLkLRDu9PsmVjIC++cAOs2HEVZUhhVY/64elEp6juxaMgQFXG/rKynlbNZoUj8ZOnJ2Z/OBkM9oRVqVKo2qs73m1aBx1LFMH+h/bwppa5qKAYK0suu+DW6uP4/biEdDOGc0NxTo+1sapq2+MIC2YB2yrS6X75b2NtmTReEhMATHf2/rsdJgD43819ukfO7gDw4wtrEJ4QjWElmsHm4SOCP4kzEXdAyo/6qVO7FL2DfMBff72HsWMv8cbkQEqXFrIpTZEfe4m7KcyYEv0fwXv3Hiiy3VPmzVDQzZJxS+XVUaSDfHRZPDm6HOPGfQFRCgkPj6AbNV7XtwTZf/zDDuQlQW965SKtfwunv6N2yw25/4ffk+OtVXrVddvPMMP08PYVSoJsC0mlW5KfcO3aJbr9L1sQrq+lV1xVlqCI06dXypVrQP1ZP7zDOLEq/0CcWHrHzQ7bBfSEJcYgiPrQK7yPwZ9EwIZawOmN04fJDlOHt8T27b+jfv3a6VXLcHv//qPoPv1T1fEoVRlF+CpfoynB5SNsWvolrWzP0LZtM5XoIFaw+gTsJTKhJx3M7OH55IT08wvkg4wNFi1aoRtHkZIV0WP4FIYbVIPE8ImGsFyj/k+8dRZzd/ecis5GsudTtIXLZMnKJ9eeyJUJmPi3ijdDOsTN2JFkxG4G4RqvMoYlpH2S5BB96ifpZ+SuY/hj6Eh8uW01RlQrS2tjPNbcYKZ+nANiEnwxsKIbLe9JmH3mCnZ5XsGF31cLuoF72dJwYrbvr+/2paatG2afuoz5qzdBvKH9u7fHpIY1sjTMmTOv49ctoegzpRkG1EnUxdSl14kocohqiSSGFHJKeZjQr+sVaK00hSU+LzPF09dGUcqUzRlPMuxofLD9IA6TgzTu6lXkcC2A7l9vxUe1rXSqINJnCF3nG287KYtgpxIRKhbyGM9ZdVt/HL/5DJetimEow220+EDDcUh7uQO48PyaYgANZyf7rpsAYDY8N9kdAI4+u5xP5Jb4ssqbWDx9GC6f2ptqFqtWraisIJ9+OkZRSrRpc5S0LHnx1VcVVT0JghYRdnm6P+Nrq+ga8hPQiTZnHb2g6FSdvmSlLN14vqxTsGg5dOg3FqXJ7WbDWMGMirijJf4vllYcIaeVeMXMFtEgnjGyNeOgwmjV+Q1NmtTPbFNdvb0Msi7YoxfKnjUOHjt06EMC4EuY/tsJo8kpuo7+HywExIZjy+PzuBfpT+mwWMaPpgYocnP5mtZmUaFIr1y7cBTXaSHLmbcw1lDmTSu5c+fig8jcTFsDvbzC0KjROlrYCALMY/nwYqncnhKnKUXiBhct+k7RzGjHSO/94sVQPqBYYGtIEZgzdktcbkKfUZGyYUWd4zG0/9tKiSS99uXLlyFfZSMmtNSHfK+cnBzTq5rh9mVeLmhDgCF0KXKjl9KjdDg/IyMR+X2k5/pvKeJiFRqUNyixphW5zsvxQScj96VWNzPvMuajjHe7SwvWm6XDVOyatBu7/zR+7dkfNOHSTUwwU2UoYu8eQZz3IUkXh1mhgjQrRiGZblGaelXcYdVJ43FgcDejhxXlDiGUFgLprJSiRXciiXRRQ2KPYPLG7qkeatPrRz7TEj4U9i4bmkZOThJTljPDeUSV4PSap9ou2blb7ziip54KilRYdOE6ps88jj4jqmJm0+qqjSTaCC+hkEOLbJ7EJR7qNReBUyYrWhfJ8JUiv9liNQyle9iwX1VB748JAOpNRjZfNAHAbHiCsjsA/OPeCUXrIlMn3F8TSreFn5cn6Tc+Utm92pQOG9YP4p41LOIG3UNGeuHva1I42nD3K60Lt9tvv63Gx3O20VpT6ZX6yEqjkwzeXz57PAmlJzKzcrCuqYqZypuLwewWaluOh49hsfsgzK/d5K/oM+TwZZwWpaySSX7tR8b/TbwZD6oYqkCwWIeEKLdcudIIY/alP1VDxNVt6+CKST/tVvFiugO9xgti3bsd4YvzJAa/FxWgrHzRTCaSImoSzla2yGXtyJcTrxFrVHItzOQiV5XVnd7HFu7Grz+UWFLeSdMpvXq9oWJOtWSg9FyXUa7VURCTVS/z51dDr16FlbtVYvnkoUHcrZkpBw/6MxbwBGrWdMOarU1w7AldoCxi6fEKtMFDWr1vXDiNL9/ubLQ7GeeDB+eVhdtohSxsFJC34pqLus66lwrDJ0yMqJInVt3QXegatSUo7Uji4r8CBAUkfOfpjrcrBadKzBLrlujRiuXRlccS4CklnGBx3U1nkiKH6fj7wuiqdc4k4BLiYanfpFDKb0gwYwGLV6GXoXRpFC1fBS6lu6N1KX8+bMYT98Vgw4o1sCLX4bg+XTGsahn4UnGksFPah0QZ1zYCKHqGYc3fqMh4c5Smm1ZAu106Wr/SZjul1loUiYR1QiwfHMyQVLktljb+AF1md0nXcqZ/CmWehGKlvBGvgCSrCK9gZiy3lwOs1Xx3KB6p3z08Rv7MSXdHXPg1JEY8hU3xhnDjb+WQpo48ZkmUdU9GfgciwgyK9H0nxFqxLqRXzQQA05uZ7LfdBACz3zlBdgeAMmVjz61UGcCyPLREE1R3K4rvP+qB2+QckyKkuT179sPo0Q/Vj+GJE01VLIna+Q/8caQF0IV0F8Ur1sfwyYv/gSOk7lJcgZ8OagAr0lD4+V1VO80YN2a9mMoh5D9LKl8Kz2i9AeP57PuPhPnte0isWQU5nvgich0tfs8tOZIpvI/an/LUvnR0a1y5cD71gfTW5m3zVryEepuy5eLDqCASfzMTPDoYAXERDBeIIddbyo1FAJTIAWpFHEcOljYoYOWEiv4xcLd3QcVazRXQ2vzrVzi1by1jNaPJFZmgSy4SdQlzZorWppJLn/dmUlXGn8kf3+Dise1M2jivkgokicbe3pY8d864QeWVNm3eVIcUC9qNGyeUdrQ2hvTep0y5yrjQOMZ3VkuvSobbb92KoGzhTSaXlGCcq4s6xwLC3q4UkqrdiRNnmUE6A7du3UUMEyD0y4kTOzPFham1EYWKs2eD8fbbxdJ8367SlXjOzxa9yoSSj89KxcGJ1V2KPIyso4xXU4IMYwBE6z+jd9HaHX84L6lYguhKTbGW6teXJAU5hjM5O1swEUqu/ZsEE0IsLP7DAI6hMF2gEQSKPQgK0wNbWp8/X3RLYxUrMHg8os+dx5WjB7HsWk4MrBCSJcvjo3ALZRETdgH944ubdqe3gzp36VkEJeZvzrmc+LBmkALaMk6x7AnvX3vGPQqTgejuFqG1zVj55YoLBlUI1WkH69cR171Iy3V/iY6w1PvjuotRneP+W4Nw6thmWkMT4WiXD/53TjD+sQySScllSbWZGZ3rqoeSMdUDaaXWP/qL5bn8fE0ZX2js/Gq1TABQm4ns/55ipsj+4zSNMBvNgNzchf6lpEMevFuqBe4T9H04vBOtfylP9jLUN9/swZtuCjCS9bt3I1GihKMs/iMlgvFXfZg8snDhcsZMCalz0X/kOFqnx5/Te6xatQBmjNOzOH8ZSQ60JhCoWB44iuQTBML2doiZ+Slix42E5aHjiH13EKyXUVWAtCOJzRsix537cC9fGm+VCVPddtm4SCkvXLjgxaxkayxjXf3i8+gOCniU1t+UbZbFqrePihA7nl5U9D/awKyYFe5gYQs7ofmIjyNws1Su+YKJ5gjZuwtVSIYsGr6/zBqFtYe3qWbztt9TPH6Htv2m4itLliymFGIk85SHYfZoPK+n++TxW4VrdPmKsoZ+EdAnL63Url2NwPACCheuqmJF8+eviOHcSXuIrujTacjGuLhnzCK9rfZnFgD+8ss93L8fxeSDgkwe8cc77xRjklMN3THuhVmq2FXdhucLkqgiyiVSxo37DNev31HcfSKDaM9rKCtl6FBPJlfRUsSA/W7d6PLUKwL6WtJCJVY+iQ/TL5KQ9E7lYCwjifGrAkAhe+5TLjRdcCCJH6JHK0Wk2gRIFXNJwHlm5QoRco9SMQp4SEKB0Im0LZragqU/XgGTArgMy+ghvfHVrt1Yc/kM3qtZS8XWXQmAyrw31AbW2oob9GawNU772HE8dMsToGvAWKsj8YZFnOJVlq08rIkbX+ZSn4JKAKPMo3DlaUVimt8k4D7ra4eSpGgRt/VBulLFmieJKPrFgnUFMKpgOv0dXPZ9FIklS4PQZJw9w2qsDfa+WL3Bz5HH7kW/okksllJ55upd0QHLOw3RVY5O6EyrZRDe6d0L5vE3cahqIYyp4Yrf+ZAiQFTGbliEJsYY+EvPmm7Y3rSevWbABACz1/l4LUaTQFemlPLU9LWxsFL0Lhr4E/fY2LHDyaKQhzXuqnryp2bN/ejQIR+WL6+VxjKhq/QXF0aOHKIAoN9j7ywBwMjwEOzftARP7t9Amcr10aBt71RkwIbDEmLovesXqs15ZsyFxdt9YTvtOySSIzCJYMViPwlx6cJNprUpdsQgPOMNXl5SkooUguWOvbDcexhJdAMnCKh5zkknoOWddwaoevJn9uxpinpj/PgpKhnkwOYllBD7+h+bP92Bs7iw1+cKtj6+gMTkZ8qF29i9NJKPHcXFPesRTutcMPuTl1Yq1W6JE16nlebs+X0bcGj7MkVhIpQo8hJQJ5QqGWVXiyVx5cr1CvzZ2trSchajuhc5P2NFEoWCgm6SomUJ+Ry/QbGvPwOYlSvAT25ehjcwyQZeu7Yu8TwRZyaKjGfcuEuqZkhIPLPcKY3VLDd5I110rcu4xTP+zhmlGFdVisvGyvjx75G3rwNlEE+njKlwAVVN+tdc18baadvWr6/Hh4iHVPbIpW3SvSckmdG69AKc6HY8X5B4N1vLzH1ew7Y3gq2URa+LXuyf1BGLlOAIDVCJHq1hMdwmbs4HdBkbKxo5sUiqDaKVzrCMp4bwN5VItP3h5xhyeBM6FE+iCok1hJ5EAI1k0Yob+kmkJc762CCKnzmvXaICvQPKh2ToAheAp1ngBHzupeU+hDFxwu0p7nxbWlPdbRPVcfTHJYkvGmGyAF5pu/WuE+XpIpS1T4CwbAtiIoW4kvVBpdaPyM5dve6AM2fiqGCTT9uc5r0qaVyOP3HDUsZ8WvJcSwxfbz5gGgNz8nl6lnHDilHDcPFgJNaP7ob1zWph+jvz0/SrbZBYbbGE1mHWsn5Cj/YAJaEGpvL6zIDJBZwNz1V2dwFHJ8bjw/MrkZ+kzxPLd0SwzyN8+V47JcWlTWeePLnw+eff05KVBxMmeNKVFsFdjoxp6sJAduM/7lrbrL7LzVvKFb4q8fX2pPmo1qC9bMpUWUVN3iM7VkDG7OcXoLR5CxYrj7ote+La+cOIZqKH3Hwfe1+luzFA54rU7/xA47qow4zTxNZNYLl2Kyy378GzujUR/cMMIX3TVXUgPU2Op75IIG9a7IT3kKxnqdJVMlgQ69qsWT/SnfgTGrXvh14jpxvU+O9WF9zaj0uhD1VSUHO3Ugjesh6XmLkqSi/6xYbKJkIaLEXm0tbWRnEnWjMY38XFCW5uLrxWyM92/6FKuNDaXr16VHHNaev670ID9Pvv6/HZZx9i8uSvCPBCKB+4Q7/Kv7YsahAdOhynu9aR56kSmTeiSYVkn+b4ApQEEJVOBwBKg4bkDHyf5Mbdu3dU7Zctu48xYy5S27oluQnT9qkqveSPgKAwJl41LJgSL5de9S2MfaueJyZdnsr02olrcBRdv0JyrF+E/kVoj4ZWDE6X9ki/viwLyMtnn4iKBm7kK/wMIfwMjV7yGdZeu4t3WnZF7YnjsHtUP133Ak7EBS6JKpKQkt8hAbfogj7HrNlWVLQQcJbVIs8HYtGT8QoHX19aQDOT7CK0LqI+IqA77rlFsUruGBUnKtY3wyKhCJ8sCcPb/QugZoGMx/kbwZ+QNhsDffr9ynwIH+L3nkHYfuYmItd8yuz2WPy07gJa0bqZyy7lQV+/jSxLssgxJuFE0VUvjxMedGlX4zUjAPhOiBVOX49CTPtyhs1MNDCGM5IN1k0AMBucBMMhZHcAKOP96eYeXA2jagf/OVMJpH+xhvCwcMCiL0fwxm7JWMDTiGPslmERygxPz72ZisHS2koWnH06wdf79vnR5XYSp041R8GC5ihXsDIqEGT1++AbrflL3/dtXIwNS6ZDyJ1Fks3L64aujchoSdyYWGBKlChK6o1qiprm+vVbavn06fOq7oafv0Gzt7ro2pmRIDrZ1UW3ri2Y01UscYLRsybr4gC1fS97Hzv2M+UW/mqlJxydc76s+j++f8713bgZ4YMi9u54v0QLzBnfAw9JSCxl9Jd/KPWVH8gPKZx1orwxggkxDRmnV7p08TTE2ZoFTsDutGnf0/2aYmGdMWMSk2wGZeqzmIucXoUyKtlGZXlmqlX2q7Rw4TJmBh/SydhVrPgnAWUMrl5tTTBs+0oDXknJxD7lUoNyYx0JeJBM1PRiwPTbSAyf6HCL+zY3XZ9i5eLXBLuZ4CWqGhLrJ/ROooKjhTnot09v+RZB8jJKpYkrujhdspKZKm7amadzKeoXcZUK8JKM6rbFjLuJS7/7KfwPHMKK7avQoUSh9A6ltsuYdxKQBURboA8/u7jI9Yskt4j7XIBPvQIplmb9/X/n8hkfW0i8oXAlGgNgf5BIvnfZ9M/jMSaSCJGzULlIafX9UnjtP0wpwjhYky9ywPChqF6kuQKez/hdO/voKv74inKc967AruNYlK/dBJOalNQl1hh+Nonz3MZkF0mQEZe4zMdd0vyULFsJTszkF0ugKQbQcNay77oJAGbDc/M6AECZttOBd3DQ9zoeRAeqLM1PKnbRzWZkWDCJcg/g5L71uHX5pG67AMALF/ar5AndxgwW5Id+G2kKosWVxB+di4xn+aaxn66Fq+tmtfzxx2VoaSyDkSM/JoHyNszecC3TChpCIPzzlMEk2T2i+rKkxW7o0L4YOPBNSPyZMddbaGiYis+SulktOa7eZJJI6TRux4z6EZ3gr776Ad9+Ox9jv16HkhVqZVT9H993huf+V++jKOmYF2/nr021gK7wpQRg2aqNqJqxFHevelLq72OEBjzBPl4DFSumsQi8dIxVqjTD1q0rCB4LGK2rWX4191OOK9dhRaLlJF5jYNJJ/LD+Rttl943Nm3elGkwbZQWUsXp7R5IAPYDXo4dKqMrq+IVnc8ElVwykVUgsaAJ0xAomIM2HVEwCgMT1W43uw9hm/XC9YBVUXvZhGouY9COJDEHUh73IeD8XArw2jNPTj62TRJObtAK1oMJGIK1/Euf2MkuU9nkEYG0luBB6HLHyCcg49MiOXHaWCmR2Io+gI0Hfs2Qz8graMb7PHlPr+6vvkfShXQeyfC80AtUqNITr++/Be/wQ2fTSEsDxSkJWLwOAJeDzdt1hOL54nUoC0T6PWCoFhGaWukpor8TCWp7xhA0ysGI65CyDHw75UuYyNM2YhVi6IV3O6VkZV1x1Rr/yKQBx/I4jWEJeRLMKFWDv7E7QnBvW1IGOvrcVyWYxeBbOmG1/f+QoWRJffjcdDvZ1M3xIENm4HTw/QmytufTTDJAbTADQ2Kxkz22mGMDseV5ei1HVdi8Bec3w2oLHzPj8/PIGNM5dBk1yl1Wya6FBfgr8tWrVRGUFV6pULl13XnofWGQou+plvhXjzeHkU1udiPqJE81w8mQQ+vUrorro3r0D5ao20OrkA3fywWWm+D25ryS0pG7FimUxc+YnKFbMI0MZMf0kA2PHMAQn+nWcGOMlRf+Gpb/fcFkUSoQTULJEazTqQBBggVljOtM1HcysYEu45MxLsGtFqbICKFa2BspUqQdntzyG3fyt65dDH6v+RpVsiatn9inwJxucXN0xZVgT8is+VlyQEyZ8jfbtvamCYanoUEqUoKJBBooC+oMU0GsMfGt1DOcvOacrYywLwpwqM8l2tinJNozLTGZyjnbT1tpmx/cbN5jI1Gc4JQ9d8N57Q3VDLFbMgdcjE4xeoQhQkng6ASpCvSSUL3no2nPn90gsc5KMIEBLYs8u8eFqz9zttP5EEfxFpTnaclrlPNhOFDX60dUp7QyLJEsIt6fsyk83a2bnPYEgay0pYYaSQua59KzqWqN40T8OIasa9y66XAWUprkOuO3sIY7fYjATjfjwmUkAKBa3fHQLr2G8YGWCYYnZlCLW0PJnFsH/YTw86ULW1DjinjGH3YxPqJksfgSA0tYw5tGweWTQDcRezKEysw3VgnqS91CSUNIriQTHUsJJfbNk3CcwK1wMM79dRdDsRp1gX8zdswm3HIshnh4KK5cKqNFmBNoUL0OlEjPGS6ZYDY31LbGcQo0jyiHGzruxNqZt2X8GTAAw+5+jbD/C8WXaY8Gd/bge/hTrHp5BGGk/3ihUAzv+mKPGvnjx91kisBVWeYkvMaaXW5dE0UKXIO9SypZ1Ui+1wj+7dh1Qi2LVyEyRhIMfJ7+wFF2hFalDh74oVKiA0pLNTB/G6hjelPTryD4BiJrbU3+fsWXRNBbw55a7AAoULYvvxncjiHJT9CCSEfv06QNSDD6D99VTjGX8XXVRqlIdvPXudKVtbKzPv7otimTNUuLBm7y63af0eObgJo6rFH74/mdmgTdHly4nEBERQNd6GBN0HpDj0J5JCnVTKmfwV5NME1m/zJZkWv7iB7z5PJWSrYg+JM5pCa1fwj2XGQ61zB5Lv54E8IsVzDD+Tb9OZpYHDBjF+WrPeMaxmamuqyNxdrcZyyauWLFiiYVMgJd8B2K5zVhMma7x8wUBiB506QkQlAxXAVaGN/qBRmLTDPuRNhJDeIBxcWJhFKteNz7AvWzul19zhYAbffBn2Lf+utQTuhJxbcu8y3G177x89edtfwyHCh1h5lRVAVuJtxProUZsrN+X/rIAThnz5ydy46fmPvq7CKAE3NrpAGAHummlXgmrUAL2i6T6ceaDDkMQnpcTJwJVNvgnn5Qlh6WZsoy2oZs8M6UzrZ0bmDD0rgH5s3xOfXoaw75k7o8zPq8+jeZvTfsUO9Y/xWefj8bnk4czzrEcWr79JjbfGap+XyVbuT4tkQUcwg27SbUeyt9joS+S8y/HN5X/PzNgAoD/f87lf/JJYpkQsvDOAdwMT/mxzGvjjJrmOekS7E7utniVySkxdEJs3K8f6VDoMhVC3q5d26dL7CvB6mK1MAYA5UMmP3/KNfzAJ0+exeLFK9C00yDkylfYcLfR9VuXT6nttpSCk+zfijWbQ+Td7l7zxKNHTwkEMw9AjB4gnY0ZAUTDJrNmfUrqB9IzMOFhy7Jv0LBRfaxe9TOlxNLGg4k+8erVm1XSyNThLdC861B0HzrZsMu/vH47wg/uJGsWZY4ty75SGrRbty6nW9w+lTTZ4sXV1Y3ZxiYHs2ofq8zYzBz888+/VjrRmambpo6e2Uk0WIeQ1mMlb2BCYVGAWYx/F/m4dlxRtZHYOZHqMkxc0Opk5v2bb76gm3c0KWseY+7c6UrzOr12Ehf7Jy1gAvDkpixasgLcGhRITBPDll4fhttFhuzbJr64wRg0kVurNaQnyhybz+QcmsAMitDkhIYmMHHKxmAPE7GYvKFRhZylNVDIznsQ3BkSPAvYFIJjAc8CosXKlJUivw+au9Ow3ZsLC6DB2CW48ecqgtkWtH46KooXbyY9FCXQTS8LW/oRsNqAUnMrGW8XHGWGaxcCkb+ALeqVt0InkmVrRS4zkaTb4O2GE4EuiLwQjdH8DOIenTfvDj791EtVldAUKyszpW0s2csCVPUuUa27VO/i4q2SK+VhV4CmoSUwVWW9FZnLUEHALD93b42+VhHYMWkZPhk2Bv1PbqPt1IYWxBxMVglLqfSSvxKCI7Qww0kRZBgb+ZKmpt2vwQyYYgCz4Ul6XWIAZeqmXtkEn5hQ5GNGcBOL3Ngx/X1F6fGyaV29ehFat25qtJrEyQhZahW6YYyVX3mzNWbV6NZtEM6eu4avVp5LF1wa9ifJHXExUbC2teePcsoN6OalE5gzsZfS9xWd379SMmvly8wxxMq37JIdBlWNe+kNRGTLxHL466+rMGXJYeTO75GZQ2SqzoPIAHx1bTua5SmHNi4l8FGvakrxRZRfXlbE1bf/oYPKGsxNl5tWxOL0+fyduHT6JCxi/HH5wgXcuXNG2/23vX97NqfK2BSjpdyIRXmhCUmJJYPxrxSxGglgEBBoyO9mrN/0rotYqll8SAqTnTv3EUB8iCHktTNW5MYcT7BhT269f6qUa38dQY/D4XsxLXXTsGGejLV9rDt0o0buzFQupehntO+RtjOOIFVARF2C1HJ0EWtFYugk0SO9eDatXlbeN996gAW7D+HS5u2IvXETFvXqIoCUTXJ9iWpIEh8eJY74MZNT9mxgDPPepyhCkqLjRxorK53hsYRYu//bV6hIk8hEs2aGu9W6JIlIssuhR/bKyqjiK5/64+zGK5g2tTxDIVJAsiR3SAJFS1oBha4lM0W+L9tIAB1FsCyJNlWZKaz/vTHsQ0C0AG5pJ+5gG0oZjnqvM33C4Tjz504ce+qKGrQSag8qkrUrsX1CEdSc3wNDYCoE4cLJ2PO5eovh8Yytm2IAjc1K9txmsgBmz/OS7UcVnRiH/T5XFfjzYBboBNLBzGXGZ3hIQLpjlyzazp3bMBasKiQu0FgRSgYJLk8P/EkbcQ8buqhERu3MmQvwKFcn0+BP+pKblaFmsL2Tq+wivcgsRUSsVl7hj9zkMyrpgYD02nj6O8CVurJmZi9uounVFXqVzz8frwCgyKT9nQDwkP8NddhGjPf03LdJLQ+cMBXIBAC84G+ryIDlJiUxYuK2lBtoYKwFfl2+CQOHDYFPDg+smBiW3kfL8vYNGx7j8OEAVOpbFw3p4pOsUq2Iy1RuyuG04rWlpeVVwYhYjbqWDKd7zUmpMBgCSjnXVwpWxbG1f6LQuyPRguonWtG/DoQKZ968WaRNCkStWm1QrVpFpQGs1dXeL3IeBdBIsoV+EVD7J2P9hJYjI6Cg3ya9ZZ8TN+HQoApJt6OYAZ86BnHy5HKKcPrcuRDV/MiRQMjrHZzEVyGzUnUpVqnBVOQ4SIAkRMgCkqWI5dJSjzQ5VaMsrjwKj0LdUZ8iav8B6rdZw6JcOTT8cgrmdW2lepJjafx6zTln8vsReiIGe3yD4F+lBIm/k8hMkNbSWbWqK1YurZQh+bK4oD2Y7DLQOVQ36st5HGFXqBmO0hz3Rq5wcqA/Q0GrSBItxymlESG/LsEMZ5kb4SQU93v1vGlBoVjdupJbUc5rELN7TzNLWDwkFuT4k7AGQ8Am151kCc845Y5JtQP5W2mGCa0/gNnhY3hnxzWMI1VVw1KFdXGTbuQttKFV8lcSgAsANCwyPguDc3SKMdgS66lv0RULrlDqCIl3uF+CYTem9Ww6AyYAmE1PTHYdViR1LpfcOaToP2SMEv8lMnBSPEpXxY2Lx5VM19SpE9C0aQPF7+bu7qa2qUov+SM/hhmBP2n+jE+r/D3XFU/Pi8q9HBefSHfnp7rtr7rgff2cajp0aJ80XUhWYmPGzRj+8KapyA36sX6yX1vX3L/au7G2+tsEIK2nq0xcXm8YEO3q1zNcfvz4qdpkaf0CbBjWyeq6X0wYzgTeVa5f0es9uPkX5CteAWuXH0DOtpNQb/PEVDcGw/5rPQdf7zCYXFyAcoMRcmLeQ3GqtBVWz3wfyebWiB42Bzny2aYCa4Z9ZXb955+9CVbIy9f5GXyHfQ2fu0eR79pG1VwC/8W6IRY8AU4ZUWy87HhyfsQVu/4maTKeB0uJla6sZSDCFu0G7cwYXi83/J3yYXXtIbBrNR05Fs9EwbsPYB2WjMKOCbo4wly53NWDTHoZ0BLrJVxyQgsi1htJzBBgJdel7BOgIC7hVh4vbur6QPNln0X2Hz3aFGNWJOFwVAGcvia0Hwkqe1XAU6FCdszubqzr5tq1cFoEHzHhp5FumyxIDKa44WVczQgwJClFe3gzBK+pGmZh5RxBXKuebyPp0SP0+fFrfNWhKZVGrDLsQU7P+8MKqdcVf0vseGSLLoy7k7k0BO+VKrno+hKlFxcXJl65ZNy/5gaXhLVlV5zx5RcXER0eh7nDHcjvWFABuvsMc5G5qER1lFXM7jUGALUDy/y58yGjfTq0N1o9AYrbOn6HuImzIBnDYo1sVs4c23/ehOK53lKWR/3fHRerRBx8EMW4xpRzpPUj75toTV18MYTWxII4+tAb1fK64YxPALO77VArf126yq3Uw7rQAbmQzFr0koVn0tIpHH/od2RazrYzYHIBZ8NTk11dwKHx0fjs0nokUPGhgK0r2uethEjPU0q1ISE+VknBiS7riBED8eWXn7zSzArYEe1MsQzltU9UfciPnzlBgriQQsIJQNcexNTBdZj84KuyimvWbKUsJr1GzUCjdn1f6bjSKIqEzzuZuHJw668KvG7YsDRNX3vptpKn/ZcFk6dp+Aob5ElbLCbxvCm1K5r5OCDtUM2bd8ONW/fw9R8XMk2Jo7U19v4kOgTfXNuBuKQEfFC6DW5sWoHda+cpvjoB+2KRiqzWFac27VGB/8b6yMy24OBQ1KvXDgv2XlSJBH8VKEi8mlh4hIBcrq+TzSfBhXrFFc4sTDUcAScOPYeg6okfU23P6kpYnHDg8U78vBx/bIOSjy4hLwFtUp7ccKEUnXYTllhXoUsRN+n9cCu67JJ1We8iXffrrz+gOWUDX1bkwaQQAWRxPTLj35kgIXOnfY+yCgANjynJBQ85XrHQp8e/Z9hmMWMJDbWPpY6MRSvaXGjrWX3P1X4AEm/exIQVC/Bx3SpZba7qi0zfWYJmAasqFpG/OfJQIskj4tKvwexd71thvC4PoG7dnHTRv/ycaAORMJP58+9i5e2cmNYNaN7QVdule1/IRCV5KPqrZfNtRxXfKK52DWj/fOE6JrXtAcu3h+LplNH8jPLJUkqjpRtxa91ZfuhoxMfeRe7SJWHh4gKfM55IevhQoULXTrMRumsSkpncpwr5A80qV8bC2VPRplhx5fZ+3p16M7mA9Wcjey+bLIDZ+/xkq9Gtun9Sgb8hxRqjhnsx/PBJH1y/cCzNGCVjVX70pBjGA6WpbLBB3Ckjqwbrtgop8wFq6169/QhLA+Nx/9Y1+Hpfx/Kpuiq6hYzcz7pKBgtCF3OCur6eh7bAjxrCMt5evbriu++mGNQkGSzjZYQGQgK//4kiup37SKwrGqPi5hZwkBnyXmNjEZf4eZJOdxv6yV8Gf+eC7mHNg1OIYOYv74voU7guLjOG88CWpRg8uLcCyzKGInmLY9/1R7CNJsrKRBEwHUsqjQSCn0akHhELx7Vrt9C79zvMxG6lyGh3UHZK3ErGEoIyC2hE1k1eUuT6KrtzFjzfmIYKBmMU68XqfHS5Gmw3tprRsfXBn7StX5CuvYKloc2KPuARq5q8pAiprlCtiM6t6OauWDGPSVQjSAJ9Se3P6I9YrYSWRL+IRWsn48d6lglXm/WPq18vs8ty3Ut2qWERN6Ncr7l4/uRhTYCHFImJE8ukYdEHf4b7XmXdtVxZBHh5YdabgzGrbFm0emcQVnZplgrovKxfAXnGYjflZ0y+hwKsYuId0OLduujW3FE9SMi1lJkivykjR5ZAjotuaG6Q1au1F2ttPKfKKuUy1TZn+T2S56GwQyzjQ58hjB6RxZduwjeSv1dt2yJhyS98iM6D3QPeQE47Gz5cJeHa6j2wK9Ue9arF4fRB0gLeuo3k6BhYlCiB7pPGYlbL+rwWLfDVqZ+pmBKEb5vWxIVHPhjG7+iwviPQZ+bn+Kll3SyP09Qge8yAyQKYPc5DqlFkVwugpvzwTbVeygU4//OBuHKWvxp6pXfvbvjpp5koU+ZPcozGUr6qxStxmMXGxmHp0j/wySdfKlBma2cPcWXmMDOHLfViS1Wqh+oN28P/6X0FSoqWrYa8BYvrjcT4oihN3Lh4DJdO7sGNC0fY/oHqX/j/OnZsTVdyDyUJZ6y13Ng20BUrmXyP6aoWWgkBROLGFFb8GkZieIz1Y7hNXKESEycuwFr5oqmuQN43xgcJYazcUDNbBHSHkN/r8uVr1BQeh+AQxgL9dpLcfLky20WaegGxpLogv2MODqQitZ/rww07f5qMe7cvY2Kp4viUQC2R3Iuwp2uPah8CXua9tw8f/txcWVPSdKi3QSyGEl8kH1HAwsy5f1BW8AEmdsqnVFmkqlhj1pEfTshn/67iyThTexIfl80Zn6ZLiQcUN64hbYlYDsVtKOcjI/CXpsNMbFh700mFNQjYcLVJpHXthdu2bM1uGP1uX4wY9IbKlBWgZ+yaECqYLYw/FJ1bXuJ8UEsJkxDeuZdZq+W4YoEStQ4X6tZmtojqxE3GfblxzPJdEIJpKTI+6VPiEDOSvNPmUXvP7HEN68l3+vAjX0zYfgi3f19FfbP7SGYyzV8FvIbHEQ3c23wIlBhSmVPDa0Tqy+eWhJALTDSR3wnNBS8Wxgd86WegPwq3wJEn9iTgTpHek3jIjAiWDcdjuC6AzmO2J2K3/YxnT27Q987zERbGC/c5Uo2hBc/SCpYVm8DKnUg+wRY5LF2otFMBHzesbthdhuuB0dEoWb4xXDt3hPecz1LVNVkAU01Htl4xWQCz9enJXoNrlreciv3b9fQSehSujbdGzkDA5H7wfXQXHh6FyWHWBuPGjURUVDQD2EOwfbs1EhP5i5hBuXnzjqI3EfBVq1ZV3L7tjbfeGsaYrQeqVfkaTTB88mJY8IfLWClRvqaxzUa3iXt62oiWdPWSyd7GmiC1FD4eN5WUNB1SUZcYbcyNAgAk8NqHWXMSi6PdiCUAeukVVxX79rKbrX7fEh90j7QUAnL6MHBb+/HXZJz062Zmedmy1czETPkxFhqcj+esfGXwl5iUiLUPzuB4wC1SRyRjXNn2sAkNx/QRrWBnZYk1LRuh271HRMTbYXX0JGIo2QYCQLFcdT27gsCu+UuHrB9E3sYjHPPvrqHiS04kVZ1MS1iK+60K46MKE1xL+atAQRtQZfb5C4PenRi3ZJj0IZmaFd1j09zcv6QMmciSibWyk89dkHJaWSbF8pURyNGOmdF7Ci1HqLq+DOvlKNuFLsOqavNlqm/INaefxKLVl4QPcbWKK1kUWrNC2SF9CsAWAFmY1khjFu6YGCqrxCfxe2KpHZJW2QRI/FeXLMSl6hpzQQNo2rv+vqws5yDAaVokP86M7I2kEW8hZ+32sC5WDH4GnTwMj8QyrzsIio5FVCQzXgkS75Fy5wGVeQLvP2BadTwsLZhgwUScHEwkEeCUxGQdx3zk7CzXDu55qmFa/QfI62AOVxVX+8IEKCTaV3ntCEmz/AYIjYw+QDRmYRRuvXE1A/+y1U8+5rHHvug66Se4FByEmNxuqN3pbdy69wAzhvdDE87NlYAQ+EbFYPuNCHhejsSzR8fhUswcY96qh4EVSxrM1MtX3e3skLtfb/gvXoqKTk7YOnYIiro4vryhqUa2mgETAMxWpyN7D0Y0X6WEx/NJkmXp16MV+BO5tAEk4D106Dj1eCurffLnzz/XolSpjH8UltRpiyWs+9NPvyBv3tzw9fWHA7Nwuw/7DCXK1UThkhV50+Md6m8oZw5uVuCvTZtmVAtZoOtXKFMEcNra2qgxvOx4+Zi9ql+EmDWPXYICcomJiWl0bvXr6i8LmbXczAVYplsS6B4kqz91mtJUEYufuHnnzVvKGLd4PHhAQMbS/I230XXIJJVEkKZRJjYIt6OouwTGRypLb79CteEQGYN5U4eQcDoRXjfOIDfPl9nl60jKlRM5HvvAcssuJJE8O5nnsOT539I9ioxx0KD3ce/eQzU+Z2dHEkWLhiznkPFxP307BnWfW/vE6qbvZvurQEEblICjYQRLQic0RE9uSzRNJbtRYjz1i7jmpEiCiNCvLCN4tDKnJTnYGp/VDUjZyb/6CjXaRnFfy/nNxWzL9EBZa48IrKDrV9yAcqk7W6XE2A38YiPcraJRqoSH6k4kxyQZSPoqphfrpx1L3sUa/SpFrmGRQJM4SEkgMQSZNWvuQ2TkM563trrvjR8tfpKRerrRONQ+8u2rHFa5PWUeNd7AV+pEr9FO7yfUq3sK295v6W0FnkREo0q7Pkj2vseLiheATDTfLfl7ZVO2NXLXbgULPtjEJCQiSb5zydQwdijIa9QaCaS5erDtD9y5PwadynVA5LlfYV6qFJyKFEbhiuXRtFoLWlxd4Gh5m7JwuZgIkS/VsY2tCHAW1/9fdflK35f8gtCxx9tMpQ9G3nEVcG36N2nc3/kc7NQwOpTIgX0VHZj41NDYsLK0beUIymXycnv8y6+odvQ4vp09HUOqlM5SH6bK/+0MmADgfzv/r9XRna3s4Eji33PB91DSLy9a9xyJtT/THXj/MTnLZqb5LHv3HlJWPcMdMTGxVIbwwp49h7CWJNEIj0Dj9v2UO7dm20HIXb8ZKuUtAascf+/l2fyNoSph5cCBY/jhh8WoXr2yko3bsGEHH/5TrExlypREEcqJDRnSJ0sUMFev38GwwQ3URz13bh/d3kUMP3aqdc2apQN/4jt6DnStv51H+TJ7JHRoDTMSaFucu4T4N9pRysAhVR/ffDOPsnVz1TYnFzflfmrYtg869R/3yuBvn48XNj1iADitSCLp1yFnWfL8VaVb8TkK4tHGjv2MhNvfI/bj0cpKkoNxmmY8hwL+XlaE306Ijm/fPqXGePToKTRs+IK6R9zhYXH04vknwSuILiprKzRl9qgkMsgUiXbtTQKGnARBFckhJ0H64nqU+EnRqRWt26EVQzJUS5Axyrwbqk4UI/CT42xkvJfwrsnpUC/WH1k1SH00aTOU4FFcpqOrBeustrJTMtjD+TAhlkWtiMv2CvVxBViJpe8NqmKI6oZ+EZLfAXoqG2u6LkDM2hGwt7VAIlGwuDjFyiWlG6lmlhK4FnMJVevG/ghgfcCEEqEZeX5JGatmdJvEQe5qOwtPHp1FAa/1qo6Ac1/fWEUIrf9wJCCxdbmCKf24btFZ9Ix2bLBRxijxiWHx5rSmRxjsffXVQT/8qk5aTY+C2HPvCZoVyYcfz1zGjHc/QnJkJJYc2ILmHgX425KD1ENJ2Psgj6Ko0eZJspYlHEPUVBpRczeXHYE7pz4pqRp+u3IX+x4WxLValnh68DDCL1zEracOuHd6P8IPf5vi/2UCxYeL5mBszYqI4sNgLsbaSZFzGBBjyfAOcVeHM1ykAPpXCE/zQb1oqXOh9bGgk53K4JWkm2heN5r8XJoG3NDkoy8BPz98x6SswZWNAzCZ741UFpHvUKtMqpEYO5b+tmiSDSZE8NyRcgeenvhx614TANSfoNdg2RQDmA1PUnaNAZSpEhqQL69uZWZqIupQB3hAsYY4vH05Vs+fnGYmrUjFcPDgJhQokA/Xr9/ib8RF6vaeJV/YaVoTxIVKd0nFOqjTojuiy5fBnz5XVKKBdCTuZnEz/93lMRNIVswZh4d3r6ofbCsra1Rt0E6NwZ+awKvmfaI7ZEUuVeBrwO41qF27mm67LMhN8fjxMwSxB2n5PIkrV66p/c7OTrQmntXdsFM1ymDF/OwFWO4+iLi3+8Jq3VZYrVhHi1p+BazMGMSd0KElEmtUIa9GIlGQHx4yMcaTFrMBv61WvX5MDdcitJa+ahGX7z7fq9jy+DxszZl8UqgOLv46F97XziKUrvMkklDrl99++4Gcjm31N2V6uV+/d1UG9/79G3Vtbl4KxJV992DNgHLzAH+UXPwjKgddRXyjethapx9CHwTDnPFM+XNboESX6vDlzVQUK8wZNyXJI+VzxkIC4AVUZFa1QNQ7jMmbieVR4j3Ts9jJoCUMYA9jNQfoxSZqsZzC0SY3bEO1CXHPisLG29S7zcjyc+yxrcroLeKciKqDlmFYiwIY0aeFbq4OUmZN+qpAV7W4sDXwolWQm70nFThE9YIYVtFzGItPleQNoVQS6pNbjG0TwCi0OEKULK5wsQhq5e7dSGpj21CdJGsPZTKXx5g9LOBYxiJFQLvEu0mGsr6bNGVvyl+Zf+FoFCujkDaLpZFdobRrXIaxtq5t+4GEoDSF2vIk5mdQJVuFPoF5xYoY/dk4fNbgxffYGBG1xDXKmMQSbFgkQUcStDQPwI/n3VDJPZLjvEmQ9oxzmYyxb3/ATAp/0J0gqA9m7jmBRi3hFF0WjnZR8Dm3G0mBD2FRgNnghUvCjWETo9r3xNrrkbh99Toir/F3hGbvboP7oKlHbp7fBMWXKZyZxsrbu49j/ZB3KXczBCFTRxurorZ9fcZd8VOmN9/pNsxgR8OVO+A16gMFAAs1qIOFo4egLj+XKQYwg0nLZrtMADCbnRAZTnYGgDI+AQsfXVjDDM4E9CtaHx6xZji8bRkkHu+XWe9JFeOFdypnV3fq2ZajlWo8XPMUwvX4QOxkTKF/bDgsmeBRxpnxKqGPUJfgsj/B5T9VIsKCEOT3GIXJYZdDXELPy94Ni7DxlxmUkiuCPAWL4eHtSwgPDSa/WQu8//4wlGjVE7dYdyJfF7RGz9+/Y1LE0N/nI4ku8cyWHHfvwXLrHiRWqQDL0+dgRhe4OTNhzenaNXsOupLlDs8xPmOM5KUihVBj1UZd93JTLVGhFj6YuTrV59BVeMnC7XBfZfG7HxVAmx8tY7xNT63UDbG+TzH93Va61hYMKBf3tn5Zt26J0l4ePnwAPv54OkaNGsxhmlMiLBydOrXWr5pqWawhdeu2RbduHamdOkrts9h3GEnfLkbA/RAUDfKG2fNjJbu6IDmnK8z8/OHZfQSud+yDKFpPRBHBMIlDXJd3SJsjFjaNbDjVgQ1WNnecgy7bPjDYmvnVywGSte2gAKehPusCWgjFEim0IvpFdFVX3XBRXH0CcsTSJHFjApAEzErij9CsSFJMn3Kh6DhmJR4GxaNjdXfMGtNR15UAUAFtAo5eliku4xTiaOnf/DkKEwApx5EkHLGGCrDaQx7EvjymxHGKYsq4mkG642V2QWIFu/Y4DfPqFdH7LfLdsaEk1RjLsM2oz61UA7Km9bQgAZCAIC05RahohDdS0wI37KPk3BUInDINaPgRHK7mR4LTY2rilsT3bUXpI/XJEECnn1wkCR5nCJwlHle/iNVMkoPsmDikURIJOBUrsIQQaIBQ2iy4cAM/rtoMG1qu87s449jceQCzah37fo+IY3NhmScP3IoWQViICyzMXZAU5MdcjCgkHN/COAHGmXbpiHsHjyCJHonD21dg0p/HcPy8F6zj4+BAmqrQh0/wLCABOfK4YAIBnwUfsqf1GAimzSPkpy9kCLoiXgYpO7yeZDhnugZZXPBjrHeFAWOQyPjJoDM7dfNrAoBZnMj/sLoJAP6Hk5/eobM7AJRxe0f44/sbu3gDk+dyKCm4sWXa4vC6BdTRPYdydVogV4lysOCPRHCQL5Ly5sEd3nBCnsUiMC4SkaQU0dpK+5o5i2Fg0RTAN9Jz2T8OAOWYmSli+dq7cRG2r/iOAOhFbFju/EVUJvKx3atRunRxrPlqMkqf8kRc/55wLt8w0+4wS1r7rMWKF5+AZLrDE1o0gjWzn81402BgH3IEhSCJN5JkxsrFkV/Rm/Ub3vJGKOPmpJjxpiagWnw78XGxSOCNQrb1ff9r1GzS2ehHFDLvfb5eOMYEjygqukgpSF7HambOiPM8g4vHd9Id/5Bdppxbo51wox1fX1Uqh5kBQUrWbwMTQhzpppaYvosXDyoScM3VbdiHZHf70JK5dOlc5LhAqa23x8I/KAGXKrXA5RJ14cAboyXdYMkkEY+KegbH+AjOtxvKwwc5enXCQX83lW3pwJuyHbMtBdAI6NOPGTQ8ZjDB1/wLOfFRrQBlgRMAIJYo+ZQiCdemaMqcGraTdXExSxJG91JhqUDdWR8bbKfVsYxbvLIaduxUG0539uA+Mz7PkTKoG12+hkWAn/A7CreljFfi9vLQ1Sg4XwCOxBkKj5uMTwh2gwIDsGzjUSwb6IaaNSrrupN6oqX75nOaF92OV1yQZCaxuAlYW0RwI2DQENi+rOtFi+5ixkFHlI9/gJ0bamZYXQMoWYntFDAm5NeSkCMuaEPrpxzQteMg+uPD8fGbM0ijU5hxyXKlpi0C6kSBQ5NFW0aLsL4rXloIBYwQboubWqMikhCBRLHAMpnoKYG7BgrTHgFo/fMfCOH1O7D5u3i3Kt2lz8u8CzkwonIiOixeg5M/LYJ7uza4PWuC2jvp0Fn8/PZoEpNGqRALC/4uJJrT8kqdbft8eZF4rRni/PmZ7Geg2Q8zceD3tWBcBXxObWdIAuv9i6XQ+K8QuWYt2sz9GqveaKGObAKA/+IJ+IuHMgHAvziB/0Tz1wEAyucWS+AexoxdD3uKO5F+aipsza0UsBMXsbEi5BQ2dDG6Wzsgp5UDyjoXQI2cRXkTt1bVxTr0XwJASXI4vnsVHt29hpDAp4gMC1HjkkxYyUS2d3CGjb2jAlxnyR3YuHE9KiAsgaWlpbGPm+ltDi17KHevZCLG/DgT5mcvEgRGqwSQhI6t8IyxifoxgEKTs23bn7hA8PTgwWMmnpgrwOXq6orZsxegSceBeHPEFHX8XU8uqbjNcBK5CuCT+D4pEmNZ2TYP8t24hxN04wfSIipWksqVCbMIziwtLZQ1L4FB8Y6OjiTdzsNQowD1usw7cUKrJqjseQkJtPbJK5Fk0FK+//5n/PjjEmaMOqmM8GLFPLBhwy9wcHBQ+x89esq4v444fWw7CjFONAczvy0PHUcCtaEtTp3jDWUxzPm5bCfNQDIBZUL7lkjo3RVmpLVJkthKPUuOWGIExJ18aqfIesWFmV5Z2eMXdPFcCft7B9KrYnT7VcbwiTtViMhTaICSSXvyDO2YCS4Ztz+cz4kPaS07S+uRbaf+OoJpcTH3LB2WZRAlBM59y4WpeC0BOAIY31nui6vH/sSJxQN0YxTOSEkkyErmua7xSxYMrWMvqa7bHR2diEWejvigER9g/oGigcajtx7iKClUxN2vi6N9frzcIz5FwrbtGPfrPHxCHjtjRdzkiy+7qfbaQ4MxjXFRiJHzO6W+v64bLXRALKuBMRZK4US3U29B4gnFpfwwworsAWFpFEa0qpLEUdDRXnHzyTZJHBpzIBDRTOwqk4vW4ry5lPtc+pN42P2bvfHboQRc9P4UuOFFMzI/TJcuCFn0pdblv/ZegRbXJ19MBYoXhwBV2woVMbZDczi8OdBwDLnHJScHGG40rf+3M2ACgP/t/Bs9+usCAPUHf5MgcPuTiwhm5qhFDnPkt3GBu40j1ymARaCRx8YZ1SgZl4vbMir/NQCUWEaJaRRLlgAYyVKVIlm2AbR0hVNUXawQkjHcp083JmF8mums34w+t+XqTXhGN7A5Ez4S+nQHxMJnxzgiPfd0Ru1FFeX339czqWWjygYe+NFcRJYugb209IUR+JkTSQg4d7W0R25aenKHx+Henk24dfmkClAXHsRRo4bQ1d2ShgYmLJAiY+PGnejepikS9h/BVPZ7nTeaHj06q88df+sRnFp2U4kBcWwX997QVMBMzqNk/Er8p2gqb9q0k/OWjPXrl2Lv3sOYPv17hDJW0nLdNlitXI84uoIt125hcBhjxejmyuEfADNSdcR8+iESBr6Z0UdX+4RTUFx0ErDP2HT8REufxDuZEaA50CUsChsS65YVih2xsP1x3QUR7FvIyfWtTZLUcfgxVWFoQRINVRdaEOdfcMMH1YN0VsgrBAj+tKiJ9mxWipAO61OrPKG798NVPgi8uAv75vXXdSXWQgGHkriR2aIBqJdZ3TZxDDFMPpCkGJFwy0pZRsulfmxkVtpmpa7My166reVY+udGdIFrvDsJ8QcOouHMqdg6oIuuW/nuSjux2r5FUKa5lqWCAH2RT5tKsBfKc34n1BqnSdUkSRNaPKckGp1iqIHo80qRc1WRlkB9BRa1g39kDsVqmFECh1bXl8lNkvAhVuaizvGKGN2C4F+K9tnkuhbQ68e6AgQtzSIx1/MqDnk/wk+U4KuUyy2lQSb/SqynJFXJNSqZ5fJAk16RY8v13piJMfqxsd+dvoLpPy1Fbn7vopkIFnnjJuwZq/uFcA6mLiYAmHo+ssWaCQBmi9OQehCvIwBM/Qlefe2/AIDi5t287Gt4ndkHn4d3MpSyExAjL8N4oix9Ysk4folWaWb6k7kS2pyztBYOHvy+GlfBEoxprF0HD4uTkoU3EHEle9x8gOQbNxAa8ETFM+pn9A4d2hcffDBMATVjx7Ra8Btsp32PJDdXJJDwObFODeS4/wjR1x5gSyytkqTXKFPcGiVJsfOkU0887dUPZdwT0gT3y1iLF69FXtowuDMwfjYpIySuUtzcMkaJ9ZOS4/5D2E6k5Y/AN6Fre/VSO9L5I3FbJwiEhELFiSEGkiwgMoKigqHd3MW1KWAmqwHwAhbmUb1hJNUbtJuwNoylV1wwiJm7sn0XCYLlJtqN7mGJn9OKWHIkmaKlnh6vtk/ejYGxX375g9bTxSoTfdCg3jTstEV0fDKqDVwKj4hj6NqxGXkyu1DWLuXBRCyStfJGo5CTcYu7/vG0Y74M/Om3EfJjsbBl5B7Xr6+B5n8DAMpxZY53EMwJrY/+OZLrLU+3d5Do7Y1LR7eQ39BBucsjmChUxDGeJNWxKltb2h8lsJEi141Y2WxIiePIh4kybnGKkF0/jlMInsVlLy56KULi3ZoAUZ/TUu3gH+l7M7kVB5YPSXVdaPu1d7HkyvUpx3tZ5rYkGolLWpJ3tNKWMpHata5tk3c5F5IhL8TUkjkvD0NVB/eC6/4l1PS1JfizxJsEwZn5Xsh3QcCiPPhI3GqiPEwVf/Ed0477ICwC2zy9YNP9xYPK830mAKhNUjZ6t8hGYzENxTQD/8kMeJ09gL3rF6Bs2ZLoP+E9fPjhiHTHIZnL+lQY6VZMZ4f3mQto3ronmtHN2oDciRUqlFEuV8mY1oq4d2P4BJ3IzEIn3rishZT2eRF37MmTnti6dTf1SPcrV63sylOgKMZ+txGTb5HShu53Nyt7FKIG6JUNy/CIFC4NWceOgKtixXKoxLg9oamZSKAlN0qx0mklB0lxGZ2OpOIeSkkgnpm+FhxzfI9OsNyxFzZfzkFii8awbVYD/TbyWPY2uJlcASeKN4ObS24UeeiFlQG1VHatPt3J24zxE3qdggVrMgv8FEm4i6cckp8tWft8BIK2YybD/PY9BQDj+3bXhpXuuxAx96MFSCxh4o4Ti5Uhf6DEsdlZ0oSRhZLiurNUSRIubiUZtH9b1/pY00movOp7BTjkxniDahjvkSZGHyjcCLZSii7v0EWZXjEEYpJkI1nl4tLv168nH0TGKwv0j2vOIDogHI9JQL1rV7Ki/rl794x6CBFr0f6HDsoNnM8+IV1+QG0MhsfUtqf33pZxkQKyZX4NXa3G2my45UwS5KxZDI31k9lt+Xi+hV5HMos1i5m0lQe0udMnYGTnvqjcpCsGzpyCjxs0wAHOlQAiod6xsYjDb+R0/KQOrc3EU5JYI258Y1ZiAYZXA22URVDI4LUisYFrqVQj8YgaKNT2ifVPtosVebBeQpC4lWW84XxYOc7QBbEit2D86suKgDbhTMzInaz1Idflp0fzkAYnSiXRaMkuXjt+xa3gFKv0NibaSOKPqBoNrhCSIRCU+ZFMZC3eVCyCy0lkrc+jKccuQo/JkMqlsFIbiOk9W8+ACQBm69NjGtw/PQOSOLFnw0K6dG1J67Ij0+BOuAyvXr1Bi1YEY+PyqkQQQ6tgUGAQPI+chD05Ah8TyIXwprR56y6E8kNtvHQVG8mnJ0XiB0uVKqbi5fz8AhX4Uzv4R57zbemSzZ3LHS4uTrhx445yz1pRiSB/kVIY3O8TqjtZo2iZajgYdk+Bv1Z5K6IKwdDX68arbNxvv53CBMOcWpe694/J49ez51B1bOE9XLBgGVzJkTiU4DOpSX3EEQwjXx5EM1FDyjOCR6uV6+ieDYLN9NkwY4KPGYFiqYE9UZZE2g+7NsefvKEUtWHMIC0oWhGQuW3bHkxgf7lzu3Oez/KzOGu7X7zT5R0z6zPYMkEkkdyAFsdOI5mWR8l+Tq9UyR2rdgk40bgCv/d0V27ft8qEUhrtxTjS68Nw+y9UdZHM1QbPXav64E9UW859uxDD3INVM7kxCjXIaR87nYLGLlpoJMHjXVoOXQkepaQHvCSmUtz2f/yxAaLfXKJEMUya9AEVXd5RIGbIkA+QlLsiijboiUGfv4/mlXJhxkcfEEDX43krDiE1b968ESztyqsxBMbEU04wJkuqKZJAc5OA1RgZs3w+zf2pPkgGfw4/skNZWrGMuUMzaPaXdomFrTutWPrgW+uwddGC4EQxs+Y41p3wxOzWdRW1jYBZyRgX8u06lF6UzyhFQgQkjMBYEZAtiT3vVE5tabTnw4VYguWci0tVupL+y5OjshjBubh/xfK2hq7lN0qEkUeQOsLcL9J5IkfYp2yo0bFrY5BYxd8JtIRPsjTnVgNy2v703oNizQlyE5V1Ur9OBY6LZne1ScbZk/J/EjbxsiKAcj/phyRJSKyPEnebXvmTc2Eqr8cMmFzA2fA8mVzA/04W8P1bl7FgyiAIJcw333xBN2rvDK+Gx6RVGMdEhUsEb+J61S8SE9iXFqtCVMO4fv02ihYtjN+Y3SuxeS8rjdr1w71bF2Bn74S8hUqgYPFyTDZxIQAgpQozqJ/cvwG/x96ICg9BwWLl0LTToDQKKUf9buKPBydgz2Sar6r0xPbl3+HApkW0EHopapb0xiAJGX36DFfj7FWoIMZRKeFWAuOWnl4x3oRgDhFRsP3ia/rKrJGD1qokEkDHjRyMhVFVFdeYoaVIc3W2a9sc0UxsuXLlOi2B+bFjx0pdYoj+wcweU8lhyrfIceceolYtzBTBtNZeOPJEokyoQ5YQyAlZc1aLgDy54RqWBRddlatNlDsMgaUkEIiWrrRbySQOURMRqhrDLFoBwwL6WtMKLJZeodcpX740ybVHkBqnhuEhU617U6lE3HYi1SbxlWJJlUSgW8wKj3Epi1ItB2FkjUhcvpRyzt97bwgjDV5YllN19nxFLJ1iAWpaKFJZxeRcpQdWjbXX3ybuUcluFrJqDVTp78/KsrjVxa3fuFBUugBJLHZH6f7vqWeR0z+GzLX7+9OQvHEjev86H/NapU4IEVAj51r/HM09l5NE38EEZ2nPvwBcoeuR6/sOraLtaP0TV7JhEWAl8YRXg2wUaKv6/CFFEmsqMV5Qe2gxbGe4LsB8BzOVhfZGPybUsJ6xdXFNd6JVUZOXNFZHEqh23nNEX4LQ2zxvYkEtxKQiQzUZsVjK2DuwP0k4EiqjxVfcqF0er7MIiha1fPfkuj97Iwo2XUsbHtLkAjackWywbgKA2eAkGA7BBAD/WQCYEM8Eh6UzcYjchQVovVu37hfl/jU8D/rrAviqVGmGHBZWKFa2OspWbUjew1pwcHZDgM8DHN35Oy6eIMkrbzo2tiTqjWNcm4AlFtEyLl2lPp7cu47fvhuDQJ+Huq7fGjldqaDoNmRxQQTgF9zeh6thT5hsY4kvKnWFCxVbVswej6tnduP+/XOZ7tFuwCiYMdmlFgHuFwu+oWu6grI6Gu1APhstmgLShL4mmZa9yHgzo7FOGgAUYKEBjF69huEypeSuXj1qtHvZmIN8iMm0XMorvSI3cQnKF7eexCU5jP8cnbaPVdU9femepstMCI0NQWl6/WW0XW5ye8n7J1aeaLqeB9BtpllPJPGhFy2OVuYqQVzRh0iigdxcJT5Lkmo+/5xUGas2qezqUaOGKiuf/vHks0h5FfAUEBCI4cM/YqJSIGMtPRTIFFeyNr/hTGoQC5dhvJdQqojE3d9VznHOExkjJq7Pv1okKWIz3ZQyPiH7Nix+UebKhSnzJeEGNWhtMyRMvkZljfo1W8LjnSG4MHGEYRdp1kXOT8IKRMv3ZeBpNsHijAb+GZ4vAZQDeZ1IjKBY83wJ4EVv+WUlgrF+YvmTLHLDc/aytrJfLPHV+ACSUVa81BOr5hHGQIq1Uop8j/QJwCU5RZRRJM5Qk0gUMvMBjGvUTwYRq7k8GMncWYYH4EDVNA8zJgCoZjh7/TG5gLPX+TCN5h+egciwYMyd1ItWtZto3qwhM2fnM5PVmkwKz2gF81M30HPMxO3Zs7PKAtaGs2TJ70ou7uvfTinQp22X95x5CqIMAd4z8gRG0krn5JpL7Y6KCIU5LTy2do5qvXi5Gpj2y1HExUbj4R0vCJegs1sete9V/lwIfoCV948rahcP+1wYU7oNrHg8KZERlEOTLOIslOgF35KUzgofUq5t3LgvqNbCLFy6uufMma6SEVJ1RbRi/d3PlKwjVx8BYEKvriiYp4RR65G+RUlb7tu3J0aPnkj+2j7Yvt14xFBSuVKpDqm/ImBJbk5XmWQhrrQ3aHVSYOw5+JO6on5hTAFDv5+sLOcmxYxGEiw3PAGX4lK7Rfep8MUJ+JMSExONx1cu4zjpUM7+cRQhN48R7F7jQ0YpWutO8nqzSan4/K9YjOSGL+THkon5Kjf8XAwR2LBhqepRYjtF7nD8+JFqfRVBnmRCb9qwFRaOufkgU1654GUOxeKT6eIfCOtFyxHHcAEJDVBFOiECE3AjBNeyKhbKv6MIUBKal9XstySJqg3JnyWOr1/5MHXMaFryJDtWSlB0LKYePYf7d+7i+JbdCpF3rV8zU0OqQ+Aqmbg76cb0obVP4u3kvBsWT3I8GiaeGNaRdbEmSmaxxMrJ9ZEZ8CeWP7FeC8hye0kIg/ZAZXjsQrSCiwJL5xIRGT78CKjzcA5VVmt5SDKM35SsZ0ky0sCfgHJLWmYF/Mm5FpewJIbkZfypxpMYkw4FmOEYTev//QyYAOB/fw5MI/gXZiAhIQ5n9m/C2oWfq5gcsfqVLFlUUZTsJ83JfWa2ivVOK198NBVlqlVS6h+SvPDTT7+gfI0macCfVl/ezS0sCehy6zY5OLnqlvUXrG3sUJLqHZkp8UwMCEmIgih13CX59pPoEASSFFnInIXPj7/Z6FywGtrkr5yqu4hQJiU8B4OpdmS0Qje2lA4dWqmXLAt9y4gRHzFhZLoClEWoRDJ79lQmdBRKMVXxLpDYroVUNQr+1A4jfyQDWIBI3brtjOw1vulc/fdxh8LzEu8VQLdUSZIBSyzV32FtMn7E9LeKtUMA4C2+qlldxdMDp7HZrR9ub/0W8+YtpYs7H0qUrQD3Ah6wKlgEk6dtha2VGTZ4s0+etOcGVHX+eB9VqhzVCVhfBfwZjtKcch/PmAlzOdxdxaNJAoOQZA/9bqQ6b8dJMGxmlkPFZDbq1wNPnvgQPG5XNECGcaxa38LLmOPmXVit2QKrrX8qbkbhu0kmVZJQAAm46U/i6FexXmrHMPau+mWSz35mykq2rDHSZTmmuGyLuSTgbkg4Ggz8ALGnzzDQjg9e1OYdMO9bTG5Uw1j3abZJX5XpshVX7W+06sYRWBorJ/c+xPC9sZjwflHUKWlm1BUs7SIJ7IVkPDNFHgLEAilWQpnLl4E/6VN7oDLsXxJSRJJQQhMMEzUM68q6WM8NSbBlu7i4V1x1xnVe58JNmY+hFcJ9KZn18y7mVBKMkgij70aXdqbyesyACQC+HufJNMpXnIE7V8/SHfoh3bQPFU1Kgwa1yUE3ETNmzCEf3SGSO1uiEOXg2tKCVbR0FdgTtAlwO0YyaE8SPQ+gW1SKC4GdqGtktohm8onA24ggUDMsFrz5WpLmxMrMQpExW5unvNvQhWtDF/OjqCAc8SdtS0JaS4oQaduRzy+/rQsquBRC63wVVRv9Y0hM470b55kcYolYckHYaH5K/UqZXO7cuY1KdBE6El9fP8bt7UPNmq257IW4scMZOU8LEudQigDo9ACEdjip4+V1A2vWbMLq1Vvothyg7Xrpe+mDP6CKRbhyZaZxz0k8HaX0zK/dRFzfHqlIs1/acQYV0rOwaNYOcdOZMV5yzbcTse2P5XBBiFJBibdyxTXGgYlLLfZZDvRgfKB+VnQGh3ylXacIHEIY+N+SlCTTp0+iNN9odOj9EZp8aIVhQ99ScatigRQrt1ghRZdbrLxTp36rrN8hJNmuV68maojetJEirn7ze6QTEnc8z6H5jdt4RsnDeAJIrWQG/EnsnMjPedjTRZyFBxThUjxCS5O4NoV2xbDIg1KHH5fj7A8/g4GleG/lYkxtXtewWqbXxQoXRUCmnWfDho5hAXC98whNcv0fe1cBGMXxfj8IcU+wYMHd3d2tuBcvFAq0hVKgUEqheJFS3K14cSvu7u7uCcQ9wP+9CXtcLncR5Efgf9OGtdnZ2dm93befvOckR56lU5Y9Y27qjbeiaGAM99eW7/snk8HjH0nmGnkkH7SdqUFc3QRtkLZPfKdM3gnFeTA8oRVCEWJ7DLjBxU6ATRoaJjDxvHk9aeV7EhyVJczjckwYI/gPMpvLpAmS0mlxHQ0KLajm8nmMgDkGMBFeJ3MM4IeLAfy+QXa4bsN0V3nEiIEyDq7LgMBgqd60m1Rr1FWsbIy7Ssn3d/PSCQkJ8pds+Uqq2D5dQwYzXqEBctDrqorFewLwF/k6fl/9Bs3oFgkSszqmVnQudgCFHgB8VE1xBb1LbIWAd/bIbhIeEiBrNy6X6zalVGICA9ZjgKbYGjKxjQAuTZp8cvLkDh19DBNJqlRpLD4+vnChXzaxJ2gv/AOkTJm6eLFA+xZWp/v3kfhiZ2eyfnw2JNu4DVrBbmJx4bLYjP4bqcqv5GXhfPIKequhP3SV17DEfaxyydtKzoEahHFPi+ctRBavr/Ri4gUsvIz5KsKYNGRixgcYGfZRP27ScJupZcbEUZuYrjyCLB73z/m7Jej6Pgm5vEW6tvtKmBhi6IJmRjEzkK9cOazUZOIC8klAiE59ZkG8qLyxGhv2yVT/6e5evS9Uij8/I5nSWMpLStslAAheAKgmsG6mJ3935NEzqf/NTxJx+oykb9VcVgz4TnK6uxh2KUHLpL7ZCz3mTqBvMXb9yLFHCzCTPTje+tQw+gf6hzGWAP/G2tDq+SCpgrKBd6A20whckvGx/Gn7xmdKCx4lCWOz0hHo0crHxBr2leEVNzEGzCQuDZd+VPZwlFShM/g26WKmlB6tgoZl3n5QMdXPZbjaHANoOCKJYNlsAUwEF8HchY83AgVKVZfHd69J1vyl5fblE6DYGC6kUPnl7y2SKl3mWA9MoJI1T7FY6xz3viWL7xwA/UoU4EsKC507ZO5yOHlAzzibZLB3i7Y/nczk6QtDvGAYpqEvtSkk4F6Gq3WOFjZQTckYL2uaD8idH9y+Iif2boCqxwHx93mugNmydSvAMZhLQL0szOJjfFkILFGVke2pTxrMB/81POj5EouPpBhdPxHIEqbUG+lvotyHm2AVLCAPHjyOdq6GC40adVCxhMOG9ZeMGYtAMzjonQAgExro+uWLyu6HXyWJn7+8gjUqFPQp4bDkJkMMJ61TtFq9/AgAkGNGGhGCLGagohvS8o+qUg8ycKNaz5bBv/WVsMKtlfUkthf/bQTgHwEPHGlBXiFxIi8sQMwQZexWbWSABz6/YjiERpc1KyVj4lq/eSHzOpGqo9zA4vL0pi3uhe919xPBy3rQpzCwn6Dg0qUDsBpOwDWsBpJuN4RDPABfZE64/X9QPJWpUqWIdlxdUk4CwR8lyyyPnJRiqbLI3d1PJUvYS3kFHW2NBFz/IKYAJIFIKNyyzEqtByLicfv2yZSe/RC7ai3DVi+SHiUL6JphXOJNqHlQto/JIbzHWXj96BbXT2LgeoLL2wBh1Bnm9a0Ceh/960fuu613HJVbNxwu04JwE9P9qU/+zXb0S35cU9KnGHNda/WYUc7t4S+DcF6uiBv00Ta995TnyvjP2MAfD8LzpNtY4zIkjyWzsJvoaVkzYYTqNqTCIUC8Bv5Lw8LfJql0Agw3mJcT5QiYLYCJ8LKYLYAfzgJoeHn9XjxTLl7G671v8Q0PloFnVuDFklSKQc+YgC+bU+r3bTbW/WmNPLLzX9kHubqnj+7oMo3tHeylNGhEWkEzt379mrqXvX5jfIFRSsq35ygJgYQdH/rMDM2OAHsSzNYGICCIIM3DKhD6ZgBBLV+IdF2yLq03dP9s339GHt/HsX3uSpakt1TCDOXjGjfuINevH9U/pJo/9NBWNhx7JBtWr5N+/QlERHYduigHdmyVNqVd5McONY3SwcRo6M0KvvwbIOmD/Uq2+4BEIlsZmRcieoTWpvZNyPo9oP3IjCB5Y4H7pGRJjuQAza1LKw456dK+vCFf16ggV6+fkGU306iXuTGOulnIpCSvW3FYCTXLLHnkeB3oCjwLN5oDXqQEGhFw49HqEptUV0LOi3Uph0aQEwLrJd2EzHq9CpfmiWeOki1jKjl++pIcmNxNbl27Ki6I8yNo/1rP3Ruf42nAVL/u0YfWcuCOlfxQHAlShghMv6LePMEaVTpIsaMBMoLYWnNvycnBDcS+TGnZOX205HB31tsr6n69WryzeK1ZolzxuO3U/gTc5XC+hteVvw9+JN0CJQotgHQDI5xSV7AbZP+i6HJ0K2OZYXt0WbPPdWKRWdOaIHULAa4GwrT1/+spz3saFHB6FX6uA810DzPznbGkHBveq7xvDUmzaYXecz5EAmrlMey22QJoOCKJYNkMABPBRTDsghkAfjwAaDjWCV1mrNHuZ5fkiPcNeRqKDEQ00CtHDbhn05hs6sqZgxIWAmuXowuyhF/IrcsnJVWazFKiSkPE6cUvXsb7yT1ZNPFnuX4B5Mhww+bOnUMYn1cQ+sE5YEXJkCEdXjR6byuTvTG+gRQuixHXQ9DBVgj+KKtWN0vMeCvjLYhUrNhAWQEzZEgLXsXfpEiRArqq/23fJ117DJZTh9cqNyM3nDp3TfrNOi53LXPLmT9yxGkNJG8ZMypJ0Kxxq+kO8A4zxgCKfjO0nhBs1nwDjPW3Gc6TToXyc82GbJXQ00tlx+b5SgqM3GhM1eF9QoshQQjBDClLSBYcWwmCFY/Xgtabc7C8nIKbkK43Ej3TAmpYqIBCChZDHjfDeobLBFPHAQYJNiumj4o7pfWMwNUNRNYzkB0+bNg4OXRok06CzrCN+CxzPBmP1h7EyQkpdKPa4sODnH08v8BwhCEM+UO+C3omjoUd5OyInwBGTDuz4rrOxvpCIMPsX1M/KWbHuiK5Q58vkh8BB/CBxcQPXm9+MDFeMS4qFh6f2bU3ADzj0l2mFW5rj6Vi37+TSnopBcJyzbJp7DwSuo5Aj4onJFHnxyCvGTPeyWnJJCUu8+OlA1zjxmIK+Tuo4XJb1uQuYXhoMwA0HJFEsGz6V5MIOmfugnkEPuUIhEYCBEXgazYyVB4H+8o+ryvyIPiF6hJdvRnskiP7Nn+s4O/u9fOgnWkV7TSo/EFJt1evX0n52qDUiKMwFnHyr20lwOeJ/PD9N0L9XrpfP2ShG+tbuHb40uLXPYFLn2LPE3SIPXvWqvqkzOnSpTdcvMHgWJytpO5qVCsvXds3VATIx49vU3yAhcELWMljsyzZMVO23F8kjXMYB5sENofhKr0EyhfSgsTTcBRn301lUGo78uXvbhupAJy2ztSUVjqCgQouN2TGtcuKhy8dXKekKYlvOXDAS06dQhxhr2xqF30yYqp05ANgpB4rrUq00tDFy6B9uu4JCPchZu0RaDmoS/xdwec6Wpq4js8XuWEwP7NvWThGzTG9fPkqPjYqycyZ46QqpAD1yxG4dYtDrSXOBCDcWwTBCS2UesviHCol04TA/RspuXsMEZ/j6yVnz+9kb1/EXMYC/nisuK6zsf4Q/JgqtIDRUkvgHIa+8T4hMKKrmQoy75LJTWukoUXS2PG3gLi5y4Im2OQnt2CdJ8UPtXkJBOMTwmGsTW2dH9y31A7+vsjb3z3X8Yrpk1cTcBoDf2yHdeNyN7OeuSSOETADwMRxHcy9+MQjQELl/QB41/2fKJDnA/euYSIHnnviCdBXJXUeKWIQo3fxxB5F8tyi+zApUq6u7mxs7R3VfNeu7ZBR3Fxpu6aGcoYHsndvXzkZLwBIBZCnD2+r2L4biGtbtWoDgvk7v5fFT9fBNzN0Ca6Epmkqu0gVi9a76NuXgH5dvuiOQS+V2ZwMELdL9kpZQnIi41CzlhCg8m/r1l2I+WunNICpfsEkkRw5silS5JEjfpGnUBOZP385OPL2yOJbVvqHiTZPrj/yvHUBQDVWaOG5imviHOIjqf0evdML31i7zHb1v/IQ6iy22Gy6f9yXlikWcu85gBuxTp1WyLTdrtbF95969Q6qqhoANNyP48tYNi0ui1ZbqnhsfwMIswIItvb0g8YsdHBPJpefinnrrolhWwlZZpIPFUUKFcqHe7gnKJFGScOGtWXhwuXQJZ4E0vMIKOAUlnnz/oTGtIPJpglM40uJot8IXcCBcD+yDNx1XHzWrJGvJoyW+a3e/s7063+I+dishgRa7wu24uojs7p5zgUQZ/gI19gTFnmqrKQBDQs/0q4i/i4nQgjYD/4mGWdIPj66ZW1gLW2kF7vHEAxa7y7jd8R9jamXsD9R8m6ooFdIYl4CcnnL8WzgRwdlD9mGqUI6GXP5fEbADAA/n2tl7ukHHAFmOt6CC4lJHNcDnwppW8irx2KFAPzkSORIb+8urpb2sOyA38/SToq4ZjJpbVg7b6SQZNrH63G0XqZMk1EKla0ts2Ytkm++eWu5Y6wepeKunj6g+ANTps0sbqnSg1HFSpzdU0mGrPnEI0M20NRYydkjUUCCCRf8o65ut27tlYawdjC+sFjexdrBWLCtIL6lKD0LrRskNubDnBQmzrBs8OEfBGtHJKbMbqU6A7n4GBdEeawNN61jxANRp/b8+T2yY8d+qVy5rNy791BGjfoLCQYVoHmbSQiEw8PDlWWp84h5cjhl8RhkvwSmG5FtyGxMU4XnbAu324G9j8Vrxnp5eTSZBNs4qj5TSSE2a45+m7Ts8GWq074NhywWrptNhxkS+uy8ftUY8+Rc493D11+3bh1k8+adUrt2S4D1OXG6trXGatRIJf/99xR9ALciACXPm25IU8TKJwDESdFRPl1QNAUMJ2Qgl4RFiNeRtCLvU0gqvWzZaqkIbeiMGTMouboePfor3ejbQY7y5+INsuq/JLJt7W3QyCyQ3393EH7saOXmzdsAjHPl+fMXipcyPH8nyf11TknrnBQhDG0RM3r7TVW4xxEc6ujoILUgGUgi9rx5c6ptTDqYf9xa0n1zXbI6IdYTFEoWIG//mCWhv6PYAGNC+sl7kB9YGeDqJ8A7jnlPWAcPwQKeEh9njMFjkhBDNebCNeuJGFXWq5KB3I5RR2K4wNzzLmINMIjHnOJIXHwJ9wLqaWTaxvrri5APYwCO1mf+Efj9e81JHZ9cifr8m8we3g0LtFs8OQ8TMibmuh9vBMwxgB9vbN+5ZXMM4IePAaRl76LfA1j3fECm/EK5dqNe2XifIIkjubWjVEiVU8qlyA43VcK/i8JDQ+TRvWsKuEVGhMu/s4fJ0/u3pGbz7yRjzkLya4fS4mBnBV36jYirclXca4MHj5abN+/AghKOF+EtRZNCRRKqbxAE6Bdm9H79dRMlz0btWAckfRgWWoT8wy2igQHDOtoyM4O3MkAdK/hi0c9SpGsrBGCPzh+6IekGYuKGlvCgtaE/ZewQLRUEih0hfQWOYJMlCSxKNuOmSji0k1+Cd466tuXK1Zfhy/aIx5BJUmjdEMmMGEn2LSEvYr7USP0S1r2D4qvjC2vGWTfpVvCFyb4YbliFFxxj9Dxg3aTlg4H8O+85RrOoGO5D1zRjuGpmCtRt4gdG376/yzooUfDFPGXKaKlevaJuu7GZSJi6fH0jQCZurfrdHOCVVBsdoSKhX64iQ3M/5LuKo398qc+54KqIg/Vdb7x9puPc20JNQt+VrN9ObPNjx06BRW+pMAN4w4ZFEpI0Sk2Ebu5Dh47J4yfeYpW9uiR3c5QT1yJlwZIH0jzbcZznYGQRn1LAvly5ekpNpkaNypIqbQa5bllI7j/B78+2gEScmCfVM/iAVHyYjrCcHwPUiZ4+fYEcPXpKgcH9+9crYHjlir/U6nlfLMLCxOfl72LtkVoerZoR2yn8v9jGuFgC/c2QHfwW97kTPtYMdaoNB4L6z3fBT8lkE2Yya8CR9TZBg5nAzhinIbOvp+GeagqqGv4+tN84+RnJQcmMaMaNhng/l3+ylTQ8rDkG0HBEEsGyGQAmgotg2AUzAPzwAPC7Y/N1Fj4bC0vxsHGRnKBqKZE8q6SydTa8BO+1fHTnauUOZiN0AY9feUEegYpmZK/aUq9uNZk7969Y23cAkLmNGkcXTVEv0JIliyjrS6w7YeNqgJcK6YNijUEKDw6Xw5ci5JZNWsiaRclTxdVuQrY/CEgmp5/ZKkuF0f0AbpMdPCbJdu2XiMZ15eWbRJEtW3ZK9+4/i1OGfDLZIq342bpK6t+rKm69//7bLU2b1heOQ5yFJg+i1TeFdBYEpnEF12v1OSV4eoRYu62It7K2eKUsLKYk5ZgMQJBGS6OpF+9pqGg0adJJ2rdvIQMH/qAAjf7xDOcJ5MNgeWUs2T+XorR6+ZJmv04+tVGccbSKaYWUL9QdJnAlSOeLmC9nrl+JbO5KLWtKhnPLtOoxpobWIKq//PrrKFm9FrJvzjmURSo1XvjS8zfxGj1c3Tc3EX927LGdovywePhQdv57HeTquSVTpvzi6uosEZYuUqV0Xpk3d4JK3pgHkPo11C0Yq8gsc7oW4ypNmnRUgLBSpTIYu5ZSuGhhmX4qUv6c9q9EJHkuB8Z9LVlcneJqJtFuNxz39+koE1BIwEw+wZ6FnscaJzv5lJt0L/RC0d6cxz78UKKLmICQ9xjvmzNetlIsdXC0OFLWm3nWFUDz7b3HPjOxh88Sgj9+PF68HignShYyPB0zADQckUSwbAaAieAiGHbBDAA/PADsdmwekjbcpV/uunG+gA2vR0KXqQF8HCoi4WGhsHxkEnIRsgztWkUypHFCbNzyhDYZr/ov8BKgcDv1TKNlmBIU+frLjT+Wyh7PilItU5BkqJJHksJS+drFOV5tJ6TSiitOUhEuKWMaqqod9Me23zB5lTmDhMFdqhW+EJctnYHs4fxSp/8meRXwRByeHVaZznR7u7m5IA4ytWTJkglJB4XhQqwRg9RYa0ub8uVUH3xxpsCZVu9dp4y7IkULXWSxFbpAO3TohXjHyyqRIi5roNYWYy3P4CXdKKuvLD6JpI80URYazfqi1dOm5GfbhXgw4EVF8cOXOEGBMcJebR/96YEDR6R379+kBfgUXav1U6AgB2iCNMBGoLEdZNOM5SOoJjBt3fooXN6PEeZQFEA3nWrutk8S2XDbVVmSCCjIl0jXdEIL4w+nT5+PMIK9chs8k+SgRCqyWLtCAadMebkz8euENvlF16d1mK5cffes4QnznqXLluoq/Mgg6KOLmbyU/HTiNS2GMA/GlGqFv81DN+7JXdSpZUBpQ7okfnjcgRWcyVCerx7LhTIFtV21qRkAaiORiKZmAJiILobWFTMA/LAAkO64704sAFdfZumYpYI2zO88ffkS7g/ItyW0jOxVV5xsIoVurY9ZaDWqTvoJqzCx6/SDJAkOkaRwNd+2SCmZn14RQfzUq3RplPs19IcuUaoOCPKP5gt6jw7SbUqLTznQtZiMQUM2Z2wKECOPJhcmouhTmpw9exEA6pL6uwDlD7oLZ8+eIIw1ZDFmUblToJWcyVBcGmz4QdX50P/w5fkc7i9jLjNjx2L/mSBC3kTGcX7/fRdj1aKtOwWr39C5B+Ts+umQaDgtyexcxCJ1ASmeNkz69u8t3g75AdTwsWGQufr4cYjUGh4mvvBMz2gZigzs1NHa1V8IDg5G7N5PcL2eltodf5K8tTtKpQzBIEWOUMTLzDg1xU8XHBwJ+p8QaGuT847QM6qQSmY+7gPGojH71wVAmQDDH/GatCgyYza+Gd1Tjp2TQU07AJkUkxQgq/ZCVrJL7Tpyu19H7XDm6ZsRYGZwS8ToxlYIyvmhwMScdtBaNvVBod8GuS9tkfTl4YALq1dIMk+1kHXgwWyeA235e5tdwHrjk5hnE/4WS8xnY+6beQSMjEDwq6gvWduk70b+zJi+iyf3ytq5I+SF1yNl2bODazdtptzSssdwlaxh5LAxVlnb2ou/3/0Y6z/0Cmbz0e2ZIuVLCV4wWZIeOy2WcKNmXrBckgQFi0A318IbQflwTSZBZm5kuZLyMlc2eVms0AfpCl/q30DNQKMr0aeQ0B0gmelHD0EVXYX64I/7FSiQR/1xnnrCdeu2RoKFLRdVMRYvWPDecdmXI8oCq9X7kFPindjAH91mjA/MiGB9lvz5cyPm8ZSiiWnZsqv888+/oMpZBnnCO1KhQgpp1ChtNBDFfVKE35KHq3+RBeN+l1y4Tv899ZTkVsGyeNMpaTPuhIReGIT09HKSK19++apeVUnvaqGsc3/MuizPH6SQV7h/x+xOITlLWYpDxFOVnELZumS4BowlDcI9wTjUGrWrS/9VF+FWjUQowVsAwczSPQjwNwYAnyEGbf+DqLjJk/i2IMkyXYGc0kpIzkJmijNb9QWAchIkyzjj2jJ7eSF0lElEXhuxk4YUKPfuBSPhZCcShlwQg1hafoXFOEkGxBEunCjudjYcFsVPFxLpG42LT234jP/p1+8cmAIsocKSM8Z9EJ/TOogEEWYMx1UYL0oLLrN7aWGmrB3j+vghQX5DjQZIv53M+BgwVtIhYWUF+DlpdmbSWMzoZGN7mdclhhEw/RRODL0z98E8Ah9gBALCQ1UrttDUNVaYcPEMNCt3r5+Tq2cPyb0b55DN+xBWPrhEEE8WCBqWSFisPD3TS8cOLRCPl07OnLkAV+5uGdmztvQc8Y9ky1NcggP95PyxnfISdZMik9gSknP30CaJn6s26iJOrink5PkjyJasrdzATk6OxrrzQdZRjkkryU6fU2DvNVyoqsCCSevbK1cXeZkzm4S3baZVfeepj0+4enElfcNKS2DEpIjDoLNIaOG+LwEMTJXVa7ZI7x8HwTU4VsqXL2Wqmm695/ObEK93RpySn27du84YszIaa4tULNRWZRZnSSRraACQdXlPeXikEvImFitWHe7s5kia6C6LFt2V7dufQmavEDK8aSuD5W7GAkW1kjt3dilasqQsv+aqLGk1s72WmtkLoQb/mqsEov79/5ARTftI0hQ5JGmmSmLz0lequt6RAwdOyqNboNGxGilP1/8qbXG9e/fupvZhZjY5Jele34lEnozOIcrqx2NrZTMTA+BK1i/3/clHiI8gvPxJlG0HkAjMF4OUmNnkKmkFwKS9nqWJma2dkNxCa/EiAEHDRBdv7zAJDn4Jhbek0n3Ndnl95Yo0nDpBB/7YF1qb6OInf2ViKLSEPnsWJv/++wCUOa5SqVKKBIG4l0DNM2feUqeSJo0t4h4zmjwtY/ch412ZsBVfxZjroJLpAFLu0+D9bAm9YmbcM46QCR3MQk9jH4FQjujX3ViH+PwsBQugRs4Ozntz+UxGwAwAP5MLZe7mu49AIIicWewsYlJHnNy/SdbMHS7Pnz5Udeiay5w5o5QtXllZRZiZmDVrZsic1VW0FPouLl9fP7jVmsuEn5tJjgKl5RrA3Stk8RorNy4e162+fPm6cmOWLVtCty4hM/pghlm3tJhpcmJspwnirfjSpsWFsUDhHVpKOKg5kuBlbw1AYblxG+LvPGH9A/UNgAjQbazuWAbtk4iZrrx04CHjF7++LNf69Q/BDxd1fk+e1MNLGybAN0WjndCW4zulPB3JqCvBSqFf9lQZLP1e+QFAL5OcAK9xlcdeN+QZkjQydflWZO/ouKrHud2YlZE7MauamdAk5SV0pf4qM3S7gbja0L12+PAJWbt2M8iyvwYnYpgsXz4c85vEy6sE5u/DnbpIrlw5rIBiQEAA7pU9kiJFcqVfy8xg/WxfrcP8mJg61fT5MQaxQruJaNNKgT/ux300qhUu+yB2rCSTPVBo2duBWD/eW3TjkzuOLt2dWEerXRDof6iVm+mNZZP7UCeWdfVJggn0CCRmnHWRYyfOwYL+DPeHpRQuXECBTlqLCVSnLj8gTSvnVOfJtgoXdgVvZAMpNexv2ff9THkdGip3apXnJl2J60NBV/EjzxCs5snzH0D8248uHpJAvk0bz3gf3QK6c9u2lZfvvjulwGNsOxq7Dw8gMzw2uiTD9nwBFunW1YBbOsdI/Lajrj/r8oOAlDJxxbcWSR1m2LR5+TMZATMA/EwulLmb7z4CGgC0tYhuAQzwey5zRn2HpIKM8ueoSaAiKSHu7m7xPpALEihoxRmZJp8svH1e2oPo+aefuqMNV/BvvYZ6wjUEr0MCrWtbWAZAm4E4O2traOvixWtrG+XGMjwYKUVoNbJB9mnznP5qM1/CAXjhMhaM2rxFUoXodgtBLA+D8injReDBF3BeaMnSEEdCWFUY34fyGhJt4c0bSBiAB0xM8trWWuzb9wIAjJCIts1VHWP/UBSevHLMMr0P+ggejzJxdDVVgHRYzpxOABaw+NX0gNWUvXj/wkBzgir9wiSXYfkKSm+AIFPgj1m5DHCni5FWOFu4usiTl+oDgD/9vmjztJ5cwEuSgK8h9ImNuc60utq0V69f8JHhCc3mtrAGF4MVs7RculQS98oBKIFcQ2JLDlmyZJhyb/v5+evuSbpW30VJg8d1pbX35k4ZMXqw1o0YU9J8kDKGkmvMKO8EN74G5mipm3bGHbFlvupjwxcfF4Zu2yu4d12Q6KG/nvfu1JMOMqF9CUnliCxl3PtUwSExOCmPJFV+sYKUYtoHK2Rs/5vSqVNrGfDLj0ryj/fwwzspxOqrbrLjtxZSBL8dSrQ9CoJsGpJj2F9mPH/qYmtrASuuGyyt3rqu1KplKUuXjkFMpRPAXBkQotdWgF5XwcRMsWJucuxYVRNbY19NAB+J8baM50+QMZiL2ywWz3/axPhI4ZHK4XfDuL64AGDsvTJvTcwjYE4CSYRXx5wE8mGTQLxC/WXwuX8lpbWT9MtTF+4qa3XVfbwfyy9tS8a4Aw4e3ASt3ewx1v8vVkwHzUIlULlQjo0AUGl/gq8vrUOk0uOkEsQbL2uM7pCCgbE8Z+HGIXlzfHR8+WJnfBeDuBPCF3f5sr/svW4h1pk/Dp2M4cmRXmbDTScZ1zyvHD20VgeK9OvRLbngoquiZGGSQVxZp8bcaPrtxbb98nMrZRXNDCk2TT9Xf19j84GBgXDxLYKlbr7cuHHUWJWPts7fPwDZ1JXl1q23lmj9g2nuv1pw28+G1mtTgGznN1m7tAAvueKi9GHdYqFvIWBfgZgyfd5FXreWPSdgjIKgJ9xf/5BqfsEFFwUqCZwDg0OleLPRkqZARbG+sVbuXz4t95/iwylNQbGq3EpGdW4pKSHNRws0QWZC7tcYBzaygu5TJjQQaOOnJHXg2tayn41UV4o2w4aNh8t3o/qgY/hDeHikWp88ubuUa9Zd/EG7dGX7Qnny5BmI4L9WZPAEwR+jMIv3NDK+9VVA4joOLb0EeZ1BtE6Lqn4heJ+Je4EJJaY4QKnSQuaBpvA6aMXMA6iNROKfmi2Aif8amXv4niOQwsZJSrhnkaOIBetzaokkA/FzWls3+Sl3Lek6aKbM+KNLtCMsWfKv/PHHgGjrPtYCX5pkz+fDl5JNdBvmgKwa/1hSgKSZljzSK+i7Z4z1h9aavNCL5V98y2HF5RZF+BzffViva9dTCCO0kFUbHRToygerI93Nhi+RhLRpqi7HiBqoIbtHSoa0yY2CP1r+juBcCD7iq8VqzI2m3wdT251JibHPO17utvuPvGXoHxNlz/btsI5a4MMiB+L8VugfRjfPF+6D/M3keqrcknPtH0guiYyXRVHXgMGMPoAl6EiXzkNRqnz7bXuDmqCMwf3FP5bCsDD/B2WYZm8s0JtuOco3+WLXYJ668ihUdVylcy265ZOA0Npfhg79E/QwOyR5rYGSp3UrxGEiUxuZwMxQ55GWI3EgJyzWBH8E1HsfuMqq2YOQbPW3pK9bWir+2VesEJvYeOE6ufj7z9Jj7yTJ36qZ7P6hfbysaTyX+JaTT2zUh1MbcBU+D0mmCM018Kc/jqSlmTNnMWKALypCbNLSFAWZeb9+PVSMsLU1FEswzozzZGFYgHW7NmL5YL+MHv23TJw4A+7tfFK2bEklGxjf/sWnXkaAYrrtE1JI1URwvoQxsrn9ou3K3zIzhPlBwN+VYSgDKz+AR4AhAebyeY6A2QKYCK+b2QL4YS2A2iW+7PdQdj25JJcwpexbn1y1xROE0Cum/SYHti5V1ZjocebMLm2XDzal6+ooAEowFDaYZcfsUMbo8aXPAOwnoGM4ijg7U1/vpGxgViUlmGIrtOjtuu+gXrTk8jJlLdTa0Nx9hg9/bbup6alTPnBpJ0UMmbOqsgWAIZNzOEiT485AVLJycF3HZk3SP+6ii85SL8MTKZivjBw/vk0XJ6Zfh9Qi0864QRIrUEyRNuvXj+88A9xXrHgAV6275MtfQO22sng7ybhsaJx6sJOXH5G/N92Sah6PZWi/TirmzdhxvQFw97WYqDZlmNFXxdstQnLD13j5xnX9jLXHdQQtLPogloorFSo0EF+4lfXXq4r4hwk7gRjHahmDlK4ss0LJBXfR21qox0wuP/1Cq+LBo+dl+urjcjvATnKEH5WTJ86Ig3saCXz+WNHzkPg6bVoP3W50z/NeIUinCzIFAAgLFVg0jWNd5TczpHFq++8eObhuvfju3CkWOaGEk9FTFv7eV8pnSC2MqcyYMb1KrDHcN7Zlgh7+Dkm6TQWNxtDPJdihVZeudlrTGR/HTGatFC9eQ2ViV6pUTipWLKXkDGfMWAhFkxkAhhOlSpVyWlXdlPcvQS9pek5gfK5evSE9egxQ48LksuSgtWndurGSENTt9A4zlHPkb1D7eIxPE6SDIWUTVW9M/W4oK/dV1gCjH1bzYcFtDdCshQrwmGYLYHxGPnHUMVsAE8d1MPfifzACuZzTCv8Gnlkp/hEgOnVMJbs3zNeBP3Zh2bIZH6UnSy67qJgqR2TnanQYrnCxaRazEMT40dpnqjAeZwmyJeMCgHyY14LrikLy5APjC470DYzfM0yo4LGYzMH4wYQWBunrF7rmXiEBIq7COEbGzZFSJL4JImz33OkzkCVLqcCfvkVGOx5dVP2Kv43B0ta/7/TiRX/59tuTeLFDt7jcfGnbt6i4eboB/EVPTtGOExLyEvJ+gQoYX7j5RBwjvWTSyB91FiGt3rFjL/Dit5JAWxc5+8JeGq/q9Sa5A8kOJOQFXcq7gj/tGAR5zMA9Bbfgc58ISWKRU5oPWyrWfRtKUXARNm/+FTKCm2vV1fXgPUOLqxc+SAiEWEgqTiqbORvPy8R/L0hkaKCi/CAXpoONBWT7Msn+MZXF2fEbueljIb/vc5JptV6IvVXM+8EDIQy0FC8GVyV1bbViCvxxO61pv1WtKWfyN5DxxSfLrR27xW/XHml4+46Uc7FHwswN5DG9hAUuLWh28qjkLQJC6hcXLVogxtizTQIfgt0uyCB+CTcmrVt0g9O1rMki0rV/5O4rGbjGV14+vSjXd8xTbS0AtZJ+IZcjs9Hr1h2OD4UwWby4UrREqBZwoc6Dbi9dqbQW8q8liLapttKnT3dQGl1G7OMPkiFDesmePTM+rCyVNVH/GPGZZ+IU3dcJKXg8IKbzlVHwN3fubekz4JIUblZYOv79FsTrt0+6JlqOST1lLp/fCJgB4Od3zcw9fs8RyOqYUo49vwUguEKKwSXX8JtBcnrvRrlz7Yxs2rRDBagfA3cehelz5swKl01+Fbz+PoelWoQWR2PMRUlLy14w9JM8mYWWQRIM8+G6E9Y/WilKwWISW6EFhy/Y9E5RQDKXO5Q+0M6LnHXENeg5/DVHjO5OqwHJowk40iDLl4kdCS3ZYflj/BfjkMjrpgFbw3Zu+FoplYH4BpbTWmgJF7glkla0DGxj1ivD43yo5bRpbVVT1yFvRW66LG0cZFRp4+Nz40YgaF12qPrjxxeQ6b80kGZ95kipzvPk6NxOui7dvRuE7PF9UDJJI9vv2EmrClby9e/ZddsJAPFpoNyHlPbTt67oKunNHMF1L5nmrZWKm+48vinrLjjB6hQp9tcvSXdkaWfP7iQtvw2T5NV+lhzVysnUWX1l8uS5ikyb/IQsjaHzyo8IhiUw7lQrVJcYv/6OTOucQ8qXKaytjjalu5OQb0YdcvPFBH9aZQJKgiG6gFvA+m14rxgD+OQRtAAo7lypl1zNXUFmdW8pr65clULd2sFCO1slWC1cuBzJNNdAR+QoFy9ekdWrN0FKEAAPCM/GxkpNmXhCq65n4UryVd/JEgAQSBDDEu59W3ZNnycDd+yVgIAogE/wmTp1SnHNWV4sS/0gfzQqjpoxrfCFCuUDR2U7JPKA0zDDJjxD6qs2+Q+vH6UC+RtrjvPlWNJ1Onz4L6oOweO6dYsAyL9B8tgr8DLCArt/o6RPn0b9flnJcIzUjgb/mOLpM6gWbZFxlFYAb3xOGB6jT5+zYuHiINduRb+39Bvg+BnLStevY55PvCNgBoCJ99qYe/aRRqBdpig3zckXd+S/51fE0dNBxkxcJzOGfYPYv/HqqEnw4H+Nh7FWGNRdpkwxqVevhorjSp06hcqs1LbHNqXLSwN2sdXjg5iJHGHIbpx5zk1p0PI1SuqPEOWmetsfZrjSmsb1nA8It1D19a0qPBYf6u5XN0WpZJg4uH4Sw79wxVHbllZEWhOMvYyNNcOXKCkoCF7mwNrRGRmkxgpjBY89sYt3ZuErKLc47Tyh4qdatmxorEm1btu2PTJv3hKAqlrSooXpeiYbMLHB1dUKGdz1wcd3Exabi9Iunx+C3j0UcDHcRT8DOnduJ7V5+sDGElCxm6ypHygN13+v1q1Z81BNi5RPK0dfJRNX2xfRmoriXgtWcXGk9jBmudV24Iv7BLLG6e7XYrRowVsGcMXrsfO/7aDogcUOpWbNVLLxn8HyU+sm0rBZVtkMsLr/xHVp3rGLWL2CaguIpG2SQDkkTQ2JsE0h668tABgJAR9fsAS9tpMQ14rimLKUasvYP7QaxqVAoe3HDwD9jwAm8DDphIk7hgB/w/W7MnTxWrmxey/AVBKxkEziWKWPtIQFM7eHmzwLC5ZUySKjWTO142jTx4+f4oPOHsTXDmrVgAHDZULrQvKi/xhpWSal0qF+9OipVK5cFvfR34qwW9tXf3rO67XwN0J3sWGZM6cing3/ScqUMa3DjCdkmMV2gGSGcvD3QsCsWRuZdHb+/F7VZLt2PeCJWKNiBB9D2m8vkrRYaKln1nNsxOOqYgL/oYXdEPyxCS+v+jJhiY+4ZUmFpbcfA1rz2+8YJwfXtpuniX8EzDGAifAamWMAP04MoLFLvezOEdn77LK4WTlIEbeMksTLC+6hUHniaI2sYUdJHhQuYbduyuPzx+Xyyb2gsIhyi9EaRSmvdJBUu3PnHri7OuLLP120Q9D9xpcz44k0SpdoFQwWll9xQuZugDD+izE5V15AqB11gMNUUHoquIhpPeBXNy07pIOhC5eZeIYyYAZNJ2iR8YrMuDXkeYtvI4HhSUCLAmRipDDTkrF6fYt56wCLkWqwfIqsQx9IN9I+r48UzF9etmxZpqwi+vWZWUtKkaJFq6sYO8ZTPXz4FNciDWhUAFpCQpQFd9iwAeLiEgXK9PdPyDx53hj3yJiur/P46XYlx96QIWPB4ReVnVypUlmARtKcvALVSw2oOgyXXM1+k9VjmitN4ufPw0AR5C/HQPfSPoeXPAy1BX9gUjFUTCHv3oyzbtIWsYBaRq7uoHozjKvbhkzxZKT9wf8uABgEFgXzlsZL/Dn6MUrVdnD4TyyK5ZdaNSsgCaGUeunTDUraoWxJb0rg1V3iGHJH0bREou/OTg7K8k26IxJFkx+TVilThTF1rWDZM1UmT74O93iQ0DqqWXO1ukMOpcD9HKoAjr7re+6ZK9KHEnBAuklz54a70kJyVSwnizo2gbXbTt0n+wCSCXqDEF9LYuP4FpJgt2zZBfdIqHwFmpbMjYeqDzV90m5jbZHv8ATc6m0R+6aBbq3e9eu3pHr1ZqBy2Wo0VlWrx+lU/A66I7nCsDRo0FbFT2rJOsxMZuYzP574m+AHImmHKiAsREtUMWwjvst0hW/FvdMo21t3vP6+jPH7+s150nLKol27WYiXNJY9bI4B1B/BxD1vBoCJ8PqYAeD/DgDy8i+8tV+OeN/Eu9M4aGEde1DHVHHPKaUd08n9mxfk8PaVcubQVuWy4XaWYsUKQue1muRv0AtJHdbqBUvrnbGv66g9ov9LGaebPpZy6bkNFA9eyg+FvXVaqYzlo24n6U3+V4XWyLjcj/HtyzWA4YNIcqEbvCisVSQIjq34BkXI8uvJpWtBP6VYkStXGQmCpjF1YClhxpcR476skDX76tJqBbjy5MkB19kGdU1WrtygZM4IXBYvXgnwuAugMC1UL1IBhNzFoV9Lq1aNFXBneyzM0jx06JgCbmwrVy4AtPZRMXJ89+3H9dl7+r7s2L5HHm0Zo3sRkt+R4QLjxw8FaXhGGTHiLxXLtWPHfriNH4AmZJ7UrPO1/LriZDQL2Ty8XHldiwL45IQEF2P17oDnEcOuQD/7REk1BuBrbkquMyz8ILAGb6TGQci+/gtqjmSXloNP8F9w0Z0DCF0sTZp4ytpHqXXWWcb1MbyAVuOEWpWMWYZ5/7K/mRFSQBeyvmvQzw8AJt82XLukoMCpiVi6JLrT4L3Be7vsm/AHbqArtMigcXJnwWJJkiuXnF8zF6ojdrp9DGfICUgAaqgoYljPcJnJVQTQLHkQl8p7NBti6WiNf4ZsYFrHrDC2ZRB+of+RdfSxrbomxuTxSPUzcuRfIPteoJMuNDwul/feR1IYYnXJeakVJtYUKFARiSKHlVtbW09r/zEAdYI+JloxxncLyN55X+jvr9XXpsauk7aNU6qH8CPLMIRAqzPuuLswM5rn3rfvWdAIhcn8+YVwv+M55W0l3hij8ggZ0T+OGQBqo5f4p2YAmAivkRkA/m8BIG8BvnAehfhIAFRDrJNCu9U+uXiFBch9xM5d8n8kh72vKxD4Z+FWujvmJSTVQoICZEL/ZhIeGiLeT+6rbbNWrZWVM/0R3O0vR45UVg9L3U5xzMxovVzazGuBAHq8SeMo+g/dOKp+ss0EOOQJY2mQ1T+GxcSwY7wOtWu3lOvXb0u4ex7JnyaZXD53Wjp0aAUXbG91nUKhCvEKEOm8ryviyJwkA9QLyuLFaOfjJXlyOeiAmX7bbJcAb8yYyfLXXyNgzUovf/45FdfoKpQ2ditrYaZMReV3ZJYmS2YJC84pFUNWp05VZfk6ej+JXLn1WKweHUJSSHsFDAn8YivM8Pzxx19BhbITbukGkq3lGFgzob0MhEd3KYHeTcREEoBQWaMMuBgTksGpHfuvk+7SFYoj+mowVHBYedRHTkMG8Nbt+3JiRjMV4znpVFRoQXAk49CSyfdFnicY5PO+Y6Gr1vAevICM4adwW5LQ/LtCL6IlslDmLl8+Z8TU2aj9mQxE8mgvWO+6IhlDX/v5yKNnUqtkTUnbqb2s6tEWANlF7WPqHx6P1kz9jF1TdU2tJyCjtd4T1jbG3/JakASd4HIvrIy8TgzlyILkGIJuytB1QaiD/rhrbe/cuR9USX2Uigst0aYK3clkBSAVCzFxmTI7YR2+A3f2Xdyrg6KBQP6WaK09D57Pysh2Z8wfM7Rv4aPBMPTD1PEM19O6yA8y8osaK7QQDjuSUinaHN9+Gx9Iz8HnmAe/lyjQTPqcIqmjx0SaAaCxkUyc68wAMBFeFzMA/N8DwLhug27H5klup7TSM2d1k1V/ap5fggL8kA1YXTZuJBi8rOqWKFFYZs2aEKv7TGuU1pu4LIZ8Ee1CXJDDz0MkaMwQFXiv7UMLTHE80OkyonWBNC90ExtLPNGO+aGn+qBgzXVH9SI1Zikxdly6dDNkKAyL1fewWNWF9eqUlCpVVJcVyfGhy4ouP2aTUo1kLmIOrb2fyODJzyTZ+fOyeGFxtGEH97xtNDB4+vR5SPp1VCTMGk8bsy+ZLMDMTPIa0s2sFQLNbt1+VjrQaXMWEfsCjaR7FfAQgsLEVGE8JrO8+bKm9YjXg9qqXHcLYK9H4RdC11lWgIgkSGrglFbdhGZvasfncZh8w2QKw5IlSzGA6arS79cBsu95enWfkPiXdfWtWYb7fYhlWvXuwMJYHZQyxgqvo6lY0fPP/KV8PXxoQTZu7JpFUiZdPjn1zFZZ5fKnCDUKkglk6PpWFlS0zVhEfUBprA/G1hGQMfGI40qaGv3ECrrkT6MfBF2FYLWlLOI28O6Z0pgeNGgEngM75L//liODPYWxw6l1HKuTsP4SVP7S6F95+cJfihQ5K3fvblYfGz/+2DVGJvNCgE/2j8lj5A8Ngfu7Ctz+CSleuBcIbGPLwKa7+xCsoh0RTxpfb4AZACbkKnzaumYA+GnH3+jRzQAw8QHAX5Ax7BMeJIVcPaVJhuLiZu0Q49rRJbxh0TgJ8n8hfj7eMbYfE0cpJgExgtxjVHyPFQjdUlaCR7DCMMOPknJ8OTyFxSkZAEdNZOjGBmDe49AxdvWBgsRmkAgbcgzqA0TDncaOnaLIinfvXotYNGcFhi2Tu8i+h1GB8AQOtFjo8w1SIq7rqOeya+5xsS+cS15jEF7D+vZ1i7RS0uU5rImB0rlzJsQCloZrNgt0hJdHO+xPPw1BfKCz/PLL9zFetNEqYsFU3+mio2XGHXQ4dKcSkNPJSYsSyY/pEmUcnwbUDdt912WOBzn0HEAvZOgKTJ++oFDVJn+Biuqe+w/AmaCFWbgfopC8mZnCphRX5oA/rgq0nI3F1PHDgJm9Gmn5XYBFZjNHItFhHCymXsdOSqfvu0p2N2fJCJBPyiBaTldcc1ZxjgSx+vF3tHhSnrAsrKjXIBO3H8AmK1y5+XCuzEynhZX9YOgBScPpruaxKJ3IsSOQ0i8c1xVXnUQjSja8bvuQsc822QbjdVnPWJk8eQ44AqfL2bO7dQkoxupxHX+77cf6y3/zz8n9M6XgPTgK5ZA+iC/ti3CFRtF2Y/9IpVMDv2cem+d/BuCUlC6UJDQG1miNnj9/mVy7dgTxie4KcFLvt4GR+D/+dtcj3tDdJjJeikL6nTMDQP3RSNzzZgCYCK+PGQAmPgDoGx4sE69skaeQlWNJitd7sqQWUt0jn9RJW1Ct0/6ha3hg2xIKBNKSxdjA4cMPIKC+kixYUDhBLmGtzQ8x5Qt0EbgEGShvCsh8iOMEwW1ERQK6AmmJiS1+Tf94jRp1QMzcQyWVNn3uM9nxLIXkjrwnP/1dVcnj6b/wtf0icVIF29wX71vPJez6PfnnnxKyatUDuKgspHbnQtKh3VEJv/cExLtZQXfSAhxxYYiLmyFVq5ZXsYRaO+86ZQLIylUbZP3ZUGnUtCFoSlK+a1PvtR9doARQzAYmqCJgKVasOoifS8vg4UNlB2Ld+EFQA4TEH6owQYCgK7V9hCKJNgRJBDTMXKUFWisEKgcB5klvQ6sw3asMEaAbtFJ6f6n85yy5veO+uFauISu7VkVsIJI7ALQIND1wHFouSXicAkDb0PWoHUObnntmLQS9jKG0A8j7746jopLhsap5BgIkvQZYtIbr9TWAYHQAqLXBGLnFhy3k5Jy98teoXPiAcNQ2qWzg7K6hINC2V0TPBJh0ieaDlVKLx2RlxgPOm7dM5s6doBRAuI7u74KoZzhm3KZfsmYtIStXzhbSzBgW/p5JXE33udbO2SeWsnqnvwxsaq0SlvT3GQW9c4ZB0Bp55cohWQmAq3SyMab6hawFtKS2zInnRCzSf/r7cJ6WUyYj+T19IaF1chtuTvnT69dehivNy592BMwA8NOOv9GjmwFg4gOA2oW6FfBMdoEU1gfUEw9DXkjYq0hJCam57yFor28VDA0Jkh8b50YiQXbEzWySbdue4KEbID17ZsXDmrah9ytn8QKhe9dU8Lap1vcgzikVLAa0fBgWJn14w1JILsH3KaQgIWdhXIkePAZj83r2HIDx2QMKjcJYbi0HDniLTd4s8tI3QJqNrCPls0HXGPQX+kkFWv9IzzJo3AOJ9PLRVomPTwPd/JMnoXD5BgAMucH6sVjWr/8P1CbBSAS5I0uXTlcvZPahb9/fYSm8CXfw/BjA8MiRkwDwE5SeKylRKP9FFzI55ZJ5lhaPQjXk0s5lsn7691K8eCHdsT/2jCGIZ+b0OVghp649K4f375dQr3vy9Xc/SJ6cGaUmgB8tdbRcUW5QU+Aw7CNdn3RbG7MgGdal5ZGAkgkorG/qtiYQZHwbKVAos5YbCS90e9+Ape7f686w0iGb3RKg6KmPLJ0yW0LSRcrp3zsD/DmperTQPQi0VPGSvEezwW1OZRK6LmNznVNnmJns+hKKdOPqgzPDczK2vGHLM+kxD1blS1fkwKFqSqKP9yKtcEyi4YcOs9u7F/JRcYJ0IzP+UX88du8+gJjAvtKmTRMZPLgPwgEsZdc9B6X2Qle14bXU+sEM8suXr6mEEm2d/nT/A1tJjxhYzcrK306LkQFSwuYxko9K61dViVNubtnVut7z9kidUlmU6ox+JY7vUpDWM/M8IYUhGcsRisCkN7ugZ/JPtpKGu5sBoOGIJIJlMwBMBBfBsAtmAJh4AaD+tSJwWHj7gNIY5vpeAIG5nNOoKk8f3JIhXSqpzNAOHVrq7/bB5vmlTpAV3/g6HpgvLQq8G4vpuYmX0n244qJ46N6tmwSlp+EOLZ3WNHmsfssMlv/hh0EyatRcvBzP6zY5OFgi8cMTerJ5latqMSyXlJzSyLT9/SNA7muhyJmHDLkIWS1bmTnzFgiWPcChVkLXjqmZrVt3qeNawKz4Cqgna9ZMQq640NAwJH1kkC5d2qpYzrNnLwppOQYO/BFgsQQ43lIoSpQXASFI4kAM1ePkyvrTpV1nIY/c7t1r4nQjm+rT+64nUCWHnJeXt9Rr2kyqfz9ZSZwFRliopnlteK/Q3apPS8R74jJkzwgeacUpmioYbmLjFjH9PtKNS4kzWhZZjIEYWsTOedsoaxtd4bTgMSyBhOBMAOlZ+LmiDLrvD9qalt3l1dWrsmDLcqmfzVP/ULr56wCNpEJyg2vyLPprir7kCUAZ3bzG+Pp0jSVgZuT4G3LeMqN0a+ikXMyZYO3TEje8Q5LKxJPJ5Y+yz1SLdDkfwrGbQDpPPw6R8a358lXEx8cxdY8QNBIsxpa5TE3lhg3bCTWIK1QoJX//PUIlLGldL1x8t7jXLiX/9rUSJ/uk+D0ESbGGl4RhE99Xw4fNTzkUrY+39wslladZAUmL1KNHR60Z3ZSJQccgWWlKklJXUW+G989C/D4bwfVMa7/ZBaw3OIl8NiqVJ5F30tw98wgkxhGgFah9lvIITk8m+72uileonw4AJvfIIKnSZoRI/DDJli2zAg8f+hxqIf6HpM3HQElBqobYrCHasWmVoOWE/HLMytW3jhD8xacNrS1jU7r74gv++EIcMWKCIt89ciQKMBYp4gpXbVFou0bF/PEYfKnUT+cl5bp6i8+Bs9KwQVpZdcZS8hd2l++6QBN2kYOyMA0dmgcv1vhZV2vWrKzcYA8fPgaFjLdysWWD1YLxVnThU6aLMVPk8ps6dYzUqlVFd7p84c2+nF5ZUalOwf4tXToTALYb6rWE5WVOnPFeusb0ZsLCQGsDnsGEWIipLXvo0HEkGKWVn3/+HYC5P+63TNK0aWfp3eeSZEyfXoFmfWsUs0bv40V/GCAlgokqcNPmgVWucTY/He2QXrdMzjK2lFmyWjEkcCbAuQBKI2PcfPlABB2C+5eqIzxunSmL5NWpU9J69mST4I/HYdygVvYgEYoWS5KPe8GyyIxqXn0rWM2sxg1Q97dW932nA3pnfdNEsMoEJsibjt9QC7hJHZGxT6uqVggObSwg3QhaGnJ1aoTXJKEuWDCv/PbbGGTS9lcxocxaplVen4xda4dTclfyo+LFC19QFv0q5cpNgIW8v9jaRoH629f95MHq05J9s5XM+c0DNFRp5ODyXFLh24cyevJ9+aZzamF2O8tTeC4CAqJCAHx8fNQ6w3+23HZU7nHD9bEtk86IGezxDfWIrS3ztv/tCJgtgP/b8Y7X0cwWwM/DAqhdzP1Pr8qSu4ekMBJEOmWpqLMAhQQHyJ+9G4Ie5i4CujfjQeyp7fJBp95wv6xDFqSdXqwVdYUZgK//4tc/KF+cJGRuCpeNfgB7lFZvFKDUt17o7/sh5ufMWQLVlXHIyq0HuovBujEz1TZj/SpWOyh3bNKIkw1eZiduyuZVRSR9nlTgMrMGLUbCMiC146xZsxk8gavwUv4JKiJtcZ22xJqxSfA3HxmY5fDCM0wcYJvffddPtm/fp/yhSREjmj5dasmRIwsAZn5hNjh5Am1scAIGZfHiu3CFn1ZrN24sCzqQ5LoaV6/egIV0ErjhboifX4Bym9vZ2eBlHgxrkI3kyZNTET77+fnL4cOb1Vgy7mwVYhNPntyha0eboQIFE2eaZA9QwFlbn9BpXMTPvMf4F9tHRVB4pBQcflgC962XMNuncm/tbFgErWLtCq2UtJyR9sUe7mG6P1Pific9Szzxv8n26a42jDUlXyJ/F4wl1P89aTFvdAHTutoTGd6GZTWspLlAJ6OFXNCiV7hwZfDpnVBVaaUkmIyL65GVv/9+vyxc+BwW7pKwdKdW+7u6rlVT+2K5JezeGfm+zQPZu/ewnDzzCH0lQPeFVTtU1TH8p3Tp4sg2zieBgcGIUVwqI1Gh1dObuL8APOOI/SPdD2NNt0ENhB8C7L9WzBZAbSQS/zRZ4u+iuYfmEUjcI1AmRTYVF3jK566cObEQQMwakm6RqtODR+HB+m1ViMV/pUiHv/22nco4fdczMuZmY9Zpp3y+0ZoktQRdxLX1SGb1K/BFyUxKxoQ11MsCLAx6CwKbVQgQN8ze1d//febpohw2bBwoXrbqwJax89I/BgmED+wuhzi8UGi9WsICkuXN5kiTHGba/suXr8OxTirlFlpjtUIXfu/eg+VbKLq0/HGqvEpqiZdhoK5PWj1tygQGkjWXRNanMfDHehMnjYblx0VZxtJY+4nPuS0AYWehMb1dZTfTAkOX8+vXr5S72S1rUUnh5giS45SIgcwHN50Lpk7qkOzfggXLwU04Fu7wlsotnT17Zpy7DdROniilCWPqJoynXLhwhYpZzJu3PBJjpkUjJNbkx7TzepcpgTD/Yiu8x+ICZL/uPCjeM3tIxn7jpVulBmKdLG4gzw+T2BRHYusTt5m61whWRx9LrrgRNbc263sgczolLKYMnaDVPcMbEnP2g/x7jm45pNMqP5WlbAh26aL+B5bA5EhaYdwlrxc/DLTChCneT6xTy/Wi/DZ4pBw/fhofRUMU/RE1jMkjyHuhYkVLXEsotgQy0SgKAN6+XRuW33NIFDmOj47hMm4cEmXSZpL8RREGAeu0lXUycXRxk11r52qH1E1Jes4/rfj1/14XYqGtMzYlfQwBeDZXb4BwKLMYiSc2tp95XeIbAbMFMPFdEzFbAD8vC6B2C+16clEOeV2XAEjJRbwGSfTLCMlg5y4tHXPIuhm/y9Wzh5R7r0qV8pCgaqDcdoUL54/T+qW1n9ApaTgYpK/v5tVvg/E+dAeW11Ng4Ha+2JcikeN9XrL6xzGcHz9+GgDKSli1BqnkC1oo6PZk/F2PHp3wQuthuIvJZb7Avv66h+Lqu3v3lNF62bKVAM9fAfCqPcALb7OyiCxbtk4Y/zdo0EgZtHCXpE6XXlFexOa+DvMsLwLVmNgso+S2a5LdL1bpNvIRfvNNb2jshsgvo8bJ/VBHOX3TT+7eviV++6aolz1BIscjZ86sMnHiH5I3b06j56a/kiCBlDabN++ANbMmXNjfgvR6FsDnNjW+c+ZMVHyH+vu86zwtV3T/Muv4XQvj/vLXaysIXJODx7ZJKofkKsOXoJGWNiajUDeX97C+5e1djxef/TZBXaMAsnON/WYIEKkcQkolZyskJmWJcqeyXcoDVk39XDzcY9pUSDBN0EjXMK9R5sxFIR8Zda8ycWfNDSdFX1Ot+3xp6PlALl+6Jrt27RfKGpJM3NoaFDew/DZsWBvA/5Hs23dENmxYpMso5vHHuuaQkQB8IxcdFWe3VFylK2vmjpAHt6/I9fNHJCL8rau6WbOvEAZxAx9UjjJlyug4eUr5AXQaH0DMoqZWuCVwLJ8VMwCMSYitgX2zBVA39Il+xgwAE+ElMgPAzxMAGt5KU6/ukPN+99Xq3jlrSaqXFrL935lyeNsyCQr0V+tHj/5VWXYM9/0Qy3xhkTKDJMSM+zNULCBFRnZwpfHFZFhmnnWVFqBw0ZIuDLe/z/KmTTsATgYJ3WGrV8/VvcgOHDgCK2l/cKbtiVfzpI05f/4SrCLTAai7IlvyQDTlBDbCFyhj+27fPgG3al0lGUdwlStXNgUI+UIuUKy4lPthHl78wVDHsJJmOaOuTbw6oVeJSTSPkK1KtQhThYkmRYpUlcmTR0qjRnWjVWNmKKlKSFFCCyPHx5iFL9pOWLh9+64Celu27ARQzIX280unTq2jWTKZ9PLttz/BfThLSpYsYthEgpYJkpwR96gv3RbfBmpMXSIn1m4UK1gxwwKD5DWSPlrM/Eum1a5gtAmSLlN3l4XxlrxXGW9GvemdyKLVdz0abcDESn+4j0NgfXuI68U/Us1Q8u01YiLjkzhyGQksJEim65lceZ6W/lKy6xOxv3EVyjVVFODmoZkdux5xuu2RVUsQO336fIC3bQDlS3Q9Y9Y8rfG5ynaULqMXSh4nJMYEXBW6aHl/8p6xtbWVXr0GABS6I44wn06ikI2cO3dJqlT4SkrUbCFteo3WtavNdKvtqc1Gm86aNV6BSguLtxbJaBWwQJf4ESSF8Do4AvQWS/5cMrtbRPtoJa/idjxLmDnMczQDQMNRTLzLZgCYCK+NGQB+GQCQt9ZF3wcy+dp2yeyQUvrmrqPuNurYBvn7SL/WRdWDfNy43+N1Fz7Eg5YapNUBEBziIRWnNcqX3SLErbXL46P2Y9A8FQBIsFsBOp7GytMgcJ/hxWvKhWxsn/isY6ZqrVotdKrL92C1c3JyVLtyW6dO38vFiwfi0xTUPgrB2rVUWcf69RuqKF6WLJmu40wjZUuFCg0kMKS9BKYqKEOG5peUqW1V7Jh6UYWEyjwQ4xYuXEBaVssjdwMspQ7oZvTdf/HqCCrRkjMNSQGGkmaG+1PrtWLFBvL8uQ+se9mUSoR+Hb5wqXRiD0Jf/WxsgvkH6B+58Gh5YSE4oIuXCia0KnftCsk0tGmqaNZSgt9p08bGafEx1g4tPky+qKQXc0mAQkWVEIxn2rSpdYkGKVOllL0hL+X5vfvyyttbLNzd5eW1a5Ikd25kBYNEGY01+a6TLO/0XbzJ0WmBY4YvrVCcGkswYb9poWTsHvn/CKhfAtgxI/keAD75/xwAYJmNTMUcyqCRaob6uiTrjm/hWNCFe9PXGr8VG5nYd5/4WDnLmXke4u5Ojd2ksuSKi3yT74XumhUpUgXWtjE6EE4qHFoUCWoZjvDM20faD5uryNoLpTQeu6ffP2pNly5dR6ztnOW3mXvg8rXR36zm79+6JCN61Iqxniv422vXrrlKUiPdDK3TtEzzWl6+DG8GQHrbLt9Ktw5fyeajd2X0nB1i7+Yhdk4u0u+rDNK0NlzNKDd8bRBOYqssp0l9veVe5fxqvd4/ZhoYvcFILLNmAJhYroReP8wA8MsBgLysYy5ulNtBXlIhZS5pkrawzB7eRcIiIuTpvetSvGhuWGVm6l1907Mk3qV7cf5F0Ljk9THKi6e/N19ADJJnicQLkJJpHbDfKqgpfI2sYYIgU4WAhvVI5PwhC0HLuHHTEOeWAxmLVaM1HR4ejizH+rBcJTfKx6dVZhtMtjhz5qLs379ex9vHhI7ff/9THjx4ipi3CXLqVDBecOHyMkdBCQVJ9IlDFRD/ZW00wJ3B7KRIMeb6045rbErARv4zYAHFtRebyooWe3YJLr46dVoqF22vXt/EaJZgb0nTOXI6Y0kp2KEUEimSIjEBGrVwI5IGhXFnjCfjOGTNWhz0N+NAbF0hRjvGVnCfCRNmgE5kNrKV58JaWMBYNbVO669hBbaxcuUGgIbi6lpSSu+77zrK8BET5Q9ktzo5OcC9biHHobO8YPZikTRpJGW1yuJ37aa45csj//X7VtI7vc3yNmw/PssEd1RdiQusMxOZVnC67Skjpy/tFp/jJKTOQQCgVZft5bti4MJDQtYmqOC0x0eXBtjZFsMRrl8/qmuWRM7t8ZuMDA/FtaiG2L/f1O+CcoFcnwy/UZJpGws7OHDgqMr2TmZlI4Ombovh+tUd5M0Mk9KePbgt926clwNbl4hL8jQSERosV88dxr1E9Zok4ujsKpZoj3GKaTPlFu7D7VrAJ93S3t4DxALEj07FkktSWz/8/u6Lvb29DO/bSsplSSqP7zyTPcVLGx7eDAANRyQRLJsBYCK4CIZdMAPALwsA8oXZ78wycKxFytiCzeSHr3KoS64estnySLfpu5WcVL7kodFeFvr3BS0Ny2BN6ARNTrq/ON8ZcTfGCrWCmelpjZeQL9y/5F/jfFVYQjbBHcUsx7jcXEwgYUzPh1SO0O9r795nYAlLIfXrp9VfrUBNjRrNYUUKkP4ISmdAPF909+8/VG4nusDIaZYlS0YVB2WlnzGKcQZvi2TN1QMWtrcWD7tCOST8/lO5cqQULDPW0Y7HBVqSqEZBC6kxsukYO+itoFwYrTdUqohvoSWuYcP2OP8ysnz5rGi7aS57J7jb6iCBJzaQzh1p+SQx9fjxQ6O1E9fC3r2HpPOPf0oyN0/JbOMtS5dM1Vlije1Lvka66A8ePI4M1jvKJfkaejhlyxRBBnVfxTFnbL+fNuyWOd1+FHF1lYId28r279shKxg3oF4Jxr3N8ef9ltE5SvZNb/NnNXsW6iNURwlCZiwteCS81i+ZMn0FKcLFyAyPsnozdjDgwBQZO2qCyoj/888hqjoz+ynTxg83akn3ALG0VvihNH36AlAWjRWP9Fnlh9HLAdzctc0JnoaHhUqAr5e4AhAmNeIO9nvxTE7sXS+rZg1TbadJMwqcl1FmaAeHZPht1lUfAlOmzAEYtBQr9O8HZKQbFDMANBiQxLBoBoCJ4SoY9MEMAL8sAMjLu+PxBfn3/nFJZ+cmbWyyyKheUe5gbrt2/Zg8fp1aSWTVyxKdm4/bmXU3+bQ7QJufjlOMWb4H4OZipq6+dYBWm0fPbqh4Py0om22Qq6s2ArfJl0b6BrqBNfUAbtcK9z92867cRHIIMx7jW7gfiyEXnP7+gwdfVMTNc+YUQSzTerWJLw++RFgYr1e2bD3EvvmpZIWnT71UEkTt2lVBe1FJ1Tly5IR4eqaHq9FDLUf7B+5V24EjxDdHbkn9q6OkkefySPBiBLBwrFhUKtndlwWLS6ldkoI/7/5rFynS/bm07pFPxnxtFYP+I1rbJhZ4bRhLSaBmiRi1OrDOGbNKUX2EL+00aVKp7E7GLD596g1AtQHgKSqjk4fYcsteueljiyPkB8W//26URYtWwMp5AZnCf8MFXE5lt7KN2K4Bt2uFMmznHr+WP5eekFOLh0idSgVAVVMEZNfuKpbS3t4O18Ze9ZsKKtWrV4C1ei0oQyZJ/or1QV9iBaWNuLN2V1+5LX3+nCa+m7aIQ6eO8nODatKzaF7VDUqincFfU9zbCQlr0M7hfz2l5ZGULzlA7WKqMBHIxTpShU/oy+Ax9MPdfZlUqpQR1y/qPpx8UOSv9iXUNWTMn7GyZN8jcfK/gGtiidjRuYr3kSo0hcrWlo59/5JkllbGdnvvdQ+ROHL13CE5c3CL3Lh4XN0T/fr1RAJTB3yY+cDafhnnkgIfAHmiHWvj8rXy5Nu+0dZhwQwADUckESxHPXkTQUfMXTCPwJc8AlU98so5n3tyPfCpeCAWsEzNlnJw61J1yufPXQQZsqtyQS5ArB4JVYEnlPs2PWKUSJrLmKes4DnTSna8gGgtoQSXfuHL304XYfd2ixYrRSUGFio/0J1YLWPMFzhj0PQtDm9biXvOlNuQe1686IfsxmeIdcqGF0hVWbfuISxJUZYEbmdcHC19Gzf+o4vj43r9YuolqeognikURM7usK5FZgkBuLGABSWpFH15R9Y9qS2X81UUx+I15LWdrUSCk6/rzVpikyOTXFx6RCzaldc/TLzn6YptAxDOkiRNUfln53XEkUVZ77iO2sMnTpxV8V3X4AI1LLlzl5NZsyZIkyZ11aZamYNkP6h51l53kHR+B4xm7TLuj2CZOtMLF07VJYrw2mtAnAkpGXDv6LsfDY/NpKDi6UVW/FxU7rWYIZMmzQKh9WollUeAwSxkWpuYNHLs2FaAFze1jbJmVWvXlrXIXqWqCOMmtfvK8BhcbpQzkzSaPUbKDEsnlyZOkWFnHkrKP2dK6Muk4ukYLmUgG7gObfGDBTZc2BVFxePRYm0sQYltfqpCy/g3JizvWp9qZAyAMoi7NNTjxuM2ulg9PM4jGclNVWUm+p/LUki7Vr1Uwoe2f+vWx1Sd7t2zIkv3uvRoUAceWMQFoNBFW7F+RylVtQlctHFnhmttJmR699o5WTZtkNy5elbtRu3g/v17Isa0vcoY5spKlVKpP2PtVoT+9DJjG8zrEt0ImAFgorsk5g59qSNQzD2LAoCzru+Wdu37yU18VT+5fwNf1D8iJmuk0NJFPj/qlbIEgGOL6ga0KJBryzCL1+otdora4c2/AUj6YMZkbO5M6qkeRBajYSGISHracG3cy/GxOg0YkBMcZi8lXTpAVGQ1lmyRElrKIWKH+CwWvmhmzPhTuUf3gRg4Q4bo7uG4e4FQJbT7KkM6sdxzUApYQxkiCJmmsGTV9wHZcHBOKCpEiKubi5z2AfeeBMjhPSdlzOYS8Wk6zjqvHh6XzEd3ycDfJ8mGSi3kFZQXNi5doALqbWztpecfiyR9ljxy48Jx0HGEyqK/fkbsVxgIfn/RAUAehNa/RgPXy97Zg6RntzZw9f0c7dhM+qD7WF9ikG5UXm/tOlgATE094w4g/zxelk2OteZ+jHYwg4UGDWpBJWW0/BboI72LvhYed8D+VDKghLeyjNngWtIaynuQSUb6AHT/wB6S/Owlccz2tZINYyLSfSS2MDyBWbAMTdAKeL8BhO0V7Qq3GbOqanXfdUpak5xQ8DD1OzL2MeMBly5l3mLLgGYcafeCLxS3X32AQC2x5NSpG3CdHgYnaDPch+EA/vsk0vo7mTz9BaxpIvXqHcSHjzMSmx6pPwLAGzduK/DXtOsQyVOkgqQEx19ClGISMjYvvB7JgnE/yrVzRyB36IpY0WHSunVjsbS0TEgzYoH701w+jxEwA8DP4zqZe/kFjEC5VDlk6+Ozctn/kTjkcJU0GXMoAEiZp9atu0EWbaAiK9ZeSEwocLcNEcpFxbcwS5KSbs3x0oytZAYAdEMQvbGSGZZF0lzkgav4Q5aiRd2Q9VpO9tyzkxfPLYTuMap4kHxaK3XrVleu0apVG6tEkPjw32n7qinoRcK/bipiYy1J4EpOtvugJEGmRgR4AAM6tRMpAOtSl+by+EVK+dXaV2akwMsq2fs/Bmkxq1GjGUifz6lunDpyCPFUcLcXqyi1W/4g6TLnQmJE1HGSp04nI3pGhQBkzpwRrtWKkjp1XmXJ00iinZ2d5PrlfeDz+1pWrFivLHHkaytRoqiylNI9e+ihrXLDUpGCgM8LcWNusBRfxZgy4aFQyhBZDFJqZrs2ACGxfqhAtDFLwEL9+jWlT5/fpFmzTrJjx78KdA4s6SXnocvLjFuqQrDwWLRk6uvcUjrR1sVZQo4tlw4bckuLfEnk6zzG71MSKjPTuAQAJq2DHzoZiX2kAs5SjE+7vL5cjFao/PHk5GOpLYHR1lfOEAiy6BT4bYTCKk97pfHCZJOwlyGyEBb9qtgnF7SVx4zxFkfHTvjA6YcPoW7YsZzY5g+RJLhnWQ4c8FJ/nP/llyygQ6JnoJxSENq7YZ5U/qoDN33wQuvi2nmjZPvqmfjZWMnYsUMUO0GyBP4uSHnDuMWXPhEfvI/mBj/OCLz/k+/j9MvcqnkEvsgReIWHrTW0g18BMFw4tlM94B88eCR0D/7yy3A8eFsopYd3OfnDj6IoToy90Azbo7vNzcQLjLF/s2F1pHVE3yqj34Yx64j+dlPztBg9DLKU1sguJkDQspT16zdu3Ub2BuWTFm17y4VTm/U3xT0P81Mk4gUjy5WUJADWtoNGyCvEDL6EBqu/k7u8+vEbEMq9lswg6j78JIU09fCPtc34nuexY6cV+CtZtakEB/pKmerNJVfhciqj0vAAzu6p1Spy9Smgd/0mXOOrYRn1QEzgMaUf7erqDFqbrSD9Xa+SLjw8UilajnXrtoI38mvJVf9HdW0M1VoIBpMiBIA2VYLC8qD58Ucm7CzwOvKjgKoxtNC9ayEoqFatoly4cFklhZQtW1IBKWOxgASjzETP6JFFWSbZt47tRsri9ftk8/dl5Tiu05YGteXvmmWj8crp941WTcIsJkEZswJyPS1yz0ClQksi+S7jWyjBxgQeY+XYE1vQt/jE2PQiNJmUhMs6NvCn7USibFrtj4NO6YyXrVTpVgqW2+SwtB4FAIyqFenlIzY5M8rQkbtg8bZGjGwY4i5XQH/aGtyO+WB9Ww1rnI08e3RXQoL8xdbeSWv+g019vB/LtlXTVXt0+Q8ePApZ3utBVzMKWeaZ4jwOk87oGue91SCbPwCgv/wT517mColhBMxJIInhKhj0wZwE8uUlgWiXuNfxhSCOdZBODrlk+Hc14cYbjRdCA+i5UubsGXRpp4IGoppWPdYpwRRdbeQ2uwSiVloliqZ+a02Ldec4Np5FYD6pR5gw8j6FySrb70bRcJTGy5Yccm1zR/ERMqxpJbJo+eLOBuJjnstpWF4eXT0hK4Z1kV9+7iJt2zZ/98MjWSIJpNdew5rGQm7DXSAPDgUQYdIB+dvOwHJlhfMshZe6saSY+B586tS5MnDgSPnpz38lS+6ice72R/fqYm8VqeTw9CtnylREII8rdBm/8HosxYoVhAVwNqyDzrIb1t17cJkS3dFyOrL8U7UrX9qk1uH022/b6wigA5B40vPvQ+JoESH2SUNk/TUbTEOlb5tS0qyMh/5hEzxPreElS1YDyATK0KH9ocbSVLVhCjA/C6Z8GAmPAcqThUuNvxbIuQWACc+eIXYSvHkFCsjon7tJo1xZYvSFAJb3DdUnNtx0kkYAeRr9Dl3OvK+Ybbv4krMuHjNGI0ZWUM+WiTDGfjPMok8BQFPACBcfFXYIri1xbCaeMwZUH1Tzvp50yl2KpKL1PjiGizkcF7hZs7mgMToHhY+qkiyVm7wOuywZXFci3rIokir6wAr3CON7X9asKQllnE6K7HnS2qtGPyiMnFqCVx3YukyO7vpX0mbMpc7lGD5IQoIDQVZeG/dTNiQCPYeGdUHoXFfUSRXyPBddclEyd9Wh1qLJ4JmJoBM8/J9sBzMA/GRDb/rAZgD4ZQJAZm9+d2KB5HVOJ/lvecmSvwfgJhguPj7NhCoOrVp9qzL9Hj06r3jUjN0hfOhSgeAKgBUtLEwYSQU1AiaK6BdTL2L9OrHNE1zSCtgZVpDYYglNtUF3EClNUqBv1ZApSj42xjMWxAvV0KpIK9GMFQdk9rQZ8vLpZSQbuCiOvFatGplq/r3W89w09QWeG8EEAQYVDfgS+yqrf4LP+dGjJ8jEbSzBYa/kj/mHYLmxjrWP6xaMlW0rp8KqdxBZt8lV3VmzFuFlP1TNk04jMjLKlWYDd/aCBZPlWbqvFFDmGCYBCtRCBX788VckaZxW6iaka1m1ao6aL9ZtreSxvgHVDSfxfeUggzpVQtzpaRn9z3EJvrZHveit3TLIS4QkMOGjY8dWcunSFdmydbfkyJ5FEQLTAsQMY8ZnkreRurRMQEmbtgAkyqyU5Y5xYsOH/2LyfBnTOu5EcvmxCEAEwL5WQtHO8H0nVGax9/adAtOXPD+706g1kOCO935dZFkbs77xmpKPUUt20o4R13QpNHhNuZeZkFUXCS6xcTsyK/hQhyniPLY39JwjlPs9GG5wJraY0ovW75O3d5gcOXIb18se1EbIyEGZO/c2rH83AaxzIQQgLbwEjeSxd5D8NmO3/q4fdZ7UMBsWjZO9G+cjXpX3Ia9bOlAvzUemvqc6Nq1+fPboh3BwgxkAquH5LP5J9ln00txJ8wh8ASPwONRPnUUaO1d5dOcAXnT2cOflUuuqVi2vpuF42JKk94cfuiouP6oE0NrAlx8fwZwSADBZJLaiJQPEVie2bQRG5BzkF34VxDBldY07rufycys5AdcZAR5du8wOdXnjZua0iAnrJLc5BV2VZL435OqNI0YBAAEtS1znRdWTh7CSxSZQz3PTj01jfzUXJq1NzIJuhJi5hJQ0aVIDeM0FkXU9GdS+tPT6459YszTL12kju9fNgVLJVwBv/ym+Q4K/tBlzysM7V3Tgj30gOOvc+UfZc7YydFdTgDMyekzo+fOX4TbuCU7FGkJ+v+bdx0rSzJWkVgFX+av/4GinUbJgFsSbNlHr6D6dc/QV9GcfIwzSAlJjv8Dll1muXzssk+88kAjnLDJvv4/s3ztJfJ9uQyboMICVLZIiRXLEsjkgQeFotLZNLSAtRN0P1PbVLzZwJw+rXFKOIzHEe9VqwQnosoD163Fey7Q2XM9lgj+So5N0OT6FgP8xuB+ZrBEOa7Cp0iqXryxhjCAkzkwVavxm+LcLrMshaNNSGuDjQT/xxdR+2vrkya0R4+cBN38AXK/dkDF+BqoqfUEV5AkycxcFti9evCplarXRdvmfTKko0rjzQGnYoT+eO6+hI3xcJg1sKXv2/AsA2Fv14dmAqVJmYfv/SX/MB/k4I2AGgB9nXM2tmkcgxgjcDHii1mW0Ty4HH98BJYSTjByZT61jpt1TZI3OnLlIGv06SgQAkIH0jJX7VIUxV7QAjjmeXPoX947m5jLWp5ygpsnl/paqxlgdU+u6deugNFKrVWsidevWgLJEB8U7ptXXgJ++ZZNgeBvcy7QuEh1TMUGz4Gn7JXTqBCmwhII/7Rh37tyDMkN9uGzXyc1LJ2IFgK7JPeTnCetleI+aUhMarreg55sWSUEFStdUAJBtFqvYUE7uWyfWcAeTGNteQlRiDhN9KutJsT18+FgyFa4o/8BKliRlLRk0u5aUdbsrWdO6aF2LMaXuMMfum5J0yaZR21eunKOmjI07Y51N8jqEytT2jmLZ6U+1nnJvdeu2VlQwz5+/kOzZS4Lf2VnR9hAUmiq8j3kfTYdUHrNj9a2A3Mcn2Rseu4ULZXizuvJbmUKmmjK6fgUsf6khkRdfSzUB/w6ce3skfySD+5/3kb4LVzsIKZYovUcJucq50osfMuRNFdLgxEaFY2o/rh88+BIoknbh979DVZs7dyvof5rJnTtnkBi2OoqKB9blT1E0YuicBUGiniodlHfegv5mi9vJ+mZTxfHPn6R8uiDF48iEkhvXb3+KrpqP+Q4jYAaA7zBo5l3MI/AuI/Ag+IXaLYtDKll64wLY970hCr9dF/OXMlUeGYQaGtiJjQz4XY7/LvvwZVkfbje+vONSyjD2Ek3IMcn/N3v2P4h9+hcEx2dBdDw12u6My6r0/LpYvFm7GuoRVE/JhIxmvsQ1l2i0neK5oA8sTe1yBEk2L6DKoL3s9OtduHBf2rTprla5p0wjJePxwk7jmV3a9ZkgC/78UZxdk0uv4f9IeFiIbF4yUbWTKm1GpYwSEhSgQgJSQcaurasvJMYclHu95hui7gjICp559EpypA+HdThEcUMmSeKi2jD2D1Um6PLuCj47Y9dsH+hXaP3VJxJnO7NmjUeM6kpYK8+Cp64YqGvqKS1iZrHHBgA5ti7Yv4c3KIaMGNxeQ7NZXUBkCU/D9W+cPaOce+aDhIIMYmcV9YpaBTLpmWevSfdCOaVBdk92R1cI5BgSsQEqN5TJi09hhjv1gpkEQis3M6UrAlQzM1i/VAdP5jHob085+EwsLkNohvcZQGOF9EHvDPj02+f8pEkFYX1dDQAYteXp0914LvCPwDSplK7WTDJkzWu42/90OSjAV54/fSBdOr4Foszib776WxDVh8hdr1CZO344Mvc3SwSy74f8T3tnPti7joAZAL7ryJn3M49AAkfANzzKdedkZavAH3fv2XOADgBqwC+BzSaoOnVmqbzAJAJKxFVD8LYhmbRhg3xZJgdlDK0m5DX7WIU0IcxwXb16o6I6MTzOCwCXCFj5TiJRhCTWTFAhIfaHKHGNPQmPyVlHyxsTEWg5yoEs6Xzow+7dzyDjBRcmStve46RYhfrxVmcoXvErVV+f2y1HgdJy9ewhkEiHiL2jqwQF+EDh7qVKumBcJKXraG3VyuDBfeS3FuWkXO8FMrRtQaUZrG0zNiXwoXveGPhjfXJIcpwNaWN4fZiUoyXmLFy4HBqwdpA1y2rsMLp12ti+sfPp1mszB/p0lIHFC8mhC1dAFD1ZyhWsqKh5vkufXn747We56u0rW4YMB39PoHQA0u+SO7f0HdpP+pZ+aynkeJD+5r5/MugMR2pNm5wWSx0iC6COQwCY0dlXJYPwA+M5lHJ4XUumeetmJw2TPhUTlUD4IfChSpo0NnDdb5AM2fJJmgw50OxryVeimrIIp/DwNCrP9qGOHd92tiz7Gxj9tbRr1zzGLlYRPvJ1vUZy9+59yV20olSu1FBkdM8Y9cwrEt8I4PveXMwjYB6B/8UIBL8MQzTUWxNIkiT2iO2a8N6HpvWLgfZMYiCBLpf1C+k3Nt50UFmSDHoneGmew09lKc5DoscWWJRuAFBphSCRf/qFlhEqj3zIMnbsFbgQ10Il46yu2cGDR4M/Lams27BUHgQkwwvZQvVl7XVH8Ba+VO7DBzhPZoIay9DUNfSBZ6jIwoxRJgQwaYAZqS+h0/oPxnP1LVexLVIJR0wiEYjXS6g0lz74Y7fbwyrITOLCkPrqjDgrrXz3XT8kx/wq7XK/EPLU8bqyEJCdObNbzi7oK22GbpanvrFnggciTpLKMqYKFWdIrRJbOXnyLLJVx8jatQtjqxavbVaIBRxbqZgc7Pm1LN62Sn5YMV8GzJ0sFjY2MvHrLrLlx5/FrVQJqT4eoRFOThJx8qSMmDgrRtu0/q1EprEKCYixNeYK/g60Qrd03SyB0hbxfqRvmQ9wyNhCY4WWr4RoPxtrQ3/dvXsPFMC/d/08LMLj8Dce176WpEqXOVGAv4gIfOSsma26bEGXgF7ZtWu/FC9eUx48eCx9x62R74bMk5wFy+jVMM8m5hEwWwAT89Ux9+2LGoGQl+GwriSVsFC4vFBev64D7riE/wRfIGv2MDKBgyKSKu4tWsMITJLbRCrXHoPc+Zzm642Pa1p0NJcVASIBALcxZomB7rQIXQLx81EkcBA8BqNdgi19TjVai/IbZBqjifcqI0ZcUfsXLeqia4dScGmK1YWb01Gdkw8sLaSHYf8ZtM+SGpmHW7CdnGOaS5HWiQMHjkIrdY7i48uFGLXSqFvlvxWIpypgNLFENRbPf67CxUiqGq1Qt7YQknH41zybhdRJm0J+bZVELp7cJeURJ/c+xSV5akUno7XR/6+NMur7umpx/vxlCMQ/KLMWz5dFFwsoIMfEgz3P0kj/hftl2a+tpOcUa2kKaTkCIsN4OzZC1/Gc827StcAL3fhpxyKopMYvk49IxGyqjBkzGW7LLpI+fRpTVd5pfR1kwvKPpTwoYZrOWSVOyEA+2v4ryfBVJ2QX2UvFmVOkjoEbmPV5L9B1TYm64qmD3ykelfc5s1p5rQkmU8DyTSt5CvdsPIQuPEMtvOc/Dx4EA0RfkIcPZ6iW6rb58T1b/Di7M6O9Xru+sumfichWLqNkGklLxOQUxp+m8MiAeNZFiuz84/TA3OrHGoGEv30+Vk/M7ZpH4AsfgYCIULjVkiED+OqbM/UAXUjCTprkyb5hFir2LSWAEF1R+gkBplq7g8D+OeedQBvzUhyQocs2CPZo7SBf2XVw4vmFiXKpGmaZmmrzfdd7e38FV28YaFBsdE1RaWLAgD9k5LcVJUO6tLr1+jOl04ZAqeSl4sVj9i71djt0+AFUOjvgMnWWbPlKyVN/H5l4/ayMgToHS86c2WT06F8hw1VKv6l4zRMck2S7vYlsUEv40GvV8pBt7ZsibnEV+NMCxNbOMV5tx6eSJ1yDg6b8h4SRWrhmr5AccF+qla0iFSuWlr8X/oMMWBdFhEw5tlLjBkFSsKVM7V1JVl1zUvx4VQDk9N29/Fhwwn3gi+QZfTJwfjjMBGE0qVbi0uANDQ1VgJsuaVLEfIxSMk1Kuf9rd13TlZo1kF2DhsqesROlx8ooi5Ru45sZyq4xa3c6zoO8jrTWmSr6Y2JYh/RKbOchrM2LML7uZbtL9TW9BZ9I0aqGhb2EssdhyZzZDh8fhaNti2vh6dMw2bp1Deh29kmrHiOkXO33+3CI63jvs71m8x5SukYLBQKvnTsoN24B+EGWrkPLflKsUgPcX0DO5vLZjYCZBzARXjIzD+CXxwMY/ipSvj+xSHI6pZG8Vx7IqplD8cLYiqzKDFCDSCAKTMA9S9cwaWTIadYKrktjFiH95ggKP/WzfNq0edAhnakyTHv16gxwVRVKGfvlmZe3dPimo7JoLgTtR2OQOYf7PQXgaSW3kUXbtOvvUrFeW93LiGorNy4dl9uXT8s+8Jm98H6ilA0IBCtVKqurp3/+hvOUWyNYrp050HBTjOXHj58qQu/chcvLt4NnJ9gVHKPBNytO7NsgF0/skdfgkTwKgl6tlChRGABiuVqk5U6L2cudu6zSliZ/H5MjaOHNAy1p/bg2Apu9SAThPaGVCNwrM865QT/4hbbK5JSJH8OGjQNR8WYlX0gamo9dyKOZut8kSXb0oTgGnpaZE4aCRod23piFFDe8R7oh69hUYVIP4wZp4UvjEGVdNlWX9EBLriDbGB9dQeD5i8R4lwDXn4eFv2TL9h+Iul+DhPuximFNl76QshSuX/9Q5q/2kb+HZwFvoq3Rpps06S7HT4KbcclJo9s/x5Wv/Z5LUMsYYDjlT69fe32O5/Ml99kMABPh1f3/DAB9w4JlwNnlktbWRXKDMLmIW0bxdPg4Fob/5aXf//SqLLl7SFpnLC0XZoyTRzdOQv7t8AfvAmPDqOPLQiBHolpaQqjvqx/I/sEP/BEa3Lp1l1K4oFRexozpwRF4T9qNWi7Zc+dV7kmL4KfgJKsrfv5ByKBdIplzxXjp6HpFF/Hpg5tl9ew/5PmzR5Dbs5XChfNB6aAOCLgbQ+Ugasx0O2CGMYjMiNUHSfrbjc3/888qJPb8Iq5w4347eI6kz5LHWLUErVs6eaDs27w42j6dO7eGtuxvRkHsuXOXQKn3tQwZ8rPSdOWOpDK5428F0Oyv7ol0iGm8htABAqCv9BJ7GAu6G8CwALSE43O/+Pr6I4mppcpS3rFjVTTqnmgdfs+FZ0Gh0nH9I7mw65JEvj4iI8rnB1nyTHVc8igaK6sRN1oJoQPGiKO1+ox1paWUmb1Us4iLSubC9QeyeO5C2b1rr/ilrS4vz6/Ah0pKSY7rbQvL6qlT52Btzgpql5/kOo4/55ibLPrZHjJvdtoho027d+8nGzbulLHLz0Vb/zkvmAHg53P1zAAwEV6r/28AMBJf9rvBgbf9MahRImMGsDsms5FczmmkYfpi4mJl/EGqfxmDI8PkjM9dBMlHSgHXDOJm7aC/+ZPM/3Vlq1zxfyx/Ff1afmtXSgrlz4Zs1/kftS+05rF8aoteVC/e79+NG7epjOkTJ7ZDKcQNxLl3FSfdC+iO9p+0SVKnyxKvAxAIMsP2+O61mB4EGHwIAQp7WNKWwXqXM1ob2+7YS1W4mLU4w2gbTSwQlIwe/bciSn72zFuSgzutQr32Uvmrju8c0E/L17i+TeTW5ehWIje3nABfAwGESsQAgszIbNKkE3ImHMCvuFgpeDCRhvGg5M37poAP6GyCZTvOkZJmFaEZrFl/CYpIsWOX7FWsls/ZsxerRBB7xOX5gvrj4MGNsIZljjYyjDklN6NhiQ/tjv4+hcedkPuzh8jrdI7yNyx/lVO4SMWKDZCBnCVaIsq1awH4sAoAt2JqOfDIARm94eIRh3WPx2GyEfkVmehjKO3G7cuXr1MWT85XqFAK2bAtjMaW8lpR0WXOnCUSHBImEQW7iPvNxeLvHyj//js3RsZ09+4/y9r122X8yots+osoZgD4+VxGMwBMhNfq/xMAPP3ijiy7c1j8AfyYIFEQgC2drbtktHISC7yJ9gbckUt+DyXkZYS6UhntU4h9MlprXiuAF4mYKKukFsjIfCXeYdB9xX/+ESHRrmoGO3fJYO+u9iuVPJuksnWOtj0+CwSpfI+RCuNdSu+T/0gynF+/tGVlUIeyIHwep0iD2RZjr/jSTYiCwLv04UPvk9CXuP7xjx9/IadP+8g332SOAV509d4gkkePHkuRItUBADtBfeK27Eeyh7fXc8TZOUifP1crugzdPgmcefLgpowHuEqfFuTcBzfF2JvnyKJRmcSoYLBi0KARAJO7QZi7AcBkC3gNFyurkFuKNNLx578lS56iBnvEf3HFjCFQD5mHYPvc8uDWJezIe9ER8Y/f4HieIA0uJNWqpYrWYL9+Q8EntwNqI1vF2sZO6TIzm/saLH8apQ9pgRjnmMM1LJpaC+PofqiWTkKfnY/WprZw+vR5xL+1h07sWACuytpq3XTiSXcVg2jdb5gEjvhVWdjelSw5Q5eV8jpwn9xd/Jf6DZIqqHfvwbAMZwB/npfKomUcmp/f14gJTQP5tJuSsVEz+bGyraTzSK7rE5MWGLOYDJnHxgrpfk4iGYpZ5ixeCDuoWrWJ+kiYMOEPZLwWMrab0XW0ut7ziRSXJ3vQrwBkcA+SUaMG43dfT50DP0ayZi0uqTzzwYId3cJrtMHPZKUZAH4mFwrdNAPARHit/j8AQAKqoedXi1dYgLoC1T3ySUFkHu5cPUu8Ht/FC+6iIsG1tLKSVGmzSJWuA+U/8ZKnkFN7Y9hSlCrwcirQx0ZsLazUuuQ2jlLG0VOskyaT/f535EbgU3UM7R8CMYJId2tHyQpS5uxOqSU55gkiQwE0L/o+UNY6HivsVYRaz2PyWLRAlk2RQyqmyiXnfO7L4xAQpIYHSAprJ6zPLu44tmHxCw+W/meWS0GXDOJ5+oqsXzgWgfwnxdbBUf6DnuYtZF1SeJ6F1jriHgLCLKDj0I/bMmz3Uy6/D/jji8/NbZ3q/osXXxkFgEmePJO9f82S8dt2y15Y+7TCJI9MOQtLUUioFYJqhpWN8dgqrX58pluXT1HXZOWJm5IteVJYjCJAi2MtF7ytJQTxXo4DflfNhIwerJaZaZqQcuzYKch7/QiqjEfgd6si9dr0USDuXQLnaWHiRwjH0PvJfZk6pKc8uX8G3XHFODaRFy9+jtG1AQOGy/r1WwA+pkF5xkvSprOTCv1qSY8ib8+DlCfkF2RiETOcWZht/g9IkpuBMog0QIaWZEr/RTw6J/XrtVYWVCba6JcloMjR3OeML6RW70WMafMcURQ++u1F4CPIFB8lfwue366U8Mdb5P7qmULaGBYCWzs7G3wcFICl01HJpp0/fw+WtgtIrDgjp/3cxevBbQm/fUDV55jZ5K4nYVe34QcWjgSsZJImjQcUTTKrzNbs0D9Ontxdbr7KJA9vXZVja2cJtZUHDOiF+L72qo2E/LMPCVtpcS9lcYn6eN237zD0fcepJB7SqVhaWiGL9pF888t0RfuSkLYTc10zAEzMVyd638wAMPp4JIqlLxUAPgr2kQNeV5Xl7kV4EEDWIynunlnKi5ssGfujPLp3XX1pp0uXRmrUqCSpU6dQD0sGmnvB4pOnaCXFj8YsWp/nT+RlRDiA0itxcHKVkCB/CUTwMV+qocGBkG3yVmgqfeZcktozhzi7p5akrm7indxRHjpYSiAoWcjLF/Wqi3nZgcOErmdHS1txwNQB+hO+eGncDfZGDBLeViaKZRILyYiYxbyIXySAvOB7X1k3Wf27bFVl9YD24mKfVP7d9p8swwuxVU5fk4oCJ2BBuAYgwr6YEqw31o2EWq2MtWG4LgksYVOxclOZ4nB91Ycrq5RUrJgCVo0z4ulpL4MG5YLb09JwN5PL+/d7qezfHDmiA+YkcJuGTpkjPf7dKKsePpFUzm6SB2CvJCTiUqfPKpZWbzOGTTaewA13rp2V0T/UlwX/LBXnfOXFv9sw8Zg9UMXBGVplV151UkogCbVkkch58uTZAGF/K21ftxSp5buhiyWNZ3TQlMCuq+oPb1+Rv3/tKn6wpt+7d1p9ONFFTn3fvHlzqd8ErZHffbccwKiB2mfFsWaSNeVrXbYveSJJK/QK3IbUtyVBMktgeBI59sTOaKb59pMPZfoeX/G5cVzyuvjKpJE/qH20f8iRyAxzkjTTFUyLYNf8L+SGn7WchdXREnF3buAjpIQf3eymVDwIxAdu2iv7+n8jNcAFuKxlHe0QRqe+AK4bQRPEOEe6trVCfsDFALRMDOHxmMhy6NAxWEdPywWQUFPeLhiqFqHInLIv971USn5HBvTuoMCl1kZCpkehIOKO88uqRx+k7c8s6l27DkCXuZuQ5oeZ3l9KMQPAz+dKmgFgIrxWXxoAvBHwVObc2CO+EW8fxhx2GwtLmVCkjYzp3UBuXzktU6aMkmbNvorhngkJCYXY/VD57789AIJeeCA7QXrKXaytrZQlhO4VxnHRtRPy0kKsQMHRsEYJxN0EyAF8cZ8CZ1VQULBQMouFRL2u7qnEPbWnOKXPKEnSppUk7slxXCuRyAixf+EvATevQgD9kESGh2O/MAn09xU7Byepjqy9yMwZ5SHAjmekpfgcOygp3TwkSerUctshqdyM8BOCW63QCulp7Sq5IqzkPigfTh3YLK1++UuK12olwYg3cgFtBS0c1qCrKOkRrOO60/bnlEHqNRCgTmqK+JT3sc7pt0+LyZw5t2Xz5oN4QU4Vb+/nYg+wHejng2p5YInyANhg7J0n3J1l4Y6LkKtXbygLJgE8S0L6wuNt375XvmvfUwJCw6VZm95SqkUPBWBUYx/pn0h8SPzYILs0qlNNZiyaEutR6K5nDB0/HGwRI9cwW5QFO9ad9DbyHtyxY5+KZ4x8ZSFD5xz4IFbMuyARJlcgP4A4jlrh74SJLrVqVUF8XhapV6+/hGUoINkq1JM5XbOA7zGq5p57dioEgcBv8y0HxRXpAvBCLkB9yxyTPnLnLqOSaPj7aorf6/mbXtJk0Fypny+ZDlCyVVLn3PS1UjF1XCa5coucbzPR2U1S0ZCz0snEvc2kprNeNtDE9hWPyo3QShJZOn+SVM+Ulk3qCmP4CPoIKvlnyIHI7cuQxds+r49Q4zquwixiXucOkJlLSOFvmeDyMbKsGUdpeDx+CJDGx8/PX44ePSW//DJcRi0+Js5u0V33CTlmYqtrBoCJ7YqY7o8ZAJoem0+25UsBgM9DA2Tyte3yBJYwKmDkd0kvOR4HSMDje/LK0VFePXsmJ3avFh/QcxDA3b17Srm3Yht4zQVmqg7pGsiL1hr0FuQ70y8EgVQw2Aa34rlzl9WDmPFD4QB5hoX9qRGIDEJseFqvBmhDSss//6xW+7OucsNhime9srhwHd6+kHLKJnlL15AISGRFvvCWZzcuyfULR1UdvpyHDesn3bt3YtVohS64/+44KBJmZiOmgMtNK3wZUX6ML8//Zenbdx1i2KbjkDfELUU6WKzmQ50gk8we+bucPXwI5/AA5xWKsUiGaXQajXbYywN/XW+fAEWGc5zdZhJBJUhI8eXokSGLdB8yX5KnzhDnfh+qwqYlE2Xj4gmw0A2Srl3Z+7gLVVV4TQyvZdx7ily5ch16unWlOM6Zyg8folw7f1SunTssbtAiToWkmJnDukgArOL6gLBnz85SulZj+WnuefmlFqiBGlXSHXraGTcFWMidR5ftXmRAk1pGX5Oa1vjvvx8oc+dORLLDYsRxXgBlzzBQ8VRVqihMoNAKLYD8HWqFVj9+9JTSk1nTtulPaS1MghUEhkxS8YI8mz24Ky882i1/dv1RScK5Nu4kvdtCIcQ2yuocAVqW5nrgUr89gjJeq7JIevGEdZMlPh8mBIwJ/c2RlP0YYghTgkC6IEjC9X/HVPzgNQ/Ec0Ur2fOXlB9GLsM9xDP+MooZAH4+19EMABPhtfoSAGBwZLgMOLMM7p2XktPRQ0oFIMvun7+ElgrNSkEQRSqOXifOSgWAP8bxfIjClxfVAKhnGok/Z2jektaCdCiGhS/HFy98AD4fwNIXCRoLS8mSJaPJvtBFdPbsRei/HhDHsVOkPVxu3OfWrXuwXu2RpUvXvLGCwRIBlue8sPh8hYNOgSWGx3Jzc0XQfguTMUXsOy0Z/uEW0hAKD06wELLwBZYQNzCD2fcgq7E0LDr6ChZsi5YXvohpCaHCBa0VzjaIf8Q6luC7J+T3fgPk/PnLYpEslTTs8JNUbtA0xkuKYJyxmmcPbxMmU6RMk0nFMi35+xdYCb3lhddjKY86rWeNU3FamTJ5qvYN/6ElqXPnH2UH4q26gT8vb7HKMY5luM/HWB7fr5k8vXsJ1/K4ohcxPIY+aCA4eIlx5DW6cdlHAgIipUQJN1iRgVriWX79dRSs3nNl2Nz94p4qfTz3il+16+ePyfh+TY1WLl26mGTPmVOOBGaVUYM6QWUlKmlq8SVnnbVO2/E4wAwBVPLgS9KgQXv8LhwAXm/IDz90lV9/7Q0AeB5W+85q2aXyj1I1Q6AupIExhPpE05tv2svx/1bKyyfnEQvXTztEjCmtdQebjpVXiNUNHfubknYjmbUTfsf+Yb7Sd8Vm2fPHKLErXVpmjuivUw6J0dCbFVNOuynpvsxvYvFM1eN6nit/BcRjU0+7SvdCPrFVj/e2mzfvwArbQoJCIqRz/6libWcvT+/flCLl64mV9YcPa4h3xz5CRTMA/AiD+pGaNAPAjzSw79PslwAA59/cJ0ef35T2mctJCKxFy6f9qgKshw//RWXBBQQEKqvfu2bVJmR8+UJhluMtuKO+Brt/Qmg94jqOPijQ6hLoRUZGKgCoreM0EGL2+/cfhQWwHyyJOwAGXfQ3R5sngfMskPKyv3SPGXs5R9vByAKB3qxzAJxIWiAQpmWH62ghpUWFADkjLCKeTuGQW7NQLs0TR49Kl5YtxRGu3gYdf5EScLu96zU6d3SHzB/bC8oYURaPQoXyKUsRgSMD75csWS3Hj59RFCL8KGjUaaBUbfSNkTP536y6cfG4olv5D/JxcWV7hgAsc2xruj6QEoWRVIDSu3d2gKLc8e4sLdLZspWQnEUqSRckAnzIsmnJX7BomrYscrxTpkwulnkaSJfeP0uudPbK7dvAhEu7cOEqSvqtffvmMbrJ2LnKuE9eJ7OVMt3+ktHNPIx+QFUAbUtgjq/F98hCqVY6u/T66SfJnSVu1yc/ihiHyDEnb2ETEICP2rFN5vfsK0ly55Eba/+JBjRZ79RTW6WS8wK/fYZXxGXJowV+6ZWo3wVPkL+/EwC/L0KhBFL7gQKEMU4cK86f98WH42t8yLoa26xbVwsxtGftHaXP2H/fK2td12AinjEDwER8cQy6ZjwX3qCSedE8AgkdAXLe2SLGr0TyrDJo1ddSsGBeBD2v1ll2PpS1Lz79oiuYbizNlbVu3UPEtT2W5v3LSeVMUdYPAiMWfvk/fRoKMtetarlNm4xQVSio5o39Y4wehC9XWv8Mi4ODg1JOoMUrS5Zi8tVXNQEGOxoFG1YWAikqH8QtuUg+WC8ZmH8aLjRqz8a38Fwo67YSsUy0CFIq7ABUIXK5RVeFiGovUnx8fOX7Du3FA+7D/n9vwjlYx/dQRuvlL1FVxq+6pNyQ54/ulLXzRiAL9S8VP0dVCwcnF8lRsLxK7CkIt7lrcjqNP3wJCoA02NBOkjVPCfmq/c8mD8DYUJbr128qy7QpqhDWIZjuVfgFXN+2cF2ngkX4KUCtIzfFu9gjTKBp0/qwGq9VcabvO976B67dspdQkWT3+nly5tB/EhEe/b7hRwrDH5J6z5UJIeEA+71kfBMn/SbUPO/V4cMnIP7zhY5U2rASeRnPnt0jZ85clF6L7+B+bq/ieIcN6w+amNqqenBwsISHhUvtVDfFc95/cm7BT9Lo101SIt1LWTAmdtDPRBwtKYXEzlNOO8r2C6FilaqAeFZrL2sRa9cxn69Su0mG+D+SOhdNHQLt6tB4xc0S7A08kEp+L/MMwC+Jai8SzwMqomRGJj5/R8bKhAnXYMm8JPnyOcu+fZWiVSGHJF3XtAfbJw2Ro7BmNoCcWtqMOaLVMy+YR+BTjoAZAH7K0f+Cj00yZldre3WGSWByu3//If4egRE/evD2hxgCBubvgXoBY3zuA+jsB8jJgAxA6sQaKz0H3ZSXSPxoDBJhyRQF1P7FS6SaZyDcxeAT9A7T7ZYyZRQo0K14z5mpU0cj868xXhq5ZdKkmQCAP8MChn6g8GU5Fi4vrZCOo2amANCR2GAaKHPPu0he99AE8QUWAGBMDwvfyCMplPUvMDypeBpxhfOYP/wwCPGQkdLzj8XvDf60c+DU0dldSldvpv64TIk2H+9HiFVLp/sg4PqPVfZuXCg3Lp5Qf9Wbfiu29jGBDmPlHt+9prrQo8cAla178uT2OJUtkuLeXr26FIDga1hKTSCFWE6sW7f2snDhCuVGLwp34Icq/AjJlLOQuIJ/8PiedSabVdfi8AKZhz+3K92Qzd07Wt1vv50Bmb2LMmPGn7r1lJFLjo8qTXpO21CgQB7paVtKsvStL2G3Dqj76eeff0cCV09Ff1KrURPp3f8nWX3TRsaMGy7jJUJK9VgLUuUeiCccH+dYLzp/TcYvXSdPr9+SiOvPJVOr9rK8U2WEdviqLtB9qx9zqPXL1JRZwZNPuyu6pQYMt4ClfQIylXsWem6UvNqwncqVUyKW2Ff6/5JbEUkzo3nbXQd5FmQheZKHgfcwSP3mbjx4gekrFZdp2IZ52TwCn3IEzC7gTzn6Jo79JbiAh5z7FzQo/tI5a0XxQHzUn30awBUYKOnTpwX1wibl/jVx+glaTcvdDBDWEiDpx/iRgoG0FpURO2RYnjwJxQP5tXh4vEmBRAXGwh3DPlYIem8GnjJTX/2GbWnLxlzB2jZtGlsd0kLUqdNK0d2kSZMadB4PwVGWWsaPHypnLUrBFeynNaOmJKgtVqwGgFoyRYZbt241JC+0haSZTbR6XFgB6pIK6YLA7/ZK/sILrl9xbxVgb1ixQIFKYoNEnR9HLTPc9Fku8xrfuXpGuUIvndqnzuGXvzdHk2dbM2+UbFs5TXd+rXuOVJnOM4d/q0D6lCkj1VRX4SPM5M9fEVDIRgZP3/lRAPHFk3tl+6oZEhYSKMk9PAG+H0uhMrWhR/17jLNhWMKyZbNwbxVEpmoE7q1Nqo6PTwM1ZVzf/AuuKqbW2AeWV7CFHHpkJyUdbykCbOoFM2aQUn57Du+WdTccJRtoYeiipYV73hlbOTyxnap7+fLBGP3RViy6eEN61W8tQIkwqSG29Zv2sqlrC22zmiY0TILPDrrxGWZBiy6fGUwiqwbgZqrQkv4gwFIpqtzDlDyRBMIMqfAJtVCWx9zQXdYv585dhHpIA+k5bKHkLlJBf9MXOW92AX8+l9VsAfx8rtVn1dMuWSvLqIsbZPaNPdIrR3XQXeyXvi0LKUvgggXLlYD8u8aWGQ4ErXb64I/b6fpcdNHZsKpaTp06JkiiZBT/5sDK9i7FmCvYsJ3Y6hC47dy5WpbtuCzevkHSsFRa2bJlpzQdukscLDbL8LOrVXOOjg4q6SQ0NAzSZTngUp4vlEljhvKkSbORvJBUcSj26NEJtDgpkYHrJLVhRaQ1keNRAbxozK40LASnmSuWlvOX7hhu+uyWX0De7crZg7IHqhn3lWLG21Nwck3xdgFz4aFRIQDaStLcFCpTSzr1+1tWTP9NvbinTRsjzZs30Kropo8ehSAD9pqULOkujRun061P6Mzw4QOkbdsecnDbcilbIzqoSWhbxurnAejgn34JCQ6QTf9MUPyZ2voUaTKK16M7Ur16U0mb1gPkzsulYkUX2bv3MX63T/Hxlkqc39C1kB7GsFyHZe766+ziigz2Ro3ayxOQeX/3XUfEDn7zhtrplaKJSQY5uvYAfwvByVc7W6D0+GealC9fH2ERxXHc1FKqVFHI6Q2O1nwWZ7jXQ0KkwK8DZE/7BtG2cYH3r8OxJ8oSZ5j9T1DH8AlS0jTBx51GOcOPvHagefEBqLVN9lI9MwwbXrLkLs4jTMV3EjDSs0BL40vEz9LFHJt2MD9AtESfJEh4S5k2k2Hz5mXzCHzSETADwE86/F/uwdPYucrQAk2QCbxc9j29Ik2cs+tOdtCgkXhpFlGZobqVCZwhVUQY4nWOPrYD+AuPsTeVBfDMVSU2y5vhjkyWSKj1z7CNuJYfBCSTfaDYoOuaSaN4n0Am7rXYIiv3nlM5+a4C+PYw37lzG7Eq4SA5Q/ZL/vw9lJwU47aYicx4vRIliii3WaNGdfHCrasOy+ST3LnLAhRuVzyJXHnlyiG1LfxVUqNjxY0Ep+XGT8PL/rDK6I2vtq5qOBH9sxygbc/6+apH7sndhC53xtqRSPynn4bI0d1rpHrjrroeN+/2u9QF3yBl5R7cviTpMkUlcRStUF8KIC5x6pAOyNjuq/YnqNYv8+ffAU3ObVm16lE0AEjy5P24vsy+NkYCrN8G5+vWrS5VqpSXJZP6i70jACiO+7GLLbgyx688rw7jCxqmE/s2SOFydZQFcvWcEXJi73pYPssrbk07uyDwaBZF3VTq46El6FZmw3LWCtx8mmoItW+H/ntXcuTNL2UbtJNMmTKIs7MTgFM33alMOgVSb1jHyqYLVeu+hR6xVvbtWw83+ivhx+HgwaNjAMAjj71U1VS21touaqr9tnn/pn8SIevPJJFCtr6IOXZR2+/BYkeNXwLWYogLZIygfqH1bs99B+Wq5RaCO3oBqMxDV/eAJRSXtJTgnJaSKSOonfB84Hb+UanHWDl71hcfZvfxu5sG7tLtUq52a6nT6vsviuvP2Hmb131+I2B2ASfCa/YluIC1Yf351FIJRDxgr+zVJeLqZXl8/7qsmTsSD/k+UJH4VqsWY0qQRCCWFkLuJIO9jOw/UkzQ2ucbZgFePEc8pCNhtQuTXO4xH8RbQGZbBA98SlglpLBdEjLr83clZP/41J0KvrU2eHlqFC/ch5mOBLQMeNePraJCgx8ydPniJK9YfEp7ECmvXbdVMnqmg9TUY7yMPeX333+WKtUqyc7tu6E/mgnZp5lVU4w/3LRpG9x+axUdztWrN8UGYGjMktOS1AKd+YzK+WM7Adg6SseOrRRFiSH/YMGClcXKKa30Hr083mf18mWk9KiXRdVv376FymAn8LbA2Ny/D4m//uchI+YCcJlD1XkE0LAV8n5fZQUJOWNRkbwTn8QdEkTXrdsGqhSnkAndBdQ7/T/p+HuBq3POqO6KtoknRpL1tWsXIDkqmzpPgtzpZ8kb6Cvnj+zFmH+v4vjateupwhAY60vd5i1blkGNJKfsBgijLFp2WNljK0WKVJE+fb6TVq0aRat22zdACrfsLoiNEJ+Le6Nt01+o1/2WHD/hK0tHp0NyTko59NBW0uAZYugh0N/HcJ6/Q7qy0zlG4qMrHMTdz/CBBbL4N1+G/K3SetkermNDazqtfoUKbQet1EY0u0Wy5i0hP45c+kmvpeH5fexlswv4Y4/wh2vfDAA/3Fh+sJa+JAB4L+i5jLm0UcXcDcnfSKyCQ6V/m2JqrHzw1W6q8CHMxA5vPIhzvQE/lOFinD0D0MnBVtVEkgfbXHPdUalnxOaiMXZsxhE1hZtIH4QZq5fQdee8rKGDaiMw+qks3MKp4gfmtONQVYExjbGds1aXoG/69AUqy7ho0YKw6h0C92AvdQ0Yg0kKHv7Z2trKM0ivMd6rYcM6kHRLK4sWrYRu7H9SrOJX0vHnSVqTiX6qD9QeP74AEBLdUsQTqFq1iTx5HiqDpm6L9/mEhgTJT83zy0tYVi2trJFNGybu7q5w/f4BVY3qRtuhq1CzIlNhg1rPjDPTV9QwtqMD3JjdOreGVfEfWCJzgnh7obhAseZTlqtnD8u8sd9DZu6psjZPmDBUucPDXyeT6fiQqWJ9UGkBr1u3UJgEol94H/Xu/av89ltfSVMemcGwvpVHopY2Nvp1OV+3bmul2LNs2UwQTS9RhMnk2NyyZZc8wJgGIZHMoV5duT9jhOGuuuUDF8Nl6hJvGf2tC1zWduDTTCqr8NzgMyQuAmpdI/GYWY9YxlJpYn4ohoW9RLzjZsj9PYMFdTtoYk4i+7yY/Dh6xTvTKcWjO4mqihkAJqrLEWtnzAAw1uH5NBu/JADIETzufUvm3torDdMVlVwhSWVEz9pqYG/ePKaIkUPxct1666HUzZoeLwn4RD9AoQWRgeb1YYlJSGEgOLP5DMmTE9KGsbo77trHC7wZ21dbZ6isoK03NaU767YfiK1BgEvXMP/0k0SYaEION/LzaaVKlcYqID9zrsLSd9wabXWin1LOredXUdap8+f3Sbp0HjH63KJFFzl+6ooMX3AkxrbYVrBtZnFaQCrw1uWTsnzqILiLr8i//84F/105tevdu0EgRfZFIo8HEnOi38NMnCBVSXa4hJmpHlfZuXOfigmMgKmp7td9pGrDbz65BenmJZz3tEFy/+YlFXv628xVciXUQ6b2bi5/9m2C864a47SaNDmMuNaniO3bAvfuDek57whol8J1ahyKhgZfdLRC9+kzWHFk0mrLxCbGAXKe4Q60BjbuOUhev3ghw8H517342/vV8KBU4qCHgO5e/XISutoPAi2VZVZ//bvOkzpm9XXjSiGBgZH4uLKAlTiJon2idZRu4FY9TAPXd+1HYtzPDAAT41Ux3qfoTyrjdcxrzSPwXiNwI+CJ2j+3cxpFCG2Ppd5jf5dm89eKR9124pG3onSoVF8yjJwJ4tWEWcZMdYzum0dByVRsj6k6xtaTwuE0XM4MGg+A9eBDlfhY7uI6FkKkElRoMfXDy5CFnHb64I/r6KbTB39cR0k2lm8GTFPTz+WfpBbJQDfjqrp78+btGN2ma+7x46egoIkuWRejopEV5Ae0tLJRFhxac/pP2iTpMueSli2/hazgHmVZbd/+hPz88xXFL2nYBNUwyFNHQuIFIOFmfGpshfGA587tAbgso8IlBnVAcs7xXSpGLrb9Pua2LLmLyC9/b5EeQxfIteu3pWu9clLy5S4JTeYqAemqy549XnADHwdgezu+mTLZqS7dftJIpOhE2bNmsaRI5ger4GDJlauMVAQxNDPZy5f/SsVAXrt2WPhReO3aEcQCTpa//hqu3MqVKpWV4/vXi0W+fDKwcVsp3HekjDh8VoJBWWRYkiKWlvQuR5TFHO6CN6VA8mDlEdCWOWX84LuWnfccpDQsgFohz+GOHXtl3LhpsnLlSgX+uI3UTj/91F32b/5HGJ/6OZbwsFCVLMQYTXP5skbAnATyZV3PRHk2EbCesCS3dpRnkAyLdHaW8X+MV+ussmcTz4b15e7ZCxIyf4FMqVQSLtiMiNlxUNZAzSJ45bmvVPpuIBIbrCW5Z3p5/OCxZMyeSZK4usuYOhWkdLqYrjLKR62Bq6aRCXUD1YE3/9Btx2Dxi89tIADvoxQAaLULe5lUgUha0+h+5hT/iwNoH6pBszc+wvL6x3nXeYLRhLilKetGpY+EupoXLpwiZcvWlbs3zotL8tTv2t3/+X7MKK/Tpo8smzII2au7kL1bOlofJk6cAVB1Sdr1jrrvom1M4IIFwOYPI5fLuJ8awh36jeTIkVW+/noACKTdpFxeS5M6s7RKpUMcHBVeSDhMt2QN3EPGCsmV6QqlUso33/SWqb91QIKIs5Ss2kxyFCilkkUCA0LhTg2XF898cG+GgUwaMa/I5M2er6RY2dgaa/a91+UpWlF+nbZTRv9QF7RDfSWzq7N4X79Fyn8AAKtmSURBVNgpew44gd7pBTg0w/FhEfVaGTu2ABRBLGXC37cke5CPnPp3onz/cL88evQEwHklXLRplDrOit03xHX2Prm9aqQk3zfOaB+zuDrJqZUzpMHf/4nXhecyoevvMrduGbkxok+0+pnBcfksONkb/WD+UgWAMgAWxvNgHsgEJZq3luHYsvKjNWpk4b5/Mnm8Z5bch77vxYtXEbt5WlHd8JyuAyCT23HVqjlIAtmjXOcMvWByUtkaLSUt3PuJvTx/el8ObVshx0De7/30ga67lqDhsbK2FWsbOxUrbGPvjA9LO0mS1EJZqZNiag2LeVPdHuaZxDwCZhdwIrw6X5oLeMntg7Lf6xoyXZNK5G+D1YjblSsju+b9JTncnWXn3UfSpHQtBgGJPMDDBoHeeJtFcX7lzw+05SAWN27Iy4sXkRWSFsFAQVHbOA0IkGTNmorX30OMXklDPVKjlbCSXHk5kVCSP0WYqSrR1psiw41W6QMu0CJJeySJneNb6M4mZ1nj7P7x2oW0Ha1afav0XX+esE4y5SgYr/0SS6UlkwfC0rJYhRXQkqRfMmUqKqkz5ZPvh/+jv/q95mlVPH1wi6yAzGFQgI9s2LBYatZqqWszNoCxFjGqTO5ph9jAuAqPc+TISXBCToXiyEF5yfTxWIqTi5uMWEQ94yggFkvVd9509cxBmTSojbJK0pLctWsH0Mc0QoKRY4w2jx59Dk7LCGT9V1Zu3cmTR6JuRVVvJz6yKN1WN0ug0H17zssmxv1Kd+uBh/ZKzYZu9DzuvlJ20nV5OLOTnAFZtycpYvQKCdNpcdXKpUvgI+18ApnF+aRcuRTa6vea9h42X3aeeSplUr1AYkxWuOybK8olrdFhw8YDBC5XuuLMhvbER+usWYsQC7hcAXStXmKY8v66B432S6f2yuXTB0CIfkUC/SGZiY8qeghIgcS416dPn4ESx0tlxDN0xMfHD0ky/oh3DFX3JC2E/LOOfCk9sd2gpPzp9Wsvg3XmxU88AmYA+IkvgLHDf2kAcM6NPXLixW1JbuUgz4f/JknCwuTs6V26OC1/SERlbvuDvDxwUPCkFJhUpED2LHL28jXB5zXI2sLFMm0aqdG+lSxqUUc3ZL6h4ZKpVmtJCoB4bskUSetop9t2+PBzpdPZuXNmPMjeuoJ0FfRmaOkjd1h8sjX1dnvv2bMAdS6IN/REpmhcZellZ2kO+o04TiVGM7RqprSLlLxQJjBVHsCaOmbM3yoTmF/yHfpOEkqzfW6FL7IBXxdHwsIzmT//b5UEo52Dh0c+KV2zjTTt8qu26oNNQ0FwPqb3V/IU1u2/fvpOeoyZrNq+9vCWbL7lqChDyqUNgiJLdJcls9upGJEQyy6zhdesuSZDhhyVFCnsYNXKJX/8cQvu1xzSBFJutQBAaWEbu/S0ODi7fbBzNNYQx3nt/NFy/8YFeXjnCiQTR0qbNk2MVYUF7jfoX59F4kwNzHfT1TEEa0x2ogWPWdRPEMJBy/e2O8yq9lf3sRUiGmiF/2mPtczrkE+SFSwg90FybYMQB63svW+n+Plocb15MxDA7woUd7LqqGG0erFNmcxhbR0VPsF6dPH+/fccWBNvwnq5W1IUrCVjRw+SKrkdYmtGt43xtgUKVJQ6rX+Quq1/1K3/lDMhQf6yavYfcnr/RqXXzedk+vTpANTzKy7RWrWqCHlHE1pCvJ/LP9lKGu5mBoCGI5IIlt/+ahJBZ8xd+DJHwMUqCphV88gr14pWlpN7NyjAMWnSCEl6+rw44Svz3qJJoHrxk3zJncVK72HOL0ofAD13O5sYg+NiYyXJXF0kEsAxb77yYl2ksCwc8ps0L39cV7d5c/KRWeqW9WeAFxDI7RRvqg79fd93fguoQlysX0oBgD+Nyyy2NqkuQCoNY+oLse1HJZQ5510lryvilSDBZrlhm7yCwkgkeBiPHDkhtFQcPnwCMYKWUqR8XWnS5Tdo9LrG1mSi3UaajiZdBoO+pAd0a3siw/kSEgosVZwerRTps+T5KH0nbc4AxAVSPaQnwN/hVo0Rv/aH2AKpfJ3bVwEW8j7uQNxY2bTBugSjhLrn2XmeT7NmedSfdjItWhRQZOCFC/dCoopl1DX8yOCPx3Z2SwmX+jgVH9a7aX5F/Kz1yXB6Gr9zxsKR81C/EAAfBF2OpvXLTN0L3tYyH1a8IFgGi6YOlRb48NEnd+ZHUGc4Bla1+0sCVwwCSERyk94zg2Tn5CkskipEyTquXPkAcXkPQHP0FVzmxj8GN4E2ikoe9+8EyN+/H5cksCoO7J5GHLOlRSiKyLo/f5FbyOh1T+UhttmriHXqXGKThNb4+AEkWkkZD7h2yV+wAJaS7PljACT9Yfmo85T/ozzgsqkDlUY0r0mnTq3BK1pYuavf5eAk274Bom2C81xvcfO7NGXe5384AmYA+D8c7P+Ph7ro+0B2PbkE92USSWcH3c3mPRUAbFi2hNiA8NVq+VoJGdpf7OpUAW+fe4whohvCGPjTKq6Y8DteFtflEaxYJwYPlT7/LMemzGrzpk1lTYI/VqClIA1isgwzBtXOH+kf0lKcfGKrHpQaLUVsrkKtG3T90mL07zWnGC4yrY6paboHl+XO7YeSZ+lMeZXMQhYC+Y5H7NoVUME4Aig0AOdchbqQkbO1N9XEZ7OeerpLJ/8iwYH+oMKZjxdbG0UAnSpdJileqcFHOw8miXQfMk82L50kSxaPV/GGM2eOU+5BCyAWDbiTPuQwpNLcbCLVOkqQfYgyefIcSYY+/DHv4P8cwE/7vRPczUkB7qoZPRVmn1MlhHGNWiGPICmavEMsYhAq01qdChyff4E4+ir4P439PvlBFLBxqLiXLQbOzpgfh7UzByiZtyrZrcAHeBCRJeFSv/4q5bIMCQmVFdt3yvNQS5UkRXk3gvE6mQPlRIS/zIpErObrEEnrgZhBgE9ixqct+8sF+U9+7tcJH6mhsmr6GOnwVV2VsKKdU1zT6dPHyqVL1+SvX1pJzebfSdXGXUBAHt19HVcb77M9KMBPdq2djVjEeRIcFKDcu0y2YdxiXIUfyywafg6KSKK8EWEA6WtvOKmP2Zy4JiTKXoREJzOwiBqvxP6v2QWcCK/Ql+ICPup9Q+bf2g/dzKTyU67aktEhBfjUIuT7Bjmkd/tm8ltxKFmsXCdJQT4cMrSfRJYuBsbZFAm+Iluu3pZW7XvhKf1UNu9aI66hjgghfA0eLudY2+LX6mIQuraNRxxWrA3FcyMBHGOcKqYPShAxrX7ztBzWgu5xQkqSO/dl4ZxbUsvvkPy2fqUs8QuQjBmySS0Av7zFKn9x/GTPIGc2b0xPuXPtnG6Yvvt9njpX3YqPOHPhxG6ZBWsgsycdHOyRHdxDSaLpSx8+B/ChLi4BYGYoSlC60LDwfiEfpvahYLhdf3no0LngJhyNVY5SuGwNqd+2m6RKF/UhpF/vQ88HB/pJn2b5wfX3k/zwQ1ejzRMAZoNL8PbtE7rtv+xPqVzf1ZEEw3M3DG0gKLsGfe7cAIPrATCyuYaprFuCRnoF1t64J53qtxHP5k3k1G+9ZMaMBYr0PBLxZ05OjnLhwhU5cfK8JMlUUdLkKi4/VrYF6AlS8aErV66XI97JpXWdouL//Kk0qpJPyJcZW6EFOVeustHOIVu2EgC2R2PbLcY2P/z2yI24evUmbCNMyivt+nQF72b1jxaz+RCUResX/SkXQJRugbErU7GMlPz6V+nTIIuybsbopJEVVHEhqT6fmbShWkIJxQIZ1wR89bIEwAL7BiFiW9Cz57I0RwwLp9kFbGRcP/UqMwD81FfAyPG/BAC4A2S8/94/LlZJk8kveerji/4tGBvRtbKksAiXg/VrisWZC2Jx8Qp5SiSyTHEJnvGnmjcyLDFWTTt1Sf7o/4eEP4bEU7pOEn7eUpbPLwjC31Qx6ppa8Q+In1vn9jO1+YOsZywTLT9MHBlS2kv3Ff1BGo+lEQZoM0Px5J5Dcg7ScHsfPBfvwOfS8rvhipcsll2/iE1ej+/K4ok/K7co6Uv+l8omBH/Xzh2GxWUOAuv3IzM2HcCHi/j7B0jmzJ4qxsrb+4UUKVFc7iWH9nA+H6NjTmJyujIZJxobqXlIyEtQqSyTy5e3AkichLc/QmUEV6zfHuCiwUezCoaFBssPjXIhpOM3ZCu3MRnOkD17yWjWMoI7Er3TkpfaPhIu3khxBV0OC2lyjkHi8QrqdHqTzPEYvx2Ga3TO7yPVBy6Tc3M3IKThuCSzSSVJI8tKztw+kr9ACkW2zTHOkycnEjOaKqvjHXBhUpXl5eskQotVPmj4hl3bCTWiUepaECyS/uibb74GyMsO4ukdyoJLsEbNbVo3CWJJlj5mzGChtZV/qdJllJ7TNisQZOhYJhdh1wIvdMCWsYwX4dq+CVLwFjn85OGDR1K48Ghcp5M4Y5BsW9tI/pLVVRiGe8q0iMkLRKatvSRPnV7sHJxNuq7VgMXyzyHoSy/Cb8DOzhZhES3k+++7SMqUySFViDjJZK/gdQmNZe+3m0irUxLu+fgUcwxgfEYpcdQxA8DEcR2i9eJzB4Cr7x2X7U8uiL2FtfyarwEE5N8mZ/BEd+Kl+O/MoXIRWq0Z8dKwhvpEJFQEIhB0HFG9okiK5NHGw9jCovPXpFeTDpLECdm7PbpLlvOZZfd5a7F3tZZ9czJKKvuXxnaLsY7ZmJWhKKIJxMeoEMeKp+ANXAcLRRc9XVPDXchLxgduPmQYM+HEmISU4T7vsswX38GDxxDvtF727TuiSJ7ZDuOe3ODqTemZQ+q2BXUHSJ6/lEIAcv/mRcmcs3A0gEdlkFP7N+Hl11epd5CeogRIrlv3HKEA4f/y/E8d2CyLJvRBzFxqcULcnNejW+L7/Jm6Lkxc6fBNBxk/5hfVJWpcM3P7GjJin4ckEx8ACWuLV8rC0jpX/D5UCFrWrt0CkDJbfQCwYbrACQSLV2ooKTw8P+jp/9Qsn9SsUR4KJhNMtlujRnOpWbMy5B+jWwlv+lrKU3wgkbT9u0IvlIVpGiTmcrs/lql7dsiNa1fl+bXr4pwlM6h9asmZG4FyaTisbkndZN728hJ8RmBdPS12RXLJsZU5JK2ryS6oDbRgEQzSEt8+j49Oy/jcuQfyyy/9VUZryZKFQWxdHRnNGZFok1wlgNjZRT3DqHk8HprZkyGdeON1VhWf6GgVBVz1j0zr7R2APVouL0ABiPyEReBmprVXs3ZG4mJv3fpYwsLuK1qcFSvW6zehm2d8rgM4Lnn/WIKCJcDnqQT6vUC/AsUB1ECeOQpJwVI1JGfBMqBmcZKwkEAFIPkBRDm/0iDVXrlydoz4PobAkBybVjxPR5Cdd+wr5bb9rvrHZ5YGyHUdieeMGQDGc6ASQTWzqz4RXIQvqQsL4PI9Atevq5W9DM7bAC8uqxinV75OG1k76w/ZhIzGLrWrSfjXzeQ1aAbwhIpR19iKwoMnyO0Fi0VSp5az21cgu9JekfE+fBgCPqokKtg+KXZ0RYZtVc/AWN0cDC73RfD3uwJAAk07cAKaKmTs4Jf2vQArqY0Yo7qITZp7wVVZfLQXgal947t+//7DsF70gQfcS+1i5+AoWXIXl9pta4lntvySGq5AJgd8aYWuQFqfWMatOKcsJZwnKJzQr5nSsaXlje5JyuGRm+3h7cvg8FsitvZOrPo/KYXL1oZbtna0Y7GPjBtcM3eEzJs1SyJS5JNS0AJOun6bZH12RTIP7CTVMwYBRFjKi1rdJP+BSdH2j22BMngtWjRQf7wn1qzZLAcG/CHbF0+QjfhzhbxcHSiMlKrSJBpojq3N2LYFBwWKa8Z8MaroJzcRHJYrV1fJDTZqVFdXlyo1/Lvha60s4zvuOkiJ1M+kRuMm8vrefXi0HYH13CTw/AXEVi4Rx/I/iGS8KqWq1ZcGuT0lPOsruH5D5NjVcNl020lSer8WJj7p62zrDoYZ/uYoR0c6mU3I0G6e0x8Wv/ugP1oPV/0AGTAgZqKQBv7YDj+y0lf5RnxtMkrnrD6qz/rta/NkFPAFl97hPdckacAD8UyfWsaMXw/d7R0SDneyK6hh8nmmk9KF80uZjOllkI2NWKX1kMWQcaydL5dkL/+9TJxyCYCZcdGP5cqV69AXvi+hwX6SNWMqWJQLg3cwHZgOLsvRo6dl8bFd2qGjTTNlygBS7b9jgD9WYrIMC0ExNaxfrx6OGGM7xVFJzXVmY9NaWjhliNIoV5XN/3xRI2AGgF/U5fx0J8OX8ZRrO+SS/0PxsHWB27cegJfx2ysZQSGexJERkWJx9YZENH77QojrDMj+f3vDZkF0vcxCpiXBHwutXOnSRX2lt04TZSmhHNwMWBNItZLZOdxoLFUmrGf2WoZ4ULHo943ALigyqWxFTJ5+/It+Hc4HQr93HgAfLTuPYelI6xCJGL4AYSwfg87fp9B69OefU2XkyIlw92WCdauPZMtXQlKmBUG2Fq39PgdI5PsG+HrrepjM0hov1hA5gQzz9QvHgMfshSxdOkNZnVipUaM6cgexkPv2HZazR7ZLSVgDP2UhkS5L486D5OG9m7J0dF/p+/qu5Dp4RJLefSARjk/lZcYMkhEyaBkPxR/8GZ5TKsTUfvttO/UXGBgk27fvkYkTZynX+Lblk6UbEldSp89quFu8l3kPkho9FITp/4GuhcTWXDXjrKsUvXxfikqUi5GJBhs3LsF1aA9WpwhFnp0jTy458cRbwvHsmLNnnyw6k0+e+9yR23O+FQkNkr+3rJJmuTIqVgA+Xy4995Ptd9NJt4Ltdb85K6uk0rdvzjf99QeBe1LZcNNRWsJaaviBRRD1/feDFE3O1183FYsCreWn2RNBGbQC+0fCsjcH5M1uyj1fo0YlgKy0oB31iAaeKFPnd2adNMzWNtYxGjRohEydOi9aHUsoyhQqW0f9Ph/cuiRXr56WfWeXSqQaw7dVfW7ekVMvrmFFesROFobiTIa3G03MvXjhAyB4CrHPESru1AHUWM7IZM4OOq24ngUcJyonsTR19Ff0O/zw4JUlBQ/jMZtBH91cvrwRMLuAE+E1/dxcwJF4OI+9tFHuBT+XrA6p5MecNWNNLGA22k/N88sCBI03hbUirF/PBF2FJrNWyM5ho6TH4tkyrHzhOPe9hwcYQR4tA4aF2Wy0OpB3LCFlITLdaGGkHJQ+RYWxNiLgjSaVhIbJ6F7Zi/inhOoUG7Zdp04rqC8cl3wlqkjXgTO+SCuf4TnrL9+6ckrG9m6oVjGGKiI8TFmCc+bMhpfv6Bgyd//9txtWsS5wFxeSn6BzHNeLUf9YH3OeSgtDOpdHPNhLqWZhIRmR+d4d6iK58ueWkLGDJdmBYxLJ0IgPWA4cOCrt2vUEma+v5C5SAVmpPWA1LprgMTkKpYj5f/6oSLAts1QESLOWQMS7EQjegguUVEf6sWOUgSOX3kEQW4NoL+pHQQAEehsE2gkC7qDu00LqNK4ofb8tKxnxYaZvzXsWbCEn4S6OKxGKbvBnz7wRWmwBN667Srro0aO/WNs6ihti7Bg2wJIEY10SllBSIB3Y8o/4ej+RB7cuqpg/bbgzAoiPHDlQ0SYtg+uXKiYEh6YK6ZXq1GkthcvVkQbt+ym3vx+STZxcU6pYP/39GKrw+N51uHW9JU3GHHL9wjFZNu5HeQU1jfKOLpKmamnJUqQgCKU9FZl0rlzZ1O7kKQwNfRUry4H+cUzN88P0Fp6N5+GmpnoQwaAHmBE47nRt8/ohBDJBxewCTtBwfdLKxk00n7RL5oN/TiMQjof2sItrxTssQAq7eso32SrH2X0S5rK4VywtYcz8TWDpULmU7BzyEpaBuJ9MEYgoX3DUUgZUCTJ6FCZopAJRckILY3qY/RafYmkRvdZlvCTzgkrifQotL5QgIwB8gJfZl+jijWt87l+/oKpQM/bSpavi4ZEKVB81JFMmT6O70qozbdoYkCf/LDcuHpdseYsbrfe/XHn51H6lqGELLkE7WAVvIkZwO4iVZ124LFvu3JPnSJKyBICpWrV8rB9VCe1zWdAwnT+/V0aMmKhc4+P6NhEnF3cpU7OVVG/aLd6UQJuXTFSJE2xPJERl9Abjo4oJK9ndwkF5Y6s4+XJAZYcfYtnb/S2eYAI49n0ricjhKoO6tkFyxmtQG2UED1+YBPm/lBbfeMvFa84KjGzDxxkwCfgTg2TrHUdpAEJoxgxqZe9eL+nX7zzUKtLJunWP5OxZX2TZZoe1sbcCmqzHDy9iTAL/jv2myLDuVbXdpWPfv6RohfpqOQ+AMEskwNcLr0fyHMD86YMbsnvdXFjhuiogeebMLiRU2KlMZFpU+TtkprcFgGtwcIjMnbsEIQdjJBWs8K16jtTRvCRPbdyKR7WWdJkQxpAJoxccAJCYXKq2/F7WLRwr/wX4isB9r/5Uz0TuY7pbCktbaanW7N9fSfLmdX6zNfaJ5pKnssqGm07K1RuKLHPS2hT3CJYUCIeJzZsRe+vmrZ/jCLz9JX2OvTf3+ZOOQGBkqAw9t0YCMK2QMpe0yFgyXv1ZNLGPuILAuUQZvICR/Wuq8IuUcTqcki6j0RtJMzdYDVnugjg6rjJj5i2ZscVCvs6WEjEzUe5iUmvQCnfX30qO4gXVLq9vXM1E237iiY3kwMvtXQuVOXjszIh94suSVDT82qbbKj6FMkylS9dRFg7Wz1W4fHx2++LqJPeIeqnSojRy5KB4Wa+aNftKBg0aqdzEfcas+uRjkiZjduWCLVOzhVRt+A0k5XxhHS+g+lULAEMAbFnqFKwsy/p8K5EtG8U7VlbtaPAPQQpBC7NC+ffHHwNkKCiY9uw5CBqZGbJr2d9y9vBW6Y2xsYcFKrbCdryePJAMxTqKfpaofrYyKWyKIov5HhJbSIKtgbHZ9bvLg9k9pe34ITJ90m2x97SSLB5O8jr1a6n/dXLpVwUJE6CAIScgQzmOIiu4SXY/WXnVWe4BGLcc0U9u3riFBJBR4PaLxAeAv1L+YX+nTr2JjOtg1XUSg0eEheA3UgHxsPnk1uWTSJKI2sYKL18BDRmUZHDVMhOXusq5CpWV8nXaypBvKojX43vgzCukyLjDoGakFbq3qT3N5KswqBpRL7kLLPK0SrMwNIFZ2abiTmkF/Hf2cNm7cYGQpJmFoDJ58uSwNKaBjF5q6O1aQ0kkj9i3ay45rgdKjrbHJHduSsxFhRKoneL4h3yjTFpbg6S1pniW6nsuGMri6RQOfep3f67FcXjz5kQ4Aqbfvomws+YuJZ4ReB4aIH9cXIfYnwipl7aQ1E5bMF6d41f1k/sAZaB7SWYC/B2Dhu1lEMAyeaICOPNI3cAkCq1ceu6rZq2sLLVVJqfpc7hLdi97cUwRtT8pJpj5y4dfOscIlXmouWYNG9G+mLX1BGvbYZGgmyS+1j9tX/0pA6yZ5VkJLmlSW7SBWgTdZbSWxMX5RjfhV1+1Ex/fAGnadYgUgZuJigz/HwtdlxXrtZMpU+YqHdafIMMWVyEXX5Mm9ZUua1x1/xfbnd1SyW8zduoOZWVjK2VrtoQFKbOkhWUofda8cg4xi8xm3pAyhdSKZ6KUrsE3MwRrQ4eOg5zZLICRV5If69eBl8/FBZY2jEnlyuXU3/79R6Rx444ysF1JafvjOOXGNGxLW6YLPUf+UnJi3SzxAr3IlKeZQW4dCE3t6CCCFnAmerAce+Qlracek/A7vmJZrIiMGnoNVrPbSrHjr78KISEB6h8lUoCexUc7jIpPS+cYZW332jJcJsBqqZXSpZ1BfVNILc6aVRRgMAIxhq+QMJFeqlVrKmcObZWOP08S1+Qeqk7mXEVA2D1Xtq2cJp7ZC0oBUK/oF38fL+nfpjhA8itY5nJKfyi80ErXY9giZJVvRNUkcuH4Lrl56YRut/v3H8nixauUFT4TrIweGbLj2Rb1bOK1mza0s6pLC+tvM3crCyNjGu2dXAAmreX2ldPKyshKVaqUh0V2sqxf7wVL9SnErfIbOSmsmtlA45JLtZM9uyPc0VXUfEL/IX9fp7w+MZI66LKfDcWghAJAEno7WL1OaDfM9RPJCJgBYCK5EJ9TNx4EPZcxlzZJxOuX0sqztJRLlSPe3T91cIuy1NSoEd1VTCkhWglOQCUjF7jB2r0hZyZ3FkEXQZJWvIPhPoXrOQQuI8Ny8nqE7HqRSgqkCpP7AI0d8Jx0zm4nL5A84o3qwUjccAP4Y0ygF7LczjyzVe4htvMSzzE3xPXVBNEyQR6/mAkCtTJii4+ie0mO/d+nJEv6WmUnUwXBCg9k0n4wbio+msDUVL0I3sQewxaK5rJ6n758zvsSgDTvNlRp8E6ePBfWoE5iaxtldTF1XowLW7LkX0njmd1UlU+6noCgda9R0fpQCkBm6ZSBinuO+qzvUn7//U/I081UVDhZ4fpeMqk/LEqVADLaK8sS4+TID1cGVnm6Odu0+U5mjewuxY82lPZ9Jhi1rjKT+cqZA6o7Idf3SufGrkh4clEUK5R2Y8KTYWn22zgJ2H9CnKqVlX1DhognlDDc3CylS5cs6nfIRBLyAt7AB1KKpIESGPgSiRi2umbOnr2g4vbqg85oHXSIe/QYIMOHD9AROVP2kWB33LgNap8biKn7pW1JSYv4us6/TENGfBbJV7yK+tM1qjdjY+sgji5u4o+YvAcgUA7wfS4uyJqmNbB6k24yG7Qq+uBPb1dFch/gfVd2rD6txqVAqRqINbygq+KPtkiarV8s8BGsWf24fufOfdAvvg0gG+Xl4DrSxcydexcZyrngJk+qkjQSCtTYDovbG67FqKW3/96Bex7DluCy5LJLrBRYCW7QvMP/dATMAPB/Otyf/8Gu+j2SSVe3qQyxrlkrS0E3zwSd1Mm96xX5KjPU+MBhlizj8HZC55auIgI/fYscLXV0j7oDmLH03rJX5g0crtxg6Q2Eyk+ceCFNh/mLZ8AhabmuirKmAcdJQVAy7EbSRXpY/OxB2VLKI0yqgfuPsmwkaGYdyk0xZmnjTQdlnTO0Yhy7eVcK+IZKQsAfyW79wi2UC7sEYmwoZM9zpgWQf/PxsmwKUljvN3xvJR1CeIomy+jRf4PSYbnUatnz/z340x+kum36yNg+DRVA6ts3ditgx47fSziycrr/Pl+/iUQ9z4xmxqVRwiwoKFjs7ePv9uOJ7dixT4E/yv216D5MnSuB0Jo5w0FsPAX3JMzibwoB4IYNi+Xk6XMyZHAfZTV0T5kOyiI/aVV0U2YypwG35KO7V1UCi1XSV/It+DBpKWf26F58bJFjjhn4BISLLt4Qvy1bJU+v7nLg5y66dgYOzK2bpzbwaXDorZt/Uf4YehHxhU6Ic337sXgVqkFpoGJTA3GK1rCWboa2Li196dJ5ACi9VGOUIoWb3LhxR9cmZx7euSq/d6kso5ecRKxj8mjb9BdogR21+ITcQxymFWh6Nv0zUZ4+vKVAud+Lp3L64FalNEI6GJbatasgmaaFOnauXFlV/OmsWYuQnT9Nti6bpLwcbqCxSZ68JLSSX6I+FHjq5EE8pDWsfmfR9hO4ct2kbFlPcf2+v7isXwQFo9z4ExBPp5WTJ32Q2ftKihd3V8cjN9+Gm3YJttSpnQ3+0UJsQuBhSQOw/g1IthNSmEASGwVWQtoy1/00I2AGgJ9m3D/Lo558flvm3NwD61hSZPrWkKyOqRN8HgG+XngoUoIN7PbYmxYwgryuePjoAz9KZW0EnYMzstA6wGWhbZs3bJyy/nWcP12GVioW7fjBds6IswmWXu2y6GgNWMEFD826SNi4j6/cFJAzuuZjrbLeePzGiCuyR8A6wSCtF2mRAacP/jSd3hOXbaVRNv9ox4ttgUBv/0N7gNBgpYNM8Mey7IqzivU7iThCBl/z2PaWESp7cullZ6WMQN1fwzJu3DQZNWoSgvRbSD0Anv91CfB7Dtf9TWUJckuRRmVS/q/7YOp4JLa2tLKGmoOvqSq69RGgHmIslot7wu9dXSP/4xkHJzeVpXpy3yaQExeX7t07ICmhkbJ2UvbM0eBDSL97BCrM9mWiQbNvf9dtypqnmPQdvxbALRKkws+Vxevy6QOKl5AyZY1h/f4RtU+ePCs7180xCgDZWJ+xK2UStG27d+8nQ4aMhe7yd0qBo0E2/ADelH33X0uXba9k+7a1gk7L8m5ttE0xprSCR3r5SAeAP5bff8+jq8O4u5ugSCEYpfW3Uv0OUgHxeeeObpd/Jg1Q1D+sbG3vptuH+tad+k+RtfNGKhC4Zu5Iadcbz5BYCtum+sbgTuV0tagokz1/SbWsgT8mFLVo0VBXR5uhogj/tLJs2T3lzqVb4UF4DnmaoRS0hv3l6AIPCbp4ARZ9kerVi0o9jDmLFnqSMqWN1KrloTWjm2YAaTOpbsgpqj0XdRuNzAQGRoIH8xk+Hl4i/CGd+OKj9DqegQy1aQ3PiimroJGmoq2ipnVRfDiby+c7AmYA+Pleu/9pz/c8vSzL7x4RyyQW0h8cf2nsXBN8fLo6ImDJcHdxEaoaGHt4EYgtAxB6Cvds/xJeStpJ/0DVO7eTbb8OlbXIjhtXpYTaxKw2qibcF2e5+A9oJJJE7cEvVLLwk/WfbldaIkh3wK9dzcVMebaVV6O4/FqhT9YApIaFcS7JkPUb3ww5Hnf2OVepiBg/fTDJ+EO6oFkuPLfR9YHLpLXg9vPoK8ExAWQm5whkVQZJrx79ZNmyNVKyahNw/Y1SLz/u86GLP3j1rp8/qtxd6bO8ffHyOGN+qCfeTx/qDumZLa/0Hbc2UWQfH96+UlHAlCxZVNc/UzNdurSRzp17y4tnD8Q9VXpT1RLVegKSzgAxpBRZNWuYStYYP3666iPj93r3/lYGDiRci1nGjp0Kq1gI+P7mGs0iZnwb4xD5ly5zbjl9YBO48gYCkFRUwPLFC1+QRZt+TRAo9ftro7pv1swbATLl3xFnOFtOnNim+PMmHj4jv/8wUJI89hLHSj9J+i5TZN8DD6UlS01sYxb1FCmskaiTS1q39gTXu43upDgOSQGi7lw/h9/Ha/U7oLxfwdI15T549WgNZGE2LWP+fLwfS2hIkBzfu04GTvlPqNGc3Mg1P39sl2xd/rfK7H2NpBBqSQ9C/QyIv6TiRkoQqVdt9I3MHvnWurxmzXypCE3d+JRS1Tylzd+ekgQcfQWzWEpHfOxS5m7CyJxybk2oBNrhw7NEFjQVFdKifXSaartihmClk8yPyWBY7+yQIEcyZxYmuPGjGkOFD0+EsSCrevjv5yQJxsnCxUE2Pw2X5nXd8WwJlx5QXmG9dy0M1SEI1H/GvWtb5v0+zQiY/mV/mv6Yj5oIR2DD/VOy+fFZsbWwUtJuVPl4l7Jv82IVU7Ny2ZQYDx5y5S294qIExhsbZKjpH2tp+4bi/vd0CQ8KRQKFI9xNSRXHHhNF6GbSHmjcFgawlRMPqbZv4gn129HmCfgM3c7aNm3KIOdmUAyIbyHdBbOE9R+Mu+HiZjZjTZBAs+QCLca2O/ZK7UFr1xJP7MKQi+IfiaYZl9OrRGdZdnOffNXuZ3C1vX0Bafu875QWoL0bF8n2VdMgT/ZUNUdutKkbb0dr2sbeUbfMF/FdULCsWzBWGnX6Rbf+U808vHNFHfrQoRNSr16NWLvh7x+kttvFkeEaayOfaCOpRL79dZYCJQ9uX0LMWaSQh49k4KSJKVGiSLSe0VI1c+ZClchBq21cRQHNAVOV5atMmTqIr+skhw+fUGTVpvZlPPCEK1tVSIhzx/aS9d5juTFljPw0YLhMGve7TF+2TuTePWm3aJa0yp1FinqkwG/UDxQkItNB0t4WFiha6PWLk5Ol9OmTQ3+VmrdCAkznvoNlxqghAGPdlWWPAJiFv41LAHh3rp1TzxiSoRcoWU3OI2EjNzKAeW75ilVWdQ3/If/frcunoq12cHaXAUgA0QrjHcMAJkm9NHr0ryrrV9sW25RZt9seOsnEVj7g0+PrNgqpUYnkpo+DJKtVSZohAYPPr3/w4csPP1Ld8GMzNpYrhqvwL67yCsjwdqEQWbX0joRlyiodqrlLBXxs+iKW8BDAG+lfIgAayWuaavAQHWdOw/Xfx9W0HIC8pTFu1Th3NFdINCNgBoCJ5lIkzo78c/ugHPC6Js6WdjIYur52yazfqaPhYaGI6xkFOaiSMV5UdPdOOe0m3fFFaswioH/AuvPXCoJppNRX/YUWBGPuC4InPmBJIRFXMXU8zQ0T1/7Gtl94YiE5wKgPVlvd5uQQu6d1UwuML+4Roh74GmearuKbGZKv+tw4Lstv7VOWv48B/i6f3i+LxvcRHwC/okULSsdhvZUr7zUyFOn+ypq3hLT5foyyHPWfuFGePLglR3aswt8KuNt8xQFZjYmhVGvcVXaumS3Tp89X1jAmNJgq585dAMednY6fzVS9xLzeAbrOOQuWVV3MVaicXDt7ULlft2xZFq3bBG/h4eFwmfaNtj62BfdU6RQFzLwxPcCvN0yNFbOSTZXZN/dI8MtwcQwMkyezx8Dk5KuqLpq3VMaO+EWeIvkKjMVy97mPFEuTQm2j9c7KIomyQBHwxKfQOv7XSXdp3K6zZHKJlP79/xCHaYOl5Xd/qN2ZPNNv4gYFjq+dOywZkY1L0KtZCmM7hsq0PrpDV+UrWFo1ChdtJa2JUcklQ5AoU0JbHed0Pfj22iOExZBMmUlmdbIEKpJrhqQQCPPjNQDPiOVXXVS7dPVWgxxgbIVAjlRZHEYMqZrS06GRb9NiOmZ4Hhk+LA+8GKKUi8Ycs0aizUsVnjIPmb9MRvuhyHOxA+ijJBzjN3EhYxzW8JlIALoCtDzNEMesT9YdY0fzikQ7AmYAmGgvzafv2LRrO+Wc7z1JZeMkg/J+hS/Sd79dti6fAo3WEASjRz2w9c+OihopkflnCMZWX7sjnb4bAKHKR3iy4Q3ApxxeMJaVK8nieq54iWCdkcKHbXzAn5FddavicsPoKhqZWTXvimzf7ycTerkiRiiDqpEvRZicg/tZH/DVR1wis+gs4Z5untMvmouZwf4tWnTFSywt3L4jjRzl3Vf5vXgmc0b3UG47kiavXL5KihQpoBqkduyGDdvk+vVbQtcqFSKYAUmiaWZSNu48UFn9+ELUqDXevScfZk87ByddQ3ElSNxEMo9jLEkAuoY+kxm6QMvUai1blk7CT8NP0bpoXXdzc1GzBMeky6HkGy1hcZUsuYvIsHmHVPIDEyFsQFJtqgRFhosVngsdkC0/ST8GE8gudZY3QKlqVfm5XJR1kiCqWrX94Mt7Be7BCqBYSaJAD92WxsIveFxmBPPjiQkQTNYq1rUdtHFvgC5lidRt86M4wlqnFYJjKnBoJT7nW6fVD9BqrgMSah+lylG8UgNtd900aVILNc9Ek/gWWvwJ9GjZN1XKQZeYAIzJcEx241+nfD6q+lHE6DGUpLNBcgYTQ6p/dUwmL6ogD+zTSS+AN23sCKiPg0lhASx6DFtheQlqHTIcECDyWXMVFFutcz2VtI4vJatLOKT4kuj2p0vXFB+p4TMxN7wrdCWvuuqkrJWZ0VYJfNiay+czArHcmp/PSZh7+mFHgBxVYy5uVOAvk30KGZy34XuBPyYQ7Fg9AxlwVY2qNHhB3skQ/PGMkkMgHel8KnA8TcOvJFuLJlJ+JOL/Rv9fe1cBVsXWRRetdAi22N3x7O7ueuZv++yOZ+ezu7u7u7u7W5QQFEFApJF/74P3erkCEle4wDnfN0ydObHmMrNnx9r/kvCn2TlrsrX+LUkgoSfxqPUB5FcU/hDm9lngY9JnReGHPT/cOUBlFz1Et5IJiANBmOqB01Z5UpquPpM2gIlpNVl2rZwkhD9uM53De3IQv6psfiCl51u/fpHIKZo+S85Ifab4pcralZi8XJUN/8ENFmi5cEYQJjeOrjg7u8LyBydcdPWS0rnK9TsI0x0TXKuWUqWKkR9dC1wi14tJvWpgTOeyeHrngmqVKLf53nKkMKdNi65USZuPBIgQ7NP3QN3W/ZRVjQoWoWTZ36BnY4NLS6eRRspOnON2mzbNgLJlrUmzHF6dg69Yax9ZOfLWFFdIKGEBp2kub2WVMWMGEYeeHqb3qyd49JQn4rDBY2JaIM6jXal++0gFXsaQC5ucjx8/i/79/6XnWVtBpWNvX5zyCC9X9sw+wPy/zMJUF9L+/a7UJO5E5iZlVgAO7mDzOJfCtuQK8vPxEX6Q/i46qweTorng+jEQvYp6KoU3ptLaRn6BChqtdvm9KcjDW7jA/I/I7tkVhoW76sSAYEfBcKzRo6krr+cOmCBfIUwqO4xmgwn6OxTwFu1yxDebkq84x81FKJpu5Kk/hIAUAP8QsEm12RB6mE96vB8O39xRyCIzhhdoIB56cZ0P57n8r38DMoHoYNas8ZE28+GbAZqS34t6qZQlHbovmwt4euLDpas4NLQ7DpAPYLlMadWratV+yZLWeHgwH3oOKUTY0RP2R2GBL5LnuYiC5ocoB6E0oUjj863m4fShsyhfqw2RyuZSXK6xdbXGXWBqbiXae0lO8ZMnzxUvNU4kz8J/hw7/0JtOX5gCWcOk7eX8oQ0iFVfjxnV/O9TPnz1gmz7rb+slpQrmVrao2awHtmzZQ9kwnkYY+uLF0+HkdJ+4D5fDwtQQi8Z2xA4ynWqq1CcC+OLWWeHk74n3pQujXO3WounAxw/Eb/e7kxPMVTJv8Mk+fXIS/Uxh5QcEa/9Us4eofjRZEl2SFTEBsLZJ1d3DxsYaR49uQ2r6NmIKoAfXTmpqSpG2w/nLuTRq1EGkhdu1+zDeOnrgi08A2NdS8RHFWr+l962Fb1zLPD5CwIq0QZWD7GPM//+dSUhj1oA9lAbvzHsTpCJc2JWFqaoUhd1lGjZIjwebyFTfxkJoDPkcZ/I4RKZgJqhnQZLTTbI2MLKSheiwOBORukaP6zIRd1wL+2CzkOlBH7CyJA0E5J1KGvcpQUbpR+acsQ/24GOAN8qnyUUJ6WvEq9/goAAS/urD39cTR45sEblaI2uwGNGeqPvIKOrNrFMRpadOEJrAS05uisNav+ZIxowZSIOpVvgBv4xeEPvoi/+Zh+EvD2n+ota7dA2BelZEOPtG7WrN7DJtyrSN1zFl3RWMXHCI/OFMxEuNNRn58pWnfKpP8Hff6UohUTO9/rlWOEpVX18PzC2pWtjcqFo4d+vXr76UYSOv6uFksd2wwxCRy7dfv9G/zIfN4kwgfefOKfQk8ykLzBzpqqnSPWdVZDexw1MfFwTXroUiFJXLhbNqGJD2umnTTkoBKbI+OQcta764bNnyHkWLniYyai+xz0IF5xFmLSFrx13JR43L+/dOWFu9ufg45fu8YkoPvCfuPtXCxzmKNz6Fgz8ObZpL7isRffHa9J0GTjP3lSLnCxbMS+NeJrrh4I0+pJVjMuu4FLaE1CN6F87Ry9o5zhb0lczDisLsCIaGZA63Isn3R2E6F6bSYmYF/sjsQ77U7Eu4iawNbA5WL8yIwCbnyErbfF6RHSb3giCRseXbt8jn5e4eqLR2VCXfbFmSBgKR/wqSxtjlKDWIgFeQHwl/u+AV7Ic66QujffYK8W6dtUlBQYEUWNAFxYsXjnN782uVAzG9okefkcRfFf4lHufGEuBCNuG+9TIQ/jH8Je/544uY5RHONJLPOoAIckF0GCaYfiON8NfZ9MQCx+grnku+u+uQo1R9ynBBTvzkNxmXwtjzElUxIN8udvhnTeDMbfcxfN4B5C9VA+a22TF4xk6UqtI4qku17jhnauAcrCzgqZaSJWsiC5m5smYtIQTcceNmiNOcCSK5FXYT+IsEoqeUO5jTBUZW2NT5999NxSl/X5/IqsT52KB8tWFnZI5bPk54VqcCFh14TYTKt/C/EYvh6eWPJk06CR7LyDpgfk0X0pxxKV7cikiTDWkOP4V3jqjvUshLkKYr/kd69BiCLVT/u174/wwLe3tWThRCJ2f+OLx5LqV0I67DtsXA6SfjWtbPHoSj2xbg6a3TogkDg/BxMi+g05twbWuVKhVE7l6uwAJcVB+zMR0D++kxOb6ihJJpVVEuUuRtfRIQVcuXQD2YGvy852x04CAz1saxwLzzuTlRYRkJFgI2L7Nw+Ir8KiMrzEsaWZk9+wVFZj+gx/BhSqP4lqiFfvbn5hZAmVweYPz4J+JSM9LaypI0EJACYNK4T390lB/8vmD8w90imq9VltJonLmERvq7eW6/aKd9++ZiHUK0Ff37j0bevOXg4uIaZR9MncAPKjZjcNBEXhtLtBg/EmGvXuOvYtVRbNrSKK+N7ISrqz8WLnxFX837iaj2AeXXjCgoRHZNXI+xVu8AJVvnL/VHn1MLpvzr5Au04I41hpxPJwhY+YVWJ9s3NM75FYNLeggaGjYBcbYT1nKc9s6IWdM7kQAXitsi/2jMR8Opr/7rXw/9GuXE0FaFxAvxd1ez8JAtT1HBNcfEvuwLlZTKu2e3xXAbN+4gIjV5J4DSBL59+x78qsxRuCIuXLyJdRSZmsE+F5mA7UX95PanINGcMNH148fPo5waE0dz8XR3ibJOXE5wgNjEIs1RliwH7BO4xuGi4A8sXLoGui09LEzDnMmGBcG7dx+S+fY0pT27BHd3D6HpcncPEDl8OfPH2bOVKSjJSjkMFlZYgFn32EpJOzJwYC9x/qOLg7IeR4kZGKUS/H9HiBOQNZBc4hP0w/l6ufDviQvju2PHKpGZ5eDGWShXqzVloFmNe/ceifOa+MPm8AykQWRzLxd+BipKDdIIMn3OO28DxSFwHt/rrsbKfdWNRvSMqU20L9wCC4/tjmRGLjIrs3k6NqVgQQtl9eHDHyJDBoq4JoJpLnZ2RpS5xJgYHtIo68iNpIGAjrqZJGkMO3mPcraOji3N8JPqLE223YWOSrSb6rn4bL/++hHzicfrO0XZds1RBSVsssWnuQjXbpw3FE9uHCdzzV1xnE2MChb9nRduIpe9nWCjDyG/E3Y8zkumnksuxiInLufGZToDJkwuYBNAjPMBGHbuFlbPXkyszx5wv3qQAlOi/355THmEH3xKhYWDz+LlDSfl2AoWNMelS9WU+5rcYO0f5/blqF+maLhJJhh3IqlOZxwiXl5s1omu8EPfknyeWItQqVIjvHz1jrRzB2OUu9bzkwv+7UzaUpUyetERZM5RUOVI8tvs3yQPEUEHiIm1a9ecuABr4fDhU9i8eTcRAB8XWTD4Oefl4SYIjxXccckNidCQYAxuWRBp7Sjw4tJBMombK6fo5PSBUsJdAOdCnjNnKfIWq4x/iBxa04W1ztOfHoKzH5EMi8Z1SCwLg62hGWq6BVEO4lERurSxsSLz8HmUG/AF/k8ccGp7QQqssIxQhwVA1ohxxCxnA3F1/UhuwV6oThrPiEUH/xs2DxaU5eXS0S1g+po8RcqRgPnzn47N31dPbCfN5CKkp6jo3xX+3bx/+QD7Ke/wiwdXRXVDQwMSVoPFdvNuY7Bn9RQiz+5B1DT9kSqV0e+ajNF5Dop5QKTw/DxgsnqmdVHwirJAyMFkPSjtnqKwqbcgZRZiF5LoyiqKKm5DjANsKo5LcXHxJx/Ou8SHaIbp0yP6OCva8yc/2y25yih2FWu7oWFh4RK54ohcJzoCUgBM9Fvw6wASSgC87/keK1+fEw/q/nlqIY9Fhl8HE48jI9uXRL7c9jh2bBtRNuygh+QY0dpr5+dw9TcmAUkPJYjWgR9aHH321tuQIn9DkNk8/MtS0fVVl9TkEB2KhRf248CgkTDt2AFO/w1TnBbrwMBQODhQ5o28P196fILNrqM3UB7SHa7QNSKzB/mKhXp9JR/HL9i5/ZeHVIQ2E3vHze0TqlRpio8fP1FWgkJo2XM8OIVXVOXY9sVgrQQXYe4j+pjoONyiaiepHf9I/ITnD60X5vKrp3aGD59e+hwYoQ1E1QmJ5yvSAM8d3hKbNi1Bgwa1BDVM166DSKt2SQyDhV/O/tFv6mbkIp7HP1UuUuagW5Q60jc0AG7+lG6R+EM7BtuBPwqZbkVRDAwMMH/+UQyd+QF6mTPC9/I9fPnSRHE6wppN23Z2+SN1bejTpwuuXLmJp89eY+D07chKGm1VwU/R0LS+9ShryBOxO5M+qlUpZBR1olrzBxZzZz69c5FIuIPRgPwuOXPIf5Qlh7MctWzZkIi350Z1eayOs0tICfrotSX+UAVDAsuxbL7dSYwBBSkohp+d/LGZlTIGxbTwc3b2rTQYRVmWVANvYnp9TOpJATAmKGlHHSkAasd9iDCKhBAAL318ga3vrxJXlB6G56+PzCY2EcagiZ3e9bKSaSAvSj96htU/GqxUuRwO7N8Qq+ZZq5Cr73h47tsPfeIUOz17DIqkjTje5cvfYNSoR/QyKUq5T7P+0j77qbAj88OH3hi8IQR+d57C063+L/W07QDzAXI2h0mT5sCICIzn7X4a6YuNx80O66zhOLN3Ffy/faXcrxNQsV47bZvSHx0P/1a+uH8grEySTBCLJgBhDejeNdPx6OZp4fN26NBmVKhQmsxyjQRnXpMuo1CGNGacuu07ZX9hXseEKEtenMJjb2f0tSuFxf/8/H/LTMLeuHFDwOn7ylW6Dx2irOlW0JO0txkoCMQy0qGxNm4y5QKfN2+F8jy7L5hQVhemAmIKID8/f3GOiZxLVm6MkpUagrO+ZKLAH56zi8NzTOlTW9TpNGSuwETZWBw2Tu5ajiNb54lUhCdOkO9sqaJRtqLwa2TDRbdut+HtzebkMoILUfUiDuoIoiCQCqTxVC/8MexAGYIoRER8uNuQgPjB1wAcSMcawKj8D/lDmK0rn8g15QWlofu3zGf1pjW2LwVAjUH5xxv66Wn6x7uSHWgLAsdcHuCgy12k0jUQBM82qcL9gjQ9vpKVGuD+teN49KNh9j9asvi/WHfz6stXeB46jPyjh+NSn3aR0tK0aZOZNAAesLEhLV8khXOKmlgb40ZIFnTp5IEuS+wiqRX3Q0xWe4GCOjjCTpFnOO6t/bzy2bOX2LZtvzhQrFzdKIU/rmCUyhi1W/amxPfhGG9dPBpla7bUOI+gGIyW/mENFwe3pLSyYnIPPL17ES3pTW8/rI8QRFgYfvz4GRp3Ho7qTboqIUko4Y87dCZ6mNR6Bnh76aSyf85MwkEaW7fuRYsWDdFjdS74X72PixfcwdHzRYpYKH/nAQGhOHnSTQiGrNEbN26oWF6/diAi6StYvXoLXrx4LdoO09EXdEAdOrQk/zRfHDiwF1dP7hDnsucthqFz9okI8CnrLgv/xJgQmTON1Z2Lh4UQWaJiPfpfMsLsIU3AtDCt/5mCa5QVh7McbTt+Cq9Mi+DlM1CAlw7YhYV983KSvx2XqVOfUcq+FxTkYo259JF6wo0+YMnOu5MoX74G6aEcRTtzCkkO6GAXkPrZfQVPnzpVS7mM/lQ3XMgVDdMfFu7ukavLdsrKQbIlFMbd9AZ+8H70DldufkHu+sVQOHsY6lEqykymqQTBdCOi3mI+wMjKpk3vyF/7PkVj14S9vUlkVeSxZICAFACTwU2MzRR2vLuO85+ewUw/FaV2a0rRY6lic3ms6nYdSf56VCb0qAp3op34++9mlLO0uXD8HjSop0hgH5mZRr2TBZfv0FPtO/4uXyJS4Y/rW1oaktnrL/VLlfuct/IFpVz6m2gOUhfUo+Oaeagxz/NhorAwIjM2k76ymSa+hTUdhw+fJK3fXOLBfgsLqzToM3Ed2Mk/JmXq+qvwIHNVasrgoGkS6Zj0L+skPAIulBu4fPm/sII0f6qFTayBajx8quf/5PbJD4/A7AL2xmmQ2jSQ/jd0YZvBHtWadBH/x+xX5+joDI9nDnh84TFu305PyxeyGljQXNKIoTVqdAW3bnmSMFeO6FYMYG1tJY7nzJkNvHTt2o7obR5g7Ng5uH79BtEB6ROR+XYsXTqTgjP+I/eJ8Mw2TJJ96/wBcJYPG+K+jGlZObUn3JzeiOq7VkxA56HzKSWig9jnbDote4zHzuXjsXj9EQxo5ki+cTmQJUtGQWx9iZ45nM2Do3ID8uWHRVVTZK5ihyuBduhe/Qt6t89AJm0f+FMeYM7OUZS0eBlMQ5RDUxf+lCfUNviZo8ghrjjFz6XuY97jJH19h303Qed0juhRIb04zf6EnFGF+Qr7FfdUXBJh7ewcLmTu3u0caV7mCJXlTpJFQAqASfbWxX7gq8jf767nO6QxMsXYAk1hSA/LhCicxJ79tFav3ooQ8p3hMmfOMpjmqoQezUr91hfl6pMX4jM3hIiK41J2EA1CJrMQZYqluLQR1TXMvF+GEqrnsPw5Nv4i54g7Ns1w4VR3TGSb0TQ4ShONon02YbVq1U34M9nRy5KF6OLl65HGgoXWmBXO3vC7DA4xa0nWSgoIhAQHIcD/W4Q0cKz9Y9cBJve2pKCIhC6+IQHYTzRGqfUM0dgoM1ZuHkGaqu/4RFG7/Cyo0qgzjmyZh5btBuIlkUZzGT16Bglz70jjfZtoe45QIhE/ZM+el6L3s1HAwR4S8K6LAJ+JE4crMwrxB6S+flYKbulAOcaz03ci+R7eukfUU8MpKGMyUqdOLQRFbt/L8yOvYlXK124rgjwKFcovSLZDCWsuGTKkowyVbkL44/0r2+fTArBx+TgtLLxVyaJiws0HTG6mEDy9gZL2fJko7APNwp8mCwudA4koeu/S88TFqIvW0yuL5teuJb9Mit7t1jt3tN2NGpWPSK8zIEcOU2W91Q8tRfSwBdG88DPOk3y4LziZIA0FuAWQEMsUMpy9JGHeKsphyY14ICB9AOMB3p+6VNM+gPwymP/iBF59dUNmYxvy+Wvw2whaTc6NfdM+uzkhbcZsOLVnBQUqzMaMGWPRqGUrbHqdSSQTtzf/KUCp9/2RXgRFWvVGIKWFW7FvI/IQLYy6D6D6Nar7ax5ZCrOsPttHNFjeENcff7nXI3ONotz9mAr3yRxTmchQs/9wzv5Eqe7O04OS+69LJpjonK+7dRuEvfuOovOQeUJboWhXrpM+Ahw4wCpiDsKIa2FeyEc3z+DJ7XO4ffEQaXe+w5w0xJ7urvTRcAT58+emYKj3aN68C60dUaBEZfQcuxLM+5iQZa/jLZxye4ze2atieeeqYpyK/tkXkf3vzu5fiz1rp8PI0Aj+fl8VpyNdpyL/V8s0Geg58p4oWIKRO3d24hUci7Rp7UgYy4xly95R1Lyt0Byy4LdixUbRDrsE8POPSzYyAw+fu19sx/QPp87j7CmKYmFtJ/wN2X9x/vyVgtUge3Z7iv4ltoICeYR5OlOmcE2b4hrVNdO3sMYuuuedan3FdlDQdxJoH9J8U2HEiLyKw79ds99zSEgYcSsaibotWlyHSf5sKNs8L/6maGDzWHD2vfpiQKnerESqOZoCAslPsVUeb8plTKZ3cn0Jpv3bH1PD7NsnOFUrpD42GQWsjogW7EsBUAtugvoQNCkAhtDDb/qTA/jg74V85hnQN3fNKM2o6uP4E/uOrx9hOqWGO3mSHaaLiS/J+XdtMIgSmkdXXL76oVDVZghzdQV5e2Pz8Z2on0PxRR3dlRCcgpwIPSoH6eivjng2OBRwpdR1TLsQTL4+rekByA90Lmx2WUk0C71U6BnCzwh3H9yhhyNTOrjR9W3yeonk9orzvF6zZguGD5+IEkT/0mX4QtVTcjuJIfCOqEOWTeyCem0HomyNFlgyvhNeProhZsHceE0pKCPdb2hI2Lds75opuH/lGAkxocLH8xsROHOAj6GhIVGRhGujrK0sMWv2BDRrVp8iaL1QslQtBFBUfI9/VwgalMSAbtHzkyIzyFDTwpg9tFmEIVSiwKS2lEmDC7s7vHtxHzMHNxH7bCbOTprCeQc3CW49Dvjw8vKmgB5L2OcuSgJtFejT3A/RR+TXH1k+WAu4a9caooWpKNrgP6xJv337Prp3Hwy/wO/KjCB2GbLCnIS4Oq36oEDJKsr6UW3cI+xXTu0lTlvSdTkpcpoFb848w4I8c5v27duVSJCH0nPgx4OAajONCwdcOFGULheO1jUn374clkG4RL7CTNIcm/LsmQ9q174stKiOjvUj9KXeDlNRHXMwE8/WhsQDaK5C+bL/aSpYGwajUk56kMWyMK0Vk9czt2B05ebzb3hYtqh6FSkAqiOiBftSANSCm6A+BE0JgAGU2o3z+n4J+obSNjnQOUcl9a4SfP/Qpjk4vmMRaSfugINCmPSZiZNVOa0iG9Q1l0/oumkfXPcdBNkw8PrWCdgYJ4xWgx/mB8nPz4/SJ3He0rRE0loirb8weSjGytk+thI3V/PcPsSxFSrOqbwPFNXEmoXItURq261+Xjg+uS0eqsz+P7rZXzAlk9XQWbsjTUgfoRG5o7UIbFs6BhcPb1KOj4NzWAvOPHHsk7aJCKnZ0ytfsQqo2bwXCYbX8fLhNcp+EQIrMtcG+PvC85MzPlMGC9bw1axZWXD6ff7sKQSAf/8dRMLNA4p6nyI0+T27/o0pM8eL/jhX7fUbdzFm6SmwsJNY5YjLfRx2uYei5pmQ66UzTuxYTMTMn8Vw0qTNiMnrroptP19v+iCsj9DAr6S1bCD49YYO7Q2OEubC1C9MGr1nz2EyAd8R+OmTX2PJyk1w/fQuUYf/dOvWTqS7mz1+Jirkz4PpOw+AnSa6/K8ttu84CEO6B96e7sr6mbLnR9s+Uyg6OD+dS608HtnGs7uX8JCiqxu0G0wRxxaCYJqfY8wraWCUGvcuHyVTcyrUqVONGAha46+/iuOokx3RWxF7wY8gEHb/YAqXx59TiUCxyplVzMORdRrJsbdvfSnjiD75DUb93LvllgqPiD+wfX4vZV+FiZNUUTZQSr0CFC3MPoMqacoVp6Ndf6CP11PvTdGWPl4NGdwoysu3PrhYooT6WSkAqiOiBftSANSCm6A+BE0IgD5B/iT87cO3kEDUSFcAzbP8pd5NouxP7l0TRroBlA3gjOiffUk4zycHUZjQA7MmRc6ppyNiE04aim4Nu3aN0jrkQMM+3bCxTf0EG/+Rt6bChy/bD5MuE9PmpC95VQGPM5cotIwbn1iiQQ6fCMnr1QfLjt/byX8wPQmTHC3IKbwqVGiAdv3/SxHcfep4JJf9z26OGNulEjp3bk1+a2NoWSCENRZqqlatIKbJKeuYF3Pq1AWkqfIjTZKu8GljgmEW8jhwI1u2LESHUhBdurRV+rspMGJCZ/YT5Zy7vh4f8fblfZiZscYnTJgkW/8zGVUadFRUT5Q1Wx6mkeXBlSwPTFgyJFdNeD++j6PbF6FN78mkSSuF968eYd6IVkRLE4xq1cbg1Stb4iusQsJuuNYssoEzofWECbOwd+/hyE6LY6yH46fD4nd3KQPQKrHM2vEQO5aNw+0LB4QJmbWvbk6vYZUmHWo06ykCU6Js8DcnrpzYIUzybC5mQf+vOi0xlO57zazffnNlxNPu5CrCz5ayFKSh+mzhWpx67ezZjxTV7YNevbITRobiYtbK8Uepwq2EBbxOBb3EOfbL20b+z01JY2eZir5iqfDH7Evqg7MRxVYA5Gf1BSdjyk2sh4Y5vor2FH/4nDOl8/tA1o1XDj4IqJ9fcUqxlgKgAgktWksBUItuhmIo8RUAPxLx6rQnB0VKpqaZSqJWhkKKphN9fXzHEhzYMBPPnl1BunR2yvGw+fTIWzPiJQxDXRWfOhb+ak5bgrvLVqPbmsWYVaei8pr4blhSntiYRtqp9sV+fqGUvYQj6dQLJ7XPZBYseLnUz0W1zy/uatWa4cWr95i59a6M3I0KqCRwfMvCkbhGZNSOjvcEL110Q+YADfbTy5IlkzKDxDfK8hAQQlQ2lFM2svL8+Svi9mtIdCb5MYz82YJI4Di0eR5F+lK6L+L0TJs5O2o07R6tiTCydv/UsQNOd3Dc9SFa2ZfGkf5tBH0Ka/BKV28BYxNz4RPMfIWpU/fC5ctfKICjBjJmjF4jx2NlHF68eEMC8gDh49dh4CwRXMKE4EXL18HdCwdhamaKnj07kL/xYnBkPAdGBfj5inqsdd27Zqpy2gsPvCTBO9xPTnkwmg3+n71weCPd612kpaX83uRrmZ+WLQtGwNrcgLSVx5RXqz9nWDA762gSwXdYUfldkb9xatMxFLfzxxsvQ6JpCcGD536Y1PUkvvuGaw0nTSqAQ0fc4GmWlrSYhWGTJhWKUBAJcwHue2mGprl/Cmf8ockp9LoXog8LFa0dZ1ZxpaUoBW1YWecS3fOzkP2Vz5CWj7OFqAuh/NtcSWno+hX3EP7MijHz+uDrcCYE1nTmwAeZCUQVHC3elgKgFt6c+AiA733dMfvZUYSQH02nbBVRxjanVs3Q3dUR47pWFPk0a9Wq8tuxddp0AAeHj0G2/v/g9oie8fJf5C9UJljNrhKx+9sBRFGBH3icTqkq5ebkcodML0wzw8fUv46jaEJ5mHPWlihRQ+QV7TBwpvK43Eh6CLCGi/3Tdu1aTZRHlWM9gWDS0OwhbjjmkyybwU+Q/bKWWPEybtiwPe7ef0YCzbUk4SbANDCj7u9AulQWKHL3FU7sXKrEpGLddjC1sMYxwqx//x7k/9oPJiZRmzeVF6ps3Lp1H7VqtSSf2UUoVaWR8oz7h/eYP6oNBcd8EMfGLjtF2r+DwoTOPITsr3eaCNM5jRuXKo3+JzTvGbPmEfvqf7599YLj68d4fOss0TLZIl2WnOTj2U29mtjnjCBD7njB/sY6pCLLRmRlA1kJouILNU6TH3eI25AtDh4Uacvp1zrND4DPs/foUic1Vu3xgpF9evg9eYNbx4qjIJlbF5x3IxoqL2FVaJvPO0KXrCXcRb+pbBaBIp0cMyIw5yALgOyj2ILcVrjwsXMUrFaS3FvY5eV/1F46+u2xZtKUnmtrH1mJIDbF85PbPUq+hqxVzEcsB5yuk4skghYwJIk/UgDUwtsUVwHwiZczlr48LWbUO3cNFLTMpHWze/PkFmYPa4GLFw8S31e+aMfnS07umSs3h66tLTwOro22bkxOspn2KrHhc27e2CZDj6x9NrdkpXzF7ODNgt/vnKO5DdYc7CT/pP37j4m8poGBQcJh/f79xyJTwcL9L+Ml5EY2Tnks4RBgP74ZlBrM6c1T8ftmXjrmhZs0aYTQ9MV0JD6UsouDhrhwTuteRT3h9/UrcuUqjfIkOLXuNTGmTSV6vTWvz+O2p4MQAku/csd5ErzsKVVbyfrtUYhy9a6f1hsPrp8WEb3bti1HsWLRWyzOnLmEadPmk8DYl7ShZcjnrragZDGzsKLAl5XI8SNdIvsXLh3fGSbmVmjRYzx9eFYSWMzb8xSpKFMMlye3zmMxBegoSqtek1ClYccIGlSHF/cwc1ATRRWkNjHD1A3XcGD9LLy4f5l4Ad/ANn0W8Mctl0GbHuDG0U+ktaN0biWslNepbnA2jssuJmic00ekenOg6GAmc2ZzLYuMqubZIUMeiA/X/40qhdTkx9hiyleUNvpAms3Cgjib2+WgNG6DAz7qZPNV7Uq5/Yn8rbc8t8RA0uApPiiu0PPQh3wTa9r7YuszS3Qg30E+d+1DasFvWjBNoMjTzowHPCb2HayY6ZsIMuHrWuX2jqBZ5M6kAKiEXOs3pACohbcoLgLgjc+vsf7tJTKh6mJIvnrIamqrhTODyFawaEwHPHhwXrwYoxukK5k88ldqAsPCBfFx/dzoqsbqHNO08EuVHbRLp/eP8bX8sCxPjP2KwpQO/FDMEg2FjaIur69evUWJ1Efi3TtHoUUwJOoIzsqgq2dA+3YUENAT+YprzsSt2rfcTjgEOC/sqb0rcPPsfujQD4RJ0Dmi18bGGgsWTEH9+jVjNZjP/nrCPeJbBVuMof/vaSR8WJLvWlIqq0kIvENCoC7pNE2JhJ55AolNjvZ0YE9pKHN+/Y7bS6Yi0M+H+PbOC6yiml+dOm1w48YdcZrpVwYN6gFPT69w6heSXmZsuQ1zyzQRLnd59wJT/qlF0cTW6Dh4Dgr9VU2c713PHkXKVMdnVye4vH8pjnHQzthlJ5WE0btWTibKmtXiXLEK9dCu338iGETRAd9bTj335DYJk+M6oXadcThx3EycdnJqIAI3FHVV1yGkOWN/YQ4AY00hu5SwaZjZBboX/qIUAps1u443b3xx7VoVcivQx5ZnFminpuXjdrtvTQXnHedwdH+ZCAKsap/cflbzoAhWkLck3F39YAzm91NYL9inL4jGYUT+hbx9/J0p+W6HISO5t+ynoD2ms1INLlHtQwqAqmho97a+dg9Pji4mCJxyfYS9TrdhqKuP0QUaIW1qi5hclih1HMnxm4upqfFv+09PdbKWL4N3l64KPx/m9FIUf/8AEXmn2Fdds88Nl6j8+5h0lZfjDqbk02KFZmQCUSRcV21HdZsZ/Zn4VLXEJgn75MlzMXfuMljbpke/yZvIXyhcG6Hantz+cwiwZu49UbPcuXQEzm+fCDqPOq37xMrvK6ajY9Lu2i3/EQtfw35nD66fIpPjCrRv/48IBmEeOQ7yiEnh3+Zf6fzQq2pTGN8+K/L5xuQ6barTLWcVlPDMhn1Ot+BH7ASZjK1J8EuDZ94uePftM97Rv3a3Sauwtl9Tim6eSiTWc6IcftWq5YUAyILcka3zKdBmoVJrbkzZb0KCfka9Khph0y7nBuYgkAPr/1MKgHz+w7uXmLT2MqV1242Nc4cI4UlP31BxKVp0H0N0Pv0jCH3Kk7TBwh8X9gE0IE7DK5fnklayLjp06Eom7YjPDFHxxx/mBe1SyEv1EJrk8hGp5PjDkt1KslOw2fbtf5GVIASfglPj0ZtUsCI6mcjK55M38MCbUst9DSGGBYPIqqASae+WUwaQnqRRJn5oUdikm93SO0J91gKy8Mdm3nXE/Vcx4zeliXfEX58j1JU7SRcBqQHUwnsXGw2ggnDVRM8IYws1gYXh7wWrxJoymz9HtiuO/Hmz49ixbTEaRo8tB7FryGj8M6g3vhEHoJGRIY4cOU1+Ma701f8yyi9ddcfrqDpjSpYl921gS0nVudDHLsqR71VG8pNhZ+335Bdzm8wrnMmDUyjFpcydu1wksa9I/GdtKH+oqiAbl/bkNTFDgAWvS8e34s6FQ3BxeCay0HCELQdfKAq/sJkgmH3ImOT3TxbWDJ4kIvSTO5cQ8bEvpQ3LiSVL/iMzYZEYdXvs2BlKp9gLmbLnw7+Lj8fomqRQadjdbSQUBqKrnj1Wje2Mxo3rUDq3RVEOnTn+ypSpCx/fQAyZtRfrZw8kjsAiqNSgA5HNZ4/ymXCX6FpWT++DBu0Hg4V//j/8RNpZTpdobZtB9MeE1F6fP8LV8SWKlY8+93ZkA3z15CZ2r5go/AVtbKwoaveSMsAnsvpRHTtKzAOcE/guuQGwsKZPAtnie9aC3sXqR0Sv+rXr7puhqK4LihU2Vz8VYX8FffT2IA2jwgwc4aTaDvv3rSbfP/YTjCowSe0SaQJWB0SL96UAqIU3J6YC4Po3F3HD4w0Re5pgbMEmZEb4+dWqhdMCP4BXkb9Pq1aNaHg6lGj8saC7sLNLQ74saYjdPxvq1atJlBamePnyDeUKnobT5O8jbBB0RWoTU8GLFvAjt2nJkkWJPHn+b03JscGCzR1HSTPICdotiM/PlqLw2Lk5pgKlel8+Puy3VYaiBKug19hV6qdT9D5/ELBWjn2sSlZuBDMLmwh4MOGuq+MrZMlZCDnylyTznVWE89Ht3Ll4GJvmDxWmV87UwBxtLFiwf9nSpeuIuHemuLxbt/aUonAz2g+YgfK120RokrNuvCB+Po4ejSo4IMIFMdxhbSQTDO9eOVFw07EgyMKCHmkOmcy5ffsWIqdtZM1lz15KkD2zD5pCaImsXlI69s/NdchhmhZ6mzbB3ekpmYAv/jaCmkmea9ZsSYTSU1GpXvsYTZcF8D4Ns4u6Q2fvEb8pxYV7KCL49J6VqN6kG84eWCN8dfn/tUjZWooqUa6Zw9HnizsRuDcUv+V1M/uLzEd8wd27p3+h8YmyITrBXIG7XpiLlGr83GFamDPvTVCFMgtx5G6E9HJqDZ13NIY1aYujMs0qqp8gc641padkkzO7sbDvIW9zcIh64eeeu8cr7CDKKqa54lKd/AXtjCPXQvJ5aQJmFJJGkQKgFt6n3wmATI2y+OUpPPP5gAypLTGqQGNBCKuFU4kwJOYA/KD0s0lNkXS54EP5OZk7i/1oOMUTf5WnTWsLV9eP5KhtjOrE0XXCVgchZiYkjFmRWUIfec3SI93L99i9fJy4rn//7pQMfrBWatZY0Fi0aLVwGrdKkz4CHil5h4W/TfOHCRoNxoEd64tVqE/+WLVgn6sQ2IF/Uq8aESDKmrswOg9dgLSZwl/ifJJf6kyc7P/NB74+XoJQmaM9OeNM8eJFsGHDIqin5mIN4MaNO4WwZWRkRLQjhVGLeCab/m+ksr9Xj29g1ZSe+OrzhQRTK8zcdl95TlMbLAheOb4Nl45uJu1kEIIDA+HxyQX29plwkDJhMD2Mejl9+iJatuyKxp2GCy2W+vmkuN/31gb6iDVF3st3ceXYVkGhYmCgT3l/OxDnOwUckLUgb95wtw7V+dWu3RqPn77GLLo3McmVzb+5fZR6jiODfTzdBIXOsHkH8dXLA6M6/CWaNiJS6EAS/Ll0GUGRxfRhwveJ61japBXH1f9M6FGNqGXeoNn/huPFo5vCF1BRx87OVmQLCaSAtiAK+FJon2/ePEEfhtlJmA/C7t3OaNMmM334GmDLUwuih/n6S5YgppfypsAg5h+NyhLBH69rH1tSznMvRfdRrtn/8BGRUrOwObikB2kaU4nMJawV5JRuHJwSmaaRr2PifmZTYIqa6vbffulDCoC/QKK1B6QAqIW3JjoBkAlWZz49DCc/D+QyS4eBeWprpeATGayc8un5/SsUZdcJRhSFp5o6iet70svvIr0Mnd8+pWCISqhEUYLMzTX9Mb3Qab7s4xhK9Da8sPN4WfMsCD18CDfO7lV2N3XqKEoE30W5n1AbQpv13glMVGtpaU45WfMQceslerD3JMGmHrqNXJJQQ9FYPzwnTml2nLI4vHt+V2hFomqcXxzpsuRGzRa9Rf5Z9XvLbXl7fhKmsZcPr+IORYF7eXzCgAE9yKzZjNZjKDDoMRHeBkToonLlciRAT6eo6aOYM2cZAr19kLNUVeFX5/Tmifh4iHAB7bDgNGBAd/yPskCoj0O9Lu/36DGEaFsOos/E9TTf+zh/YC045VrWrFkoyrQ0Nm3ahaFz9iJHvhK/XB4UFAC3H1rKX07G4cAbygzDaeOsLE2JQPoU0hOHp8KX9dWrtyhXrh6MzawwnAQXG9JMJoey+MVJPCFfwL/Tl8Sh4R1gaW5M2r1KWLt2G6W/Sw19Uj3t378BrPFXLXfvPqT0b81FruOiZWurnvrt9sh2JeBNmUkmrr6ANOmyYNvi0fhCmT1s0mamLC4bKYjEGhNWXxTPornDW4n2uo9eiuL0kaJeTu5eLgRLC2tb8RHCOZ/zUpYXcwrsYp5GzsOsS2TfN86EP6f4o+PKlcPC6mFvf1o0N3ZOGViUyocy6f0oU8evPoyKPk+TNrCGmtDl7OyKzYUqgR36H3UbihI5zJEnWzrBs8pCprFxeDS5og3Fml1cLjobowIFtimIpPkcH2ctIQt5sWVL4P9zh8fPcZbSWaoVSQStBog27EoBUBvugtoYohIAg00sMPnJPnwO9EVx66zonrOq2pXJd5e1ngrfuTseDtjleBPewX6oli4/vLduIoHikJg8m4/UNT4xQYUfXGxWevfOSWgf3dw+4dOnzyIPKZuk06SxQdmyJeHu7oFTpy6IPKycyo61lcWLF8by5RvI3+eZsis26XFkYpacBclPabd4CShPatmGAwnmJ8gc6UwClS9pYHVIqxZKeIQSwS3jbmpqQoEL5aP1ZQoMDBaO+ZzqjH3Ueo1dTS/TTOTr9hUXj2zGOTKrKVJxcR7bIkUKYNiwPsSVV0kppPE9YE5EzorCEbNZs2ZGhgzplGix79cS0tatIcz5JVqpUhnRDt+bEyfOktbsBAUPzKUo24iaQ2UDUWyEkZnLWuVcpUplRWox9s0rW7ae0EINn3uA/MwKq9QK3+QoUi4Dp21DnqLlwg/G8y8LttMHNIClhbnSdM2//Q0bdlAO7QukjbyXJDgAYwpD0PcQDLy9SZiBm+tlwCzKHcyaXRao+k7aSAJxZwT5fxXmVA6m4rRwVlYWOH9+v7g/mXOXQq9xv3evYP+8M3tXkybOHw07DCZ3kjBkz1dcOcwdy8fjPrmpNO8+jvz/6uDAxlk4tXuFOM/p4phLMA0JiOrlE3EOju9WKcLhCnX/FjmOP1K2keDgIOW5jBnTU5q7DCKtHR+0tMyOPMXaoPHovqid5gVRquiK88oLYrBRsmRNvH/vQsEoRuK5FKLSH38A8UfUhAnDxP+KanP074aPlK94/ysij6bgk7QmEc26rBV84WmEWpSp6Hf+f/y/u2npWswcNxNe9MyYoNpR+LYUAH/FJNGPSAEw0W/BrwOITADEpmuY9P4CvhJ9QhW7fGidtcyvF6awI73Jd4i1oIWeOglW/23bVogXpgKGsmXPksAWiDt3qitTJynOqa4DyfTWvn0fcIotLvzQ5OAANkFzrs8Q0vIE+H0j3r5w7ZQ1UXDo01d9cKAfmSp9xHFTM8oD3H0sBRQUx0eXtyLSMFP2AmhPxM6xyTCgOi5NbvMLlf2UPD9/EJpW3ufoxfMH1ws/t0yUhqyZsTFMKcjGgEIQDYNDEEwZGgqRFqx6icIIa/B7Xyh+CVyxzo1/aODOJLCwhoq1uixElipVDL17d6aAh8Iiz2tMNHMxnT/ft759R+HMmT1kzo29md2EBMCh1NluWqq2a47586cIf7waNVrg8ZOXGL/irPADjGw8nBP26sntJICsFabryOrE5dirRzdwjHJmv3l8k17qPzVCbCqfu+txXJrU6msGkABooKuHmUXbiBRtyyb8D5WadYNX4fwoY5QWs3rVQuZM6cQHQhHS9j2kqOrSpYsJP+LiRBUVEwL1QS3yi/9jBqL/1C0iF7MqKIvGtCeaqktIlzmHuOdssdhO6eM4Arhw6fCPirfPKLMJZTOq07qvUni8cHgDti8dJ1wTSpLmq0bzHkQ5UxMeH11Um4+w3YKETH7GnN2/ip4X78THLf+f8P/F5cuHyYKQO0L96Hbs7PKLdHaNOg0T1VgD+dXbk4JZXPHwxhmcP7SOXCS+ko91DvHRVa9ejQhaQfYxtCKfQOb4Uy8cBbz9hYXgB1RP0amo+/TpS3Tu1BevXjsgNwVUVa/zN7KTe4dakQKgGiDasCsFQG24C2pjiEwAnD5yNDxTGaJRxuKom7GI2hUpc3fcg91wpyTy/dOXxarBLcRDTuFbw4hMnvwUDx96Y8uW0jA01FWCxGbGEyfOiXRSQUHBlJVkv9D6tSJy3bLkWG5IQl9kAgqbMPXJJM0J4RWFhR72VzM2tYj0GkW9P73msT25cx6P6IHP/kr+37zpZfdVmKACSeMRTD5IkRUzSmI/lTQEPUjjCXrZ6pLWM7BXJ3zPnBHfKXhCBOCwfTeaoh4gw4EvHPnMGtUyZUpQuq6/I2jyomkq1qdGjpwszLfbtq0k6o1isb4+sgv4ni5Zspb8Sv9Dt1FLUaLir2a/yK77E8fY/8zrsxv4pf7h/QsSRDMJrfKf6Csx29zleANn3Z4iNXFi5rPIiG/BgXjx1VUMqTrlMn8yZQQRLr+lwJd0mLzuGtH5HMbaGf3EeSZ1nr3zIQnt+tFO4ei2hcJXL3fhsiLrjvr/OGvOnlA+XxP6X85ZMNwnULXBr94eGN42XGPIH08L9r1Qnla1UPBBrvvoxml4k5vDwU2zRT2ONmeKmoYkqBUqVU15Lbu8PKIMIwYUjbxn9VQ0alSbtL2Lleej22CtOPuwth8wk4KYWkdalX9DtykS/ujWeRT1/F4Im0ygvW7dAtKkWgoS6afEi8o5hbl8/9EK/RsIfkJ3IpBmbsJuFDmsairmahcuXEXz5l3omUgk0oPmoCC5Z4TR3L/9wOlHU7ySAqAKGNqyGf1/jLaMUo4DgZQwvV3WKqhgl0ei8QOB3rlqYNLjfVjnfg+jl5zE2M5lhWDHfi9cxo7N/6Nm+IodsHv3Hi6ycISSBkyPsjToErEum5pGLDgMezLXRlciownhlwg//BKj8EuHXzL71k4jLYKDGAKbTq2tLZHRzpwe7hmEKdXW1ob8gWxFNGKmTBmEiZwzVPDLg/0VjW/eQ8gHN4SQOTaMzLMgk6+y/Eb443oKHzXFNWwaZ5NTQhTOCsFz0pTwxyboVq26EfHuO6H1SUzhj/FjoYZN6VzsMmYT6+T4p2WW0jDVS4UTlDf4ruc7MUVTfSMijA7El6BvIhKcBcAMWekjhbTLHJzhS1qunWS2DfD/JnyLCxAPX3SFNXnRFaaDOUNZSjgAaMT8g8hK1DKqhUmkc+QvIQJ2uo5YonpK6Z6iOMgR7eVqhQtkthmzwonSyKW3z41Lx7ZgF43ZPldhJVl1puz5yW0i/Fnl+ekD0VxtREBAYLQuF4p+Fi5cJT48cxQoqTj0y5p/Q6WrNRXLJ3pOMN/hmX0rSStfGzdvHqcIYIgo4F8upAOepAG0JtoZDyIj3/fKHGShRjrjYBEg8uHOEcFryfMaOnuvMrtKZO3IY9qJgNQAauF9iUwD+HbpQRTOGvGBpIVDT/AhHaRk88fopVHQIhOC16+lYIU75It2jCIpf/XV6dt3JGkD96Ba4y6o3KBjkn2hujg8x8ndy8gMdpJefn6CSmfIkH/Icb4ymFInKRZ1LWJs5pAnT1m65yeEMBub69TrcgBPqVI1yTRnjHZEC8OmRnUtkfo1cl/zCHCgmz4JeVxG398pBMAyNjlQzCMYGYg5gIM22J1hRLtwoad2qz6o3ao3UhubxXswozqUpuAkN+FTyBHpmii3zh/EloXDldHF3Ob4lWeRLlOOX5pnloQp/9QWH2kzZ45DtWoVyHpBH2U/Clsvzpy5SFRG60nzlw63bt2joA0DTFh1XlElRmvmP+TMKNmzZRa+lKlSGcXoOg4QeezgiQXzl+Pw9g3IRhH7g2bsiODjLDWAMYJSKypJDaBW3IbfD6KQVbgW4Pc1U1aNRplL4IGXIx57O6Nb79FwHtaOIiXri8jS8eOHiQAGRmTWpDlC+Kvbph8adRyaJEFiTrwtC0fgzdM7IgtKndpV0bNnJ/KFKh5BUImtMBXb+n8CPHUtYmz6YG0uazLjW5aSE3toaBim0Ms0NpyD8e1XXh8RAYXwx0dHF2yEWU+P4Drxnd7TNcA4q9KiMvu0Kcr9K0fRpPNwxW681qz5c3N6hbxFK8SrHcXFT+9cxLpZ/UWg2OTJI4kVoAes0maNVPjjazKQNm3IzJ1Ebj0Abdv2JLOwgbi2YME8lArujnBbYc2/uaUNbt95SK4dgfi77zRFdzFe22XIKgJs5o9qS5HuDQXxdoECEa1L/H/l7PxBaMNZM86UPMePnyWt4T163uiiApmcOSc1a05lSZoISAEwad43OWoVBHrkrIbJj/djjdttNBm7CA/XLySC6G3YunUf+afUp4eovqCU4EsqNu+mcmXS2eRgg2PbF1GKJzPMnDkenTu3Fi8H9RmwMMclNkJdfIQv9f4Tep8d0BkTTZSQkBDBJ5dYJn2OTp3QvSpKVG6I5l3/1cSUknwbnDd4YmEKzHl2XPgEOvi6w9rIFLYkwLAf3ePb51Dv74Eamydz/fGiqXL9zG5Bn8TaPKas4TJ0bkTzsXpfOQqUotR0VwQFDVNcPbh6DPcfPCXNZ2bUbUPCJPmksqDIROX8mzG1sFZvIkb7uQuXwaD/tmPllB6oUKGBiD4uX760YDN48eKNkrNQ2Ri5g9hQxpR6bQegWpMuSTIloXIuckMgIE3AWvhDiMwEbLLtLnTIr0SWyBFw9fPC9CcHERwWKiqY+HxDyPFjCHr5HGH0JUupFSjHWzmQsxj0Tc1QkXwpW9mXibwxLTvKGr/ZRI3RqlVjLFgwNUa+QVo2hT82nFmzllCgzxPi6lsa7z7Yn7BFiy7oNX41ipSuGe/2YtvA6ye3MGdYC6HNzVXoL7TrPxOsqZEFuOr+EpscrqBbDsopbPPTF5IDdrTZTL9tyViiQdooMrvokFm7MJmVtY0TlINfrp7aKQJFnN88JsHOHCWrNIGZZRqkz5yThO1sIoc5/w719A1++3OUJuDfQqQ1FaQGUGtuhRxIfBBIb2yJ2cX+xpq3F/D6qxu+e39AMLHwhxHHGLEzA9++ccgarL394NmkPq59fq11AiDnrr1ycgcuHFwHH4rkTZclJwWopMVTikzMkiWjyBvLwRuy/ETg5s27pFmp+PNAPLbY36oKXb+Ookt7jFlFZsDyvzj3x6P5316akzQ/zDXHGXNePrwhfLRmbb9PZMjGv702xVTQiThTbRL+gily+fzBDcJ31C6DvRhoevof5sKC/OBZexItYEwMIoo/bMLldHoxTakXRTPycBJEINzTNgkOXA5ZIqCOgCEJR71zV8fffhbwX7MSRufPIc1nd5QvWkAZldq26T8w0TOiPCJEd0C+NPEp3kHEA0hRivEtTMS8ZPz/MKRVYcoPOwnp7czQulUD6If64NO7B6hOgsm5c/uizA8b3/6T8vVM0s1+SpooLEwse3YFdrbWYE64CUTu+56iNxOysGmvIfmoshaGo1C1gUMyIecfVV9MSRKbwqklWTuYkOXkruWCj/TG2T3Kbjnr0fy9zzB2+RmtFP6UA9XABqdjZOJ3WZIOAtIErIX3SpqA43dT/qMsCiF+nym12DlB6MutMa1C/gIVYJDKEvmIO+6C5xvYGZljUL66sKSoz9iUt18/YQnlYvYLDRKXWRikxoA8dcBayNgUDupYP3ugSI/GgszQr774HzVg9uVVbJpJ0XVdXFxRhcxVr17diBMOkflKsvM785v17j2CssG4Y/jc/UTwXSxO7cuLNIPAlU8vsfndFfSg7EfFKAtSVIU/6h4Tp95KyuNcgDjpuo5YjOM7l4hc0S26jYmRCTOqtqM7zv54A5rlFVUGkl9dHuIaTAmFBb6bZ/dh54oJInsL+ylOmndA8gAmkZsv7UlJ5EbJYcYMAea5ev/qEQVKjFMKf3wl0xxs2bwMDRq0w+u5U1DqnyG45fcB/xLNRK30hdAwYzGluY/9Cc9/fApn/y8k5AXiK2UCYYLa0mlyIoeZHQl/p4V2oQztMz/jvS/vsfDlCUwv2jpmg6RaV05sx5ZFo2BBAQxLl84Q/n16enoxvl5WDEeAM3/ExwwYWQAM34dq1SpSBplT5BhfFJz9QQqASeMXd47yOO9eNVkMlrOFDGj6M7KVs9+07TNFnGMN4bevXwR3nVEqExH8E58ZhoSEfwxyG2/IlzM5C4CMoyP5CjK59Jl9qyLA1nHw3Aj7cke7EZACoHbfHzm6WCJwkKJlWdjr0KHVL1dyLt+9e9ehZctu8JgwCM0GTMMJYz8cJx7Bk66PYEzEsyzQKQJJKCuuIIo21jeEV7A/jny4L9pk83HXjGVwdHJfyv9aEXkqlxERitscrsItwBvmpBEsam2PEtbZfhkDZw05vXclRfQuJt+1Sti4cXGEtEy/XCAP/BaB75TT9U8UzoHMAiZHmlZv2u1PdCHbjDUC/N8XdXl275I4uX79IpEv2NHRBceOncHRo6cpGGOTSE340fk13F0dlY3wB0QhSvWWI38pWFGaxwxZ8+KLuwtlXnlJ6eIqInOOAsq6TD6dytj0F+oTzgTEARKcsYX9dpNyeUCC827KDW6XMTult8tBASAZhKD82c2R0kZehcvbZxHyG/Ncm/xvJKWj6y6IyzkIRJakgYAUAJPGfZKjjAECLFzdo2TuXbq0jTJSlhOj37lzGq1bd8fe6QOQLU9R2FaqhfdpjOGn+x2W+qmR0TcY1k5u8H7/RjzQnd48QtF8JWGUOx98w4Jh6vYZm6bPoBzA/vjg+BqDW/wPr33dcNH9hfAtZHHktqcD7AqYI7OJjRg5m6ZYO7F//QzRZrNm9bFq1Vyl1jEG05NVokDAyspCmGz53mq6FCyYF2fPXRXp2CxJOJAlcRAIQ8yEfFNiSmDap2vXbsHD4wtlwLFDO8rv3KlTK4waNQ2eLk+RPUsGDOjTnnLjZhfcdnfvPsSuxWtEVh11v8FrmbZj0MzdeP/igUi1eOHwRhQrXxc9/l3+CxATiNyZ+QlVBcZfKmnxAX5GcZaVqxSI9tnNCYa6wXj75Aa5zwSIUTMnYWZKEdmlSxusXr2FODNDUZIysrT5Z3Ky92/U4tsWr6FJATBe8MmLtQmBs/vXkmn2O4YM6R3tsDJnzkAJ1w9h2bJ1tGzAzVUzRX1++PlTQnp3+opnrQCnVGNzYFaq/+j6CQReOCjq8YOwQoXSop+GDdtj04gOKEERnDAxhlHwd7jqBOG1AS2hdgiySIv7V0/gxpldlB/0C2XrqEJULlOQPn3S1hJEC3ACnyxMOYx37z6EPyEAZstmj2DKG+35+QOkAJjANzaS7qLX/wH1iROQhZZ163dS9HRqCkrwxWIS7hQlNeW+ZqHQzMwE/LtJm9YWTZvWo7zhI0UVDw9PIlt+LbJstG//D548eYERf5cQ5/j5wCVjtnxirf6HM5QktcIfzSz4rZvZD8/uX6EPWB3aD0VtIplnDTgTQN+59whjRg/AsGF9xfS8vX2wYsVG1GndB407DU9qU5bjVUFACoAqYMjNpIsA+6WcO7BGCAH8gP9d4Yd5nz5dxeLo6Ixnz15S0MgTYovxFynVihcv/Itp9ts3PwQFBVHWCQul39mIEf0ocfsOPL56hHKEhlLaJgN8o3RN9HmMnfv3i2HoUgLNCkSwynXLlSNBURaNIeDr6ytSYz18eEFjbSoaOnXqPJYvX48qDTsje97iisNynQgIxDSg1zZ9FkGirBgiCzdfvdzxmUy+Hz84wPHVYzy+Sb6BA/4VAo6Dw22h9eIsF1++eImI8nfvnITZ2NnZVdEMSpQoIjSK7945oiilCEwOhbWdw9oUFQKgvr4e+vXtKj6kPD298PTpCzRpUo+Wupj14SPmjp8JJofmYLWLF6+J6R/fsQSlqjQlUupcyQGOFDkHKQCmyNue/CZ9l9JB+ZH5Zdy4IbGeXJYsmYhnLxN99VaL9loT0vDxolpYqONFtbzw8EaZjnTs3n1YNG6Mu/8Ng7VF/FOVqfYht8MR4NzOxYsX0Vg2EAWunHO1Z69hSE+5Z1v2HK84LNeJjIAuaeZjU/hDj33yeOEMG+Vqkm/wP5Nw9/IRrJr2D+XOzh8pXYyBoRGMTcyQLnN24hL9jrfvPxINkwFqNu9JPoJ5YjMEra3LVg4WkLncv39O+LtOnBi5Ru/Tp8/CrL5nz+EI83n/6oEUACMgkrR2pACYtO6XHG0UCBzZPBc5cmRDsWKFoqiRcIfz2FjAad9qVJ28BK/Xrkeuhw8xiTJ49ClZUDmIyOhHlCeT6cafmPMdyoeq6XvOmhHOw+r1xQdjpu6GB/lDbV8yGjkKlqY0WP2T6d3R7mnF1AcwprMoTJlemhMtjK+PJ+XVtYWZVRrhx8YBD1a0pBTybdZuXzi8QfjzRYUdUy15en7B1au3iBy9HJl9R4ioacs06Qm7NFFdJo8nAQSkAJgEbpIcYvQI3DpPCdyd35IpdnH0FRPwrKmhIW5NHoR1LepicJvuGDNsIvqc26UcQWT0I8qTyXTjT8z54cOnwodLU5Cx8Ddw4BgRVNJpyFyh3ehdz140z0E/UgDUFNKJ2w5nv+Co1ZReGnceTv7Ju1GyZC3hnlK+/F/w9vYWH1XTpi2Eq+tH+Pv7g9PYVW3cBS26j5WBa8noRyMFwGR0M1PiVJiIdOuiEcJHp2HDWloHQXpzU2ahhkmxYlo3tqQ+IM4DHEx5TOvWra6RqbBz+//+N4CyrlxGvb8HoEz15qLdCnX+FgEgtVr20kg/spG4IBCzKOC4tJySr0mV2gRjlp4UZNkPrp/ExUvXhcmbMeFsNKWqtUCugn8hX/FKMtI3Gf5QpACYDG9qSprStkWjEUxEzevWLVQGZmjT/CtktEOq2rXwbetW9KhVGSvrlNem4SXJsTCv26hRU2BkZIhTp/bEew7sB7VmzVbyH/2PgnxC0GHQrHBfsR8tt+s/Pd59yAY0gwBzc8qiWQSs7TLi777TAF6ouL5/hT1rpqBum37EjVhSs53J1rQKASkAatXtkIOJDQIOz+/hFlGzcBAGU7toY2FTsDVRvnygdYMcmbRxiElqTHv3Hsbw4ZOwfv1CouIpE++xs39Tkyad8Pq1A/IVrYCOg+dIupd4o6r5BsJDFTTfrmzxVwTSU1Rv30kbfj0hjyQ7BMKJjZLdtOSEkjsCoaEhWD29N4rSRIcO/Uerp9utZgWQrRJ3P3zS6nFq++DuER/Z4MHjsWfPOo0If5wfumrVpnB2+YQB07ai/7QtUvjT9h9BLKOAtX06cnwSgcREQAqAiYm+7DvOCOxeOQme7q6YdWoX0TNotyJ7ULni0LG1xdrt++M835R+4aRJc9CiRVfi5ZuFIkV+puaKDy7MZ+bu7oGB/+2g6EZpmo8Pln/6Wk1HAf/p8cr2JQJJAQHtfnMmBQTlGBMUASZ8PrZjMc4f2oB+/bpR9FrROPX/JyhJohtIahtr+BGXlixxQ2Ddum1E1n2ZUvylilsDkVxl8YOb0eeL1MxGAo88JBGQCCRzBKQGMJnf4OQyPabneHr3AsZ2KY/DxPnXunUTREVaqj5nFvbUy5+gJFHvQ3XfjzINpLWxUj0kt2OBAKff06Twx13/9VcxlKX1jqVjyEIfqBzNJxcHjGpfCkNaFcT62QPA6bJkSWQEZBBwIt8A2X1yREAKgMnxriajObGv3/XTuzH2f+WwaExHmBnr4yrNb/uO/TGO+k1oYU8dfsG2TynLLDJqZ6AKjzcyIVl9Hom5z/lJNV04E8LYo1uFK8G0PnXg5vRaZIW4e+UYvDw/IZh4/26c3Y8dy8ZpumvZXhwRkDHAcQROXiYRiAQBaQKOBBR5KPER8PjoRAz1m3Dl+FaR4i1fvlxYtng1atSoJAQ/r8QfYoxHcNvNA5R1Hs8uX6XUS321kkg1sYXk34GZNq0dduw4QJrfxr+rGqvzZcuWwlESAlu27IaJPasLwltO/cUlODgE+qR5LFm5UazalJU1j4D0AdQ8prJFiYAUAOVvQGsQ+PbVG9eJlf7SkU34SGY4YxoZU/F2Pb9fY47/iTHZvzLYonHf7jgwYDhWPniJXsXyJsYwknSfy5bNRKtW3TQuADIoZcqUJBqYG9i16xDc3D5RoMl6eHh8AeeDDQ4KpJyxvTB90y1JhKsFv6DY5gLWgiHLIUgEtBYBaQLW2luTMgbGvn0Pb5zGzMFNMKxNEXB0bxpLQyyg3LkvnO5jI8FQuUqTJA+GmQF9a+npwc5Ec0EMSR6UWEygcOH8sLNLI7SAsbgsxlWNjIzQvn0LQSm0fv0i5MyZDUWL5MfOnatICAwSbgiKxk67PgYvQd9DFIfk+g8jwM8JWSQCEgHNIiA1gJrFU7YWCwTY52rNjL5wfvsMGTKko+wOA9CxYyuyltoqW9F206RyoL/ZOH7qAnSsLNEsd9bf1JSno0Jg3LghlK1jxh/RAqr2WaFCady6dVJ5qESJwti9ajL8/XxRsF5r7HG6Jc7xOo2RGRpnLI6SabIr68uNP4mA9AL8k+jKtlMWAjryy0r7bvhsHR2WgCJwU5hsuwsdCxvtG2wcR/T41lksm9gV2cjfagylcWvcuE6Mgzri2GWiXlZh+Q48mTwNK07tRav8ORJ1LEm1cw6mKVCgIooWLYC5cycjPWVYSYjy+PFzVKzYMLwrJiLOnh0VW/WEk7khHAO98R1hsDAwRkv7v1DCOlukQ/rg9wWPvJygp6OLIlZZYJvKPNJ6KfUg39uL7i9wz/MdQsO+I6upLYz1DPHW9xMMdPRgbmiMi5+eo3+e2shnob3BVCn1/qnOO8zbA9/aFlc9xNt2Q8PC3NUPyv3ERUAKgImLf6S9J3cB8N2L+5g1pBkKF86Hw4e3wMSEvf2Sd/HwC0Cuio0RZpsG+1fORuUs6ZP3hP/Q7EJCQjBhwkxhCs6aNQuOHduWIETgbkQldGbxf+g7dgbg7Q2K5hEzzFW4DIwbNsUjgwAhCJobpEYus3RIl8pCCHsfA7zxxNsZviE/aWb4QhZu/rLJgQyprWBFwk1ei/TQ101ZBpkgupf6urpw9PPA0pen8TUkQGCqQ/l+owr6GEgCYB4pAAqctPWPFAC19c78Oi4pAP6KSaIfSc4CoJvzG0zvVw8Z0tvh8uVDKUL4U/ygBh88i3V9h6LzumWYV7204rBcxxGBLl0GwNPTC/v3b4hjC7G7jLVUNrnKotyAPhhfphAuXbqG+fNXwpc4HnMXKQfjRk3wRD8QwWERKWsMdfSRK0gPmb2DiFPQB765suFJqDcCvwdHGAALhSwQVrbLm2xNyj5B/thLpvO7pOlTxYn0qiija42vhw8iNCQI6fIXRxCtzaCH7+Q/65zeCt/SWGFw/nowTGGCcoQfSRLYkQJgErhJP4aYsj45k859SZYjfXbvEpZN6AIrK3OcPr0rRQl/fENDjUnTSZlMSGJIlvc3oSe1evU85M9fAc+fv0LevL+SfWt6PM89SPMXGIiiFJDCJNK89O3bFStWbMTMmYvxbfJVWNmkhaVdJhhaWMGYli+Ob+D69imeBAbgicqAMtjnRuYiZaBrZoEwKwt8SW+LD2H+eO37USz7nG9jYN46ycZU/ML7Awl+t4W2j2Ew00uFPF+BIP9vIC0EbJ0+4cqhSTBOnRpmZiZ4dveioEvSo8Ap1vqyq5KNXQbcbP0N+UtWhrWtNAOr/JzkpkQgTghIDWCcYPuzFyVHDeD5wxuxkwh18+fPLcy+lpYWfxZELWw9hDRIWToOhP+FS6g0diS2dW4OY0P5DRafWzV9+gLcvfuQKFzWxKeZGF370N0TlYtVw6Ad6zCufLEI1wQEBGLz5l04efI83r1zJiuxN3Qo9Z9RlkyoXLks+bjWpbSFRchy/B3btu3Dxo074eDwHkFBP7WALOAUKF0DARUr4KbfBzKE6iCjsRVKkl8hm5XtyG/Q1CBpRZF7BvoK866L/xeBVxZDC+R85Yy7BzYS2fZPlzAm5W7evAHmzZsMU1MTIfDxMS6M7fnzVyjzz2wh7PMxExKcC/1VEzWa90DGrHn4kCxagoDUAGrJjYjBMKQAGAOQErpKchMAd62ciLP716JevRpYv34hOK1XSi0vSItU799Z8Dx4ELq5c2PZ0hkyKCQeP4YgomjhwJBXr27Eo5WYXXrSwQWtK9RD0/n/YW3LujG76De1WLPl6OiMU6cu4sCBY7h27TbxD6ZCvf5TcM48GN7B/sK3UNGMLgmFHHncJHMJFLPOqjisles9jjdxxu0J+fMB+VPbIs3V27h+fBuCSBtarFgh/PvvQLAfp66uDgX0pKNUf0a/nYe7uweuX7+N7dv34+zZS0I4LF6hLrqPXv7ba2WFhEFACoAJg7MmepECoCZQ1HAbyUkA5BdcnwbZ0KRJXaxZMz9ZR/rG5mew+cFz9OvcDzA0xJK1i/B3AUkjEhv8VOsWKVKFMsRUxqxZ4/9olhXW4Nq37Qe/589xgVwYCttaqw5DI9vOzq6C8PrZs5cwNjFDmvT2MM6SHcY5ckMnfXq4pdaFa6CPEAqLWdmjR65qGulXk42wlnPpq9MU/OICDoqp4v4dp1ZNQwDR6DRsWAtjxw4RPIvx7TM4OJh4GyeQ5nU3Fu5/CT39lPthGV8sNXm9FAA1ieafbUv3zzYvW0/pCHhTTlUWAtm8ozDppHRMeP7LrtwFvpBZjPwCc1ubSUjigcC1a0fx5o0DChWqjIcPn8ajpagv5Sjuv5dsgt9dum/m5mBN3J8omTKlF8FR27evQONGNWBiEIT3107h7qrZuDNpCFz/HYpqT9yQiYJF7n15jxMfHv6JYcS5TRaSpz89JIS/7MZpUOD8HRycPwq5c2bBzZsnsGHDYo0IfzxAtiQUKVJQmNUD/P3iPGZ5oWYR4Oe9LEkDAemAlDTuU5Id5UeK+uWSK1f2JDsHTQ/cLygETxcvh3WF8ji7ZArsLaQAGB+MjUmI3r9/Iw4ePIGmTTuhZs3KWEyULfr68X+8HXvjjFGrt+H9jl2C+sWkZg3MH9ITBW2t4jPkaK/VJWqU2rWriUVRkU2fT5++wNateyk7yTK0zzgLu60NcMTlPqqnKyjoVBR1E3O99OUpOPt5ophZBnxduwrXntwSBO/DhvX5Ix+AlpbmYrqLxrRD7dZ9YZ0mAyzTpIWZRRroUgCJLAmPgLurI0wSvlvZYxwQiP8TMg6dyktSDgJXT+0Ep9nKli1Lspz0VeePuO3qjpuPnuPGtZsI9PKBPvkyGZFQkpqc2VObmsLU3Azm9KIyp+hGKwtzXHD1oDBIM3j6B0jhT4O/ikaNaqNOnaro0WMI8uQpRybhcWjWrEGcenjw0QP1Zq+E3979IBU2crZri3k926JCpnRxai++F9na2lAwSTmxODg4Yg/51VadtxHHXB/i1VdXIkfOGN8u4n39669ueObzAdlMbBG8bSvePLuNdesWkPtHvXi3HVUDzZrVpwyLehSNPRIrp/RUVmNrgxlFYZeu3hINOw4hbeHv/QuVF8uNeCFw+8JBVI5XC/LihEJACoAJhXQK7Mfh2V3cOn8Af7dtmiwDP7rtOYE9g0eDeCpAHuyU6s0K+iTYfSffpO9EFxJGAQriHO0L+hemgOGFTST0gtIvWSIF/ir+7JQNyaeSc/ly9o6GDdsTPUxuEXke014D6F4uvf0E/42YiGCHd6jdrwdWD+gCU2pXW0pb+n8aPHgc0hmFa7+8iVtPG8qOd9eFYbzqVwOsvX0eG2hQjf6g8KeYM2cRYt9CD48v+PDBTSxv3rzHhQtXcG7PClw9sR1Z8xaHUWpjpEptghD6f/zy2RUmphbInr8kCYnN6AMtjaI5uY4HAt/p+Xbt9E4pAMYDw4S8VAqACYl2CuqLzQDzR7dFtqyZSRMzIVnO/MjqzdCjtGB7NiwkPz4LpDc1jnaebPr1IEqLLyQcevkHobCddbT1k/pJS8qekVi5nAsWzAvWCK5bt00Eh8QUy667juPo0H+BjBkxaPNKjKtUMqaXJlg9G5vw342pX6Do04HSpZWxzZlg/UfV0Qd/L0FkfWXretjZpUHD51ejqqrx42w2Zy0pL0WKFBDt9+3bhT4EnlHQyQyKEn9OlDtBgnaH61oR96KL2ys8uH4ShzbNxrDZe5E5Z0GNjyulNfjg2kkK9vmW0qadZOcrBcAke+u0d+Dfvnph9pAmROpqhBMndiB16qTFXaaKLEc0BoR8F3x9nLpq6zMHrNx9BC+On8L3d+9QZOyoGKd1Y84/XjKnEA+ZxBL+FPevXbsW6NNnhGI32rVXQBBef/HG0f/mQadoUdzavAg5iLA8MYXYqAZcrVoFYfZ8eGwnDErmFMEgbbOVi6p6ghz3pTRunBM5G+XwvfvmEerXrfpHfP5iO5mCBfNh3771UV7GvpUVKjTE6v/+wYRVF7RizFEOVstPML3PtiWjkCN7VuDtOy0frRweI6ArYZAIaBqB5ZO6IdD/K3Gb7UKaNDaabj7B2rv+4RPSEO1HxiJVYV2kGtLmq4hBtZvj2XbKYpIzO1qtXoyjPdok2HhkR7FDgDN1cKq435V5V+8iW/EaqFmyhqi6ft4kIfzxTmILsZGNnYmS2ex59cQ2lDShYAsSvnYT515iFn9K28bFSM+AhFMDkb0jMccT075ZY7h48XR8+vAe18/s+eUyL4+PcHh+D/5+lLYkiRQ2w/p/80nw0e5aMQG+Pl5YsvS/BO9bdhg3BKQGMG64yauiQODD+5d4TZF/M2eOixPdgzZpXLY8fo2wCxdQflBfvCTfIqM0tujXoBo6FsqFVL+JMNWmeURxq1LE4TSUP5bJlcuWLRnpfK+5fMKkLv1hkD0b+vw7GC1zZ0X+PxjhG+kg4nBwwoThRBx9HJ/Xr4Blm2aCcJnz65ZLkwtV0uZL8IwhNoamYhZuZAa2oHR4r187xGFWiXMJm4OZjHrH0jH04foNX9w/4OXDa/jo/JoEv5/mTFNzS6TNlBM5yG8wZ8HSsM1gD5u0mRI9wIQFPsfXj3CK/B0f3zonQAwK9BfZUsrWbI3qTbpSZHQ6eH5ywe2LhyjXcgiqN+0GQyPNWWae3DmPy0Ty3b17B+TIkRXXE+dWyl5jiYAkgo4lYAlRPSkTQS8e1wnvnt3E27e3iONYexzn43Lfuhy9jH1de6PfxuWYVLN8XJqQ1yQyAiNHTqbUy98ELUxkQ8naYyS8KTXf2YsHUSxt0tJW79lzGN26DULuwmVh0LI1Xnz3QUjYdzFNQ109mOingh4FG3HMkQHt21IqudyUUu4vmxwwN0wdGRzxOjb07laEkstExQeOOLNvlXgGWFDUuzYX5o+sWbMVpk4dhQkTZuPTJ3dhXs+e3R6lSxcXlEJZKJ3fgwdPcPHiNdy79xjOzi4IDqbArx/F1MwSdplykFD4F3IXKkOBJSWQ2thMcfqPrU/vXYVj2xbA79uv2klOPfjt2zfKlnJZ8CQam5rDzzeiVnDOzocwpkCY+Jb7V09g5dReqEG/vYFHtqJY7uzYkquMerN2Q8PC3NUPyv3ERUAKgImLf6S9J2UBcEirgmjUoDqWLZsV6dyS0kGmeKlfswX0qlTB52WTk9LQ5Vh/IPD8+Su0adMT9++fjRSTrP0nwufsWXx+eO6PZhGJtHMNHNy+fR/69/9XmFztcxWGfeW6+JLJDh/0QvEtLFiQsHNKXSZoDg6jCPQfhQVEO4oiLmCZCfUyFoWhbvyNQbscb+Cs21O0sS6InQNbg30Vd+1arehS69bs35snT1nMmDFWSRfEgSJMK8NLVIWv44wtnMKPf1+sYb5//zGcnMIFQwP68K3bpj9qteil8ewkQQH+8Pz8AU9vn8eulZOEkHrjxl3lUEuVKiZyTw8f3lcwL/j4fMWWLXuEAJuBxjzvyg1lXd7oOGg2/qraJE7jDAzww+HNc8GCKJcVK+ZQFptG8P/sIQVAgYj2/5ECoBbeo6QsAPZrlBMDB3anPJ+DtBDZ2A2JueCqFK8Gi1Ej8faf1klSQIjdjJNn7Zw5/8LLl9cjvX+zrj/AtGYdMO/EHnQm035SLF5e3vTBtV7kx2WhRFFSEe2JoVFq6BsYwoDWmXIUhGXh4vBIZwNnnUB4Bn0TgRuc1SSzsQ1K2GRFRds85N4QN809B0kNvrdFZEmp7xSA/aun4s6d02BtmjYWFuTy5i1Pv41rGhket/fmzTvSJM7C0aOnif/TkrSzbDkgHknSDlZp2DnOQSYsbG1fOhY3z+4VGj0ecPnypYkAfb0gPOfsG9FlWtKl38XyQWMxgjSCTJDNZmNFsbC2w5CZu4VJW3GMfR7fPb+PjNnzEZ+iDX1ghPt4hgQF4v2rR8KUfJN8JoODg0S/7G97/PgOcbkUABUoav9aCoBaeI+SqgAYGhqCvg1zYM6cSejSpa0WIhu7IW168hr9W3cDvL2RrkM7PJkyOFIhInatytoJjUCFCg3opTxM5AtW75s1Y7Z5ysO+TSvcn5z0P1pYM8VaqbcUhfny5VvKNuhFpkA/sJD47Nlr+PiEmwFZOMmSqwjSlqmCx2lT4/P3ABJTwkt2Uzt0zFYBaVPH3jx4xvUxdjvdQiOb/DjYrxVphWaTVqixOuxas587dxnxcaDpAT169BQjR06ltt+QgARwtLGNXUaR25l9JK1tM5D/YGZkzVMUmbLli7b7hzfPYMOsAfhOpt5OPTuixYqNYP1kIfdnUWa70T95HiG1qvxslwU+EhIfUY7pzZv3YOXKjcpzRkaGJOCFonjFBnD/4EC+j2/J99FXnGdhUU9PH8Ek+KkWdu/h/O7//juQNL3NSeDdity5c4gqUgBURUq7t+Ov99fu+cnRJSAC7HjMRZGeKQG71nhXH+mlGRIahmKjh+PhwaNw27kbL4d0Q14bS433JRv8swhwarjt2/dHKgDqEyecZYnicDpzDkgGAiDnEuYFqBQpqC4urrhy5abQUF29egtP71wQHzVsPs5coSbe58yAt8QrOOHRXlgYpEat9IVQLV2BSNuK7KArBYFwMaT/Hy5mZuHBIWJHC/8wJ6Cr60ekT59Wo6MrVCg/jhzZomzzyJFTmD9/JZFVO+OV41PhnxcUFCzOW5FAWL7u3xSs0Q2pjH/i5fj6MWn9xogo5Hz5cmPTpr0iwAL/jVW2G1Wwmf4J+j3TPTDtMiA8kv2HSZvHNWNGfmTIkI60sw9Emj7e5kwqFy6eRbq0tmjSuCaYPoe5HJcv34CiRQuKhQNlOL1i4cL5hbDH2HHp2LEVJk6cTabmZeHjCgtfyb/aj4DUAGrhPSINoA0N67Pq0Iw334Iuqeq1ubDZYHCLglizZj751NTX5qFGO7ZFNx9iXL9RIEcfkbGDolmQrm0rPJksNYDRAqelJ9+/d0KDBu3x6NGFSEdYbeoS3Nu8HR8fnYPhb6K7I20gCR/kaF3OL8wBJWw+trBKg9JN/geXbBnwMsxX+A3mNE2LAXlrUb7h6PUF191fY4PDJVhTRLDlpq346PicTKI3RSpIbYVowIB/xdAWLJia4EMMIFL406cvYNGi1bh16z757BmiROVGwmz/+NYZeHx0oY9pCxKuhqNDh5bRmngjDN79M4xHTYWu8wcEEhdmMF0LCoQi6Y0yFhlFqKqJnYCAAOTLV57Itm8IAdHP7RO20r5asaEgEE+1Y3I3kREIF+ETeRCy+18QCHe4UD0cHFEFr3pKW7Zd378SQ0mbNo22DClW43hFRMDlR0zHuCbtYUAp3Vad2Yf7Ty7B4ellPJs6VJp/Y4Wm9lS2t88Mf3//SLnpfMnp/8H+Q9DLli3FCX98h3LmzIZx44ZQkMA5XL58CJky2ODkull4Mm4Q8p+4gtyp0+C170eMuLcDH/y+RHlTb35+jY0k/HEwSaknTnhLaSBnzRqv1cIfT6Z//244d+5ylPP6kydSkTDWoEEtIsvfiYcPL6B27Sp4cOUwbpzegSwZrLF69Tzhn8gatuj8+1THaLhyE8woqln3xWvovnOC4d4jMK3YEHq37tG+o2pVjW2nojSYxYoVxoIFq0SboZwC89cS6cFfq8kjCYlA9J90CTkS2ZcqAvS5FrGEqfBRRTyjPXvuH96Jwbx65SAclLVnZFGP5Nz7D/jv4Bk8PnYSfo+fEF+GAbL074tzAzvCmh5ssiQPBOztM+Hw4VPCb0kxI3baL9DrX3wnjcnsVQsUh1PsukCBvCQEHiby7C9Yu3Yrpk1bAKtXD1Bz4AScDnbFlMf7UTVtfjTNXIq0geG6A68gP2x1uIpH3iRskPBX6bUnTmxZIDRWbdo01XosfX39RLRsYg+UzfYbNy6O8zB0b96D4cFj0PlMSjY9XejSOszGCjrePtD99Bmpp85HwCjyI8ybK859RHch+9i2b98bQ4b0RpCPb2RVw30CIjsjjyUaAlIDmGjQR90xqcq/09kIn9thPtqvPS9avg6y5yuOQRRtxqYNbS49Dp+HTZ12aFauLm7OXUjRbMEoPGIIrt88iQcje6Q44Y99iZJzqVOnOnbvPhhhik2XbYHPsePoMv8/dC2aJ8K5lLxjbW2FoUP74Pz5/UBoAM6M+wf1P+sKXsGzH59i0J1NWPHyDGY8OYxR93cI4S8tUcrUfv8NpzfORfPmDUgbNDXGWqvExHrBgpWoU6daYg5BI31/pyjcoL9b4Dv5Cuq6uEGHTb4Ula3r6AJ8D0MYfdjqfvz0S1+a+r9nv0D2aWQf00D6gFArnj/eaWqH5W5iIyB9ABP7DkTRP/kBPqNTeRWnjYbOg0G1ZopdrV0zvcD0AfXh7e6Ed+/uaKXZdOaVu5je6n8wypkDHYf1x4hqZWBjLLV9Wvuj0sDAPn50R5UqTSgS9gree3/F5CPnsWfov7D/X0fcJ/O+LJEjwDxyHTr0ESTIeYqURfbmnXBJ9wt8Q8JdUjIaWaCYqx9u7VmNjy4OaNiwNtavX6iV//fqM+Tgj7Jl6+Hx4wswNf0ZfKFeL8nse/nApOcQ6N1+gDBTY4rEMcR3Dm4x0EcoCYaBA3sg7A+m5uzUqS/Y3aJD/jy40HuYKmzPSADMr3pAbmsHAtIErB33IbJRONJBpQAY9tE5sjpad4xpA+q2GYBV03ohwCYPjL+E+wVqy0CZq+y//iNhULAAPhzbnCReVNqCXVIeR1qKbvQLDEKuHiPw+fwlipD8BvOG9XF94sCkPK0/PnZzczPimtsgaEOmkhnxxbhegi8uS/qsYFJi57dPcJjSpzHX3+bNy1C/fo0/PiZNddCgQTuiaumXPIQ/BsXSHCHFCuG7lSV0AgOhQxo5NgkHEyF3EAWD/Enhj7ufPHkUqlZtiiY9O/CuauF3mSxaiICuFo5JDikcgdeqQHwnbqakUpzfPhVM+qk8XmjVkJnzrWDP0QgjE8Xc2ZOk8KdVd+fPDebYG2dk7TocPl998fnCZRTp0Bbrzh3EqyWTf5vT+c+NKum0zAEIPXt2EhG98+ZNRtaM1vB2e4nv/h9Rs0YFYSpmwuekJPxt2rSLBD8T9OrVOenciBiMNHBkf9L8GUCHeAdB0blhRMMT1LYZwmyZWOLPlixZMhJ9TB5cOnROvaMI7zL1k3I/8RCQGsDEw/53PbMJWFm+v3uu3Nb2jUc3TiFz5oxaJWB5BQShWLeh8Dp3Ac3mTUf7Qjm1HUY5Pg0h0HXuSvgfOoxKU8dhU6fmMCfiW1lij4AB+ZF17txGLLG/WruusLKyEGnytGtUmhmNPwV7mHQdBD3KX8xmYB0/PyXJt2Z6iLqVGTPGYX/t/8EkYpWk8/KKOO5kvyc1gNp7ix+oDu37+5cII5OLtpdn9y7B2eEZhg37J1GHyhGeFx3dMPPsdZTsOhTZClSE16076LB0Dta0qpeoY5OdJywC03q2B4yNcYd8o4zJH0oWiUC9ejWI5vMD/Eg4SnaF/P503T5Ch/w3/eZOwvds9tFOkTkI/6IgsLZte0ZbLyYnrWztYf31k3rVCO8y9ZNyP/EQkE/DxMP+dz3fowocDRwupH8PRejL+9AvXPZ31yXaeR+vz1g5padgiW/dukmCjOOp+xfUGTwB+qHfoUP0B55EQqrr64vvHmQCIT8vUkOCaO+RrlljLO3bGVXtMyTIuGQn2oNA58K58WbRDCzuMRB1KRPCqX4dtWdwciSJggBnsShSpCBRr+xMdmZgBvQrcTqaEvm5/vmrCPlNlPPy5evBntqvjp+lwD1HZM2aJc735BNlF9GllHMqhd9hd1X25aYWISA1gFp0M1SHQlFTvrTPQqCyhD64qtzWtg0/X2/MGNiA0sp/x96964QPoKbHyFq9AAriUC1bH7/C1xOn8OW9IzzfOMCMyFUtcuVA0a4dMXLneuy/chQvz+3Bs1mjpfCnClwK257csDpykO/f7bmLcNzBOYXNXk43MgRKlSqKm8SflywLabzZFzAsjVW00+Pc0fv2HUWRcrWhR5lC1q7dFm393530u/nLO+ouvcvoS1wWbURAagC18a78HNM52iyh2A29cxHoMESxqzVrFswWjG4LXy8PnD27Bxkzptfo2Dh4o+eeE9g3cyHC3N2FOU/HwgJWWe3hQ6YOpEuHjxf2pMhMDhoFOpk3dnoSuQIcOoquE+fDZf3sZD5bOb3fIdC6dWNKWfm/31VLsufDiMg+jAJdois7duwTvpCdBs/BglFtcezYGUyaNCK6S6I85xesA8fT9I6KWPgdJouWIiA1gFp6Y34M67jq8L6TCfj7ZzfVQ1qxfXjzXDi+foINGxaBswlosrBwWWLYdOztPxzGWTKjxsR/kbNlU1jnzgkvytUbGhSIHjMnSeFPk6An07YsUxmi9oA+8Dt/Ho/JdUCWlI1AjhzZyEvED/yMSY4lNHcOBBEpd3SlUqVy4vSmecNQpGwdivR+h7dv30d3SZTnvji5wfvBQ/XzJ9QPyH3tQUBqALXnXkQ2kkt00JsWC8XJkMtHYNikq2I30dfP71/Gse2LKQ1QS40y6k84eRkLJswEPpKGj3JLVp88Hru7tUj0+coBJG0EprdpgBNTZ2Ly0QvY0alJ0p6MHH28EUiXzo6sFpdQo0bleLelTQ3ofPYg6hdrwDZNtMNiM/i0af9i9OipCAr0h7GpBaVL7IT798/GmsXB8/hR9b743fWLSlC9ktxPPASkBjDxsP9tz+Q7EUSVDqhWDDm9W3X3l+2ndy5g88KRCAnmS/9scXrzBIvHdkL+/LkxZ84EjXTGWRpy9RiJBZ16w8jGGn8N7IOJezdJ4U8j6MpGslmawTBvHpzbtkuCIRGgyNdmWLRodbJDIowym4RUKBOjefXu3Zly+P6DJ/Tu6DhoNpycXMgMPCdG1yoqhVHgx6ttexW7ivUBeocFK3bkWvsQkAKg9t0T9RFtUT3wnUiWQ18+UD0ktlkYWzqxCxaN7Ygrx7eRSfbRL3U0eeCzmyPmDGuO9OntyG9kG9FNGcareTbDLLx8B0UrNsbnM+dQb+ZkOB9YixMUsdm/VMF4tS0vlgioIlCjbUsEP3+BFx6soJAlJSPQs2dHPH36MvlBQMFwoWVLxnheLVs2BDkDwjBValRp2JlyOa/ErFlLYnz953uP4Pn4uXr9zeoH5L52ISAFQO26H5GN5gwddFQ9EXxwveoubp0/gGn96uH1wyviuA5RHGTLWzxCHU3ucH+Te9eEcWojnDmzB5wuKj7l6BtHpG34P4xv2Rn69llw6vJhbOnQBPpM4SKLREDDCPSpVQEIDsaRt04ablk2l9QQ0KfIVx2d5PecsSRev9gUQ4oYDi86aNVrAsrWbEmm4flgjsCYlCcrNqpXc6QDZ9UPyn3tQiD5/fK1C994j4ZU6KHUyArVhkIuHMT3Ty7KQ6f3rRLbAQGBYl2uZit6qOkoz2tqw+XdC/w3oAHWzuyPIoXz4fr1Y7Czi97H5Hd9d9t2GO2qNkEo+fr137QCLvtWo2R6299dJs9LBOKMQMl0NuLaN1++xrkNeaFEQJsR8IplDnZLSwvxzrh5dq9Yt+8/AxZWafDvv9N+O01fpw94s/eIer3lP95d6sflvhYhIAVALboZ0QxlJZ37mQYkNARBO5eK6h8oQ0gQJWPnUql+B0xcdR7tB8wQ+5r64+35Ccsn98CUf2rB290Ry5fPxokTO+Il/J15/wHZOg7CnsGjYFmvLp6c34uJNcrJaF5N3TTZTpQIGJLWh95yCAzlbytZJAISASsrSwwd+g+ukY/5+UMboKunhwp12+P27fvw9vaJFqD785YjLCI/K7+rwrUS0V4pTyY2AlIATOw7EIP+6UvqM1WL4KkccmI7fJ7fw9Q+teHm/BaZsuVFm38mwy5jthi0GPMqLx9ex7guFfDszjmMohyTz59fAfNnxUfD6O4XgBb12sLrzh3UmzMdLxZPRHpT45gPStaUCMQDAQevcM2frXGqeLQiL5UIJC8E+PlepUp57F83XQQR5i9RSXAEvnsXtauEN1HGvNj0S0DV6h/vrOQFUDKcjRQAk85NZbVeuI2Xx0xawI9kimVBrCw5+/p6e2h8JtfJv2/+qDbIli0zOUpfwvDhfWFkZBTvfhx9SGPp7Y1Raxdjy98NpNYv3ojKBmKDwGWXj6J6ibThpuDYXCvrSgS0FYHY+v2pz4PfJdOn/4tAyjm/e9VkZMyWj6hg9KKNkr45boa69o/fUfyukiUJICAFwCRwk3iI9EXlQqtFqsNNT5G43SuWwadPn2Ftl1n1VLy37109gQ1zBqNy5XK4cGE/bIiSRaOFHjZ6tMgiEUhoBO599BRdls+UNqG7lv1JBLQagbx5c2HgwJ64cHgjjm1biLpt+mHPnsOYMGGW0AaqDt7p1AW8P3JK9RBvL/zxrlI/Lve1EAEpAGrhTYlmSFPpHJuDlaXAi9dwIfb2gn9VUx6Lz4YT0cwsHtcZK6f0QKlSxbBr12qNaP3iMyZ5rURAkwi89Aynf5FuB5pEVbaVqAj4fIXPrZMaGcL48UPRrVs7nNi1DFnzFEGVRuG0MK1adReZU7iTIOrvyuBx6v2504HfR42oXyX3Ew0BKQAmGvSx75i+rLzoqhGqVwa4fkQj0qRVqt9R9XCstoODA3Ht1C5M6F4Z0/rWxbtnNzBmzGAcPboVTJMgi0QgOSHg+SNaPrmmAEtO90rOJWYIGO44AB3f8GDAmF0Rfa3//huLggXzYdXUXqhUrz3a9p0qMqbUI9/tUAqeuj5mOnydP6g3MuLHO0r9uNzXUgSkAKilNyaaYa2jc+dVz5diAs+7F1UPRbsdHBSA109uCT+PSb2qY2DTvNg4bygsTfWxatU8ODjcJmb43lL4ixZFeTKpIlDDPgPox421V+4m1SnIcUsEIiAQ3Lw+wjToUqNHUcD79q2DlZU5pvevj4z2edF7wlo8fPgUE5p2xstfAz/O04DWRxiU3NF6BHQ4hYssSQuB2To62WnED2kxUY48tQmM5+2HbpbcykOKDTbrHtwwC46v7sP/my9x4AaJUwYGBiKNW61aVdCckobnyZNTcckfXd9x80CNUjUwZs9GDPmr0B/tSzYuEVBHIIAoK9IXrYG0tWvg+Zwx6qflfgpDIHfusnj58lrSnjUrATbuRFCTepQ53kxjc/nyxQs1iRT6DbkZ1WrZCxZ+31D4yCaoxc/7UoeFSfvnoLGOZUMJgoC07yUIzJrthP7R3pIQ2IdaXa9smbgA/Sd2g/HcfdCx+Bnd6Or4CtMpS4ipqQmqVatAEb32tGRB4cL5UahQPvCXniwSgZSEQCrS/mUm85bTnftwooj0zOY/v6NSEg5yrskIAdL+hRQvDB36uNekSof5AZnwfzD5++3dtBzj7TIiktC9PlL4S5q/JSkAJs37xlHBG0gIrEzD/59iCmGu7+E/oStST9sCHdIIen12w8LRf5Ma3xKPH19E6tRq322KC+VaIpDCEMhYMD+cHuzE97DvKWzmcrrJFQG9904Ic/+MkGoVNTpF9gPv1LYpLMjsq6OSgepHJ2vpXbRRox3KxhIMAd0E60l29CcQYC3gLdWGv7+4h4DJ3RHi50v5gesgJOgbDhzYoFXCX/sazQA/P9Vhy22JQIIhwETkNw8dQ6pCBWCvQXNZgk1AdiQRUEeATMBhhoZAQLh7j/rp+OyH+AfgzdgZsP+1EX739P31sDySVBCQAmBSuVORjJO+vPzpcBNanFRPh96/gi/DWyLQ+ws58q4X0Vyq5xN7+5svuYykktrIxL4PKbX/f05cwXdHJ3Tt1TWlQiDnndwQIBOw3sMnCC2SX6MzC6bI4pNte8LnzgP1dh3pQOMf7yD1c3I/iSAgBcAkcqOiGib9A3Isfh1aPFTrpKbAjz76eshvn1n1sFZsm5A/Ir5L05tW3IwUOIjLR0+BmM0xqEyhFDh7OeXkikBoyaLQ/eypsen5f/bA0aad8OHCVfU2+V1Tl949ruon5H7SQkAKgEnrfkU6WvpHfEonatPipVohQ0goDtVqiS/PX6keTvRtf39SXJLJ4sS9pwiRgmCi34+UNACXr34IuHMXRWpXh43MBZySbn2yn2sI5fENpcA+TRR+Zxyk6F/3279o/ryo/do/3jma6Eq2kYgISAEwEcHXZNf0D3mH2qtBC3+dKctXSuTN/8hv9h5RHkvsjXy5c1JW40DcmjAF6Wq0xoYHzxN7SLL/FIKAbWryk6LId4+v5IYgi0QguSGgG/5K57zAcc0NzO+KAzVagN8daoXfLTV+vGvUTsndpIiAFACT4l2LYsw//jEr0ekI/7nsx3Gu60Bc7D8aQXF88XHWhDdvHHD16k14eflEMYKfh7l+374jkTNnaeTOXUa55MpVGk5OLjAzM8XE/VsQFhSMgXVbIXevf/Hcw+tnA3JLIvAHEDCkiEYbCv5wufuAvBCkG8IfgFg2qQUIeH15BV5iU/jdwO8IfleEfPNTv9SRDlSSwp86LEl7XxJBJ+37F+noiR4mA53YT0sp9QqmmTOi/JyJyFyTGWSiL++JVmD79v04Qgm/nZ1dYW5uBhOT1PD09CIFXiCKFCmIhQunIXPmDLh8+Trs7bOIbRcXV9Su3RoFCuTBhg2LKN7j14APBfkqk/K2W7cHZ2fOF2bhSkP6YVvX1jA21I9+cPKsRCCOCAw/fgmruvbFjstHUStbxji2Ii9LLggonkXJZT5xmYfTqQu4MmQ8fOnjPJJyi45xwIf0+YsEnKR8SAqASfnuRTN2EgJT0+nFtHSJrJp9/Zr4a9IIWGSPJLifLnhOPiAVKzZE3brV8fffzcHZQnR/mBe4PdaeTJu2AJs27aTckN9haWkuNIO6ujoIIq0eJxT/3//aRta1OKb+0H3zxQcNxs2F214iss6SBaNnTcTQCsWjvF6ekAjEFYEXHt4oU7UpdLNng8f+NXFtRl6XTBBQfxYlk2mFT8PPH/qXbyCEnt+RFe+373Fz3Ay8p4/8KMpaOt6XhD9y3JYluSEgBcDkdkfV5kOCYEc6tIQWU7VT0CFzWJ4OLVF0UC+YkhZPveTJU5Y0gCtRrFgh9VPx3o/qobv98Sv0I9b5kEePYVa1CnbOHosyGezi3Z9sQCKgikDmFj3g7/EFn8/tUj0st1MgAlE9i5IDFDpun5B67H8IqVIOwQ1qIczCXEzL1+kD7s9bjhdE7hxGVphICjvJcoaPjZGck4eSCQK6yWQechpRIPDjH7gwnT6vXoX/8Z+v24YdxavjfM+hcL/7UFmFk34HBAQSW4a18lhCbLQpmAsfj29Bm0Wz8fXhQ9QtWxeVpi+DZ0BAQnQv+0ghCASRG4OemVkKma2cZkpFICydHQL/+R/0T19EqoFj8JlMvfys52c+P/ujEP7OE16c21cKf8n8hyMFwGR+g3l69I/sQKtqtDDz7WdaIhR+CLzeeQAHqjfHvkqNcH7SbLRr0A5r1sxDliwJ7yPFpuZlLWrjKeWgzNmhLR4tXYkcZRpg+JEL0nE/wp2TO7FBgN0W1j54gUz1OyLo2TOUaVw3NpfLuhKBJImAT1pb3ChaCNsuXMP+Vt3Esz4Kwc+dJtiFlmo/3hlJcr5y0DFHQJqAY45VsqhJJmFLmshoWvrTYkRLlMWWkotnql4R6SuWgV2JItA3ZrdCzZTYmF3OODijw5CJ8L92HUYlS2DN3EmonyuLZgYiW0kRCHhQ+rcC/cYh8PgJ6KRPj9ojB2NNo2oy2ChF3P3oJxmbZ1H0LWnH2RDy+/tEmTtcL12H85lLESw7UYwwkI4vpGUaCX5eUdSRh5MhAlIATIY3NSZTIkGQVXsjaOlGy28lOx3iTrPMkxPWFNlrmSsb+QxmgkmGtDCytoKhuSn0KA8l14FOTHoHypSph+vXj8as8o9aMy7cxJrJcyjhuTsyN2+CzYO6IYOZcazakJVTJgLVF2+F44qVqDV6CBa2bgA9ClaSRSLACMTlWZRoyIURWUJoKEKDghDk44tAzy/49uEjRe86w+uVAzyfvIDXi9eiTgzGyIEdq2mZQYJfpOG/MWhDVknCCEgBMAnfPE0MnQTBNNROd1p60SLVapoAVbYhEZAISAS0FwFHGtpyWlaR4PeLS5D2DluOTNMISAFQ04gm0fZIECT1nfATbE/rxrRYJNGpyGFLBCQCEgGJQEQEvGn3AC2baTlLgl9oxNNyLyUiIAXAlHjXfzNnEgYpXxYq0lKblqq0FKdFlxZZJAISAYmARED7EfhOQ7xLyzlaTtBykYS+YFrLIhFQIiAFQCUUciMqBEggNKFzLAQWoSUvLTlpYXNxWlqsaZFFIiARkAhIBBIeAU/q8iMtjrS8puU5LQ9ouUsC3zdayyIRiBIBKQBGCY08ERMESDhkzSBHYrDWkM3IskgEJAISAYnAn0OAzbdBtPiRkMeaPlkkAnFCQAqAcYJNXiQRkAhIBCQCEgGJgEQg6SLA2htZJAISAYmAREAiIBGQCEgEUhACUgBMQTdbTlUiIBGQCEgEJAISAYkAIyAFQPk7kAhIBCQCEgGJgERAIpDCEJACYAq74XK6EgGJgERAIiARkAhIBKQAKH8DEgGJgERAIiARkAhIBFIYAlIATGE3XE5XIiARkAhIBCQCEgGJgBQA5W9AIiARkAhIBCQCEgGJQApDQAqAKeyGy+lKBCQCEgGJgERAIiARkAKg/A1IBCQCEgGJgERAIiARSGEISAEwhd1wOV2JgERAIiARkAhIBCQCUgCUvwGJgERAIiARkAhIBCQCKQwBKQCmsBsupysRkAhIBCQCEgGJgERACoDyNyARkAhIBCQCEgGJgEQghSEgBcAUdsPldCUCEgGJgERAIiARkAhIAVD+BiQCEgGJgERAIiARkAikMASkAJjCbricrkRAIiARkAhIBCQCEgEpAMrfgERAIiARkAhIBCQCEoEUhoAUAFPYDZfTlQhIBCQCEgGJgERAIiAFQPkbkAhIBCQCEgGJgERAIpDCEJACYAq74XK6EgGJgERAIiARkAhIBP4PkGeJQI2iUJsAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m.add_feature.cultural.time_zones(fc=\"none\", ec=\"teal\")\n",
+    "m.add_feature.cultural.admin_0_countries(fc=\"none\", ec=\"k\", lw=0.5)\n",
+    "m.add_feature.cultural.urban_areas_landscan(fc=\"r\", ec=\"none\")\n",
+    "m.add_feature.physical.lakes_europe(fc=\"b\", ec=\"darkblue\")\n",
+    "m.add_feature.physical.rivers_europe(fc=\"none\", ec=\"dodgerblue\", lw=0.3)\n",
+    "m.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e3b3f84-6c79-4ae6-b6ed-189b1248a648",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Advanced usage - getting a hand on the data\n",
+    "\n",
+    "For more advanced use-cases, it can be necessary to access the underlying datasets.\n",
+    "\n",
+    ":::{tip}\n",
+    "With EOmaps, you can quickly load the data of a selected feature as a `geopandas.GeoDataFrame` with:\n",
+    "```python\n",
+    "gdf = m.add_feature.cultural.<FEATURE-NAME>.get_gdf()\n",
+    "```\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c9971a4f-7a69-4763-8823-5ae41854bd1b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps(facecolor=\"none\")\n",
+    "m.set_frame(rounded=0.3)\n",
+    "m.add_feature.preset(\"coastline\", \"ocean\", \"land\", alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e2a2f2f4-74ee-41a4-a13b-1aab4ff6ae54",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "places = m.add_feature.cultural.populated_places.get_gdf(scale=110)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "452c8a5f-6492-404c-aedf-eb7574d920dc",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "This will load the corresponding NaturalEarth dataset of the feature, containing the gometries and all associated metadata. \n",
+    "\n",
+    "For example, the first 10 rows of the `places` GeoDataFrame look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "68b3a623-8d4c-4282-92b5-fa20414ffce0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style='width: 100%; height: 40ex; overflow: auto'><style type=\"text/css\">\n",
+       "#T_78f41 tr {\n",
+       "  font-size: 8pt;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_78f41\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_78f41_level0_col0\" class=\"col_heading level0 col0\" >SCALERANK</th>\n",
+       "      <th id=\"T_78f41_level0_col1\" class=\"col_heading level0 col1\" >NATSCALE</th>\n",
+       "      <th id=\"T_78f41_level0_col2\" class=\"col_heading level0 col2\" >LABELRANK</th>\n",
+       "      <th id=\"T_78f41_level0_col3\" class=\"col_heading level0 col3\" >FEATURECLA</th>\n",
+       "      <th id=\"T_78f41_level0_col4\" class=\"col_heading level0 col4\" >NAME</th>\n",
+       "      <th id=\"T_78f41_level0_col5\" class=\"col_heading level0 col5\" >NAMEPAR</th>\n",
+       "      <th id=\"T_78f41_level0_col6\" class=\"col_heading level0 col6\" >NAMEALT</th>\n",
+       "      <th id=\"T_78f41_level0_col7\" class=\"col_heading level0 col7\" >NAMEASCII</th>\n",
+       "      <th id=\"T_78f41_level0_col8\" class=\"col_heading level0 col8\" >ADM0CAP</th>\n",
+       "      <th id=\"T_78f41_level0_col9\" class=\"col_heading level0 col9\" >CAPIN</th>\n",
+       "      <th id=\"T_78f41_level0_col10\" class=\"col_heading level0 col10\" >WORLDCITY</th>\n",
+       "      <th id=\"T_78f41_level0_col11\" class=\"col_heading level0 col11\" >MEGACITY</th>\n",
+       "      <th id=\"T_78f41_level0_col12\" class=\"col_heading level0 col12\" >SOV0NAME</th>\n",
+       "      <th id=\"T_78f41_level0_col13\" class=\"col_heading level0 col13\" >SOV_A3</th>\n",
+       "      <th id=\"T_78f41_level0_col14\" class=\"col_heading level0 col14\" >ADM0NAME</th>\n",
+       "      <th id=\"T_78f41_level0_col15\" class=\"col_heading level0 col15\" >ADM0_A3</th>\n",
+       "      <th id=\"T_78f41_level0_col16\" class=\"col_heading level0 col16\" >ADM1NAME</th>\n",
+       "      <th id=\"T_78f41_level0_col17\" class=\"col_heading level0 col17\" >ISO_A2</th>\n",
+       "      <th id=\"T_78f41_level0_col18\" class=\"col_heading level0 col18\" >NOTE</th>\n",
+       "      <th id=\"T_78f41_level0_col19\" class=\"col_heading level0 col19\" >LATITUDE</th>\n",
+       "      <th id=\"T_78f41_level0_col20\" class=\"col_heading level0 col20\" >LONGITUDE</th>\n",
+       "      <th id=\"T_78f41_level0_col21\" class=\"col_heading level0 col21\" >POP_MAX</th>\n",
+       "      <th id=\"T_78f41_level0_col22\" class=\"col_heading level0 col22\" >POP_MIN</th>\n",
+       "      <th id=\"T_78f41_level0_col23\" class=\"col_heading level0 col23\" >POP_OTHER</th>\n",
+       "      <th id=\"T_78f41_level0_col24\" class=\"col_heading level0 col24\" >RANK_MAX</th>\n",
+       "      <th id=\"T_78f41_level0_col25\" class=\"col_heading level0 col25\" >RANK_MIN</th>\n",
+       "      <th id=\"T_78f41_level0_col26\" class=\"col_heading level0 col26\" >MEGANAME</th>\n",
+       "      <th id=\"T_78f41_level0_col27\" class=\"col_heading level0 col27\" >LS_NAME</th>\n",
+       "      <th id=\"T_78f41_level0_col28\" class=\"col_heading level0 col28\" >MAX_POP10</th>\n",
+       "      <th id=\"T_78f41_level0_col29\" class=\"col_heading level0 col29\" >MAX_POP20</th>\n",
+       "      <th id=\"T_78f41_level0_col30\" class=\"col_heading level0 col30\" >MAX_POP50</th>\n",
+       "      <th id=\"T_78f41_level0_col31\" class=\"col_heading level0 col31\" >MAX_POP300</th>\n",
+       "      <th id=\"T_78f41_level0_col32\" class=\"col_heading level0 col32\" >MAX_POP310</th>\n",
+       "      <th id=\"T_78f41_level0_col33\" class=\"col_heading level0 col33\" >MAX_NATSCA</th>\n",
+       "      <th id=\"T_78f41_level0_col34\" class=\"col_heading level0 col34\" >MIN_AREAKM</th>\n",
+       "      <th id=\"T_78f41_level0_col35\" class=\"col_heading level0 col35\" >MAX_AREAKM</th>\n",
+       "      <th id=\"T_78f41_level0_col36\" class=\"col_heading level0 col36\" >MIN_AREAMI</th>\n",
+       "      <th id=\"T_78f41_level0_col37\" class=\"col_heading level0 col37\" >MAX_AREAMI</th>\n",
+       "      <th id=\"T_78f41_level0_col38\" class=\"col_heading level0 col38\" >MIN_PERKM</th>\n",
+       "      <th id=\"T_78f41_level0_col39\" class=\"col_heading level0 col39\" >MAX_PERKM</th>\n",
+       "      <th id=\"T_78f41_level0_col40\" class=\"col_heading level0 col40\" >MIN_PERMI</th>\n",
+       "      <th id=\"T_78f41_level0_col41\" class=\"col_heading level0 col41\" >MAX_PERMI</th>\n",
+       "      <th id=\"T_78f41_level0_col42\" class=\"col_heading level0 col42\" >MIN_BBXMIN</th>\n",
+       "      <th id=\"T_78f41_level0_col43\" class=\"col_heading level0 col43\" >MAX_BBXMIN</th>\n",
+       "      <th id=\"T_78f41_level0_col44\" class=\"col_heading level0 col44\" >MIN_BBXMAX</th>\n",
+       "      <th id=\"T_78f41_level0_col45\" class=\"col_heading level0 col45\" >MAX_BBXMAX</th>\n",
+       "      <th id=\"T_78f41_level0_col46\" class=\"col_heading level0 col46\" >MIN_BBYMIN</th>\n",
+       "      <th id=\"T_78f41_level0_col47\" class=\"col_heading level0 col47\" >MAX_BBYMIN</th>\n",
+       "      <th id=\"T_78f41_level0_col48\" class=\"col_heading level0 col48\" >MIN_BBYMAX</th>\n",
+       "      <th id=\"T_78f41_level0_col49\" class=\"col_heading level0 col49\" >MAX_BBYMAX</th>\n",
+       "      <th id=\"T_78f41_level0_col50\" class=\"col_heading level0 col50\" >MEAN_BBXC</th>\n",
+       "      <th id=\"T_78f41_level0_col51\" class=\"col_heading level0 col51\" >MEAN_BBYC</th>\n",
+       "      <th id=\"T_78f41_level0_col52\" class=\"col_heading level0 col52\" >TIMEZONE</th>\n",
+       "      <th id=\"T_78f41_level0_col53\" class=\"col_heading level0 col53\" >UN_FID</th>\n",
+       "      <th id=\"T_78f41_level0_col54\" class=\"col_heading level0 col54\" >POP1950</th>\n",
+       "      <th id=\"T_78f41_level0_col55\" class=\"col_heading level0 col55\" >POP1955</th>\n",
+       "      <th id=\"T_78f41_level0_col56\" class=\"col_heading level0 col56\" >POP1960</th>\n",
+       "      <th id=\"T_78f41_level0_col57\" class=\"col_heading level0 col57\" >POP1965</th>\n",
+       "      <th id=\"T_78f41_level0_col58\" class=\"col_heading level0 col58\" >POP1970</th>\n",
+       "      <th id=\"T_78f41_level0_col59\" class=\"col_heading level0 col59\" >POP1975</th>\n",
+       "      <th id=\"T_78f41_level0_col60\" class=\"col_heading level0 col60\" >POP1980</th>\n",
+       "      <th id=\"T_78f41_level0_col61\" class=\"col_heading level0 col61\" >POP1985</th>\n",
+       "      <th id=\"T_78f41_level0_col62\" class=\"col_heading level0 col62\" >POP1990</th>\n",
+       "      <th id=\"T_78f41_level0_col63\" class=\"col_heading level0 col63\" >POP1995</th>\n",
+       "      <th id=\"T_78f41_level0_col64\" class=\"col_heading level0 col64\" >POP2000</th>\n",
+       "      <th id=\"T_78f41_level0_col65\" class=\"col_heading level0 col65\" >POP2005</th>\n",
+       "      <th id=\"T_78f41_level0_col66\" class=\"col_heading level0 col66\" >POP2010</th>\n",
+       "      <th id=\"T_78f41_level0_col67\" class=\"col_heading level0 col67\" >POP2015</th>\n",
+       "      <th id=\"T_78f41_level0_col68\" class=\"col_heading level0 col68\" >POP2020</th>\n",
+       "      <th id=\"T_78f41_level0_col69\" class=\"col_heading level0 col69\" >POP2025</th>\n",
+       "      <th id=\"T_78f41_level0_col70\" class=\"col_heading level0 col70\" >POP2050</th>\n",
+       "      <th id=\"T_78f41_level0_col71\" class=\"col_heading level0 col71\" >MIN_ZOOM</th>\n",
+       "      <th id=\"T_78f41_level0_col72\" class=\"col_heading level0 col72\" >WIKIDATAID</th>\n",
+       "      <th id=\"T_78f41_level0_col73\" class=\"col_heading level0 col73\" >WOF_ID</th>\n",
+       "      <th id=\"T_78f41_level0_col74\" class=\"col_heading level0 col74\" >CAPALT</th>\n",
+       "      <th id=\"T_78f41_level0_col75\" class=\"col_heading level0 col75\" >NAME_EN</th>\n",
+       "      <th id=\"T_78f41_level0_col76\" class=\"col_heading level0 col76\" >NAME_DE</th>\n",
+       "      <th id=\"T_78f41_level0_col77\" class=\"col_heading level0 col77\" >NAME_ES</th>\n",
+       "      <th id=\"T_78f41_level0_col78\" class=\"col_heading level0 col78\" >NAME_FR</th>\n",
+       "      <th id=\"T_78f41_level0_col79\" class=\"col_heading level0 col79\" >NAME_PT</th>\n",
+       "      <th id=\"T_78f41_level0_col80\" class=\"col_heading level0 col80\" >NAME_RU</th>\n",
+       "      <th id=\"T_78f41_level0_col81\" class=\"col_heading level0 col81\" >NAME_ZH</th>\n",
+       "      <th id=\"T_78f41_level0_col82\" class=\"col_heading level0 col82\" >LABEL</th>\n",
+       "      <th id=\"T_78f41_level0_col83\" class=\"col_heading level0 col83\" >NAME_AR</th>\n",
+       "      <th id=\"T_78f41_level0_col84\" class=\"col_heading level0 col84\" >NAME_BN</th>\n",
+       "      <th id=\"T_78f41_level0_col85\" class=\"col_heading level0 col85\" >NAME_EL</th>\n",
+       "      <th id=\"T_78f41_level0_col86\" class=\"col_heading level0 col86\" >NAME_HI</th>\n",
+       "      <th id=\"T_78f41_level0_col87\" class=\"col_heading level0 col87\" >NAME_HU</th>\n",
+       "      <th id=\"T_78f41_level0_col88\" class=\"col_heading level0 col88\" >NAME_ID</th>\n",
+       "      <th id=\"T_78f41_level0_col89\" class=\"col_heading level0 col89\" >NAME_IT</th>\n",
+       "      <th id=\"T_78f41_level0_col90\" class=\"col_heading level0 col90\" >NAME_JA</th>\n",
+       "      <th id=\"T_78f41_level0_col91\" class=\"col_heading level0 col91\" >NAME_KO</th>\n",
+       "      <th id=\"T_78f41_level0_col92\" class=\"col_heading level0 col92\" >NAME_NL</th>\n",
+       "      <th id=\"T_78f41_level0_col93\" class=\"col_heading level0 col93\" >NAME_PL</th>\n",
+       "      <th id=\"T_78f41_level0_col94\" class=\"col_heading level0 col94\" >NAME_SV</th>\n",
+       "      <th id=\"T_78f41_level0_col95\" class=\"col_heading level0 col95\" >NAME_TR</th>\n",
+       "      <th id=\"T_78f41_level0_col96\" class=\"col_heading level0 col96\" >NAME_VI</th>\n",
+       "      <th id=\"T_78f41_level0_col97\" class=\"col_heading level0 col97\" >NE_ID</th>\n",
+       "      <th id=\"T_78f41_level0_col98\" class=\"col_heading level0 col98\" >NAME_FA</th>\n",
+       "      <th id=\"T_78f41_level0_col99\" class=\"col_heading level0 col99\" >NAME_HE</th>\n",
+       "      <th id=\"T_78f41_level0_col100\" class=\"col_heading level0 col100\" >NAME_UK</th>\n",
+       "      <th id=\"T_78f41_level0_col101\" class=\"col_heading level0 col101\" >NAME_UR</th>\n",
+       "      <th id=\"T_78f41_level0_col102\" class=\"col_heading level0 col102\" >NAME_ZHT</th>\n",
+       "      <th id=\"T_78f41_level0_col103\" class=\"col_heading level0 col103\" >GEONAMESID</th>\n",
+       "      <th id=\"T_78f41_level0_col104\" class=\"col_heading level0 col104\" >FCLASS_ISO</th>\n",
+       "      <th id=\"T_78f41_level0_col105\" class=\"col_heading level0 col105\" >FCLASS_US</th>\n",
+       "      <th id=\"T_78f41_level0_col106\" class=\"col_heading level0 col106\" >FCLASS_FR</th>\n",
+       "      <th id=\"T_78f41_level0_col107\" class=\"col_heading level0 col107\" >FCLASS_RU</th>\n",
+       "      <th id=\"T_78f41_level0_col108\" class=\"col_heading level0 col108\" >FCLASS_ES</th>\n",
+       "      <th id=\"T_78f41_level0_col109\" class=\"col_heading level0 col109\" >FCLASS_CN</th>\n",
+       "      <th id=\"T_78f41_level0_col110\" class=\"col_heading level0 col110\" >FCLASS_TW</th>\n",
+       "      <th id=\"T_78f41_level0_col111\" class=\"col_heading level0 col111\" >FCLASS_IN</th>\n",
+       "      <th id=\"T_78f41_level0_col112\" class=\"col_heading level0 col112\" >FCLASS_NP</th>\n",
+       "      <th id=\"T_78f41_level0_col113\" class=\"col_heading level0 col113\" >FCLASS_PK</th>\n",
+       "      <th id=\"T_78f41_level0_col114\" class=\"col_heading level0 col114\" >FCLASS_DE</th>\n",
+       "      <th id=\"T_78f41_level0_col115\" class=\"col_heading level0 col115\" >FCLASS_GB</th>\n",
+       "      <th id=\"T_78f41_level0_col116\" class=\"col_heading level0 col116\" >FCLASS_BR</th>\n",
+       "      <th id=\"T_78f41_level0_col117\" class=\"col_heading level0 col117\" >FCLASS_IL</th>\n",
+       "      <th id=\"T_78f41_level0_col118\" class=\"col_heading level0 col118\" >FCLASS_PS</th>\n",
+       "      <th id=\"T_78f41_level0_col119\" class=\"col_heading level0 col119\" >FCLASS_SA</th>\n",
+       "      <th id=\"T_78f41_level0_col120\" class=\"col_heading level0 col120\" >FCLASS_EG</th>\n",
+       "      <th id=\"T_78f41_level0_col121\" class=\"col_heading level0 col121\" >FCLASS_MA</th>\n",
+       "      <th id=\"T_78f41_level0_col122\" class=\"col_heading level0 col122\" >FCLASS_PT</th>\n",
+       "      <th id=\"T_78f41_level0_col123\" class=\"col_heading level0 col123\" >FCLASS_AR</th>\n",
+       "      <th id=\"T_78f41_level0_col124\" class=\"col_heading level0 col124\" >FCLASS_JP</th>\n",
+       "      <th id=\"T_78f41_level0_col125\" class=\"col_heading level0 col125\" >FCLASS_KO</th>\n",
+       "      <th id=\"T_78f41_level0_col126\" class=\"col_heading level0 col126\" >FCLASS_VN</th>\n",
+       "      <th id=\"T_78f41_level0_col127\" class=\"col_heading level0 col127\" >FCLASS_TR</th>\n",
+       "      <th id=\"T_78f41_level0_col128\" class=\"col_heading level0 col128\" >FCLASS_ID</th>\n",
+       "      <th id=\"T_78f41_level0_col129\" class=\"col_heading level0 col129\" >FCLASS_PL</th>\n",
+       "      <th id=\"T_78f41_level0_col130\" class=\"col_heading level0 col130\" >FCLASS_GR</th>\n",
+       "      <th id=\"T_78f41_level0_col131\" class=\"col_heading level0 col131\" >FCLASS_IT</th>\n",
+       "      <th id=\"T_78f41_level0_col132\" class=\"col_heading level0 col132\" >FCLASS_NL</th>\n",
+       "      <th id=\"T_78f41_level0_col133\" class=\"col_heading level0 col133\" >FCLASS_SE</th>\n",
+       "      <th id=\"T_78f41_level0_col134\" class=\"col_heading level0 col134\" >FCLASS_BD</th>\n",
+       "      <th id=\"T_78f41_level0_col135\" class=\"col_heading level0 col135\" >FCLASS_UA</th>\n",
+       "      <th id=\"T_78f41_level0_col136\" class=\"col_heading level0 col136\" >FCLASS_TLC</th>\n",
+       "      <th id=\"T_78f41_level0_col137\" class=\"col_heading level0 col137\" >geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_78f41_row0_col0\" class=\"data row0 col0\" >8</td>\n",
+       "      <td id=\"T_78f41_row0_col1\" class=\"data row0 col1\" >10</td>\n",
+       "      <td id=\"T_78f41_row0_col2\" class=\"data row0 col2\" >3</td>\n",
+       "      <td id=\"T_78f41_row0_col3\" class=\"data row0 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row0_col4\" class=\"data row0 col4\" >Vatican City</td>\n",
+       "      <td id=\"T_78f41_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col7\" class=\"data row0 col7\" >Vatican City</td>\n",
+       "      <td id=\"T_78f41_row0_col8\" class=\"data row0 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row0_col9\" class=\"data row0 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col10\" class=\"data row0 col10\" >1</td>\n",
+       "      <td id=\"T_78f41_row0_col11\" class=\"data row0 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col12\" class=\"data row0 col12\" >Vatican</td>\n",
+       "      <td id=\"T_78f41_row0_col13\" class=\"data row0 col13\" >VAT</td>\n",
+       "      <td id=\"T_78f41_row0_col14\" class=\"data row0 col14\" >Vatican</td>\n",
+       "      <td id=\"T_78f41_row0_col15\" class=\"data row0 col15\" >VAT</td>\n",
+       "      <td id=\"T_78f41_row0_col16\" class=\"data row0 col16\" >Lazio</td>\n",
+       "      <td id=\"T_78f41_row0_col17\" class=\"data row0 col17\" >VA</td>\n",
+       "      <td id=\"T_78f41_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col19\" class=\"data row0 col19\" >41.903282</td>\n",
+       "      <td id=\"T_78f41_row0_col20\" class=\"data row0 col20\" >12.453387</td>\n",
+       "      <td id=\"T_78f41_row0_col21\" class=\"data row0 col21\" >832</td>\n",
+       "      <td id=\"T_78f41_row0_col22\" class=\"data row0 col22\" >832</td>\n",
+       "      <td id=\"T_78f41_row0_col23\" class=\"data row0 col23\" >562430</td>\n",
+       "      <td id=\"T_78f41_row0_col24\" class=\"data row0 col24\" >2</td>\n",
+       "      <td id=\"T_78f41_row0_col25\" class=\"data row0 col25\" >2</td>\n",
+       "      <td id=\"T_78f41_row0_col26\" class=\"data row0 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col27\" class=\"data row0 col27\" >Vatican City</td>\n",
+       "      <td id=\"T_78f41_row0_col28\" class=\"data row0 col28\" >636762</td>\n",
+       "      <td id=\"T_78f41_row0_col29\" class=\"data row0 col29\" >636762</td>\n",
+       "      <td id=\"T_78f41_row0_col30\" class=\"data row0 col30\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col31\" class=\"data row0 col31\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col32\" class=\"data row0 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col33\" class=\"data row0 col33\" >20</td>\n",
+       "      <td id=\"T_78f41_row0_col34\" class=\"data row0 col34\" >177</td>\n",
+       "      <td id=\"T_78f41_row0_col35\" class=\"data row0 col35\" >177</td>\n",
+       "      <td id=\"T_78f41_row0_col36\" class=\"data row0 col36\" >68</td>\n",
+       "      <td id=\"T_78f41_row0_col37\" class=\"data row0 col37\" >68</td>\n",
+       "      <td id=\"T_78f41_row0_col38\" class=\"data row0 col38\" >160</td>\n",
+       "      <td id=\"T_78f41_row0_col39\" class=\"data row0 col39\" >160</td>\n",
+       "      <td id=\"T_78f41_row0_col40\" class=\"data row0 col40\" >99</td>\n",
+       "      <td id=\"T_78f41_row0_col41\" class=\"data row0 col41\" >99</td>\n",
+       "      <td id=\"T_78f41_row0_col42\" class=\"data row0 col42\" >12.333333</td>\n",
+       "      <td id=\"T_78f41_row0_col43\" class=\"data row0 col43\" >12.333333</td>\n",
+       "      <td id=\"T_78f41_row0_col44\" class=\"data row0 col44\" >12.481009</td>\n",
+       "      <td id=\"T_78f41_row0_col45\" class=\"data row0 col45\" >12.481009</td>\n",
+       "      <td id=\"T_78f41_row0_col46\" class=\"data row0 col46\" >41.766667</td>\n",
+       "      <td id=\"T_78f41_row0_col47\" class=\"data row0 col47\" >41.766667</td>\n",
+       "      <td id=\"T_78f41_row0_col48\" class=\"data row0 col48\" >42.050000</td>\n",
+       "      <td id=\"T_78f41_row0_col49\" class=\"data row0 col49\" >42.050000</td>\n",
+       "      <td id=\"T_78f41_row0_col50\" class=\"data row0 col50\" >12.419907</td>\n",
+       "      <td id=\"T_78f41_row0_col51\" class=\"data row0 col51\" >41.903477</td>\n",
+       "      <td id=\"T_78f41_row0_col52\" class=\"data row0 col52\" >Europe/Vatican</td>\n",
+       "      <td id=\"T_78f41_row0_col53\" class=\"data row0 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col54\" class=\"data row0 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col55\" class=\"data row0 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col56\" class=\"data row0 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col57\" class=\"data row0 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col58\" class=\"data row0 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col59\" class=\"data row0 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col60\" class=\"data row0 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col61\" class=\"data row0 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col62\" class=\"data row0 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col63\" class=\"data row0 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col64\" class=\"data row0 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col65\" class=\"data row0 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col66\" class=\"data row0 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col67\" class=\"data row0 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col68\" class=\"data row0 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col69\" class=\"data row0 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col70\" class=\"data row0 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col71\" class=\"data row0 col71\" >7.000000</td>\n",
+       "      <td id=\"T_78f41_row0_col72\" class=\"data row0 col72\" >Q237</td>\n",
+       "      <td id=\"T_78f41_row0_col73\" class=\"data row0 col73\" >101914185</td>\n",
+       "      <td id=\"T_78f41_row0_col74\" class=\"data row0 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row0_col75\" class=\"data row0 col75\" >Vatican City</td>\n",
+       "      <td id=\"T_78f41_row0_col76\" class=\"data row0 col76\" >Vatikanstadt</td>\n",
+       "      <td id=\"T_78f41_row0_col77\" class=\"data row0 col77\" >Ciudad del Vaticano</td>\n",
+       "      <td id=\"T_78f41_row0_col78\" class=\"data row0 col78\" >Cité du Vatican</td>\n",
+       "      <td id=\"T_78f41_row0_col79\" class=\"data row0 col79\" >Vaticano</td>\n",
+       "      <td id=\"T_78f41_row0_col80\" class=\"data row0 col80\" >Ватикан</td>\n",
+       "      <td id=\"T_78f41_row0_col81\" class=\"data row0 col81\" >梵蒂冈</td>\n",
+       "      <td id=\"T_78f41_row0_col82\" class=\"data row0 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col83\" class=\"data row0 col83\" >الفاتيكان</td>\n",
+       "      <td id=\"T_78f41_row0_col84\" class=\"data row0 col84\" >ভ্যাটিকান সিটি</td>\n",
+       "      <td id=\"T_78f41_row0_col85\" class=\"data row0 col85\" >Βατικανό</td>\n",
+       "      <td id=\"T_78f41_row0_col86\" class=\"data row0 col86\" >वैटिकन नगर</td>\n",
+       "      <td id=\"T_78f41_row0_col87\" class=\"data row0 col87\" >Vatikán</td>\n",
+       "      <td id=\"T_78f41_row0_col88\" class=\"data row0 col88\" >Vatikan</td>\n",
+       "      <td id=\"T_78f41_row0_col89\" class=\"data row0 col89\" >Città del Vaticano</td>\n",
+       "      <td id=\"T_78f41_row0_col90\" class=\"data row0 col90\" >バチカン</td>\n",
+       "      <td id=\"T_78f41_row0_col91\" class=\"data row0 col91\" >바티칸 시국</td>\n",
+       "      <td id=\"T_78f41_row0_col92\" class=\"data row0 col92\" >Vaticaanstad</td>\n",
+       "      <td id=\"T_78f41_row0_col93\" class=\"data row0 col93\" >Watykan</td>\n",
+       "      <td id=\"T_78f41_row0_col94\" class=\"data row0 col94\" >Vatikanstaten</td>\n",
+       "      <td id=\"T_78f41_row0_col95\" class=\"data row0 col95\" >Vatikan</td>\n",
+       "      <td id=\"T_78f41_row0_col96\" class=\"data row0 col96\" >Thành Vatican</td>\n",
+       "      <td id=\"T_78f41_row0_col97\" class=\"data row0 col97\" >1159127243</td>\n",
+       "      <td id=\"T_78f41_row0_col98\" class=\"data row0 col98\" >واتیکان</td>\n",
+       "      <td id=\"T_78f41_row0_col99\" class=\"data row0 col99\" >קריית הוותיקן</td>\n",
+       "      <td id=\"T_78f41_row0_col100\" class=\"data row0 col100\" >Ватикан</td>\n",
+       "      <td id=\"T_78f41_row0_col101\" class=\"data row0 col101\" >ویٹیکن سٹی</td>\n",
+       "      <td id=\"T_78f41_row0_col102\" class=\"data row0 col102\" >梵蒂岡</td>\n",
+       "      <td id=\"T_78f41_row0_col103\" class=\"data row0 col103\" >6691831</td>\n",
+       "      <td id=\"T_78f41_row0_col104\" class=\"data row0 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col105\" class=\"data row0 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col106\" class=\"data row0 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col107\" class=\"data row0 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col108\" class=\"data row0 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col109\" class=\"data row0 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col110\" class=\"data row0 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col111\" class=\"data row0 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col112\" class=\"data row0 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col113\" class=\"data row0 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col114\" class=\"data row0 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col115\" class=\"data row0 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col116\" class=\"data row0 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col117\" class=\"data row0 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col118\" class=\"data row0 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col119\" class=\"data row0 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col120\" class=\"data row0 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col121\" class=\"data row0 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col122\" class=\"data row0 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col123\" class=\"data row0 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col124\" class=\"data row0 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col125\" class=\"data row0 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col126\" class=\"data row0 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col127\" class=\"data row0 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col128\" class=\"data row0 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col129\" class=\"data row0 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col130\" class=\"data row0 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col131\" class=\"data row0 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col132\" class=\"data row0 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col133\" class=\"data row0 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col134\" class=\"data row0 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col135\" class=\"data row0 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col136\" class=\"data row0 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row0_col137\" class=\"data row0 col137\" >POINT (12.4533865 41.9032822)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_78f41_row1_col0\" class=\"data row1 col0\" >7</td>\n",
+       "      <td id=\"T_78f41_row1_col1\" class=\"data row1 col1\" >20</td>\n",
+       "      <td id=\"T_78f41_row1_col2\" class=\"data row1 col2\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col3\" class=\"data row1 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row1_col4\" class=\"data row1 col4\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col7\" class=\"data row1 col7\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col8\" class=\"data row1 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col10\" class=\"data row1 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col11\" class=\"data row1 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col12\" class=\"data row1 col12\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col13\" class=\"data row1 col13\" >SMR</td>\n",
+       "      <td id=\"T_78f41_row1_col14\" class=\"data row1 col14\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col15\" class=\"data row1 col15\" >SMR</td>\n",
+       "      <td id=\"T_78f41_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col17\" class=\"data row1 col17\" >SM</td>\n",
+       "      <td id=\"T_78f41_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col19\" class=\"data row1 col19\" >43.936096</td>\n",
+       "      <td id=\"T_78f41_row1_col20\" class=\"data row1 col20\" >12.441770</td>\n",
+       "      <td id=\"T_78f41_row1_col21\" class=\"data row1 col21\" >29579</td>\n",
+       "      <td id=\"T_78f41_row1_col22\" class=\"data row1 col22\" >29000</td>\n",
+       "      <td id=\"T_78f41_row1_col23\" class=\"data row1 col23\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col24\" class=\"data row1 col24\" >7</td>\n",
+       "      <td id=\"T_78f41_row1_col25\" class=\"data row1 col25\" >7</td>\n",
+       "      <td id=\"T_78f41_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col27\" class=\"data row1 col27\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col28\" class=\"data row1 col28\" >29088</td>\n",
+       "      <td id=\"T_78f41_row1_col29\" class=\"data row1 col29\" >29579</td>\n",
+       "      <td id=\"T_78f41_row1_col30\" class=\"data row1 col30\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col31\" class=\"data row1 col31\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col32\" class=\"data row1 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col33\" class=\"data row1 col33\" >20</td>\n",
+       "      <td id=\"T_78f41_row1_col34\" class=\"data row1 col34\" >30</td>\n",
+       "      <td id=\"T_78f41_row1_col35\" class=\"data row1 col35\" >30</td>\n",
+       "      <td id=\"T_78f41_row1_col36\" class=\"data row1 col36\" >11</td>\n",
+       "      <td id=\"T_78f41_row1_col37\" class=\"data row1 col37\" >11</td>\n",
+       "      <td id=\"T_78f41_row1_col38\" class=\"data row1 col38\" >63</td>\n",
+       "      <td id=\"T_78f41_row1_col39\" class=\"data row1 col39\" >63</td>\n",
+       "      <td id=\"T_78f41_row1_col40\" class=\"data row1 col40\" >39</td>\n",
+       "      <td id=\"T_78f41_row1_col41\" class=\"data row1 col41\" >39</td>\n",
+       "      <td id=\"T_78f41_row1_col42\" class=\"data row1 col42\" >12.391667</td>\n",
+       "      <td id=\"T_78f41_row1_col43\" class=\"data row1 col43\" >12.391667</td>\n",
+       "      <td id=\"T_78f41_row1_col44\" class=\"data row1 col44\" >12.541667</td>\n",
+       "      <td id=\"T_78f41_row1_col45\" class=\"data row1 col45\" >12.541667</td>\n",
+       "      <td id=\"T_78f41_row1_col46\" class=\"data row1 col46\" >43.900000</td>\n",
+       "      <td id=\"T_78f41_row1_col47\" class=\"data row1 col47\" >43.900000</td>\n",
+       "      <td id=\"T_78f41_row1_col48\" class=\"data row1 col48\" >44.000000</td>\n",
+       "      <td id=\"T_78f41_row1_col49\" class=\"data row1 col49\" >44.000000</td>\n",
+       "      <td id=\"T_78f41_row1_col50\" class=\"data row1 col50\" >12.462153</td>\n",
+       "      <td id=\"T_78f41_row1_col51\" class=\"data row1 col51\" >43.953472</td>\n",
+       "      <td id=\"T_78f41_row1_col52\" class=\"data row1 col52\" >Europe/San_Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col53\" class=\"data row1 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col54\" class=\"data row1 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col55\" class=\"data row1 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col56\" class=\"data row1 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col57\" class=\"data row1 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col58\" class=\"data row1 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col59\" class=\"data row1 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col60\" class=\"data row1 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col61\" class=\"data row1 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col62\" class=\"data row1 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col63\" class=\"data row1 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col64\" class=\"data row1 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col65\" class=\"data row1 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col66\" class=\"data row1 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col67\" class=\"data row1 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col68\" class=\"data row1 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col69\" class=\"data row1 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col70\" class=\"data row1 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col71\" class=\"data row1 col71\" >6.100000</td>\n",
+       "      <td id=\"T_78f41_row1_col72\" class=\"data row1 col72\" >Q1848</td>\n",
+       "      <td id=\"T_78f41_row1_col73\" class=\"data row1 col73\" >101837381</td>\n",
+       "      <td id=\"T_78f41_row1_col74\" class=\"data row1 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row1_col75\" class=\"data row1 col75\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col76\" class=\"data row1 col76\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col77\" class=\"data row1 col77\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col78\" class=\"data row1 col78\" >Saint-Marin</td>\n",
+       "      <td id=\"T_78f41_row1_col79\" class=\"data row1 col79\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col80\" class=\"data row1 col80\" >Сан-Марино</td>\n",
+       "      <td id=\"T_78f41_row1_col81\" class=\"data row1 col81\" >圣马力诺</td>\n",
+       "      <td id=\"T_78f41_row1_col82\" class=\"data row1 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col83\" class=\"data row1 col83\" >مدينة سان مارينو</td>\n",
+       "      <td id=\"T_78f41_row1_col84\" class=\"data row1 col84\" >সান মারিনো</td>\n",
+       "      <td id=\"T_78f41_row1_col85\" class=\"data row1 col85\" >Άγιος Μαρίνος</td>\n",
+       "      <td id=\"T_78f41_row1_col86\" class=\"data row1 col86\" >सैन मारिनो नगर</td>\n",
+       "      <td id=\"T_78f41_row1_col87\" class=\"data row1 col87\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col88\" class=\"data row1 col88\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col89\" class=\"data row1 col89\" >Città di San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col90\" class=\"data row1 col90\" >サンマリノ市</td>\n",
+       "      <td id=\"T_78f41_row1_col91\" class=\"data row1 col91\" >산마리노</td>\n",
+       "      <td id=\"T_78f41_row1_col92\" class=\"data row1 col92\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col93\" class=\"data row1 col93\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col94\" class=\"data row1 col94\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col95\" class=\"data row1 col95\" >San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col96\" class=\"data row1 col96\" >Thành phố San Marino</td>\n",
+       "      <td id=\"T_78f41_row1_col97\" class=\"data row1 col97\" >1159146051</td>\n",
+       "      <td id=\"T_78f41_row1_col98\" class=\"data row1 col98\" >سن مارینو</td>\n",
+       "      <td id=\"T_78f41_row1_col99\" class=\"data row1 col99\" >סן מרינו</td>\n",
+       "      <td id=\"T_78f41_row1_col100\" class=\"data row1 col100\" >Сан-Марино</td>\n",
+       "      <td id=\"T_78f41_row1_col101\" class=\"data row1 col101\" >سان مارینو شہر</td>\n",
+       "      <td id=\"T_78f41_row1_col102\" class=\"data row1 col102\" >聖馬力諾</td>\n",
+       "      <td id=\"T_78f41_row1_col103\" class=\"data row1 col103\" >3168070</td>\n",
+       "      <td id=\"T_78f41_row1_col104\" class=\"data row1 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col105\" class=\"data row1 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col106\" class=\"data row1 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col107\" class=\"data row1 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col108\" class=\"data row1 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col109\" class=\"data row1 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col110\" class=\"data row1 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col111\" class=\"data row1 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col112\" class=\"data row1 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col113\" class=\"data row1 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col114\" class=\"data row1 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col115\" class=\"data row1 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col116\" class=\"data row1 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col117\" class=\"data row1 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col118\" class=\"data row1 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col119\" class=\"data row1 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col120\" class=\"data row1 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col121\" class=\"data row1 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col122\" class=\"data row1 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col123\" class=\"data row1 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col124\" class=\"data row1 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col125\" class=\"data row1 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col126\" class=\"data row1 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col127\" class=\"data row1 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col128\" class=\"data row1 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col129\" class=\"data row1 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col130\" class=\"data row1 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col131\" class=\"data row1 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col132\" class=\"data row1 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col133\" class=\"data row1 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col134\" class=\"data row1 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col135\" class=\"data row1 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col136\" class=\"data row1 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row1_col137\" class=\"data row1 col137\" >POINT (12.4417702 43.9360958)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_78f41_row2_col0\" class=\"data row2 col0\" >7</td>\n",
+       "      <td id=\"T_78f41_row2_col1\" class=\"data row2 col1\" >20</td>\n",
+       "      <td id=\"T_78f41_row2_col2\" class=\"data row2 col2\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col3\" class=\"data row2 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row2_col4\" class=\"data row2 col4\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col7\" class=\"data row2 col7\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col8\" class=\"data row2 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row2_col9\" class=\"data row2 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col10\" class=\"data row2 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col11\" class=\"data row2 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col12\" class=\"data row2 col12\" >Liechtenstein</td>\n",
+       "      <td id=\"T_78f41_row2_col13\" class=\"data row2 col13\" >LIE</td>\n",
+       "      <td id=\"T_78f41_row2_col14\" class=\"data row2 col14\" >Liechtenstein</td>\n",
+       "      <td id=\"T_78f41_row2_col15\" class=\"data row2 col15\" >LIE</td>\n",
+       "      <td id=\"T_78f41_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col17\" class=\"data row2 col17\" >LI</td>\n",
+       "      <td id=\"T_78f41_row2_col18\" class=\"data row2 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col19\" class=\"data row2 col19\" >47.133724</td>\n",
+       "      <td id=\"T_78f41_row2_col20\" class=\"data row2 col20\" >9.516670</td>\n",
+       "      <td id=\"T_78f41_row2_col21\" class=\"data row2 col21\" >36281</td>\n",
+       "      <td id=\"T_78f41_row2_col22\" class=\"data row2 col22\" >5342</td>\n",
+       "      <td id=\"T_78f41_row2_col23\" class=\"data row2 col23\" >33009</td>\n",
+       "      <td id=\"T_78f41_row2_col24\" class=\"data row2 col24\" >7</td>\n",
+       "      <td id=\"T_78f41_row2_col25\" class=\"data row2 col25\" >5</td>\n",
+       "      <td id=\"T_78f41_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col27\" class=\"data row2 col27\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col28\" class=\"data row2 col28\" >45442</td>\n",
+       "      <td id=\"T_78f41_row2_col29\" class=\"data row2 col29\" >45442</td>\n",
+       "      <td id=\"T_78f41_row2_col30\" class=\"data row2 col30\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col31\" class=\"data row2 col31\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col32\" class=\"data row2 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col33\" class=\"data row2 col33\" >20</td>\n",
+       "      <td id=\"T_78f41_row2_col34\" class=\"data row2 col34\" >45</td>\n",
+       "      <td id=\"T_78f41_row2_col35\" class=\"data row2 col35\" >45</td>\n",
+       "      <td id=\"T_78f41_row2_col36\" class=\"data row2 col36\" >17</td>\n",
+       "      <td id=\"T_78f41_row2_col37\" class=\"data row2 col37\" >17</td>\n",
+       "      <td id=\"T_78f41_row2_col38\" class=\"data row2 col38\" >90</td>\n",
+       "      <td id=\"T_78f41_row2_col39\" class=\"data row2 col39\" >90</td>\n",
+       "      <td id=\"T_78f41_row2_col40\" class=\"data row2 col40\" >56</td>\n",
+       "      <td id=\"T_78f41_row2_col41\" class=\"data row2 col41\" >56</td>\n",
+       "      <td id=\"T_78f41_row2_col42\" class=\"data row2 col42\" >9.433333</td>\n",
+       "      <td id=\"T_78f41_row2_col43\" class=\"data row2 col43\" >9.433333</td>\n",
+       "      <td id=\"T_78f41_row2_col44\" class=\"data row2 col44\" >9.558333</td>\n",
+       "      <td id=\"T_78f41_row2_col45\" class=\"data row2 col45\" >9.558333</td>\n",
+       "      <td id=\"T_78f41_row2_col46\" class=\"data row2 col46\" >47.091667</td>\n",
+       "      <td id=\"T_78f41_row2_col47\" class=\"data row2 col47\" >47.091667</td>\n",
+       "      <td id=\"T_78f41_row2_col48\" class=\"data row2 col48\" >47.233333</td>\n",
+       "      <td id=\"T_78f41_row2_col49\" class=\"data row2 col49\" >47.233333</td>\n",
+       "      <td id=\"T_78f41_row2_col50\" class=\"data row2 col50\" >9.503734</td>\n",
+       "      <td id=\"T_78f41_row2_col51\" class=\"data row2 col51\" >47.167478</td>\n",
+       "      <td id=\"T_78f41_row2_col52\" class=\"data row2 col52\" >Europe/Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col53\" class=\"data row2 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col54\" class=\"data row2 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col55\" class=\"data row2 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col56\" class=\"data row2 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col57\" class=\"data row2 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col58\" class=\"data row2 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col59\" class=\"data row2 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col60\" class=\"data row2 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col61\" class=\"data row2 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col62\" class=\"data row2 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col63\" class=\"data row2 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col64\" class=\"data row2 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col65\" class=\"data row2 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col66\" class=\"data row2 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col67\" class=\"data row2 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col68\" class=\"data row2 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col69\" class=\"data row2 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col70\" class=\"data row2 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col71\" class=\"data row2 col71\" >6.700000</td>\n",
+       "      <td id=\"T_78f41_row2_col72\" class=\"data row2 col72\" >Q1844</td>\n",
+       "      <td id=\"T_78f41_row2_col73\" class=\"data row2 col73\" >101828603</td>\n",
+       "      <td id=\"T_78f41_row2_col74\" class=\"data row2 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row2_col75\" class=\"data row2 col75\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col76\" class=\"data row2 col76\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col77\" class=\"data row2 col77\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col78\" class=\"data row2 col78\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col79\" class=\"data row2 col79\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col80\" class=\"data row2 col80\" >Вадуц</td>\n",
+       "      <td id=\"T_78f41_row2_col81\" class=\"data row2 col81\" >瓦都兹</td>\n",
+       "      <td id=\"T_78f41_row2_col82\" class=\"data row2 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col83\" class=\"data row2 col83\" >فادوتس</td>\n",
+       "      <td id=\"T_78f41_row2_col84\" class=\"data row2 col84\" >ফাডুৎস</td>\n",
+       "      <td id=\"T_78f41_row2_col85\" class=\"data row2 col85\" >Βαντούζ</td>\n",
+       "      <td id=\"T_78f41_row2_col86\" class=\"data row2 col86\" >वादुज़</td>\n",
+       "      <td id=\"T_78f41_row2_col87\" class=\"data row2 col87\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col88\" class=\"data row2 col88\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col89\" class=\"data row2 col89\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col90\" class=\"data row2 col90\" >ファドゥーツ</td>\n",
+       "      <td id=\"T_78f41_row2_col91\" class=\"data row2 col91\" >파두츠</td>\n",
+       "      <td id=\"T_78f41_row2_col92\" class=\"data row2 col92\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col93\" class=\"data row2 col93\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col94\" class=\"data row2 col94\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col95\" class=\"data row2 col95\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col96\" class=\"data row2 col96\" >Vaduz</td>\n",
+       "      <td id=\"T_78f41_row2_col97\" class=\"data row2 col97\" >1159146061</td>\n",
+       "      <td id=\"T_78f41_row2_col98\" class=\"data row2 col98\" >فادوتس</td>\n",
+       "      <td id=\"T_78f41_row2_col99\" class=\"data row2 col99\" >ואדוץ</td>\n",
+       "      <td id=\"T_78f41_row2_col100\" class=\"data row2 col100\" >Вадуц</td>\n",
+       "      <td id=\"T_78f41_row2_col101\" class=\"data row2 col101\" >واڈوز</td>\n",
+       "      <td id=\"T_78f41_row2_col102\" class=\"data row2 col102\" >華杜茲</td>\n",
+       "      <td id=\"T_78f41_row2_col103\" class=\"data row2 col103\" >3042030</td>\n",
+       "      <td id=\"T_78f41_row2_col104\" class=\"data row2 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col105\" class=\"data row2 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col106\" class=\"data row2 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col107\" class=\"data row2 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col108\" class=\"data row2 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col109\" class=\"data row2 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col110\" class=\"data row2 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col111\" class=\"data row2 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col112\" class=\"data row2 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col113\" class=\"data row2 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col114\" class=\"data row2 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col115\" class=\"data row2 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col116\" class=\"data row2 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col117\" class=\"data row2 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col118\" class=\"data row2 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col119\" class=\"data row2 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col120\" class=\"data row2 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col121\" class=\"data row2 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col122\" class=\"data row2 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col123\" class=\"data row2 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col124\" class=\"data row2 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col125\" class=\"data row2 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col126\" class=\"data row2 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col127\" class=\"data row2 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col128\" class=\"data row2 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col129\" class=\"data row2 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col130\" class=\"data row2 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col131\" class=\"data row2 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col132\" class=\"data row2 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col133\" class=\"data row2 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col134\" class=\"data row2 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col135\" class=\"data row2 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col136\" class=\"data row2 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row2_col137\" class=\"data row2 col137\" >POINT (9.5166695 47.1337238)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_78f41_row3_col0\" class=\"data row3 col0\" >6</td>\n",
+       "      <td id=\"T_78f41_row3_col1\" class=\"data row3 col1\" >30</td>\n",
+       "      <td id=\"T_78f41_row3_col2\" class=\"data row3 col2\" >8</td>\n",
+       "      <td id=\"T_78f41_row3_col3\" class=\"data row3 col3\" >Admin-0 capital alt</td>\n",
+       "      <td id=\"T_78f41_row3_col4\" class=\"data row3 col4\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col7\" class=\"data row3 col7\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col8\" class=\"data row3 col8\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col9\" class=\"data row3 col9\" >Legislative and</td>\n",
+       "      <td id=\"T_78f41_row3_col10\" class=\"data row3 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col11\" class=\"data row3 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col12\" class=\"data row3 col12\" >eSwatini</td>\n",
+       "      <td id=\"T_78f41_row3_col13\" class=\"data row3 col13\" >SWZ</td>\n",
+       "      <td id=\"T_78f41_row3_col14\" class=\"data row3 col14\" >eSwatini</td>\n",
+       "      <td id=\"T_78f41_row3_col15\" class=\"data row3 col15\" >SWZ</td>\n",
+       "      <td id=\"T_78f41_row3_col16\" class=\"data row3 col16\" >Manzini</td>\n",
+       "      <td id=\"T_78f41_row3_col17\" class=\"data row3 col17\" >SZ</td>\n",
+       "      <td id=\"T_78f41_row3_col18\" class=\"data row3 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col19\" class=\"data row3 col19\" >-26.466668</td>\n",
+       "      <td id=\"T_78f41_row3_col20\" class=\"data row3 col20\" >31.199997</td>\n",
+       "      <td id=\"T_78f41_row3_col21\" class=\"data row3 col21\" >9782</td>\n",
+       "      <td id=\"T_78f41_row3_col22\" class=\"data row3 col22\" >4557</td>\n",
+       "      <td id=\"T_78f41_row3_col23\" class=\"data row3 col23\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col24\" class=\"data row3 col24\" >5</td>\n",
+       "      <td id=\"T_78f41_row3_col25\" class=\"data row3 col25\" >4</td>\n",
+       "      <td id=\"T_78f41_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col27\" class=\"data row3 col27\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col28\" class=\"data row3 col28\" >9782</td>\n",
+       "      <td id=\"T_78f41_row3_col29\" class=\"data row3 col29\" >9782</td>\n",
+       "      <td id=\"T_78f41_row3_col30\" class=\"data row3 col30\" >9782</td>\n",
+       "      <td id=\"T_78f41_row3_col31\" class=\"data row3 col31\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col32\" class=\"data row3 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col33\" class=\"data row3 col33\" >50</td>\n",
+       "      <td id=\"T_78f41_row3_col34\" class=\"data row3 col34\" >18</td>\n",
+       "      <td id=\"T_78f41_row3_col35\" class=\"data row3 col35\" >18</td>\n",
+       "      <td id=\"T_78f41_row3_col36\" class=\"data row3 col36\" >7</td>\n",
+       "      <td id=\"T_78f41_row3_col37\" class=\"data row3 col37\" >7</td>\n",
+       "      <td id=\"T_78f41_row3_col38\" class=\"data row3 col38\" >32</td>\n",
+       "      <td id=\"T_78f41_row3_col39\" class=\"data row3 col39\" >32</td>\n",
+       "      <td id=\"T_78f41_row3_col40\" class=\"data row3 col40\" >20</td>\n",
+       "      <td id=\"T_78f41_row3_col41\" class=\"data row3 col41\" >20</td>\n",
+       "      <td id=\"T_78f41_row3_col42\" class=\"data row3 col42\" >31.183333</td>\n",
+       "      <td id=\"T_78f41_row3_col43\" class=\"data row3 col43\" >31.183333</td>\n",
+       "      <td id=\"T_78f41_row3_col44\" class=\"data row3 col44\" >31.233333</td>\n",
+       "      <td id=\"T_78f41_row3_col45\" class=\"data row3 col45\" >31.233333</td>\n",
+       "      <td id=\"T_78f41_row3_col46\" class=\"data row3 col46\" >-26.458333</td>\n",
+       "      <td id=\"T_78f41_row3_col47\" class=\"data row3 col47\" >-26.458333</td>\n",
+       "      <td id=\"T_78f41_row3_col48\" class=\"data row3 col48\" >-26.391667</td>\n",
+       "      <td id=\"T_78f41_row3_col49\" class=\"data row3 col49\" >-26.391667</td>\n",
+       "      <td id=\"T_78f41_row3_col50\" class=\"data row3 col50\" >31.201993</td>\n",
+       "      <td id=\"T_78f41_row3_col51\" class=\"data row3 col51\" >-26.430254</td>\n",
+       "      <td id=\"T_78f41_row3_col52\" class=\"data row3 col52\" >Africa/Mbabane</td>\n",
+       "      <td id=\"T_78f41_row3_col53\" class=\"data row3 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col54\" class=\"data row3 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col55\" class=\"data row3 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col56\" class=\"data row3 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col57\" class=\"data row3 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col58\" class=\"data row3 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col59\" class=\"data row3 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col60\" class=\"data row3 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col61\" class=\"data row3 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col62\" class=\"data row3 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col63\" class=\"data row3 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col64\" class=\"data row3 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col65\" class=\"data row3 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col66\" class=\"data row3 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col67\" class=\"data row3 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col68\" class=\"data row3 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col69\" class=\"data row3 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col70\" class=\"data row3 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row3_col71\" class=\"data row3 col71\" >6.000000</td>\n",
+       "      <td id=\"T_78f41_row3_col72\" class=\"data row3 col72\" >Q101418</td>\n",
+       "      <td id=\"T_78f41_row3_col73\" class=\"data row3 col73\" >421199783</td>\n",
+       "      <td id=\"T_78f41_row3_col74\" class=\"data row3 col74\" >1</td>\n",
+       "      <td id=\"T_78f41_row3_col75\" class=\"data row3 col75\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col76\" class=\"data row3 col76\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col77\" class=\"data row3 col77\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col78\" class=\"data row3 col78\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col79\" class=\"data row3 col79\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col80\" class=\"data row3 col80\" >Лобамба</td>\n",
+       "      <td id=\"T_78f41_row3_col81\" class=\"data row3 col81\" >洛班巴</td>\n",
+       "      <td id=\"T_78f41_row3_col82\" class=\"data row3 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col83\" class=\"data row3 col83\" >لوبامبا</td>\n",
+       "      <td id=\"T_78f41_row3_col84\" class=\"data row3 col84\" >লোবাম্বা</td>\n",
+       "      <td id=\"T_78f41_row3_col85\" class=\"data row3 col85\" >Λομπάμπα</td>\n",
+       "      <td id=\"T_78f41_row3_col86\" class=\"data row3 col86\" >लोबम्बा</td>\n",
+       "      <td id=\"T_78f41_row3_col87\" class=\"data row3 col87\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col88\" class=\"data row3 col88\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col89\" class=\"data row3 col89\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col90\" class=\"data row3 col90\" >ロバンバ</td>\n",
+       "      <td id=\"T_78f41_row3_col91\" class=\"data row3 col91\" >로밤바</td>\n",
+       "      <td id=\"T_78f41_row3_col92\" class=\"data row3 col92\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col93\" class=\"data row3 col93\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col94\" class=\"data row3 col94\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col95\" class=\"data row3 col95\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col96\" class=\"data row3 col96\" >Lobamba</td>\n",
+       "      <td id=\"T_78f41_row3_col97\" class=\"data row3 col97\" >1159146343</td>\n",
+       "      <td id=\"T_78f41_row3_col98\" class=\"data row3 col98\" >لوبامبا</td>\n",
+       "      <td id=\"T_78f41_row3_col99\" class=\"data row3 col99\" >לובמבה</td>\n",
+       "      <td id=\"T_78f41_row3_col100\" class=\"data row3 col100\" >Лобамба</td>\n",
+       "      <td id=\"T_78f41_row3_col101\" class=\"data row3 col101\" >لوبامبا</td>\n",
+       "      <td id=\"T_78f41_row3_col102\" class=\"data row3 col102\" >洛班巴</td>\n",
+       "      <td id=\"T_78f41_row3_col103\" class=\"data row3 col103\" >935048</td>\n",
+       "      <td id=\"T_78f41_row3_col104\" class=\"data row3 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col105\" class=\"data row3 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col106\" class=\"data row3 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col107\" class=\"data row3 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col108\" class=\"data row3 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col109\" class=\"data row3 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col110\" class=\"data row3 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col111\" class=\"data row3 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col112\" class=\"data row3 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col113\" class=\"data row3 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col114\" class=\"data row3 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col115\" class=\"data row3 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col116\" class=\"data row3 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col117\" class=\"data row3 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col118\" class=\"data row3 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col119\" class=\"data row3 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col120\" class=\"data row3 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col121\" class=\"data row3 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col122\" class=\"data row3 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col123\" class=\"data row3 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col124\" class=\"data row3 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col125\" class=\"data row3 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col126\" class=\"data row3 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col127\" class=\"data row3 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col128\" class=\"data row3 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col129\" class=\"data row3 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col130\" class=\"data row3 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col131\" class=\"data row3 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col132\" class=\"data row3 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col133\" class=\"data row3 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col134\" class=\"data row3 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col135\" class=\"data row3 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col136\" class=\"data row3 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row3_col137\" class=\"data row3 col137\" >POINT (31.1999971 -26.4666675)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_78f41_row4_col0\" class=\"data row4 col0\" >6</td>\n",
+       "      <td id=\"T_78f41_row4_col1\" class=\"data row4 col1\" >30</td>\n",
+       "      <td id=\"T_78f41_row4_col2\" class=\"data row4 col2\" >8</td>\n",
+       "      <td id=\"T_78f41_row4_col3\" class=\"data row4 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row4_col4\" class=\"data row4 col4\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col7\" class=\"data row4 col7\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col8\" class=\"data row4 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row4_col9\" class=\"data row4 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col10\" class=\"data row4 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col11\" class=\"data row4 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col12\" class=\"data row4 col12\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col13\" class=\"data row4 col13\" >LUX</td>\n",
+       "      <td id=\"T_78f41_row4_col14\" class=\"data row4 col14\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col15\" class=\"data row4 col15\" >LUX</td>\n",
+       "      <td id=\"T_78f41_row4_col16\" class=\"data row4 col16\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col17\" class=\"data row4 col17\" >LU</td>\n",
+       "      <td id=\"T_78f41_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col19\" class=\"data row4 col19\" >49.611660</td>\n",
+       "      <td id=\"T_78f41_row4_col20\" class=\"data row4 col20\" >6.130003</td>\n",
+       "      <td id=\"T_78f41_row4_col21\" class=\"data row4 col21\" >107260</td>\n",
+       "      <td id=\"T_78f41_row4_col22\" class=\"data row4 col22\" >76684</td>\n",
+       "      <td id=\"T_78f41_row4_col23\" class=\"data row4 col23\" >106219</td>\n",
+       "      <td id=\"T_78f41_row4_col24\" class=\"data row4 col24\" >9</td>\n",
+       "      <td id=\"T_78f41_row4_col25\" class=\"data row4 col25\" >8</td>\n",
+       "      <td id=\"T_78f41_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col27\" class=\"data row4 col27\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col28\" class=\"data row4 col28\" >107260</td>\n",
+       "      <td id=\"T_78f41_row4_col29\" class=\"data row4 col29\" >107260</td>\n",
+       "      <td id=\"T_78f41_row4_col30\" class=\"data row4 col30\" >107260</td>\n",
+       "      <td id=\"T_78f41_row4_col31\" class=\"data row4 col31\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col32\" class=\"data row4 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col33\" class=\"data row4 col33\" >50</td>\n",
+       "      <td id=\"T_78f41_row4_col34\" class=\"data row4 col34\" >60</td>\n",
+       "      <td id=\"T_78f41_row4_col35\" class=\"data row4 col35\" >60</td>\n",
+       "      <td id=\"T_78f41_row4_col36\" class=\"data row4 col36\" >23</td>\n",
+       "      <td id=\"T_78f41_row4_col37\" class=\"data row4 col37\" >23</td>\n",
+       "      <td id=\"T_78f41_row4_col38\" class=\"data row4 col38\" >71</td>\n",
+       "      <td id=\"T_78f41_row4_col39\" class=\"data row4 col39\" >71</td>\n",
+       "      <td id=\"T_78f41_row4_col40\" class=\"data row4 col40\" >44</td>\n",
+       "      <td id=\"T_78f41_row4_col41\" class=\"data row4 col41\" >44</td>\n",
+       "      <td id=\"T_78f41_row4_col42\" class=\"data row4 col42\" >6.041667</td>\n",
+       "      <td id=\"T_78f41_row4_col43\" class=\"data row4 col43\" >6.041667</td>\n",
+       "      <td id=\"T_78f41_row4_col44\" class=\"data row4 col44\" >6.183333</td>\n",
+       "      <td id=\"T_78f41_row4_col45\" class=\"data row4 col45\" >6.183333</td>\n",
+       "      <td id=\"T_78f41_row4_col46\" class=\"data row4 col46\" >49.558333</td>\n",
+       "      <td id=\"T_78f41_row4_col47\" class=\"data row4 col47\" >49.558333</td>\n",
+       "      <td id=\"T_78f41_row4_col48\" class=\"data row4 col48\" >49.708333</td>\n",
+       "      <td id=\"T_78f41_row4_col49\" class=\"data row4 col49\" >49.708333</td>\n",
+       "      <td id=\"T_78f41_row4_col50\" class=\"data row4 col50\" >6.125273</td>\n",
+       "      <td id=\"T_78f41_row4_col51\" class=\"data row4 col51\" >49.620833</td>\n",
+       "      <td id=\"T_78f41_row4_col52\" class=\"data row4 col52\" >Europe/Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col53\" class=\"data row4 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col54\" class=\"data row4 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col55\" class=\"data row4 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col56\" class=\"data row4 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col57\" class=\"data row4 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col58\" class=\"data row4 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col59\" class=\"data row4 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col60\" class=\"data row4 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col61\" class=\"data row4 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col62\" class=\"data row4 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col63\" class=\"data row4 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col64\" class=\"data row4 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col65\" class=\"data row4 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col66\" class=\"data row4 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col67\" class=\"data row4 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col68\" class=\"data row4 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col69\" class=\"data row4 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col70\" class=\"data row4 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col71\" class=\"data row4 col71\" >6.000000</td>\n",
+       "      <td id=\"T_78f41_row4_col72\" class=\"data row4 col72\" >Q1842</td>\n",
+       "      <td id=\"T_78f41_row4_col73\" class=\"data row4 col73\" >101751765</td>\n",
+       "      <td id=\"T_78f41_row4_col74\" class=\"data row4 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row4_col75\" class=\"data row4 col75\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col76\" class=\"data row4 col76\" >Luxemburg</td>\n",
+       "      <td id=\"T_78f41_row4_col77\" class=\"data row4 col77\" >Luxemburgo</td>\n",
+       "      <td id=\"T_78f41_row4_col78\" class=\"data row4 col78\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col79\" class=\"data row4 col79\" >Luxemburgo</td>\n",
+       "      <td id=\"T_78f41_row4_col80\" class=\"data row4 col80\" >Люксембург</td>\n",
+       "      <td id=\"T_78f41_row4_col81\" class=\"data row4 col81\" >卢森堡</td>\n",
+       "      <td id=\"T_78f41_row4_col82\" class=\"data row4 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col83\" class=\"data row4 col83\" >مدينة لوكسمبورغ</td>\n",
+       "      <td id=\"T_78f41_row4_col84\" class=\"data row4 col84\" >লুক্সেমবুর্গ শহর</td>\n",
+       "      <td id=\"T_78f41_row4_col85\" class=\"data row4 col85\" >Λουξεμβούργο</td>\n",
+       "      <td id=\"T_78f41_row4_col86\" class=\"data row4 col86\" >लक्ज़मबर्ग नगर</td>\n",
+       "      <td id=\"T_78f41_row4_col87\" class=\"data row4 col87\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col88\" class=\"data row4 col88\" >Luksemburg</td>\n",
+       "      <td id=\"T_78f41_row4_col89\" class=\"data row4 col89\" >Lussemburgo</td>\n",
+       "      <td id=\"T_78f41_row4_col90\" class=\"data row4 col90\" >ルクセンブルク市</td>\n",
+       "      <td id=\"T_78f41_row4_col91\" class=\"data row4 col91\" >룩셈부르크</td>\n",
+       "      <td id=\"T_78f41_row4_col92\" class=\"data row4 col92\" >Luxemburg</td>\n",
+       "      <td id=\"T_78f41_row4_col93\" class=\"data row4 col93\" >Luksemburg</td>\n",
+       "      <td id=\"T_78f41_row4_col94\" class=\"data row4 col94\" >Luxemburg</td>\n",
+       "      <td id=\"T_78f41_row4_col95\" class=\"data row4 col95\" >Lüksemburg</td>\n",
+       "      <td id=\"T_78f41_row4_col96\" class=\"data row4 col96\" >Luxembourg</td>\n",
+       "      <td id=\"T_78f41_row4_col97\" class=\"data row4 col97\" >1159146437</td>\n",
+       "      <td id=\"T_78f41_row4_col98\" class=\"data row4 col98\" >لوکزامبورگ</td>\n",
+       "      <td id=\"T_78f41_row4_col99\" class=\"data row4 col99\" >לוקסמבורג</td>\n",
+       "      <td id=\"T_78f41_row4_col100\" class=\"data row4 col100\" >Люксембург</td>\n",
+       "      <td id=\"T_78f41_row4_col101\" class=\"data row4 col101\" >لکسمبرگ</td>\n",
+       "      <td id=\"T_78f41_row4_col102\" class=\"data row4 col102\" >盧森堡市</td>\n",
+       "      <td id=\"T_78f41_row4_col103\" class=\"data row4 col103\" >2960316</td>\n",
+       "      <td id=\"T_78f41_row4_col104\" class=\"data row4 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col105\" class=\"data row4 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col106\" class=\"data row4 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col107\" class=\"data row4 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col108\" class=\"data row4 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col109\" class=\"data row4 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col110\" class=\"data row4 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col111\" class=\"data row4 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col112\" class=\"data row4 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col113\" class=\"data row4 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col114\" class=\"data row4 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col115\" class=\"data row4 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col116\" class=\"data row4 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col117\" class=\"data row4 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col118\" class=\"data row4 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col119\" class=\"data row4 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col120\" class=\"data row4 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col121\" class=\"data row4 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col122\" class=\"data row4 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col123\" class=\"data row4 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col124\" class=\"data row4 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col125\" class=\"data row4 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col126\" class=\"data row4 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col127\" class=\"data row4 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col128\" class=\"data row4 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col129\" class=\"data row4 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col130\" class=\"data row4 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col131\" class=\"data row4 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col132\" class=\"data row4 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col133\" class=\"data row4 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col134\" class=\"data row4 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col135\" class=\"data row4 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col136\" class=\"data row4 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row4_col137\" class=\"data row4 col137\" >POINT (6.1300028 49.6116604)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_78f41_row5_col0\" class=\"data row5 col0\" >6</td>\n",
+       "      <td id=\"T_78f41_row5_col1\" class=\"data row5 col1\" >30</td>\n",
+       "      <td id=\"T_78f41_row5_col2\" class=\"data row5 col2\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col3\" class=\"data row5 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row5_col4\" class=\"data row5 col4\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col7\" class=\"data row5 col7\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col8\" class=\"data row5 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row5_col9\" class=\"data row5 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col10\" class=\"data row5 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col11\" class=\"data row5 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col12\" class=\"data row5 col12\" >Federated States of Micronesia</td>\n",
+       "      <td id=\"T_78f41_row5_col13\" class=\"data row5 col13\" >FSM</td>\n",
+       "      <td id=\"T_78f41_row5_col14\" class=\"data row5 col14\" >Federated States of Micronesia</td>\n",
+       "      <td id=\"T_78f41_row5_col15\" class=\"data row5 col15\" >FSM</td>\n",
+       "      <td id=\"T_78f41_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col17\" class=\"data row5 col17\" >FM</td>\n",
+       "      <td id=\"T_78f41_row5_col18\" class=\"data row5 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col19\" class=\"data row5 col19\" >6.916644</td>\n",
+       "      <td id=\"T_78f41_row5_col20\" class=\"data row5 col20\" >158.149974</td>\n",
+       "      <td id=\"T_78f41_row5_col21\" class=\"data row5 col21\" >4645</td>\n",
+       "      <td id=\"T_78f41_row5_col22\" class=\"data row5 col22\" >4645</td>\n",
+       "      <td id=\"T_78f41_row5_col23\" class=\"data row5 col23\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col24\" class=\"data row5 col24\" >4</td>\n",
+       "      <td id=\"T_78f41_row5_col25\" class=\"data row5 col25\" >4</td>\n",
+       "      <td id=\"T_78f41_row5_col26\" class=\"data row5 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col27\" class=\"data row5 col27\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col28\" class=\"data row5 col28\" >412</td>\n",
+       "      <td id=\"T_78f41_row5_col29\" class=\"data row5 col29\" >412</td>\n",
+       "      <td id=\"T_78f41_row5_col30\" class=\"data row5 col30\" >412</td>\n",
+       "      <td id=\"T_78f41_row5_col31\" class=\"data row5 col31\" >412</td>\n",
+       "      <td id=\"T_78f41_row5_col32\" class=\"data row5 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col33\" class=\"data row5 col33\" >100</td>\n",
+       "      <td id=\"T_78f41_row5_col34\" class=\"data row5 col34\" >1</td>\n",
+       "      <td id=\"T_78f41_row5_col35\" class=\"data row5 col35\" >1</td>\n",
+       "      <td id=\"T_78f41_row5_col36\" class=\"data row5 col36\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col37\" class=\"data row5 col37\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col38\" class=\"data row5 col38\" >4</td>\n",
+       "      <td id=\"T_78f41_row5_col39\" class=\"data row5 col39\" >4</td>\n",
+       "      <td id=\"T_78f41_row5_col40\" class=\"data row5 col40\" >2</td>\n",
+       "      <td id=\"T_78f41_row5_col41\" class=\"data row5 col41\" >2</td>\n",
+       "      <td id=\"T_78f41_row5_col42\" class=\"data row5 col42\" >158.158333</td>\n",
+       "      <td id=\"T_78f41_row5_col43\" class=\"data row5 col43\" >158.158333</td>\n",
+       "      <td id=\"T_78f41_row5_col44\" class=\"data row5 col44\" >158.166667</td>\n",
+       "      <td id=\"T_78f41_row5_col45\" class=\"data row5 col45\" >158.166667</td>\n",
+       "      <td id=\"T_78f41_row5_col46\" class=\"data row5 col46\" >6.908333</td>\n",
+       "      <td id=\"T_78f41_row5_col47\" class=\"data row5 col47\" >6.908333</td>\n",
+       "      <td id=\"T_78f41_row5_col48\" class=\"data row5 col48\" >6.916667</td>\n",
+       "      <td id=\"T_78f41_row5_col49\" class=\"data row5 col49\" >6.916667</td>\n",
+       "      <td id=\"T_78f41_row5_col50\" class=\"data row5 col50\" >158.162500</td>\n",
+       "      <td id=\"T_78f41_row5_col51\" class=\"data row5 col51\" >6.912500</td>\n",
+       "      <td id=\"T_78f41_row5_col52\" class=\"data row5 col52\" >Pacific/Ponape</td>\n",
+       "      <td id=\"T_78f41_row5_col53\" class=\"data row5 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col54\" class=\"data row5 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col55\" class=\"data row5 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col56\" class=\"data row5 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col57\" class=\"data row5 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col58\" class=\"data row5 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col59\" class=\"data row5 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col60\" class=\"data row5 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col61\" class=\"data row5 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col62\" class=\"data row5 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col63\" class=\"data row5 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col64\" class=\"data row5 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col65\" class=\"data row5 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col66\" class=\"data row5 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col67\" class=\"data row5 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col68\" class=\"data row5 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col69\" class=\"data row5 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col70\" class=\"data row5 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col71\" class=\"data row5 col71\" >6.000000</td>\n",
+       "      <td id=\"T_78f41_row5_col72\" class=\"data row5 col72\" >Q42751</td>\n",
+       "      <td id=\"T_78f41_row5_col73\" class=\"data row5 col73\" >1141909141</td>\n",
+       "      <td id=\"T_78f41_row5_col74\" class=\"data row5 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row5_col75\" class=\"data row5 col75\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col76\" class=\"data row5 col76\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col77\" class=\"data row5 col77\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col78\" class=\"data row5 col78\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col79\" class=\"data row5 col79\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col80\" class=\"data row5 col80\" >Паликир</td>\n",
+       "      <td id=\"T_78f41_row5_col81\" class=\"data row5 col81\" >帕利基尔</td>\n",
+       "      <td id=\"T_78f41_row5_col82\" class=\"data row5 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col83\" class=\"data row5 col83\" >باليكير</td>\n",
+       "      <td id=\"T_78f41_row5_col84\" class=\"data row5 col84\" >পালিকির</td>\n",
+       "      <td id=\"T_78f41_row5_col85\" class=\"data row5 col85\" >Παλικίρ</td>\n",
+       "      <td id=\"T_78f41_row5_col86\" class=\"data row5 col86\" >पेलिकियर</td>\n",
+       "      <td id=\"T_78f41_row5_col87\" class=\"data row5 col87\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col88\" class=\"data row5 col88\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col89\" class=\"data row5 col89\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col90\" class=\"data row5 col90\" >パリキール</td>\n",
+       "      <td id=\"T_78f41_row5_col91\" class=\"data row5 col91\" >팔리키르</td>\n",
+       "      <td id=\"T_78f41_row5_col92\" class=\"data row5 col92\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col93\" class=\"data row5 col93\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col94\" class=\"data row5 col94\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col95\" class=\"data row5 col95\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col96\" class=\"data row5 col96\" >Palikir</td>\n",
+       "      <td id=\"T_78f41_row5_col97\" class=\"data row5 col97\" >1159149061</td>\n",
+       "      <td id=\"T_78f41_row5_col98\" class=\"data row5 col98\" >پالیکیر</td>\n",
+       "      <td id=\"T_78f41_row5_col99\" class=\"data row5 col99\" >פליקיר</td>\n",
+       "      <td id=\"T_78f41_row5_col100\" class=\"data row5 col100\" >Палікір</td>\n",
+       "      <td id=\"T_78f41_row5_col101\" class=\"data row5 col101\" >پالیکیر</td>\n",
+       "      <td id=\"T_78f41_row5_col102\" class=\"data row5 col102\" >帕利基尔</td>\n",
+       "      <td id=\"T_78f41_row5_col103\" class=\"data row5 col103\" >2081986</td>\n",
+       "      <td id=\"T_78f41_row5_col104\" class=\"data row5 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col105\" class=\"data row5 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col106\" class=\"data row5 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col107\" class=\"data row5 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col108\" class=\"data row5 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col109\" class=\"data row5 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col110\" class=\"data row5 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col111\" class=\"data row5 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col112\" class=\"data row5 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col113\" class=\"data row5 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col114\" class=\"data row5 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col115\" class=\"data row5 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col116\" class=\"data row5 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col117\" class=\"data row5 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col118\" class=\"data row5 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col119\" class=\"data row5 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col120\" class=\"data row5 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col121\" class=\"data row5 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col122\" class=\"data row5 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col123\" class=\"data row5 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col124\" class=\"data row5 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col125\" class=\"data row5 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col126\" class=\"data row5 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col127\" class=\"data row5 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col128\" class=\"data row5 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col129\" class=\"data row5 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col130\" class=\"data row5 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col131\" class=\"data row5 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col132\" class=\"data row5 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col133\" class=\"data row5 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col134\" class=\"data row5 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col135\" class=\"data row5 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col136\" class=\"data row5 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row5_col137\" class=\"data row5 col137\" >POINT (158.1499743 6.9166437)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_78f41_row6_col0\" class=\"data row6 col0\" >6</td>\n",
+       "      <td id=\"T_78f41_row6_col1\" class=\"data row6 col1\" >30</td>\n",
+       "      <td id=\"T_78f41_row6_col2\" class=\"data row6 col2\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col3\" class=\"data row6 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row6_col4\" class=\"data row6 col4\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col5\" class=\"data row6 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col6\" class=\"data row6 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col7\" class=\"data row6 col7\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col8\" class=\"data row6 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col10\" class=\"data row6 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col11\" class=\"data row6 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col12\" class=\"data row6 col12\" >Marshall Islands</td>\n",
+       "      <td id=\"T_78f41_row6_col13\" class=\"data row6 col13\" >MHL</td>\n",
+       "      <td id=\"T_78f41_row6_col14\" class=\"data row6 col14\" >Marshall Islands</td>\n",
+       "      <td id=\"T_78f41_row6_col15\" class=\"data row6 col15\" >MHL</td>\n",
+       "      <td id=\"T_78f41_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col17\" class=\"data row6 col17\" >MH</td>\n",
+       "      <td id=\"T_78f41_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col19\" class=\"data row6 col19\" >7.103004</td>\n",
+       "      <td id=\"T_78f41_row6_col20\" class=\"data row6 col20\" >171.380000</td>\n",
+       "      <td id=\"T_78f41_row6_col21\" class=\"data row6 col21\" >25400</td>\n",
+       "      <td id=\"T_78f41_row6_col22\" class=\"data row6 col22\" >20500</td>\n",
+       "      <td id=\"T_78f41_row6_col23\" class=\"data row6 col23\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col24\" class=\"data row6 col24\" >7</td>\n",
+       "      <td id=\"T_78f41_row6_col25\" class=\"data row6 col25\" >7</td>\n",
+       "      <td id=\"T_78f41_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col27\" class=\"data row6 col27\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col28\" class=\"data row6 col28\" >2084</td>\n",
+       "      <td id=\"T_78f41_row6_col29\" class=\"data row6 col29\" >2084</td>\n",
+       "      <td id=\"T_78f41_row6_col30\" class=\"data row6 col30\" >2084</td>\n",
+       "      <td id=\"T_78f41_row6_col31\" class=\"data row6 col31\" >2084</td>\n",
+       "      <td id=\"T_78f41_row6_col32\" class=\"data row6 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col33\" class=\"data row6 col33\" >100</td>\n",
+       "      <td id=\"T_78f41_row6_col34\" class=\"data row6 col34\" >3</td>\n",
+       "      <td id=\"T_78f41_row6_col35\" class=\"data row6 col35\" >3</td>\n",
+       "      <td id=\"T_78f41_row6_col36\" class=\"data row6 col36\" >1</td>\n",
+       "      <td id=\"T_78f41_row6_col37\" class=\"data row6 col37\" >1</td>\n",
+       "      <td id=\"T_78f41_row6_col38\" class=\"data row6 col38\" >7</td>\n",
+       "      <td id=\"T_78f41_row6_col39\" class=\"data row6 col39\" >7</td>\n",
+       "      <td id=\"T_78f41_row6_col40\" class=\"data row6 col40\" >5</td>\n",
+       "      <td id=\"T_78f41_row6_col41\" class=\"data row6 col41\" >5</td>\n",
+       "      <td id=\"T_78f41_row6_col42\" class=\"data row6 col42\" >171.366667</td>\n",
+       "      <td id=\"T_78f41_row6_col43\" class=\"data row6 col43\" >171.366667</td>\n",
+       "      <td id=\"T_78f41_row6_col44\" class=\"data row6 col44\" >171.375000</td>\n",
+       "      <td id=\"T_78f41_row6_col45\" class=\"data row6 col45\" >171.375000</td>\n",
+       "      <td id=\"T_78f41_row6_col46\" class=\"data row6 col46\" >7.091667</td>\n",
+       "      <td id=\"T_78f41_row6_col47\" class=\"data row6 col47\" >7.091667</td>\n",
+       "      <td id=\"T_78f41_row6_col48\" class=\"data row6 col48\" >7.116667</td>\n",
+       "      <td id=\"T_78f41_row6_col49\" class=\"data row6 col49\" >7.116667</td>\n",
+       "      <td id=\"T_78f41_row6_col50\" class=\"data row6 col50\" >171.370833</td>\n",
+       "      <td id=\"T_78f41_row6_col51\" class=\"data row6 col51\" >7.104167</td>\n",
+       "      <td id=\"T_78f41_row6_col52\" class=\"data row6 col52\" >Pacific/Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col53\" class=\"data row6 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col54\" class=\"data row6 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col55\" class=\"data row6 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col56\" class=\"data row6 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col57\" class=\"data row6 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col58\" class=\"data row6 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col59\" class=\"data row6 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col60\" class=\"data row6 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col61\" class=\"data row6 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col62\" class=\"data row6 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col63\" class=\"data row6 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col64\" class=\"data row6 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col65\" class=\"data row6 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col66\" class=\"data row6 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col67\" class=\"data row6 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col68\" class=\"data row6 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col69\" class=\"data row6 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col70\" class=\"data row6 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col71\" class=\"data row6 col71\" >6.000000</td>\n",
+       "      <td id=\"T_78f41_row6_col72\" class=\"data row6 col72\" >Q12919</td>\n",
+       "      <td id=\"T_78f41_row6_col73\" class=\"data row6 col73\" >890451463</td>\n",
+       "      <td id=\"T_78f41_row6_col74\" class=\"data row6 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row6_col75\" class=\"data row6 col75\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col76\" class=\"data row6 col76\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col77\" class=\"data row6 col77\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col78\" class=\"data row6 col78\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col79\" class=\"data row6 col79\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col80\" class=\"data row6 col80\" >Маджуро</td>\n",
+       "      <td id=\"T_78f41_row6_col81\" class=\"data row6 col81\" >马朱罗</td>\n",
+       "      <td id=\"T_78f41_row6_col82\" class=\"data row6 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col83\" class=\"data row6 col83\" >ماجورو</td>\n",
+       "      <td id=\"T_78f41_row6_col84\" class=\"data row6 col84\" >মাজুরো</td>\n",
+       "      <td id=\"T_78f41_row6_col85\" class=\"data row6 col85\" >Ματζούρο</td>\n",
+       "      <td id=\"T_78f41_row6_col86\" class=\"data row6 col86\" >माजुरो</td>\n",
+       "      <td id=\"T_78f41_row6_col87\" class=\"data row6 col87\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col88\" class=\"data row6 col88\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col89\" class=\"data row6 col89\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col90\" class=\"data row6 col90\" >マジュロ</td>\n",
+       "      <td id=\"T_78f41_row6_col91\" class=\"data row6 col91\" >마주로</td>\n",
+       "      <td id=\"T_78f41_row6_col92\" class=\"data row6 col92\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col93\" class=\"data row6 col93\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col94\" class=\"data row6 col94\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col95\" class=\"data row6 col95\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col96\" class=\"data row6 col96\" >Majuro</td>\n",
+       "      <td id=\"T_78f41_row6_col97\" class=\"data row6 col97\" >1159149063</td>\n",
+       "      <td id=\"T_78f41_row6_col98\" class=\"data row6 col98\" >ماجورو</td>\n",
+       "      <td id=\"T_78f41_row6_col99\" class=\"data row6 col99\" >מג'ורו</td>\n",
+       "      <td id=\"T_78f41_row6_col100\" class=\"data row6 col100\" >Маджуро</td>\n",
+       "      <td id=\"T_78f41_row6_col101\" class=\"data row6 col101\" >ماجورو</td>\n",
+       "      <td id=\"T_78f41_row6_col102\" class=\"data row6 col102\" >馬久羅</td>\n",
+       "      <td id=\"T_78f41_row6_col103\" class=\"data row6 col103\" >2113779</td>\n",
+       "      <td id=\"T_78f41_row6_col104\" class=\"data row6 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col105\" class=\"data row6 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col106\" class=\"data row6 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col107\" class=\"data row6 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col108\" class=\"data row6 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col109\" class=\"data row6 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col110\" class=\"data row6 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col111\" class=\"data row6 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col112\" class=\"data row6 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col113\" class=\"data row6 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col114\" class=\"data row6 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col115\" class=\"data row6 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col116\" class=\"data row6 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col117\" class=\"data row6 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col118\" class=\"data row6 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col119\" class=\"data row6 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col120\" class=\"data row6 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col121\" class=\"data row6 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col122\" class=\"data row6 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col123\" class=\"data row6 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col124\" class=\"data row6 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col125\" class=\"data row6 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col126\" class=\"data row6 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col127\" class=\"data row6 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col128\" class=\"data row6 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col129\" class=\"data row6 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col130\" class=\"data row6 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col131\" class=\"data row6 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col132\" class=\"data row6 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col133\" class=\"data row6 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col134\" class=\"data row6 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col135\" class=\"data row6 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col136\" class=\"data row6 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row6_col137\" class=\"data row6 col137\" >POINT (171.3800002 7.1030043)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_78f41_row7_col0\" class=\"data row7 col0\" >6</td>\n",
+       "      <td id=\"T_78f41_row7_col1\" class=\"data row7 col1\" >30</td>\n",
+       "      <td id=\"T_78f41_row7_col2\" class=\"data row7 col2\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col3\" class=\"data row7 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row7_col4\" class=\"data row7 col4\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col5\" class=\"data row7 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col6\" class=\"data row7 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col7\" class=\"data row7 col7\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col8\" class=\"data row7 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col10\" class=\"data row7 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col11\" class=\"data row7 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col12\" class=\"data row7 col12\" >Tuvalu</td>\n",
+       "      <td id=\"T_78f41_row7_col13\" class=\"data row7 col13\" >TUV</td>\n",
+       "      <td id=\"T_78f41_row7_col14\" class=\"data row7 col14\" >Tuvalu</td>\n",
+       "      <td id=\"T_78f41_row7_col15\" class=\"data row7 col15\" >TUV</td>\n",
+       "      <td id=\"T_78f41_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col17\" class=\"data row7 col17\" >TV</td>\n",
+       "      <td id=\"T_78f41_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col19\" class=\"data row7 col19\" >-8.516652</td>\n",
+       "      <td id=\"T_78f41_row7_col20\" class=\"data row7 col20\" >179.216647</td>\n",
+       "      <td id=\"T_78f41_row7_col21\" class=\"data row7 col21\" >4749</td>\n",
+       "      <td id=\"T_78f41_row7_col22\" class=\"data row7 col22\" >4749</td>\n",
+       "      <td id=\"T_78f41_row7_col23\" class=\"data row7 col23\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col24\" class=\"data row7 col24\" >4</td>\n",
+       "      <td id=\"T_78f41_row7_col25\" class=\"data row7 col25\" >4</td>\n",
+       "      <td id=\"T_78f41_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col27\" class=\"data row7 col27\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col28\" class=\"data row7 col28\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col29\" class=\"data row7 col29\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col30\" class=\"data row7 col30\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col31\" class=\"data row7 col31\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col32\" class=\"data row7 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col33\" class=\"data row7 col33\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col34\" class=\"data row7 col34\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col35\" class=\"data row7 col35\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col36\" class=\"data row7 col36\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col37\" class=\"data row7 col37\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col38\" class=\"data row7 col38\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col39\" class=\"data row7 col39\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col40\" class=\"data row7 col40\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col41\" class=\"data row7 col41\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col42\" class=\"data row7 col42\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col43\" class=\"data row7 col43\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col44\" class=\"data row7 col44\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col45\" class=\"data row7 col45\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col46\" class=\"data row7 col46\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col47\" class=\"data row7 col47\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col48\" class=\"data row7 col48\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col49\" class=\"data row7 col49\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col50\" class=\"data row7 col50\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col51\" class=\"data row7 col51\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col52\" class=\"data row7 col52\" >Pacific/Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col53\" class=\"data row7 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col54\" class=\"data row7 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col55\" class=\"data row7 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col56\" class=\"data row7 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col57\" class=\"data row7 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col58\" class=\"data row7 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col59\" class=\"data row7 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col60\" class=\"data row7 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col61\" class=\"data row7 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col62\" class=\"data row7 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col63\" class=\"data row7 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col64\" class=\"data row7 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col65\" class=\"data row7 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col66\" class=\"data row7 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col67\" class=\"data row7 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col68\" class=\"data row7 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col69\" class=\"data row7 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col70\" class=\"data row7 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col71\" class=\"data row7 col71\" >6.000000</td>\n",
+       "      <td id=\"T_78f41_row7_col72\" class=\"data row7 col72\" >Q34126</td>\n",
+       "      <td id=\"T_78f41_row7_col73\" class=\"data row7 col73\" >1141909143</td>\n",
+       "      <td id=\"T_78f41_row7_col74\" class=\"data row7 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row7_col75\" class=\"data row7 col75\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col76\" class=\"data row7 col76\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col77\" class=\"data row7 col77\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col78\" class=\"data row7 col78\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col79\" class=\"data row7 col79\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col80\" class=\"data row7 col80\" >Фунафути</td>\n",
+       "      <td id=\"T_78f41_row7_col81\" class=\"data row7 col81\" >富纳富提</td>\n",
+       "      <td id=\"T_78f41_row7_col82\" class=\"data row7 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col83\" class=\"data row7 col83\" >فونافوتي</td>\n",
+       "      <td id=\"T_78f41_row7_col84\" class=\"data row7 col84\" >ফুনাফুতি</td>\n",
+       "      <td id=\"T_78f41_row7_col85\" class=\"data row7 col85\" >Φουναφούτι</td>\n",
+       "      <td id=\"T_78f41_row7_col86\" class=\"data row7 col86\" >फुनाफुति</td>\n",
+       "      <td id=\"T_78f41_row7_col87\" class=\"data row7 col87\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col88\" class=\"data row7 col88\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col89\" class=\"data row7 col89\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col90\" class=\"data row7 col90\" >フナフティ島</td>\n",
+       "      <td id=\"T_78f41_row7_col91\" class=\"data row7 col91\" >푸나푸티</td>\n",
+       "      <td id=\"T_78f41_row7_col92\" class=\"data row7 col92\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col93\" class=\"data row7 col93\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col94\" class=\"data row7 col94\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col95\" class=\"data row7 col95\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col96\" class=\"data row7 col96\" >Funafuti</td>\n",
+       "      <td id=\"T_78f41_row7_col97\" class=\"data row7 col97\" >1159149071</td>\n",
+       "      <td id=\"T_78f41_row7_col98\" class=\"data row7 col98\" >فونافوتی</td>\n",
+       "      <td id=\"T_78f41_row7_col99\" class=\"data row7 col99\" >פנאפוטי</td>\n",
+       "      <td id=\"T_78f41_row7_col100\" class=\"data row7 col100\" >Фунафуті</td>\n",
+       "      <td id=\"T_78f41_row7_col101\" class=\"data row7 col101\" >فونافوتی</td>\n",
+       "      <td id=\"T_78f41_row7_col102\" class=\"data row7 col102\" >富纳富提</td>\n",
+       "      <td id=\"T_78f41_row7_col103\" class=\"data row7 col103\" >2110394</td>\n",
+       "      <td id=\"T_78f41_row7_col104\" class=\"data row7 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col105\" class=\"data row7 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col106\" class=\"data row7 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col107\" class=\"data row7 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col108\" class=\"data row7 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col109\" class=\"data row7 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col110\" class=\"data row7 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col111\" class=\"data row7 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col112\" class=\"data row7 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col113\" class=\"data row7 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col114\" class=\"data row7 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col115\" class=\"data row7 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col116\" class=\"data row7 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col117\" class=\"data row7 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col118\" class=\"data row7 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col119\" class=\"data row7 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col120\" class=\"data row7 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col121\" class=\"data row7 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col122\" class=\"data row7 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col123\" class=\"data row7 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col124\" class=\"data row7 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col125\" class=\"data row7 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col126\" class=\"data row7 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col127\" class=\"data row7 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col128\" class=\"data row7 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col129\" class=\"data row7 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col130\" class=\"data row7 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col131\" class=\"data row7 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col132\" class=\"data row7 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col133\" class=\"data row7 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col134\" class=\"data row7 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col135\" class=\"data row7 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col136\" class=\"data row7 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row7_col137\" class=\"data row7 col137\" >POINT (179.2166471 -8.516652)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_78f41_row8_col0\" class=\"data row8 col0\" >6</td>\n",
+       "      <td id=\"T_78f41_row8_col1\" class=\"data row8 col1\" >30</td>\n",
+       "      <td id=\"T_78f41_row8_col2\" class=\"data row8 col2\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col3\" class=\"data row8 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_78f41_row8_col4\" class=\"data row8 col4\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col5\" class=\"data row8 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col6\" class=\"data row8 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col7\" class=\"data row8 col7\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col8\" class=\"data row8 col8\" >1</td>\n",
+       "      <td id=\"T_78f41_row8_col9\" class=\"data row8 col9\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col10\" class=\"data row8 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col11\" class=\"data row8 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col12\" class=\"data row8 col12\" >Palau</td>\n",
+       "      <td id=\"T_78f41_row8_col13\" class=\"data row8 col13\" >PLW</td>\n",
+       "      <td id=\"T_78f41_row8_col14\" class=\"data row8 col14\" >Palau</td>\n",
+       "      <td id=\"T_78f41_row8_col15\" class=\"data row8 col15\" >PLW</td>\n",
+       "      <td id=\"T_78f41_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col17\" class=\"data row8 col17\" >PW</td>\n",
+       "      <td id=\"T_78f41_row8_col18\" class=\"data row8 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col19\" class=\"data row8 col19\" >7.487396</td>\n",
+       "      <td id=\"T_78f41_row8_col20\" class=\"data row8 col20\" >134.626549</td>\n",
+       "      <td id=\"T_78f41_row8_col21\" class=\"data row8 col21\" >7026</td>\n",
+       "      <td id=\"T_78f41_row8_col22\" class=\"data row8 col22\" >7026</td>\n",
+       "      <td id=\"T_78f41_row8_col23\" class=\"data row8 col23\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col24\" class=\"data row8 col24\" >5</td>\n",
+       "      <td id=\"T_78f41_row8_col25\" class=\"data row8 col25\" >5</td>\n",
+       "      <td id=\"T_78f41_row8_col26\" class=\"data row8 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col27\" class=\"data row8 col27\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col28\" class=\"data row8 col28\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col29\" class=\"data row8 col29\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col30\" class=\"data row8 col30\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col31\" class=\"data row8 col31\" >7026</td>\n",
+       "      <td id=\"T_78f41_row8_col32\" class=\"data row8 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col33\" class=\"data row8 col33\" >100</td>\n",
+       "      <td id=\"T_78f41_row8_col34\" class=\"data row8 col34\" >6</td>\n",
+       "      <td id=\"T_78f41_row8_col35\" class=\"data row8 col35\" >6</td>\n",
+       "      <td id=\"T_78f41_row8_col36\" class=\"data row8 col36\" >2</td>\n",
+       "      <td id=\"T_78f41_row8_col37\" class=\"data row8 col37\" >2</td>\n",
+       "      <td id=\"T_78f41_row8_col38\" class=\"data row8 col38\" >15</td>\n",
+       "      <td id=\"T_78f41_row8_col39\" class=\"data row8 col39\" >15</td>\n",
+       "      <td id=\"T_78f41_row8_col40\" class=\"data row8 col40\" >9</td>\n",
+       "      <td id=\"T_78f41_row8_col41\" class=\"data row8 col41\" >9</td>\n",
+       "      <td id=\"T_78f41_row8_col42\" class=\"data row8 col42\" >134.466667</td>\n",
+       "      <td id=\"T_78f41_row8_col43\" class=\"data row8 col43\" >134.466667</td>\n",
+       "      <td id=\"T_78f41_row8_col44\" class=\"data row8 col44\" >134.500000</td>\n",
+       "      <td id=\"T_78f41_row8_col45\" class=\"data row8 col45\" >134.500000</td>\n",
+       "      <td id=\"T_78f41_row8_col46\" class=\"data row8 col46\" >7.325000</td>\n",
+       "      <td id=\"T_78f41_row8_col47\" class=\"data row8 col47\" >7.325000</td>\n",
+       "      <td id=\"T_78f41_row8_col48\" class=\"data row8 col48\" >7.350000</td>\n",
+       "      <td id=\"T_78f41_row8_col49\" class=\"data row8 col49\" >7.350000</td>\n",
+       "      <td id=\"T_78f41_row8_col50\" class=\"data row8 col50\" >134.481548</td>\n",
+       "      <td id=\"T_78f41_row8_col51\" class=\"data row8 col51\" >7.339881</td>\n",
+       "      <td id=\"T_78f41_row8_col52\" class=\"data row8 col52\" >Pacific/Palau</td>\n",
+       "      <td id=\"T_78f41_row8_col53\" class=\"data row8 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col54\" class=\"data row8 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col55\" class=\"data row8 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col56\" class=\"data row8 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col57\" class=\"data row8 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col58\" class=\"data row8 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col59\" class=\"data row8 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col60\" class=\"data row8 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col61\" class=\"data row8 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col62\" class=\"data row8 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col63\" class=\"data row8 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col64\" class=\"data row8 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col65\" class=\"data row8 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col66\" class=\"data row8 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col67\" class=\"data row8 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col68\" class=\"data row8 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col69\" class=\"data row8 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col70\" class=\"data row8 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col71\" class=\"data row8 col71\" >6.000000</td>\n",
+       "      <td id=\"T_78f41_row8_col72\" class=\"data row8 col72\" >Q154002</td>\n",
+       "      <td id=\"T_78f41_row8_col73\" class=\"data row8 col73\" >890443883</td>\n",
+       "      <td id=\"T_78f41_row8_col74\" class=\"data row8 col74\" >0</td>\n",
+       "      <td id=\"T_78f41_row8_col75\" class=\"data row8 col75\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col76\" class=\"data row8 col76\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col77\" class=\"data row8 col77\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col78\" class=\"data row8 col78\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col79\" class=\"data row8 col79\" >Melequeoque</td>\n",
+       "      <td id=\"T_78f41_row8_col80\" class=\"data row8 col80\" >Мелекеок</td>\n",
+       "      <td id=\"T_78f41_row8_col81\" class=\"data row8 col81\" >梅莱凯奥克</td>\n",
+       "      <td id=\"T_78f41_row8_col82\" class=\"data row8 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col83\" class=\"data row8 col83\" >ميلكيوك</td>\n",
+       "      <td id=\"T_78f41_row8_col84\" class=\"data row8 col84\" >মেলেকেওক</td>\n",
+       "      <td id=\"T_78f41_row8_col85\" class=\"data row8 col85\" >Μελεκέοκ</td>\n",
+       "      <td id=\"T_78f41_row8_col86\" class=\"data row8 col86\" >मेलेकियोक</td>\n",
+       "      <td id=\"T_78f41_row8_col87\" class=\"data row8 col87\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col88\" class=\"data row8 col88\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col89\" class=\"data row8 col89\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col90\" class=\"data row8 col90\" >マルキョク州</td>\n",
+       "      <td id=\"T_78f41_row8_col91\" class=\"data row8 col91\" >멜레케오크</td>\n",
+       "      <td id=\"T_78f41_row8_col92\" class=\"data row8 col92\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col93\" class=\"data row8 col93\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col94\" class=\"data row8 col94\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col95\" class=\"data row8 col95\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col96\" class=\"data row8 col96\" >Melekeok</td>\n",
+       "      <td id=\"T_78f41_row8_col97\" class=\"data row8 col97\" >1159149073</td>\n",
+       "      <td id=\"T_78f41_row8_col98\" class=\"data row8 col98\" >ملکئوک</td>\n",
+       "      <td id=\"T_78f41_row8_col99\" class=\"data row8 col99\" >מלקאוק</td>\n",
+       "      <td id=\"T_78f41_row8_col100\" class=\"data row8 col100\" >Мелекеок</td>\n",
+       "      <td id=\"T_78f41_row8_col101\" class=\"data row8 col101\" >میلیکوک</td>\n",
+       "      <td id=\"T_78f41_row8_col102\" class=\"data row8 col102\" >梅萊凱奧克</td>\n",
+       "      <td id=\"T_78f41_row8_col103\" class=\"data row8 col103\" >1559804</td>\n",
+       "      <td id=\"T_78f41_row8_col104\" class=\"data row8 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col105\" class=\"data row8 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col106\" class=\"data row8 col106\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col107\" class=\"data row8 col107\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col108\" class=\"data row8 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col109\" class=\"data row8 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col110\" class=\"data row8 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col111\" class=\"data row8 col111\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col112\" class=\"data row8 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col113\" class=\"data row8 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col114\" class=\"data row8 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col115\" class=\"data row8 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col116\" class=\"data row8 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col117\" class=\"data row8 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col118\" class=\"data row8 col118\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col119\" class=\"data row8 col119\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col120\" class=\"data row8 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col121\" class=\"data row8 col121\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col122\" class=\"data row8 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col123\" class=\"data row8 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col124\" class=\"data row8 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col125\" class=\"data row8 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col126\" class=\"data row8 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col127\" class=\"data row8 col127\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col128\" class=\"data row8 col128\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col129\" class=\"data row8 col129\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col130\" class=\"data row8 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col131\" class=\"data row8 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col132\" class=\"data row8 col132\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col133\" class=\"data row8 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col134\" class=\"data row8 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col135\" class=\"data row8 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col136\" class=\"data row8 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row8_col137\" class=\"data row8 col137\" >POINT (134.6265485 7.4873962)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78f41_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_78f41_row9_col0\" class=\"data row9 col0\" >6</td>\n",
+       "      <td id=\"T_78f41_row9_col1\" class=\"data row9 col1\" >30</td>\n",
+       "      <td id=\"T_78f41_row9_col2\" class=\"data row9 col2\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col3\" class=\"data row9 col3\" >Admin-0 capital alt</td>\n",
+       "      <td id=\"T_78f41_row9_col4\" class=\"data row9 col4\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col5\" class=\"data row9 col5\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col6\" class=\"data row9 col6\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col7\" class=\"data row9 col7\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col8\" class=\"data row9 col8\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col9\" class=\"data row9 col9\" >Claimed as inte</td>\n",
+       "      <td id=\"T_78f41_row9_col10\" class=\"data row9 col10\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col11\" class=\"data row9 col11\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col12\" class=\"data row9 col12\" >Western Sahara</td>\n",
+       "      <td id=\"T_78f41_row9_col13\" class=\"data row9 col13\" >SAH</td>\n",
+       "      <td id=\"T_78f41_row9_col14\" class=\"data row9 col14\" >Western Sahara</td>\n",
+       "      <td id=\"T_78f41_row9_col15\" class=\"data row9 col15\" >SAH</td>\n",
+       "      <td id=\"T_78f41_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col17\" class=\"data row9 col17\" >EH</td>\n",
+       "      <td id=\"T_78f41_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col19\" class=\"data row9 col19\" >26.119167</td>\n",
+       "      <td id=\"T_78f41_row9_col20\" class=\"data row9 col20\" >-9.652522</td>\n",
+       "      <td id=\"T_78f41_row9_col21\" class=\"data row9 col21\" >500</td>\n",
+       "      <td id=\"T_78f41_row9_col22\" class=\"data row9 col22\" >200</td>\n",
+       "      <td id=\"T_78f41_row9_col23\" class=\"data row9 col23\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col24\" class=\"data row9 col24\" >2</td>\n",
+       "      <td id=\"T_78f41_row9_col25\" class=\"data row9 col25\" >1</td>\n",
+       "      <td id=\"T_78f41_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col27\" class=\"data row9 col27\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col28\" class=\"data row9 col28\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col29\" class=\"data row9 col29\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col30\" class=\"data row9 col30\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col31\" class=\"data row9 col31\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col32\" class=\"data row9 col32\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col33\" class=\"data row9 col33\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col34\" class=\"data row9 col34\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col35\" class=\"data row9 col35\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col36\" class=\"data row9 col36\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col37\" class=\"data row9 col37\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col38\" class=\"data row9 col38\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col39\" class=\"data row9 col39\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col40\" class=\"data row9 col40\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col41\" class=\"data row9 col41\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col42\" class=\"data row9 col42\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col43\" class=\"data row9 col43\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col44\" class=\"data row9 col44\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col45\" class=\"data row9 col45\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col46\" class=\"data row9 col46\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col47\" class=\"data row9 col47\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col48\" class=\"data row9 col48\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col49\" class=\"data row9 col49\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col50\" class=\"data row9 col50\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col51\" class=\"data row9 col51\" >0.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col52\" class=\"data row9 col52\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col53\" class=\"data row9 col53\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col54\" class=\"data row9 col54\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col55\" class=\"data row9 col55\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col56\" class=\"data row9 col56\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col57\" class=\"data row9 col57\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col58\" class=\"data row9 col58\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col59\" class=\"data row9 col59\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col60\" class=\"data row9 col60\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col61\" class=\"data row9 col61\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col62\" class=\"data row9 col62\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col63\" class=\"data row9 col63\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col64\" class=\"data row9 col64\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col65\" class=\"data row9 col65\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col66\" class=\"data row9 col66\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col67\" class=\"data row9 col67\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col68\" class=\"data row9 col68\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col69\" class=\"data row9 col69\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col70\" class=\"data row9 col70\" >0</td>\n",
+       "      <td id=\"T_78f41_row9_col71\" class=\"data row9 col71\" >6.000000</td>\n",
+       "      <td id=\"T_78f41_row9_col72\" class=\"data row9 col72\" >Q614754</td>\n",
+       "      <td id=\"T_78f41_row9_col73\" class=\"data row9 col73\" >1141909145</td>\n",
+       "      <td id=\"T_78f41_row9_col74\" class=\"data row9 col74\" >1</td>\n",
+       "      <td id=\"T_78f41_row9_col75\" class=\"data row9 col75\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col76\" class=\"data row9 col76\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_78f41_row9_col77\" class=\"data row9 col77\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_78f41_row9_col78\" class=\"data row9 col78\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col79\" class=\"data row9 col79\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col80\" class=\"data row9 col80\" >Бир-Лелу</td>\n",
+       "      <td id=\"T_78f41_row9_col81\" class=\"data row9 col81\" >比鄂-雷楼</td>\n",
+       "      <td id=\"T_78f41_row9_col82\" class=\"data row9 col82\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col83\" class=\"data row9 col83\" >بئر لحلو</td>\n",
+       "      <td id=\"T_78f41_row9_col84\" class=\"data row9 col84\" >বির লেহলোউ</td>\n",
+       "      <td id=\"T_78f41_row9_col85\" class=\"data row9 col85\" >Μπιρ Λελού</td>\n",
+       "      <td id=\"T_78f41_row9_col86\" class=\"data row9 col86\" >बीर लहलू</td>\n",
+       "      <td id=\"T_78f41_row9_col87\" class=\"data row9 col87\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col88\" class=\"data row9 col88\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col89\" class=\"data row9 col89\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_78f41_row9_col90\" class=\"data row9 col90\" >ビル・ラルフー</td>\n",
+       "      <td id=\"T_78f41_row9_col91\" class=\"data row9 col91\" >비르레흘루</td>\n",
+       "      <td id=\"T_78f41_row9_col92\" class=\"data row9 col92\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col93\" class=\"data row9 col93\" >Al-Bir al-Hilw</td>\n",
+       "      <td id=\"T_78f41_row9_col94\" class=\"data row9 col94\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col95\" class=\"data row9 col95\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_78f41_row9_col96\" class=\"data row9 col96\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_78f41_row9_col97\" class=\"data row9 col97\" >1159149075</td>\n",
+       "      <td id=\"T_78f41_row9_col98\" class=\"data row9 col98\" >بئر لحلو</td>\n",
+       "      <td id=\"T_78f41_row9_col99\" class=\"data row9 col99\" >ביר להלו</td>\n",
+       "      <td id=\"T_78f41_row9_col100\" class=\"data row9 col100\" >Бір-Лелу</td>\n",
+       "      <td id=\"T_78f41_row9_col101\" class=\"data row9 col101\" >بر لیہلو</td>\n",
+       "      <td id=\"T_78f41_row9_col102\" class=\"data row9 col102\" >比鄂-雷楼</td>\n",
+       "      <td id=\"T_78f41_row9_col103\" class=\"data row9 col103\" >-1</td>\n",
+       "      <td id=\"T_78f41_row9_col104\" class=\"data row9 col104\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col105\" class=\"data row9 col105\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col106\" class=\"data row9 col106\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col107\" class=\"data row9 col107\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col108\" class=\"data row9 col108\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col109\" class=\"data row9 col109\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col110\" class=\"data row9 col110\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col111\" class=\"data row9 col111\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col112\" class=\"data row9 col112\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col113\" class=\"data row9 col113\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col114\" class=\"data row9 col114\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col115\" class=\"data row9 col115\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col116\" class=\"data row9 col116\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col117\" class=\"data row9 col117\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col118\" class=\"data row9 col118\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col119\" class=\"data row9 col119\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col120\" class=\"data row9 col120\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col121\" class=\"data row9 col121\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col122\" class=\"data row9 col122\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col123\" class=\"data row9 col123\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col124\" class=\"data row9 col124\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col125\" class=\"data row9 col125\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col126\" class=\"data row9 col126\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col127\" class=\"data row9 col127\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col128\" class=\"data row9 col128\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col129\" class=\"data row9 col129\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col130\" class=\"data row9 col130\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col131\" class=\"data row9 col131\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col132\" class=\"data row9 col132\" >Populated place</td>\n",
+       "      <td id=\"T_78f41_row9_col133\" class=\"data row9 col133\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col134\" class=\"data row9 col134\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col135\" class=\"data row9 col135\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col136\" class=\"data row9 col136\" >nan</td>\n",
+       "      <td id=\"T_78f41_row9_col137\" class=\"data row9 col137\" >POINT (-9.6525222 26.1191667)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, HTML\n",
+    "style = places[:10].style.set_table_styles([dict(selector=\"tr\", props=[(\"font-size\", \"8pt\")])])\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"<div style='width: 100%; height: 40ex; overflow: auto'>\" +\n",
+    "        style.to_html() +\n",
+    "        \"</div>\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a83349f-3d08-4d16-9f2c-7ebcc62c260f",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "You can then modify the obtained `GeoDataFrame` as you need and finally add it to the map with [`Maps.add_gdf`](eomaps.Maps.add_gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f128bf4c-31a2-42a7-836c-9f9c640c75e5",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "remove-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.collections.PathCollection at 0x137d52427d0>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m.add_gdf(places, markersize=places.NATSCALE/10, column=\"NATSCALE\", ec=\"k\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3321ad8d-38e6-4b5e-a7c0-76378cc9dba0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5dec36838b7973cff53b4e8ad8f65a70b4f8b1fc Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:30:42 +0100
Subject: [PATCH 010/240] update path to docs_env.yml

---
 .readthedocs.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 05593cd47..2c6a4ac65 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -6,7 +6,7 @@ build:
     python: "mambaforge-4.10"
 
 conda:
-  environment: tests/docs_env.yml
+  environment: docs/docs_env.yml
 
 python:
   install:

From 95c377979c7e75c83c06e3f20cee98b277400ba0 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:31:52 +0100
Subject: [PATCH 011/240] update mambaforge version for docs

---
 .readthedocs.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 2c6a4ac65..84eb1cd96 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -3,7 +3,7 @@ version: 2
 build:
   os: "ubuntu-20.04"
   tools:
-    python: "mambaforge-4.10"
+    python: "mambaforge-22.9"
 
 conda:
   environment: docs/docs_env.yml

From dd5143caf26600e38ab98953cea9191d716da5cf Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:32:54 +0100
Subject: [PATCH 012/240] update docs env

---
 docs/docs_env.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs_env.yml b/docs/docs_env.yml
index 029052838..2104f3465 100644
--- a/docs/docs_env.yml
+++ b/docs/docs_env.yml
@@ -4,7 +4,6 @@ channels:
 
 dependencies:
 
-  - python = 3.10
   - numpy
   - scipy
   - pandas
@@ -27,6 +26,7 @@ dependencies:
   - xmltodict
   - cairosvg
 
+  - pip
   # --------------for building the docs
   - docutils<0.19
   - sphinx=7.2.6

From 6857d0b6c8a79350f172c0b1485aa9833db6747d Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Dec 2023 22:35:29 +0100
Subject: [PATCH 013/240] update docs env

---
 docs/docs_env.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs_env.yml b/docs/docs_env.yml
index 2104f3465..3bbd51341 100644
--- a/docs/docs_env.yml
+++ b/docs/docs_env.yml
@@ -31,6 +31,6 @@ dependencies:
   - docutils<0.19
   - sphinx=7.2.6
   - sphinx-copybutton=0.5.2
-  - myst_nb=1.0.0
+  - myst-nb=1.0.0
   - sphinx-design=0.5.0
   - sphinx_rtd_theme=1.3

From 3ac16a4ffe93515127c18fccdea823a8ba031fe7 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 00:43:43 +0100
Subject: [PATCH 014/240] update docs

---
 docs/index.rst                                |   4 +-
 docs/notebooks/inset_maps.ipynb               | 352 ++++++++++++++++++
 ...ures.ipynb => naturalearth_features.ipynb} |   0
 3 files changed, 354 insertions(+), 2 deletions(-)
 create mode 100644 docs/notebooks/inset_maps.ipynb
 rename docs/notebooks/{basic_features.ipynb => naturalearth_features.ipynb} (100%)

diff --git a/docs/index.rst b/docs/index.rst
index 6de7b81e6..0a70f4780 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -188,8 +188,8 @@ Make sure to check out the :doc:`EOmaps_examples` for an overview of the capabil
     :maxdepth: 1
     :caption: Map Features
 
-    api_inset_maps
-    notebooks/basic_features.ipynb
+    notebooks/inset_maps.ipynb
+    notebooks/naturalearth_features.ipynb
     api_webmaps
     api_vector_data
 
diff --git a/docs/notebooks/inset_maps.ipynb b/docs/notebooks/inset_maps.ipynb
new file mode 100644
index 000000000..059a28fac
--- /dev/null
+++ b/docs/notebooks/inset_maps.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a5ea710d-94b1-4396-aa3b-481ceb117a8b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "563102a7-ac5a-4b46-af2b-b29d068fbf4d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# 🔬 Inset Maps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "780a75df-00d3-4148-8251-d17130e04b81",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## How to create inset maps\n",
+    "\n",
+    "Inset maps are used to show zoomed-in regions of a map and can be created with [`Maps.new_inset_map`](eomaps.Maps.new_inset_map).\n",
+    "\n",
+    "```{eval-rst}\n",
+    ".. currentmodule:: eomaps\n",
+    "\n",
+    ".. autosummary::\n",
+    "    :nosignatures:\n",
+    "\n",
+    "    Maps.new_inset_map\n",
+    "```\n",
+    "\n",
+    "\n",
+    "An inset-map is defined by a **center-position** and a **radius** (in a given coordinate-system).\n",
+    "\n",
+    "The shape of the inset boundary can be \n",
+    "- projected \"ellipses\" or \"rectangles\" (with dimensions defined in a given coordinate-system)\n",
+    "- \"geodesic_circles\" (with a radius defined in meters).\n",
+    "\n",
+    "\n",
+    ":::{note}\n",
+    "[`InsetMaps`](eomaps.inset_maps.InsetMaps) can be used just like any other [`Maps`](eomaps.Maps) object!  \n",
+    "For convenience, [`InsetMaps`](eomaps.inset_maps.InsetMaps) have the following additional methods defined:\n",
+    "\n",
+    "```{eval-rst}\n",
+    ".. currentmodule:: eomaps.inset_maps\n",
+    "\n",
+    ".. autosummary::\n",
+    "    :nosignatures:\n",
+    "\n",
+    "    InsetMaps.set_inset_position\n",
+    "    InsetMaps.add_extent_indicator\n",
+    "    InsetMaps.add_indicator_line\n",
+    "```\n",
+    ":::\n",
+    "\n",
+    ":::{tip}\n",
+    "To quickly re-position (and re-size) inset-maps, make sure to have a look at the {ref}`layout_editor`!\n",
+    "\n",
+    "A more advanced example on how to use inset-maps is available here: {ref}`ex_11`\n",
+    ":::\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "052d389f-a7f0-4a35-83fc-57d0bfa992f1",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps()\n",
+    "m.add_feature.preset.ocean(alpha=0.5)\n",
+    "mi = m.new_inset_map(xy=(125, -5), radius=10)\n",
+    "mi.add_feature.preset(\"ocean\", \"land\")\n",
+    "m.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b9039fa-c055-4d1a-9192-74db4855a6bf",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "There are many ways to customize the appearance of an inset-map.  \n",
+    "\n",
+    ":::{tip}\n",
+    "The [`InsetMaps`](eomaps.inset_maps.InsetMaps) can be in a different projection than the parent map!\n",
+    "\n",
+    "You can also specify the position and radius of an [`InsetMap`](eomaps.inset_maps.InsetMaps) in a coordinate system of your choice.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "35d0a654-5a34-40d2-8053-a53f77c2ab35",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps(Maps.CRS.PlateCarree(central_longitude=-60))\n",
+    "m.add_feature.preset.ocean()\n",
+    "m.set_frame(ec=\"none\", rounded=0.3)\n",
+    "\n",
+    "m_i = m.new_inset_map(xy=(5, 45), xy_crs=4326,\n",
+    "                      radius=2e6, radius_crs=3035,\n",
+    "                      plot_position=(.3, .5), plot_size=.5,\n",
+    "                      shape=\"rectangles\",\n",
+    "                      inset_crs=3035,\n",
+    "                      boundary=dict(ec=\"orange\", lw=4),\n",
+    "                      indicate_extent=dict(fc=(1, 0.6, 0, .5), ec=\"0.5\", lw=0.5, hatch=\"xxxxx\"),\n",
+    "                      indicator_line=dict(lw=3, marker=\"o\", ms=10)\n",
+    "                      )\n",
+    "\n",
+    "m_i.add_feature.preset(\"coastline\", \"countries\", \"ocean\", \"urban_areas\")\n",
+    "m_i.add_feature.physical.rivers_europe(ec=\"b\", lw=0.25, fc=\"none\")\n",
+    "m_i.add_feature.physical.lakes_europe(fc=\"b\")\n",
+    "m_i.add_gridlines()\n",
+    "m.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cecfa72-98e9-4472-976e-14b0b271a4af",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Zoomed in views on datasets\n",
+    "\n",
+    "To create a \"zoomed-in\" view of a dataset, the data must be plotted on the inset-map with the same classification as the initial plot.\n",
+    "\n",
+    "For this purpose, EOmaps provides 2 convenience-functions:\n",
+    "\n",
+    "- [`Maps.inherit_data`](eomaps.Maps.inherit_data) : Use the same dataset as another [`Maps`](eomaps.Maps) object\n",
+    "- [`Maps.inherit_classification`](eomaps.Maps.inherit_classification): Use the same classification as another [`Maps`](eomaps.Maps) object\n",
+    "\n",
+    ":::{note}\n",
+    "If the classification is inherited, also the the used ``colormap`` and the associated data-limits ``vmin`` and ``vmax`` will be the inherited!\n",
+    ":::\n",
+    "\n",
+    ":::{tip}\n",
+    "You can still change the shape that is used for plotting to adjust the appearance of the data in the zoomed-in view!\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2a2560d1-dfaf-4995-bd9b-cd905710d6c2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "x, y = np.meshgrid(np.linspace(-20, 20, 50), np.linspace(-50, 60, 100))\n",
+    "data = x + y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9521c322-3b26-4c9f-bfa3-e7cae7dc0408",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Create a map and plot a classified dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3a96a635-3c2b-49d0-a25b-f58c555f85bd",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from eomaps import Maps\n",
+    "m = Maps(ax=131)\n",
+    "m.add_feature.preset.coastline()\n",
+    "m.set_data(data, x, y)\n",
+    "m.set_shape.raster()\n",
+    "m.set_classify.Quantiles(k=10)\n",
+    "m.plot_map(cmap=\"tab10\", vmin=-10, vmax=40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e19988aa-eb39-49bf-b4c2-52059b32973a",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Create a new inset-map that shows a zoomed-in view on the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b5bfc055-ec33-4672-9a6b-570defce9f9e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "m_i = m.new_inset_map(xy=(-5, 35), radius=8, \n",
+    "                      plot_position=(0.7, .5), plot_size=.7, indicator_line=True)\n",
+    "m_i.add_title(\"A zoomed-in view on a dataset!\", y=1.05)\n",
+    "\n",
+    "m_i.add_feature.preset.coastline()\n",
+    "m_i.add_feature.preset.ocean(alpha=0.4, zorder=10, hatch=\"///\", ec=\"darkblue\")\n",
+    "\n",
+    "m_i.inherit_data(m)            # inherit the data from \"m\"\n",
+    "m_i.inherit_classification(m)  # inherit the classification from \"m\"\n",
+    "m_i.set_shape.rectangles()\n",
+    "m_i.plot_map(ec=\"k\", lw=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "11d46e5c-4769-412a-a74a-b5a1dd38dc0d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/basic_features.ipynb b/docs/notebooks/naturalearth_features.ipynb
similarity index 100%
rename from docs/notebooks/basic_features.ipynb
rename to docs/notebooks/naturalearth_features.ipynb

From 1468cbdfb90049f3550b595c1209872efd039cbc Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 01:08:35 +0100
Subject: [PATCH 015/240] update docs

---
 docs/notebooks/inset_maps.ipynb | 41 ++++++++++++++++++---------------
 1 file changed, 22 insertions(+), 19 deletions(-)

diff --git a/docs/notebooks/inset_maps.ipynb b/docs/notebooks/inset_maps.ipynb
index 059a28fac..713e89a3f 100644
--- a/docs/notebooks/inset_maps.ipynb
+++ b/docs/notebooks/inset_maps.ipynb
@@ -46,7 +46,7 @@
    "source": [
     "## How to create inset maps\n",
     "\n",
-    "Inset maps are used to show zoomed-in regions of a map and can be created with [`Maps.new_inset_map`](eomaps.Maps.new_inset_map).\n",
+    "Inset maps are used to show **zoomed-in regions of a map** and can be created with {py:meth}`Maps.new_inset_map`.\n",
     "\n",
     "```{eval-rst}\n",
     ".. currentmodule:: eomaps\n",
@@ -58,16 +58,17 @@
     "```\n",
     "\n",
     "\n",
-    "An inset-map is defined by a **center-position** and a **radius** (in a given coordinate-system).\n",
+    "An inset-map is defined by providing a **center-position** and a **radius**.\n",
     "\n",
-    "The shape of the inset boundary can be \n",
+    "The shape of the inset boundary can be one of the following options:\n",
     "- projected \"ellipses\" or \"rectangles\" (with dimensions defined in a given coordinate-system)\n",
     "- \"geodesic_circles\" (with a radius defined in meters).\n",
     "\n",
     "\n",
     ":::{note}\n",
-    "[`InsetMaps`](eomaps.inset_maps.InsetMaps) can be used just like any other [`Maps`](eomaps.Maps) object!  \n",
-    "For convenience, [`InsetMaps`](eomaps.inset_maps.InsetMaps) have the following additional methods defined:\n",
+    "[`InsetMaps`](eomaps.inset_maps.InsetMaps) can be used just like any other [`Maps`](eomaps.Maps) object!\n",
+    "\n",
+    "For convenience, [`InsetMaps`](eomaps.inset_maps.InsetMaps) have the following **additional methods** defined:\n",
     "\n",
     "```{eval-rst}\n",
     ".. currentmodule:: eomaps.inset_maps\n",
@@ -90,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "id": "052d389f-a7f0-4a35-83fc-57d0bfa992f1",
    "metadata": {
     "editable": true,
@@ -102,7 +103,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -110,10 +111,12 @@
    ],
    "source": [
     "from eomaps import Maps\n",
-    "m = Maps()\n",
+    "m = Maps(figsize=(4, 2.5))\n",
     "m.add_feature.preset.ocean(alpha=0.5)\n",
-    "mi = m.new_inset_map(xy=(125, -5), radius=10)\n",
-    "mi.add_feature.preset(\"ocean\", \"land\")\n",
+    "\n",
+    "mi = m.new_inset_map(xy=(125, -5), radius=10)   # create a new inset-map\n",
+    "mi.add_feature.preset(\"ocean\", \"land\")          # populate the inset-map with some features\n",
+    "\n",
     "m.show()"
    ]
   },
@@ -133,13 +136,13 @@
     ":::{tip}\n",
     "The [`InsetMaps`](eomaps.inset_maps.InsetMaps) can be in a different projection than the parent map!\n",
     "\n",
-    "You can also specify the position and radius of an [`InsetMap`](eomaps.inset_maps.InsetMaps) in a coordinate system of your choice.\n",
+    "You can also specify the position and radius of an inset-map in a coordinate system of your choice.\n",
     ":::"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "35d0a654-5a34-40d2-8053-a53f77c2ab35",
    "metadata": {
     "editable": true,
@@ -151,7 +154,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -159,7 +162,7 @@
    ],
    "source": [
     "from eomaps import Maps\n",
-    "m = Maps(Maps.CRS.PlateCarree(central_longitude=-60))\n",
+    "m = Maps(Maps.CRS.PlateCarree(central_longitude=-60), figsize=(6, 3.5))\n",
     "m.add_feature.preset.ocean()\n",
     "m.set_frame(ec=\"none\", rounded=0.3)\n",
     "\n",
@@ -191,7 +194,7 @@
     "tags": []
    },
    "source": [
-    "# Zoomed in views on datasets\n",
+    "## Zoomed in views on datasets\n",
     "\n",
     "To create a \"zoomed-in\" view of a dataset, the data must be plotted on the inset-map with the same classification as the initial plot.\n",
     "\n",
@@ -243,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "id": "3a96a635-3c2b-49d0-a25b-f58c555f85bd",
    "metadata": {
     "editable": true,
@@ -279,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "id": "b5bfc055-ec33-4672-9a6b-570defce9f9e",
    "metadata": {
     "editable": true,
@@ -299,13 +302,13 @@
     "\n",
     "m_i.inherit_data(m)            # inherit the data from \"m\"\n",
     "m_i.inherit_classification(m)  # inherit the classification from \"m\"\n",
-    "m_i.set_shape.rectangles()\n",
+    "m_i.set_shape.rectangles()     # switch to a different shape for plotting\n",
     "m_i.plot_map(ec=\"k\", lw=0.5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "id": "11d46e5c-4769-412a-a74a-b5a1dd38dc0d",
    "metadata": {
     "editable": true,

From 58330c2b6b6077608424073a9d40b5bbd9733c5b Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 01:11:32 +0100
Subject: [PATCH 016/240] update docs

---
 docs/notebooks/inset_maps.ipynb | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/docs/notebooks/inset_maps.ipynb b/docs/notebooks/inset_maps.ipynb
index 713e89a3f..6e8c2e13e 100644
--- a/docs/notebooks/inset_maps.ipynb
+++ b/docs/notebooks/inset_maps.ipynb
@@ -45,26 +45,25 @@
    },
    "source": [
     "## How to create inset maps\n",
+    "```{eval-rst}\n",
+    ".. currentmodule:: eomaps\n",
+    "```\n",
     "\n",
     "Inset maps are used to show **zoomed-in regions of a map** and can be created with {py:meth}`Maps.new_inset_map`.\n",
     "\n",
     "```{eval-rst}\n",
-    ".. currentmodule:: eomaps\n",
-    "\n",
     ".. autosummary::\n",
     "    :nosignatures:\n",
     "\n",
     "    Maps.new_inset_map\n",
     "```\n",
     "\n",
-    "\n",
     "An inset-map is defined by providing a **center-position** and a **radius**.\n",
     "\n",
     "The shape of the inset boundary can be one of the following options:\n",
     "- projected \"ellipses\" or \"rectangles\" (with dimensions defined in a given coordinate-system)\n",
     "- \"geodesic_circles\" (with a radius defined in meters).\n",
     "\n",
-    "\n",
     ":::{note}\n",
     "[`InsetMaps`](eomaps.inset_maps.InsetMaps) can be used just like any other [`Maps`](eomaps.Maps) object!\n",
     "\n",
@@ -134,7 +133,7 @@
     "There are many ways to customize the appearance of an inset-map.  \n",
     "\n",
     ":::{tip}\n",
-    "The [`InsetMaps`](eomaps.inset_maps.InsetMaps) can be in a different projection than the parent map!\n",
+    "Inset-maps can have a different projection than the parent map!\n",
     "\n",
     "You can also specify the position and radius of an inset-map in a coordinate system of your choice.\n",
     ":::"

From 2be67ce28bff1ae72f47cae053ea2c28e4c21239 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 11:44:21 +0100
Subject: [PATCH 017/240] add unittest to test all Jupyter Notebook code-cells
 in the docs

---
 tests/test_doc_notebooks.py | 47 +++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 tests/test_doc_notebooks.py

diff --git a/tests/test_doc_notebooks.py b/tests/test_doc_notebooks.py
new file mode 100644
index 000000000..71e9ca604
--- /dev/null
+++ b/tests/test_doc_notebooks.py
@@ -0,0 +1,47 @@
+"""
+Test all Jupyter Notebook code-cells from the docs.
+
+NOTE:
+All cod cells of a notebook are concatenated as if they have been writtin as a
+singe pyhton-script that is executed in one go!
+
+This is done to avoid issues with cells that are not "standalone"
+(e.g. that require previous cells to be executed)
+"""
+
+from pathlib import Path
+
+import pytest
+import nbformat
+import matplotlib.pyplot as plt
+
+plt.ion()  # use interactive mode to avoid blocking images
+
+basepath = Path(__file__).parent.parent / "docs" / "notebooks"
+
+
+class TestDocNotebooks:
+    @pytest.mark.parametrize(
+        "notebook",
+        filter(lambda x: x.suffix == ".ipynb", basepath.iterdir()),
+        ids=lambda x: x.stem,
+    )
+    def test_doc_notebook(self, notebook):
+        with open(notebook, encoding="utf-8") as f:
+            nb = nbformat.read(f, as_version=4)
+            # parse all code-cells from notebook
+            code_cells = [i["source"] for i in nb["cells"] if i["cell_type"] == "code"]
+            code = ""
+
+            for c in code_cells:
+                for l in c.split("\n"):
+                    # exclude lines that use magic commands (e.g. starting with %)
+                    if not l.startswith("%"):
+                        code += f"{l}\n"
+
+            # run code (use a shared dict for locals and globals to avoid issues
+            # with undefined variables)
+            d = dict()
+            exec(code, d, d)
+
+        plt.close("all")

From 759d9b25a4c78aafc9263f8d709250b09c23aad3 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 19:20:01 +0100
Subject: [PATCH 018/240] add basic info on how to use VSCode/VSCodium

---
 docs/FAQ.rst | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 587dc1ec3..d581e7099 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -25,6 +25,52 @@ the plot-settings must be adjusted to ensure that ``matplotlib`` plots remain in
 
 .. image:: _static/spyder_preferences.png
 
+.. _config_vscode:
+
+VSCode / VSCodium
+-----------------
+
+.. currentmodule:: eomaps
+
+In general, EOmaps works "out of the box" with `VSCode <https://code.visualstudio.com/>`_ or the open-source variant `VSCodium <https://vscodium.com/>`_ (together with the standard `Python <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_ extension).
+
+However, there are some tipps that might help with your mapping workflow:
+
+- In a normal python-terminal, the default matplotlib backend will be ``QtAgg`` in a **non-interactive mode**.
+  This means that you must call `m.show()` at the end of the script to actually show the figure.
+  Once the figure is shown, **the terminal is blocked until the figure is closed**.
+
+- To **avoid blocking the terminal** while a figure is running, you can **activate matplotlib's interactive-mode** using
+
+  .. code-block:: python
+
+      from eomaps import Maps
+      Maps.config(use_interactive_mode=True)
+
+  Once activated, figures are immediately shown as soon as a new :py:class:`Maps` object is created and the terminal is **not blocked** (e.g. you can continue to execute commands that update the figure).
+
+.. note::
+
+   If you run a whole script using the **interactive mode**, the script will run until the end and then usually terminate the associated kernel... and in turn also closing the figure! If you want to keep the figure open, either make sure that the terminal is kept alive by entering debug-mode, or avoid activating the interactive mode and block the terminal with `m.show()`.
+
+Interactive IPython (e.g. Jupyter)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you enjoy interactive coding in a Jupyter-Notebook style, make sure to have a look at the nice `Jupyter <https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter>`_ extension!
+
+It allows you to work with an interactive IPython terminal and execute code-blocks (separated by the ``# %%`` indicator).
+
+- With IPython, the default behavior is to create static (inline) figures (same as with Jupyter Notebooks)!
+  To print a snapshot of the current state of a figure to the IPython terminal, call :py:meth:`Maps.show` or :py:meth:`Maps.snapshot`.
+
+- Same as with Jupyter Notebooks, you can use *"magic"* commands to set the used matpltolib backend.
+
+  - For interactive (popup) figures, switch to the default Qt backend using ``%matplotlib qt``
+  - For interactive (inline) figures, you'll need to install `ipympl <https://github.com/matplotlib/ipympl>`_ and then activate the ``widget`` with ``%matplotlib widget``.
+
+  - For more details, see the associated section for :ref:`config_jupyter_notebook`
+
+
 .. _config_pycharm:
 
 PyCharm IDE

From de6ca1248076090272701f17d4f054aac02e3692 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 19:21:22 +0100
Subject: [PATCH 019/240] minor

---
 docs/FAQ.rst | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index d581e7099..647477c5a 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -30,7 +30,7 @@ the plot-settings must be adjusted to ensure that ``matplotlib`` plots remain in
 VSCode / VSCodium
 -----------------
 
-.. currentmodule:: eomaps
+.. currentmodule:: eomaps.eomaps
 
 In general, EOmaps works "out of the box" with `VSCode <https://code.visualstudio.com/>`_ or the open-source variant `VSCodium <https://vscodium.com/>`_ (together with the standard `Python <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_ extension).
 
@@ -96,7 +96,6 @@ To my knowledge there are 2 possibilities to force ``pycharm`` to use the origin
 
   .. image:: _static/pycharm_preferences.png
 
-
 In addition, if you use a **commercial version** of PyCharm, make sure to **disable** *"Show plots in tool window"*
 in the **Python Scientific** preferences since it forces plots to be rendered as static images.
 

From d0c8dcbe79539f423f1b67ef6be7be63397a042d Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Dec 2023 19:22:04 +0100
Subject: [PATCH 020/240] minor

---
 docs/FAQ.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 647477c5a..9d62793e7 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -37,7 +37,7 @@ In general, EOmaps works "out of the box" with `VSCode <https://code.visualstudi
 However, there are some tipps that might help with your mapping workflow:
 
 - In a normal python-terminal, the default matplotlib backend will be ``QtAgg`` in a **non-interactive mode**.
-  This means that you must call `m.show()` at the end of the script to actually show the figure.
+  This means that you must call :py:meth:`Maps.show` at the end of the script to actually show the figure.
   Once the figure is shown, **the terminal is blocked until the figure is closed**.
 
 - To **avoid blocking the terminal** while a figure is running, you can **activate matplotlib's interactive-mode** using

From aac3287ac990c88e906d569cf3c85192d6ff22dc Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 10 Dec 2023 11:21:37 +0100
Subject: [PATCH 021/240] update test env

---
 tests/test_env.yml | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/tests/test_env.yml b/tests/test_env.yml
index 805b76630..a3e273b18 100644
--- a/tests/test_env.yml
+++ b/tests/test_env.yml
@@ -30,12 +30,7 @@ dependencies:
   - coveralls
   - pytest
   - pytest-cov
-  # --------------to support multiple Qt versions
-  - qtpy
-  # --------------for building the docs
-  - sphinx-copybutton
-  - sphinx
+  # --------------for testing the docs
   - docutils
-  - pip
-  - pip:
-    - sphinx_rtd_theme
+  - jupyter
+  - nbformat

From 5577d34af00560098e35c86013da1c9564d2eb93 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 10 Dec 2023 14:31:00 +0100
Subject: [PATCH 022/240] update docs

---
 docs/FAQ.rst                                  | 393 +++++++++---------
 docs/conf.py                                  |   1 -
 docs/installation.rst                         |   7 +-
 ...{introduction.rst => quickstart_quide.rst} |   0
 4 files changed, 207 insertions(+), 194 deletions(-)
 rename docs/{introduction.rst => quickstart_quide.rst} (100%)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 9d62793e7..df9067218 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -5,153 +5,175 @@
   :local:
   :depth: 1
 
-.. include:: introduction.rst
+
+Need some help to setup python for EOmaps?
+******************************************
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   quickstart_quide
+
+
+Have a look at the :ref:`quickstart_guide` to learn how to setup a python environment that can be
+used to create EOmaps maps!
+
 
 .. _configuring_the_editor:
 
 Configuring the editor (IDE)
 ****************************
 
+EOmaps can be used with whatever editor you like... however, some editors have special settings that can be
+adjusted to improve your mapping experience with **EOmaps**:
+
 .. _config_spyder:
 
-Spyder IDE
-----------
+.. dropdown:: Spyder
+
+    .. currentmodule:: eomaps.eomaps
+
+    To use the whole potential of EOmaps with the awesome `Spyder IDE <https://www.spyder-ide.org>`_  ,
+    the plot-settings must be adjusted to ensure that ``matplotlib`` plots are created as interactive Qt widgets.
+
+    - By default, figures are rendered as static images into the `plots-pane <http://docs.spyder-ide.org/current/panes/plots.html>`_. To avoid this and **create interactive (popup) figures** instead, go to the preferences and set the "Graphics Backend" to "Automatic" :
 
-To use the whole potential of EOmaps with the awesome `Spyder IDE <https://www.spyder-ide.org>`_  ,
-the plot-settings must be adjusted to ensure that ``matplotlib`` plots remain interactive.
+    .. image:: _static/spyder_preferences.png
 
-- By default, plots are rendered as static images into the "plots-pane"... to avoid this and create
-  interactive ``matplotlib`` widgets instead, go to the preferences and set the "Graphics backend" to "Automatic" :
+    .. raw:: html
+
+       <p/>
+
+    .. note::
+
+      If the gaphics-backend is set to *"Automatic"*, you can still plot static snapshots of a figure to the "plots-pane" with :py:meth:`Maps.snapshot`!
 
-.. image:: _static/spyder_preferences.png
 
 .. _config_vscode:
 
-VSCode / VSCodium
------------------
 
-.. currentmodule:: eomaps.eomaps
+.. dropdown:: VSCode / VSCodium
 
-In general, EOmaps works "out of the box" with `VSCode <https://code.visualstudio.com/>`_ or the open-source variant `VSCodium <https://vscodium.com/>`_ (together with the standard `Python <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_ extension).
+    .. currentmodule:: eomaps.eomaps
 
-However, there are some tipps that might help with your mapping workflow:
+    In general, EOmaps works "out of the box" with `VSCode <https://code.visualstudio.com/>`_ or the open-source variant `VSCodium <https://vscodium.com/>`_ (together with the standard `Python <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_ extension).
 
-- In a normal python-terminal, the default matplotlib backend will be ``QtAgg`` in a **non-interactive mode**.
-  This means that you must call :py:meth:`Maps.show` at the end of the script to actually show the figure.
-  Once the figure is shown, **the terminal is blocked until the figure is closed**.
+    However, there are some tipps that might help with your mapping workflow:
 
-- To **avoid blocking the terminal** while a figure is running, you can **activate matplotlib's interactive-mode** using
+    - In a normal python-terminal, the default matplotlib backend will be ``QtAgg`` in a **non-interactive mode**.
+      This means that you must call :py:meth:`Maps.show` at the end of the script to actually show the figure.
+      Once the figure is shown, **the terminal is blocked until the figure is closed**.
 
-  .. code-block:: python
+    - To **avoid blocking the terminal** while a figure is running, you can **activate matplotlib's interactive-mode** using
 
-      from eomaps import Maps
-      Maps.config(use_interactive_mode=True)
+      .. code-block:: python
 
-  Once activated, figures are immediately shown as soon as a new :py:class:`Maps` object is created and the terminal is **not blocked** (e.g. you can continue to execute commands that update the figure).
+          from eomaps import Maps
+          Maps.config(use_interactive_mode=True)
 
-.. note::
+      Once activated, figures are immediately shown as soon as a new :py:class:`Maps` object is created and the terminal is **not blocked** (e.g. you can continue to execute commands that update the figure).
 
-   If you run a whole script using the **interactive mode**, the script will run until the end and then usually terminate the associated kernel... and in turn also closing the figure! If you want to keep the figure open, either make sure that the terminal is kept alive by entering debug-mode, or avoid activating the interactive mode and block the terminal with `m.show()`.
+    .. note::
 
-Interactive IPython (e.g. Jupyter)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+       If you run a whole script using the **interactive mode**, the script will run until the end and then usually terminate the associated kernel... and in turn also closing the figure! If you want to keep the figure open, either make sure that the terminal is kept alive by entering debug-mode, or avoid activating the interactive mode and block the terminal with `m.show()`.
 
-If you enjoy interactive coding in a Jupyter-Notebook style, make sure to have a look at the nice `Jupyter <https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter>`_ extension!
+    Interactive IPython (e.g. Jupyter)
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-It allows you to work with an interactive IPython terminal and execute code-blocks (separated by the ``# %%`` indicator).
+    If you enjoy interactive coding in a Jupyter-Notebook style, make sure to have a look at the nice `Jupyter <https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter>`_ extension!
 
-- With IPython, the default behavior is to create static (inline) figures (same as with Jupyter Notebooks)!
-  To print a snapshot of the current state of a figure to the IPython terminal, call :py:meth:`Maps.show` or :py:meth:`Maps.snapshot`.
+    It allows you to work with an interactive IPython terminal and execute code-blocks (separated by the ``# %%`` indicator).
 
-- Same as with Jupyter Notebooks, you can use *"magic"* commands to set the used matpltolib backend.
+    - With IPython, the default behavior is to create static (inline) figures (same as with Jupyter Notebooks)!
+      To print a snapshot of the current state of a figure to the IPython terminal, call :py:meth:`Maps.show` or :py:meth:`Maps.snapshot`.
 
-  - For interactive (popup) figures, switch to the default Qt backend using ``%matplotlib qt``
-  - For interactive (inline) figures, you'll need to install `ipympl <https://github.com/matplotlib/ipympl>`_ and then activate the ``widget`` with ``%matplotlib widget``.
+    - Same as with Jupyter Notebooks, you can use *"magic"* commands to set the used matpltolib backend.
 
-  - For more details, see the associated section for :ref:`config_jupyter_notebook`
+      - For interactive (popup) figures, switch to the default Qt backend using ``%matplotlib qt``
+      - For interactive (inline) figures, you'll need to install `ipympl <https://github.com/matplotlib/ipympl>`_ and then activate the ``widget`` with ``%matplotlib widget``.
 
+      - For more details, see the associated section for :ref:`config_jupyter_notebook`
 
-.. _config_pycharm:
 
-PyCharm IDE
------------
+.. _config_pycharm:
 
-The `PyCharm IDE <https://www.jetbrains.com/pycharm/>`_  automatically registers its own matplotlib backend
-which (for some unknown reason) freezes on interactive plots.
+.. dropdown:: PyCharm
 
-To my knowledge there are 2 possibilities to force ``pycharm`` to use the original ``matplotlib`` backends:
+    The `PyCharm IDE <https://www.jetbrains.com/pycharm/>`_  automatically registers its own matplotlib backend
+    which (for some unknown reason) freezes on interactive plots.
 
-- | 🚲 The "manual" way:
-  | Add the following lines to the start of each script:
-  | (for more info and alternative backends see `matplotlib docs <https://matplotlib.org/stable/users/explain/backends.html>`_)
+    To my knowledge there are 2 possibilities to force ``pycharm`` to use the original ``matplotlib`` backends:
 
-  .. code-block:: python
+    - | 🚲 The "manual" way:
+      | Add the following lines to the start of each script:
+      | (for more info and alternative backends see `matplotlib docs <https://matplotlib.org/stable/users/explain/backends.html>`_)
 
-    import matplotlib
-    matplotlib.use("Qt5Agg")
+      .. code-block:: python
 
-- | 🚗 The "automatic" way:
-  | Go to the preferences and add the aforementioned lines to the *"Starting script"*
-  | (to ensure that the ``matplotlib`` backend is always set prior to running a script)
+        import matplotlib
+        matplotlib.use("Qt5Agg")
 
-  .. image:: _static/pycharm_preferences.png
+    - | 🚗 The "automatic" way:
+      | Go to the preferences and add the aforementioned lines to the *"Starting script"*
+      | (to ensure that the ``matplotlib`` backend is always set prior to running a script)
 
-In addition, if you use a **commercial version** of PyCharm, make sure to **disable** *"Show plots in tool window"*
-in the **Python Scientific** preferences since it forces plots to be rendered as static images.
+      .. image:: _static/pycharm_preferences.png
 
-.. image:: _static/pycharm_preferences_2.png
+    In addition, if you use a **commercial version** of PyCharm, make sure to **disable** *"Show plots in tool window"*
+    in the **Python Scientific** preferences since it forces plots to be rendered as static images.
 
-.. _config_jupyter_notebook:
+    .. image:: _static/pycharm_preferences_2.png
 
-Jupyter Notebooks
------------------
 
-To get the most out of EOmaps in Jupyter Notebooks, use `jupyter lab <https://jupyterlab.readthedocs.io/en/stable/>`_  together with the `ipympl <https://github.com/matplotlib/ipympl>`_ (``widget``) backend.
+.. _config_jupyter_notebook:
 
-- To install, use ``conda install -c conda-forge ipympl``
+.. dropdown:: Jupyter Notebooks
 
-Once it's installed, use the command ``%matplotlib widget`` at the start of the code to activate the backend.
+    To get the most out of EOmaps in Jupyter Notebooks, use `jupyter lab <https://jupyterlab.readthedocs.io/en/stable/>`_  together with the `ipympl <https://github.com/matplotlib/ipympl>`_ (``widget``) backend.
 
-Using the Companion Widget
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-To use the :ref:`companion_widget` in backends other than ``Qt`` the Qt event-loop must be integrated.
-This can be done with the ``%gui qt`` command.
+    - To install, use ``conda install -c conda-forge ipympl``
 
-.. code-block:: python
+    Once it's installed, use the command ``%matplotlib widget`` at the start of the code to activate the backend.
 
-  %matplotlib widget
-  %gui qt
+    Using the Companion Widget
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~
+    To use the :ref:`companion_widget` in backends other than ``Qt`` the Qt event-loop must be integrated.
+    This can be done with the ``%gui qt`` command.
 
-  from eomaps import Maps
-  m = Maps()
-  m.add_feature.preset("coastline", "ocean")
+    .. code-block:: python
 
+      %matplotlib widget
+      %gui qt
 
-Alternative backends:
-~~~~~~~~~~~~~~~~~~~~~
-For classical notebooks, there's also the ``nbagg`` backend provided by matplotlib
+      from eomaps import Maps
+      m = Maps()
+      m.add_feature.preset("coastline", "ocean")
 
-- To use it, simply execute the magic ``%matplotlib notebook`` before starting to plot.
+    Alternative backends:
+    ~~~~~~~~~~~~~~~~~~~~~
+    For classical notebooks, there's also the ``nbagg`` backend provided by matplotlib
 
-And you can also use the magic ``%matplotlib qt`` to use the default ``qt5agg`` backend.
+    - To use it, simply execute the magic ``%matplotlib notebook`` before starting to plot.
 
-- This way the plots will NOT be embedded in the notebook, they show up as popups.
+    And you can also use the magic ``%matplotlib qt`` to use the default ``qt5agg`` backend.
 
+    - This way the plots will NOT be embedded in the notebook, they show up as popups.
 
-.. note::
+    .. note::
 
-  It is possible to plot **static snapshots** of the current state of a map to a Jupyter Notebook
-  irrespective of the used backend by using `m.snapshot()`, e.g.:
+      It is possible to plot **static snapshots** of the current state of a map to a Jupyter Notebook
+      irrespective of the used backend by using `m.snapshot()`, e.g.:
 
-  .. code-block:: python
+      .. code-block:: python
 
-    m = Maps()
-    m.add_feature.preset.coastline()
-    m.snapshot()
+        m = Maps()
+        m.add_feature.preset.coastline()
+        m.snapshot()
 
 
-Checkout the `matplotlib doc <https://matplotlib.org/stable/users/explain/interactive.html#jupyter-notebooks-jupyterlab>`_
-for more info!
+    Checkout the `matplotlib doc <https://matplotlib.org/stable/users/explain/interactive.html#jupyter-notebooks-jupyterlab>`_
+    for more info!
 
 Record interactive maps to create animations
 ********************************************
@@ -164,162 +186,159 @@ All animated gifs in this documentation have been created with this awesome piec
 Important changes between major versions
 ****************************************
 
-⚙ From EOmaps v3.x to v4.x
----------------------------
+.. dropdown:: ⚙ From EOmaps v3.x to v4.x
 
-Changes between **EOmaps v3.x** and **EOmaps v4.0**:
+    Changes between **EOmaps v3.x** and **EOmaps v4.0**:
 
-- the following properties and functions have been removed:
+    - the following properties and functions have been removed:
 
-  - ❌ ``m.plot_specs.``
-  - ❌ ``m.set_plot_specs()``
-  - | arguments are now directly passed to relevant functions:
-    | ``m.plot_map()``, ``m.add_colorbar()`` and ``m.set_data()``
+      - ❌ ``m.plot_specs.``
+      - ❌ ``m.set_plot_specs()``
+      - | arguments are now directly passed to relevant functions:
+        | ``m.plot_map()``, ``m.add_colorbar()`` and ``m.set_data()``
 
-- 🔶 ``m.set_shape.voroni_diagram()`` is renamed to ``m.set_shape.voronoi_diagram()``
-- | 🔷 custom callbacks are no longer bound to the Maps-object
-  | the call-signature of custom callbacks has changed to:
-  | ``def cb(self, *args, **kwargs)`` >> ``def cb(*args, **kwargs)``
+    - 🔶 ``m.set_shape.voroni_diagram()`` is renamed to ``m.set_shape.voronoi_diagram()``
+    - | 🔷 custom callbacks are no longer bound to the Maps-object
+      | the call-signature of custom callbacks has changed to:
+      | ``def cb(self, *args, **kwargs)`` >> ``def cb(*args, **kwargs)``
 
-Porting a script from v3.x to v4.x is quick and easy and involves the following steps:
+    Porting a script from v3.x to v4.x is quick and easy and involves the following steps:
 
-1. Search your script for all occurrences of the words ``.plot_specs`` and ``.set_plot_specs(``,
-   move the affected arguments to the correct functions (and remove the calls once you're done):
+    1. Search your script for all occurrences of the words ``.plot_specs`` and ``.set_plot_specs(``,
+       move the affected arguments to the correct functions (and remove the calls once you're done):
 
-    - | ``vmin``, ``vmax`` ``alpha`` and ``cmap`` are now set in
-      | ``m.plot_map(vmin=..., vmax=..., alpha=..., cmap=...)``
-    - | ``histbins``, ``label``, ``tick_precision`` and ``density`` are now set in
-      | ``m.add_colorbar(histbins=..., label=..., tick_precision=..., density=...)``
-    - | ``cpos`` and ``cpos_radius`` are now (optionally) set in
-      | ``m.set_data(data, x, y, cpos=..., cpos_radius=...)``
+        - | ``vmin``, ``vmax`` ``alpha`` and ``cmap`` are now set in
+          | ``m.plot_map(vmin=..., vmax=..., alpha=..., cmap=...)``
+        - | ``histbins``, ``label``, ``tick_precision`` and ``density`` are now set in
+          | ``m.add_colorbar(histbins=..., label=..., tick_precision=..., density=...)``
+        - | ``cpos`` and ``cpos_radius`` are now (optionally) set in
+          | ``m.set_data(data, x, y, cpos=..., cpos_radius=...)``
 
-2. Search your script for all occurrences of the words ``xcoord`` and ``ycoord`` and replace them with ``x`` and ``y``
+    2. Search your script for all occurrences of the words ``xcoord`` and ``ycoord`` and replace them with ``x`` and ``y``
 
-3. **ONLY** if you used **voronoi diagrams**:
+    3. **ONLY** if you used **voronoi diagrams**:
 
-  - search in your script for all occurrences of the word ``voroni_diagram`` and replace it with ``voronoi_diagram``
+      - search in your script for all occurrences of the word ``voroni_diagram`` and replace it with ``voronoi_diagram``
 
-4. **ONLY** if you used **custom callback functions**:
+    4. **ONLY** if you used **custom callback functions**:
 
-  - the first argument of custom callbacks is no longer identified as the ``Maps`` object.
-  - if you really need access to the ``Maps`` object within the callback, pass it as an explicit argument!
+      - the first argument of custom callbacks is no longer identified as the ``Maps`` object.
+      - if you really need access to the ``Maps`` object within the callback, pass it as an explicit argument!
 
 
-**EOmaps v3.x:**
+    **EOmaps v3.x:**
 
-.. code-block:: python
+    .. code-block:: python
 
-  m = Maps()
-  m.set_data(data=..., xcoord=..., ycoord=...)
-  m.set_plot_specs(vmin=1, vmax=20, cmap="viridis", histbins=100, cpos="ul", cpos_radius=1)
-  m.set_shape.voroni_diagram()
-  m.add_colorbar()
-  m.plot_map()
+      m = Maps()
+      m.set_data(data=..., xcoord=..., ycoord=...)
+      m.set_plot_specs(vmin=1, vmax=20, cmap="viridis", histbins=100, cpos="ul", cpos_radius=1)
+      m.set_shape.voroni_diagram()
+      m.add_colorbar()
+      m.plot_map()
 
-  # ---------------------------- custom callback signature:
-  def custom_cb(m, asdf=1):
-      print(asdf)
+      # ---------------------------- custom callback signature:
+      def custom_cb(m, asdf=1):
+          print(asdf)
 
-  m.cb.click.attach(custom_cb)
+      m.cb.click.attach(custom_cb)
 
 
-**EOmaps v4.x:**
+    **EOmaps v4.x:**
 
-.. code-block:: python
+    .. code-block:: python
 
-  m = Maps()
-  m.set_data(data=..., x=..., y=..., cpos="ul", cpos_radius=1)
-  m.plot_map(vmin=1, vmax=20, cmap="viridis")
-  m.set_shape.voronoi_diagram()
-  m.add_colorbar(histbins=100)
+      m = Maps()
+      m.set_data(data=..., x=..., y=..., cpos="ul", cpos_radius=1)
+      m.plot_map(vmin=1, vmax=20, cmap="viridis")
+      m.set_shape.voronoi_diagram()
+      m.add_colorbar(histbins=100)
 
-  # ---------------------------- custom callback signature:
-  def custom_cb(**kwargs, asdf=None):
-      print(asdf)
+      # ---------------------------- custom callback signature:
+      def custom_cb(**kwargs, asdf=None):
+          print(asdf)
 
-  m.cb.click.attach(custom_cb, asdf=1)
+      m.cb.click.attach(custom_cb, asdf=1)
 
 
-Note: if you really need access to the maps-object within custom callbacks,
-simply provide it as an explicit argument!
+    Note: if you really need access to the maps-object within custom callbacks,
+    simply provide it as an explicit argument!
 
-.. code-block:: python
+    .. code-block:: python
 
-  def custom_cb(**kwargs, m=None, asdf=None):
-      ...
+      def custom_cb(**kwargs, m=None, asdf=None):
+          ...
 
-  m.cb.click.attach(custom_cb, m=m, asdf=1)
+      m.cb.click.attach(custom_cb, m=m, asdf=1)
 
 
-⚙ From EOmaps v5.x to v6.x
----------------------------
+.. dropdown:: ⚙ From EOmaps v5.x to v6.x
 
-General changes in behavior
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    General changes in behavior
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- | 🔶 Starting with EOmaps v6.0 multiple calls to ``m.plot_map()``
-  | **on the same Maps-object completely remove (and replace)** the previous dataset!
-  | (use a new Maps-object on the same layer for datasets that should be visible at the same time!)
-- | 🔶 WebMap services are no longer re-fetched by default when exporting images with ``m.savefig()``
-  | To force a re-fetch of WebMap services prior to saving the image at the desired dpi, use ``m.savefig(refetch_wms=True)``
-  | (see ``m.refetch_wms_on_size_change()`` for more details)
-- | 🔷 ``m.add_gdf`` now uses only valid geometries
-  | (to revert to the old behavior, use: ``m.add_gdf(..., only_valid=False)``)
-- 🔷 the order at which multi-layers are combined now determines the stacking of the artists
+    - | 🔶 Starting with EOmaps v6.0 multiple calls to ``m.plot_map()``
+      | **on the same Maps-object completely remove (and replace)** the previous dataset!
+      | (use a new Maps-object on the same layer for datasets that should be visible at the same time!)
+    - | 🔶 WebMap services are no longer re-fetched by default when exporting images with ``m.savefig()``
+      | To force a re-fetch of WebMap services prior to saving the image at the desired dpi, use ``m.savefig(refetch_wms=True)``
+      | (see ``m.refetch_wms_on_size_change()`` for more details)
+    - | 🔷 ``m.add_gdf`` now uses only valid geometries
+      | (to revert to the old behavior, use: ``m.add_gdf(..., only_valid=False)``)
+    - 🔷 the order at which multi-layers are combined now determines the stacking of the artists
 
-  - ``m.show_layer("A|B")`` plots all artists of the layer ``"A"`` **on top of** the layer ``"B"``
-  - the ordering of artists inside a layer is determined by their ``zorder``  (e.g. ``m.plot_map(zorder=123)``)
+      - ``m.show_layer("A|B")`` plots all artists of the layer ``"A"`` **on top of** the layer ``"B"``
+      - the ordering of artists inside a layer is determined by their ``zorder``  (e.g. ``m.plot_map(zorder=123)``)
 
-Removed (previously depreciated) functionalities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    Removed (previously depreciated) functionalities
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- ❌ the ``m.figure`` accessor has been removed!
+    - ❌ the ``m.figure`` accessor has been removed!
 
-  - Use ``m.ax``, ``m.f``, ``m.colorbar.ax_cb``, ``m.colorbar.ax_cb_plot`` instead
+      - Use ``m.ax``, ``m.f``, ``m.colorbar.ax_cb``, ``m.colorbar.ax_cb_plot`` instead
 
-- ❌ kwargs for ``m.plot_map(...)``
+    - ❌ kwargs for ``m.plot_map(...)``
 
-  - ``"coastlines"`` use ``m.add_feature.preset.coastline()`` instead
+      - ``"coastlines"`` use ``m.add_feature.preset.coastline()`` instead
 
-- ❌ kwargs for ``m.set_data(...)``
+    - ❌ kwargs for ``m.set_data(...)``
 
-   - ``"in_crs"`` use ``"crs"`` instead
-   - ``"xcoord"`` use ``"x"`` instead
-   - ``"ycoord"`` use ``"y"`` instead
+       - ``"in_crs"`` use ``"crs"`` instead
+       - ``"xcoord"`` use ``"x"`` instead
+       - ``"ycoord"`` use ``"y"`` instead
 
-- ❌ kwargs for ``Maps(...)``
+    - ❌ kwargs for ``Maps(...)``
 
-  - ``"parent"`` ... no longer needed
-  - ``"gs_ax"`` use ``"ax"`` instead
+      - ``"parent"`` ... no longer needed
+      - ``"gs_ax"`` use ``"ax"`` instead
 
-- ❌ kwargs for ``m.new_inset_maps(...)``
+    - ❌ kwargs for ``m.new_inset_maps(...)``
 
-  - ``"edgecolor"`` and ``"facecolor"`` use ``boundary=dict(ec=..., fc=...)`` instead
+      - ``"edgecolor"`` and ``"facecolor"`` use ``boundary=dict(ec=..., fc=...)`` instead
 
-- ❌ kwargs for ``m.add_colorbar(...)``
+    - ❌ kwargs for ``m.add_colorbar(...)``
 
-  - ``"histbins"`` use ``"hist_bins"`` instead
-  - ``"histogram_size"`` use ``"hist_size"`` instead
-  - ``"density"`` use ``"hist_kwargs=dict(density=...)"`` instead
-  - ``"top", "bottom", "left", "right"`` use ``margin=dict(top=..., bottom=..., left=..., right=...)`` instead
-  - ``"add_extend_arrows"``
+      - ``"histbins"`` use ``"hist_bins"`` instead
+      - ``"histogram_size"`` use ``"hist_size"`` instead
+      - ``"density"`` use ``"hist_kwargs=dict(density=...)"`` instead
+      - ``"top", "bottom", "left", "right"`` use ``margin=dict(top=..., bottom=..., left=..., right=...)`` instead
+      - ``"add_extend_arrows"``
 
-- ❌ ``m.indicate_masked_points()`` has been removed, use ``m.plot_map(indicate_masked_points=True)`` instead
-- ❌ ``m.shape.get_transformer`` is now a private (e.g. ``m.shape._get_transformer``)
-- ❌ ``m.shape.radius_estimation_range`` is now a private (e.g. ``m.shape._radius_estimation_range``)
+    - ❌ ``m.indicate_masked_points()`` has been removed, use ``m.plot_map(indicate_masked_points=True)`` instead
+    - ❌ ``m.shape.get_transformer`` is now a private (e.g. ``m.shape._get_transformer``)
+    - ❌ ``m.shape.radius_estimation_range`` is now a private (e.g. ``m.shape._radius_estimation_range``)
 
 
-⚙ From EOmaps v6.x to v7.x
----------------------------
+.. dropdown:: ⚙ From EOmaps v6.x to v7.x
 
-- ⚠️ A lot of internal functions and classes have been re-named to better follow PEP8 naming conventions.
-  While this should not interfere with the public API, more extensive customizations might need to be
-  adjusted with respect to the new names.
+    - ⚠️ A lot of internal functions and classes have been re-named to better follow PEP8 naming conventions.
+      While this should not interfere with the public API, more extensive customizations might need to be
+      adjusted with respect to the new names.
 
-  If you encounter any problems, feel free to open an `issue <https://github.com/raphaelquast/EOmaps/issues>`_ , and I'll see what I can do!
+      If you encounter any problems, feel free to open an `issue <https://github.com/raphaelquast/EOmaps/issues>`_ , and I'll see what I can do!
 
-  - For example: the module `_shapes.py` is now called `shapes.py` and the class `shapes` is now called `Shapes`
+      - For example: the module `_shapes.py` is now called `shapes.py` and the class `shapes` is now called `Shapes`
 
-- ⚠️ The use of ``m.set_data_specs(...)`` is depreciated. Use ``m.set_data(...)`` instead!
+    - ⚠️ The use of ``m.set_data_specs(...)`` is depreciated. Use ``m.set_data(...)`` instead!
 
-- Figure export routines have been completely re-worked (but should result in the exact same output as in v6.x)
+    - Figure export routines have been completely re-worked (but should result in the exact same output as in v6.x)
diff --git a/docs/conf.py b/docs/conf.py
index fb88f677c..ba190908f 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -74,7 +74,6 @@ def setup(app):
 # handle compiler warnings for duplicate labels due to documents
 # included via the  ..include:: directive
 exclude_patterns = [
-    "introduction.rst",
     "build",
     "jupyter_execute/*",
     ".jupyter_cache/*",
diff --git a/docs/installation.rst b/docs/installation.rst
index d6808084e..e51ade5bc 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -43,12 +43,7 @@ This should make sure all required dependencies are correctly installed.
 A quick tutorial on how to **get started from scratch** is available here: :ref:`quickstart_guide`
 
 More details on how to **configure your favorite IDE** to work with EOmaps can be found in the FAQ section
-:ref:`configuring_the_editor`:
-
-- :ref:`config_spyder`
-- :ref:`config_pycharm`
-- :ref:`config_jupyter_notebook`
-
+:ref:`configuring_the_editor`.
 
 Alternative way (via ``pip``)
 -----------------------------
diff --git a/docs/introduction.rst b/docs/quickstart_quide.rst
similarity index 100%
rename from docs/introduction.rst
rename to docs/quickstart_quide.rst

From c902eac682199e46a1ce23fdb5f9352be7db3298 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 10 Dec 2023 15:14:07 +0100
Subject: [PATCH 023/240] update docs

---
 docs/FAQ.rst | 64 ++++++++++++++++++++++++++++------------------------
 1 file changed, 34 insertions(+), 30 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index df9067218..0434cd4e9 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -25,10 +25,9 @@ used to create EOmaps maps!
 Configuring the editor (IDE)
 ****************************
 
-EOmaps can be used with whatever editor you like... however, some editors have special settings that can be
-adjusted to improve your mapping experience with **EOmaps**:
+EOmaps can be used with whatever editor you like!
 
-.. _config_spyder:
+However, for some editors there are special settings that can be adjusted to improve your mapping experience with **EOmaps**:
 
 .. dropdown:: Spyder
 
@@ -49,15 +48,11 @@ adjusted to improve your mapping experience with **EOmaps**:
 
       If the gaphics-backend is set to *"Automatic"*, you can still plot static snapshots of a figure to the "plots-pane" with :py:meth:`Maps.snapshot`!
 
-
-.. _config_vscode:
-
-
 .. dropdown:: VSCode / VSCodium
 
     .. currentmodule:: eomaps.eomaps
 
-    In general, EOmaps works "out of the box" with `VSCode <https://code.visualstudio.com/>`_ or the open-source variant `VSCodium <https://vscodium.com/>`_ (together with the standard `Python <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_ extension).
+    In general, EOmaps should work "out of the box" with `VSCode <https://code.visualstudio.com/>`_ or the open-source variant `VSCodium <https://vscodium.com/>`_ (together with the standard `Python <https://marketplace.visualstudio.com/items?itemName=ms-python.python>`_ extension).
 
     However, there are some tipps that might help with your mapping workflow:
 
@@ -78,8 +73,9 @@ adjusted to improve your mapping experience with **EOmaps**:
 
        If you run a whole script using the **interactive mode**, the script will run until the end and then usually terminate the associated kernel... and in turn also closing the figure! If you want to keep the figure open, either make sure that the terminal is kept alive by entering debug-mode, or avoid activating the interactive mode and block the terminal with `m.show()`.
 
-    Interactive IPython (e.g. Jupyter)
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    .. raw:: html
+
+       <font size=+1><b>Interactive IPython (e.g. Jupyter)</b></font><p>
 
     If you enjoy interactive coding in a Jupyter-Notebook style, make sure to have a look at the nice `Jupyter <https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter>`_ extension!
 
@@ -95,9 +91,6 @@ adjusted to improve your mapping experience with **EOmaps**:
 
       - For more details, see the associated section for :ref:`config_jupyter_notebook`
 
-
-.. _config_pycharm:
-
 .. dropdown:: PyCharm
 
     The `PyCharm IDE <https://www.jetbrains.com/pycharm/>`_  automatically registers its own matplotlib backend
@@ -125,19 +118,23 @@ adjusted to improve your mapping experience with **EOmaps**:
 
     .. image:: _static/pycharm_preferences_2.png
 
+.. dropdown:: Jupyter Notebooks
+
+    .. currentmodule:: eomaps.eomaps
 
-.. _config_jupyter_notebook:
+    When working with Jupyter Notebooks, we recommend to use `Jupyter Lab <https://jupyterlab.readthedocs.io/en/stable/>`_.
 
-.. dropdown:: Jupyter Notebooks
+    - By default, EOmaps will use the ``inline`` backend and put a **static snapshot** of the current state of the figure to the Jupyter Notebook cell whenever you call :py:meth:`Maps.show` or :py:meth:`Maps.snapshot`.
 
-    To get the most out of EOmaps in Jupyter Notebooks, use `jupyter lab <https://jupyterlab.readthedocs.io/en/stable/>`_  together with the `ipympl <https://github.com/matplotlib/ipympl>`_ (``widget``) backend.
+    - To get **interactive inline figures** in Jupyter Notebooks, you have to switch to the `ipympl <https://github.com/matplotlib/ipympl>`_ (``widget``) backend.
 
-    - To install, use ``conda install -c conda-forge ipympl``
+      - To install, use ``conda install -c conda-forge ipympl``
+      - Once it's installed, you can use the *"magic command"* ``%matplotlib widget`` at the start of the code to activate the backend.
 
-    Once it's installed, use the command ``%matplotlib widget`` at the start of the code to activate the backend.
+    .. raw:: html
+
+       <font size=+1><b>Using the Companion Widget</b></font><p>
 
-    Using the Companion Widget
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~
     To use the :ref:`companion_widget` in backends other than ``Qt`` the Qt event-loop must be integrated.
     This can be done with the ``%gui qt`` command.
 
@@ -150,26 +147,33 @@ adjusted to improve your mapping experience with **EOmaps**:
       m = Maps()
       m.add_feature.preset("coastline", "ocean")
 
-    Alternative backends:
-    ~~~~~~~~~~~~~~~~~~~~~
-    For classical notebooks, there's also the ``nbagg`` backend provided by matplotlib
+    .. raw:: html
 
-    - To use it, simply execute the magic ``%matplotlib notebook`` before starting to plot.
+       <font size=+1><b>Alternative backends</b></font><p>
 
-    And you can also use the magic ``%matplotlib qt`` to use the default ``qt5agg`` backend.
+    - For classical notebooks, there's also the ``nbagg`` backend provided by matplotlib
 
-    - This way the plots will NOT be embedded in the notebook, they show up as popups.
+      - To use it, simply execute the magic ``%matplotlib notebook`` before starting to plot.
+
+    - You can also use the magic ``%matplotlib qt`` to use the default ``qt5agg`` backend.
+
+      - This way the plots will NOT be embedded in the notebook, they show up as **interactive popup figures**!
 
     .. note::
 
-      It is possible to plot **static snapshots** of the current state of a map to a Jupyter Notebook
-      irrespective of the used backend by using `m.snapshot()`, e.g.:
+      .. currentmodule:: eomaps.eomaps
+
+      Irrespective of the used backend, you can always plot a **static snapshots** of the current state of a map to a Jupyter Notebook
+      with :py:meth:`Maps.snapshot`!
 
       .. code-block:: python
 
+        %matplotlib qt    # Create figures as interactive popup widgets
+
+        from eomaps import Maps
         m = Maps()
-        m.add_feature.preset.coastline()
-        m.snapshot()
+        m.add_feature.preset("coastline", "ocean")
+        m.snapshot()      # Print a snapshot of the current state of the figure to the Jupyter Notebook
 
 
     Checkout the `matplotlib doc <https://matplotlib.org/stable/users/explain/interactive.html#jupyter-notebooks-jupyterlab>`_

From ed57e316c456ddd667c8837cbb93aa6eda6eace3 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Dec 2023 14:18:39 +0100
Subject: [PATCH 024/240] minor

---
 docs/FAQ.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 0434cd4e9..bca3cbbd7 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -89,7 +89,7 @@ However, for some editors there are special settings that can be adjusted to imp
       - For interactive (popup) figures, switch to the default Qt backend using ``%matplotlib qt``
       - For interactive (inline) figures, you'll need to install `ipympl <https://github.com/matplotlib/ipympl>`_ and then activate the ``widget`` with ``%matplotlib widget``.
 
-      - For more details, see the associated section for :ref:`config_jupyter_notebook`
+      - For more details, see details on Jupyter Notebooks below!
 
 .. dropdown:: PyCharm
 

From e4b8cafd325207c51fd4faf792a63ac0dc9a2998 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Dec 2023 15:40:56 +0100
Subject: [PATCH 025/240] minor

---
 docs/FAQ.rst        | 3 ++-
 eomaps/callbacks.py | 5 ++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index bca3cbbd7..41ef4572d 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -82,9 +82,10 @@ However, for some editors there are special settings that can be adjusted to imp
     It allows you to work with an interactive IPython terminal and execute code-blocks (separated by the ``# %%`` indicator).
 
     - With IPython, the default behavior is to create static (inline) figures (same as with Jupyter Notebooks)!
+
       To print a snapshot of the current state of a figure to the IPython terminal, call :py:meth:`Maps.show` or :py:meth:`Maps.snapshot`.
 
-    - Same as with Jupyter Notebooks, you can use *"magic"* commands to set the used matpltolib backend.
+    - Similar to Jupyter Notebooks, you can use *"magic"* commands to set the used matpltolib backend.
 
       - For interactive (popup) figures, switch to the default Qt backend using ``%matplotlib qt``
       - For interactive (inline) figures, you'll need to install `ipympl <https://github.com/matplotlib/ipympl>`_ and then activate the ``widget`` with ``%matplotlib widget``.
diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 95334ace6..251452355 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -1293,9 +1293,8 @@ def overlay_layer(self, layer, key="x"):
         """
         Toggle displaying a layer on top of the currently visible layers.
 
-        - If the layer is not part of the currently visible layers, it will be
-          added on top.
-        - If the layer is part of the currently visible layers, it will be removed.
+        This callback is useful to quickly show/hide a data-layer on top
+        of a basemap by pressing a key on the keyboard.
 
         Parameters
         ----------

From 42dfc2e24fa67e2d8255c194876631ae299b434f Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Dec 2023 22:05:07 +0100
Subject: [PATCH 026/240] allow overriding the layer at which a colorbar is
 plotted

---
 eomaps/colorbar.py | 37 +++++++++++++++++++++++++------------
 1 file changed, 25 insertions(+), 12 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 17af04bd4..a6e8194db 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -220,6 +220,11 @@ def __init__(
             The default is None.
         ylabel : str, optional
             The label used for the y-axis of the colorbar. The default is None
+        layer : str
+            The layer at which the colorbar will be drawn.
+            NOTE: In most cases you should NOT need to adjust the layer!
+            The layer is automatically assigned to the layer at which the
+            data was plotted and Colorbars are only visible on the assigned layer!
         kwargs :
             All additional kwargs are passed to the creation of the colorbar
             (e.g. `plt.colorbar()`)
@@ -248,6 +253,10 @@ def __init__(
 
         """
         self._m = m
+
+        # allow overriding the layer on which to draw the colorbar
+        self._layer = kwargs.pop("layer", self._m.layer)
+
         self._pos = pos
         self._margin = margin
         self._orientation = orientation
@@ -316,6 +325,10 @@ def __init__(
         if ylabel is not None:
             self.ax_cb_plot.set_ylabel(ylabel)
 
+    @property
+    def layer(self):
+        return self._layer
+
     def set_visible(self, vis):
         """
         Set the visibility of the colorbar.
@@ -420,7 +433,7 @@ def set_labels(self, cb_label=None, hist_label=None, **kwargs):
 
         if not self._dynamic_shade_indicator:
             # no need to redraw the background for dynamically updated artists
-            self._m.redraw(self._m.layer)
+            self._m.redraw(self.layer)
         else:
             self._m.BM.update()
 
@@ -530,7 +543,7 @@ def set_hist_size(self, size=None):
 
         self.set_labels(**self._label_kwargs)
         # tag layer for refetch
-        self._m.redraw(self._m.layer)
+        self._m.redraw(self.layer)
 
     def _identify_parent_cb(self):
         parent_cb = None
@@ -745,12 +758,12 @@ def _setup_axes(self):
             a.set_navigate(False)
             if a is not None:
                 if self._dynamic_shade_indicator is True:
-                    self._m.BM.add_artist(a, self._m.layer)
+                    self._m.BM.add_artist(a, self.layer)
                 else:
-                    self._m.BM.add_bg_artist(a, self._m.layer)
+                    self._m.BM.add_bg_artist(a, self.layer)
 
         # we need to re-draw since the background axis size has changed!
-        self._m.BM._refetch_layer(self._m.layer)
+        self._m.BM._refetch_layer(self.layer)
         self._m.BM._refetch_layer("__SPINES__")
         self._m.redraw("__SPINES__")
 
@@ -819,7 +832,7 @@ def _set_data(self):
             try:
                 z_data = self._coll.get_ds_data().values
             except:
-                self._m.redraw(self._m.layer)
+                self._m.redraw(self.layer)
                 z_data = self._coll.get_ds_data().values
 
             if "count" in aggname:
@@ -861,7 +874,7 @@ def check_data_updated(*args, **kwargs):
 
                 self._m.BM.on_layer(
                     lambda *args, **kwargs: self._redraw_colorbar,
-                    layer=self._m.layer,
+                    layer=self.layer,
                     persistent=True,
                 )
 
@@ -870,7 +883,7 @@ def check_data_updated(*args, **kwargs):
                 # TODO colorbar not properly updated on layer change after zoom?
                 self._m.BM.on_layer(
                     self._redraw_colorbar,
-                    layer=self._m.layer,
+                    layer=self.layer,
                     persistent=True,
                     m=self._m,
                 )
@@ -1127,7 +1140,7 @@ def _plot_histogram(self):
 
     def _redraw_colorbar(self, *args, **kwargs):
         # only re-draw if the corresponding layer is visible
-        if self._m.layer not in self._m.BM.bg_layer.split("|"):
+        if self.layer not in self._m.BM.bg_layer.split("|"):
             return
 
         self._set_data()
@@ -1292,7 +1305,7 @@ def set_bin_labels(self, bins, names, tick_lines="center", show_values=False):
                 left=False, top=False, labelleft=False, labeltop=False, which="both"
             )
 
-        self._m.BM._refetch_layer(self._m.layer)
+        self._m.BM._refetch_layer(self.layer)
 
     def remove(self):
         """
@@ -1328,7 +1341,7 @@ def tick_params(self, what="colorbar", **kwargs):
         elif what == "histogram":
             self.ax_cb_plot.tick_params(**kwargs)
 
-        self._m.redraw(self._m.layer)
+        self._m.redraw(self.layer)
 
     tick_params.__doc__ = (
         "Set the appearance of the colorbar (or histogram) ticks.\n\n"
@@ -1549,4 +1562,4 @@ def indicate_contours(
         for level, label in zip(used_levels, label_names):
             self.ax_cb_plot.annotate(xy=(level, y), text=label, **label_kwargs)
 
-        self._m.redraw(self._m.layer)
+        self._m.redraw(self.layer)

From b3f926ef059d1a62ef57dd4b47761aaf9f6401ab Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Dec 2023 23:57:40 +0100
Subject: [PATCH 027/240] fix forcing colorbar position inheritance

---
 eomaps/colorbar.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index a6e8194db..66c22612f 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -264,8 +264,6 @@ def __init__(
         self._init_extend = extend
         self._extend_frac = extend_frac
 
-        self._parent_cb = self._identify_parent_cb()
-
         if inherit_position is None:
             if not self._m.colorbar:
                 inherit_position = True
@@ -274,6 +272,8 @@ def __init__(
 
         self._inherit_position = inherit_position
 
+        self._parent_cb = self._identify_parent_cb()
+
         if hist_size is None:
             self._hist_size = 0
         else:
@@ -551,7 +551,7 @@ def _identify_parent_cb(self):
         # the same plot-axis.
         # If yes, use the position of this colorbar to creat a new one
 
-        if self._m.colorbar is not None:
+        if self._m.colorbar is not None and not self._inherit_position:
             parent_cb = None  # self._m.colorbar
         else:
             # check if self is actually just another layer of an existing Maps object

From 3533ef4db9f50f1e1080c158fb7a34a38ec7790e Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 13 Dec 2023 22:16:42 +0100
Subject: [PATCH 028/240] fix treatment of histogram style kwargs for colorbars
 (lw, ec, fc, ...)

---
 eomaps/colorbar.py | 42 +++++++++++++++++++++++++++++++++++++++---
 1 file changed, 39 insertions(+), 3 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 66c22612f..d2551afd9 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -1064,6 +1064,26 @@ def _plot_histogram(self):
                 maxval,
             ]
 
+            args = dict(
+                edgecolor=self._hist_kwargs.get(
+                    "edgecolor", self._hist_kwargs.get("ec", None)
+                ),
+                linewidth=self._hist_kwargs.get(
+                    "linewidth", self._hist_kwargs.get("lw", None)
+                ),
+                linestyle=self._hist_kwargs.get(
+                    "linestyle", self._hist_kwargs.get("ls", None)
+                ),
+                alpha=self._hist_kwargs.get("alpha", None),
+                hatch=self._hist_kwargs.get("hatch", None),
+            )
+            # drop all unset values to avoi overriding defaults
+            args = {key: val for key, val in args.items() if val is not None}
+            # handle facecolors explicitly
+            facecolor = self._hist_kwargs.get(
+                "facecolor", self._hist_kwargs.get("fc", None)
+            )
+
             if len(splitbins) > 2:
                 patch.remove()
                 # add in-between patches
@@ -1073,19 +1093,35 @@ def _plot_histogram(self):
                             (b0, 0),
                             (b1 - b0),
                             height,
-                            facecolor=self._cmap(self._norm((b0 + b1) / 2)),
+                            facecolor=(
+                                facecolor
+                                if facecolor
+                                else self._cmap(self._norm((b0 + b1) / 2))
+                            ),
+                            **args,
                         )
                     else:
                         pi = mpl.patches.Rectangle(
                             (0, b0),
                             width,
                             (b1 - b0),
-                            facecolor=self._cmap(self._norm((b0 + b1) / 2)),
+                            facecolor=(
+                                facecolor
+                                if facecolor
+                                else self._cmap(self._norm((b0 + b1) / 2))
+                            ),
+                            **args,
                         )
 
                     self.ax_cb_plot.add_patch(pi)
             else:
-                patch.set_facecolor(self._cmap(self._norm((minval + maxval) / 2)))
+                patch.set_facecolor(
+                    facecolor
+                    if facecolor
+                    else self._cmap(self._norm((minval + maxval) / 2))
+                )
+                for key, val in args.items():
+                    getattr(patch, f"set_{key}")(self._hist_kwargs[key])
 
         # setup appearance of histogram
         if horizontal:

From 4d6b313352f3e645033306f44f534338e15f566d Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 13 Dec 2023 23:08:35 +0100
Subject: [PATCH 029/240] address numpy binary incompatibility warning when
 reading NetCDFs

---
 eomaps/__init__.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/eomaps/__init__.py b/eomaps/__init__.py
index 694620546..959c260aa 100644
--- a/eomaps/__init__.py
+++ b/eomaps/__init__.py
@@ -1,3 +1,9 @@
+from .helpers import register_modules as _register_modules
+
+# address numpy runtime warning concerning binary incompatibility when
+# reading NetCDF files (see https://github.com/pydata/xarray/issues/7259)
+_register_modules("netCDF4", raise_exception=False)
+
 from .eomaps import Maps
 from .mapsgrid import MapsGrid
 from ._version import __version__

From da22b42bec1b8f9a4016aa2867351147a6789e51 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 15 Dec 2023 17:03:37 +0100
Subject: [PATCH 030/240] fix linewidths of colorbar patches after layout
 editor exit

---
 eomaps/colorbar.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index d2551afd9..bb20b7ea3 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -1069,7 +1069,7 @@ def _plot_histogram(self):
                     "edgecolor", self._hist_kwargs.get("ec", None)
                 ),
                 linewidth=self._hist_kwargs.get(
-                    "linewidth", self._hist_kwargs.get("lw", None)
+                    "linewidth", self._hist_kwargs.get("lw", 0)
                 ),
                 linestyle=self._hist_kwargs.get(
                     "linestyle", self._hist_kwargs.get("ls", None)
@@ -1121,7 +1121,7 @@ def _plot_histogram(self):
                     else self._cmap(self._norm((minval + maxval) / 2))
                 )
                 for key, val in args.items():
-                    getattr(patch, f"set_{key}")(self._hist_kwargs[key])
+                    getattr(patch, f"set_{key}")(val)
 
         # setup appearance of histogram
         if horizontal:

From 7d18dbc629413c40fcb2d7d0823398e96d4f337e Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 15 Dec 2023 17:03:55 +0100
Subject: [PATCH 031/240] fix identifying colorbar position from another
 colorbar instance

---
 eomaps/colorbar.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index bb20b7ea3..811298e82 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -272,7 +272,10 @@ def __init__(
 
         self._inherit_position = inherit_position
 
-        self._parent_cb = self._identify_parent_cb()
+        if isinstance(self._inherit_position, ColorBar):
+            self._parent_cb = self._inherit_position
+        else:
+            self._parent_cb = self._identify_parent_cb()
 
         if hist_size is None:
             self._hist_size = 0

From 62ce4a8d46a8c74adaaf971f489af6e02e8e64b3 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 21 Dec 2023 10:06:46 +0100
Subject: [PATCH 032/240] fix issues with contour-plot unittest

---
 eomaps/helpers.py         | 14 ++++++++++++--
 tests/test_plot_shapes.py | 11 ++++++++---
 2 files changed, 20 insertions(+), 5 deletions(-)

diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index c46a6b60c..11556f9d7 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -2121,8 +2121,18 @@ def _do_fetch_bg(self, layer, bbox=None):
             if renderer:
                 for art in allartists:
                     if art not in self._hidden_artists:
-                        art.draw(renderer)
-                        art.stale = False
+                        try:
+                            art.draw(renderer)
+                            art.stale = False
+                        except Exception:
+                            if _log.getEffectiveLevel() <= logging.DEBUG:
+                                _log.error(
+                                    "Unable to draw artist:"
+                                    f"{art} ("
+                                    f"figure={getattr(art, 'figure', '??')}, "
+                                    f"axes={getattr(art, 'axes', '??')})"
+                                )
+
                 self._bg_layers[layer] = renderer.copy_from_bbox(bbox)
 
     def fetch_bg(self, layer=None, bbox=None):
diff --git a/tests/test_plot_shapes.py b/tests/test_plot_shapes.py
index 96338eed5..79affcb5c 100644
--- a/tests/test_plot_shapes.py
+++ b/tests/test_plot_shapes.py
@@ -76,9 +76,14 @@ def test_contour(self):
                 ),
             )
 
-            arts = m3_1.ax.clabel(m3_1.coll.contour_set)
-            for a in arts:
-                m3_1.BM.add_bg_artist(a, layer=m3_1.layer)
+            # TODO using 'clabel' causes collections to be re-drawn
+            # which puts the new contours on the default layer and leaves
+            # the old contours as "artists without a figure" in the blit-manager!
+            # see https://github.com/raphaelquast/EOmaps/issues/218
+
+            # arts = m3_1.ax.clabel(m3_1.coll.contour_set)
+            # for a in arts:
+            #     m3_1.BM.add_bg_artist(a, layer=m3_1.layer)
 
             m.show_layer("base", "contours")
             plt.close("all")

From 2aeb6a3f6dda9289a6e605543d5124aa5567a7bb Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 21 Dec 2023 19:04:37 +0100
Subject: [PATCH 033/240] minor - fix unexpected indent in docstring

---
 eomaps/annotation_editor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/annotation_editor.py b/eomaps/annotation_editor.py
index a8d1b4eaa..5f7af62a2 100644
--- a/eomaps/annotation_editor.py
+++ b/eomaps/annotation_editor.py
@@ -615,7 +615,7 @@ def print_code(
         use_ids : bool, optional
             If True, ID values are used to identify the annotation-positions.
             (NOTE: this can cause problems because the Maps-object must have
-             the correct data assigned to identify the annotation!)
+            the correct data assigned to identify the annotation!)
             If False, always use the current position of the annotation
             as input-argument for the returned code.
 

From 53e97f4226ceda31d05b13d1f56a24b43f64e8c1 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 19 Dec 2023 15:27:00 +0100
Subject: [PATCH 034/240] switch from setup.py to pyproject.toml for install

- single-source version number to pyproject.toml
- implement optional dependency groups
---
 eomaps/__init__.py |  3 +-
 eomaps/_version.py |  1 -
 eomaps/eomaps.py   |  3 +-
 pyproject.toml     | 69 ++++++++++++++++++++++++++++++++++++++++++++
 setup.py           | 72 ----------------------------------------------
 5 files changed, 73 insertions(+), 75 deletions(-)
 delete mode 100644 eomaps/_version.py
 create mode 100644 pyproject.toml
 delete mode 100644 setup.py

diff --git a/eomaps/__init__.py b/eomaps/__init__.py
index 959c260aa..bc2a416c9 100644
--- a/eomaps/__init__.py
+++ b/eomaps/__init__.py
@@ -1,3 +1,4 @@
+import importlib.metadata
 from .helpers import register_modules as _register_modules
 
 # address numpy runtime warning concerning binary incompatibility when
@@ -6,8 +7,8 @@
 
 from .eomaps import Maps
 from .mapsgrid import MapsGrid
-from ._version import __version__
 
+__version__ = importlib.metadata.version("eomaps")
 __author__ = "Raphael Quast"
 
 # Follow conventions used by cartopy to setup cache directory
diff --git a/eomaps/_version.py b/eomaps/_version.py
deleted file mode 100644
index 40d114c5b..000000000
--- a/eomaps/_version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "7.3.3"
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index d9b3cbedd..98ca1b986 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -13,6 +13,7 @@
 import gc
 from textwrap import fill
 from contextlib import contextmanager, ExitStack
+import importlib.metadata
 
 import numpy as np
 
@@ -71,7 +72,7 @@
 
 from ._data_manager import DataManager
 
-from ._version import __version__
+__version__ = importlib.metadata.version("eomaps")
 
 
 def _handle_backends():
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..3489937aa
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,69 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+include = ["eomaps"]
+
+[project]
+name = "eomaps"
+version = "7.4"
+description = "A library to create interactive maps of geographical datasets."
+requires-python = ">=3.8"
+
+authors = [
+  { name="Raphael Quast", email="raphael.quast@geo.tuwien.ac.at" },
+]
+
+keywords = ["Visualization", "Plotting", "Maps", "Geographical Data"]
+
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
+    "Operating System :: OS Independent",
+]
+
+dependencies = [
+    "numpy",
+    "scipy",
+    "matplotlib>=3.4",
+    "cartopy>=0.20.0",
+    "descartes",
+    "pyproj",
+    "packaging",
+    "click"
+]
+
+[project.optional-dependencies]
+
+all = ["eomaps[io, classify, wms, shade, gui]"]
+
+all_nogui = ["eomaps[io, classify, wms, shade]"]
+
+io = [
+    "pandas",
+    "geopandas",
+    "xarray",
+    "netcdf4",
+    "rioxarray"
+]
+
+classify = ["mapclassify"]
+
+wms = [
+    "owslib",
+    "requests",
+    "cairosvg",
+]
+
+shade = ["datashader"]
+
+gui = [
+    "PyQt5",
+    "qtpy"
+    ]
+
+
+[project.urls]
+Documentation = "https://eomaps.readthedocs.io/"
+Repository = "https://github.com/raphaelquast/eomaps"
diff --git a/setup.py b/setup.py
deleted file mode 100644
index a1cbf5775..000000000
--- a/setup.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# -*- coding: UTF-8 -*-
-
-"""
-This file is part of EOmaps.
-For COPYING and LICENSE details, please refer to the LICENSE file
-"""
-from setuptools import setup, find_packages
-from pathlib import Path
-import re
-
-# add the README as long-description
-this_directory = Path(__file__).parent
-try:
-    long_description = (this_directory / "README.md").read_text()
-except Exception:
-    long_description = "A library to create interactive maps of geographical datasets."
-
-# get version-number from _version.py
-try:
-    with open(this_directory / "eomaps" / "_version.py") as file:
-        (version,) = re.findall('__version__ = "(.*)"', file.read())
-except Exception:
-    version = "undefined"
-
-setup(
-    name="EOmaps",
-    version=version,
-    description="A library to create interactive maps of geographical datasets.",
-    packages=find_packages(),
-    package_dir={"eomaps": "eomaps"},
-    package_data={"eomaps": ["logo.png", "NE_features.json", "qtcompanion/icons/*"]},
-    # include_package_data=True,
-    author="Raphael Quast",
-    author_email="raphael.quast@geo.tuwien.ac.at",
-    maintainer="Raphael Quast",
-    maintainer_email="raphael.quast@geo.tuwien.ac.at",
-    license="GNU General Public License v3 or later (GPLv3+)",
-    url="https://github.com/raphaelquast/maps",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    install_requires=[
-        "numpy",
-        "scipy",
-        "pandas",
-        "matplotlib>=3.4",
-        "cartopy>=0.20.0",
-        "descartes",
-        "mapclassify",
-        "pyproj",
-        "pyepsg",
-        "geopandas",
-        "owslib",
-        "requests",
-        "xmltodict",
-        "cairosvg",
-        "packaging",
-        "click",
-        "qtpy",
-    ],
-    entry_points={"console_scripts": ["eomaps = eomaps.scripts.open:cli"]},
-    keywords=["visualization", "plotting", "maps", "geographical data"],
-    # See https://pypi.python.org/pypi?%3Aaction=list_classifiers
-    classifiers=[
-        "Intended Audience :: Science/Research",
-        "Topic :: Scientific/Engineering :: Visualization",
-        "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
-        # Pick your license as you wish (should match "license" above)
-        # ~ 'License :: OSI Approved :: MIT License',
-        "Programming Language :: Python :: 3.7",
-    ],
-    license_files=("LICENSE",),
-)

From f5ca61f493193b3e2c82e4e9ded50f2b084057af Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 19 Dec 2023 16:14:05 +0100
Subject: [PATCH 035/240] make sure package is installed in unittests

---
 .github/workflows/testMaps.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/testMaps.yml b/.github/workflows/testMaps.yml
index f783dc134..959ba4333 100644
--- a/.github/workflows/testMaps.yml
+++ b/.github/workflows/testMaps.yml
@@ -46,6 +46,7 @@ jobs:
         # do this to ensure proper activation of conda environment
         shell: bash -l {0}
         run: |
+          pip install -e .[all]
           python -m pytest --cov=eomaps --cov-report=xml
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3

From 64eb25846d669ae7ad865f0d5a65ce11d6fbde84 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 22 Dec 2023 08:37:29 +0100
Subject: [PATCH 036/240] add entry point to pyproject.toml

---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 3489937aa..47a74acdc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,6 +63,8 @@ gui = [
     "qtpy"
     ]
 
+[project.scripts]
+eomaps = "eomaps.scripts.open:cli"
 
 [project.urls]
 Documentation = "https://eomaps.readthedocs.io/"

From c39819862770e1823736add57d48b7a6d6c265d7 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 22 Dec 2023 08:58:19 +0100
Subject: [PATCH 037/240] update version to v8.0

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 47a74acdc..8607913ba 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ include = ["eomaps"]
 
 [project]
 name = "eomaps"
-version = "7.4"
+version = "8.0"
 description = "A library to create interactive maps of geographical datasets."
 requires-python = ">=3.8"
 

From 2ffa9c37c756282a5f0be06ae054f7983a84e4a0 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 30 Dec 2023 12:49:18 +0100
Subject: [PATCH 038/240] run unittests on python 3.11 and 3.12

---
 .github/workflows/testMaps.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/testMaps.yml b/.github/workflows/testMaps.yml
index 959ba4333..620b31bf8 100644
--- a/.github/workflows/testMaps.yml
+++ b/.github/workflows/testMaps.yml
@@ -13,7 +13,7 @@ jobs:
         # set operating systems to test
         os: [ubuntu-latest]
         # set python versions to test
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     name: test_Maps ${{ matrix.os }}  ${{ matrix.python-version }}
     steps:

From 89f06b2655474e12e65551a0193988320b8fceb3 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 30 Dec 2023 13:34:27 +0100
Subject: [PATCH 039/240] run unittests also on windows

---
 .github/workflows/testMaps.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/testMaps.yml b/.github/workflows/testMaps.yml
index 620b31bf8..3af00a87f 100644
--- a/.github/workflows/testMaps.yml
+++ b/.github/workflows/testMaps.yml
@@ -11,7 +11,7 @@ jobs:
     strategy:
       matrix:
         # set operating systems to test
-        os: [ubuntu-latest]
+        os: [windows-latest, ubuntu-latest]
         # set python versions to test
         python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 

From 781efa97bc146515ac2f0a508fa324db48f13afb Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 30 Dec 2023 21:00:45 +0100
Subject: [PATCH 040/240] avoid calling m.show in tested code-blocks - required
 to avoid blocking the terminal on windows

---
 docs/api_data_visualization.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/api_data_visualization.rst b/docs/api_data_visualization.rst
index 82cfe62ae..7823c98b6 100644
--- a/docs/api_data_visualization.rst
+++ b/docs/api_data_visualization.rst
@@ -849,7 +849,7 @@ Once a dataset has been plotted, a colorbar with a colored histogram on top can
     |     cbB.tick_params(labelcolor="darkblue", labelsize=9)         |                                          |
     |                                                                 |                                          |
     |     m.subplots_adjust(bottom=0.1)                               |                                          |
-    |     mA.show()                                                   |                                          |
+    |     m.show_layer(mA.layer)                                      |                                          |
     +-----------------------------------------------------------------+------------------------------------------+
 
 .. autosummary::

From 2938914a681ffed027b21815f61570b335ce51f0 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 30 Dec 2023 21:01:39 +0100
Subject: [PATCH 041/240] test python 3.8, 3.9 and 3.10

---
 .github/workflows/testMaps.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/testMaps.yml b/.github/workflows/testMaps.yml
index 3af00a87f..404a98a39 100644
--- a/.github/workflows/testMaps.yml
+++ b/.github/workflows/testMaps.yml
@@ -13,7 +13,7 @@ jobs:
         # set operating systems to test
         os: [windows-latest, ubuntu-latest]
         # set python versions to test
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.8", "3.9", "3.10"]
 
     name: test_Maps ${{ matrix.os }}  ${{ matrix.python-version }}
     steps:

From 0c8a55b2a808b8b72781b234b580171146ada3f2 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 30 Dec 2023 21:03:12 +0100
Subject: [PATCH 042/240] add more descriptive name for doc-unittests

---
 tests/test_doc_codeblocks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_doc_codeblocks.py b/tests/test_doc_codeblocks.py
index 45027d320..22dd683fe 100644
--- a/tests/test_doc_codeblocks.py
+++ b/tests/test_doc_codeblocks.py
@@ -58,7 +58,7 @@ def __new__(mcs, name, bases, tests):
                 source_code = node.astext()
                 name = node.attributes["names"][0]
 
-                test_name = f"test_{i}"
+                test_name = f"test_{p.stem}_{i}"
                 tests[test_name] = gen_test(i, name, source_code)
 
         os.chdir(cwd)

From b6abc3291e5f67ade6c7b60de1e79d9a9b4a31ad Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 30 Dec 2023 21:03:54 +0100
Subject: [PATCH 043/240] run pytest with -v (e.g. verbose) flag

---
 .github/workflows/testMaps.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/testMaps.yml b/.github/workflows/testMaps.yml
index 404a98a39..466073ddb 100644
--- a/.github/workflows/testMaps.yml
+++ b/.github/workflows/testMaps.yml
@@ -47,7 +47,7 @@ jobs:
         shell: bash -l {0}
         run: |
           pip install -e .[all]
-          python -m pytest --cov=eomaps --cov-report=xml
+          python -m pytest -v --cov=eomaps --cov-report=xml
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3
         with:

From 439b7246cfa628747e462254742880b1fa0cb28f Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 30 Dec 2023 21:56:58 +0100
Subject: [PATCH 044/240] make sure notebook-tests don't block the terminal

---
 tests/test_doc_notebooks.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/test_doc_notebooks.py b/tests/test_doc_notebooks.py
index 71e9ca604..072ab2a1f 100644
--- a/tests/test_doc_notebooks.py
+++ b/tests/test_doc_notebooks.py
@@ -31,7 +31,8 @@ def test_doc_notebook(self, notebook):
             nb = nbformat.read(f, as_version=4)
             # parse all code-cells from notebook
             code_cells = [i["source"] for i in nb["cells"] if i["cell_type"] == "code"]
-            code = ""
+            # make sure plt.ion() is called before each test!
+            code = "import matplotlib.pyplot as plt\n" "plt.ion()\n" "\n"
 
             for c in code_cells:
                 for l in c.split("\n"):

From b8fe3b2747f85bfda2124060ef64f22e083a8d19 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 31 Dec 2023 01:11:00 +0100
Subject: [PATCH 045/240] switch to micromamba to setup (and cache) test
 environments

---
 .github/workflows/testMaps.yml | 35 +++++++++++-----------------------
 1 file changed, 11 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/testMaps.yml b/.github/workflows/testMaps.yml
index 466073ddb..37139204c 100644
--- a/.github/workflows/testMaps.yml
+++ b/.github/workflows/testMaps.yml
@@ -11,39 +11,26 @@ jobs:
     strategy:
       matrix:
         # set operating systems to test
-        os: [windows-latest, ubuntu-latest]
+        os: [ubuntu-latest]
         # set python versions to test
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     name: test_Maps ${{ matrix.os }}  ${{ matrix.python-version }}
     steps:
       # checkout repository
       - uses: actions/checkout@v3
       # install miniconda environment
-      - uses: conda-incubator/setup-miniconda@v2
+
+      - uses: mamba-org/setup-micromamba@v1
         with:
           environment-file: tests/test_env.yml
-
-          # use mamba to speed up installation
-          #mamba-version: "*"
-          #channels: conda-forge
-          #channel-priority: true
-
-          miniforge-variant: Mambaforge
-          miniforge-version: latest
-
-          activate-environment: eomaps
-
-          show-channel-urls: true
-          use-only-tar-bz2: true
-
-          python-version: ${{ matrix.python-version }}
-
-          auto-activate-base: false
-          auto-update-conda: true
-      # run pytest
-      - name: test Maps
-        # do this to ensure proper activation of conda environment
+          init-shell: >-
+            bash
+          cache-environment: true
+          post-cleanup: 'all'
+          create-args: >-
+            python=${{ matrix.python-version }}
+      - name: Test Maps
         shell: bash -l {0}
         run: |
           pip install -e .[all]

From bd88c28d0af14173222a5a26fdd3bb96c46b27a7 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 2 Jan 2024 18:47:16 +0100
Subject: [PATCH 046/240] add info on optional dependency groups to the docs

---
 docs/installation.rst | 65 ++++++++++++++++++++++++++++++++-----------
 1 file changed, 48 insertions(+), 17 deletions(-)

diff --git a/docs/installation.rst b/docs/installation.rst
index e51ade5bc..e447a37db 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -8,6 +8,7 @@
     :local:
     :depth: 1
 
+
 Recommended way (via ``conda`` and ``mamba``)
 ---------------------------------------------
 
@@ -17,27 +18,35 @@ EOmaps is available via the ``conda-forge`` channel and can be installed via:
 
     conda install -c conda-forge eomaps
 
-This should make sure all required dependencies are correctly installed.
+
+This will install all required and optional dependencies.
+
 
 .. admonition:: Greatly speed up the installation!
 
-  Since the dependencies of EOmaps can be demanding to solve for the classic ``conda`` solver, it is **highly recommended**
-  that you use `mamba <https://github.com/mamba-org/mamba>`_ to install EOmaps!
+    Since the dependencies of EOmaps can be demanding to solve for the classic ``conda`` solver, it is **highly recommended**
+    that you use `mamba <https://github.com/mamba-org/mamba>`_ to install EOmaps!
 
-  To install ``mamba``, simply use:
+    ``mamba`` is a reimplementation of the conda package manager in C++, capable of solving environments a lot faster.
 
-  .. code-block:: console
+    The best way to get started is to use `miniforge <https://github.com/conda-forge/miniforge>`_.
 
-    conda install -c conda-forge mamba
+    However, you can also install ``mamba`` into an existing ``conda`` environment with:
 
-  Once ``mamba`` is installed, you just need to replace the term ``conda`` with ``mamba`` and you're good to go!
+    .. code-block:: console
+
+        conda install -c conda-forge mamba
 
-  .. code-block:: console
 
-    mamba install -c conda-forge eomaps
+    Once ``mamba`` is installed, you just need to replace the term ``conda`` with ``mamba`` and you're good to go!
 
-  Alternatively you can also configure ``conda`` to use the ``libmamba`` solver by default.
-  (More info here: `A Faster Solver for Conda: Libmamba <https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community>`_  )
+    .. code-block:: console
+
+        mamba install -c conda-forge eomaps
+
+
+    Alternatively you can also configure ``conda`` to use the ``libmamba`` solver by default.
+    (More info here: `A Faster Solver for Conda: Libmamba <https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community>`_  )
 
 
 A quick tutorial on how to **get started from scratch** is available here: :ref:`quickstart_guide`
@@ -45,18 +54,40 @@ A quick tutorial on how to **get started from scratch** is available here: :ref:
 More details on how to **configure your favorite IDE** to work with EOmaps can be found in the FAQ section
 :ref:`configuring_the_editor`.
 
+
+
 Alternative way (via ``pip``)
 -----------------------------
-EOmaps is also available on ``pip`` and can be installed via
+
+EOmaps is also available on ``pip``.
+
+To install EOmaps with a **minimal set of dependencies**, use:
 
   .. code-block:: console
 
     pip install eomaps
 
 
-However, it is **not guaranteed that all dependencies are correctly resolved** and some manual
-tweaking of the environment might be required to ensure that all packages work as expected.
-Especially dependencies on C/C++ libraries such as ``geos`` or ``pyproj`` have to be configured
-carefully to set up everying correctly. If you are not sure what you're doing, use ``conda + mamba``!
+Optional features (WebMap services, CompanionWidget, etc.) require additional dependencies.
+You can install them by installing ``eomaps`` with the required dependency-groups.
+
+To get all features of EOmaps, you can use one of:
+
+.. code-block:: console
+
+    pip install eomaps[all]       # ALL optional dependencies
+    pip install eomaps[all_nogui] # All optional dependencies (except ``Qt`` GUI framework)
+
+
+In addition, you can also select only specific dependency-groups to activate only selected features:
+
+.. code-block:: console
+
+    pip install eomaps[wms]       # dependencies required for WebMap services
+    pip install eomaps[gui]       # dependencies for ``Qt`` GUI framework and the CompanionWidget
+    pip install eomaps[io]        # add support for ``pandas``, ``xarray``, ``geopandas`` and ``rioxarray``
+    pip install eomaps[shade]     # add capabilities to visualize extremely large datasets (via ``datashader``)
+    pip install eomaps[classify]  # add support for ``mapclassify`` to classify datasets
+
 
-A list of all required dependencies can be found in :ref:`setup_a_dev_env`
+A list of all associated packages can be found in :ref:`setup_a_dev_env` or in the ``pyproject.toml`` file.

From 5aeee73519b575b6ec8f69b6eea5825e1be07e88 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 2 Jan 2024 19:11:57 +0100
Subject: [PATCH 047/240] update docs

---
 docs/installation.rst | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/docs/installation.rst b/docs/installation.rst
index e447a37db..16fdcc550 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -24,7 +24,7 @@ This will install all required and optional dependencies.
 
 .. admonition:: Greatly speed up the installation!
 
-    Since the dependencies of EOmaps can be demanding to solve for the classic ``conda`` solver, it is **highly recommended**
+    Since the dependencies of EOmaps can be demanding to solve for ``conda``, it is **highly recommended**
     that you use `mamba <https://github.com/mamba-org/mamba>`_ to install EOmaps!
 
     ``mamba`` is a reimplementation of the conda package manager in C++, capable of solving environments a lot faster.
@@ -68,8 +68,11 @@ To install EOmaps with a **minimal set of dependencies**, use:
     pip install eomaps
 
 
-Optional features (WebMap services, CompanionWidget, etc.) require additional dependencies.
-You can install them by installing ``eomaps`` with the required dependency-groups.
+Optional dependencies
+~~~~~~~~~~~~~~~~~~~~~
+
+Some features (:ref:`webmap_layers`, :ref:`companion_widget`, etc.) require additional dependencies.
+To use them you have to install the required dependency-groups:
 
 To get all features of EOmaps, you can use one of:
 
@@ -79,7 +82,7 @@ To get all features of EOmaps, you can use one of:
     pip install eomaps[all_nogui] # All optional dependencies (except ``Qt`` GUI framework)
 
 
-In addition, you can also select only specific dependency-groups to activate only selected features:
+In addition, you can use the following dependency-groups to activate only selected features:
 
 .. code-block:: console
 
@@ -90,4 +93,6 @@ In addition, you can also select only specific dependency-groups to activate onl
     pip install eomaps[classify]  # add support for ``mapclassify`` to classify datasets
 
 
+(It is also possible to combine dependency-groups, e.g.: ``pip install eomaps[wms, gui]``)
+
 A list of all associated packages can be found in :ref:`setup_a_dev_env` or in the ``pyproject.toml`` file.

From e2905e7bb91067b3cc8f3182d63da68457f5bfe0 Mon Sep 17 00:00:00 2001
From: Bane Sullivan <banesullivan@gmail.com>
Date: Sun, 17 Dec 2023 13:07:59 -0800
Subject: [PATCH 048/240] Draft improvements to contributing guide

---
 CONTRIBUTING.md | 87 ++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 83 insertions(+), 4 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 545850a56..89acfb0b5 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,5 +1,3 @@
-#
-
 # Contributing to EOmaps
 
 Interested in contributing to EOmaps? Awesome! Any contributions are welcome!
@@ -17,8 +15,6 @@ A detailed introduction on how to setup a development environment to contribute
 
 If you got some questions or need help to get started, don't hesitate to get in touch by opening a new [issue](https://github.com/raphaelquast/EOmaps/issues), or [discussion](https://github.com/raphaelquast/EOmaps/discussions) on GitHub!
 
-
-
 ## General infos
 
 - All of the `EOmaps` codebase is hosted on [GitHub](https://github.com/)
@@ -32,3 +28,86 @@ If you got some questions or need help to get started, don't hesitate to get in
 - The documentation is generated with [sphinx](https://www.sphinx-doc.org/en/master/) and hosted on [ReadTheDocs](https://docs.readthedocs.io)
 
 - Releases are hosted on [pypi](https://pypi.org/project/EOmaps/) and [conda-forge](https://anaconda.org/conda-forge/eomaps)
+
+## Development Practices
+
+This section provides a guide to how we conduct development in the EOmaps repository. Please follow the practices outlined here when contributing directly to this repository.
+
+### Testing
+
+After making changes, please test changes locally before creating a pull request. The following tests will be executed after any commit or pull request, so we ask that you perform the following sequence locally to track down any new issues from your changes.
+
+To run our comprehensive suite of unit tests, install all the dependencies listed in `tests/test_env.yml`:
+
+```bash
+# Install dependencies (to be filled in)
+```
+
+Then, if you have everything installed, you can run the various test suites.
+
+#### Unit Testing
+
+Run the primary test suite and generate coverage report:
+
+.. code:: bash
+
+   python -m pytest -v --cov eomaps
+
+Unit testing can take some time, if you wish to speed it up, set the
+number of processors with the ``-n`` flag. This uses ``pytest-xdist`` to
+leverage multiple processes. Example usage:
+
+.. code:: bash
+
+   python -m pytest -n <NUMCORE> --cov eomaps
+
+#### Style Checking
+
+To ensure your code meets minimum code styling standards, run::
+
+  pip install pre-commit
+  pre-commit run --all-files
+
+If you have issues related to ``setuptools`` when installing ``pre-commit``, see
+`pre-commit Issue #2178 comment <https://github.com/pre-commit/pre-commit/issues/2178#issuecomment-1002163763>`_
+for a potential resolution.
+
+You can also install this as a pre-commit hook by running::
+
+  pre-commit install
+
+This way, it's not possible for you to push code that fails the style
+checks. For example, each commit automatically checks that you meet the style
+requirements::
+
+  $ pre-commit install
+  $ git commit -m "added my cool feature"
+  check python ast.........................................................Passed
+  check for merge conflicts................................................Passed
+  fix end of files.........................................................Passed
+  trim trailing whitespace.................................................Passed
+  mixed line ending........................................................Passed
+  black....................................................................Passed
+
+The actual installation of the environment happens before the first commit
+following ``pre-commit install``. This will take a bit longer, but subsequent
+commits will only trigger the actual style checks.
+
+### Building the Documentation
+
+Build the documentation on Linux or Mac OS with:
+
+.. code:: bash
+
+   make -C doc html
+
+Build the documentation on Windows with:
+
+.. code:: winbatch
+
+   cd doc
+   python -msphinx -M html source build
+   python -msphinx -M html . build
+
+The generated documentation can be found in the ``doc/build/html``
+directory.

From 6b0717fbcba34fd13914106a21b5134ef5aa5508 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 21 Dec 2023 15:28:27 +0100
Subject: [PATCH 049/240] update contribution guides - add more comprehensive
 quick-info to CONTRIBUTING.md - update contribution guide in the docs to
 include testing etc. - rename `contribute_env.yml` to `environment.yml` and
 move to main dir

---
 CONTRIBUTING.md                            |  83 ++++--------
 docs/contribute.rst                        | 149 ++++++++++++++++-----
 docs/contribute_env.yml => environment.yml |   7 +-
 3 files changed, 145 insertions(+), 94 deletions(-)
 rename docs/contribute_env.yml => environment.yml (85%)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 89acfb0b5..b67211de8 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -11,8 +11,6 @@ We greatly appreciate any ideas how we can improve the code and documentation.
 
 EOmaps aims to be a feature-rich, performant and user-friendly tool for geographic data visualization and analysis. We highly welcome pull-requests for bug-fixes, improvements or entirely new features to improve the package!
 
-A detailed introduction on how to setup a development environment to contribute code to EOmaps is provided in the [Contribution Guide](https://eomaps.readthedocs.io/en/latest/contribute.html) of the documentation.
-
 If you got some questions or need help to get started, don't hesitate to get in touch by opening a new [issue](https://github.com/raphaelquast/EOmaps/issues), or [discussion](https://github.com/raphaelquast/EOmaps/discussions) on GitHub!
 
 ## General infos
@@ -31,83 +29,54 @@ If you got some questions or need help to get started, don't hesitate to get in
 
 ## Development Practices
 
-This section provides a guide to how we conduct development in the EOmaps repository. Please follow the practices outlined here when contributing directly to this repository.
+This section provides a **quick overview** how we conduct development in the EOmaps repository.
+
+**More detailed instructions** on how to setup a development environment and contribute code to EOmaps are provided in the [Contribution Guide](https://eomaps.readthedocs.io/en/latest/contribute.html) of the documentation!
+
 
 ### Testing
 
 After making changes, please test changes locally before creating a pull request. The following tests will be executed after any commit or pull request, so we ask that you perform the following sequence locally to track down any new issues from your changes.
 
-To run our comprehensive suite of unit tests, install all the dependencies listed in `tests/test_env.yml`:
+Run the primary test suite and generate coverage report:
 
 ```bash
-# Install dependencies (to be filled in)
+python -m pytest -v --cov eomaps
 ```
 
-Then, if you have everything installed, you can run the various test suites.
-
-#### Unit Testing
-
-Run the primary test suite and generate coverage report:
-
-.. code:: bash
-
-   python -m pytest -v --cov eomaps
-
 Unit testing can take some time, if you wish to speed it up, set the
-number of processors with the ``-n`` flag. This uses ``pytest-xdist`` to
-leverage multiple processes. Example usage:
+number of processors with the ``-n`` flag. This uses [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) to leverage multiple processes. Example usage:
 
-.. code:: bash
-
-   python -m pytest -n <NUMCORE> --cov eomaps
+```bash
+python -m pytest -n <NUMCORE> --cov eomaps
+```
 
-#### Style Checking
+> NOTE: During the tests, a lot of figures will be created and destroyed!
 
-To ensure your code meets minimum code styling standards, run::
+### Style Checking
 
-  pip install pre-commit
-  pre-commit run --all-files
+To ensure your code meets minimum code styling standards, run:
 
-If you have issues related to ``setuptools`` when installing ``pre-commit``, see
-`pre-commit Issue #2178 comment <https://github.com/pre-commit/pre-commit/issues/2178#issuecomment-1002163763>`_
-for a potential resolution.
+```bash
+pip install pre-commit
+pre-commit run --all-files
+```
 
 You can also install this as a pre-commit hook by running::
 
-  pre-commit install
-
-This way, it's not possible for you to push code that fails the style
-checks. For example, each commit automatically checks that you meet the style
-requirements::
-
-  $ pre-commit install
-  $ git commit -m "added my cool feature"
-  check python ast.........................................................Passed
-  check for merge conflicts................................................Passed
-  fix end of files.........................................................Passed
-  trim trailing whitespace.................................................Passed
-  mixed line ending........................................................Passed
-  black....................................................................Passed
-
-The actual installation of the environment happens before the first commit
-following ``pre-commit install``. This will take a bit longer, but subsequent
-commits will only trigger the actual style checks.
-
-### Building the Documentation
-
-Build the documentation on Linux or Mac OS with:
-
-.. code:: bash
+```bash
+pre-commit install
+```
 
-   make -C doc html
+This way, pre-commits will run automatically before each commit to ensure that you do not push code that fails the style checks.
 
-Build the documentation on Windows with:
+## Building the Documentation
 
-.. code:: winbatch
+Build the documentation, navigate to the `eomaps/docs` directory (containing the `make.bat`) file and then run:
 
-   cd doc
-   python -msphinx -M html source build
-   python -msphinx -M html . build
+```bash
+make html
+```
 
 The generated documentation can be found in the ``doc/build/html``
 directory.
diff --git a/docs/contribute.rst b/docs/contribute.rst
index dd3472ad5..bf8538c8d 100644
--- a/docs/contribute.rst
+++ b/docs/contribute.rst
@@ -22,8 +22,8 @@ Any contributions are welcome!
 - ...
 
 
-🛞Getting started
-------------------
+Getting started
+---------------
 
 The source code of EOmaps is managed on `GitHub <https://github.com/raphaelquast/EOmaps>`_.
 
@@ -53,41 +53,102 @@ Once you're done or in case you want/need some feedback, open a `pull request <h
 
 .. _setup_a_dev_env:
 
-⚙ How to set up a development environment
-------------------------------------------
+How to set up a development environment
+---------------------------------------
 
 To contribute to ``EOmaps``, you need a working ``python`` installation.
-I recommend using `miniconda <https://docs.conda.io/en/latest/miniconda.html>`_ to set up ``python`` with the following steps:
 
-- Download the latest `miniconda <https://docs.conda.io/en/latest/miniconda.html>`_ installer and install
-- Open `anaconda prompt <https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html#starting-conda>`_ (or normal terminal on ``linux``)
-- | Install `mamba <https://github.com/mamba-org/mamba>`_ with
-  | ``conda install -c conda-forge mamba``
-- | Create a development environment from the ``eomaps.yml`` file with
-  | ``mamba env create -f < path to eomaps.yml >``
+I recommend using `miniforge <https://github.com/conda-forge/miniforge>`__, a minimalistic installer that contains the package-managers `conda <https://docs.conda.io/en/latest/>`_ and `mamba <https://github.com/mamba-org/mamba>`_ already pre-configured to use the `conda-forge <https://anaconda.org/conda-forge>`_ channel by default.
+
+
+You can set up ``python`` with the following steps:
+
+- Download the latest `miniforge <https://github.com/conda-forge/miniforge#download>`__ installer and install
+- On Windows, miniforge is not added to the system path by default. To use `conda/mamba`, open `Miniforge Prompt <https://github.com/conda-forge/miniforge#usage>`__! For `Linux` and `OS X` you should be able to use the normal command prompt.
+- | Create a new development environment from the ``eomaps/environment.yml`` file with
+  | ``mamba env create -f < path to environment.yml >``
+
+Content of ``environment.yml``:
+
+.. literalinclude:: ../environment.yml
+
 
-Content of ``eomaps.yml``:
 
-.. literalinclude:: ../docs/contribute_env.yml
+.. tip::
+
+    As editor, I recommend using the awesome, free and open-source `Spyder IDE <https://github.com/spyder-ide/spyder>`_.
+    You can install it directly into your environment via:
+
+    .. code-block:: console
+
+        mamba install -c conda-forge spyder
+
+
+
+Development Practices
+---------------------
 
-As editor, I recommend using the awesome, free and open-source `Spyder IDE <https://github.com/spyder-ide/spyder>`_.
-You can install it directly into your environment via:
+This section provides a guide to how we conduct development in the EOmaps repository. Please follow the practices outlined here when contributing directly to this repository.
+
+Testing
+~~~~~~~
+
+After making changes, please test changes locally before creating a pull request. The following tests will be executed after any commit or pull request, so we ask that you perform the following sequence locally to track down any new issues from your changes.
+
+The `environment.yml` file already contains the packages required to run the tests locally, e.g.:
+
+- `pytest <https://docs.pytest.org>`__ to run the tests
+- `pytest-cov <https://github.com/pytest-dev/pytest-cov>`__ and `coveralls <https://github.com/TheKevJames/coveralls-python>`__ to track lines covered by the tests
+
+
+To run the primary test suite and generate coverage report, navigate to the parent `eomaps` directory and run:
 
 .. code-block:: console
 
-    mamba install -c conda-forge spyder
+    python -m pytest -v --cov eomaps
+
+.. note::
+
+    During the tests, a lot of figures will be created and destroyed!
+
+
+.. tip::
+
+   You can run only a subset of the tests by using the ``-k`` flag! (This will select only tests whose names contain the provided keyword)
+
+   .. code-block:: console
 
+      python -m pytest -k <KEYWORD>
 
-Pre-commit hooks
-~~~~~~~~~~~~~~~~
+   (see `pytest command-line-flags <https://docs.pytest.org/en/7.3.x/reference/reference.html#command-line-flags>`_ for more details)
 
-To ensure uniform code formatting, EOmaps uses `pre-commit hooks <https://pre-commit.com/>`_ to automatically check (and fix) code-style issues such as:
 
-- Trailing spaces in `.py` files
+.. tip::
+
+    Unit testing can take some time, if you wish to speed it up, you can install `pytest-xdist <https://github.com/pytest-dev/pytest-xdist>`_ to leverage multiple processes with the ``-n`` flag.
+
+    .. code-block:: console
+
+        python -m pytest -n <NUMCORE> --cov eomaps
+
+Style Checking
+~~~~~~~~~~~~~~
+
+To ensure uniform code style, EOmaps uses `pre-commit hooks <https://pre-commit.com/>`_ to automatically check (and fix) code-style issues such as:
+
+- Removal of trailing whitespaces in `.py` files
+- Making sure that files end with a newline
 - Compliance to the used `black <https://github.com/psf/black>`_ code formatting standards
 
-To initialize pre-commit hooks in your current environment, navigate to the directory where you cloned the EOmaps repository and run the following command:
-(e.g. the parent directory containing the file ``.pre-commit-config.yaml``)
+
+To initialize pre-commit hooks in your current environment, first install `pre-commit hooks <https://pre-commit.com/>`_ with
+
+.. code-block:: console
+
+  mamba install -c conda-forge pre-commit
+
+
+Then navigate to the directory where you cloned the EOmaps repository and run the following command: (e.g. the  directory that contains the ``.pre-commit-config.yaml`` file)
 
 
 .. code-block:: console
@@ -98,21 +159,43 @@ This will install the required pre-commit hooks in your current environment so t
 (The first time pre-commit is run, the necessary packages will have to be installed which might take a short moment)
 
 
+.. code-block:: console
+
+    git commit -m "added my cool feature"
+
+    check python ast.........................................................Passed
+    check for merge conflicts................................................Passed
+    fix end of files.........................................................Passed
+    trim trailing whitespace.................................................Passed
+    mixed line ending........................................................Passed
+    black....................................................................Passed
+
+
 .. note::
 
-  This means that all files will be auto-formatted prior to each commit to comply with the used code-formatting standards and
-  only commits that comply with all pre-commit hooks can be pushed to GitHub.
+  This means that all files will be **auto-formatted** prior to each commit to comply with the used code-formatting standards and only commits that comply with all pre-commit hooks can be pushed to GitHub.
 
 
-- To run the pre-commit hooks manually on selected files, simply **add the files you want to commit** with ``git add < filename >`` and then run ``pre-commit``
-- If you want to run the hooks on all files, use ``pre-commit run --all-files``
+  ⚠ After running the pre-commit hooks, some files might have new changes that must be staged for commit again!
 
-Note: after running the pre-commit hooks, some files might have new changes that must be staged for commit again!
 
+.. tip::
 
+    - To run the pre-commit hooks manually on selected files, simply **add the files you want to commit** with ``git add < filename >`` and then run ``pre-commit``
+    - If you want to run the hooks on all files, use ``pre-commit run --all-files``
+
+.. tip::
+
+  If you have issues related to ``setuptools`` when installing ``pre-commit``, see
+  `pre-commit Issue #2178 comment <https://github.com/pre-commit/pre-commit/issues/2178#issuecomment-1002163763>`_
+  for a potential resolution.
 
-📖 Building the docs
----------------------
+
+
+
+
+Building the Documentation
+--------------------------
 
 The documentation of EOmaps is written with `Sphinx <https://www.sphinx-doc.org/en/master/>`_ using the markup language `ReStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_.
 
@@ -148,7 +231,11 @@ The first time the documentation is built, all auto-generated files parsed from
     This will trigger a first build of the documentation and start a http server that hosts the local
     documentation (and live reloads it as soon as the build process finished)
 
+    The url of the http-server will be printed to the console (the default is: ``127.0.0.1:8000``).
+
+
+.. tip::
 
+    As editor for the docs I recommend the nice minimalistic free and open-source editor `retext <https://github.com/retext-project/retext>`_.
 
-As editor for the docs I recommend the nice free and open-source editor `retext <https://github.com/retext-project/retext>`_.
-It provides syntax-highlighting for ReStructuredText and a useful table-edit mode.
+    It provides syntax-highlighting and live-preview for both Markdown and ReStructuredText.
diff --git a/docs/contribute_env.yml b/environment.yml
similarity index 85%
rename from docs/contribute_env.yml
rename to environment.yml
index 2f037a0ff..e8c42e781 100644
--- a/docs/contribute_env.yml
+++ b/environment.yml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
 
 dependencies:
-  - python = 3.10
+  - python = 3.11
   - numpy
   - scipy
   - pandas
@@ -13,7 +13,6 @@ dependencies:
   - descartes
   - mapclassify
   - pyproj
-  - pyepsg
   # --------------for data-shading
   - datashader
   # --------------for GeoTIFF and NetCDF files
@@ -23,7 +22,6 @@ dependencies:
   # --------------for WebMaps
   - owslib
   - requests
-  - xmltodict
   - cairosvg
   # --------------to support multiple Qt versions
   - qtpy
@@ -31,9 +29,6 @@ dependencies:
   - coveralls
   - pytest
   - pytest-cov
-  # --------------for version control
-  - git
-  - pre-commit
   # --------------for building the docs
   - sphinx-copybutton
   - sphinx

From c6d58793f765388f2d1d1a508735ce10d77dddb1 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 2 Jan 2024 23:00:37 +0100
Subject: [PATCH 050/240] rename "utilities" class to "Utilities"

---
 docs/api_utils.rst  | 2 +-
 eomaps/eomaps.py    | 6 +++---
 eomaps/utilities.py | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/api_utils.rst b/docs/api_utils.rst
index 140bb55ef..7b9ebae00 100644
--- a/docs/api_utils.rst
+++ b/docs/api_utils.rst
@@ -31,7 +31,7 @@ By default, the widgets will show all available layers (except the "all" layer)
 - To exclude certain layers from the widget, use ``exclude_layers=[...layer-names to exclude...]``
 - To remove a previously created widget ``s`` from the map, simply use ``s.remove()``
 
-.. currentmodule:: eomaps.utilities.utilities
+.. currentmodule:: eomaps.utilities.Utilities
 
 .. autosummary::
     :nosignatures:
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 98ca1b986..ab164e999 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -66,7 +66,7 @@
 from .reader import read_file, from_file, new_layer_from_file
 from .grid import GridFactory
 
-from .utilities import utilities
+from .utilities import Utilities
 from .draw import ShapeDrawer
 from .annotation_editor import AnnotationEditor
 
@@ -666,11 +666,11 @@ def cb(self):
         return self._cb
 
     @property
-    @wraps(utilities)
+    @wraps(Utilities)
     def util(self):
         """Add utilities to the map."""
         if self.parent._util is None:
-            self.parent._util = utilities(self.parent)
+            self.parent._util = Utilities(self.parent)
         return self.parent._util
 
     @property
diff --git a/eomaps/utilities.py b/eomaps/utilities.py
index 987f80adc..489eadc73 100644
--- a/eomaps/utilities.py
+++ b/eomaps/utilities.py
@@ -231,7 +231,7 @@ def __init__(
 
         See Also
         --------
-        utilities.layer_slider : A slider widget to switch between layers.
+        Utilities.layer_slider : A slider widget to switch between layers.
         Maps.show_layer: Set the currently visible layer.
 
         """
@@ -573,7 +573,7 @@ def remove(self):
         self._m.BM.update()
 
 
-class utilities:
+class Utilities:
     """
     A collection of utility tools that can be added to EOmaps plots.
 

From e5aaa37327d61f7a01aabf4093d792f47e84ee6d Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 2 Jan 2024 23:02:01 +0100
Subject: [PATCH 051/240] update docs - remove obsolete files - fix missing
 links - update references

---
 docs/FAQ.rst                               |   6 +-
 docs/api.rst                               |  99 ----------------
 docs/api_inset_maps.rst                    | 129 ---------------------
 docs/api_naturalearth_features.rst         |  89 --------------
 docs/index.rst                             |   4 +-
 docs/notebooks/inset_maps.ipynb            |   1 +
 docs/notebooks/naturalearth_features.ipynb |   1 +
 eomaps/callbacks.py                        |   2 +-
 8 files changed, 7 insertions(+), 324 deletions(-)
 delete mode 100644 docs/api.rst
 delete mode 100644 docs/api_inset_maps.rst
 delete mode 100644 docs/api_naturalearth_features.rst

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 41ef4572d..40865eaab 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -279,8 +279,7 @@ Important changes between major versions
 
 .. dropdown:: ⚙ From EOmaps v5.x to v6.x
 
-    General changes in behavior
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    **General changes in behavior**
 
     - | 🔶 Starting with EOmaps v6.0 multiple calls to ``m.plot_map()``
       | **on the same Maps-object completely remove (and replace)** the previous dataset!
@@ -295,8 +294,7 @@ Important changes between major versions
       - ``m.show_layer("A|B")`` plots all artists of the layer ``"A"`` **on top of** the layer ``"B"``
       - the ordering of artists inside a layer is determined by their ``zorder``  (e.g. ``m.plot_map(zorder=123)``)
 
-    Removed (previously depreciated) functionalities
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    **Removed (previously depreciated) functionalities**
 
     - ❌ the ``m.figure`` accessor has been removed!
 
diff --git a/docs/api.rst b/docs/api.rst
deleted file mode 100644
index a5c2f606e..000000000
--- a/docs/api.rst
+++ /dev/null
@@ -1,99 +0,0 @@
-🌍 Users Guide
-===============
-
-The following chapters provide comprehensive overview on the capabilities of EOmaps.
-
-|  :doc:`api_basics`
-|  The basic concepts how to create beautiful interactive maps.
-
-|  :doc:`api_data_visualization`
-|  How to visualize datasets (plot-shapes, classification, colorbars etc.).
-
-.. raw:: html
-
-    <hr style="height:1px;border:none;color:darkblue;background-color:darkblue;">
-
-|  :doc:`api_companion_widget`
-|  The graphical user interface that ships with EOmaps!
-
-|  :doc:`api_layout_editor`
-|  Interactively re-arrange and re-size axes of a figure.
-
-.. raw:: html
-
-    <hr style="height:1px;border:none;color:darkblue;background-color:darkblue;">
-
-|  :doc:`api_inset_maps`
-|  Create zoomed-in views on specific regions of a map.
-
-|  :doc:`api_callbacks`
-|  Turn your maps into interactive data-analysis widgets.
-
-.. raw:: html
-
-    <hr style="height:1px;border:none;color:darkblue;background-color:darkblue;">
-
-|  :doc:`api_naturalearth_features`
-|  Add basic map features (coastlines, ocean-coloring etc. ) to the map.
-
-|  :doc:`api_webmaps`
-|  Add imagery provided by WebMap services (ts, wms, wmts, xyz) to the map.
-
-|  :doc:`api_vector_data`
-|  Add vector geometries to the map.
-
-|  :doc:`api_annotations_markers_etc`
-|  Add markers, annotations, lines, logos etc. to the map.
-
-|  :doc:`api_scalebar`
-|  Add a scalebar to the map.
-
-|  :doc:`api_compass`
-|  Add a compass (or North Arrow) to the map.
-
-|  :doc:`api_gridlines`
-|  Add grid-lines (and optionally grid-labels) to the map.
-
-.. raw:: html
-
-    <hr style="height:1px;border:none;color:darkblue;background-color:darkblue;">
-
-|  :doc:`api_draw`
-|  Interactively draw geometries on a map and export them as shapefiles.
-
-|  :doc:`api_utils`
-|  A collection of utility widgets (layer-sliders, layer-selectors)
-
-|  :doc:`api_read_data`
-|  Read data from NetCDF, GeoTIFF or CSV files.
-
-|  :doc:`api_misc`
-|  Additional functions and properties that might come in handy.
-
-
-.. toctree::
-    :hidden:
-    :maxdepth: 3
-
-    api_basics
-    api_data_visualization
-
-    api_companion_widget
-    api_layout_editor
-
-    api_inset_maps
-    api_callbacks
-
-    api_naturalearth_features
-    api_webmaps
-    api_vector_data
-
-    api_annotations_markers_etc
-    api_scalebar
-    api_compass
-    api_gridlines
-
-    api_draw
-    api_utils
-    api_read_data
-    api_misc
diff --git a/docs/api_inset_maps.rst b/docs/api_inset_maps.rst
deleted file mode 100644
index 764aa5c8a..000000000
--- a/docs/api_inset_maps.rst
+++ /dev/null
@@ -1,129 +0,0 @@
-
-🔬 Inset Maps
---------------
-
-.. contents:: Contents:
-    :local:
-    :depth: 1
-
-How to create inset maps
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. currentmodule:: eomaps.eomaps
-
-Inset maps are used to show zoomed-in regions of a map and can be created with :py:meth:`Maps.new_inset_map`.
-
-.. code-block:: python
-    :name: test_inset_maps_01
-
-    from eomaps import Maps
-    m = Maps()                                      # the "parent" Maps-object (e.g. the "big" map)
-    m.add_feature.preset.coastline()
-    m_i = m.new_inset_map(xy=(125, 40), radius=10)  # a new Maps-object that represents the inset-map
-    m_i.add_feature.preset.ocean()                  # it can be used just like any other Maps-objects!
-    m_i.add_indicator_line()
-
-- An inset-map is defined by it's center-position and a radius
-- The used boundary-shape can be one of:
-
-  - "ellipses" (e.g. projected ellipses with a radius defined in a given crs)
-  - "rectangles" (e.g. projected rectangles with a radius defined in a given crs)
-  - "geod_circles" (e.g. geodesic circles with a radius defined in meters)
-
-
-For convenience, inset-map objects have the following special methods defined:
-
-.. currentmodule:: eomaps.inset_maps
-
-.. autosummary::
-    :nosignatures:
-
-    InsetMaps.set_inset_position
-    InsetMaps.add_extent_indicator
-    InsetMaps.add_indicator_line
-
-
-Checkout the associated example on how to use inset-maps: :ref:`ex_11`
-
-To quickly re-position (and re-size) inset-maps, have a look at the :ref:`layout_editor`!
-
-.. table::
-    :widths: 60 40
-    :align: center
-
-    +-----------------------------------------------------------------+--------------------------------------------+
-    | .. code-block:: python                                          | .. image:: _static/minigifs/inset_maps.png |
-    |     :name: test_inset_maps_02                                   |   :align: center                           |
-    |                                                                 |                                            |
-    |     from eomaps import Maps                                     | |img_minsize|                              |
-    |     m = Maps(Maps.CRS.PlateCarree(central_longitude=-60))       |                                            |
-    |     m.add_feature.preset.ocean()                                |                                            |
-    |                                                                 |                                            |
-    |     m_i = m.new_inset_map(xy=(5, 45), radius=10,                |                                            |
-    |                           plot_position=(.3, .5), plot_size=.7, |                                            |
-    |                           boundary=dict(ec="r", lw=4),          |                                            |
-    |                           indicate_extent=dict(fc=(1,0,0,.5),   |                                            |
-    |                                                ec="r", lw=1)    |                                            |
-    |                           )                                     |                                            |
-    |     m_i.add_indicator_line(m, c="r")                            |                                            |
-    |                                                                 |                                            |
-    |     m_i.add_feature.preset.coastline()                          |                                            |
-    |     m_i.add_feature.preset.countries()                          |                                            |
-    |     m_i.add_feature.preset.ocean()                              |                                            |
-    |     m_i.add_feature.cultural.urban_areas(fc="r", scale=10)      |                                            |
-    |     m_i.add_feature.physical.rivers_europe(ec="b", lw=0.25,     |                                            |
-    |                                            fc="none", scale=10) |                                            |
-    |     m_i.add_feature.physical.lakes_europe(fc="b", scale=10)     |                                            |
-    |                                                                 |                                            |
-    +-----------------------------------------------------------------+--------------------------------------------+
-
-.. currentmodule:: eomaps.eomaps.Maps
-
-.. autosummary::
-    :nosignatures:
-
-    new_inset_map
-
-
-.. _zoomed_in_views_on_datasets:
-
-Zoomed in views on datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. currentmodule:: eomaps.eomaps
-
-To simplify the creation of "zoomed-in" views on datasets, both the data and the classification
-of the data must be the same.
-
-For this purpose, EOmaps provides 2 convenience-functions:
-
-- :py:meth:`Maps.inherit_data` : Use the same dataset as another :py:class:`Maps` object
-- :py:meth:`Maps.inherit_classification`: Use the same classification as another :py:class:`Maps` object
-
-  - Note that this means that the classification specs as well as ``vmin``, ``vmax`` and the used ``colormap`` will be the same!
-
-
-.. code-block:: python
-    :name: test_zoomed_in_data_maps
-
-    from eomaps import Maps
-    import numpy as np
-
-    x, y = np.meshgrid(np.linspace(-20, 20, 50), np.linspace(-50, 60, 100))
-    data = x + y
-
-    m = Maps(ax=131)
-    m.set_data(data, x, y)
-    m.set_shape.raster()
-    m.set_classify.Quantiles(k=10)
-    m.plot_map(cmap="tab10", vmin=-10, vmax=40)
-
-    # Create a new inset-map that shows a zoomed-in view on a given dataset
-    m_inset = m.new_inset_map(xy=(5, 20), radius=8, plot_position=(0.75, .5))
-
-    # inherit both the data and the classification specs from "m"
-    m_inset.inherit_data(m)
-    m_inset.inherit_classification(m)
-
-    m_inset.set_shape.rectangles()
-    m_inset.plot_map(ec="k", lw=0.25)
diff --git a/docs/api_naturalearth_features.rst b/docs/api_naturalearth_features.rst
deleted file mode 100644
index a439bda00..000000000
--- a/docs/api_naturalearth_features.rst
+++ /dev/null
@@ -1,89 +0,0 @@
-
-.. _ne_features:
-
-🌵 NaturalEarth features
-------------------------
-
-.. currentmodule:: eomaps.eomaps
-
-Feature-layers provided by `NaturalEarth <https://www.naturalearthdata.com>`_ can be directly added to the map via :py:meth:`Maps.add_feature`.
-
-.. autosummary::
-    :nosignatures:
-
-    Maps.add_feature
-
-
-The call-signature is: ``m.add_feature.< CATEGORY >.< FEATURE >(...)``:
-
-``< CATEGORY >`` specifies the general category of the feature, e.g.:
-
-- ``cultural``: **cultural** features (e.g. countries, states etc.)
-- ``physical``: **physical** features (e.g. coastlines, land, ocean etc.)
-- ``preset``: a set of pre-defined layers for convenience (see below)
-
-``< FEATURE >`` is the name of the NaturalEarth feature, e.g. ``"coastlines", "admin_0_countries"`` etc..
-
-.. table::
-
-    +-------------------------------------------------------------------------+-------------------------------------------------+
-    | .. code-block:: python                                                  | .. image:: _static/minigifs/add_feature.gif     |
-    |     :name: test_add_features                                            |   :align: center                                |
-    |                                                                         |                                                 |
-    |     from eomaps import Maps                                             | |img_minsize|                                   |
-    |     m = Maps()                                                          |                                                 |
-    |     m.add_feature.preset.coastline()                                    |                                                 |
-    |     m.add_feature.preset.ocean()                                        |                                                 |
-    |     m.add_feature.preset.land()                                         |                                                 |
-    |     m.add_feature.preset.countries()                                    |                                                 |
-    |                                                                         |                                                 |
-    |     m.add_feature.physical.lakes(scale=110, ec="b")                     |                                                 |
-    |     m.add_feature.cultural.admin_0_pacific_groupings(fc="none", ec="m") |                                                 |
-    |                                                                         |                                                 |
-    |     # (only if geopandas is installed)                                  |                                                 |
-    |     places = m.add_feature.cultural.populated_places.get_gdf(scale=110) |                                                 |
-    |     m.add_gdf(places, markersize=places.NATSCALE/10, fc="r")            |                                                 |
-    |                                                                         |                                                 |
-    +-------------------------------------------------------------------------+-------------------------------------------------+
-
-
-`NaturalEarth <https://www.naturalearthdata.com>`_ provides features in 3 different scales: 1:10m, 1:50m, 1:110m.
-By default EOmaps uses features at 1:50m scale. To set the scale manually, simply use the ``scale`` argument
-when calling the feature.
-
-- It is also possible to automatically update the scale based on the map-extent by using ``scale="auto"``.
-  (Note that if you zoom into a new scale the data might need to be downloaded and reprojected so the map might be irresponsive for a couple of seconds until everything is properly cached.)
-
-For convenience, multiple preset-features can also be added in one go via:
-
-.. code-block:: python
-    :name: test_add_multi_preset_features
-
-    from eomaps import Maps
-    m = Maps()
-    m.add_feature.preset("coastline", "ocean", "land", scale=50)
-
-If you want to get a ``geopandas.GeoDataFrame`` containing all shapes and metadata of a feature, use:
-(Have a look at :ref:`vector_data` on how to add the obtained ``GeoDataFrame`` to the map)
-
-.. code-block:: python
-    :name: test_get_gdf
-
-    from eomaps import Maps
-    m = Maps()
-    gdf = m.add_feature.physical.coastline.get_gdf(scale=10)
-
-The most commonly used features are accessible with pre-defined colors via the ``preset`` category:
-
-.. currentmodule:: eomaps.ne_features.NaturalEarth_presets
-
-.. autosummary::
-    :nosignatures:
-
-    coastline
-    ocean
-    land
-    countries
-    urban_areas
-    lakes
-    rivers_lake_centerlines
diff --git a/docs/index.rst b/docs/index.rst
index 0a70f4780..ca5eb6d26 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -88,9 +88,9 @@ EOmaps provides many useful tools to customize your maps.
     :width: 100%
     :widths: 50 50
 
-    * - |  :doc:`api_inset_maps`
+    * - |  :doc:`notebooks/inset_maps`
         |  Create zoomed-in views on specific regions of a map.
-      - |  :doc:`api_naturalearth_features`
+      - |  :doc:`notebooks/naturalearth_features`
         |  Add basic map features (coastlines, ocean-coloring etc. ) to the map.
 
     * - |  :doc:`api_webmaps`
diff --git a/docs/notebooks/inset_maps.ipynb b/docs/notebooks/inset_maps.ipynb
index 6e8c2e13e..0fdd9b1af 100644
--- a/docs/notebooks/inset_maps.ipynb
+++ b/docs/notebooks/inset_maps.ipynb
@@ -193,6 +193,7 @@
     "tags": []
    },
    "source": [
+    "(zoomed_in_views_on_datasets)=\n",
     "## Zoomed in views on datasets\n",
     "\n",
     "To create a \"zoomed-in\" view of a dataset, the data must be plotted on the inset-map with the same classification as the initial plot.\n",
diff --git a/docs/notebooks/naturalearth_features.ipynb b/docs/notebooks/naturalearth_features.ipynb
index 009c1d5a3..54b4b1117 100644
--- a/docs/notebooks/naturalearth_features.ipynb
+++ b/docs/notebooks/naturalearth_features.ipynb
@@ -30,6 +30,7 @@
     "tags": []
    },
    "source": [
+    "(ne_features)=\n",
     "# 🌵 NaturalEarth features\n",
     "<br>\n",
     "\n",
diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 251452355..17b783143 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -1316,7 +1316,7 @@ def overlay_layer(self, layer, key="x"):
         If the visible layer changes **while the overlay-layer is active**,
         triggering the callback again might not properly remove the previous overlay!
         (e.g. the overlay is only removed if the top-layer corresponds exactly to
-         the overlay-layer specifications)
+        the overlay-layer specifications)
 
         Examples
         --------

From e61ab45c11330fd86f12723d152eeb8b307446a0 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 2 Jan 2024 23:33:34 +0100
Subject: [PATCH 052/240] avoid executing notebooks when compiling docs

---
 docs/conf.py                               |    2 +-
 docs/notebooks/inset_maps.ipynb            |   12 +-
 docs/notebooks/naturalearth_features.ipynb | 3062 ++++++++++----------
 3 files changed, 1538 insertions(+), 1538 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index ba190908f..1666f5ab3 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -68,7 +68,7 @@ def setup(app):
 myst_enable_extensions = ["dollarmath", "colon_fence"]
 myst_title_to_header = True
 
-nb_execution_mode = "cache"
+nb_execution_mode = "off"
 nb_execution_timeout = 120
 
 # handle compiler warnings for duplicate labels due to documents
diff --git a/docs/notebooks/inset_maps.ipynb b/docs/notebooks/inset_maps.ipynb
index 0fdd9b1af..54f57c4ee 100644
--- a/docs/notebooks/inset_maps.ipynb
+++ b/docs/notebooks/inset_maps.ipynb
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "id": "052d389f-a7f0-4a35-83fc-57d0bfa992f1",
    "metadata": {
     "editable": true,
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "35d0a654-5a34-40d2-8053-a53f77c2ab35",
    "metadata": {
     "editable": true,
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "2a2560d1-dfaf-4995-bd9b-cd905710d6c2",
    "metadata": {
     "editable": true,
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "id": "3a96a635-3c2b-49d0-a25b-f58c555f85bd",
    "metadata": {
     "editable": true,
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "id": "b5bfc055-ec33-4672-9a6b-570defce9f9e",
    "metadata": {
     "editable": true,
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "id": "11d46e5c-4769-412a-a74a-b5a1dd38dc0d",
    "metadata": {
     "editable": true,
diff --git a/docs/notebooks/naturalearth_features.ipynb b/docs/notebooks/naturalearth_features.ipynb
index 54b4b1117..02aa78872 100644
--- a/docs/notebooks/naturalearth_features.ipynb
+++ b/docs/notebooks/naturalearth_features.ipynb
@@ -377,1564 +377,1564 @@
      "data": {
       "text/html": [
        "<div style='width: 100%; height: 40ex; overflow: auto'><style type=\"text/css\">\n",
-       "#T_78f41 tr {\n",
+       "#T_f91f0 tr {\n",
        "  font-size: 8pt;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_78f41\">\n",
+       "<table id=\"T_f91f0\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_78f41_level0_col0\" class=\"col_heading level0 col0\" >SCALERANK</th>\n",
-       "      <th id=\"T_78f41_level0_col1\" class=\"col_heading level0 col1\" >NATSCALE</th>\n",
-       "      <th id=\"T_78f41_level0_col2\" class=\"col_heading level0 col2\" >LABELRANK</th>\n",
-       "      <th id=\"T_78f41_level0_col3\" class=\"col_heading level0 col3\" >FEATURECLA</th>\n",
-       "      <th id=\"T_78f41_level0_col4\" class=\"col_heading level0 col4\" >NAME</th>\n",
-       "      <th id=\"T_78f41_level0_col5\" class=\"col_heading level0 col5\" >NAMEPAR</th>\n",
-       "      <th id=\"T_78f41_level0_col6\" class=\"col_heading level0 col6\" >NAMEALT</th>\n",
-       "      <th id=\"T_78f41_level0_col7\" class=\"col_heading level0 col7\" >NAMEASCII</th>\n",
-       "      <th id=\"T_78f41_level0_col8\" class=\"col_heading level0 col8\" >ADM0CAP</th>\n",
-       "      <th id=\"T_78f41_level0_col9\" class=\"col_heading level0 col9\" >CAPIN</th>\n",
-       "      <th id=\"T_78f41_level0_col10\" class=\"col_heading level0 col10\" >WORLDCITY</th>\n",
-       "      <th id=\"T_78f41_level0_col11\" class=\"col_heading level0 col11\" >MEGACITY</th>\n",
-       "      <th id=\"T_78f41_level0_col12\" class=\"col_heading level0 col12\" >SOV0NAME</th>\n",
-       "      <th id=\"T_78f41_level0_col13\" class=\"col_heading level0 col13\" >SOV_A3</th>\n",
-       "      <th id=\"T_78f41_level0_col14\" class=\"col_heading level0 col14\" >ADM0NAME</th>\n",
-       "      <th id=\"T_78f41_level0_col15\" class=\"col_heading level0 col15\" >ADM0_A3</th>\n",
-       "      <th id=\"T_78f41_level0_col16\" class=\"col_heading level0 col16\" >ADM1NAME</th>\n",
-       "      <th id=\"T_78f41_level0_col17\" class=\"col_heading level0 col17\" >ISO_A2</th>\n",
-       "      <th id=\"T_78f41_level0_col18\" class=\"col_heading level0 col18\" >NOTE</th>\n",
-       "      <th id=\"T_78f41_level0_col19\" class=\"col_heading level0 col19\" >LATITUDE</th>\n",
-       "      <th id=\"T_78f41_level0_col20\" class=\"col_heading level0 col20\" >LONGITUDE</th>\n",
-       "      <th id=\"T_78f41_level0_col21\" class=\"col_heading level0 col21\" >POP_MAX</th>\n",
-       "      <th id=\"T_78f41_level0_col22\" class=\"col_heading level0 col22\" >POP_MIN</th>\n",
-       "      <th id=\"T_78f41_level0_col23\" class=\"col_heading level0 col23\" >POP_OTHER</th>\n",
-       "      <th id=\"T_78f41_level0_col24\" class=\"col_heading level0 col24\" >RANK_MAX</th>\n",
-       "      <th id=\"T_78f41_level0_col25\" class=\"col_heading level0 col25\" >RANK_MIN</th>\n",
-       "      <th id=\"T_78f41_level0_col26\" class=\"col_heading level0 col26\" >MEGANAME</th>\n",
-       "      <th id=\"T_78f41_level0_col27\" class=\"col_heading level0 col27\" >LS_NAME</th>\n",
-       "      <th id=\"T_78f41_level0_col28\" class=\"col_heading level0 col28\" >MAX_POP10</th>\n",
-       "      <th id=\"T_78f41_level0_col29\" class=\"col_heading level0 col29\" >MAX_POP20</th>\n",
-       "      <th id=\"T_78f41_level0_col30\" class=\"col_heading level0 col30\" >MAX_POP50</th>\n",
-       "      <th id=\"T_78f41_level0_col31\" class=\"col_heading level0 col31\" >MAX_POP300</th>\n",
-       "      <th id=\"T_78f41_level0_col32\" class=\"col_heading level0 col32\" >MAX_POP310</th>\n",
-       "      <th id=\"T_78f41_level0_col33\" class=\"col_heading level0 col33\" >MAX_NATSCA</th>\n",
-       "      <th id=\"T_78f41_level0_col34\" class=\"col_heading level0 col34\" >MIN_AREAKM</th>\n",
-       "      <th id=\"T_78f41_level0_col35\" class=\"col_heading level0 col35\" >MAX_AREAKM</th>\n",
-       "      <th id=\"T_78f41_level0_col36\" class=\"col_heading level0 col36\" >MIN_AREAMI</th>\n",
-       "      <th id=\"T_78f41_level0_col37\" class=\"col_heading level0 col37\" >MAX_AREAMI</th>\n",
-       "      <th id=\"T_78f41_level0_col38\" class=\"col_heading level0 col38\" >MIN_PERKM</th>\n",
-       "      <th id=\"T_78f41_level0_col39\" class=\"col_heading level0 col39\" >MAX_PERKM</th>\n",
-       "      <th id=\"T_78f41_level0_col40\" class=\"col_heading level0 col40\" >MIN_PERMI</th>\n",
-       "      <th id=\"T_78f41_level0_col41\" class=\"col_heading level0 col41\" >MAX_PERMI</th>\n",
-       "      <th id=\"T_78f41_level0_col42\" class=\"col_heading level0 col42\" >MIN_BBXMIN</th>\n",
-       "      <th id=\"T_78f41_level0_col43\" class=\"col_heading level0 col43\" >MAX_BBXMIN</th>\n",
-       "      <th id=\"T_78f41_level0_col44\" class=\"col_heading level0 col44\" >MIN_BBXMAX</th>\n",
-       "      <th id=\"T_78f41_level0_col45\" class=\"col_heading level0 col45\" >MAX_BBXMAX</th>\n",
-       "      <th id=\"T_78f41_level0_col46\" class=\"col_heading level0 col46\" >MIN_BBYMIN</th>\n",
-       "      <th id=\"T_78f41_level0_col47\" class=\"col_heading level0 col47\" >MAX_BBYMIN</th>\n",
-       "      <th id=\"T_78f41_level0_col48\" class=\"col_heading level0 col48\" >MIN_BBYMAX</th>\n",
-       "      <th id=\"T_78f41_level0_col49\" class=\"col_heading level0 col49\" >MAX_BBYMAX</th>\n",
-       "      <th id=\"T_78f41_level0_col50\" class=\"col_heading level0 col50\" >MEAN_BBXC</th>\n",
-       "      <th id=\"T_78f41_level0_col51\" class=\"col_heading level0 col51\" >MEAN_BBYC</th>\n",
-       "      <th id=\"T_78f41_level0_col52\" class=\"col_heading level0 col52\" >TIMEZONE</th>\n",
-       "      <th id=\"T_78f41_level0_col53\" class=\"col_heading level0 col53\" >UN_FID</th>\n",
-       "      <th id=\"T_78f41_level0_col54\" class=\"col_heading level0 col54\" >POP1950</th>\n",
-       "      <th id=\"T_78f41_level0_col55\" class=\"col_heading level0 col55\" >POP1955</th>\n",
-       "      <th id=\"T_78f41_level0_col56\" class=\"col_heading level0 col56\" >POP1960</th>\n",
-       "      <th id=\"T_78f41_level0_col57\" class=\"col_heading level0 col57\" >POP1965</th>\n",
-       "      <th id=\"T_78f41_level0_col58\" class=\"col_heading level0 col58\" >POP1970</th>\n",
-       "      <th id=\"T_78f41_level0_col59\" class=\"col_heading level0 col59\" >POP1975</th>\n",
-       "      <th id=\"T_78f41_level0_col60\" class=\"col_heading level0 col60\" >POP1980</th>\n",
-       "      <th id=\"T_78f41_level0_col61\" class=\"col_heading level0 col61\" >POP1985</th>\n",
-       "      <th id=\"T_78f41_level0_col62\" class=\"col_heading level0 col62\" >POP1990</th>\n",
-       "      <th id=\"T_78f41_level0_col63\" class=\"col_heading level0 col63\" >POP1995</th>\n",
-       "      <th id=\"T_78f41_level0_col64\" class=\"col_heading level0 col64\" >POP2000</th>\n",
-       "      <th id=\"T_78f41_level0_col65\" class=\"col_heading level0 col65\" >POP2005</th>\n",
-       "      <th id=\"T_78f41_level0_col66\" class=\"col_heading level0 col66\" >POP2010</th>\n",
-       "      <th id=\"T_78f41_level0_col67\" class=\"col_heading level0 col67\" >POP2015</th>\n",
-       "      <th id=\"T_78f41_level0_col68\" class=\"col_heading level0 col68\" >POP2020</th>\n",
-       "      <th id=\"T_78f41_level0_col69\" class=\"col_heading level0 col69\" >POP2025</th>\n",
-       "      <th id=\"T_78f41_level0_col70\" class=\"col_heading level0 col70\" >POP2050</th>\n",
-       "      <th id=\"T_78f41_level0_col71\" class=\"col_heading level0 col71\" >MIN_ZOOM</th>\n",
-       "      <th id=\"T_78f41_level0_col72\" class=\"col_heading level0 col72\" >WIKIDATAID</th>\n",
-       "      <th id=\"T_78f41_level0_col73\" class=\"col_heading level0 col73\" >WOF_ID</th>\n",
-       "      <th id=\"T_78f41_level0_col74\" class=\"col_heading level0 col74\" >CAPALT</th>\n",
-       "      <th id=\"T_78f41_level0_col75\" class=\"col_heading level0 col75\" >NAME_EN</th>\n",
-       "      <th id=\"T_78f41_level0_col76\" class=\"col_heading level0 col76\" >NAME_DE</th>\n",
-       "      <th id=\"T_78f41_level0_col77\" class=\"col_heading level0 col77\" >NAME_ES</th>\n",
-       "      <th id=\"T_78f41_level0_col78\" class=\"col_heading level0 col78\" >NAME_FR</th>\n",
-       "      <th id=\"T_78f41_level0_col79\" class=\"col_heading level0 col79\" >NAME_PT</th>\n",
-       "      <th id=\"T_78f41_level0_col80\" class=\"col_heading level0 col80\" >NAME_RU</th>\n",
-       "      <th id=\"T_78f41_level0_col81\" class=\"col_heading level0 col81\" >NAME_ZH</th>\n",
-       "      <th id=\"T_78f41_level0_col82\" class=\"col_heading level0 col82\" >LABEL</th>\n",
-       "      <th id=\"T_78f41_level0_col83\" class=\"col_heading level0 col83\" >NAME_AR</th>\n",
-       "      <th id=\"T_78f41_level0_col84\" class=\"col_heading level0 col84\" >NAME_BN</th>\n",
-       "      <th id=\"T_78f41_level0_col85\" class=\"col_heading level0 col85\" >NAME_EL</th>\n",
-       "      <th id=\"T_78f41_level0_col86\" class=\"col_heading level0 col86\" >NAME_HI</th>\n",
-       "      <th id=\"T_78f41_level0_col87\" class=\"col_heading level0 col87\" >NAME_HU</th>\n",
-       "      <th id=\"T_78f41_level0_col88\" class=\"col_heading level0 col88\" >NAME_ID</th>\n",
-       "      <th id=\"T_78f41_level0_col89\" class=\"col_heading level0 col89\" >NAME_IT</th>\n",
-       "      <th id=\"T_78f41_level0_col90\" class=\"col_heading level0 col90\" >NAME_JA</th>\n",
-       "      <th id=\"T_78f41_level0_col91\" class=\"col_heading level0 col91\" >NAME_KO</th>\n",
-       "      <th id=\"T_78f41_level0_col92\" class=\"col_heading level0 col92\" >NAME_NL</th>\n",
-       "      <th id=\"T_78f41_level0_col93\" class=\"col_heading level0 col93\" >NAME_PL</th>\n",
-       "      <th id=\"T_78f41_level0_col94\" class=\"col_heading level0 col94\" >NAME_SV</th>\n",
-       "      <th id=\"T_78f41_level0_col95\" class=\"col_heading level0 col95\" >NAME_TR</th>\n",
-       "      <th id=\"T_78f41_level0_col96\" class=\"col_heading level0 col96\" >NAME_VI</th>\n",
-       "      <th id=\"T_78f41_level0_col97\" class=\"col_heading level0 col97\" >NE_ID</th>\n",
-       "      <th id=\"T_78f41_level0_col98\" class=\"col_heading level0 col98\" >NAME_FA</th>\n",
-       "      <th id=\"T_78f41_level0_col99\" class=\"col_heading level0 col99\" >NAME_HE</th>\n",
-       "      <th id=\"T_78f41_level0_col100\" class=\"col_heading level0 col100\" >NAME_UK</th>\n",
-       "      <th id=\"T_78f41_level0_col101\" class=\"col_heading level0 col101\" >NAME_UR</th>\n",
-       "      <th id=\"T_78f41_level0_col102\" class=\"col_heading level0 col102\" >NAME_ZHT</th>\n",
-       "      <th id=\"T_78f41_level0_col103\" class=\"col_heading level0 col103\" >GEONAMESID</th>\n",
-       "      <th id=\"T_78f41_level0_col104\" class=\"col_heading level0 col104\" >FCLASS_ISO</th>\n",
-       "      <th id=\"T_78f41_level0_col105\" class=\"col_heading level0 col105\" >FCLASS_US</th>\n",
-       "      <th id=\"T_78f41_level0_col106\" class=\"col_heading level0 col106\" >FCLASS_FR</th>\n",
-       "      <th id=\"T_78f41_level0_col107\" class=\"col_heading level0 col107\" >FCLASS_RU</th>\n",
-       "      <th id=\"T_78f41_level0_col108\" class=\"col_heading level0 col108\" >FCLASS_ES</th>\n",
-       "      <th id=\"T_78f41_level0_col109\" class=\"col_heading level0 col109\" >FCLASS_CN</th>\n",
-       "      <th id=\"T_78f41_level0_col110\" class=\"col_heading level0 col110\" >FCLASS_TW</th>\n",
-       "      <th id=\"T_78f41_level0_col111\" class=\"col_heading level0 col111\" >FCLASS_IN</th>\n",
-       "      <th id=\"T_78f41_level0_col112\" class=\"col_heading level0 col112\" >FCLASS_NP</th>\n",
-       "      <th id=\"T_78f41_level0_col113\" class=\"col_heading level0 col113\" >FCLASS_PK</th>\n",
-       "      <th id=\"T_78f41_level0_col114\" class=\"col_heading level0 col114\" >FCLASS_DE</th>\n",
-       "      <th id=\"T_78f41_level0_col115\" class=\"col_heading level0 col115\" >FCLASS_GB</th>\n",
-       "      <th id=\"T_78f41_level0_col116\" class=\"col_heading level0 col116\" >FCLASS_BR</th>\n",
-       "      <th id=\"T_78f41_level0_col117\" class=\"col_heading level0 col117\" >FCLASS_IL</th>\n",
-       "      <th id=\"T_78f41_level0_col118\" class=\"col_heading level0 col118\" >FCLASS_PS</th>\n",
-       "      <th id=\"T_78f41_level0_col119\" class=\"col_heading level0 col119\" >FCLASS_SA</th>\n",
-       "      <th id=\"T_78f41_level0_col120\" class=\"col_heading level0 col120\" >FCLASS_EG</th>\n",
-       "      <th id=\"T_78f41_level0_col121\" class=\"col_heading level0 col121\" >FCLASS_MA</th>\n",
-       "      <th id=\"T_78f41_level0_col122\" class=\"col_heading level0 col122\" >FCLASS_PT</th>\n",
-       "      <th id=\"T_78f41_level0_col123\" class=\"col_heading level0 col123\" >FCLASS_AR</th>\n",
-       "      <th id=\"T_78f41_level0_col124\" class=\"col_heading level0 col124\" >FCLASS_JP</th>\n",
-       "      <th id=\"T_78f41_level0_col125\" class=\"col_heading level0 col125\" >FCLASS_KO</th>\n",
-       "      <th id=\"T_78f41_level0_col126\" class=\"col_heading level0 col126\" >FCLASS_VN</th>\n",
-       "      <th id=\"T_78f41_level0_col127\" class=\"col_heading level0 col127\" >FCLASS_TR</th>\n",
-       "      <th id=\"T_78f41_level0_col128\" class=\"col_heading level0 col128\" >FCLASS_ID</th>\n",
-       "      <th id=\"T_78f41_level0_col129\" class=\"col_heading level0 col129\" >FCLASS_PL</th>\n",
-       "      <th id=\"T_78f41_level0_col130\" class=\"col_heading level0 col130\" >FCLASS_GR</th>\n",
-       "      <th id=\"T_78f41_level0_col131\" class=\"col_heading level0 col131\" >FCLASS_IT</th>\n",
-       "      <th id=\"T_78f41_level0_col132\" class=\"col_heading level0 col132\" >FCLASS_NL</th>\n",
-       "      <th id=\"T_78f41_level0_col133\" class=\"col_heading level0 col133\" >FCLASS_SE</th>\n",
-       "      <th id=\"T_78f41_level0_col134\" class=\"col_heading level0 col134\" >FCLASS_BD</th>\n",
-       "      <th id=\"T_78f41_level0_col135\" class=\"col_heading level0 col135\" >FCLASS_UA</th>\n",
-       "      <th id=\"T_78f41_level0_col136\" class=\"col_heading level0 col136\" >FCLASS_TLC</th>\n",
-       "      <th id=\"T_78f41_level0_col137\" class=\"col_heading level0 col137\" >geometry</th>\n",
+       "      <th id=\"T_f91f0_level0_col0\" class=\"col_heading level0 col0\" >SCALERANK</th>\n",
+       "      <th id=\"T_f91f0_level0_col1\" class=\"col_heading level0 col1\" >NATSCALE</th>\n",
+       "      <th id=\"T_f91f0_level0_col2\" class=\"col_heading level0 col2\" >LABELRANK</th>\n",
+       "      <th id=\"T_f91f0_level0_col3\" class=\"col_heading level0 col3\" >FEATURECLA</th>\n",
+       "      <th id=\"T_f91f0_level0_col4\" class=\"col_heading level0 col4\" >NAME</th>\n",
+       "      <th id=\"T_f91f0_level0_col5\" class=\"col_heading level0 col5\" >NAMEPAR</th>\n",
+       "      <th id=\"T_f91f0_level0_col6\" class=\"col_heading level0 col6\" >NAMEALT</th>\n",
+       "      <th id=\"T_f91f0_level0_col7\" class=\"col_heading level0 col7\" >NAMEASCII</th>\n",
+       "      <th id=\"T_f91f0_level0_col8\" class=\"col_heading level0 col8\" >ADM0CAP</th>\n",
+       "      <th id=\"T_f91f0_level0_col9\" class=\"col_heading level0 col9\" >CAPIN</th>\n",
+       "      <th id=\"T_f91f0_level0_col10\" class=\"col_heading level0 col10\" >WORLDCITY</th>\n",
+       "      <th id=\"T_f91f0_level0_col11\" class=\"col_heading level0 col11\" >MEGACITY</th>\n",
+       "      <th id=\"T_f91f0_level0_col12\" class=\"col_heading level0 col12\" >SOV0NAME</th>\n",
+       "      <th id=\"T_f91f0_level0_col13\" class=\"col_heading level0 col13\" >SOV_A3</th>\n",
+       "      <th id=\"T_f91f0_level0_col14\" class=\"col_heading level0 col14\" >ADM0NAME</th>\n",
+       "      <th id=\"T_f91f0_level0_col15\" class=\"col_heading level0 col15\" >ADM0_A3</th>\n",
+       "      <th id=\"T_f91f0_level0_col16\" class=\"col_heading level0 col16\" >ADM1NAME</th>\n",
+       "      <th id=\"T_f91f0_level0_col17\" class=\"col_heading level0 col17\" >ISO_A2</th>\n",
+       "      <th id=\"T_f91f0_level0_col18\" class=\"col_heading level0 col18\" >NOTE</th>\n",
+       "      <th id=\"T_f91f0_level0_col19\" class=\"col_heading level0 col19\" >LATITUDE</th>\n",
+       "      <th id=\"T_f91f0_level0_col20\" class=\"col_heading level0 col20\" >LONGITUDE</th>\n",
+       "      <th id=\"T_f91f0_level0_col21\" class=\"col_heading level0 col21\" >POP_MAX</th>\n",
+       "      <th id=\"T_f91f0_level0_col22\" class=\"col_heading level0 col22\" >POP_MIN</th>\n",
+       "      <th id=\"T_f91f0_level0_col23\" class=\"col_heading level0 col23\" >POP_OTHER</th>\n",
+       "      <th id=\"T_f91f0_level0_col24\" class=\"col_heading level0 col24\" >RANK_MAX</th>\n",
+       "      <th id=\"T_f91f0_level0_col25\" class=\"col_heading level0 col25\" >RANK_MIN</th>\n",
+       "      <th id=\"T_f91f0_level0_col26\" class=\"col_heading level0 col26\" >MEGANAME</th>\n",
+       "      <th id=\"T_f91f0_level0_col27\" class=\"col_heading level0 col27\" >LS_NAME</th>\n",
+       "      <th id=\"T_f91f0_level0_col28\" class=\"col_heading level0 col28\" >MAX_POP10</th>\n",
+       "      <th id=\"T_f91f0_level0_col29\" class=\"col_heading level0 col29\" >MAX_POP20</th>\n",
+       "      <th id=\"T_f91f0_level0_col30\" class=\"col_heading level0 col30\" >MAX_POP50</th>\n",
+       "      <th id=\"T_f91f0_level0_col31\" class=\"col_heading level0 col31\" >MAX_POP300</th>\n",
+       "      <th id=\"T_f91f0_level0_col32\" class=\"col_heading level0 col32\" >MAX_POP310</th>\n",
+       "      <th id=\"T_f91f0_level0_col33\" class=\"col_heading level0 col33\" >MAX_NATSCA</th>\n",
+       "      <th id=\"T_f91f0_level0_col34\" class=\"col_heading level0 col34\" >MIN_AREAKM</th>\n",
+       "      <th id=\"T_f91f0_level0_col35\" class=\"col_heading level0 col35\" >MAX_AREAKM</th>\n",
+       "      <th id=\"T_f91f0_level0_col36\" class=\"col_heading level0 col36\" >MIN_AREAMI</th>\n",
+       "      <th id=\"T_f91f0_level0_col37\" class=\"col_heading level0 col37\" >MAX_AREAMI</th>\n",
+       "      <th id=\"T_f91f0_level0_col38\" class=\"col_heading level0 col38\" >MIN_PERKM</th>\n",
+       "      <th id=\"T_f91f0_level0_col39\" class=\"col_heading level0 col39\" >MAX_PERKM</th>\n",
+       "      <th id=\"T_f91f0_level0_col40\" class=\"col_heading level0 col40\" >MIN_PERMI</th>\n",
+       "      <th id=\"T_f91f0_level0_col41\" class=\"col_heading level0 col41\" >MAX_PERMI</th>\n",
+       "      <th id=\"T_f91f0_level0_col42\" class=\"col_heading level0 col42\" >MIN_BBXMIN</th>\n",
+       "      <th id=\"T_f91f0_level0_col43\" class=\"col_heading level0 col43\" >MAX_BBXMIN</th>\n",
+       "      <th id=\"T_f91f0_level0_col44\" class=\"col_heading level0 col44\" >MIN_BBXMAX</th>\n",
+       "      <th id=\"T_f91f0_level0_col45\" class=\"col_heading level0 col45\" >MAX_BBXMAX</th>\n",
+       "      <th id=\"T_f91f0_level0_col46\" class=\"col_heading level0 col46\" >MIN_BBYMIN</th>\n",
+       "      <th id=\"T_f91f0_level0_col47\" class=\"col_heading level0 col47\" >MAX_BBYMIN</th>\n",
+       "      <th id=\"T_f91f0_level0_col48\" class=\"col_heading level0 col48\" >MIN_BBYMAX</th>\n",
+       "      <th id=\"T_f91f0_level0_col49\" class=\"col_heading level0 col49\" >MAX_BBYMAX</th>\n",
+       "      <th id=\"T_f91f0_level0_col50\" class=\"col_heading level0 col50\" >MEAN_BBXC</th>\n",
+       "      <th id=\"T_f91f0_level0_col51\" class=\"col_heading level0 col51\" >MEAN_BBYC</th>\n",
+       "      <th id=\"T_f91f0_level0_col52\" class=\"col_heading level0 col52\" >TIMEZONE</th>\n",
+       "      <th id=\"T_f91f0_level0_col53\" class=\"col_heading level0 col53\" >UN_FID</th>\n",
+       "      <th id=\"T_f91f0_level0_col54\" class=\"col_heading level0 col54\" >POP1950</th>\n",
+       "      <th id=\"T_f91f0_level0_col55\" class=\"col_heading level0 col55\" >POP1955</th>\n",
+       "      <th id=\"T_f91f0_level0_col56\" class=\"col_heading level0 col56\" >POP1960</th>\n",
+       "      <th id=\"T_f91f0_level0_col57\" class=\"col_heading level0 col57\" >POP1965</th>\n",
+       "      <th id=\"T_f91f0_level0_col58\" class=\"col_heading level0 col58\" >POP1970</th>\n",
+       "      <th id=\"T_f91f0_level0_col59\" class=\"col_heading level0 col59\" >POP1975</th>\n",
+       "      <th id=\"T_f91f0_level0_col60\" class=\"col_heading level0 col60\" >POP1980</th>\n",
+       "      <th id=\"T_f91f0_level0_col61\" class=\"col_heading level0 col61\" >POP1985</th>\n",
+       "      <th id=\"T_f91f0_level0_col62\" class=\"col_heading level0 col62\" >POP1990</th>\n",
+       "      <th id=\"T_f91f0_level0_col63\" class=\"col_heading level0 col63\" >POP1995</th>\n",
+       "      <th id=\"T_f91f0_level0_col64\" class=\"col_heading level0 col64\" >POP2000</th>\n",
+       "      <th id=\"T_f91f0_level0_col65\" class=\"col_heading level0 col65\" >POP2005</th>\n",
+       "      <th id=\"T_f91f0_level0_col66\" class=\"col_heading level0 col66\" >POP2010</th>\n",
+       "      <th id=\"T_f91f0_level0_col67\" class=\"col_heading level0 col67\" >POP2015</th>\n",
+       "      <th id=\"T_f91f0_level0_col68\" class=\"col_heading level0 col68\" >POP2020</th>\n",
+       "      <th id=\"T_f91f0_level0_col69\" class=\"col_heading level0 col69\" >POP2025</th>\n",
+       "      <th id=\"T_f91f0_level0_col70\" class=\"col_heading level0 col70\" >POP2050</th>\n",
+       "      <th id=\"T_f91f0_level0_col71\" class=\"col_heading level0 col71\" >MIN_ZOOM</th>\n",
+       "      <th id=\"T_f91f0_level0_col72\" class=\"col_heading level0 col72\" >WIKIDATAID</th>\n",
+       "      <th id=\"T_f91f0_level0_col73\" class=\"col_heading level0 col73\" >WOF_ID</th>\n",
+       "      <th id=\"T_f91f0_level0_col74\" class=\"col_heading level0 col74\" >CAPALT</th>\n",
+       "      <th id=\"T_f91f0_level0_col75\" class=\"col_heading level0 col75\" >NAME_EN</th>\n",
+       "      <th id=\"T_f91f0_level0_col76\" class=\"col_heading level0 col76\" >NAME_DE</th>\n",
+       "      <th id=\"T_f91f0_level0_col77\" class=\"col_heading level0 col77\" >NAME_ES</th>\n",
+       "      <th id=\"T_f91f0_level0_col78\" class=\"col_heading level0 col78\" >NAME_FR</th>\n",
+       "      <th id=\"T_f91f0_level0_col79\" class=\"col_heading level0 col79\" >NAME_PT</th>\n",
+       "      <th id=\"T_f91f0_level0_col80\" class=\"col_heading level0 col80\" >NAME_RU</th>\n",
+       "      <th id=\"T_f91f0_level0_col81\" class=\"col_heading level0 col81\" >NAME_ZH</th>\n",
+       "      <th id=\"T_f91f0_level0_col82\" class=\"col_heading level0 col82\" >LABEL</th>\n",
+       "      <th id=\"T_f91f0_level0_col83\" class=\"col_heading level0 col83\" >NAME_AR</th>\n",
+       "      <th id=\"T_f91f0_level0_col84\" class=\"col_heading level0 col84\" >NAME_BN</th>\n",
+       "      <th id=\"T_f91f0_level0_col85\" class=\"col_heading level0 col85\" >NAME_EL</th>\n",
+       "      <th id=\"T_f91f0_level0_col86\" class=\"col_heading level0 col86\" >NAME_HI</th>\n",
+       "      <th id=\"T_f91f0_level0_col87\" class=\"col_heading level0 col87\" >NAME_HU</th>\n",
+       "      <th id=\"T_f91f0_level0_col88\" class=\"col_heading level0 col88\" >NAME_ID</th>\n",
+       "      <th id=\"T_f91f0_level0_col89\" class=\"col_heading level0 col89\" >NAME_IT</th>\n",
+       "      <th id=\"T_f91f0_level0_col90\" class=\"col_heading level0 col90\" >NAME_JA</th>\n",
+       "      <th id=\"T_f91f0_level0_col91\" class=\"col_heading level0 col91\" >NAME_KO</th>\n",
+       "      <th id=\"T_f91f0_level0_col92\" class=\"col_heading level0 col92\" >NAME_NL</th>\n",
+       "      <th id=\"T_f91f0_level0_col93\" class=\"col_heading level0 col93\" >NAME_PL</th>\n",
+       "      <th id=\"T_f91f0_level0_col94\" class=\"col_heading level0 col94\" >NAME_SV</th>\n",
+       "      <th id=\"T_f91f0_level0_col95\" class=\"col_heading level0 col95\" >NAME_TR</th>\n",
+       "      <th id=\"T_f91f0_level0_col96\" class=\"col_heading level0 col96\" >NAME_VI</th>\n",
+       "      <th id=\"T_f91f0_level0_col97\" class=\"col_heading level0 col97\" >NE_ID</th>\n",
+       "      <th id=\"T_f91f0_level0_col98\" class=\"col_heading level0 col98\" >NAME_FA</th>\n",
+       "      <th id=\"T_f91f0_level0_col99\" class=\"col_heading level0 col99\" >NAME_HE</th>\n",
+       "      <th id=\"T_f91f0_level0_col100\" class=\"col_heading level0 col100\" >NAME_UK</th>\n",
+       "      <th id=\"T_f91f0_level0_col101\" class=\"col_heading level0 col101\" >NAME_UR</th>\n",
+       "      <th id=\"T_f91f0_level0_col102\" class=\"col_heading level0 col102\" >NAME_ZHT</th>\n",
+       "      <th id=\"T_f91f0_level0_col103\" class=\"col_heading level0 col103\" >GEONAMESID</th>\n",
+       "      <th id=\"T_f91f0_level0_col104\" class=\"col_heading level0 col104\" >FCLASS_ISO</th>\n",
+       "      <th id=\"T_f91f0_level0_col105\" class=\"col_heading level0 col105\" >FCLASS_US</th>\n",
+       "      <th id=\"T_f91f0_level0_col106\" class=\"col_heading level0 col106\" >FCLASS_FR</th>\n",
+       "      <th id=\"T_f91f0_level0_col107\" class=\"col_heading level0 col107\" >FCLASS_RU</th>\n",
+       "      <th id=\"T_f91f0_level0_col108\" class=\"col_heading level0 col108\" >FCLASS_ES</th>\n",
+       "      <th id=\"T_f91f0_level0_col109\" class=\"col_heading level0 col109\" >FCLASS_CN</th>\n",
+       "      <th id=\"T_f91f0_level0_col110\" class=\"col_heading level0 col110\" >FCLASS_TW</th>\n",
+       "      <th id=\"T_f91f0_level0_col111\" class=\"col_heading level0 col111\" >FCLASS_IN</th>\n",
+       "      <th id=\"T_f91f0_level0_col112\" class=\"col_heading level0 col112\" >FCLASS_NP</th>\n",
+       "      <th id=\"T_f91f0_level0_col113\" class=\"col_heading level0 col113\" >FCLASS_PK</th>\n",
+       "      <th id=\"T_f91f0_level0_col114\" class=\"col_heading level0 col114\" >FCLASS_DE</th>\n",
+       "      <th id=\"T_f91f0_level0_col115\" class=\"col_heading level0 col115\" >FCLASS_GB</th>\n",
+       "      <th id=\"T_f91f0_level0_col116\" class=\"col_heading level0 col116\" >FCLASS_BR</th>\n",
+       "      <th id=\"T_f91f0_level0_col117\" class=\"col_heading level0 col117\" >FCLASS_IL</th>\n",
+       "      <th id=\"T_f91f0_level0_col118\" class=\"col_heading level0 col118\" >FCLASS_PS</th>\n",
+       "      <th id=\"T_f91f0_level0_col119\" class=\"col_heading level0 col119\" >FCLASS_SA</th>\n",
+       "      <th id=\"T_f91f0_level0_col120\" class=\"col_heading level0 col120\" >FCLASS_EG</th>\n",
+       "      <th id=\"T_f91f0_level0_col121\" class=\"col_heading level0 col121\" >FCLASS_MA</th>\n",
+       "      <th id=\"T_f91f0_level0_col122\" class=\"col_heading level0 col122\" >FCLASS_PT</th>\n",
+       "      <th id=\"T_f91f0_level0_col123\" class=\"col_heading level0 col123\" >FCLASS_AR</th>\n",
+       "      <th id=\"T_f91f0_level0_col124\" class=\"col_heading level0 col124\" >FCLASS_JP</th>\n",
+       "      <th id=\"T_f91f0_level0_col125\" class=\"col_heading level0 col125\" >FCLASS_KO</th>\n",
+       "      <th id=\"T_f91f0_level0_col126\" class=\"col_heading level0 col126\" >FCLASS_VN</th>\n",
+       "      <th id=\"T_f91f0_level0_col127\" class=\"col_heading level0 col127\" >FCLASS_TR</th>\n",
+       "      <th id=\"T_f91f0_level0_col128\" class=\"col_heading level0 col128\" >FCLASS_ID</th>\n",
+       "      <th id=\"T_f91f0_level0_col129\" class=\"col_heading level0 col129\" >FCLASS_PL</th>\n",
+       "      <th id=\"T_f91f0_level0_col130\" class=\"col_heading level0 col130\" >FCLASS_GR</th>\n",
+       "      <th id=\"T_f91f0_level0_col131\" class=\"col_heading level0 col131\" >FCLASS_IT</th>\n",
+       "      <th id=\"T_f91f0_level0_col132\" class=\"col_heading level0 col132\" >FCLASS_NL</th>\n",
+       "      <th id=\"T_f91f0_level0_col133\" class=\"col_heading level0 col133\" >FCLASS_SE</th>\n",
+       "      <th id=\"T_f91f0_level0_col134\" class=\"col_heading level0 col134\" >FCLASS_BD</th>\n",
+       "      <th id=\"T_f91f0_level0_col135\" class=\"col_heading level0 col135\" >FCLASS_UA</th>\n",
+       "      <th id=\"T_f91f0_level0_col136\" class=\"col_heading level0 col136\" >FCLASS_TLC</th>\n",
+       "      <th id=\"T_f91f0_level0_col137\" class=\"col_heading level0 col137\" >geometry</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_78f41_row0_col0\" class=\"data row0 col0\" >8</td>\n",
-       "      <td id=\"T_78f41_row0_col1\" class=\"data row0 col1\" >10</td>\n",
-       "      <td id=\"T_78f41_row0_col2\" class=\"data row0 col2\" >3</td>\n",
-       "      <td id=\"T_78f41_row0_col3\" class=\"data row0 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row0_col4\" class=\"data row0 col4\" >Vatican City</td>\n",
-       "      <td id=\"T_78f41_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col7\" class=\"data row0 col7\" >Vatican City</td>\n",
-       "      <td id=\"T_78f41_row0_col8\" class=\"data row0 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row0_col9\" class=\"data row0 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col10\" class=\"data row0 col10\" >1</td>\n",
-       "      <td id=\"T_78f41_row0_col11\" class=\"data row0 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col12\" class=\"data row0 col12\" >Vatican</td>\n",
-       "      <td id=\"T_78f41_row0_col13\" class=\"data row0 col13\" >VAT</td>\n",
-       "      <td id=\"T_78f41_row0_col14\" class=\"data row0 col14\" >Vatican</td>\n",
-       "      <td id=\"T_78f41_row0_col15\" class=\"data row0 col15\" >VAT</td>\n",
-       "      <td id=\"T_78f41_row0_col16\" class=\"data row0 col16\" >Lazio</td>\n",
-       "      <td id=\"T_78f41_row0_col17\" class=\"data row0 col17\" >VA</td>\n",
-       "      <td id=\"T_78f41_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col19\" class=\"data row0 col19\" >41.903282</td>\n",
-       "      <td id=\"T_78f41_row0_col20\" class=\"data row0 col20\" >12.453387</td>\n",
-       "      <td id=\"T_78f41_row0_col21\" class=\"data row0 col21\" >832</td>\n",
-       "      <td id=\"T_78f41_row0_col22\" class=\"data row0 col22\" >832</td>\n",
-       "      <td id=\"T_78f41_row0_col23\" class=\"data row0 col23\" >562430</td>\n",
-       "      <td id=\"T_78f41_row0_col24\" class=\"data row0 col24\" >2</td>\n",
-       "      <td id=\"T_78f41_row0_col25\" class=\"data row0 col25\" >2</td>\n",
-       "      <td id=\"T_78f41_row0_col26\" class=\"data row0 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col27\" class=\"data row0 col27\" >Vatican City</td>\n",
-       "      <td id=\"T_78f41_row0_col28\" class=\"data row0 col28\" >636762</td>\n",
-       "      <td id=\"T_78f41_row0_col29\" class=\"data row0 col29\" >636762</td>\n",
-       "      <td id=\"T_78f41_row0_col30\" class=\"data row0 col30\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col31\" class=\"data row0 col31\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col32\" class=\"data row0 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col33\" class=\"data row0 col33\" >20</td>\n",
-       "      <td id=\"T_78f41_row0_col34\" class=\"data row0 col34\" >177</td>\n",
-       "      <td id=\"T_78f41_row0_col35\" class=\"data row0 col35\" >177</td>\n",
-       "      <td id=\"T_78f41_row0_col36\" class=\"data row0 col36\" >68</td>\n",
-       "      <td id=\"T_78f41_row0_col37\" class=\"data row0 col37\" >68</td>\n",
-       "      <td id=\"T_78f41_row0_col38\" class=\"data row0 col38\" >160</td>\n",
-       "      <td id=\"T_78f41_row0_col39\" class=\"data row0 col39\" >160</td>\n",
-       "      <td id=\"T_78f41_row0_col40\" class=\"data row0 col40\" >99</td>\n",
-       "      <td id=\"T_78f41_row0_col41\" class=\"data row0 col41\" >99</td>\n",
-       "      <td id=\"T_78f41_row0_col42\" class=\"data row0 col42\" >12.333333</td>\n",
-       "      <td id=\"T_78f41_row0_col43\" class=\"data row0 col43\" >12.333333</td>\n",
-       "      <td id=\"T_78f41_row0_col44\" class=\"data row0 col44\" >12.481009</td>\n",
-       "      <td id=\"T_78f41_row0_col45\" class=\"data row0 col45\" >12.481009</td>\n",
-       "      <td id=\"T_78f41_row0_col46\" class=\"data row0 col46\" >41.766667</td>\n",
-       "      <td id=\"T_78f41_row0_col47\" class=\"data row0 col47\" >41.766667</td>\n",
-       "      <td id=\"T_78f41_row0_col48\" class=\"data row0 col48\" >42.050000</td>\n",
-       "      <td id=\"T_78f41_row0_col49\" class=\"data row0 col49\" >42.050000</td>\n",
-       "      <td id=\"T_78f41_row0_col50\" class=\"data row0 col50\" >12.419907</td>\n",
-       "      <td id=\"T_78f41_row0_col51\" class=\"data row0 col51\" >41.903477</td>\n",
-       "      <td id=\"T_78f41_row0_col52\" class=\"data row0 col52\" >Europe/Vatican</td>\n",
-       "      <td id=\"T_78f41_row0_col53\" class=\"data row0 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col54\" class=\"data row0 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col55\" class=\"data row0 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col56\" class=\"data row0 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col57\" class=\"data row0 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col58\" class=\"data row0 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col59\" class=\"data row0 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col60\" class=\"data row0 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col61\" class=\"data row0 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col62\" class=\"data row0 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col63\" class=\"data row0 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col64\" class=\"data row0 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col65\" class=\"data row0 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col66\" class=\"data row0 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col67\" class=\"data row0 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col68\" class=\"data row0 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col69\" class=\"data row0 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col70\" class=\"data row0 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col71\" class=\"data row0 col71\" >7.000000</td>\n",
-       "      <td id=\"T_78f41_row0_col72\" class=\"data row0 col72\" >Q237</td>\n",
-       "      <td id=\"T_78f41_row0_col73\" class=\"data row0 col73\" >101914185</td>\n",
-       "      <td id=\"T_78f41_row0_col74\" class=\"data row0 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row0_col75\" class=\"data row0 col75\" >Vatican City</td>\n",
-       "      <td id=\"T_78f41_row0_col76\" class=\"data row0 col76\" >Vatikanstadt</td>\n",
-       "      <td id=\"T_78f41_row0_col77\" class=\"data row0 col77\" >Ciudad del Vaticano</td>\n",
-       "      <td id=\"T_78f41_row0_col78\" class=\"data row0 col78\" >Cité du Vatican</td>\n",
-       "      <td id=\"T_78f41_row0_col79\" class=\"data row0 col79\" >Vaticano</td>\n",
-       "      <td id=\"T_78f41_row0_col80\" class=\"data row0 col80\" >Ватикан</td>\n",
-       "      <td id=\"T_78f41_row0_col81\" class=\"data row0 col81\" >梵蒂冈</td>\n",
-       "      <td id=\"T_78f41_row0_col82\" class=\"data row0 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col83\" class=\"data row0 col83\" >الفاتيكان</td>\n",
-       "      <td id=\"T_78f41_row0_col84\" class=\"data row0 col84\" >ভ্যাটিকান সিটি</td>\n",
-       "      <td id=\"T_78f41_row0_col85\" class=\"data row0 col85\" >Βατικανό</td>\n",
-       "      <td id=\"T_78f41_row0_col86\" class=\"data row0 col86\" >वैटिकन नगर</td>\n",
-       "      <td id=\"T_78f41_row0_col87\" class=\"data row0 col87\" >Vatikán</td>\n",
-       "      <td id=\"T_78f41_row0_col88\" class=\"data row0 col88\" >Vatikan</td>\n",
-       "      <td id=\"T_78f41_row0_col89\" class=\"data row0 col89\" >Città del Vaticano</td>\n",
-       "      <td id=\"T_78f41_row0_col90\" class=\"data row0 col90\" >バチカン</td>\n",
-       "      <td id=\"T_78f41_row0_col91\" class=\"data row0 col91\" >바티칸 시국</td>\n",
-       "      <td id=\"T_78f41_row0_col92\" class=\"data row0 col92\" >Vaticaanstad</td>\n",
-       "      <td id=\"T_78f41_row0_col93\" class=\"data row0 col93\" >Watykan</td>\n",
-       "      <td id=\"T_78f41_row0_col94\" class=\"data row0 col94\" >Vatikanstaten</td>\n",
-       "      <td id=\"T_78f41_row0_col95\" class=\"data row0 col95\" >Vatikan</td>\n",
-       "      <td id=\"T_78f41_row0_col96\" class=\"data row0 col96\" >Thành Vatican</td>\n",
-       "      <td id=\"T_78f41_row0_col97\" class=\"data row0 col97\" >1159127243</td>\n",
-       "      <td id=\"T_78f41_row0_col98\" class=\"data row0 col98\" >واتیکان</td>\n",
-       "      <td id=\"T_78f41_row0_col99\" class=\"data row0 col99\" >קריית הוותיקן</td>\n",
-       "      <td id=\"T_78f41_row0_col100\" class=\"data row0 col100\" >Ватикан</td>\n",
-       "      <td id=\"T_78f41_row0_col101\" class=\"data row0 col101\" >ویٹیکن سٹی</td>\n",
-       "      <td id=\"T_78f41_row0_col102\" class=\"data row0 col102\" >梵蒂岡</td>\n",
-       "      <td id=\"T_78f41_row0_col103\" class=\"data row0 col103\" >6691831</td>\n",
-       "      <td id=\"T_78f41_row0_col104\" class=\"data row0 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col105\" class=\"data row0 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col106\" class=\"data row0 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col107\" class=\"data row0 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col108\" class=\"data row0 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col109\" class=\"data row0 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col110\" class=\"data row0 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col111\" class=\"data row0 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col112\" class=\"data row0 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col113\" class=\"data row0 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col114\" class=\"data row0 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col115\" class=\"data row0 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col116\" class=\"data row0 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col117\" class=\"data row0 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col118\" class=\"data row0 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col119\" class=\"data row0 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col120\" class=\"data row0 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col121\" class=\"data row0 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col122\" class=\"data row0 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col123\" class=\"data row0 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col124\" class=\"data row0 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col125\" class=\"data row0 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col126\" class=\"data row0 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col127\" class=\"data row0 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col128\" class=\"data row0 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col129\" class=\"data row0 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col130\" class=\"data row0 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col131\" class=\"data row0 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col132\" class=\"data row0 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col133\" class=\"data row0 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col134\" class=\"data row0 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col135\" class=\"data row0 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col136\" class=\"data row0 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row0_col137\" class=\"data row0 col137\" >POINT (12.4533865 41.9032822)</td>\n",
+       "      <th id=\"T_f91f0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_f91f0_row0_col0\" class=\"data row0 col0\" >8</td>\n",
+       "      <td id=\"T_f91f0_row0_col1\" class=\"data row0 col1\" >10</td>\n",
+       "      <td id=\"T_f91f0_row0_col2\" class=\"data row0 col2\" >3</td>\n",
+       "      <td id=\"T_f91f0_row0_col3\" class=\"data row0 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row0_col4\" class=\"data row0 col4\" >Vatican City</td>\n",
+       "      <td id=\"T_f91f0_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col7\" class=\"data row0 col7\" >Vatican City</td>\n",
+       "      <td id=\"T_f91f0_row0_col8\" class=\"data row0 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row0_col9\" class=\"data row0 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col10\" class=\"data row0 col10\" >1</td>\n",
+       "      <td id=\"T_f91f0_row0_col11\" class=\"data row0 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col12\" class=\"data row0 col12\" >Vatican</td>\n",
+       "      <td id=\"T_f91f0_row0_col13\" class=\"data row0 col13\" >VAT</td>\n",
+       "      <td id=\"T_f91f0_row0_col14\" class=\"data row0 col14\" >Vatican</td>\n",
+       "      <td id=\"T_f91f0_row0_col15\" class=\"data row0 col15\" >VAT</td>\n",
+       "      <td id=\"T_f91f0_row0_col16\" class=\"data row0 col16\" >Lazio</td>\n",
+       "      <td id=\"T_f91f0_row0_col17\" class=\"data row0 col17\" >VA</td>\n",
+       "      <td id=\"T_f91f0_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col19\" class=\"data row0 col19\" >41.903282</td>\n",
+       "      <td id=\"T_f91f0_row0_col20\" class=\"data row0 col20\" >12.453387</td>\n",
+       "      <td id=\"T_f91f0_row0_col21\" class=\"data row0 col21\" >832</td>\n",
+       "      <td id=\"T_f91f0_row0_col22\" class=\"data row0 col22\" >832</td>\n",
+       "      <td id=\"T_f91f0_row0_col23\" class=\"data row0 col23\" >562430</td>\n",
+       "      <td id=\"T_f91f0_row0_col24\" class=\"data row0 col24\" >2</td>\n",
+       "      <td id=\"T_f91f0_row0_col25\" class=\"data row0 col25\" >2</td>\n",
+       "      <td id=\"T_f91f0_row0_col26\" class=\"data row0 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col27\" class=\"data row0 col27\" >Vatican City</td>\n",
+       "      <td id=\"T_f91f0_row0_col28\" class=\"data row0 col28\" >636762</td>\n",
+       "      <td id=\"T_f91f0_row0_col29\" class=\"data row0 col29\" >636762</td>\n",
+       "      <td id=\"T_f91f0_row0_col30\" class=\"data row0 col30\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col31\" class=\"data row0 col31\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col32\" class=\"data row0 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col33\" class=\"data row0 col33\" >20</td>\n",
+       "      <td id=\"T_f91f0_row0_col34\" class=\"data row0 col34\" >177</td>\n",
+       "      <td id=\"T_f91f0_row0_col35\" class=\"data row0 col35\" >177</td>\n",
+       "      <td id=\"T_f91f0_row0_col36\" class=\"data row0 col36\" >68</td>\n",
+       "      <td id=\"T_f91f0_row0_col37\" class=\"data row0 col37\" >68</td>\n",
+       "      <td id=\"T_f91f0_row0_col38\" class=\"data row0 col38\" >160</td>\n",
+       "      <td id=\"T_f91f0_row0_col39\" class=\"data row0 col39\" >160</td>\n",
+       "      <td id=\"T_f91f0_row0_col40\" class=\"data row0 col40\" >99</td>\n",
+       "      <td id=\"T_f91f0_row0_col41\" class=\"data row0 col41\" >99</td>\n",
+       "      <td id=\"T_f91f0_row0_col42\" class=\"data row0 col42\" >12.333333</td>\n",
+       "      <td id=\"T_f91f0_row0_col43\" class=\"data row0 col43\" >12.333333</td>\n",
+       "      <td id=\"T_f91f0_row0_col44\" class=\"data row0 col44\" >12.481009</td>\n",
+       "      <td id=\"T_f91f0_row0_col45\" class=\"data row0 col45\" >12.481009</td>\n",
+       "      <td id=\"T_f91f0_row0_col46\" class=\"data row0 col46\" >41.766667</td>\n",
+       "      <td id=\"T_f91f0_row0_col47\" class=\"data row0 col47\" >41.766667</td>\n",
+       "      <td id=\"T_f91f0_row0_col48\" class=\"data row0 col48\" >42.050000</td>\n",
+       "      <td id=\"T_f91f0_row0_col49\" class=\"data row0 col49\" >42.050000</td>\n",
+       "      <td id=\"T_f91f0_row0_col50\" class=\"data row0 col50\" >12.419907</td>\n",
+       "      <td id=\"T_f91f0_row0_col51\" class=\"data row0 col51\" >41.903477</td>\n",
+       "      <td id=\"T_f91f0_row0_col52\" class=\"data row0 col52\" >Europe/Vatican</td>\n",
+       "      <td id=\"T_f91f0_row0_col53\" class=\"data row0 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col54\" class=\"data row0 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col55\" class=\"data row0 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col56\" class=\"data row0 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col57\" class=\"data row0 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col58\" class=\"data row0 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col59\" class=\"data row0 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col60\" class=\"data row0 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col61\" class=\"data row0 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col62\" class=\"data row0 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col63\" class=\"data row0 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col64\" class=\"data row0 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col65\" class=\"data row0 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col66\" class=\"data row0 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col67\" class=\"data row0 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col68\" class=\"data row0 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col69\" class=\"data row0 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col70\" class=\"data row0 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col71\" class=\"data row0 col71\" >7.000000</td>\n",
+       "      <td id=\"T_f91f0_row0_col72\" class=\"data row0 col72\" >Q237</td>\n",
+       "      <td id=\"T_f91f0_row0_col73\" class=\"data row0 col73\" >101914185</td>\n",
+       "      <td id=\"T_f91f0_row0_col74\" class=\"data row0 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row0_col75\" class=\"data row0 col75\" >Vatican City</td>\n",
+       "      <td id=\"T_f91f0_row0_col76\" class=\"data row0 col76\" >Vatikanstadt</td>\n",
+       "      <td id=\"T_f91f0_row0_col77\" class=\"data row0 col77\" >Ciudad del Vaticano</td>\n",
+       "      <td id=\"T_f91f0_row0_col78\" class=\"data row0 col78\" >Cité du Vatican</td>\n",
+       "      <td id=\"T_f91f0_row0_col79\" class=\"data row0 col79\" >Vaticano</td>\n",
+       "      <td id=\"T_f91f0_row0_col80\" class=\"data row0 col80\" >Ватикан</td>\n",
+       "      <td id=\"T_f91f0_row0_col81\" class=\"data row0 col81\" >梵蒂冈</td>\n",
+       "      <td id=\"T_f91f0_row0_col82\" class=\"data row0 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col83\" class=\"data row0 col83\" >الفاتيكان</td>\n",
+       "      <td id=\"T_f91f0_row0_col84\" class=\"data row0 col84\" >ভ্যাটিকান সিটি</td>\n",
+       "      <td id=\"T_f91f0_row0_col85\" class=\"data row0 col85\" >Βατικανό</td>\n",
+       "      <td id=\"T_f91f0_row0_col86\" class=\"data row0 col86\" >वैटिकन नगर</td>\n",
+       "      <td id=\"T_f91f0_row0_col87\" class=\"data row0 col87\" >Vatikán</td>\n",
+       "      <td id=\"T_f91f0_row0_col88\" class=\"data row0 col88\" >Vatikan</td>\n",
+       "      <td id=\"T_f91f0_row0_col89\" class=\"data row0 col89\" >Città del Vaticano</td>\n",
+       "      <td id=\"T_f91f0_row0_col90\" class=\"data row0 col90\" >バチカン</td>\n",
+       "      <td id=\"T_f91f0_row0_col91\" class=\"data row0 col91\" >바티칸 시국</td>\n",
+       "      <td id=\"T_f91f0_row0_col92\" class=\"data row0 col92\" >Vaticaanstad</td>\n",
+       "      <td id=\"T_f91f0_row0_col93\" class=\"data row0 col93\" >Watykan</td>\n",
+       "      <td id=\"T_f91f0_row0_col94\" class=\"data row0 col94\" >Vatikanstaten</td>\n",
+       "      <td id=\"T_f91f0_row0_col95\" class=\"data row0 col95\" >Vatikan</td>\n",
+       "      <td id=\"T_f91f0_row0_col96\" class=\"data row0 col96\" >Thành Vatican</td>\n",
+       "      <td id=\"T_f91f0_row0_col97\" class=\"data row0 col97\" >1159127243</td>\n",
+       "      <td id=\"T_f91f0_row0_col98\" class=\"data row0 col98\" >واتیکان</td>\n",
+       "      <td id=\"T_f91f0_row0_col99\" class=\"data row0 col99\" >קריית הוותיקן</td>\n",
+       "      <td id=\"T_f91f0_row0_col100\" class=\"data row0 col100\" >Ватикан</td>\n",
+       "      <td id=\"T_f91f0_row0_col101\" class=\"data row0 col101\" >ویٹیکن سٹی</td>\n",
+       "      <td id=\"T_f91f0_row0_col102\" class=\"data row0 col102\" >梵蒂岡</td>\n",
+       "      <td id=\"T_f91f0_row0_col103\" class=\"data row0 col103\" >6691831</td>\n",
+       "      <td id=\"T_f91f0_row0_col104\" class=\"data row0 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col105\" class=\"data row0 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col106\" class=\"data row0 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col107\" class=\"data row0 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col108\" class=\"data row0 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col109\" class=\"data row0 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col110\" class=\"data row0 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col111\" class=\"data row0 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col112\" class=\"data row0 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col113\" class=\"data row0 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col114\" class=\"data row0 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col115\" class=\"data row0 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col116\" class=\"data row0 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col117\" class=\"data row0 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col118\" class=\"data row0 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col119\" class=\"data row0 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col120\" class=\"data row0 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col121\" class=\"data row0 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col122\" class=\"data row0 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col123\" class=\"data row0 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col124\" class=\"data row0 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col125\" class=\"data row0 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col126\" class=\"data row0 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col127\" class=\"data row0 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col128\" class=\"data row0 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col129\" class=\"data row0 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col130\" class=\"data row0 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col131\" class=\"data row0 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col132\" class=\"data row0 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col133\" class=\"data row0 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col134\" class=\"data row0 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col135\" class=\"data row0 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col136\" class=\"data row0 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row0_col137\" class=\"data row0 col137\" >POINT (12.4533865 41.9032822)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_78f41_row1_col0\" class=\"data row1 col0\" >7</td>\n",
-       "      <td id=\"T_78f41_row1_col1\" class=\"data row1 col1\" >20</td>\n",
-       "      <td id=\"T_78f41_row1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col3\" class=\"data row1 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row1_col4\" class=\"data row1 col4\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col7\" class=\"data row1 col7\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col8\" class=\"data row1 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col10\" class=\"data row1 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col11\" class=\"data row1 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col12\" class=\"data row1 col12\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col13\" class=\"data row1 col13\" >SMR</td>\n",
-       "      <td id=\"T_78f41_row1_col14\" class=\"data row1 col14\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col15\" class=\"data row1 col15\" >SMR</td>\n",
-       "      <td id=\"T_78f41_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col17\" class=\"data row1 col17\" >SM</td>\n",
-       "      <td id=\"T_78f41_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col19\" class=\"data row1 col19\" >43.936096</td>\n",
-       "      <td id=\"T_78f41_row1_col20\" class=\"data row1 col20\" >12.441770</td>\n",
-       "      <td id=\"T_78f41_row1_col21\" class=\"data row1 col21\" >29579</td>\n",
-       "      <td id=\"T_78f41_row1_col22\" class=\"data row1 col22\" >29000</td>\n",
-       "      <td id=\"T_78f41_row1_col23\" class=\"data row1 col23\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col24\" class=\"data row1 col24\" >7</td>\n",
-       "      <td id=\"T_78f41_row1_col25\" class=\"data row1 col25\" >7</td>\n",
-       "      <td id=\"T_78f41_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col27\" class=\"data row1 col27\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col28\" class=\"data row1 col28\" >29088</td>\n",
-       "      <td id=\"T_78f41_row1_col29\" class=\"data row1 col29\" >29579</td>\n",
-       "      <td id=\"T_78f41_row1_col30\" class=\"data row1 col30\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col31\" class=\"data row1 col31\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col32\" class=\"data row1 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col33\" class=\"data row1 col33\" >20</td>\n",
-       "      <td id=\"T_78f41_row1_col34\" class=\"data row1 col34\" >30</td>\n",
-       "      <td id=\"T_78f41_row1_col35\" class=\"data row1 col35\" >30</td>\n",
-       "      <td id=\"T_78f41_row1_col36\" class=\"data row1 col36\" >11</td>\n",
-       "      <td id=\"T_78f41_row1_col37\" class=\"data row1 col37\" >11</td>\n",
-       "      <td id=\"T_78f41_row1_col38\" class=\"data row1 col38\" >63</td>\n",
-       "      <td id=\"T_78f41_row1_col39\" class=\"data row1 col39\" >63</td>\n",
-       "      <td id=\"T_78f41_row1_col40\" class=\"data row1 col40\" >39</td>\n",
-       "      <td id=\"T_78f41_row1_col41\" class=\"data row1 col41\" >39</td>\n",
-       "      <td id=\"T_78f41_row1_col42\" class=\"data row1 col42\" >12.391667</td>\n",
-       "      <td id=\"T_78f41_row1_col43\" class=\"data row1 col43\" >12.391667</td>\n",
-       "      <td id=\"T_78f41_row1_col44\" class=\"data row1 col44\" >12.541667</td>\n",
-       "      <td id=\"T_78f41_row1_col45\" class=\"data row1 col45\" >12.541667</td>\n",
-       "      <td id=\"T_78f41_row1_col46\" class=\"data row1 col46\" >43.900000</td>\n",
-       "      <td id=\"T_78f41_row1_col47\" class=\"data row1 col47\" >43.900000</td>\n",
-       "      <td id=\"T_78f41_row1_col48\" class=\"data row1 col48\" >44.000000</td>\n",
-       "      <td id=\"T_78f41_row1_col49\" class=\"data row1 col49\" >44.000000</td>\n",
-       "      <td id=\"T_78f41_row1_col50\" class=\"data row1 col50\" >12.462153</td>\n",
-       "      <td id=\"T_78f41_row1_col51\" class=\"data row1 col51\" >43.953472</td>\n",
-       "      <td id=\"T_78f41_row1_col52\" class=\"data row1 col52\" >Europe/San_Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col53\" class=\"data row1 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col54\" class=\"data row1 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col55\" class=\"data row1 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col56\" class=\"data row1 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col57\" class=\"data row1 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col58\" class=\"data row1 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col59\" class=\"data row1 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col60\" class=\"data row1 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col61\" class=\"data row1 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col62\" class=\"data row1 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col63\" class=\"data row1 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col64\" class=\"data row1 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col65\" class=\"data row1 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col66\" class=\"data row1 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col67\" class=\"data row1 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col68\" class=\"data row1 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col69\" class=\"data row1 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col70\" class=\"data row1 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col71\" class=\"data row1 col71\" >6.100000</td>\n",
-       "      <td id=\"T_78f41_row1_col72\" class=\"data row1 col72\" >Q1848</td>\n",
-       "      <td id=\"T_78f41_row1_col73\" class=\"data row1 col73\" >101837381</td>\n",
-       "      <td id=\"T_78f41_row1_col74\" class=\"data row1 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row1_col75\" class=\"data row1 col75\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col76\" class=\"data row1 col76\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col77\" class=\"data row1 col77\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col78\" class=\"data row1 col78\" >Saint-Marin</td>\n",
-       "      <td id=\"T_78f41_row1_col79\" class=\"data row1 col79\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col80\" class=\"data row1 col80\" >Сан-Марино</td>\n",
-       "      <td id=\"T_78f41_row1_col81\" class=\"data row1 col81\" >圣马力诺</td>\n",
-       "      <td id=\"T_78f41_row1_col82\" class=\"data row1 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col83\" class=\"data row1 col83\" >مدينة سان مارينو</td>\n",
-       "      <td id=\"T_78f41_row1_col84\" class=\"data row1 col84\" >সান মারিনো</td>\n",
-       "      <td id=\"T_78f41_row1_col85\" class=\"data row1 col85\" >Άγιος Μαρίνος</td>\n",
-       "      <td id=\"T_78f41_row1_col86\" class=\"data row1 col86\" >सैन मारिनो नगर</td>\n",
-       "      <td id=\"T_78f41_row1_col87\" class=\"data row1 col87\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col88\" class=\"data row1 col88\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col89\" class=\"data row1 col89\" >Città di San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col90\" class=\"data row1 col90\" >サンマリノ市</td>\n",
-       "      <td id=\"T_78f41_row1_col91\" class=\"data row1 col91\" >산마리노</td>\n",
-       "      <td id=\"T_78f41_row1_col92\" class=\"data row1 col92\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col93\" class=\"data row1 col93\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col94\" class=\"data row1 col94\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col95\" class=\"data row1 col95\" >San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col96\" class=\"data row1 col96\" >Thành phố San Marino</td>\n",
-       "      <td id=\"T_78f41_row1_col97\" class=\"data row1 col97\" >1159146051</td>\n",
-       "      <td id=\"T_78f41_row1_col98\" class=\"data row1 col98\" >سن مارینو</td>\n",
-       "      <td id=\"T_78f41_row1_col99\" class=\"data row1 col99\" >סן מרינו</td>\n",
-       "      <td id=\"T_78f41_row1_col100\" class=\"data row1 col100\" >Сан-Марино</td>\n",
-       "      <td id=\"T_78f41_row1_col101\" class=\"data row1 col101\" >سان مارینو شہر</td>\n",
-       "      <td id=\"T_78f41_row1_col102\" class=\"data row1 col102\" >聖馬力諾</td>\n",
-       "      <td id=\"T_78f41_row1_col103\" class=\"data row1 col103\" >3168070</td>\n",
-       "      <td id=\"T_78f41_row1_col104\" class=\"data row1 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col105\" class=\"data row1 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col106\" class=\"data row1 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col107\" class=\"data row1 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col108\" class=\"data row1 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col109\" class=\"data row1 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col110\" class=\"data row1 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col111\" class=\"data row1 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col112\" class=\"data row1 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col113\" class=\"data row1 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col114\" class=\"data row1 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col115\" class=\"data row1 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col116\" class=\"data row1 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col117\" class=\"data row1 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col118\" class=\"data row1 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col119\" class=\"data row1 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col120\" class=\"data row1 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col121\" class=\"data row1 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col122\" class=\"data row1 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col123\" class=\"data row1 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col124\" class=\"data row1 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col125\" class=\"data row1 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col126\" class=\"data row1 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col127\" class=\"data row1 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col128\" class=\"data row1 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col129\" class=\"data row1 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col130\" class=\"data row1 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col131\" class=\"data row1 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col132\" class=\"data row1 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col133\" class=\"data row1 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col134\" class=\"data row1 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col135\" class=\"data row1 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col136\" class=\"data row1 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row1_col137\" class=\"data row1 col137\" >POINT (12.4417702 43.9360958)</td>\n",
+       "      <th id=\"T_f91f0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_f91f0_row1_col0\" class=\"data row1 col0\" >7</td>\n",
+       "      <td id=\"T_f91f0_row1_col1\" class=\"data row1 col1\" >20</td>\n",
+       "      <td id=\"T_f91f0_row1_col2\" class=\"data row1 col2\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col3\" class=\"data row1 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row1_col4\" class=\"data row1 col4\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col7\" class=\"data row1 col7\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col8\" class=\"data row1 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col10\" class=\"data row1 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col11\" class=\"data row1 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col12\" class=\"data row1 col12\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col13\" class=\"data row1 col13\" >SMR</td>\n",
+       "      <td id=\"T_f91f0_row1_col14\" class=\"data row1 col14\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col15\" class=\"data row1 col15\" >SMR</td>\n",
+       "      <td id=\"T_f91f0_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col17\" class=\"data row1 col17\" >SM</td>\n",
+       "      <td id=\"T_f91f0_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col19\" class=\"data row1 col19\" >43.936096</td>\n",
+       "      <td id=\"T_f91f0_row1_col20\" class=\"data row1 col20\" >12.441770</td>\n",
+       "      <td id=\"T_f91f0_row1_col21\" class=\"data row1 col21\" >29579</td>\n",
+       "      <td id=\"T_f91f0_row1_col22\" class=\"data row1 col22\" >29000</td>\n",
+       "      <td id=\"T_f91f0_row1_col23\" class=\"data row1 col23\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col24\" class=\"data row1 col24\" >7</td>\n",
+       "      <td id=\"T_f91f0_row1_col25\" class=\"data row1 col25\" >7</td>\n",
+       "      <td id=\"T_f91f0_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col27\" class=\"data row1 col27\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col28\" class=\"data row1 col28\" >29088</td>\n",
+       "      <td id=\"T_f91f0_row1_col29\" class=\"data row1 col29\" >29579</td>\n",
+       "      <td id=\"T_f91f0_row1_col30\" class=\"data row1 col30\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col31\" class=\"data row1 col31\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col32\" class=\"data row1 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col33\" class=\"data row1 col33\" >20</td>\n",
+       "      <td id=\"T_f91f0_row1_col34\" class=\"data row1 col34\" >30</td>\n",
+       "      <td id=\"T_f91f0_row1_col35\" class=\"data row1 col35\" >30</td>\n",
+       "      <td id=\"T_f91f0_row1_col36\" class=\"data row1 col36\" >11</td>\n",
+       "      <td id=\"T_f91f0_row1_col37\" class=\"data row1 col37\" >11</td>\n",
+       "      <td id=\"T_f91f0_row1_col38\" class=\"data row1 col38\" >63</td>\n",
+       "      <td id=\"T_f91f0_row1_col39\" class=\"data row1 col39\" >63</td>\n",
+       "      <td id=\"T_f91f0_row1_col40\" class=\"data row1 col40\" >39</td>\n",
+       "      <td id=\"T_f91f0_row1_col41\" class=\"data row1 col41\" >39</td>\n",
+       "      <td id=\"T_f91f0_row1_col42\" class=\"data row1 col42\" >12.391667</td>\n",
+       "      <td id=\"T_f91f0_row1_col43\" class=\"data row1 col43\" >12.391667</td>\n",
+       "      <td id=\"T_f91f0_row1_col44\" class=\"data row1 col44\" >12.541667</td>\n",
+       "      <td id=\"T_f91f0_row1_col45\" class=\"data row1 col45\" >12.541667</td>\n",
+       "      <td id=\"T_f91f0_row1_col46\" class=\"data row1 col46\" >43.900000</td>\n",
+       "      <td id=\"T_f91f0_row1_col47\" class=\"data row1 col47\" >43.900000</td>\n",
+       "      <td id=\"T_f91f0_row1_col48\" class=\"data row1 col48\" >44.000000</td>\n",
+       "      <td id=\"T_f91f0_row1_col49\" class=\"data row1 col49\" >44.000000</td>\n",
+       "      <td id=\"T_f91f0_row1_col50\" class=\"data row1 col50\" >12.462153</td>\n",
+       "      <td id=\"T_f91f0_row1_col51\" class=\"data row1 col51\" >43.953472</td>\n",
+       "      <td id=\"T_f91f0_row1_col52\" class=\"data row1 col52\" >Europe/San_Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col53\" class=\"data row1 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col54\" class=\"data row1 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col55\" class=\"data row1 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col56\" class=\"data row1 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col57\" class=\"data row1 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col58\" class=\"data row1 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col59\" class=\"data row1 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col60\" class=\"data row1 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col61\" class=\"data row1 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col62\" class=\"data row1 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col63\" class=\"data row1 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col64\" class=\"data row1 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col65\" class=\"data row1 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col66\" class=\"data row1 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col67\" class=\"data row1 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col68\" class=\"data row1 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col69\" class=\"data row1 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col70\" class=\"data row1 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col71\" class=\"data row1 col71\" >6.100000</td>\n",
+       "      <td id=\"T_f91f0_row1_col72\" class=\"data row1 col72\" >Q1848</td>\n",
+       "      <td id=\"T_f91f0_row1_col73\" class=\"data row1 col73\" >101837381</td>\n",
+       "      <td id=\"T_f91f0_row1_col74\" class=\"data row1 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row1_col75\" class=\"data row1 col75\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col76\" class=\"data row1 col76\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col77\" class=\"data row1 col77\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col78\" class=\"data row1 col78\" >Saint-Marin</td>\n",
+       "      <td id=\"T_f91f0_row1_col79\" class=\"data row1 col79\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col80\" class=\"data row1 col80\" >Сан-Марино</td>\n",
+       "      <td id=\"T_f91f0_row1_col81\" class=\"data row1 col81\" >圣马力诺</td>\n",
+       "      <td id=\"T_f91f0_row1_col82\" class=\"data row1 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col83\" class=\"data row1 col83\" >مدينة سان مارينو</td>\n",
+       "      <td id=\"T_f91f0_row1_col84\" class=\"data row1 col84\" >সান মারিনো</td>\n",
+       "      <td id=\"T_f91f0_row1_col85\" class=\"data row1 col85\" >Άγιος Μαρίνος</td>\n",
+       "      <td id=\"T_f91f0_row1_col86\" class=\"data row1 col86\" >सैन मारिनो नगर</td>\n",
+       "      <td id=\"T_f91f0_row1_col87\" class=\"data row1 col87\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col88\" class=\"data row1 col88\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col89\" class=\"data row1 col89\" >Città di San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col90\" class=\"data row1 col90\" >サンマリノ市</td>\n",
+       "      <td id=\"T_f91f0_row1_col91\" class=\"data row1 col91\" >산마리노</td>\n",
+       "      <td id=\"T_f91f0_row1_col92\" class=\"data row1 col92\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col93\" class=\"data row1 col93\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col94\" class=\"data row1 col94\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col95\" class=\"data row1 col95\" >San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col96\" class=\"data row1 col96\" >Thành phố San Marino</td>\n",
+       "      <td id=\"T_f91f0_row1_col97\" class=\"data row1 col97\" >1159146051</td>\n",
+       "      <td id=\"T_f91f0_row1_col98\" class=\"data row1 col98\" >سن مارینو</td>\n",
+       "      <td id=\"T_f91f0_row1_col99\" class=\"data row1 col99\" >סן מרינו</td>\n",
+       "      <td id=\"T_f91f0_row1_col100\" class=\"data row1 col100\" >Сан-Марино</td>\n",
+       "      <td id=\"T_f91f0_row1_col101\" class=\"data row1 col101\" >سان مارینو شہر</td>\n",
+       "      <td id=\"T_f91f0_row1_col102\" class=\"data row1 col102\" >聖馬力諾</td>\n",
+       "      <td id=\"T_f91f0_row1_col103\" class=\"data row1 col103\" >3168070</td>\n",
+       "      <td id=\"T_f91f0_row1_col104\" class=\"data row1 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col105\" class=\"data row1 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col106\" class=\"data row1 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col107\" class=\"data row1 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col108\" class=\"data row1 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col109\" class=\"data row1 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col110\" class=\"data row1 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col111\" class=\"data row1 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col112\" class=\"data row1 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col113\" class=\"data row1 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col114\" class=\"data row1 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col115\" class=\"data row1 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col116\" class=\"data row1 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col117\" class=\"data row1 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col118\" class=\"data row1 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col119\" class=\"data row1 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col120\" class=\"data row1 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col121\" class=\"data row1 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col122\" class=\"data row1 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col123\" class=\"data row1 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col124\" class=\"data row1 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col125\" class=\"data row1 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col126\" class=\"data row1 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col127\" class=\"data row1 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col128\" class=\"data row1 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col129\" class=\"data row1 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col130\" class=\"data row1 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col131\" class=\"data row1 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col132\" class=\"data row1 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col133\" class=\"data row1 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col134\" class=\"data row1 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col135\" class=\"data row1 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col136\" class=\"data row1 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row1_col137\" class=\"data row1 col137\" >POINT (12.4417702 43.9360958)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_78f41_row2_col0\" class=\"data row2 col0\" >7</td>\n",
-       "      <td id=\"T_78f41_row2_col1\" class=\"data row2 col1\" >20</td>\n",
-       "      <td id=\"T_78f41_row2_col2\" class=\"data row2 col2\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col3\" class=\"data row2 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row2_col4\" class=\"data row2 col4\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col7\" class=\"data row2 col7\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col8\" class=\"data row2 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row2_col9\" class=\"data row2 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col10\" class=\"data row2 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col11\" class=\"data row2 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col12\" class=\"data row2 col12\" >Liechtenstein</td>\n",
-       "      <td id=\"T_78f41_row2_col13\" class=\"data row2 col13\" >LIE</td>\n",
-       "      <td id=\"T_78f41_row2_col14\" class=\"data row2 col14\" >Liechtenstein</td>\n",
-       "      <td id=\"T_78f41_row2_col15\" class=\"data row2 col15\" >LIE</td>\n",
-       "      <td id=\"T_78f41_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col17\" class=\"data row2 col17\" >LI</td>\n",
-       "      <td id=\"T_78f41_row2_col18\" class=\"data row2 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col19\" class=\"data row2 col19\" >47.133724</td>\n",
-       "      <td id=\"T_78f41_row2_col20\" class=\"data row2 col20\" >9.516670</td>\n",
-       "      <td id=\"T_78f41_row2_col21\" class=\"data row2 col21\" >36281</td>\n",
-       "      <td id=\"T_78f41_row2_col22\" class=\"data row2 col22\" >5342</td>\n",
-       "      <td id=\"T_78f41_row2_col23\" class=\"data row2 col23\" >33009</td>\n",
-       "      <td id=\"T_78f41_row2_col24\" class=\"data row2 col24\" >7</td>\n",
-       "      <td id=\"T_78f41_row2_col25\" class=\"data row2 col25\" >5</td>\n",
-       "      <td id=\"T_78f41_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col27\" class=\"data row2 col27\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col28\" class=\"data row2 col28\" >45442</td>\n",
-       "      <td id=\"T_78f41_row2_col29\" class=\"data row2 col29\" >45442</td>\n",
-       "      <td id=\"T_78f41_row2_col30\" class=\"data row2 col30\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col31\" class=\"data row2 col31\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col32\" class=\"data row2 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col33\" class=\"data row2 col33\" >20</td>\n",
-       "      <td id=\"T_78f41_row2_col34\" class=\"data row2 col34\" >45</td>\n",
-       "      <td id=\"T_78f41_row2_col35\" class=\"data row2 col35\" >45</td>\n",
-       "      <td id=\"T_78f41_row2_col36\" class=\"data row2 col36\" >17</td>\n",
-       "      <td id=\"T_78f41_row2_col37\" class=\"data row2 col37\" >17</td>\n",
-       "      <td id=\"T_78f41_row2_col38\" class=\"data row2 col38\" >90</td>\n",
-       "      <td id=\"T_78f41_row2_col39\" class=\"data row2 col39\" >90</td>\n",
-       "      <td id=\"T_78f41_row2_col40\" class=\"data row2 col40\" >56</td>\n",
-       "      <td id=\"T_78f41_row2_col41\" class=\"data row2 col41\" >56</td>\n",
-       "      <td id=\"T_78f41_row2_col42\" class=\"data row2 col42\" >9.433333</td>\n",
-       "      <td id=\"T_78f41_row2_col43\" class=\"data row2 col43\" >9.433333</td>\n",
-       "      <td id=\"T_78f41_row2_col44\" class=\"data row2 col44\" >9.558333</td>\n",
-       "      <td id=\"T_78f41_row2_col45\" class=\"data row2 col45\" >9.558333</td>\n",
-       "      <td id=\"T_78f41_row2_col46\" class=\"data row2 col46\" >47.091667</td>\n",
-       "      <td id=\"T_78f41_row2_col47\" class=\"data row2 col47\" >47.091667</td>\n",
-       "      <td id=\"T_78f41_row2_col48\" class=\"data row2 col48\" >47.233333</td>\n",
-       "      <td id=\"T_78f41_row2_col49\" class=\"data row2 col49\" >47.233333</td>\n",
-       "      <td id=\"T_78f41_row2_col50\" class=\"data row2 col50\" >9.503734</td>\n",
-       "      <td id=\"T_78f41_row2_col51\" class=\"data row2 col51\" >47.167478</td>\n",
-       "      <td id=\"T_78f41_row2_col52\" class=\"data row2 col52\" >Europe/Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col53\" class=\"data row2 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col54\" class=\"data row2 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col55\" class=\"data row2 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col56\" class=\"data row2 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col57\" class=\"data row2 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col58\" class=\"data row2 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col59\" class=\"data row2 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col60\" class=\"data row2 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col61\" class=\"data row2 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col62\" class=\"data row2 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col63\" class=\"data row2 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col64\" class=\"data row2 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col65\" class=\"data row2 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col66\" class=\"data row2 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col67\" class=\"data row2 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col68\" class=\"data row2 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col69\" class=\"data row2 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col70\" class=\"data row2 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col71\" class=\"data row2 col71\" >6.700000</td>\n",
-       "      <td id=\"T_78f41_row2_col72\" class=\"data row2 col72\" >Q1844</td>\n",
-       "      <td id=\"T_78f41_row2_col73\" class=\"data row2 col73\" >101828603</td>\n",
-       "      <td id=\"T_78f41_row2_col74\" class=\"data row2 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row2_col75\" class=\"data row2 col75\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col76\" class=\"data row2 col76\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col77\" class=\"data row2 col77\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col78\" class=\"data row2 col78\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col79\" class=\"data row2 col79\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col80\" class=\"data row2 col80\" >Вадуц</td>\n",
-       "      <td id=\"T_78f41_row2_col81\" class=\"data row2 col81\" >瓦都兹</td>\n",
-       "      <td id=\"T_78f41_row2_col82\" class=\"data row2 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col83\" class=\"data row2 col83\" >فادوتس</td>\n",
-       "      <td id=\"T_78f41_row2_col84\" class=\"data row2 col84\" >ফাডুৎস</td>\n",
-       "      <td id=\"T_78f41_row2_col85\" class=\"data row2 col85\" >Βαντούζ</td>\n",
-       "      <td id=\"T_78f41_row2_col86\" class=\"data row2 col86\" >वादुज़</td>\n",
-       "      <td id=\"T_78f41_row2_col87\" class=\"data row2 col87\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col88\" class=\"data row2 col88\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col89\" class=\"data row2 col89\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col90\" class=\"data row2 col90\" >ファドゥーツ</td>\n",
-       "      <td id=\"T_78f41_row2_col91\" class=\"data row2 col91\" >파두츠</td>\n",
-       "      <td id=\"T_78f41_row2_col92\" class=\"data row2 col92\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col93\" class=\"data row2 col93\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col94\" class=\"data row2 col94\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col95\" class=\"data row2 col95\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col96\" class=\"data row2 col96\" >Vaduz</td>\n",
-       "      <td id=\"T_78f41_row2_col97\" class=\"data row2 col97\" >1159146061</td>\n",
-       "      <td id=\"T_78f41_row2_col98\" class=\"data row2 col98\" >فادوتس</td>\n",
-       "      <td id=\"T_78f41_row2_col99\" class=\"data row2 col99\" >ואדוץ</td>\n",
-       "      <td id=\"T_78f41_row2_col100\" class=\"data row2 col100\" >Вадуц</td>\n",
-       "      <td id=\"T_78f41_row2_col101\" class=\"data row2 col101\" >واڈوز</td>\n",
-       "      <td id=\"T_78f41_row2_col102\" class=\"data row2 col102\" >華杜茲</td>\n",
-       "      <td id=\"T_78f41_row2_col103\" class=\"data row2 col103\" >3042030</td>\n",
-       "      <td id=\"T_78f41_row2_col104\" class=\"data row2 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col105\" class=\"data row2 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col106\" class=\"data row2 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col107\" class=\"data row2 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col108\" class=\"data row2 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col109\" class=\"data row2 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col110\" class=\"data row2 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col111\" class=\"data row2 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col112\" class=\"data row2 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col113\" class=\"data row2 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col114\" class=\"data row2 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col115\" class=\"data row2 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col116\" class=\"data row2 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col117\" class=\"data row2 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col118\" class=\"data row2 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col119\" class=\"data row2 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col120\" class=\"data row2 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col121\" class=\"data row2 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col122\" class=\"data row2 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col123\" class=\"data row2 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col124\" class=\"data row2 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col125\" class=\"data row2 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col126\" class=\"data row2 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col127\" class=\"data row2 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col128\" class=\"data row2 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col129\" class=\"data row2 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col130\" class=\"data row2 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col131\" class=\"data row2 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col132\" class=\"data row2 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col133\" class=\"data row2 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col134\" class=\"data row2 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col135\" class=\"data row2 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col136\" class=\"data row2 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row2_col137\" class=\"data row2 col137\" >POINT (9.5166695 47.1337238)</td>\n",
+       "      <th id=\"T_f91f0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_f91f0_row2_col0\" class=\"data row2 col0\" >7</td>\n",
+       "      <td id=\"T_f91f0_row2_col1\" class=\"data row2 col1\" >20</td>\n",
+       "      <td id=\"T_f91f0_row2_col2\" class=\"data row2 col2\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col3\" class=\"data row2 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row2_col4\" class=\"data row2 col4\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col7\" class=\"data row2 col7\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col8\" class=\"data row2 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row2_col9\" class=\"data row2 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col10\" class=\"data row2 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col11\" class=\"data row2 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col12\" class=\"data row2 col12\" >Liechtenstein</td>\n",
+       "      <td id=\"T_f91f0_row2_col13\" class=\"data row2 col13\" >LIE</td>\n",
+       "      <td id=\"T_f91f0_row2_col14\" class=\"data row2 col14\" >Liechtenstein</td>\n",
+       "      <td id=\"T_f91f0_row2_col15\" class=\"data row2 col15\" >LIE</td>\n",
+       "      <td id=\"T_f91f0_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col17\" class=\"data row2 col17\" >LI</td>\n",
+       "      <td id=\"T_f91f0_row2_col18\" class=\"data row2 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col19\" class=\"data row2 col19\" >47.133724</td>\n",
+       "      <td id=\"T_f91f0_row2_col20\" class=\"data row2 col20\" >9.516670</td>\n",
+       "      <td id=\"T_f91f0_row2_col21\" class=\"data row2 col21\" >36281</td>\n",
+       "      <td id=\"T_f91f0_row2_col22\" class=\"data row2 col22\" >5342</td>\n",
+       "      <td id=\"T_f91f0_row2_col23\" class=\"data row2 col23\" >33009</td>\n",
+       "      <td id=\"T_f91f0_row2_col24\" class=\"data row2 col24\" >7</td>\n",
+       "      <td id=\"T_f91f0_row2_col25\" class=\"data row2 col25\" >5</td>\n",
+       "      <td id=\"T_f91f0_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col27\" class=\"data row2 col27\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col28\" class=\"data row2 col28\" >45442</td>\n",
+       "      <td id=\"T_f91f0_row2_col29\" class=\"data row2 col29\" >45442</td>\n",
+       "      <td id=\"T_f91f0_row2_col30\" class=\"data row2 col30\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col31\" class=\"data row2 col31\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col32\" class=\"data row2 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col33\" class=\"data row2 col33\" >20</td>\n",
+       "      <td id=\"T_f91f0_row2_col34\" class=\"data row2 col34\" >45</td>\n",
+       "      <td id=\"T_f91f0_row2_col35\" class=\"data row2 col35\" >45</td>\n",
+       "      <td id=\"T_f91f0_row2_col36\" class=\"data row2 col36\" >17</td>\n",
+       "      <td id=\"T_f91f0_row2_col37\" class=\"data row2 col37\" >17</td>\n",
+       "      <td id=\"T_f91f0_row2_col38\" class=\"data row2 col38\" >90</td>\n",
+       "      <td id=\"T_f91f0_row2_col39\" class=\"data row2 col39\" >90</td>\n",
+       "      <td id=\"T_f91f0_row2_col40\" class=\"data row2 col40\" >56</td>\n",
+       "      <td id=\"T_f91f0_row2_col41\" class=\"data row2 col41\" >56</td>\n",
+       "      <td id=\"T_f91f0_row2_col42\" class=\"data row2 col42\" >9.433333</td>\n",
+       "      <td id=\"T_f91f0_row2_col43\" class=\"data row2 col43\" >9.433333</td>\n",
+       "      <td id=\"T_f91f0_row2_col44\" class=\"data row2 col44\" >9.558333</td>\n",
+       "      <td id=\"T_f91f0_row2_col45\" class=\"data row2 col45\" >9.558333</td>\n",
+       "      <td id=\"T_f91f0_row2_col46\" class=\"data row2 col46\" >47.091667</td>\n",
+       "      <td id=\"T_f91f0_row2_col47\" class=\"data row2 col47\" >47.091667</td>\n",
+       "      <td id=\"T_f91f0_row2_col48\" class=\"data row2 col48\" >47.233333</td>\n",
+       "      <td id=\"T_f91f0_row2_col49\" class=\"data row2 col49\" >47.233333</td>\n",
+       "      <td id=\"T_f91f0_row2_col50\" class=\"data row2 col50\" >9.503734</td>\n",
+       "      <td id=\"T_f91f0_row2_col51\" class=\"data row2 col51\" >47.167478</td>\n",
+       "      <td id=\"T_f91f0_row2_col52\" class=\"data row2 col52\" >Europe/Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col53\" class=\"data row2 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col54\" class=\"data row2 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col55\" class=\"data row2 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col56\" class=\"data row2 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col57\" class=\"data row2 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col58\" class=\"data row2 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col59\" class=\"data row2 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col60\" class=\"data row2 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col61\" class=\"data row2 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col62\" class=\"data row2 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col63\" class=\"data row2 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col64\" class=\"data row2 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col65\" class=\"data row2 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col66\" class=\"data row2 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col67\" class=\"data row2 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col68\" class=\"data row2 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col69\" class=\"data row2 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col70\" class=\"data row2 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col71\" class=\"data row2 col71\" >6.700000</td>\n",
+       "      <td id=\"T_f91f0_row2_col72\" class=\"data row2 col72\" >Q1844</td>\n",
+       "      <td id=\"T_f91f0_row2_col73\" class=\"data row2 col73\" >101828603</td>\n",
+       "      <td id=\"T_f91f0_row2_col74\" class=\"data row2 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row2_col75\" class=\"data row2 col75\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col76\" class=\"data row2 col76\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col77\" class=\"data row2 col77\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col78\" class=\"data row2 col78\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col79\" class=\"data row2 col79\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col80\" class=\"data row2 col80\" >Вадуц</td>\n",
+       "      <td id=\"T_f91f0_row2_col81\" class=\"data row2 col81\" >瓦都兹</td>\n",
+       "      <td id=\"T_f91f0_row2_col82\" class=\"data row2 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col83\" class=\"data row2 col83\" >فادوتس</td>\n",
+       "      <td id=\"T_f91f0_row2_col84\" class=\"data row2 col84\" >ফাডুৎস</td>\n",
+       "      <td id=\"T_f91f0_row2_col85\" class=\"data row2 col85\" >Βαντούζ</td>\n",
+       "      <td id=\"T_f91f0_row2_col86\" class=\"data row2 col86\" >वादुज़</td>\n",
+       "      <td id=\"T_f91f0_row2_col87\" class=\"data row2 col87\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col88\" class=\"data row2 col88\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col89\" class=\"data row2 col89\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col90\" class=\"data row2 col90\" >ファドゥーツ</td>\n",
+       "      <td id=\"T_f91f0_row2_col91\" class=\"data row2 col91\" >파두츠</td>\n",
+       "      <td id=\"T_f91f0_row2_col92\" class=\"data row2 col92\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col93\" class=\"data row2 col93\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col94\" class=\"data row2 col94\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col95\" class=\"data row2 col95\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col96\" class=\"data row2 col96\" >Vaduz</td>\n",
+       "      <td id=\"T_f91f0_row2_col97\" class=\"data row2 col97\" >1159146061</td>\n",
+       "      <td id=\"T_f91f0_row2_col98\" class=\"data row2 col98\" >فادوتس</td>\n",
+       "      <td id=\"T_f91f0_row2_col99\" class=\"data row2 col99\" >ואדוץ</td>\n",
+       "      <td id=\"T_f91f0_row2_col100\" class=\"data row2 col100\" >Вадуц</td>\n",
+       "      <td id=\"T_f91f0_row2_col101\" class=\"data row2 col101\" >واڈوز</td>\n",
+       "      <td id=\"T_f91f0_row2_col102\" class=\"data row2 col102\" >華杜茲</td>\n",
+       "      <td id=\"T_f91f0_row2_col103\" class=\"data row2 col103\" >3042030</td>\n",
+       "      <td id=\"T_f91f0_row2_col104\" class=\"data row2 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col105\" class=\"data row2 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col106\" class=\"data row2 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col107\" class=\"data row2 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col108\" class=\"data row2 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col109\" class=\"data row2 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col110\" class=\"data row2 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col111\" class=\"data row2 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col112\" class=\"data row2 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col113\" class=\"data row2 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col114\" class=\"data row2 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col115\" class=\"data row2 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col116\" class=\"data row2 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col117\" class=\"data row2 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col118\" class=\"data row2 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col119\" class=\"data row2 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col120\" class=\"data row2 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col121\" class=\"data row2 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col122\" class=\"data row2 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col123\" class=\"data row2 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col124\" class=\"data row2 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col125\" class=\"data row2 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col126\" class=\"data row2 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col127\" class=\"data row2 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col128\" class=\"data row2 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col129\" class=\"data row2 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col130\" class=\"data row2 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col131\" class=\"data row2 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col132\" class=\"data row2 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col133\" class=\"data row2 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col134\" class=\"data row2 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col135\" class=\"data row2 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col136\" class=\"data row2 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row2_col137\" class=\"data row2 col137\" >POINT (9.5166695 47.1337238)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_78f41_row3_col0\" class=\"data row3 col0\" >6</td>\n",
-       "      <td id=\"T_78f41_row3_col1\" class=\"data row3 col1\" >30</td>\n",
-       "      <td id=\"T_78f41_row3_col2\" class=\"data row3 col2\" >8</td>\n",
-       "      <td id=\"T_78f41_row3_col3\" class=\"data row3 col3\" >Admin-0 capital alt</td>\n",
-       "      <td id=\"T_78f41_row3_col4\" class=\"data row3 col4\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col7\" class=\"data row3 col7\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col8\" class=\"data row3 col8\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col9\" class=\"data row3 col9\" >Legislative and</td>\n",
-       "      <td id=\"T_78f41_row3_col10\" class=\"data row3 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col11\" class=\"data row3 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col12\" class=\"data row3 col12\" >eSwatini</td>\n",
-       "      <td id=\"T_78f41_row3_col13\" class=\"data row3 col13\" >SWZ</td>\n",
-       "      <td id=\"T_78f41_row3_col14\" class=\"data row3 col14\" >eSwatini</td>\n",
-       "      <td id=\"T_78f41_row3_col15\" class=\"data row3 col15\" >SWZ</td>\n",
-       "      <td id=\"T_78f41_row3_col16\" class=\"data row3 col16\" >Manzini</td>\n",
-       "      <td id=\"T_78f41_row3_col17\" class=\"data row3 col17\" >SZ</td>\n",
-       "      <td id=\"T_78f41_row3_col18\" class=\"data row3 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col19\" class=\"data row3 col19\" >-26.466668</td>\n",
-       "      <td id=\"T_78f41_row3_col20\" class=\"data row3 col20\" >31.199997</td>\n",
-       "      <td id=\"T_78f41_row3_col21\" class=\"data row3 col21\" >9782</td>\n",
-       "      <td id=\"T_78f41_row3_col22\" class=\"data row3 col22\" >4557</td>\n",
-       "      <td id=\"T_78f41_row3_col23\" class=\"data row3 col23\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col24\" class=\"data row3 col24\" >5</td>\n",
-       "      <td id=\"T_78f41_row3_col25\" class=\"data row3 col25\" >4</td>\n",
-       "      <td id=\"T_78f41_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col27\" class=\"data row3 col27\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col28\" class=\"data row3 col28\" >9782</td>\n",
-       "      <td id=\"T_78f41_row3_col29\" class=\"data row3 col29\" >9782</td>\n",
-       "      <td id=\"T_78f41_row3_col30\" class=\"data row3 col30\" >9782</td>\n",
-       "      <td id=\"T_78f41_row3_col31\" class=\"data row3 col31\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col32\" class=\"data row3 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col33\" class=\"data row3 col33\" >50</td>\n",
-       "      <td id=\"T_78f41_row3_col34\" class=\"data row3 col34\" >18</td>\n",
-       "      <td id=\"T_78f41_row3_col35\" class=\"data row3 col35\" >18</td>\n",
-       "      <td id=\"T_78f41_row3_col36\" class=\"data row3 col36\" >7</td>\n",
-       "      <td id=\"T_78f41_row3_col37\" class=\"data row3 col37\" >7</td>\n",
-       "      <td id=\"T_78f41_row3_col38\" class=\"data row3 col38\" >32</td>\n",
-       "      <td id=\"T_78f41_row3_col39\" class=\"data row3 col39\" >32</td>\n",
-       "      <td id=\"T_78f41_row3_col40\" class=\"data row3 col40\" >20</td>\n",
-       "      <td id=\"T_78f41_row3_col41\" class=\"data row3 col41\" >20</td>\n",
-       "      <td id=\"T_78f41_row3_col42\" class=\"data row3 col42\" >31.183333</td>\n",
-       "      <td id=\"T_78f41_row3_col43\" class=\"data row3 col43\" >31.183333</td>\n",
-       "      <td id=\"T_78f41_row3_col44\" class=\"data row3 col44\" >31.233333</td>\n",
-       "      <td id=\"T_78f41_row3_col45\" class=\"data row3 col45\" >31.233333</td>\n",
-       "      <td id=\"T_78f41_row3_col46\" class=\"data row3 col46\" >-26.458333</td>\n",
-       "      <td id=\"T_78f41_row3_col47\" class=\"data row3 col47\" >-26.458333</td>\n",
-       "      <td id=\"T_78f41_row3_col48\" class=\"data row3 col48\" >-26.391667</td>\n",
-       "      <td id=\"T_78f41_row3_col49\" class=\"data row3 col49\" >-26.391667</td>\n",
-       "      <td id=\"T_78f41_row3_col50\" class=\"data row3 col50\" >31.201993</td>\n",
-       "      <td id=\"T_78f41_row3_col51\" class=\"data row3 col51\" >-26.430254</td>\n",
-       "      <td id=\"T_78f41_row3_col52\" class=\"data row3 col52\" >Africa/Mbabane</td>\n",
-       "      <td id=\"T_78f41_row3_col53\" class=\"data row3 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col54\" class=\"data row3 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col55\" class=\"data row3 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col56\" class=\"data row3 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col57\" class=\"data row3 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col58\" class=\"data row3 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col59\" class=\"data row3 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col60\" class=\"data row3 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col61\" class=\"data row3 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col62\" class=\"data row3 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col63\" class=\"data row3 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col64\" class=\"data row3 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col65\" class=\"data row3 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col66\" class=\"data row3 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col67\" class=\"data row3 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col68\" class=\"data row3 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col69\" class=\"data row3 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col70\" class=\"data row3 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row3_col71\" class=\"data row3 col71\" >6.000000</td>\n",
-       "      <td id=\"T_78f41_row3_col72\" class=\"data row3 col72\" >Q101418</td>\n",
-       "      <td id=\"T_78f41_row3_col73\" class=\"data row3 col73\" >421199783</td>\n",
-       "      <td id=\"T_78f41_row3_col74\" class=\"data row3 col74\" >1</td>\n",
-       "      <td id=\"T_78f41_row3_col75\" class=\"data row3 col75\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col76\" class=\"data row3 col76\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col77\" class=\"data row3 col77\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col78\" class=\"data row3 col78\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col79\" class=\"data row3 col79\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col80\" class=\"data row3 col80\" >Лобамба</td>\n",
-       "      <td id=\"T_78f41_row3_col81\" class=\"data row3 col81\" >洛班巴</td>\n",
-       "      <td id=\"T_78f41_row3_col82\" class=\"data row3 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col83\" class=\"data row3 col83\" >لوبامبا</td>\n",
-       "      <td id=\"T_78f41_row3_col84\" class=\"data row3 col84\" >লোবাম্বা</td>\n",
-       "      <td id=\"T_78f41_row3_col85\" class=\"data row3 col85\" >Λομπάμπα</td>\n",
-       "      <td id=\"T_78f41_row3_col86\" class=\"data row3 col86\" >लोबम्बा</td>\n",
-       "      <td id=\"T_78f41_row3_col87\" class=\"data row3 col87\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col88\" class=\"data row3 col88\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col89\" class=\"data row3 col89\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col90\" class=\"data row3 col90\" >ロバンバ</td>\n",
-       "      <td id=\"T_78f41_row3_col91\" class=\"data row3 col91\" >로밤바</td>\n",
-       "      <td id=\"T_78f41_row3_col92\" class=\"data row3 col92\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col93\" class=\"data row3 col93\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col94\" class=\"data row3 col94\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col95\" class=\"data row3 col95\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col96\" class=\"data row3 col96\" >Lobamba</td>\n",
-       "      <td id=\"T_78f41_row3_col97\" class=\"data row3 col97\" >1159146343</td>\n",
-       "      <td id=\"T_78f41_row3_col98\" class=\"data row3 col98\" >لوبامبا</td>\n",
-       "      <td id=\"T_78f41_row3_col99\" class=\"data row3 col99\" >לובמבה</td>\n",
-       "      <td id=\"T_78f41_row3_col100\" class=\"data row3 col100\" >Лобамба</td>\n",
-       "      <td id=\"T_78f41_row3_col101\" class=\"data row3 col101\" >لوبامبا</td>\n",
-       "      <td id=\"T_78f41_row3_col102\" class=\"data row3 col102\" >洛班巴</td>\n",
-       "      <td id=\"T_78f41_row3_col103\" class=\"data row3 col103\" >935048</td>\n",
-       "      <td id=\"T_78f41_row3_col104\" class=\"data row3 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col105\" class=\"data row3 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col106\" class=\"data row3 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col107\" class=\"data row3 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col108\" class=\"data row3 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col109\" class=\"data row3 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col110\" class=\"data row3 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col111\" class=\"data row3 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col112\" class=\"data row3 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col113\" class=\"data row3 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col114\" class=\"data row3 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col115\" class=\"data row3 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col116\" class=\"data row3 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col117\" class=\"data row3 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col118\" class=\"data row3 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col119\" class=\"data row3 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col120\" class=\"data row3 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col121\" class=\"data row3 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col122\" class=\"data row3 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col123\" class=\"data row3 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col124\" class=\"data row3 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col125\" class=\"data row3 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col126\" class=\"data row3 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col127\" class=\"data row3 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col128\" class=\"data row3 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col129\" class=\"data row3 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col130\" class=\"data row3 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col131\" class=\"data row3 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col132\" class=\"data row3 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col133\" class=\"data row3 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col134\" class=\"data row3 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col135\" class=\"data row3 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col136\" class=\"data row3 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row3_col137\" class=\"data row3 col137\" >POINT (31.1999971 -26.4666675)</td>\n",
+       "      <th id=\"T_f91f0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_f91f0_row3_col0\" class=\"data row3 col0\" >6</td>\n",
+       "      <td id=\"T_f91f0_row3_col1\" class=\"data row3 col1\" >30</td>\n",
+       "      <td id=\"T_f91f0_row3_col2\" class=\"data row3 col2\" >8</td>\n",
+       "      <td id=\"T_f91f0_row3_col3\" class=\"data row3 col3\" >Admin-0 capital alt</td>\n",
+       "      <td id=\"T_f91f0_row3_col4\" class=\"data row3 col4\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col7\" class=\"data row3 col7\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col8\" class=\"data row3 col8\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col9\" class=\"data row3 col9\" >Legislative and</td>\n",
+       "      <td id=\"T_f91f0_row3_col10\" class=\"data row3 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col11\" class=\"data row3 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col12\" class=\"data row3 col12\" >eSwatini</td>\n",
+       "      <td id=\"T_f91f0_row3_col13\" class=\"data row3 col13\" >SWZ</td>\n",
+       "      <td id=\"T_f91f0_row3_col14\" class=\"data row3 col14\" >eSwatini</td>\n",
+       "      <td id=\"T_f91f0_row3_col15\" class=\"data row3 col15\" >SWZ</td>\n",
+       "      <td id=\"T_f91f0_row3_col16\" class=\"data row3 col16\" >Manzini</td>\n",
+       "      <td id=\"T_f91f0_row3_col17\" class=\"data row3 col17\" >SZ</td>\n",
+       "      <td id=\"T_f91f0_row3_col18\" class=\"data row3 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col19\" class=\"data row3 col19\" >-26.466668</td>\n",
+       "      <td id=\"T_f91f0_row3_col20\" class=\"data row3 col20\" >31.199997</td>\n",
+       "      <td id=\"T_f91f0_row3_col21\" class=\"data row3 col21\" >9782</td>\n",
+       "      <td id=\"T_f91f0_row3_col22\" class=\"data row3 col22\" >4557</td>\n",
+       "      <td id=\"T_f91f0_row3_col23\" class=\"data row3 col23\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col24\" class=\"data row3 col24\" >5</td>\n",
+       "      <td id=\"T_f91f0_row3_col25\" class=\"data row3 col25\" >4</td>\n",
+       "      <td id=\"T_f91f0_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col27\" class=\"data row3 col27\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col28\" class=\"data row3 col28\" >9782</td>\n",
+       "      <td id=\"T_f91f0_row3_col29\" class=\"data row3 col29\" >9782</td>\n",
+       "      <td id=\"T_f91f0_row3_col30\" class=\"data row3 col30\" >9782</td>\n",
+       "      <td id=\"T_f91f0_row3_col31\" class=\"data row3 col31\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col32\" class=\"data row3 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col33\" class=\"data row3 col33\" >50</td>\n",
+       "      <td id=\"T_f91f0_row3_col34\" class=\"data row3 col34\" >18</td>\n",
+       "      <td id=\"T_f91f0_row3_col35\" class=\"data row3 col35\" >18</td>\n",
+       "      <td id=\"T_f91f0_row3_col36\" class=\"data row3 col36\" >7</td>\n",
+       "      <td id=\"T_f91f0_row3_col37\" class=\"data row3 col37\" >7</td>\n",
+       "      <td id=\"T_f91f0_row3_col38\" class=\"data row3 col38\" >32</td>\n",
+       "      <td id=\"T_f91f0_row3_col39\" class=\"data row3 col39\" >32</td>\n",
+       "      <td id=\"T_f91f0_row3_col40\" class=\"data row3 col40\" >20</td>\n",
+       "      <td id=\"T_f91f0_row3_col41\" class=\"data row3 col41\" >20</td>\n",
+       "      <td id=\"T_f91f0_row3_col42\" class=\"data row3 col42\" >31.183333</td>\n",
+       "      <td id=\"T_f91f0_row3_col43\" class=\"data row3 col43\" >31.183333</td>\n",
+       "      <td id=\"T_f91f0_row3_col44\" class=\"data row3 col44\" >31.233333</td>\n",
+       "      <td id=\"T_f91f0_row3_col45\" class=\"data row3 col45\" >31.233333</td>\n",
+       "      <td id=\"T_f91f0_row3_col46\" class=\"data row3 col46\" >-26.458333</td>\n",
+       "      <td id=\"T_f91f0_row3_col47\" class=\"data row3 col47\" >-26.458333</td>\n",
+       "      <td id=\"T_f91f0_row3_col48\" class=\"data row3 col48\" >-26.391667</td>\n",
+       "      <td id=\"T_f91f0_row3_col49\" class=\"data row3 col49\" >-26.391667</td>\n",
+       "      <td id=\"T_f91f0_row3_col50\" class=\"data row3 col50\" >31.201993</td>\n",
+       "      <td id=\"T_f91f0_row3_col51\" class=\"data row3 col51\" >-26.430254</td>\n",
+       "      <td id=\"T_f91f0_row3_col52\" class=\"data row3 col52\" >Africa/Mbabane</td>\n",
+       "      <td id=\"T_f91f0_row3_col53\" class=\"data row3 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col54\" class=\"data row3 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col55\" class=\"data row3 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col56\" class=\"data row3 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col57\" class=\"data row3 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col58\" class=\"data row3 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col59\" class=\"data row3 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col60\" class=\"data row3 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col61\" class=\"data row3 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col62\" class=\"data row3 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col63\" class=\"data row3 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col64\" class=\"data row3 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col65\" class=\"data row3 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col66\" class=\"data row3 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col67\" class=\"data row3 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col68\" class=\"data row3 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col69\" class=\"data row3 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col70\" class=\"data row3 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row3_col71\" class=\"data row3 col71\" >6.000000</td>\n",
+       "      <td id=\"T_f91f0_row3_col72\" class=\"data row3 col72\" >Q101418</td>\n",
+       "      <td id=\"T_f91f0_row3_col73\" class=\"data row3 col73\" >421199783</td>\n",
+       "      <td id=\"T_f91f0_row3_col74\" class=\"data row3 col74\" >1</td>\n",
+       "      <td id=\"T_f91f0_row3_col75\" class=\"data row3 col75\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col76\" class=\"data row3 col76\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col77\" class=\"data row3 col77\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col78\" class=\"data row3 col78\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col79\" class=\"data row3 col79\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col80\" class=\"data row3 col80\" >Лобамба</td>\n",
+       "      <td id=\"T_f91f0_row3_col81\" class=\"data row3 col81\" >洛班巴</td>\n",
+       "      <td id=\"T_f91f0_row3_col82\" class=\"data row3 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col83\" class=\"data row3 col83\" >لوبامبا</td>\n",
+       "      <td id=\"T_f91f0_row3_col84\" class=\"data row3 col84\" >লোবাম্বা</td>\n",
+       "      <td id=\"T_f91f0_row3_col85\" class=\"data row3 col85\" >Λομπάμπα</td>\n",
+       "      <td id=\"T_f91f0_row3_col86\" class=\"data row3 col86\" >लोबम्बा</td>\n",
+       "      <td id=\"T_f91f0_row3_col87\" class=\"data row3 col87\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col88\" class=\"data row3 col88\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col89\" class=\"data row3 col89\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col90\" class=\"data row3 col90\" >ロバンバ</td>\n",
+       "      <td id=\"T_f91f0_row3_col91\" class=\"data row3 col91\" >로밤바</td>\n",
+       "      <td id=\"T_f91f0_row3_col92\" class=\"data row3 col92\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col93\" class=\"data row3 col93\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col94\" class=\"data row3 col94\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col95\" class=\"data row3 col95\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col96\" class=\"data row3 col96\" >Lobamba</td>\n",
+       "      <td id=\"T_f91f0_row3_col97\" class=\"data row3 col97\" >1159146343</td>\n",
+       "      <td id=\"T_f91f0_row3_col98\" class=\"data row3 col98\" >لوبامبا</td>\n",
+       "      <td id=\"T_f91f0_row3_col99\" class=\"data row3 col99\" >לובמבה</td>\n",
+       "      <td id=\"T_f91f0_row3_col100\" class=\"data row3 col100\" >Лобамба</td>\n",
+       "      <td id=\"T_f91f0_row3_col101\" class=\"data row3 col101\" >لوبامبا</td>\n",
+       "      <td id=\"T_f91f0_row3_col102\" class=\"data row3 col102\" >洛班巴</td>\n",
+       "      <td id=\"T_f91f0_row3_col103\" class=\"data row3 col103\" >935048</td>\n",
+       "      <td id=\"T_f91f0_row3_col104\" class=\"data row3 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col105\" class=\"data row3 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col106\" class=\"data row3 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col107\" class=\"data row3 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col108\" class=\"data row3 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col109\" class=\"data row3 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col110\" class=\"data row3 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col111\" class=\"data row3 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col112\" class=\"data row3 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col113\" class=\"data row3 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col114\" class=\"data row3 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col115\" class=\"data row3 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col116\" class=\"data row3 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col117\" class=\"data row3 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col118\" class=\"data row3 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col119\" class=\"data row3 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col120\" class=\"data row3 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col121\" class=\"data row3 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col122\" class=\"data row3 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col123\" class=\"data row3 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col124\" class=\"data row3 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col125\" class=\"data row3 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col126\" class=\"data row3 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col127\" class=\"data row3 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col128\" class=\"data row3 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col129\" class=\"data row3 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col130\" class=\"data row3 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col131\" class=\"data row3 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col132\" class=\"data row3 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col133\" class=\"data row3 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col134\" class=\"data row3 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col135\" class=\"data row3 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col136\" class=\"data row3 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row3_col137\" class=\"data row3 col137\" >POINT (31.1999971 -26.4666675)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_78f41_row4_col0\" class=\"data row4 col0\" >6</td>\n",
-       "      <td id=\"T_78f41_row4_col1\" class=\"data row4 col1\" >30</td>\n",
-       "      <td id=\"T_78f41_row4_col2\" class=\"data row4 col2\" >8</td>\n",
-       "      <td id=\"T_78f41_row4_col3\" class=\"data row4 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row4_col4\" class=\"data row4 col4\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col7\" class=\"data row4 col7\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col8\" class=\"data row4 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row4_col9\" class=\"data row4 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col10\" class=\"data row4 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col11\" class=\"data row4 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col12\" class=\"data row4 col12\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col13\" class=\"data row4 col13\" >LUX</td>\n",
-       "      <td id=\"T_78f41_row4_col14\" class=\"data row4 col14\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col15\" class=\"data row4 col15\" >LUX</td>\n",
-       "      <td id=\"T_78f41_row4_col16\" class=\"data row4 col16\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col17\" class=\"data row4 col17\" >LU</td>\n",
-       "      <td id=\"T_78f41_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col19\" class=\"data row4 col19\" >49.611660</td>\n",
-       "      <td id=\"T_78f41_row4_col20\" class=\"data row4 col20\" >6.130003</td>\n",
-       "      <td id=\"T_78f41_row4_col21\" class=\"data row4 col21\" >107260</td>\n",
-       "      <td id=\"T_78f41_row4_col22\" class=\"data row4 col22\" >76684</td>\n",
-       "      <td id=\"T_78f41_row4_col23\" class=\"data row4 col23\" >106219</td>\n",
-       "      <td id=\"T_78f41_row4_col24\" class=\"data row4 col24\" >9</td>\n",
-       "      <td id=\"T_78f41_row4_col25\" class=\"data row4 col25\" >8</td>\n",
-       "      <td id=\"T_78f41_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col27\" class=\"data row4 col27\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col28\" class=\"data row4 col28\" >107260</td>\n",
-       "      <td id=\"T_78f41_row4_col29\" class=\"data row4 col29\" >107260</td>\n",
-       "      <td id=\"T_78f41_row4_col30\" class=\"data row4 col30\" >107260</td>\n",
-       "      <td id=\"T_78f41_row4_col31\" class=\"data row4 col31\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col32\" class=\"data row4 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col33\" class=\"data row4 col33\" >50</td>\n",
-       "      <td id=\"T_78f41_row4_col34\" class=\"data row4 col34\" >60</td>\n",
-       "      <td id=\"T_78f41_row4_col35\" class=\"data row4 col35\" >60</td>\n",
-       "      <td id=\"T_78f41_row4_col36\" class=\"data row4 col36\" >23</td>\n",
-       "      <td id=\"T_78f41_row4_col37\" class=\"data row4 col37\" >23</td>\n",
-       "      <td id=\"T_78f41_row4_col38\" class=\"data row4 col38\" >71</td>\n",
-       "      <td id=\"T_78f41_row4_col39\" class=\"data row4 col39\" >71</td>\n",
-       "      <td id=\"T_78f41_row4_col40\" class=\"data row4 col40\" >44</td>\n",
-       "      <td id=\"T_78f41_row4_col41\" class=\"data row4 col41\" >44</td>\n",
-       "      <td id=\"T_78f41_row4_col42\" class=\"data row4 col42\" >6.041667</td>\n",
-       "      <td id=\"T_78f41_row4_col43\" class=\"data row4 col43\" >6.041667</td>\n",
-       "      <td id=\"T_78f41_row4_col44\" class=\"data row4 col44\" >6.183333</td>\n",
-       "      <td id=\"T_78f41_row4_col45\" class=\"data row4 col45\" >6.183333</td>\n",
-       "      <td id=\"T_78f41_row4_col46\" class=\"data row4 col46\" >49.558333</td>\n",
-       "      <td id=\"T_78f41_row4_col47\" class=\"data row4 col47\" >49.558333</td>\n",
-       "      <td id=\"T_78f41_row4_col48\" class=\"data row4 col48\" >49.708333</td>\n",
-       "      <td id=\"T_78f41_row4_col49\" class=\"data row4 col49\" >49.708333</td>\n",
-       "      <td id=\"T_78f41_row4_col50\" class=\"data row4 col50\" >6.125273</td>\n",
-       "      <td id=\"T_78f41_row4_col51\" class=\"data row4 col51\" >49.620833</td>\n",
-       "      <td id=\"T_78f41_row4_col52\" class=\"data row4 col52\" >Europe/Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col53\" class=\"data row4 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col54\" class=\"data row4 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col55\" class=\"data row4 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col56\" class=\"data row4 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col57\" class=\"data row4 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col58\" class=\"data row4 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col59\" class=\"data row4 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col60\" class=\"data row4 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col61\" class=\"data row4 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col62\" class=\"data row4 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col63\" class=\"data row4 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col64\" class=\"data row4 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col65\" class=\"data row4 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col66\" class=\"data row4 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col67\" class=\"data row4 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col68\" class=\"data row4 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col69\" class=\"data row4 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col70\" class=\"data row4 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col71\" class=\"data row4 col71\" >6.000000</td>\n",
-       "      <td id=\"T_78f41_row4_col72\" class=\"data row4 col72\" >Q1842</td>\n",
-       "      <td id=\"T_78f41_row4_col73\" class=\"data row4 col73\" >101751765</td>\n",
-       "      <td id=\"T_78f41_row4_col74\" class=\"data row4 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row4_col75\" class=\"data row4 col75\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col76\" class=\"data row4 col76\" >Luxemburg</td>\n",
-       "      <td id=\"T_78f41_row4_col77\" class=\"data row4 col77\" >Luxemburgo</td>\n",
-       "      <td id=\"T_78f41_row4_col78\" class=\"data row4 col78\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col79\" class=\"data row4 col79\" >Luxemburgo</td>\n",
-       "      <td id=\"T_78f41_row4_col80\" class=\"data row4 col80\" >Люксембург</td>\n",
-       "      <td id=\"T_78f41_row4_col81\" class=\"data row4 col81\" >卢森堡</td>\n",
-       "      <td id=\"T_78f41_row4_col82\" class=\"data row4 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col83\" class=\"data row4 col83\" >مدينة لوكسمبورغ</td>\n",
-       "      <td id=\"T_78f41_row4_col84\" class=\"data row4 col84\" >লুক্সেমবুর্গ শহর</td>\n",
-       "      <td id=\"T_78f41_row4_col85\" class=\"data row4 col85\" >Λουξεμβούργο</td>\n",
-       "      <td id=\"T_78f41_row4_col86\" class=\"data row4 col86\" >लक्ज़मबर्ग नगर</td>\n",
-       "      <td id=\"T_78f41_row4_col87\" class=\"data row4 col87\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col88\" class=\"data row4 col88\" >Luksemburg</td>\n",
-       "      <td id=\"T_78f41_row4_col89\" class=\"data row4 col89\" >Lussemburgo</td>\n",
-       "      <td id=\"T_78f41_row4_col90\" class=\"data row4 col90\" >ルクセンブルク市</td>\n",
-       "      <td id=\"T_78f41_row4_col91\" class=\"data row4 col91\" >룩셈부르크</td>\n",
-       "      <td id=\"T_78f41_row4_col92\" class=\"data row4 col92\" >Luxemburg</td>\n",
-       "      <td id=\"T_78f41_row4_col93\" class=\"data row4 col93\" >Luksemburg</td>\n",
-       "      <td id=\"T_78f41_row4_col94\" class=\"data row4 col94\" >Luxemburg</td>\n",
-       "      <td id=\"T_78f41_row4_col95\" class=\"data row4 col95\" >Lüksemburg</td>\n",
-       "      <td id=\"T_78f41_row4_col96\" class=\"data row4 col96\" >Luxembourg</td>\n",
-       "      <td id=\"T_78f41_row4_col97\" class=\"data row4 col97\" >1159146437</td>\n",
-       "      <td id=\"T_78f41_row4_col98\" class=\"data row4 col98\" >لوکزامبورگ</td>\n",
-       "      <td id=\"T_78f41_row4_col99\" class=\"data row4 col99\" >לוקסמבורג</td>\n",
-       "      <td id=\"T_78f41_row4_col100\" class=\"data row4 col100\" >Люксембург</td>\n",
-       "      <td id=\"T_78f41_row4_col101\" class=\"data row4 col101\" >لکسمبرگ</td>\n",
-       "      <td id=\"T_78f41_row4_col102\" class=\"data row4 col102\" >盧森堡市</td>\n",
-       "      <td id=\"T_78f41_row4_col103\" class=\"data row4 col103\" >2960316</td>\n",
-       "      <td id=\"T_78f41_row4_col104\" class=\"data row4 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col105\" class=\"data row4 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col106\" class=\"data row4 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col107\" class=\"data row4 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col108\" class=\"data row4 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col109\" class=\"data row4 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col110\" class=\"data row4 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col111\" class=\"data row4 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col112\" class=\"data row4 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col113\" class=\"data row4 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col114\" class=\"data row4 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col115\" class=\"data row4 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col116\" class=\"data row4 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col117\" class=\"data row4 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col118\" class=\"data row4 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col119\" class=\"data row4 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col120\" class=\"data row4 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col121\" class=\"data row4 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col122\" class=\"data row4 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col123\" class=\"data row4 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col124\" class=\"data row4 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col125\" class=\"data row4 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col126\" class=\"data row4 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col127\" class=\"data row4 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col128\" class=\"data row4 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col129\" class=\"data row4 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col130\" class=\"data row4 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col131\" class=\"data row4 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col132\" class=\"data row4 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col133\" class=\"data row4 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col134\" class=\"data row4 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col135\" class=\"data row4 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col136\" class=\"data row4 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row4_col137\" class=\"data row4 col137\" >POINT (6.1300028 49.6116604)</td>\n",
+       "      <th id=\"T_f91f0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_f91f0_row4_col0\" class=\"data row4 col0\" >6</td>\n",
+       "      <td id=\"T_f91f0_row4_col1\" class=\"data row4 col1\" >30</td>\n",
+       "      <td id=\"T_f91f0_row4_col2\" class=\"data row4 col2\" >8</td>\n",
+       "      <td id=\"T_f91f0_row4_col3\" class=\"data row4 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row4_col4\" class=\"data row4 col4\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col7\" class=\"data row4 col7\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col8\" class=\"data row4 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row4_col9\" class=\"data row4 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col10\" class=\"data row4 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col11\" class=\"data row4 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col12\" class=\"data row4 col12\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col13\" class=\"data row4 col13\" >LUX</td>\n",
+       "      <td id=\"T_f91f0_row4_col14\" class=\"data row4 col14\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col15\" class=\"data row4 col15\" >LUX</td>\n",
+       "      <td id=\"T_f91f0_row4_col16\" class=\"data row4 col16\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col17\" class=\"data row4 col17\" >LU</td>\n",
+       "      <td id=\"T_f91f0_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col19\" class=\"data row4 col19\" >49.611660</td>\n",
+       "      <td id=\"T_f91f0_row4_col20\" class=\"data row4 col20\" >6.130003</td>\n",
+       "      <td id=\"T_f91f0_row4_col21\" class=\"data row4 col21\" >107260</td>\n",
+       "      <td id=\"T_f91f0_row4_col22\" class=\"data row4 col22\" >76684</td>\n",
+       "      <td id=\"T_f91f0_row4_col23\" class=\"data row4 col23\" >106219</td>\n",
+       "      <td id=\"T_f91f0_row4_col24\" class=\"data row4 col24\" >9</td>\n",
+       "      <td id=\"T_f91f0_row4_col25\" class=\"data row4 col25\" >8</td>\n",
+       "      <td id=\"T_f91f0_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col27\" class=\"data row4 col27\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col28\" class=\"data row4 col28\" >107260</td>\n",
+       "      <td id=\"T_f91f0_row4_col29\" class=\"data row4 col29\" >107260</td>\n",
+       "      <td id=\"T_f91f0_row4_col30\" class=\"data row4 col30\" >107260</td>\n",
+       "      <td id=\"T_f91f0_row4_col31\" class=\"data row4 col31\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col32\" class=\"data row4 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col33\" class=\"data row4 col33\" >50</td>\n",
+       "      <td id=\"T_f91f0_row4_col34\" class=\"data row4 col34\" >60</td>\n",
+       "      <td id=\"T_f91f0_row4_col35\" class=\"data row4 col35\" >60</td>\n",
+       "      <td id=\"T_f91f0_row4_col36\" class=\"data row4 col36\" >23</td>\n",
+       "      <td id=\"T_f91f0_row4_col37\" class=\"data row4 col37\" >23</td>\n",
+       "      <td id=\"T_f91f0_row4_col38\" class=\"data row4 col38\" >71</td>\n",
+       "      <td id=\"T_f91f0_row4_col39\" class=\"data row4 col39\" >71</td>\n",
+       "      <td id=\"T_f91f0_row4_col40\" class=\"data row4 col40\" >44</td>\n",
+       "      <td id=\"T_f91f0_row4_col41\" class=\"data row4 col41\" >44</td>\n",
+       "      <td id=\"T_f91f0_row4_col42\" class=\"data row4 col42\" >6.041667</td>\n",
+       "      <td id=\"T_f91f0_row4_col43\" class=\"data row4 col43\" >6.041667</td>\n",
+       "      <td id=\"T_f91f0_row4_col44\" class=\"data row4 col44\" >6.183333</td>\n",
+       "      <td id=\"T_f91f0_row4_col45\" class=\"data row4 col45\" >6.183333</td>\n",
+       "      <td id=\"T_f91f0_row4_col46\" class=\"data row4 col46\" >49.558333</td>\n",
+       "      <td id=\"T_f91f0_row4_col47\" class=\"data row4 col47\" >49.558333</td>\n",
+       "      <td id=\"T_f91f0_row4_col48\" class=\"data row4 col48\" >49.708333</td>\n",
+       "      <td id=\"T_f91f0_row4_col49\" class=\"data row4 col49\" >49.708333</td>\n",
+       "      <td id=\"T_f91f0_row4_col50\" class=\"data row4 col50\" >6.125273</td>\n",
+       "      <td id=\"T_f91f0_row4_col51\" class=\"data row4 col51\" >49.620833</td>\n",
+       "      <td id=\"T_f91f0_row4_col52\" class=\"data row4 col52\" >Europe/Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col53\" class=\"data row4 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col54\" class=\"data row4 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col55\" class=\"data row4 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col56\" class=\"data row4 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col57\" class=\"data row4 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col58\" class=\"data row4 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col59\" class=\"data row4 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col60\" class=\"data row4 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col61\" class=\"data row4 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col62\" class=\"data row4 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col63\" class=\"data row4 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col64\" class=\"data row4 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col65\" class=\"data row4 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col66\" class=\"data row4 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col67\" class=\"data row4 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col68\" class=\"data row4 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col69\" class=\"data row4 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col70\" class=\"data row4 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col71\" class=\"data row4 col71\" >6.000000</td>\n",
+       "      <td id=\"T_f91f0_row4_col72\" class=\"data row4 col72\" >Q1842</td>\n",
+       "      <td id=\"T_f91f0_row4_col73\" class=\"data row4 col73\" >101751765</td>\n",
+       "      <td id=\"T_f91f0_row4_col74\" class=\"data row4 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row4_col75\" class=\"data row4 col75\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col76\" class=\"data row4 col76\" >Luxemburg</td>\n",
+       "      <td id=\"T_f91f0_row4_col77\" class=\"data row4 col77\" >Luxemburgo</td>\n",
+       "      <td id=\"T_f91f0_row4_col78\" class=\"data row4 col78\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col79\" class=\"data row4 col79\" >Luxemburgo</td>\n",
+       "      <td id=\"T_f91f0_row4_col80\" class=\"data row4 col80\" >Люксембург</td>\n",
+       "      <td id=\"T_f91f0_row4_col81\" class=\"data row4 col81\" >卢森堡</td>\n",
+       "      <td id=\"T_f91f0_row4_col82\" class=\"data row4 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col83\" class=\"data row4 col83\" >مدينة لوكسمبورغ</td>\n",
+       "      <td id=\"T_f91f0_row4_col84\" class=\"data row4 col84\" >লুক্সেমবুর্গ শহর</td>\n",
+       "      <td id=\"T_f91f0_row4_col85\" class=\"data row4 col85\" >Λουξεμβούργο</td>\n",
+       "      <td id=\"T_f91f0_row4_col86\" class=\"data row4 col86\" >लक्ज़मबर्ग नगर</td>\n",
+       "      <td id=\"T_f91f0_row4_col87\" class=\"data row4 col87\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col88\" class=\"data row4 col88\" >Luksemburg</td>\n",
+       "      <td id=\"T_f91f0_row4_col89\" class=\"data row4 col89\" >Lussemburgo</td>\n",
+       "      <td id=\"T_f91f0_row4_col90\" class=\"data row4 col90\" >ルクセンブルク市</td>\n",
+       "      <td id=\"T_f91f0_row4_col91\" class=\"data row4 col91\" >룩셈부르크</td>\n",
+       "      <td id=\"T_f91f0_row4_col92\" class=\"data row4 col92\" >Luxemburg</td>\n",
+       "      <td id=\"T_f91f0_row4_col93\" class=\"data row4 col93\" >Luksemburg</td>\n",
+       "      <td id=\"T_f91f0_row4_col94\" class=\"data row4 col94\" >Luxemburg</td>\n",
+       "      <td id=\"T_f91f0_row4_col95\" class=\"data row4 col95\" >Lüksemburg</td>\n",
+       "      <td id=\"T_f91f0_row4_col96\" class=\"data row4 col96\" >Luxembourg</td>\n",
+       "      <td id=\"T_f91f0_row4_col97\" class=\"data row4 col97\" >1159146437</td>\n",
+       "      <td id=\"T_f91f0_row4_col98\" class=\"data row4 col98\" >لوکزامبورگ</td>\n",
+       "      <td id=\"T_f91f0_row4_col99\" class=\"data row4 col99\" >לוקסמבורג</td>\n",
+       "      <td id=\"T_f91f0_row4_col100\" class=\"data row4 col100\" >Люксембург</td>\n",
+       "      <td id=\"T_f91f0_row4_col101\" class=\"data row4 col101\" >لکسمبرگ</td>\n",
+       "      <td id=\"T_f91f0_row4_col102\" class=\"data row4 col102\" >盧森堡市</td>\n",
+       "      <td id=\"T_f91f0_row4_col103\" class=\"data row4 col103\" >2960316</td>\n",
+       "      <td id=\"T_f91f0_row4_col104\" class=\"data row4 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col105\" class=\"data row4 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col106\" class=\"data row4 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col107\" class=\"data row4 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col108\" class=\"data row4 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col109\" class=\"data row4 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col110\" class=\"data row4 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col111\" class=\"data row4 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col112\" class=\"data row4 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col113\" class=\"data row4 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col114\" class=\"data row4 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col115\" class=\"data row4 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col116\" class=\"data row4 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col117\" class=\"data row4 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col118\" class=\"data row4 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col119\" class=\"data row4 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col120\" class=\"data row4 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col121\" class=\"data row4 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col122\" class=\"data row4 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col123\" class=\"data row4 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col124\" class=\"data row4 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col125\" class=\"data row4 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col126\" class=\"data row4 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col127\" class=\"data row4 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col128\" class=\"data row4 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col129\" class=\"data row4 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col130\" class=\"data row4 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col131\" class=\"data row4 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col132\" class=\"data row4 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col133\" class=\"data row4 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col134\" class=\"data row4 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col135\" class=\"data row4 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col136\" class=\"data row4 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row4_col137\" class=\"data row4 col137\" >POINT (6.1300028 49.6116604)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_78f41_row5_col0\" class=\"data row5 col0\" >6</td>\n",
-       "      <td id=\"T_78f41_row5_col1\" class=\"data row5 col1\" >30</td>\n",
-       "      <td id=\"T_78f41_row5_col2\" class=\"data row5 col2\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col3\" class=\"data row5 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row5_col4\" class=\"data row5 col4\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col7\" class=\"data row5 col7\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col8\" class=\"data row5 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row5_col9\" class=\"data row5 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col10\" class=\"data row5 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col11\" class=\"data row5 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col12\" class=\"data row5 col12\" >Federated States of Micronesia</td>\n",
-       "      <td id=\"T_78f41_row5_col13\" class=\"data row5 col13\" >FSM</td>\n",
-       "      <td id=\"T_78f41_row5_col14\" class=\"data row5 col14\" >Federated States of Micronesia</td>\n",
-       "      <td id=\"T_78f41_row5_col15\" class=\"data row5 col15\" >FSM</td>\n",
-       "      <td id=\"T_78f41_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col17\" class=\"data row5 col17\" >FM</td>\n",
-       "      <td id=\"T_78f41_row5_col18\" class=\"data row5 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col19\" class=\"data row5 col19\" >6.916644</td>\n",
-       "      <td id=\"T_78f41_row5_col20\" class=\"data row5 col20\" >158.149974</td>\n",
-       "      <td id=\"T_78f41_row5_col21\" class=\"data row5 col21\" >4645</td>\n",
-       "      <td id=\"T_78f41_row5_col22\" class=\"data row5 col22\" >4645</td>\n",
-       "      <td id=\"T_78f41_row5_col23\" class=\"data row5 col23\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col24\" class=\"data row5 col24\" >4</td>\n",
-       "      <td id=\"T_78f41_row5_col25\" class=\"data row5 col25\" >4</td>\n",
-       "      <td id=\"T_78f41_row5_col26\" class=\"data row5 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col27\" class=\"data row5 col27\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col28\" class=\"data row5 col28\" >412</td>\n",
-       "      <td id=\"T_78f41_row5_col29\" class=\"data row5 col29\" >412</td>\n",
-       "      <td id=\"T_78f41_row5_col30\" class=\"data row5 col30\" >412</td>\n",
-       "      <td id=\"T_78f41_row5_col31\" class=\"data row5 col31\" >412</td>\n",
-       "      <td id=\"T_78f41_row5_col32\" class=\"data row5 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col33\" class=\"data row5 col33\" >100</td>\n",
-       "      <td id=\"T_78f41_row5_col34\" class=\"data row5 col34\" >1</td>\n",
-       "      <td id=\"T_78f41_row5_col35\" class=\"data row5 col35\" >1</td>\n",
-       "      <td id=\"T_78f41_row5_col36\" class=\"data row5 col36\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col37\" class=\"data row5 col37\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col38\" class=\"data row5 col38\" >4</td>\n",
-       "      <td id=\"T_78f41_row5_col39\" class=\"data row5 col39\" >4</td>\n",
-       "      <td id=\"T_78f41_row5_col40\" class=\"data row5 col40\" >2</td>\n",
-       "      <td id=\"T_78f41_row5_col41\" class=\"data row5 col41\" >2</td>\n",
-       "      <td id=\"T_78f41_row5_col42\" class=\"data row5 col42\" >158.158333</td>\n",
-       "      <td id=\"T_78f41_row5_col43\" class=\"data row5 col43\" >158.158333</td>\n",
-       "      <td id=\"T_78f41_row5_col44\" class=\"data row5 col44\" >158.166667</td>\n",
-       "      <td id=\"T_78f41_row5_col45\" class=\"data row5 col45\" >158.166667</td>\n",
-       "      <td id=\"T_78f41_row5_col46\" class=\"data row5 col46\" >6.908333</td>\n",
-       "      <td id=\"T_78f41_row5_col47\" class=\"data row5 col47\" >6.908333</td>\n",
-       "      <td id=\"T_78f41_row5_col48\" class=\"data row5 col48\" >6.916667</td>\n",
-       "      <td id=\"T_78f41_row5_col49\" class=\"data row5 col49\" >6.916667</td>\n",
-       "      <td id=\"T_78f41_row5_col50\" class=\"data row5 col50\" >158.162500</td>\n",
-       "      <td id=\"T_78f41_row5_col51\" class=\"data row5 col51\" >6.912500</td>\n",
-       "      <td id=\"T_78f41_row5_col52\" class=\"data row5 col52\" >Pacific/Ponape</td>\n",
-       "      <td id=\"T_78f41_row5_col53\" class=\"data row5 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col54\" class=\"data row5 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col55\" class=\"data row5 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col56\" class=\"data row5 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col57\" class=\"data row5 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col58\" class=\"data row5 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col59\" class=\"data row5 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col60\" class=\"data row5 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col61\" class=\"data row5 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col62\" class=\"data row5 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col63\" class=\"data row5 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col64\" class=\"data row5 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col65\" class=\"data row5 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col66\" class=\"data row5 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col67\" class=\"data row5 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col68\" class=\"data row5 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col69\" class=\"data row5 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col70\" class=\"data row5 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col71\" class=\"data row5 col71\" >6.000000</td>\n",
-       "      <td id=\"T_78f41_row5_col72\" class=\"data row5 col72\" >Q42751</td>\n",
-       "      <td id=\"T_78f41_row5_col73\" class=\"data row5 col73\" >1141909141</td>\n",
-       "      <td id=\"T_78f41_row5_col74\" class=\"data row5 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row5_col75\" class=\"data row5 col75\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col76\" class=\"data row5 col76\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col77\" class=\"data row5 col77\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col78\" class=\"data row5 col78\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col79\" class=\"data row5 col79\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col80\" class=\"data row5 col80\" >Паликир</td>\n",
-       "      <td id=\"T_78f41_row5_col81\" class=\"data row5 col81\" >帕利基尔</td>\n",
-       "      <td id=\"T_78f41_row5_col82\" class=\"data row5 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col83\" class=\"data row5 col83\" >باليكير</td>\n",
-       "      <td id=\"T_78f41_row5_col84\" class=\"data row5 col84\" >পালিকির</td>\n",
-       "      <td id=\"T_78f41_row5_col85\" class=\"data row5 col85\" >Παλικίρ</td>\n",
-       "      <td id=\"T_78f41_row5_col86\" class=\"data row5 col86\" >पेलिकियर</td>\n",
-       "      <td id=\"T_78f41_row5_col87\" class=\"data row5 col87\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col88\" class=\"data row5 col88\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col89\" class=\"data row5 col89\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col90\" class=\"data row5 col90\" >パリキール</td>\n",
-       "      <td id=\"T_78f41_row5_col91\" class=\"data row5 col91\" >팔리키르</td>\n",
-       "      <td id=\"T_78f41_row5_col92\" class=\"data row5 col92\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col93\" class=\"data row5 col93\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col94\" class=\"data row5 col94\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col95\" class=\"data row5 col95\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col96\" class=\"data row5 col96\" >Palikir</td>\n",
-       "      <td id=\"T_78f41_row5_col97\" class=\"data row5 col97\" >1159149061</td>\n",
-       "      <td id=\"T_78f41_row5_col98\" class=\"data row5 col98\" >پالیکیر</td>\n",
-       "      <td id=\"T_78f41_row5_col99\" class=\"data row5 col99\" >פליקיר</td>\n",
-       "      <td id=\"T_78f41_row5_col100\" class=\"data row5 col100\" >Палікір</td>\n",
-       "      <td id=\"T_78f41_row5_col101\" class=\"data row5 col101\" >پالیکیر</td>\n",
-       "      <td id=\"T_78f41_row5_col102\" class=\"data row5 col102\" >帕利基尔</td>\n",
-       "      <td id=\"T_78f41_row5_col103\" class=\"data row5 col103\" >2081986</td>\n",
-       "      <td id=\"T_78f41_row5_col104\" class=\"data row5 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col105\" class=\"data row5 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col106\" class=\"data row5 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col107\" class=\"data row5 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col108\" class=\"data row5 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col109\" class=\"data row5 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col110\" class=\"data row5 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col111\" class=\"data row5 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col112\" class=\"data row5 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col113\" class=\"data row5 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col114\" class=\"data row5 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col115\" class=\"data row5 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col116\" class=\"data row5 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col117\" class=\"data row5 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col118\" class=\"data row5 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col119\" class=\"data row5 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col120\" class=\"data row5 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col121\" class=\"data row5 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col122\" class=\"data row5 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col123\" class=\"data row5 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col124\" class=\"data row5 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col125\" class=\"data row5 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col126\" class=\"data row5 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col127\" class=\"data row5 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col128\" class=\"data row5 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col129\" class=\"data row5 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col130\" class=\"data row5 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col131\" class=\"data row5 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col132\" class=\"data row5 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col133\" class=\"data row5 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col134\" class=\"data row5 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col135\" class=\"data row5 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col136\" class=\"data row5 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row5_col137\" class=\"data row5 col137\" >POINT (158.1499743 6.9166437)</td>\n",
+       "      <th id=\"T_f91f0_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_f91f0_row5_col0\" class=\"data row5 col0\" >6</td>\n",
+       "      <td id=\"T_f91f0_row5_col1\" class=\"data row5 col1\" >30</td>\n",
+       "      <td id=\"T_f91f0_row5_col2\" class=\"data row5 col2\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col3\" class=\"data row5 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row5_col4\" class=\"data row5 col4\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col7\" class=\"data row5 col7\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col8\" class=\"data row5 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row5_col9\" class=\"data row5 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col10\" class=\"data row5 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col11\" class=\"data row5 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col12\" class=\"data row5 col12\" >Federated States of Micronesia</td>\n",
+       "      <td id=\"T_f91f0_row5_col13\" class=\"data row5 col13\" >FSM</td>\n",
+       "      <td id=\"T_f91f0_row5_col14\" class=\"data row5 col14\" >Federated States of Micronesia</td>\n",
+       "      <td id=\"T_f91f0_row5_col15\" class=\"data row5 col15\" >FSM</td>\n",
+       "      <td id=\"T_f91f0_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col17\" class=\"data row5 col17\" >FM</td>\n",
+       "      <td id=\"T_f91f0_row5_col18\" class=\"data row5 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col19\" class=\"data row5 col19\" >6.916644</td>\n",
+       "      <td id=\"T_f91f0_row5_col20\" class=\"data row5 col20\" >158.149974</td>\n",
+       "      <td id=\"T_f91f0_row5_col21\" class=\"data row5 col21\" >4645</td>\n",
+       "      <td id=\"T_f91f0_row5_col22\" class=\"data row5 col22\" >4645</td>\n",
+       "      <td id=\"T_f91f0_row5_col23\" class=\"data row5 col23\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col24\" class=\"data row5 col24\" >4</td>\n",
+       "      <td id=\"T_f91f0_row5_col25\" class=\"data row5 col25\" >4</td>\n",
+       "      <td id=\"T_f91f0_row5_col26\" class=\"data row5 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col27\" class=\"data row5 col27\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col28\" class=\"data row5 col28\" >412</td>\n",
+       "      <td id=\"T_f91f0_row5_col29\" class=\"data row5 col29\" >412</td>\n",
+       "      <td id=\"T_f91f0_row5_col30\" class=\"data row5 col30\" >412</td>\n",
+       "      <td id=\"T_f91f0_row5_col31\" class=\"data row5 col31\" >412</td>\n",
+       "      <td id=\"T_f91f0_row5_col32\" class=\"data row5 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col33\" class=\"data row5 col33\" >100</td>\n",
+       "      <td id=\"T_f91f0_row5_col34\" class=\"data row5 col34\" >1</td>\n",
+       "      <td id=\"T_f91f0_row5_col35\" class=\"data row5 col35\" >1</td>\n",
+       "      <td id=\"T_f91f0_row5_col36\" class=\"data row5 col36\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col37\" class=\"data row5 col37\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col38\" class=\"data row5 col38\" >4</td>\n",
+       "      <td id=\"T_f91f0_row5_col39\" class=\"data row5 col39\" >4</td>\n",
+       "      <td id=\"T_f91f0_row5_col40\" class=\"data row5 col40\" >2</td>\n",
+       "      <td id=\"T_f91f0_row5_col41\" class=\"data row5 col41\" >2</td>\n",
+       "      <td id=\"T_f91f0_row5_col42\" class=\"data row5 col42\" >158.158333</td>\n",
+       "      <td id=\"T_f91f0_row5_col43\" class=\"data row5 col43\" >158.158333</td>\n",
+       "      <td id=\"T_f91f0_row5_col44\" class=\"data row5 col44\" >158.166667</td>\n",
+       "      <td id=\"T_f91f0_row5_col45\" class=\"data row5 col45\" >158.166667</td>\n",
+       "      <td id=\"T_f91f0_row5_col46\" class=\"data row5 col46\" >6.908333</td>\n",
+       "      <td id=\"T_f91f0_row5_col47\" class=\"data row5 col47\" >6.908333</td>\n",
+       "      <td id=\"T_f91f0_row5_col48\" class=\"data row5 col48\" >6.916667</td>\n",
+       "      <td id=\"T_f91f0_row5_col49\" class=\"data row5 col49\" >6.916667</td>\n",
+       "      <td id=\"T_f91f0_row5_col50\" class=\"data row5 col50\" >158.162500</td>\n",
+       "      <td id=\"T_f91f0_row5_col51\" class=\"data row5 col51\" >6.912500</td>\n",
+       "      <td id=\"T_f91f0_row5_col52\" class=\"data row5 col52\" >Pacific/Ponape</td>\n",
+       "      <td id=\"T_f91f0_row5_col53\" class=\"data row5 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col54\" class=\"data row5 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col55\" class=\"data row5 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col56\" class=\"data row5 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col57\" class=\"data row5 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col58\" class=\"data row5 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col59\" class=\"data row5 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col60\" class=\"data row5 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col61\" class=\"data row5 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col62\" class=\"data row5 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col63\" class=\"data row5 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col64\" class=\"data row5 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col65\" class=\"data row5 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col66\" class=\"data row5 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col67\" class=\"data row5 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col68\" class=\"data row5 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col69\" class=\"data row5 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col70\" class=\"data row5 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col71\" class=\"data row5 col71\" >6.000000</td>\n",
+       "      <td id=\"T_f91f0_row5_col72\" class=\"data row5 col72\" >Q42751</td>\n",
+       "      <td id=\"T_f91f0_row5_col73\" class=\"data row5 col73\" >1141909141</td>\n",
+       "      <td id=\"T_f91f0_row5_col74\" class=\"data row5 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row5_col75\" class=\"data row5 col75\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col76\" class=\"data row5 col76\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col77\" class=\"data row5 col77\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col78\" class=\"data row5 col78\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col79\" class=\"data row5 col79\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col80\" class=\"data row5 col80\" >Паликир</td>\n",
+       "      <td id=\"T_f91f0_row5_col81\" class=\"data row5 col81\" >帕利基尔</td>\n",
+       "      <td id=\"T_f91f0_row5_col82\" class=\"data row5 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col83\" class=\"data row5 col83\" >باليكير</td>\n",
+       "      <td id=\"T_f91f0_row5_col84\" class=\"data row5 col84\" >পালিকির</td>\n",
+       "      <td id=\"T_f91f0_row5_col85\" class=\"data row5 col85\" >Παλικίρ</td>\n",
+       "      <td id=\"T_f91f0_row5_col86\" class=\"data row5 col86\" >पेलिकियर</td>\n",
+       "      <td id=\"T_f91f0_row5_col87\" class=\"data row5 col87\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col88\" class=\"data row5 col88\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col89\" class=\"data row5 col89\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col90\" class=\"data row5 col90\" >パリキール</td>\n",
+       "      <td id=\"T_f91f0_row5_col91\" class=\"data row5 col91\" >팔리키르</td>\n",
+       "      <td id=\"T_f91f0_row5_col92\" class=\"data row5 col92\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col93\" class=\"data row5 col93\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col94\" class=\"data row5 col94\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col95\" class=\"data row5 col95\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col96\" class=\"data row5 col96\" >Palikir</td>\n",
+       "      <td id=\"T_f91f0_row5_col97\" class=\"data row5 col97\" >1159149061</td>\n",
+       "      <td id=\"T_f91f0_row5_col98\" class=\"data row5 col98\" >پالیکیر</td>\n",
+       "      <td id=\"T_f91f0_row5_col99\" class=\"data row5 col99\" >פליקיר</td>\n",
+       "      <td id=\"T_f91f0_row5_col100\" class=\"data row5 col100\" >Палікір</td>\n",
+       "      <td id=\"T_f91f0_row5_col101\" class=\"data row5 col101\" >پالیکیر</td>\n",
+       "      <td id=\"T_f91f0_row5_col102\" class=\"data row5 col102\" >帕利基尔</td>\n",
+       "      <td id=\"T_f91f0_row5_col103\" class=\"data row5 col103\" >2081986</td>\n",
+       "      <td id=\"T_f91f0_row5_col104\" class=\"data row5 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col105\" class=\"data row5 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col106\" class=\"data row5 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col107\" class=\"data row5 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col108\" class=\"data row5 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col109\" class=\"data row5 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col110\" class=\"data row5 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col111\" class=\"data row5 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col112\" class=\"data row5 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col113\" class=\"data row5 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col114\" class=\"data row5 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col115\" class=\"data row5 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col116\" class=\"data row5 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col117\" class=\"data row5 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col118\" class=\"data row5 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col119\" class=\"data row5 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col120\" class=\"data row5 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col121\" class=\"data row5 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col122\" class=\"data row5 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col123\" class=\"data row5 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col124\" class=\"data row5 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col125\" class=\"data row5 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col126\" class=\"data row5 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col127\" class=\"data row5 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col128\" class=\"data row5 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col129\" class=\"data row5 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col130\" class=\"data row5 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col131\" class=\"data row5 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col132\" class=\"data row5 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col133\" class=\"data row5 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col134\" class=\"data row5 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col135\" class=\"data row5 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col136\" class=\"data row5 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row5_col137\" class=\"data row5 col137\" >POINT (158.1499743 6.9166437)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_78f41_row6_col0\" class=\"data row6 col0\" >6</td>\n",
-       "      <td id=\"T_78f41_row6_col1\" class=\"data row6 col1\" >30</td>\n",
-       "      <td id=\"T_78f41_row6_col2\" class=\"data row6 col2\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col3\" class=\"data row6 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row6_col4\" class=\"data row6 col4\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col5\" class=\"data row6 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col6\" class=\"data row6 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col7\" class=\"data row6 col7\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col8\" class=\"data row6 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col10\" class=\"data row6 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col11\" class=\"data row6 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col12\" class=\"data row6 col12\" >Marshall Islands</td>\n",
-       "      <td id=\"T_78f41_row6_col13\" class=\"data row6 col13\" >MHL</td>\n",
-       "      <td id=\"T_78f41_row6_col14\" class=\"data row6 col14\" >Marshall Islands</td>\n",
-       "      <td id=\"T_78f41_row6_col15\" class=\"data row6 col15\" >MHL</td>\n",
-       "      <td id=\"T_78f41_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col17\" class=\"data row6 col17\" >MH</td>\n",
-       "      <td id=\"T_78f41_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col19\" class=\"data row6 col19\" >7.103004</td>\n",
-       "      <td id=\"T_78f41_row6_col20\" class=\"data row6 col20\" >171.380000</td>\n",
-       "      <td id=\"T_78f41_row6_col21\" class=\"data row6 col21\" >25400</td>\n",
-       "      <td id=\"T_78f41_row6_col22\" class=\"data row6 col22\" >20500</td>\n",
-       "      <td id=\"T_78f41_row6_col23\" class=\"data row6 col23\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col24\" class=\"data row6 col24\" >7</td>\n",
-       "      <td id=\"T_78f41_row6_col25\" class=\"data row6 col25\" >7</td>\n",
-       "      <td id=\"T_78f41_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col27\" class=\"data row6 col27\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col28\" class=\"data row6 col28\" >2084</td>\n",
-       "      <td id=\"T_78f41_row6_col29\" class=\"data row6 col29\" >2084</td>\n",
-       "      <td id=\"T_78f41_row6_col30\" class=\"data row6 col30\" >2084</td>\n",
-       "      <td id=\"T_78f41_row6_col31\" class=\"data row6 col31\" >2084</td>\n",
-       "      <td id=\"T_78f41_row6_col32\" class=\"data row6 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col33\" class=\"data row6 col33\" >100</td>\n",
-       "      <td id=\"T_78f41_row6_col34\" class=\"data row6 col34\" >3</td>\n",
-       "      <td id=\"T_78f41_row6_col35\" class=\"data row6 col35\" >3</td>\n",
-       "      <td id=\"T_78f41_row6_col36\" class=\"data row6 col36\" >1</td>\n",
-       "      <td id=\"T_78f41_row6_col37\" class=\"data row6 col37\" >1</td>\n",
-       "      <td id=\"T_78f41_row6_col38\" class=\"data row6 col38\" >7</td>\n",
-       "      <td id=\"T_78f41_row6_col39\" class=\"data row6 col39\" >7</td>\n",
-       "      <td id=\"T_78f41_row6_col40\" class=\"data row6 col40\" >5</td>\n",
-       "      <td id=\"T_78f41_row6_col41\" class=\"data row6 col41\" >5</td>\n",
-       "      <td id=\"T_78f41_row6_col42\" class=\"data row6 col42\" >171.366667</td>\n",
-       "      <td id=\"T_78f41_row6_col43\" class=\"data row6 col43\" >171.366667</td>\n",
-       "      <td id=\"T_78f41_row6_col44\" class=\"data row6 col44\" >171.375000</td>\n",
-       "      <td id=\"T_78f41_row6_col45\" class=\"data row6 col45\" >171.375000</td>\n",
-       "      <td id=\"T_78f41_row6_col46\" class=\"data row6 col46\" >7.091667</td>\n",
-       "      <td id=\"T_78f41_row6_col47\" class=\"data row6 col47\" >7.091667</td>\n",
-       "      <td id=\"T_78f41_row6_col48\" class=\"data row6 col48\" >7.116667</td>\n",
-       "      <td id=\"T_78f41_row6_col49\" class=\"data row6 col49\" >7.116667</td>\n",
-       "      <td id=\"T_78f41_row6_col50\" class=\"data row6 col50\" >171.370833</td>\n",
-       "      <td id=\"T_78f41_row6_col51\" class=\"data row6 col51\" >7.104167</td>\n",
-       "      <td id=\"T_78f41_row6_col52\" class=\"data row6 col52\" >Pacific/Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col53\" class=\"data row6 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col54\" class=\"data row6 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col55\" class=\"data row6 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col56\" class=\"data row6 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col57\" class=\"data row6 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col58\" class=\"data row6 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col59\" class=\"data row6 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col60\" class=\"data row6 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col61\" class=\"data row6 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col62\" class=\"data row6 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col63\" class=\"data row6 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col64\" class=\"data row6 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col65\" class=\"data row6 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col66\" class=\"data row6 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col67\" class=\"data row6 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col68\" class=\"data row6 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col69\" class=\"data row6 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col70\" class=\"data row6 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col71\" class=\"data row6 col71\" >6.000000</td>\n",
-       "      <td id=\"T_78f41_row6_col72\" class=\"data row6 col72\" >Q12919</td>\n",
-       "      <td id=\"T_78f41_row6_col73\" class=\"data row6 col73\" >890451463</td>\n",
-       "      <td id=\"T_78f41_row6_col74\" class=\"data row6 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row6_col75\" class=\"data row6 col75\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col76\" class=\"data row6 col76\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col77\" class=\"data row6 col77\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col78\" class=\"data row6 col78\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col79\" class=\"data row6 col79\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col80\" class=\"data row6 col80\" >Маджуро</td>\n",
-       "      <td id=\"T_78f41_row6_col81\" class=\"data row6 col81\" >马朱罗</td>\n",
-       "      <td id=\"T_78f41_row6_col82\" class=\"data row6 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col83\" class=\"data row6 col83\" >ماجورو</td>\n",
-       "      <td id=\"T_78f41_row6_col84\" class=\"data row6 col84\" >মাজুরো</td>\n",
-       "      <td id=\"T_78f41_row6_col85\" class=\"data row6 col85\" >Ματζούρο</td>\n",
-       "      <td id=\"T_78f41_row6_col86\" class=\"data row6 col86\" >माजुरो</td>\n",
-       "      <td id=\"T_78f41_row6_col87\" class=\"data row6 col87\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col88\" class=\"data row6 col88\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col89\" class=\"data row6 col89\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col90\" class=\"data row6 col90\" >マジュロ</td>\n",
-       "      <td id=\"T_78f41_row6_col91\" class=\"data row6 col91\" >마주로</td>\n",
-       "      <td id=\"T_78f41_row6_col92\" class=\"data row6 col92\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col93\" class=\"data row6 col93\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col94\" class=\"data row6 col94\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col95\" class=\"data row6 col95\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col96\" class=\"data row6 col96\" >Majuro</td>\n",
-       "      <td id=\"T_78f41_row6_col97\" class=\"data row6 col97\" >1159149063</td>\n",
-       "      <td id=\"T_78f41_row6_col98\" class=\"data row6 col98\" >ماجورو</td>\n",
-       "      <td id=\"T_78f41_row6_col99\" class=\"data row6 col99\" >מג'ורו</td>\n",
-       "      <td id=\"T_78f41_row6_col100\" class=\"data row6 col100\" >Маджуро</td>\n",
-       "      <td id=\"T_78f41_row6_col101\" class=\"data row6 col101\" >ماجورو</td>\n",
-       "      <td id=\"T_78f41_row6_col102\" class=\"data row6 col102\" >馬久羅</td>\n",
-       "      <td id=\"T_78f41_row6_col103\" class=\"data row6 col103\" >2113779</td>\n",
-       "      <td id=\"T_78f41_row6_col104\" class=\"data row6 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col105\" class=\"data row6 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col106\" class=\"data row6 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col107\" class=\"data row6 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col108\" class=\"data row6 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col109\" class=\"data row6 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col110\" class=\"data row6 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col111\" class=\"data row6 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col112\" class=\"data row6 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col113\" class=\"data row6 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col114\" class=\"data row6 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col115\" class=\"data row6 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col116\" class=\"data row6 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col117\" class=\"data row6 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col118\" class=\"data row6 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col119\" class=\"data row6 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col120\" class=\"data row6 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col121\" class=\"data row6 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col122\" class=\"data row6 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col123\" class=\"data row6 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col124\" class=\"data row6 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col125\" class=\"data row6 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col126\" class=\"data row6 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col127\" class=\"data row6 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col128\" class=\"data row6 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col129\" class=\"data row6 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col130\" class=\"data row6 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col131\" class=\"data row6 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col132\" class=\"data row6 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col133\" class=\"data row6 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col134\" class=\"data row6 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col135\" class=\"data row6 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col136\" class=\"data row6 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row6_col137\" class=\"data row6 col137\" >POINT (171.3800002 7.1030043)</td>\n",
+       "      <th id=\"T_f91f0_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_f91f0_row6_col0\" class=\"data row6 col0\" >6</td>\n",
+       "      <td id=\"T_f91f0_row6_col1\" class=\"data row6 col1\" >30</td>\n",
+       "      <td id=\"T_f91f0_row6_col2\" class=\"data row6 col2\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col3\" class=\"data row6 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row6_col4\" class=\"data row6 col4\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col5\" class=\"data row6 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col6\" class=\"data row6 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col7\" class=\"data row6 col7\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col8\" class=\"data row6 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col10\" class=\"data row6 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col11\" class=\"data row6 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col12\" class=\"data row6 col12\" >Marshall Islands</td>\n",
+       "      <td id=\"T_f91f0_row6_col13\" class=\"data row6 col13\" >MHL</td>\n",
+       "      <td id=\"T_f91f0_row6_col14\" class=\"data row6 col14\" >Marshall Islands</td>\n",
+       "      <td id=\"T_f91f0_row6_col15\" class=\"data row6 col15\" >MHL</td>\n",
+       "      <td id=\"T_f91f0_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col17\" class=\"data row6 col17\" >MH</td>\n",
+       "      <td id=\"T_f91f0_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col19\" class=\"data row6 col19\" >7.103004</td>\n",
+       "      <td id=\"T_f91f0_row6_col20\" class=\"data row6 col20\" >171.380000</td>\n",
+       "      <td id=\"T_f91f0_row6_col21\" class=\"data row6 col21\" >25400</td>\n",
+       "      <td id=\"T_f91f0_row6_col22\" class=\"data row6 col22\" >20500</td>\n",
+       "      <td id=\"T_f91f0_row6_col23\" class=\"data row6 col23\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col24\" class=\"data row6 col24\" >7</td>\n",
+       "      <td id=\"T_f91f0_row6_col25\" class=\"data row6 col25\" >7</td>\n",
+       "      <td id=\"T_f91f0_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col27\" class=\"data row6 col27\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col28\" class=\"data row6 col28\" >2084</td>\n",
+       "      <td id=\"T_f91f0_row6_col29\" class=\"data row6 col29\" >2084</td>\n",
+       "      <td id=\"T_f91f0_row6_col30\" class=\"data row6 col30\" >2084</td>\n",
+       "      <td id=\"T_f91f0_row6_col31\" class=\"data row6 col31\" >2084</td>\n",
+       "      <td id=\"T_f91f0_row6_col32\" class=\"data row6 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col33\" class=\"data row6 col33\" >100</td>\n",
+       "      <td id=\"T_f91f0_row6_col34\" class=\"data row6 col34\" >3</td>\n",
+       "      <td id=\"T_f91f0_row6_col35\" class=\"data row6 col35\" >3</td>\n",
+       "      <td id=\"T_f91f0_row6_col36\" class=\"data row6 col36\" >1</td>\n",
+       "      <td id=\"T_f91f0_row6_col37\" class=\"data row6 col37\" >1</td>\n",
+       "      <td id=\"T_f91f0_row6_col38\" class=\"data row6 col38\" >7</td>\n",
+       "      <td id=\"T_f91f0_row6_col39\" class=\"data row6 col39\" >7</td>\n",
+       "      <td id=\"T_f91f0_row6_col40\" class=\"data row6 col40\" >5</td>\n",
+       "      <td id=\"T_f91f0_row6_col41\" class=\"data row6 col41\" >5</td>\n",
+       "      <td id=\"T_f91f0_row6_col42\" class=\"data row6 col42\" >171.366667</td>\n",
+       "      <td id=\"T_f91f0_row6_col43\" class=\"data row6 col43\" >171.366667</td>\n",
+       "      <td id=\"T_f91f0_row6_col44\" class=\"data row6 col44\" >171.375000</td>\n",
+       "      <td id=\"T_f91f0_row6_col45\" class=\"data row6 col45\" >171.375000</td>\n",
+       "      <td id=\"T_f91f0_row6_col46\" class=\"data row6 col46\" >7.091667</td>\n",
+       "      <td id=\"T_f91f0_row6_col47\" class=\"data row6 col47\" >7.091667</td>\n",
+       "      <td id=\"T_f91f0_row6_col48\" class=\"data row6 col48\" >7.116667</td>\n",
+       "      <td id=\"T_f91f0_row6_col49\" class=\"data row6 col49\" >7.116667</td>\n",
+       "      <td id=\"T_f91f0_row6_col50\" class=\"data row6 col50\" >171.370833</td>\n",
+       "      <td id=\"T_f91f0_row6_col51\" class=\"data row6 col51\" >7.104167</td>\n",
+       "      <td id=\"T_f91f0_row6_col52\" class=\"data row6 col52\" >Pacific/Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col53\" class=\"data row6 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col54\" class=\"data row6 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col55\" class=\"data row6 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col56\" class=\"data row6 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col57\" class=\"data row6 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col58\" class=\"data row6 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col59\" class=\"data row6 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col60\" class=\"data row6 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col61\" class=\"data row6 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col62\" class=\"data row6 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col63\" class=\"data row6 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col64\" class=\"data row6 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col65\" class=\"data row6 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col66\" class=\"data row6 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col67\" class=\"data row6 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col68\" class=\"data row6 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col69\" class=\"data row6 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col70\" class=\"data row6 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col71\" class=\"data row6 col71\" >6.000000</td>\n",
+       "      <td id=\"T_f91f0_row6_col72\" class=\"data row6 col72\" >Q12919</td>\n",
+       "      <td id=\"T_f91f0_row6_col73\" class=\"data row6 col73\" >890451463</td>\n",
+       "      <td id=\"T_f91f0_row6_col74\" class=\"data row6 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row6_col75\" class=\"data row6 col75\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col76\" class=\"data row6 col76\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col77\" class=\"data row6 col77\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col78\" class=\"data row6 col78\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col79\" class=\"data row6 col79\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col80\" class=\"data row6 col80\" >Маджуро</td>\n",
+       "      <td id=\"T_f91f0_row6_col81\" class=\"data row6 col81\" >马朱罗</td>\n",
+       "      <td id=\"T_f91f0_row6_col82\" class=\"data row6 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col83\" class=\"data row6 col83\" >ماجورو</td>\n",
+       "      <td id=\"T_f91f0_row6_col84\" class=\"data row6 col84\" >মাজুরো</td>\n",
+       "      <td id=\"T_f91f0_row6_col85\" class=\"data row6 col85\" >Ματζούρο</td>\n",
+       "      <td id=\"T_f91f0_row6_col86\" class=\"data row6 col86\" >माजुरो</td>\n",
+       "      <td id=\"T_f91f0_row6_col87\" class=\"data row6 col87\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col88\" class=\"data row6 col88\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col89\" class=\"data row6 col89\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col90\" class=\"data row6 col90\" >マジュロ</td>\n",
+       "      <td id=\"T_f91f0_row6_col91\" class=\"data row6 col91\" >마주로</td>\n",
+       "      <td id=\"T_f91f0_row6_col92\" class=\"data row6 col92\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col93\" class=\"data row6 col93\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col94\" class=\"data row6 col94\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col95\" class=\"data row6 col95\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col96\" class=\"data row6 col96\" >Majuro</td>\n",
+       "      <td id=\"T_f91f0_row6_col97\" class=\"data row6 col97\" >1159149063</td>\n",
+       "      <td id=\"T_f91f0_row6_col98\" class=\"data row6 col98\" >ماجورو</td>\n",
+       "      <td id=\"T_f91f0_row6_col99\" class=\"data row6 col99\" >מג'ורו</td>\n",
+       "      <td id=\"T_f91f0_row6_col100\" class=\"data row6 col100\" >Маджуро</td>\n",
+       "      <td id=\"T_f91f0_row6_col101\" class=\"data row6 col101\" >ماجورو</td>\n",
+       "      <td id=\"T_f91f0_row6_col102\" class=\"data row6 col102\" >馬久羅</td>\n",
+       "      <td id=\"T_f91f0_row6_col103\" class=\"data row6 col103\" >2113779</td>\n",
+       "      <td id=\"T_f91f0_row6_col104\" class=\"data row6 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col105\" class=\"data row6 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col106\" class=\"data row6 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col107\" class=\"data row6 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col108\" class=\"data row6 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col109\" class=\"data row6 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col110\" class=\"data row6 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col111\" class=\"data row6 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col112\" class=\"data row6 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col113\" class=\"data row6 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col114\" class=\"data row6 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col115\" class=\"data row6 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col116\" class=\"data row6 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col117\" class=\"data row6 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col118\" class=\"data row6 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col119\" class=\"data row6 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col120\" class=\"data row6 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col121\" class=\"data row6 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col122\" class=\"data row6 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col123\" class=\"data row6 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col124\" class=\"data row6 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col125\" class=\"data row6 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col126\" class=\"data row6 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col127\" class=\"data row6 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col128\" class=\"data row6 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col129\" class=\"data row6 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col130\" class=\"data row6 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col131\" class=\"data row6 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col132\" class=\"data row6 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col133\" class=\"data row6 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col134\" class=\"data row6 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col135\" class=\"data row6 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col136\" class=\"data row6 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row6_col137\" class=\"data row6 col137\" >POINT (171.3800002 7.1030043)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_78f41_row7_col0\" class=\"data row7 col0\" >6</td>\n",
-       "      <td id=\"T_78f41_row7_col1\" class=\"data row7 col1\" >30</td>\n",
-       "      <td id=\"T_78f41_row7_col2\" class=\"data row7 col2\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col3\" class=\"data row7 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row7_col4\" class=\"data row7 col4\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col5\" class=\"data row7 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col6\" class=\"data row7 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col7\" class=\"data row7 col7\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col8\" class=\"data row7 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col10\" class=\"data row7 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col11\" class=\"data row7 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col12\" class=\"data row7 col12\" >Tuvalu</td>\n",
-       "      <td id=\"T_78f41_row7_col13\" class=\"data row7 col13\" >TUV</td>\n",
-       "      <td id=\"T_78f41_row7_col14\" class=\"data row7 col14\" >Tuvalu</td>\n",
-       "      <td id=\"T_78f41_row7_col15\" class=\"data row7 col15\" >TUV</td>\n",
-       "      <td id=\"T_78f41_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col17\" class=\"data row7 col17\" >TV</td>\n",
-       "      <td id=\"T_78f41_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col19\" class=\"data row7 col19\" >-8.516652</td>\n",
-       "      <td id=\"T_78f41_row7_col20\" class=\"data row7 col20\" >179.216647</td>\n",
-       "      <td id=\"T_78f41_row7_col21\" class=\"data row7 col21\" >4749</td>\n",
-       "      <td id=\"T_78f41_row7_col22\" class=\"data row7 col22\" >4749</td>\n",
-       "      <td id=\"T_78f41_row7_col23\" class=\"data row7 col23\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col24\" class=\"data row7 col24\" >4</td>\n",
-       "      <td id=\"T_78f41_row7_col25\" class=\"data row7 col25\" >4</td>\n",
-       "      <td id=\"T_78f41_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col27\" class=\"data row7 col27\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col28\" class=\"data row7 col28\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col29\" class=\"data row7 col29\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col30\" class=\"data row7 col30\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col31\" class=\"data row7 col31\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col32\" class=\"data row7 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col33\" class=\"data row7 col33\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col34\" class=\"data row7 col34\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col35\" class=\"data row7 col35\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col36\" class=\"data row7 col36\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col37\" class=\"data row7 col37\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col38\" class=\"data row7 col38\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col39\" class=\"data row7 col39\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col40\" class=\"data row7 col40\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col41\" class=\"data row7 col41\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col42\" class=\"data row7 col42\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col43\" class=\"data row7 col43\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col44\" class=\"data row7 col44\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col45\" class=\"data row7 col45\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col46\" class=\"data row7 col46\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col47\" class=\"data row7 col47\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col48\" class=\"data row7 col48\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col49\" class=\"data row7 col49\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col50\" class=\"data row7 col50\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col51\" class=\"data row7 col51\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col52\" class=\"data row7 col52\" >Pacific/Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col53\" class=\"data row7 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col54\" class=\"data row7 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col55\" class=\"data row7 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col56\" class=\"data row7 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col57\" class=\"data row7 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col58\" class=\"data row7 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col59\" class=\"data row7 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col60\" class=\"data row7 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col61\" class=\"data row7 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col62\" class=\"data row7 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col63\" class=\"data row7 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col64\" class=\"data row7 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col65\" class=\"data row7 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col66\" class=\"data row7 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col67\" class=\"data row7 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col68\" class=\"data row7 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col69\" class=\"data row7 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col70\" class=\"data row7 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col71\" class=\"data row7 col71\" >6.000000</td>\n",
-       "      <td id=\"T_78f41_row7_col72\" class=\"data row7 col72\" >Q34126</td>\n",
-       "      <td id=\"T_78f41_row7_col73\" class=\"data row7 col73\" >1141909143</td>\n",
-       "      <td id=\"T_78f41_row7_col74\" class=\"data row7 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row7_col75\" class=\"data row7 col75\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col76\" class=\"data row7 col76\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col77\" class=\"data row7 col77\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col78\" class=\"data row7 col78\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col79\" class=\"data row7 col79\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col80\" class=\"data row7 col80\" >Фунафути</td>\n",
-       "      <td id=\"T_78f41_row7_col81\" class=\"data row7 col81\" >富纳富提</td>\n",
-       "      <td id=\"T_78f41_row7_col82\" class=\"data row7 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col83\" class=\"data row7 col83\" >فونافوتي</td>\n",
-       "      <td id=\"T_78f41_row7_col84\" class=\"data row7 col84\" >ফুনাফুতি</td>\n",
-       "      <td id=\"T_78f41_row7_col85\" class=\"data row7 col85\" >Φουναφούτι</td>\n",
-       "      <td id=\"T_78f41_row7_col86\" class=\"data row7 col86\" >फुनाफुति</td>\n",
-       "      <td id=\"T_78f41_row7_col87\" class=\"data row7 col87\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col88\" class=\"data row7 col88\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col89\" class=\"data row7 col89\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col90\" class=\"data row7 col90\" >フナフティ島</td>\n",
-       "      <td id=\"T_78f41_row7_col91\" class=\"data row7 col91\" >푸나푸티</td>\n",
-       "      <td id=\"T_78f41_row7_col92\" class=\"data row7 col92\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col93\" class=\"data row7 col93\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col94\" class=\"data row7 col94\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col95\" class=\"data row7 col95\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col96\" class=\"data row7 col96\" >Funafuti</td>\n",
-       "      <td id=\"T_78f41_row7_col97\" class=\"data row7 col97\" >1159149071</td>\n",
-       "      <td id=\"T_78f41_row7_col98\" class=\"data row7 col98\" >فونافوتی</td>\n",
-       "      <td id=\"T_78f41_row7_col99\" class=\"data row7 col99\" >פנאפוטי</td>\n",
-       "      <td id=\"T_78f41_row7_col100\" class=\"data row7 col100\" >Фунафуті</td>\n",
-       "      <td id=\"T_78f41_row7_col101\" class=\"data row7 col101\" >فونافوتی</td>\n",
-       "      <td id=\"T_78f41_row7_col102\" class=\"data row7 col102\" >富纳富提</td>\n",
-       "      <td id=\"T_78f41_row7_col103\" class=\"data row7 col103\" >2110394</td>\n",
-       "      <td id=\"T_78f41_row7_col104\" class=\"data row7 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col105\" class=\"data row7 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col106\" class=\"data row7 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col107\" class=\"data row7 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col108\" class=\"data row7 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col109\" class=\"data row7 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col110\" class=\"data row7 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col111\" class=\"data row7 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col112\" class=\"data row7 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col113\" class=\"data row7 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col114\" class=\"data row7 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col115\" class=\"data row7 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col116\" class=\"data row7 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col117\" class=\"data row7 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col118\" class=\"data row7 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col119\" class=\"data row7 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col120\" class=\"data row7 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col121\" class=\"data row7 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col122\" class=\"data row7 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col123\" class=\"data row7 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col124\" class=\"data row7 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col125\" class=\"data row7 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col126\" class=\"data row7 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col127\" class=\"data row7 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col128\" class=\"data row7 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col129\" class=\"data row7 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col130\" class=\"data row7 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col131\" class=\"data row7 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col132\" class=\"data row7 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col133\" class=\"data row7 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col134\" class=\"data row7 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col135\" class=\"data row7 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col136\" class=\"data row7 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row7_col137\" class=\"data row7 col137\" >POINT (179.2166471 -8.516652)</td>\n",
+       "      <th id=\"T_f91f0_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_f91f0_row7_col0\" class=\"data row7 col0\" >6</td>\n",
+       "      <td id=\"T_f91f0_row7_col1\" class=\"data row7 col1\" >30</td>\n",
+       "      <td id=\"T_f91f0_row7_col2\" class=\"data row7 col2\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col3\" class=\"data row7 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row7_col4\" class=\"data row7 col4\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col5\" class=\"data row7 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col6\" class=\"data row7 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col7\" class=\"data row7 col7\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col8\" class=\"data row7 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col10\" class=\"data row7 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col11\" class=\"data row7 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col12\" class=\"data row7 col12\" >Tuvalu</td>\n",
+       "      <td id=\"T_f91f0_row7_col13\" class=\"data row7 col13\" >TUV</td>\n",
+       "      <td id=\"T_f91f0_row7_col14\" class=\"data row7 col14\" >Tuvalu</td>\n",
+       "      <td id=\"T_f91f0_row7_col15\" class=\"data row7 col15\" >TUV</td>\n",
+       "      <td id=\"T_f91f0_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col17\" class=\"data row7 col17\" >TV</td>\n",
+       "      <td id=\"T_f91f0_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col19\" class=\"data row7 col19\" >-8.516652</td>\n",
+       "      <td id=\"T_f91f0_row7_col20\" class=\"data row7 col20\" >179.216647</td>\n",
+       "      <td id=\"T_f91f0_row7_col21\" class=\"data row7 col21\" >4749</td>\n",
+       "      <td id=\"T_f91f0_row7_col22\" class=\"data row7 col22\" >4749</td>\n",
+       "      <td id=\"T_f91f0_row7_col23\" class=\"data row7 col23\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col24\" class=\"data row7 col24\" >4</td>\n",
+       "      <td id=\"T_f91f0_row7_col25\" class=\"data row7 col25\" >4</td>\n",
+       "      <td id=\"T_f91f0_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col27\" class=\"data row7 col27\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col28\" class=\"data row7 col28\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col29\" class=\"data row7 col29\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col30\" class=\"data row7 col30\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col31\" class=\"data row7 col31\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col32\" class=\"data row7 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col33\" class=\"data row7 col33\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col34\" class=\"data row7 col34\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col35\" class=\"data row7 col35\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col36\" class=\"data row7 col36\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col37\" class=\"data row7 col37\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col38\" class=\"data row7 col38\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col39\" class=\"data row7 col39\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col40\" class=\"data row7 col40\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col41\" class=\"data row7 col41\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col42\" class=\"data row7 col42\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col43\" class=\"data row7 col43\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col44\" class=\"data row7 col44\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col45\" class=\"data row7 col45\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col46\" class=\"data row7 col46\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col47\" class=\"data row7 col47\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col48\" class=\"data row7 col48\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col49\" class=\"data row7 col49\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col50\" class=\"data row7 col50\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col51\" class=\"data row7 col51\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col52\" class=\"data row7 col52\" >Pacific/Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col53\" class=\"data row7 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col54\" class=\"data row7 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col55\" class=\"data row7 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col56\" class=\"data row7 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col57\" class=\"data row7 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col58\" class=\"data row7 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col59\" class=\"data row7 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col60\" class=\"data row7 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col61\" class=\"data row7 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col62\" class=\"data row7 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col63\" class=\"data row7 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col64\" class=\"data row7 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col65\" class=\"data row7 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col66\" class=\"data row7 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col67\" class=\"data row7 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col68\" class=\"data row7 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col69\" class=\"data row7 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col70\" class=\"data row7 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col71\" class=\"data row7 col71\" >6.000000</td>\n",
+       "      <td id=\"T_f91f0_row7_col72\" class=\"data row7 col72\" >Q34126</td>\n",
+       "      <td id=\"T_f91f0_row7_col73\" class=\"data row7 col73\" >1141909143</td>\n",
+       "      <td id=\"T_f91f0_row7_col74\" class=\"data row7 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row7_col75\" class=\"data row7 col75\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col76\" class=\"data row7 col76\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col77\" class=\"data row7 col77\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col78\" class=\"data row7 col78\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col79\" class=\"data row7 col79\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col80\" class=\"data row7 col80\" >Фунафути</td>\n",
+       "      <td id=\"T_f91f0_row7_col81\" class=\"data row7 col81\" >富纳富提</td>\n",
+       "      <td id=\"T_f91f0_row7_col82\" class=\"data row7 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col83\" class=\"data row7 col83\" >فونافوتي</td>\n",
+       "      <td id=\"T_f91f0_row7_col84\" class=\"data row7 col84\" >ফুনাফুতি</td>\n",
+       "      <td id=\"T_f91f0_row7_col85\" class=\"data row7 col85\" >Φουναφούτι</td>\n",
+       "      <td id=\"T_f91f0_row7_col86\" class=\"data row7 col86\" >फुनाफुति</td>\n",
+       "      <td id=\"T_f91f0_row7_col87\" class=\"data row7 col87\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col88\" class=\"data row7 col88\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col89\" class=\"data row7 col89\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col90\" class=\"data row7 col90\" >フナフティ島</td>\n",
+       "      <td id=\"T_f91f0_row7_col91\" class=\"data row7 col91\" >푸나푸티</td>\n",
+       "      <td id=\"T_f91f0_row7_col92\" class=\"data row7 col92\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col93\" class=\"data row7 col93\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col94\" class=\"data row7 col94\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col95\" class=\"data row7 col95\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col96\" class=\"data row7 col96\" >Funafuti</td>\n",
+       "      <td id=\"T_f91f0_row7_col97\" class=\"data row7 col97\" >1159149071</td>\n",
+       "      <td id=\"T_f91f0_row7_col98\" class=\"data row7 col98\" >فونافوتی</td>\n",
+       "      <td id=\"T_f91f0_row7_col99\" class=\"data row7 col99\" >פנאפוטי</td>\n",
+       "      <td id=\"T_f91f0_row7_col100\" class=\"data row7 col100\" >Фунафуті</td>\n",
+       "      <td id=\"T_f91f0_row7_col101\" class=\"data row7 col101\" >فونافوتی</td>\n",
+       "      <td id=\"T_f91f0_row7_col102\" class=\"data row7 col102\" >富纳富提</td>\n",
+       "      <td id=\"T_f91f0_row7_col103\" class=\"data row7 col103\" >2110394</td>\n",
+       "      <td id=\"T_f91f0_row7_col104\" class=\"data row7 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col105\" class=\"data row7 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col106\" class=\"data row7 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col107\" class=\"data row7 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col108\" class=\"data row7 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col109\" class=\"data row7 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col110\" class=\"data row7 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col111\" class=\"data row7 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col112\" class=\"data row7 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col113\" class=\"data row7 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col114\" class=\"data row7 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col115\" class=\"data row7 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col116\" class=\"data row7 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col117\" class=\"data row7 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col118\" class=\"data row7 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col119\" class=\"data row7 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col120\" class=\"data row7 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col121\" class=\"data row7 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col122\" class=\"data row7 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col123\" class=\"data row7 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col124\" class=\"data row7 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col125\" class=\"data row7 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col126\" class=\"data row7 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col127\" class=\"data row7 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col128\" class=\"data row7 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col129\" class=\"data row7 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col130\" class=\"data row7 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col131\" class=\"data row7 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col132\" class=\"data row7 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col133\" class=\"data row7 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col134\" class=\"data row7 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col135\" class=\"data row7 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col136\" class=\"data row7 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row7_col137\" class=\"data row7 col137\" >POINT (179.2166471 -8.516652)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_78f41_row8_col0\" class=\"data row8 col0\" >6</td>\n",
-       "      <td id=\"T_78f41_row8_col1\" class=\"data row8 col1\" >30</td>\n",
-       "      <td id=\"T_78f41_row8_col2\" class=\"data row8 col2\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col3\" class=\"data row8 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_78f41_row8_col4\" class=\"data row8 col4\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col5\" class=\"data row8 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col6\" class=\"data row8 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col7\" class=\"data row8 col7\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col8\" class=\"data row8 col8\" >1</td>\n",
-       "      <td id=\"T_78f41_row8_col9\" class=\"data row8 col9\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col10\" class=\"data row8 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col11\" class=\"data row8 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col12\" class=\"data row8 col12\" >Palau</td>\n",
-       "      <td id=\"T_78f41_row8_col13\" class=\"data row8 col13\" >PLW</td>\n",
-       "      <td id=\"T_78f41_row8_col14\" class=\"data row8 col14\" >Palau</td>\n",
-       "      <td id=\"T_78f41_row8_col15\" class=\"data row8 col15\" >PLW</td>\n",
-       "      <td id=\"T_78f41_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col17\" class=\"data row8 col17\" >PW</td>\n",
-       "      <td id=\"T_78f41_row8_col18\" class=\"data row8 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col19\" class=\"data row8 col19\" >7.487396</td>\n",
-       "      <td id=\"T_78f41_row8_col20\" class=\"data row8 col20\" >134.626549</td>\n",
-       "      <td id=\"T_78f41_row8_col21\" class=\"data row8 col21\" >7026</td>\n",
-       "      <td id=\"T_78f41_row8_col22\" class=\"data row8 col22\" >7026</td>\n",
-       "      <td id=\"T_78f41_row8_col23\" class=\"data row8 col23\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col24\" class=\"data row8 col24\" >5</td>\n",
-       "      <td id=\"T_78f41_row8_col25\" class=\"data row8 col25\" >5</td>\n",
-       "      <td id=\"T_78f41_row8_col26\" class=\"data row8 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col27\" class=\"data row8 col27\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col28\" class=\"data row8 col28\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col29\" class=\"data row8 col29\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col30\" class=\"data row8 col30\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col31\" class=\"data row8 col31\" >7026</td>\n",
-       "      <td id=\"T_78f41_row8_col32\" class=\"data row8 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col33\" class=\"data row8 col33\" >100</td>\n",
-       "      <td id=\"T_78f41_row8_col34\" class=\"data row8 col34\" >6</td>\n",
-       "      <td id=\"T_78f41_row8_col35\" class=\"data row8 col35\" >6</td>\n",
-       "      <td id=\"T_78f41_row8_col36\" class=\"data row8 col36\" >2</td>\n",
-       "      <td id=\"T_78f41_row8_col37\" class=\"data row8 col37\" >2</td>\n",
-       "      <td id=\"T_78f41_row8_col38\" class=\"data row8 col38\" >15</td>\n",
-       "      <td id=\"T_78f41_row8_col39\" class=\"data row8 col39\" >15</td>\n",
-       "      <td id=\"T_78f41_row8_col40\" class=\"data row8 col40\" >9</td>\n",
-       "      <td id=\"T_78f41_row8_col41\" class=\"data row8 col41\" >9</td>\n",
-       "      <td id=\"T_78f41_row8_col42\" class=\"data row8 col42\" >134.466667</td>\n",
-       "      <td id=\"T_78f41_row8_col43\" class=\"data row8 col43\" >134.466667</td>\n",
-       "      <td id=\"T_78f41_row8_col44\" class=\"data row8 col44\" >134.500000</td>\n",
-       "      <td id=\"T_78f41_row8_col45\" class=\"data row8 col45\" >134.500000</td>\n",
-       "      <td id=\"T_78f41_row8_col46\" class=\"data row8 col46\" >7.325000</td>\n",
-       "      <td id=\"T_78f41_row8_col47\" class=\"data row8 col47\" >7.325000</td>\n",
-       "      <td id=\"T_78f41_row8_col48\" class=\"data row8 col48\" >7.350000</td>\n",
-       "      <td id=\"T_78f41_row8_col49\" class=\"data row8 col49\" >7.350000</td>\n",
-       "      <td id=\"T_78f41_row8_col50\" class=\"data row8 col50\" >134.481548</td>\n",
-       "      <td id=\"T_78f41_row8_col51\" class=\"data row8 col51\" >7.339881</td>\n",
-       "      <td id=\"T_78f41_row8_col52\" class=\"data row8 col52\" >Pacific/Palau</td>\n",
-       "      <td id=\"T_78f41_row8_col53\" class=\"data row8 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col54\" class=\"data row8 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col55\" class=\"data row8 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col56\" class=\"data row8 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col57\" class=\"data row8 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col58\" class=\"data row8 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col59\" class=\"data row8 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col60\" class=\"data row8 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col61\" class=\"data row8 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col62\" class=\"data row8 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col63\" class=\"data row8 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col64\" class=\"data row8 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col65\" class=\"data row8 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col66\" class=\"data row8 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col67\" class=\"data row8 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col68\" class=\"data row8 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col69\" class=\"data row8 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col70\" class=\"data row8 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col71\" class=\"data row8 col71\" >6.000000</td>\n",
-       "      <td id=\"T_78f41_row8_col72\" class=\"data row8 col72\" >Q154002</td>\n",
-       "      <td id=\"T_78f41_row8_col73\" class=\"data row8 col73\" >890443883</td>\n",
-       "      <td id=\"T_78f41_row8_col74\" class=\"data row8 col74\" >0</td>\n",
-       "      <td id=\"T_78f41_row8_col75\" class=\"data row8 col75\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col76\" class=\"data row8 col76\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col77\" class=\"data row8 col77\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col78\" class=\"data row8 col78\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col79\" class=\"data row8 col79\" >Melequeoque</td>\n",
-       "      <td id=\"T_78f41_row8_col80\" class=\"data row8 col80\" >Мелекеок</td>\n",
-       "      <td id=\"T_78f41_row8_col81\" class=\"data row8 col81\" >梅莱凯奥克</td>\n",
-       "      <td id=\"T_78f41_row8_col82\" class=\"data row8 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col83\" class=\"data row8 col83\" >ميلكيوك</td>\n",
-       "      <td id=\"T_78f41_row8_col84\" class=\"data row8 col84\" >মেলেকেওক</td>\n",
-       "      <td id=\"T_78f41_row8_col85\" class=\"data row8 col85\" >Μελεκέοκ</td>\n",
-       "      <td id=\"T_78f41_row8_col86\" class=\"data row8 col86\" >मेलेकियोक</td>\n",
-       "      <td id=\"T_78f41_row8_col87\" class=\"data row8 col87\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col88\" class=\"data row8 col88\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col89\" class=\"data row8 col89\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col90\" class=\"data row8 col90\" >マルキョク州</td>\n",
-       "      <td id=\"T_78f41_row8_col91\" class=\"data row8 col91\" >멜레케오크</td>\n",
-       "      <td id=\"T_78f41_row8_col92\" class=\"data row8 col92\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col93\" class=\"data row8 col93\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col94\" class=\"data row8 col94\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col95\" class=\"data row8 col95\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col96\" class=\"data row8 col96\" >Melekeok</td>\n",
-       "      <td id=\"T_78f41_row8_col97\" class=\"data row8 col97\" >1159149073</td>\n",
-       "      <td id=\"T_78f41_row8_col98\" class=\"data row8 col98\" >ملکئوک</td>\n",
-       "      <td id=\"T_78f41_row8_col99\" class=\"data row8 col99\" >מלקאוק</td>\n",
-       "      <td id=\"T_78f41_row8_col100\" class=\"data row8 col100\" >Мелекеок</td>\n",
-       "      <td id=\"T_78f41_row8_col101\" class=\"data row8 col101\" >میلیکوک</td>\n",
-       "      <td id=\"T_78f41_row8_col102\" class=\"data row8 col102\" >梅萊凱奧克</td>\n",
-       "      <td id=\"T_78f41_row8_col103\" class=\"data row8 col103\" >1559804</td>\n",
-       "      <td id=\"T_78f41_row8_col104\" class=\"data row8 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col105\" class=\"data row8 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col106\" class=\"data row8 col106\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col107\" class=\"data row8 col107\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col108\" class=\"data row8 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col109\" class=\"data row8 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col110\" class=\"data row8 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col111\" class=\"data row8 col111\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col112\" class=\"data row8 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col113\" class=\"data row8 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col114\" class=\"data row8 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col115\" class=\"data row8 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col116\" class=\"data row8 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col117\" class=\"data row8 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col118\" class=\"data row8 col118\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col119\" class=\"data row8 col119\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col120\" class=\"data row8 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col121\" class=\"data row8 col121\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col122\" class=\"data row8 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col123\" class=\"data row8 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col124\" class=\"data row8 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col125\" class=\"data row8 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col126\" class=\"data row8 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col127\" class=\"data row8 col127\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col128\" class=\"data row8 col128\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col129\" class=\"data row8 col129\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col130\" class=\"data row8 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col131\" class=\"data row8 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col132\" class=\"data row8 col132\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col133\" class=\"data row8 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col134\" class=\"data row8 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col135\" class=\"data row8 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col136\" class=\"data row8 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row8_col137\" class=\"data row8 col137\" >POINT (134.6265485 7.4873962)</td>\n",
+       "      <th id=\"T_f91f0_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_f91f0_row8_col0\" class=\"data row8 col0\" >6</td>\n",
+       "      <td id=\"T_f91f0_row8_col1\" class=\"data row8 col1\" >30</td>\n",
+       "      <td id=\"T_f91f0_row8_col2\" class=\"data row8 col2\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col3\" class=\"data row8 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_f91f0_row8_col4\" class=\"data row8 col4\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col5\" class=\"data row8 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col6\" class=\"data row8 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col7\" class=\"data row8 col7\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col8\" class=\"data row8 col8\" >1</td>\n",
+       "      <td id=\"T_f91f0_row8_col9\" class=\"data row8 col9\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col10\" class=\"data row8 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col11\" class=\"data row8 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col12\" class=\"data row8 col12\" >Palau</td>\n",
+       "      <td id=\"T_f91f0_row8_col13\" class=\"data row8 col13\" >PLW</td>\n",
+       "      <td id=\"T_f91f0_row8_col14\" class=\"data row8 col14\" >Palau</td>\n",
+       "      <td id=\"T_f91f0_row8_col15\" class=\"data row8 col15\" >PLW</td>\n",
+       "      <td id=\"T_f91f0_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col17\" class=\"data row8 col17\" >PW</td>\n",
+       "      <td id=\"T_f91f0_row8_col18\" class=\"data row8 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col19\" class=\"data row8 col19\" >7.487396</td>\n",
+       "      <td id=\"T_f91f0_row8_col20\" class=\"data row8 col20\" >134.626549</td>\n",
+       "      <td id=\"T_f91f0_row8_col21\" class=\"data row8 col21\" >7026</td>\n",
+       "      <td id=\"T_f91f0_row8_col22\" class=\"data row8 col22\" >7026</td>\n",
+       "      <td id=\"T_f91f0_row8_col23\" class=\"data row8 col23\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col24\" class=\"data row8 col24\" >5</td>\n",
+       "      <td id=\"T_f91f0_row8_col25\" class=\"data row8 col25\" >5</td>\n",
+       "      <td id=\"T_f91f0_row8_col26\" class=\"data row8 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col27\" class=\"data row8 col27\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col28\" class=\"data row8 col28\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col29\" class=\"data row8 col29\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col30\" class=\"data row8 col30\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col31\" class=\"data row8 col31\" >7026</td>\n",
+       "      <td id=\"T_f91f0_row8_col32\" class=\"data row8 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col33\" class=\"data row8 col33\" >100</td>\n",
+       "      <td id=\"T_f91f0_row8_col34\" class=\"data row8 col34\" >6</td>\n",
+       "      <td id=\"T_f91f0_row8_col35\" class=\"data row8 col35\" >6</td>\n",
+       "      <td id=\"T_f91f0_row8_col36\" class=\"data row8 col36\" >2</td>\n",
+       "      <td id=\"T_f91f0_row8_col37\" class=\"data row8 col37\" >2</td>\n",
+       "      <td id=\"T_f91f0_row8_col38\" class=\"data row8 col38\" >15</td>\n",
+       "      <td id=\"T_f91f0_row8_col39\" class=\"data row8 col39\" >15</td>\n",
+       "      <td id=\"T_f91f0_row8_col40\" class=\"data row8 col40\" >9</td>\n",
+       "      <td id=\"T_f91f0_row8_col41\" class=\"data row8 col41\" >9</td>\n",
+       "      <td id=\"T_f91f0_row8_col42\" class=\"data row8 col42\" >134.466667</td>\n",
+       "      <td id=\"T_f91f0_row8_col43\" class=\"data row8 col43\" >134.466667</td>\n",
+       "      <td id=\"T_f91f0_row8_col44\" class=\"data row8 col44\" >134.500000</td>\n",
+       "      <td id=\"T_f91f0_row8_col45\" class=\"data row8 col45\" >134.500000</td>\n",
+       "      <td id=\"T_f91f0_row8_col46\" class=\"data row8 col46\" >7.325000</td>\n",
+       "      <td id=\"T_f91f0_row8_col47\" class=\"data row8 col47\" >7.325000</td>\n",
+       "      <td id=\"T_f91f0_row8_col48\" class=\"data row8 col48\" >7.350000</td>\n",
+       "      <td id=\"T_f91f0_row8_col49\" class=\"data row8 col49\" >7.350000</td>\n",
+       "      <td id=\"T_f91f0_row8_col50\" class=\"data row8 col50\" >134.481548</td>\n",
+       "      <td id=\"T_f91f0_row8_col51\" class=\"data row8 col51\" >7.339881</td>\n",
+       "      <td id=\"T_f91f0_row8_col52\" class=\"data row8 col52\" >Pacific/Palau</td>\n",
+       "      <td id=\"T_f91f0_row8_col53\" class=\"data row8 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col54\" class=\"data row8 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col55\" class=\"data row8 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col56\" class=\"data row8 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col57\" class=\"data row8 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col58\" class=\"data row8 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col59\" class=\"data row8 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col60\" class=\"data row8 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col61\" class=\"data row8 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col62\" class=\"data row8 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col63\" class=\"data row8 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col64\" class=\"data row8 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col65\" class=\"data row8 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col66\" class=\"data row8 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col67\" class=\"data row8 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col68\" class=\"data row8 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col69\" class=\"data row8 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col70\" class=\"data row8 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col71\" class=\"data row8 col71\" >6.000000</td>\n",
+       "      <td id=\"T_f91f0_row8_col72\" class=\"data row8 col72\" >Q154002</td>\n",
+       "      <td id=\"T_f91f0_row8_col73\" class=\"data row8 col73\" >890443883</td>\n",
+       "      <td id=\"T_f91f0_row8_col74\" class=\"data row8 col74\" >0</td>\n",
+       "      <td id=\"T_f91f0_row8_col75\" class=\"data row8 col75\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col76\" class=\"data row8 col76\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col77\" class=\"data row8 col77\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col78\" class=\"data row8 col78\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col79\" class=\"data row8 col79\" >Melequeoque</td>\n",
+       "      <td id=\"T_f91f0_row8_col80\" class=\"data row8 col80\" >Мелекеок</td>\n",
+       "      <td id=\"T_f91f0_row8_col81\" class=\"data row8 col81\" >梅莱凯奥克</td>\n",
+       "      <td id=\"T_f91f0_row8_col82\" class=\"data row8 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col83\" class=\"data row8 col83\" >ميلكيوك</td>\n",
+       "      <td id=\"T_f91f0_row8_col84\" class=\"data row8 col84\" >মেলেকেওক</td>\n",
+       "      <td id=\"T_f91f0_row8_col85\" class=\"data row8 col85\" >Μελεκέοκ</td>\n",
+       "      <td id=\"T_f91f0_row8_col86\" class=\"data row8 col86\" >मेलेकियोक</td>\n",
+       "      <td id=\"T_f91f0_row8_col87\" class=\"data row8 col87\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col88\" class=\"data row8 col88\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col89\" class=\"data row8 col89\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col90\" class=\"data row8 col90\" >マルキョク州</td>\n",
+       "      <td id=\"T_f91f0_row8_col91\" class=\"data row8 col91\" >멜레케오크</td>\n",
+       "      <td id=\"T_f91f0_row8_col92\" class=\"data row8 col92\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col93\" class=\"data row8 col93\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col94\" class=\"data row8 col94\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col95\" class=\"data row8 col95\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col96\" class=\"data row8 col96\" >Melekeok</td>\n",
+       "      <td id=\"T_f91f0_row8_col97\" class=\"data row8 col97\" >1159149073</td>\n",
+       "      <td id=\"T_f91f0_row8_col98\" class=\"data row8 col98\" >ملکئوک</td>\n",
+       "      <td id=\"T_f91f0_row8_col99\" class=\"data row8 col99\" >מלקאוק</td>\n",
+       "      <td id=\"T_f91f0_row8_col100\" class=\"data row8 col100\" >Мелекеок</td>\n",
+       "      <td id=\"T_f91f0_row8_col101\" class=\"data row8 col101\" >میلیکوک</td>\n",
+       "      <td id=\"T_f91f0_row8_col102\" class=\"data row8 col102\" >梅萊凱奧克</td>\n",
+       "      <td id=\"T_f91f0_row8_col103\" class=\"data row8 col103\" >1559804</td>\n",
+       "      <td id=\"T_f91f0_row8_col104\" class=\"data row8 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col105\" class=\"data row8 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col106\" class=\"data row8 col106\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col107\" class=\"data row8 col107\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col108\" class=\"data row8 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col109\" class=\"data row8 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col110\" class=\"data row8 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col111\" class=\"data row8 col111\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col112\" class=\"data row8 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col113\" class=\"data row8 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col114\" class=\"data row8 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col115\" class=\"data row8 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col116\" class=\"data row8 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col117\" class=\"data row8 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col118\" class=\"data row8 col118\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col119\" class=\"data row8 col119\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col120\" class=\"data row8 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col121\" class=\"data row8 col121\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col122\" class=\"data row8 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col123\" class=\"data row8 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col124\" class=\"data row8 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col125\" class=\"data row8 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col126\" class=\"data row8 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col127\" class=\"data row8 col127\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col128\" class=\"data row8 col128\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col129\" class=\"data row8 col129\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col130\" class=\"data row8 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col131\" class=\"data row8 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col132\" class=\"data row8 col132\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col133\" class=\"data row8 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col134\" class=\"data row8 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col135\" class=\"data row8 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col136\" class=\"data row8 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row8_col137\" class=\"data row8 col137\" >POINT (134.6265485 7.4873962)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78f41_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_78f41_row9_col0\" class=\"data row9 col0\" >6</td>\n",
-       "      <td id=\"T_78f41_row9_col1\" class=\"data row9 col1\" >30</td>\n",
-       "      <td id=\"T_78f41_row9_col2\" class=\"data row9 col2\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col3\" class=\"data row9 col3\" >Admin-0 capital alt</td>\n",
-       "      <td id=\"T_78f41_row9_col4\" class=\"data row9 col4\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col5\" class=\"data row9 col5\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col6\" class=\"data row9 col6\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col7\" class=\"data row9 col7\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col8\" class=\"data row9 col8\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col9\" class=\"data row9 col9\" >Claimed as inte</td>\n",
-       "      <td id=\"T_78f41_row9_col10\" class=\"data row9 col10\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col11\" class=\"data row9 col11\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col12\" class=\"data row9 col12\" >Western Sahara</td>\n",
-       "      <td id=\"T_78f41_row9_col13\" class=\"data row9 col13\" >SAH</td>\n",
-       "      <td id=\"T_78f41_row9_col14\" class=\"data row9 col14\" >Western Sahara</td>\n",
-       "      <td id=\"T_78f41_row9_col15\" class=\"data row9 col15\" >SAH</td>\n",
-       "      <td id=\"T_78f41_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col17\" class=\"data row9 col17\" >EH</td>\n",
-       "      <td id=\"T_78f41_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col19\" class=\"data row9 col19\" >26.119167</td>\n",
-       "      <td id=\"T_78f41_row9_col20\" class=\"data row9 col20\" >-9.652522</td>\n",
-       "      <td id=\"T_78f41_row9_col21\" class=\"data row9 col21\" >500</td>\n",
-       "      <td id=\"T_78f41_row9_col22\" class=\"data row9 col22\" >200</td>\n",
-       "      <td id=\"T_78f41_row9_col23\" class=\"data row9 col23\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col24\" class=\"data row9 col24\" >2</td>\n",
-       "      <td id=\"T_78f41_row9_col25\" class=\"data row9 col25\" >1</td>\n",
-       "      <td id=\"T_78f41_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col27\" class=\"data row9 col27\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col28\" class=\"data row9 col28\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col29\" class=\"data row9 col29\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col30\" class=\"data row9 col30\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col31\" class=\"data row9 col31\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col32\" class=\"data row9 col32\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col33\" class=\"data row9 col33\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col34\" class=\"data row9 col34\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col35\" class=\"data row9 col35\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col36\" class=\"data row9 col36\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col37\" class=\"data row9 col37\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col38\" class=\"data row9 col38\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col39\" class=\"data row9 col39\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col40\" class=\"data row9 col40\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col41\" class=\"data row9 col41\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col42\" class=\"data row9 col42\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col43\" class=\"data row9 col43\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col44\" class=\"data row9 col44\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col45\" class=\"data row9 col45\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col46\" class=\"data row9 col46\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col47\" class=\"data row9 col47\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col48\" class=\"data row9 col48\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col49\" class=\"data row9 col49\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col50\" class=\"data row9 col50\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col51\" class=\"data row9 col51\" >0.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col52\" class=\"data row9 col52\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col53\" class=\"data row9 col53\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col54\" class=\"data row9 col54\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col55\" class=\"data row9 col55\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col56\" class=\"data row9 col56\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col57\" class=\"data row9 col57\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col58\" class=\"data row9 col58\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col59\" class=\"data row9 col59\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col60\" class=\"data row9 col60\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col61\" class=\"data row9 col61\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col62\" class=\"data row9 col62\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col63\" class=\"data row9 col63\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col64\" class=\"data row9 col64\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col65\" class=\"data row9 col65\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col66\" class=\"data row9 col66\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col67\" class=\"data row9 col67\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col68\" class=\"data row9 col68\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col69\" class=\"data row9 col69\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col70\" class=\"data row9 col70\" >0</td>\n",
-       "      <td id=\"T_78f41_row9_col71\" class=\"data row9 col71\" >6.000000</td>\n",
-       "      <td id=\"T_78f41_row9_col72\" class=\"data row9 col72\" >Q614754</td>\n",
-       "      <td id=\"T_78f41_row9_col73\" class=\"data row9 col73\" >1141909145</td>\n",
-       "      <td id=\"T_78f41_row9_col74\" class=\"data row9 col74\" >1</td>\n",
-       "      <td id=\"T_78f41_row9_col75\" class=\"data row9 col75\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col76\" class=\"data row9 col76\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_78f41_row9_col77\" class=\"data row9 col77\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_78f41_row9_col78\" class=\"data row9 col78\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col79\" class=\"data row9 col79\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col80\" class=\"data row9 col80\" >Бир-Лелу</td>\n",
-       "      <td id=\"T_78f41_row9_col81\" class=\"data row9 col81\" >比鄂-雷楼</td>\n",
-       "      <td id=\"T_78f41_row9_col82\" class=\"data row9 col82\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col83\" class=\"data row9 col83\" >بئر لحلو</td>\n",
-       "      <td id=\"T_78f41_row9_col84\" class=\"data row9 col84\" >বির লেহলোউ</td>\n",
-       "      <td id=\"T_78f41_row9_col85\" class=\"data row9 col85\" >Μπιρ Λελού</td>\n",
-       "      <td id=\"T_78f41_row9_col86\" class=\"data row9 col86\" >बीर लहलू</td>\n",
-       "      <td id=\"T_78f41_row9_col87\" class=\"data row9 col87\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col88\" class=\"data row9 col88\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col89\" class=\"data row9 col89\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_78f41_row9_col90\" class=\"data row9 col90\" >ビル・ラルフー</td>\n",
-       "      <td id=\"T_78f41_row9_col91\" class=\"data row9 col91\" >비르레흘루</td>\n",
-       "      <td id=\"T_78f41_row9_col92\" class=\"data row9 col92\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col93\" class=\"data row9 col93\" >Al-Bir al-Hilw</td>\n",
-       "      <td id=\"T_78f41_row9_col94\" class=\"data row9 col94\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col95\" class=\"data row9 col95\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_78f41_row9_col96\" class=\"data row9 col96\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_78f41_row9_col97\" class=\"data row9 col97\" >1159149075</td>\n",
-       "      <td id=\"T_78f41_row9_col98\" class=\"data row9 col98\" >بئر لحلو</td>\n",
-       "      <td id=\"T_78f41_row9_col99\" class=\"data row9 col99\" >ביר להלו</td>\n",
-       "      <td id=\"T_78f41_row9_col100\" class=\"data row9 col100\" >Бір-Лелу</td>\n",
-       "      <td id=\"T_78f41_row9_col101\" class=\"data row9 col101\" >بر لیہلو</td>\n",
-       "      <td id=\"T_78f41_row9_col102\" class=\"data row9 col102\" >比鄂-雷楼</td>\n",
-       "      <td id=\"T_78f41_row9_col103\" class=\"data row9 col103\" >-1</td>\n",
-       "      <td id=\"T_78f41_row9_col104\" class=\"data row9 col104\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col105\" class=\"data row9 col105\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col106\" class=\"data row9 col106\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col107\" class=\"data row9 col107\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col108\" class=\"data row9 col108\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col109\" class=\"data row9 col109\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col110\" class=\"data row9 col110\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col111\" class=\"data row9 col111\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col112\" class=\"data row9 col112\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col113\" class=\"data row9 col113\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col114\" class=\"data row9 col114\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col115\" class=\"data row9 col115\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col116\" class=\"data row9 col116\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col117\" class=\"data row9 col117\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col118\" class=\"data row9 col118\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col119\" class=\"data row9 col119\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col120\" class=\"data row9 col120\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col121\" class=\"data row9 col121\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col122\" class=\"data row9 col122\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col123\" class=\"data row9 col123\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col124\" class=\"data row9 col124\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col125\" class=\"data row9 col125\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col126\" class=\"data row9 col126\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col127\" class=\"data row9 col127\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col128\" class=\"data row9 col128\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col129\" class=\"data row9 col129\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col130\" class=\"data row9 col130\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col131\" class=\"data row9 col131\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col132\" class=\"data row9 col132\" >Populated place</td>\n",
-       "      <td id=\"T_78f41_row9_col133\" class=\"data row9 col133\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col134\" class=\"data row9 col134\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col135\" class=\"data row9 col135\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col136\" class=\"data row9 col136\" >nan</td>\n",
-       "      <td id=\"T_78f41_row9_col137\" class=\"data row9 col137\" >POINT (-9.6525222 26.1191667)</td>\n",
+       "      <th id=\"T_f91f0_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_f91f0_row9_col0\" class=\"data row9 col0\" >6</td>\n",
+       "      <td id=\"T_f91f0_row9_col1\" class=\"data row9 col1\" >30</td>\n",
+       "      <td id=\"T_f91f0_row9_col2\" class=\"data row9 col2\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col3\" class=\"data row9 col3\" >Admin-0 capital alt</td>\n",
+       "      <td id=\"T_f91f0_row9_col4\" class=\"data row9 col4\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col5\" class=\"data row9 col5\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col6\" class=\"data row9 col6\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col7\" class=\"data row9 col7\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col8\" class=\"data row9 col8\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col9\" class=\"data row9 col9\" >Claimed as inte</td>\n",
+       "      <td id=\"T_f91f0_row9_col10\" class=\"data row9 col10\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col11\" class=\"data row9 col11\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col12\" class=\"data row9 col12\" >Western Sahara</td>\n",
+       "      <td id=\"T_f91f0_row9_col13\" class=\"data row9 col13\" >SAH</td>\n",
+       "      <td id=\"T_f91f0_row9_col14\" class=\"data row9 col14\" >Western Sahara</td>\n",
+       "      <td id=\"T_f91f0_row9_col15\" class=\"data row9 col15\" >SAH</td>\n",
+       "      <td id=\"T_f91f0_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col17\" class=\"data row9 col17\" >EH</td>\n",
+       "      <td id=\"T_f91f0_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col19\" class=\"data row9 col19\" >26.119167</td>\n",
+       "      <td id=\"T_f91f0_row9_col20\" class=\"data row9 col20\" >-9.652522</td>\n",
+       "      <td id=\"T_f91f0_row9_col21\" class=\"data row9 col21\" >500</td>\n",
+       "      <td id=\"T_f91f0_row9_col22\" class=\"data row9 col22\" >200</td>\n",
+       "      <td id=\"T_f91f0_row9_col23\" class=\"data row9 col23\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col24\" class=\"data row9 col24\" >2</td>\n",
+       "      <td id=\"T_f91f0_row9_col25\" class=\"data row9 col25\" >1</td>\n",
+       "      <td id=\"T_f91f0_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col27\" class=\"data row9 col27\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col28\" class=\"data row9 col28\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col29\" class=\"data row9 col29\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col30\" class=\"data row9 col30\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col31\" class=\"data row9 col31\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col32\" class=\"data row9 col32\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col33\" class=\"data row9 col33\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col34\" class=\"data row9 col34\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col35\" class=\"data row9 col35\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col36\" class=\"data row9 col36\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col37\" class=\"data row9 col37\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col38\" class=\"data row9 col38\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col39\" class=\"data row9 col39\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col40\" class=\"data row9 col40\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col41\" class=\"data row9 col41\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col42\" class=\"data row9 col42\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col43\" class=\"data row9 col43\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col44\" class=\"data row9 col44\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col45\" class=\"data row9 col45\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col46\" class=\"data row9 col46\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col47\" class=\"data row9 col47\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col48\" class=\"data row9 col48\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col49\" class=\"data row9 col49\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col50\" class=\"data row9 col50\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col51\" class=\"data row9 col51\" >0.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col52\" class=\"data row9 col52\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col53\" class=\"data row9 col53\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col54\" class=\"data row9 col54\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col55\" class=\"data row9 col55\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col56\" class=\"data row9 col56\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col57\" class=\"data row9 col57\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col58\" class=\"data row9 col58\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col59\" class=\"data row9 col59\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col60\" class=\"data row9 col60\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col61\" class=\"data row9 col61\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col62\" class=\"data row9 col62\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col63\" class=\"data row9 col63\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col64\" class=\"data row9 col64\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col65\" class=\"data row9 col65\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col66\" class=\"data row9 col66\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col67\" class=\"data row9 col67\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col68\" class=\"data row9 col68\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col69\" class=\"data row9 col69\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col70\" class=\"data row9 col70\" >0</td>\n",
+       "      <td id=\"T_f91f0_row9_col71\" class=\"data row9 col71\" >6.000000</td>\n",
+       "      <td id=\"T_f91f0_row9_col72\" class=\"data row9 col72\" >Q614754</td>\n",
+       "      <td id=\"T_f91f0_row9_col73\" class=\"data row9 col73\" >1141909145</td>\n",
+       "      <td id=\"T_f91f0_row9_col74\" class=\"data row9 col74\" >1</td>\n",
+       "      <td id=\"T_f91f0_row9_col75\" class=\"data row9 col75\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col76\" class=\"data row9 col76\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_f91f0_row9_col77\" class=\"data row9 col77\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_f91f0_row9_col78\" class=\"data row9 col78\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col79\" class=\"data row9 col79\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col80\" class=\"data row9 col80\" >Бир-Лелу</td>\n",
+       "      <td id=\"T_f91f0_row9_col81\" class=\"data row9 col81\" >比鄂-雷楼</td>\n",
+       "      <td id=\"T_f91f0_row9_col82\" class=\"data row9 col82\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col83\" class=\"data row9 col83\" >بئر لحلو</td>\n",
+       "      <td id=\"T_f91f0_row9_col84\" class=\"data row9 col84\" >বির লেহলোউ</td>\n",
+       "      <td id=\"T_f91f0_row9_col85\" class=\"data row9 col85\" >Μπιρ Λελού</td>\n",
+       "      <td id=\"T_f91f0_row9_col86\" class=\"data row9 col86\" >बीर लहलू</td>\n",
+       "      <td id=\"T_f91f0_row9_col87\" class=\"data row9 col87\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col88\" class=\"data row9 col88\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col89\" class=\"data row9 col89\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_f91f0_row9_col90\" class=\"data row9 col90\" >ビル・ラルフー</td>\n",
+       "      <td id=\"T_f91f0_row9_col91\" class=\"data row9 col91\" >비르레흘루</td>\n",
+       "      <td id=\"T_f91f0_row9_col92\" class=\"data row9 col92\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col93\" class=\"data row9 col93\" >Al-Bir al-Hilw</td>\n",
+       "      <td id=\"T_f91f0_row9_col94\" class=\"data row9 col94\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col95\" class=\"data row9 col95\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_f91f0_row9_col96\" class=\"data row9 col96\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_f91f0_row9_col97\" class=\"data row9 col97\" >1159149075</td>\n",
+       "      <td id=\"T_f91f0_row9_col98\" class=\"data row9 col98\" >بئر لحلو</td>\n",
+       "      <td id=\"T_f91f0_row9_col99\" class=\"data row9 col99\" >ביר להלו</td>\n",
+       "      <td id=\"T_f91f0_row9_col100\" class=\"data row9 col100\" >Бір-Лелу</td>\n",
+       "      <td id=\"T_f91f0_row9_col101\" class=\"data row9 col101\" >بر لیہلو</td>\n",
+       "      <td id=\"T_f91f0_row9_col102\" class=\"data row9 col102\" >比鄂-雷楼</td>\n",
+       "      <td id=\"T_f91f0_row9_col103\" class=\"data row9 col103\" >-1</td>\n",
+       "      <td id=\"T_f91f0_row9_col104\" class=\"data row9 col104\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col105\" class=\"data row9 col105\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col106\" class=\"data row9 col106\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col107\" class=\"data row9 col107\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col108\" class=\"data row9 col108\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col109\" class=\"data row9 col109\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col110\" class=\"data row9 col110\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col111\" class=\"data row9 col111\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col112\" class=\"data row9 col112\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col113\" class=\"data row9 col113\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col114\" class=\"data row9 col114\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col115\" class=\"data row9 col115\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col116\" class=\"data row9 col116\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col117\" class=\"data row9 col117\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col118\" class=\"data row9 col118\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col119\" class=\"data row9 col119\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col120\" class=\"data row9 col120\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col121\" class=\"data row9 col121\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col122\" class=\"data row9 col122\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col123\" class=\"data row9 col123\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col124\" class=\"data row9 col124\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col125\" class=\"data row9 col125\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col126\" class=\"data row9 col126\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col127\" class=\"data row9 col127\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col128\" class=\"data row9 col128\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col129\" class=\"data row9 col129\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col130\" class=\"data row9 col130\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col131\" class=\"data row9 col131\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col132\" class=\"data row9 col132\" >Populated place</td>\n",
+       "      <td id=\"T_f91f0_row9_col133\" class=\"data row9 col133\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col134\" class=\"data row9 col134\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col135\" class=\"data row9 col135\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col136\" class=\"data row9 col136\" >nan</td>\n",
+       "      <td id=\"T_f91f0_row9_col137\" class=\"data row9 col137\" >POINT (-9.6525222 26.1191667)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1992,7 +1992,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.collections.PathCollection at 0x137d52427d0>]"
+       "[<matplotlib.collections.PathCollection at 0x1def5d209d0>]"
       ]
      },
      "execution_count": 10,

From b9f568ac0eeba35753ea82db7996df95db14c42b Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 18:12:03 +0100
Subject: [PATCH 053/240] minor - fix spelling mistakes

---
 README.md                                  |  2 +-
 docs/EOmaps_examples.rst                   |  4 ++--
 docs/api_basics.rst                        |  2 +-
 docs/api_command_line_interface.rst        |  2 +-
 docs/api_compass.rst                       |  2 +-
 docs/api_scalebar.rst                      |  2 +-
 docs/notebooks/naturalearth_features.ipynb |  8 ++++----
 eomaps/__init__.py                         |  2 +-
 eomaps/_data_manager.py                    |  2 +-
 eomaps/annotation_editor.py                |  2 +-
 eomaps/callbacks.py                        |  2 +-
 eomaps/colorbar.py                         |  2 +-
 eomaps/draw.py                             |  2 +-
 eomaps/eomaps.py                           | 12 +++++------
 eomaps/grid.py                             |  2 +-
 eomaps/helpers.py                          | 10 ++++-----
 eomaps/mapsgrid.py                         |  2 +-
 eomaps/qtcompanion/app.py                  |  2 +-
 eomaps/qtcompanion/widgets/annotate.py     |  2 +-
 eomaps/qtcompanion/widgets/save.py         | 14 ++++++-------
 eomaps/qtcompanion/widgets/wms.py          |  2 +-
 eomaps/scalebar.py                         |  4 ++--
 eomaps/scripts/open.py                     |  2 +-
 eomaps/shapes.py                           |  2 +-
 eomaps/webmap_containers.py                | 24 +++++++++++-----------
 tests/example5.py                          |  2 +-
 tests/test_doc_notebooks.py                |  4 ++--
 27 files changed, 59 insertions(+), 59 deletions(-)

diff --git a/README.md b/README.md
index 2a6520cea..5c9b3c39c 100644
--- a/README.md
+++ b/README.md
@@ -40,7 +40,7 @@ It is built on top of [matplotlib](https://matplotlib.org/) and [cartopy](https:
 - Use [🛸 Callbacks](https://eomaps.readthedocs.io/en/latest/api_callbacks.html) to interact with the figure
 - Interactively re-arrange multiple maps in a figure with the [🏗️ LayoutEditor](https://eomaps.readthedocs.io/en/latest/api_layout_editor.html)
 - [🗺 Export](https://eomaps.readthedocs.io/en/latest/api_basics.html#image-export-jpeg-png-svg-etc) publication ready high resolution images (png, jpeg, tiff, ...)  
-or export figures as vektor graphics (svg, eps, pdf ...)
+or export figures as vector graphics (svg, eps, pdf ...)
 - . . . and much more!  
 
 Checkout the [🌱 Basics](https://eomaps.readthedocs.io/en/latest/api_basics.html) in the documentation to get started!
diff --git a/docs/EOmaps_examples.rst b/docs/EOmaps_examples.rst
index ae116c208..76c53508a 100644
--- a/docs/EOmaps_examples.rst
+++ b/docs/EOmaps_examples.rst
@@ -100,7 +100,7 @@ Callbacks - turn your maps into interactive widgets
 -  **Callback functions** can easily be attached to the plot to turn it
    into an interactive plot-widget!
 
-   - | there’s a nice list of (customizeable) pre-defined callbacks accessible via:
+   - | there’s a nice list of (customizable) pre-defined callbacks accessible via:
      | ``m.cb.click``, ``m.cb.pick``, ``m.cb.keypress`` and ``m.cb.dynamic``
 
       -  use ``annotate`` (and ``clear_annotations``) to create text-annotations
@@ -169,7 +169,7 @@ The data displayed in the above gif is taken from:
 
 .. _ex_7:
 
-Vektor data - interactive geometries
+Vector data - interactive geometries
 -------------------------------------
 
 EOmaps can be used to assign callbacks to vektor-data (e.g. ``geopandas.GeoDataFrames``).
diff --git a/docs/api_basics.rst b/docs/api_basics.rst
index f9a111fd5..7526b7417 100644
--- a/docs/api_basics.rst
+++ b/docs/api_basics.rst
@@ -506,7 +506,7 @@ To set the absolute position of the map, provide a list of 4 floats representing
 Using already existing figures / axes
 *************************************
 
-It is also possible to insert an EOmaps map into an existing figure or re-use an existing axes.
+It is also possible to insert an EOmaps map into an existing figure or reuse an existing axes.
 
   - To put a map on an existing figure, provide the figure-instance via ``m = Maps(f= <the figure instance>)``
   - To use an existing axes, provide the axes-instance via ``m = Maps(ax= <the axes instance>)``
diff --git a/docs/api_command_line_interface.rst b/docs/api_command_line_interface.rst
index 292184241..4bf073d1f 100644
--- a/docs/api_command_line_interface.rst
+++ b/docs/api_command_line_interface.rst
@@ -9,7 +9,7 @@ To start a new EOmaps map from the command-line, simply type ``eomaps`` and hit
 
 In addition, the following optional parameters can be provided:
 
-- ``--help`` get additionnal help on how to use the ``eomaps`` command
+- ``--help`` get additional help on how to use the ``eomaps`` command
 - ``--crs`` set the coordinate reference system of the map
 - ``--location`` query the location via the OpenStreetMap Nominatim service and set the map-extent
 - ``--file`` a path to a file that should be opened for plotting
diff --git a/docs/api_compass.rst b/docs/api_compass.rst
index 7889c4f66..b06b57c5b 100644
--- a/docs/api_compass.rst
+++ b/docs/api_compass.rst
@@ -19,7 +19,7 @@ A compass can be added to the map via :py:meth:`Maps.add_compass`:
     While a compass is picked (and the LEFT mouse button is pressed), the following
     additional interactions are available:
 
-    - press ``delte`` on the keyboard: remove the compass from the plot
+    - press ``delete`` on the keyboard: remove the compass from the plot
     - rotate the ``mouse wheel``: scale the size of the compass
 
 .. autosummary::
diff --git a/docs/api_scalebar.rst b/docs/api_scalebar.rst
index 3e99882ff..16146a1ee 100644
--- a/docs/api_scalebar.rst
+++ b/docs/api_scalebar.rst
@@ -52,7 +52,7 @@ In addition, many style properties of the scalebar can be adjusted to get the lo
     - use the ``scroll-wheel`` to adjust the auto-scale of the scalebar (hold down ``shift`` for larger steps)
     - use ``control`` + ``scroll-wheel`` to adjust the size of the labels
 
-    - press ``delte`` to remove the scalebar from the plot
+    - press ``delete`` to remove the scalebar from the plot
     - press ``+``  or ``-`` to rotate the scalebar
     - press ``up/down/left/right`` to increase the size of the frame
     - press ``alt + up/down/left/right``: decrease the size of the frame
diff --git a/docs/notebooks/naturalearth_features.ipynb b/docs/notebooks/naturalearth_features.ipynb
index 02aa78872..6bcf02040 100644
--- a/docs/notebooks/naturalearth_features.ipynb
+++ b/docs/notebooks/naturalearth_features.ipynb
@@ -34,7 +34,7 @@
     "# 🌵 NaturalEarth features\n",
     "<br>\n",
     "\n",
-    "EOmaps provies access to a large amount of basic map features provided by [NaturalEarth](https://www.naturalearthdata.com/) via [`Maps.add_feature`](eomaps.Maps.add_feature).\n",
+    "EOmaps provides access to a large amount of basic map features provided by [NaturalEarth](https://www.naturalearthdata.com/) via [`Maps.add_feature`](eomaps.Maps.add_feature).\n",
     "\n",
     ":::{note}\n",
     "The first time a feature is added to a map, the corresponding dataset is downloaded and stored locally for subsequent use.\n",
@@ -220,7 +220,7 @@
     "\n",
     "[NaturalEarth](https://www.naturalearthdata.com/)  provides features in 2 categories: **physical** and **cultural**. \n",
     "\n",
-    "You can access all available featues of a corresponding category with:\n",
+    "You can access all available features of a corresponding category with:\n",
     "```python\n",
     "m.add_feature.cultural.<FEATURE-NAME>(**STYLE-KWARGS)\n",
     "```\n",
@@ -229,7 +229,7 @@
     "```\n",
     "\n",
     ":::{note}\n",
-    "NaturalEarth provies features in 3 different scales: 1/10, 1/50 and 1/110.  \n",
+    "NaturalEarth provides features in 3 different scales: 1/10, 1/50 and 1/110.  \n",
     "By default, an appropriate scale is selected based on the visible extent.  \n",
     "You can manually select the scale to use with the ``scale`` argument (e.g. ``m.add_feature.physical.coastline(scale=10)``)\n",
     ":::"
@@ -354,7 +354,7 @@
     "tags": []
    },
    "source": [
-    "This will load the corresponding NaturalEarth dataset of the feature, containing the gometries and all associated metadata. \n",
+    "This will load the corresponding NaturalEarth dataset of the feature, containing the geometries and all associated metadata. \n",
     "\n",
     "For example, the first 10 rows of the `places` GeoDataFrame look like this:"
    ]
diff --git a/eomaps/__init__.py b/eomaps/__init__.py
index bc2a416c9..df9883218 100644
--- a/eomaps/__init__.py
+++ b/eomaps/__init__.py
@@ -24,7 +24,7 @@
 
 
 # -----------------------------------------------------------------------------------
-# The following loggin config is adapted from matplotlibs way of dealing with logging
+# The following login config is adapted from matplotlibs way of dealing with logging
 # (see https://github.com/matplotlib/matplotlib/blob/main/lib/matplotlib/__init__.py)
 
 import logging
diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index 61b32fd29..eea7372fd 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -894,7 +894,7 @@ def get_props(self, *args, **kwargs):
         # (qs = [<2d mask>, <1d x mask>, <1d y mask>]
         qs = self._get_q()
 
-        # estimate slices (and optional blocksize if requred) for 2D data
+        # estimate slices (and optional blocksize if required) for 2D data
         if len(self.z_data.shape) == 2 and all(i is not None for i in qs[1:]):
             slices, blocksize = self._estimate_slice_blocksize(*qs[1:])
         else:
diff --git a/eomaps/annotation_editor.py b/eomaps/annotation_editor.py
index 5f7af62a2..587ab9e22 100644
--- a/eomaps/annotation_editor.py
+++ b/eomaps/annotation_editor.py
@@ -187,7 +187,7 @@ def update_offset(self, dx, dy):
                 return
 
             # don't allow moving the annotation anchor outside the axis bounds
-            # (otherwhise it would disappear if "annotation_clip=True")
+            # (otherwise it would disappear if "annotation_clip=True")
             ann.xy = ann.axes.transData.inverted().transform(
                 (
                     np.clip(self.x + dx, self._ax_bbox.x0 + 1, self._ax_bbox.x1 - 1),
diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 17b783143..eff6c6a3d 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -810,7 +810,7 @@ def peek_layer(
         of the currently visible layer if you click on the map.
 
         You can show a rectangular or circular area of the "peek-layer" centered at
-        the mouse-position or swipe beween layers (e.g. from left/right/top or bottom).
+        the mouse-position or swipe between layers (e.g. from left/right/top or bottom).
 
 
         Parameters
diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 811298e82..51265c45d 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -102,7 +102,7 @@ def __init__(
         pos : float or 4-tuple, optional
 
             - float: fraction of the axis size that is used to create the colorbar.
-              The axes of the Maps-object will be shrinked accordingly to make space
+              The axes of the Maps-object will be shrunk accordingly to make space
               for the colorbar.
             - 4-tuple (x0, y0, width, height):
               Absolute position of the colorbar in relative figure-units (0-1).
diff --git a/eomaps/draw.py b/eomaps/draw.py
index 807e65b9e..5439fdcd8 100644
--- a/eomaps/draw.py
+++ b/eomaps/draw.py
@@ -610,7 +610,7 @@ def handler(event):
 
     def polygon(self, smooth=False, draw_on_drag=True, **kwargs):
         """
-        Draw arbitarary polygons
+        Draw arbitrary polygons
 
         - RIGHT click to add points
           (or drag while holding RIGHT mouse button)
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index ab164e999..71217b334 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -332,7 +332,7 @@ class Maps(metaclass=_MapsMeta):
 
     CRS = ccrs
 
-    # the keybord shortcut to activate the companion-widget
+    # the keyboard shortcut to activate the companion-widget
     _companion_widget_key = "w"
     # max. number of layers to show all layers as tabs in the widget
     # (otherwise only recently active layers are shown as tabs)
@@ -2514,10 +2514,10 @@ def add_line(
         Returns
         -------
         out_d_int : list
-            Only relevant for `connect="geod"`! (An empty ist is returned otherwise.)
+            Only relevant for `connect="geod"`! (An empty list is returned otherwise.)
             A list of the subdivision distances of the line-segments (in meters).
         out_d_tot : list
-            Only relevant for `connect="geod"` (An empty ist is returned otherwise.)
+            Only relevant for `connect="geod"` (An empty list is returned otherwise.)
             A list of total distances of the line-segments (in meters).
 
         """
@@ -2809,7 +2809,7 @@ def set_extent(self, extents, crs=None):
 
         """
         # just a wrapper to make sure that previously set extents are not
-        # resetted when plotting data!
+        # reset when plotting data!
 
         # ( e.g. once .set_extent is called .plot_map does NOT set the extent!)
         if crs is not None:
@@ -3576,7 +3576,7 @@ def savefig(self, *args, refetch_wms=False, rasterize_data=True, **kwargs):
                     for a in cb._axes:
                         stack.enter_context(a._cm_set(animated=False))
 
-                # set if data should be rasterized on vektor export
+                # set if data should be rasterized on vector export
                 if m.coll is not None:
                     stack.enter_context(m.coll._cm_set(rasterized=rasterize_data))
 
@@ -4848,7 +4848,7 @@ def _handle_explicit_colors(self, color):
 
     def _get_coll(self, props, **kwargs):
         # handle selection of explicitly provided facecolors
-        # (e.g. for rgb composits)
+        # (e.g. for rgb composites)
 
         # allow only one of the synonyms "color", "fc" and "facecolor"
         if (
diff --git a/eomaps/grid.py b/eomaps/grid.py
index 15af1902a..459e2bc99 100644
--- a/eomaps/grid.py
+++ b/eomaps/grid.py
@@ -122,7 +122,7 @@ def set_d(self, d):
             - If `int` or `float`, the provided number is used as grid-spacing.
             - If a `list` or `numpy.array` is provided, it is used to draw gridlines
               at the provided coordinates.
-            - If a `tuple` of lengh 2 is provided, it represents separate assignments of
+            - If a `2-tuple` is provided, it represents separate assignments of
               the aforementioned types for longitude/latitude , e.g.: `(d_lon, d_lat)`.
             - If `None`, gridlines are automatically determined based on the "auto_n"
               parameter.
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index 11556f9d7..967f384bb 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -1133,7 +1133,7 @@ def _snap(self):
         return snap
 
     def _make_draggable(self, filepath=None):
-        # Uncheck avtive pan/zoom actions of the matplotlib toolbar.
+        # Uncheck active pan/zoom actions of the matplotlib toolbar.
         # use a try-except block to avoid issues with ipympl in jupyter notebooks
         # (see https://github.com/matplotlib/ipympl/issues/530#issue-1780919042)
         try:
@@ -1225,7 +1225,7 @@ def _make_draggable(self, filepath=None):
                     child.set_alpha(0.75)  # for overlapping axes
 
                 else:
-                    # make all other childs invisible (to avoid drawing them)
+                    # make all other children invisible (to avoid drawing them)
                     child.set_visible(False)
                     child.set_animated(True)
 
@@ -1421,7 +1421,7 @@ def get_layout(self, filepath=None, override=False, precision=5):
         Returns
         -------
         layout : dict or None
-            A dict of the positons of all axes, e.g.: {1:(x0, y0, width height), ...}
+            A dict of the positions of all axes, e.g.: {1:(x0, y0, width height), ...}
         """
         figsize = [*self.f.get_size_inches()]
 
@@ -1598,7 +1598,7 @@ def __init__(self, m):
         self._mpl_backend_force_full = False
         self._mpl_backend_blit_fix = False
 
-        # True = persistant, False = execute only once
+        # True = persistent, False = execute only once
         self._on_layer_change = {True: list(), False: list()}
         self._on_layer_activation = {True: dict(), False: dict()}
 
@@ -1626,7 +1626,7 @@ def _get_renderer(self):
         # in this case the normal draw-routines are used (see m.savefig) so there is
         # no need to trigger updates (also `canvas.get_renderer` is undefined for
         # pdf/svg exports since those canvas do not expose the renderer)
-        # ... this is required to support vektor format outputs!
+        # ... this is required to support vector format outputs!
         if self.canvas.is_saving():
             return None
 
diff --git a/eomaps/mapsgrid.py b/eomaps/mapsgrid.py
index d208b0797..b1f4be9b2 100644
--- a/eomaps/mapsgrid.py
+++ b/eomaps/mapsgrid.py
@@ -314,7 +314,7 @@ def create_axes(self, ax_init, name=None):
         Parameters
         ----------
         ax_init : set
-            The GridSpec speciffications for the axis.
+            The GridSpec specifications for the axis.
             use `ax_inits = (<row>, <col>)` to get an axis in a given grid-cell
             use `slice(<start>, <stop>)` for `<row>` or `<col>` to get an axis
             that spans over multiple rows/columns.
diff --git a/eomaps/qtcompanion/app.py b/eomaps/qtcompanion/app.py
index 8080c7661..ca0591173 100644
--- a/eomaps/qtcompanion/app.py
+++ b/eomaps/qtcompanion/app.py
@@ -183,7 +183,7 @@ def __init__(self, *args, m=None, **kwargs):
         # self.resize(int(sh.width() * 1.35), sh.height())
 
         # clear the colormaps-dropdown pixmap cache if the colormaps have changed
-        # (the Signal is emmited by Maps-objects if a new colormap is registered)
+        # (the Signal is emitted by Maps-objects if a new colormap is registered)
         self.m._connect_signal("cmapsChanged", self.clear_pixmap_cache)
 
     def show(self):
diff --git a/eomaps/qtcompanion/widgets/annotate.py b/eomaps/qtcompanion/widgets/annotate.py
index 21b050caf..f7e8116a7 100644
--- a/eomaps/qtcompanion/widgets/annotate.py
+++ b/eomaps/qtcompanion/widgets/annotate.py
@@ -169,7 +169,7 @@ def __init__(self, *args, m=None, **kwargs):
 
         self.text_inp.textChanged.connect(self.update_selected_text)
         self.text_inp.setPlaceholderText(
-            "Enter annotation text (or edit text of existing annoation)\n\n"
+            "Enter annotation text (or edit text of existing annotation)\n\n"
             "Press < SHIFT + ENTER > and click on the map to draw the annotation!"
         )
         self.text_inp.setSizePolicy(
diff --git a/eomaps/qtcompanion/widgets/save.py b/eomaps/qtcompanion/widgets/save.py
index bab0a6322..385c5f4cf 100644
--- a/eomaps/qtcompanion/widgets/save.py
+++ b/eomaps/qtcompanion/widgets/save.py
@@ -98,12 +98,12 @@ def enterEvent(self, e):
         if self.window().showhelp is True:
             QtWidgets.QToolTip.showText(
                 e.globalPos(),
-                "<h3>Rasterize datasets on vektor export</h3>"
-                "Toggle if data is rasterized (True) or treated as vektor (False) "
-                "when exporting the figure to vektor-formats (svg, pdf, eps)."
+                "<h3>Rasterize datasets on vector export</h3>"
+                "Toggle if data is rasterized (True) or treated as vector (False) "
+                "when exporting the figure to vector-formats (svg, pdf, eps)."
                 "<p>"
                 "If checked, datasets will appear as rasterized images in the exported "
-                "vektor file (to avoid creating very large files for big datasets)."
+                "vector file (to avoid creating very large files for big datasets)."
                 "<p>"
                 "<b>NOTE:</b> The current value is also used for clipboard-export!"
                 " (<code>ctrl+c</code>)",
@@ -157,8 +157,8 @@ def __init__(self, *args, m=None, **kwargs):
 
         # transparent
         self.transp_cb = TransparentCheckBox()
-        transp_label = QtWidgets.QLabel("Tranparent\nBackground")
-        width = transp_label.fontMetrics().boundingRect("Tranparent").width()
+        transp_label = QtWidgets.QLabel("Transparent\nBackground")
+        width = transp_label.fontMetrics().boundingRect("Transparent").width()
         transp_label.setFixedWidth(width + 5)
         self.transp_cb.stateChanged.connect(self.update_clipboard_kwargs)
 
@@ -314,7 +314,7 @@ def update_clipboard_kwargs(self, *args, **kwargs):
 
     @Slot()
     def set_export_props(self, *args, **kwargs):
-        # callback that is triggerd on Maps.set_clipboard_kwargs
+        # callback that is triggered on Maps.set_clipboard_kwargs
 
         clipboard_kwargs = self.m.__class__._clipboard_kwargs
 
diff --git a/eomaps/qtcompanion/widgets/wms.py b/eomaps/qtcompanion/widgets/wms.py
index d9dac08e6..08f152a60 100644
--- a/eomaps/qtcompanion/widgets/wms.py
+++ b/eomaps/qtcompanion/widgets/wms.py
@@ -870,7 +870,7 @@ def wms_cb():
             self.window().statusBar().showMessage(
                 f"Adding WebMap service:   {wmsname} - {wmslayer}   . . ."
             )
-            # trigger an immediate repaint of the statusbar to show the messge
+            # trigger an immediate repaint of the statusbar to show the message
             # before fetching the service
             self.window().statusBar().repaint()
 
diff --git a/eomaps/scalebar.py b/eomaps/scalebar.py
index 632dd9118..016190631 100644
--- a/eomaps/scalebar.py
+++ b/eomaps/scalebar.py
@@ -197,7 +197,7 @@ def __init__(
         self._scale_factor_base = 1000
         # multipliers for changing the label size
         self._size_factor_base = 50
-        # inverval for adjusting the text-offset
+        # interval for adjusting the text-offset
         self._cb_offset_interval = 0.05
         # interval for rotating the scalebar
         self._cb_rotate_interval = 1
@@ -1027,7 +1027,7 @@ def _get_d(self):
     def _get_patch_verts(self, pts, lon, lat, ang, d):
         offsets = self._patch_offsets
 
-        # top bottom left right referrs to a horizontally oriented colorbar!
+        # top bottom left right refers to a horizontally oriented colorbar!
         ot = d * offsets[0]
         ob = self._maxw + d * (self._label_props["offset"] + offsets[1])
         o_l = d * offsets[2]
diff --git a/eomaps/scripts/open.py b/eomaps/scripts/open.py
index c18d02ecf..f901ecb62 100644
--- a/eomaps/scripts/open.py
+++ b/eomaps/scripts/open.py
@@ -18,7 +18,7 @@ def _identify_crs(crs):
     if crs == "web":
         crs = "google_mercator"
 
-    # if crs can be idenified as integer, return it
+    # if crs can be identified as integer, return it
     try:
         return int(crs)
     except ValueError:
diff --git a/eomaps/shapes.py b/eomaps/shapes.py
index bfe5640df..c18dbee6f 100644
--- a/eomaps/shapes.py
+++ b/eomaps/shapes.py
@@ -423,7 +423,7 @@ def _selected_radius(self):
             if not self._m._data_manager._current_data:
                 return self.radius
 
-            # check if mutiple individual x-y radius was provided
+            # check if multiple individual x-y radius was provided
             q1 = isinstance(self.radius, tuple) and isinstance(
                 self.radius[0], np.ndarray
             )
diff --git a/eomaps/webmap_containers.py b/eomaps/webmap_containers.py
index 112970c4d..3aaabaa9d 100644
--- a/eomaps/webmap_containers.py
+++ b/eomaps/webmap_containers.py
@@ -449,7 +449,7 @@ class _OpenStreetMap:
 
             - default: standard OSM layer
             - default_german: standard OSM layer in german
-            - CyclOSM: a bycicle oriented style
+            - CyclOSM: a bicycle oriented style
             - OEPNV_public_transport: a layer indicating global public transportation
             - OpenRiverboatMap: a style to navigate waterways
             - OpenTopoMap: SRTM + OSM for nice topography
@@ -1254,7 +1254,7 @@ def OSM_landuse(self):
                 Technology (https://heigit.org)
 
 
-                For more details, plese visit:
+                For more details, please visit:
                 https://osmlanduse.org
                 """
             WMS._EOmaps_source_code = (
@@ -1294,7 +1294,7 @@ def EEA_DiscoMap(self):
 
         check: https://discomap.eea.europa.eu/
 
-        EEA standard re-use policy: Unless otherwise indicated, reuse of
+        EEA standard reuse policy: Unless otherwise indicated, reuse of
         content on the EEA website for commercial or non-commercial
         purposes is permitted free of charge, provided that the source is
         acknowledged.
@@ -1336,7 +1336,7 @@ def Image(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1369,7 +1369,7 @@ def Land(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1402,7 +1402,7 @@ def Climate(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1435,7 +1435,7 @@ def Bio(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1468,7 +1468,7 @@ def Copernicus(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1501,7 +1501,7 @@ def Water(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1534,7 +1534,7 @@ def SOER(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1567,7 +1567,7 @@ def MARATLAS(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
@@ -1600,7 +1600,7 @@ def MARINE(self):
 
                 ... make sure to check the link above...
 
-                EEA standard re-use policy: Unless otherwise indicated, reuse of
+                EEA standard reuse policy: Unless otherwise indicated, reuse of
                 content on the EEA website for commercial or non-commercial
                 purposes is permitted free of charge, provided that the source is
                 acknowledged.
diff --git a/tests/example5.py b/tests/example5.py
index e2d7d68ee..2ee68eeaf 100644
--- a/tests/example5.py
+++ b/tests/example5.py
@@ -93,7 +93,7 @@ def callback(m, **kwargs):
         fc=(1, 0, 0, 0.1),
         ec="r",
         buffer=buffer * 5,
-        n=100,  # use 100 points to represet the ellipses
+        n=100,  # use 100 points to represent the ellipses
     )
 m.add_marker(
     ID=mark_id, shape="rectangles", radius="pixel", fc="g", ec="y", buffer=3, alpha=0.5
diff --git a/tests/test_doc_notebooks.py b/tests/test_doc_notebooks.py
index 072ab2a1f..a9a7a2c9f 100644
--- a/tests/test_doc_notebooks.py
+++ b/tests/test_doc_notebooks.py
@@ -2,8 +2,8 @@
 Test all Jupyter Notebook code-cells from the docs.
 
 NOTE:
-All cod cells of a notebook are concatenated as if they have been writtin as a
-singe pyhton-script that is executed in one go!
+All code cells of a notebook are concatenated as if they have been written as a
+single pyhton-script that is executed in one go!
 
 This is done to avoid issues with cells that are not "standalone"
 (e.g. that require previous cells to be executed)

From 2dfac767fc596087dbe330837c39d43b3b37e058 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 18:23:59 +0100
Subject: [PATCH 054/240] add info of major changes between v7.x and v8.x

---
 docs/FAQ.rst | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 40865eaab..b59a49efd 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -345,3 +345,17 @@ Important changes between major versions
     - ⚠️ The use of ``m.set_data_specs(...)`` is depreciated. Use ``m.set_data(...)`` instead!
 
     - Figure export routines have been completely re-worked (but should result in the exact same output as in v6.x)
+
+
+.. dropdown:: ⚙ From EOmaps v7.x to v8.x
+
+    - ⚠️ Some functions and classes have been re-named to better follow PEP8 naming conventions.
+      While this should not interfere with the public API, more extensive customizations might need to be
+      adjusted with respect to the new names.
+
+      If you encounter any problems, feel free to open an `issue <https://github.com/raphaelquast/EOmaps/issues>`_ , and I'll see what I can do!
+
+    - ⚠️ ``pip`` install has been updated to implement optional dependency groups
+      Have a look at the :ref:`installation` instructions for more details!
+
+    - ``setup.py`` and ``_version.py`` have been removed in favor of using a ``pyproject.toml`` file

From 4672d1da049fea142b97745cec89cfb7d9520ca5 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 18:28:12 +0100
Subject: [PATCH 055/240] update pyproject.toml with more metadata

---
 pyproject.toml | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 8607913ba..88795d6df 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,9 @@ include = ["eomaps"]
 name = "eomaps"
 version = "8.0"
 description = "A library to create interactive maps of geographical datasets."
+readme = "README.md"
+license = {file = "LICENSE"}
+
 requires-python = ">=3.8"
 
 authors = [
@@ -21,6 +24,10 @@ classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
     "Operating System :: OS Independent",
+    "Framework :: Matplotlib",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: GIS",
+    "Topic :: Scientific/Engineering :: Visualization",
 ]
 
 dependencies = [

From c6945c4370a0180dc54fbebd7bb5c1a017440008 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 18:51:29 +0100
Subject: [PATCH 056/240] fix usage of deprecated kwarg in docs

---
 docs/api_callbacks.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/api_callbacks.rst b/docs/api_callbacks.rst
index c8a42ced2..7f0e8df20 100644
--- a/docs/api_callbacks.rst
+++ b/docs/api_callbacks.rst
@@ -83,7 +83,7 @@ The ``< EVENT CATEGORY >`` hereby specifies the event that will trigger the call
     |     m.set_data(data=x+y**2, x=x, y=y, crs=4326)                                   |                                                  |
     |     m.plot_map()                                                                  |                                                  |
     |                                                                                   |                                                  |
-    |     m2 = m.new_layer(copy_data_specs=True, layer="second_layer")                  |                                                  |
+    |     m2 = m.new_layer(inherit_data=True, layer="second_layer")                     |                                                  |
     |     m2.plot_map(cmap="tab10")                                                     |                                                  |
     |                                                                                   |                                                  |
     |     # get an annotation if you RIGHT-click anywhere on the map                    |                                                  |

From 83228f620c82abd3d3aa62785969ea19c9050fcf Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 21:57:07 +0100
Subject: [PATCH 057/240] make deprecation of m.set_data_specs final

---
 docs/EOmaps_examples.rst |  2 +-
 docs/FAQ.rst             |  2 ++
 eomaps/eomaps.py         | 17 -----------------
 3 files changed, 3 insertions(+), 18 deletions(-)

diff --git a/docs/EOmaps_examples.rst b/docs/EOmaps_examples.rst
index 76c53508a..3330b4dda 100644
--- a/docs/EOmaps_examples.rst
+++ b/docs/EOmaps_examples.rst
@@ -33,7 +33,7 @@ Basic data visualization
 There are 3 basic steps required to visualize your data:
 
 1. Initialize a Maps-object with ``m = Maps()``
-2. Set the data and its specifications via ``m.set_data`` (or ``m.set_data_specs``)
+2. Set the data and its specifications via ``m.set_data``
 3. Call ``m.plot_map()`` to generate the map!
 
 |toggleStart|
diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index b59a49efd..4bcc129a4 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -359,3 +359,5 @@ Important changes between major versions
       Have a look at the :ref:`installation` instructions for more details!
 
     - ``setup.py`` and ``_version.py`` have been removed in favor of using a ``pyproject.toml`` file
+
+    - deprecated method ``m.set_data_specs(...)`` is now removed. Use ``m.set_data(...)`` instead!
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 71217b334..6101f4c66 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -1393,23 +1393,6 @@ def set_data(
         if parameter is not None:
             self.data_specs.parameter = parameter
 
-    @wraps(set_data)
-    def set_data_specs(self, *args, **kwargs):
-        from warnings import warn
-
-        warn(
-            "EOmaps: `m.set_data_specs(...)` is depreciated and will raise  an "
-            "error in future versions! Use `m.set_data(...)` instead!",
-            FutureWarning,
-            stacklevel=2,
-        )
-        self.set_data(*args, **kwargs)
-
-    set_data_specs.__doc__ = (
-        "WARNING: `m.set_data_specs(...)` is depreciated! "
-        "Use `m.set_data(...)` instead!\n\n"
-    ) + set_data_specs.__doc__
-
     @property
     def set_classify(self):
         """

From fbc0fbe8362162b2999db24809cc805ebaebc2d1 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 22:01:52 +0100
Subject: [PATCH 058/240] make deprecation of InsetMaps.indicate_inset_extent
 final

---
 eomaps/inset_maps.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/eomaps/inset_maps.py b/eomaps/inset_maps.py
index 4cd7eaa6b..f1f26d850 100644
--- a/eomaps/inset_maps.py
+++ b/eomaps/inset_maps.py
@@ -5,7 +5,6 @@
 from matplotlib.path import Path
 
 from . import Maps
-from .helpers import _deprecated
 from .grid import _intersect, _get_intersect
 
 
@@ -215,10 +214,6 @@ def plot_map(self, *args, **kwargs):
         set_extent = kwargs.pop("set_extent", False)
         super().plot_map(*args, **kwargs, set_extent=set_extent)
 
-    @_deprecated("Use `add_extent_indicator` instead!")
-    def indicate_inset_extent(self, *args, **kwargs):
-        return self.add_extent_indicator(*args, **kwargs)
-
     # a convenience-method to add a boundary-polygon to a map
     def add_extent_indicator(self, m=None, n=100, **kwargs):
         """

From 218a564efd82d8c940fde24ed0ce4e7b8bdd2f58 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 22:05:02 +0100
Subject: [PATCH 059/240] make deprecation of m.add_wms.DLR_basemaps final

---
 eomaps/scripts/open.py      |  4 ++--
 eomaps/webmap_containers.py | 21 ---------------------
 2 files changed, 2 insertions(+), 23 deletions(-)

diff --git a/eomaps/scripts/open.py b/eomaps/scripts/open.py
index f901ecb62..cc3e08a5e 100644
--- a/eomaps/scripts/open.py
+++ b/eomaps/scripts/open.py
@@ -207,9 +207,9 @@ def cli(crs=None, file=None, ne=None, wms=None, location=None, loglevel=None):
         elif wms in ["terrain_light"]:
             m.add_wms.S2_cloudless.add_layer.terrain_light_3857()
         elif wms in ["basemap"]:
-            m.add_wms.DLR_basemaps.add_layer.basemap()
+            m.add_wms.DLR.basemap.add_layer.basemap()
         elif wms in ["basemap_light"]:
-            m.add_wms.DLR_basemaps.add_layer.litemap()
+            m.add_wms.DLR.basemap.add_layer.litemap()
         elif wms in ["s1_vv"]:
             m.add_wms.S1GBM.add_layer.vv()
 
diff --git a/eomaps/webmap_containers.py b/eomaps/webmap_containers.py
index 3aaabaa9d..3e8f0788b 100644
--- a/eomaps/webmap_containers.py
+++ b/eomaps/webmap_containers.py
@@ -2289,27 +2289,6 @@ def DLR(self):
         WMS.__doc__ = self._DLR.__doc__
         return WMS
 
-    @property
-    @lru_cache()
-    def DLR_basemaps(self):
-        class _DepreciatedWebServiceCollection(_WebServiceCollection):
-            def __init__(self, *args, **kwargs):
-                print(
-                    "EOmaps: The WebMap service 'DLR_basemaps' is depreciated and will "
-                    "be removed in the next minor version!\n"
-                    "Use `m.add_wms.DLR.basemaps...` instead!",
-                )
-                super().__init__(*args, **kwargs)
-
-        WMS = _DepreciatedWebServiceCollection(
-            m=self._m,
-            service_type="wms",
-            url="https://geoservice.dlr.de/eoc/basemap/wms?SERVICE=WMS&REQUEST=GetCapabilities",
-        )
-
-        WMS.__doc__ = type(self).DLR.__doc__
-        return WMS
-
     @property
     @lru_cache()
     def ESRI_ArcGIS(self):

From 6c80db21e8e9bef4772e2d5da92b4977603a19d7 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 22:09:35 +0100
Subject: [PATCH 060/240] add info on removed (previously deprecated) methods
 to docs

---
 docs/FAQ.rst | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 4bcc129a4..0613e8f98 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -360,4 +360,9 @@ Important changes between major versions
 
     - ``setup.py`` and ``_version.py`` have been removed in favor of using a ``pyproject.toml`` file
 
-    - deprecated method ``m.set_data_specs(...)`` is now removed. Use ``m.set_data(...)`` instead!
+
+    The following (previously deprecated) methods are now **removed**:
+
+    - `m.set_data_specs` -> use ``m.set_data`` instead
+    - `m.add_wms.DLR_basemaps...` -> use ``m.add_wms.DLR.basemap...`` instead
+    - `m_inset.indicate_inset_extent` -> use ``m_inset.add_extent_indicator`` instead

From 20d56f2f71a68cd00d3fa18246dc0c82ff744797 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 5 Jan 2024 13:30:17 +0100
Subject: [PATCH 061/240] update docs env - use more recent versions of
 docs-build dependencies - install only minimal eomaps dependencies required
 for import

---
 docs/docs_env.yml | 40 ++++++++++++----------------------------
 1 file changed, 12 insertions(+), 28 deletions(-)

diff --git a/docs/docs_env.yml b/docs/docs_env.yml
index 3bbd51341..503958ea3 100644
--- a/docs/docs_env.yml
+++ b/docs/docs_env.yml
@@ -4,33 +4,17 @@ channels:
 
 dependencies:
 
-  - numpy
-  - scipy
-  - pandas
-  - geopandas
-  - matplotlib >=3.4
-  - cartopy >=0.20.0
-  - descartes
-  - mapclassify
-  - pyproj
-  - pyepsg
-  # --------------for data-shading
-  - datashader
-  # --------------for GeoTIFF and NetCDF files
-  - netcdf4
-  - xarray
-  - rioxarray
-  # --------------for WebMaps
-  - owslib
-  - requests
-  - xmltodict
-  - cairosvg
+  - python =3.11
 
-  - pip
   # --------------for building the docs
-  - docutils<0.19
-  - sphinx=7.2.6
-  - sphinx-copybutton=0.5.2
-  - myst-nb=1.0.0
-  - sphinx-design=0.5.0
-  - sphinx_rtd_theme=1.3
+  - docutils =0.20.1
+  - sphinx =7.2.6
+  - sphinx-copybutton =0.5.2
+  - myst-nb =1.0.0
+  - sphinx-design =0.5.0
+  - sphinx_rtd_theme =2.0.0
+
+  # install minimal dependencies to import eomaps
+  - pip
+  - pip:
+    - ../.

From 75a386ebdd650f5a49e44507b9ad901fb4ddf8c3 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 7 Jan 2024 23:06:09 +0100
Subject: [PATCH 062/240] fix docstring for DLR webmap service

---
 eomaps/webmap_containers.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/eomaps/webmap_containers.py b/eomaps/webmap_containers.py
index 3e8f0788b..8de45ff3b 100644
--- a/eomaps/webmap_containers.py
+++ b/eomaps/webmap_containers.py
@@ -2289,6 +2289,8 @@ def DLR(self):
         WMS.__doc__ = self._DLR.__doc__
         return WMS
 
+    DLR.__doc__ = _DLR.__doc__
+
     @property
     @lru_cache()
     def ESRI_ArcGIS(self):

From 674f359cf9b2794fa024cb8cb4f013011797c704 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 7 Jan 2024 23:44:09 +0100
Subject: [PATCH 063/240] update NaturalEarth feature implementation - apply
 better naming conventions - make namespaces accessible to sphinx

---
 docs/notebooks/naturalearth_features.ipynb |   2 +-
 eomaps/eomaps.py                           |  16 +-
 eomaps/mapsgrid.py                         |   4 +-
 eomaps/ne_features.py                      | 892 +++++++++++----------
 4 files changed, 478 insertions(+), 436 deletions(-)

diff --git a/docs/notebooks/naturalearth_features.ipynb b/docs/notebooks/naturalearth_features.ipynb
index 6bcf02040..cb89be0f5 100644
--- a/docs/notebooks/naturalearth_features.ipynb
+++ b/docs/notebooks/naturalearth_features.ipynb
@@ -67,7 +67,7 @@
     "\n",
     "\n",
     "```{eval-rst}\n",
-    ".. currentmodule:: eomaps.ne_features.NaturalEarth_presets\n",
+    ".. currentmodule:: eomaps.ne_features.NaturalEarthPresets\n",
     "\n",
     ".. autosummary::\n",
     "    :nosignatures:\n",
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 6101f4c66..5c26f6356 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -57,7 +57,7 @@
     _cx_refetch_wms_on_size_change = None
     WebMapContainer = None
 
-from .ne_features import NaturalEarth_features
+from .ne_features import NaturalEarthFeatures
 
 from .cb_container import CallbackContainer, GeoDataFramePicker
 from .scalebar import ScaleBar
@@ -344,6 +344,9 @@ class Maps(metaclass=_MapsMeta):
     # arguments passed to m.savefig when using "ctrl+c" to export figure to clipboard
     _clipboard_kwargs = dict()
 
+    # to make namespace accessible for sphinx
+    add_feature = NaturalEarthFeatures
+
     def __init__(
         self,
         crs=None,
@@ -502,6 +505,8 @@ def __init__(
             if Maps._always_on_top:
                 self._set_always_on_top(True)
 
+        self.add_feature = self.add_feature(weakref.proxy(self))
+
     def _handle_spines(self):
         spine = self.ax.spines["geo"]
         if spine not in self.BM._bg_artists.get("__SPINES__", []):
@@ -1712,15 +1717,6 @@ def edit_layout(self, filepath=None):
     def edit_annotations(self, b=True, **kwargs):
         self._edit_annotations(b, **kwargs)
 
-    @property
-    @wraps(NaturalEarth_features)
-    def add_feature(self):
-        """Add features from NaturalEarth."""
-        # lazily initialize NaturalEarth features
-        if not hasattr(self, "_add_feature"):
-            self._add_feature = NaturalEarth_features(self)
-        return self._add_feature
-
     @contextmanager
     def _disable_autoscale(self, set_extent):
         if set_extent is False:
diff --git a/eomaps/mapsgrid.py b/eomaps/mapsgrid.py
index b1f4be9b2..13ed7f2b8 100644
--- a/eomaps/mapsgrid.py
+++ b/eomaps/mapsgrid.py
@@ -9,7 +9,7 @@
 from .shapes import Shapes
 from .eomaps import Maps
 
-from .ne_features import NaturalEarth_features
+from .ne_features import NaturalEarthFeatures
 
 try:
     from .webmap_containers import WebMapContainer
@@ -421,7 +421,7 @@ def add_wms(self):
     @property
     @wraps(Maps.add_feature)
     def add_feature(self):
-        x = NaturalEarth_features(self)
+        x = NaturalEarthFeatures(self)
         return x
 
     @wraps(Maps.add_gdf)
diff --git a/eomaps/ne_features.py b/eomaps/ne_features.py
index f6986d485..cccb54e8a 100644
--- a/eomaps/ne_features.py
+++ b/eomaps/ne_features.py
@@ -12,12 +12,457 @@
 _log = logging.getLogger(__name__)
 
 
-def combdoc(*args):
+# ------ Load available features from NE_features.json file.
+
+_NE_features_path = Path(__file__).parent / "NE_features.json"
+
+try:
+    with open(_NE_features_path, "r") as file:
+        _NE_features = json.load(file)
+
+        _NE_features_all = dict()
+        for scale, scale_items in _NE_features.items():
+            for category, category_items in scale_items.items():
+                _NE_features_all.setdefault(category, set()).update(category_items)
+
+
+except Exception:
+    _log.error(
+        "EOmaps: Could not load available NaturalEarth features from\n"
+        f"{_NE_features_path}",
+        exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
+    )
+    _NE_features = dict()
+    _NE_features_all = dict()
+
+# ---------------------
+
+
+def _combdoc(*args):
     """Dedent and combine strings."""
     return "\n".join(dedent(str(i)) for i in args)
 
 
-class NaturalEarth_presets:
+class _Category:
+    """
+    Interface to features provided by NaturalEarth.
+
+    (see https://www.naturalearthdata.com)
+
+    The features are grouped into the categories "cultural" and "physical"
+    and available at 3 different scales:
+
+    - 10 : Large-scale data (1:10m)
+    - 50 : Medium-scale data (1:50m)
+    - 110 : Small-scale data (1:110m)
+
+    If you use scale="auto", the appropriate scale of the feature will be determined
+    based on the map-extent.
+
+    >>> m.add_feature.< category >.< feature-name >(scale=10, ... style-kwargs ...)
+
+    Examples
+    --------
+    - add black (coarse resolution) coastlines
+
+      >>> from eomaps import Maps
+      >>> m = Maps()
+      >>> m.add_feature.physical.coastline(scale=110, fc="none", ec="k")
+
+    - color all land red with 50% transparency and automatically determine the
+      appropriate scale if you zoom the map
+
+      >>> from eomaps import Maps
+      >>> m = Maps()
+      >>> m.add_feature.physical.land(scale="auto", fc="r", alpha=0.5)
+
+    - fetch features as geopandas.GeoDataFrame
+      (to color all countries with respect to the area)
+
+      >>> from eomaps import Maps
+      >>> m = Maps()
+      >>> countries = m.add_feature.cultural.admin_0_countries.get_gdf(scale=10)
+      >>> countries["area_rank"] = countries.area.rank()
+      >>> m.add_gdf(countries, column="area_rank")
+
+    """
+
+    _category = "???"
+
+    def __repr__(self):
+        return f"EOmaps interface for {self._category} " + "NaturalEarth features"
+
+    @classmethod
+    def _setup(cls, category):
+        # this is used to setup the namespace so that it is accessible for sphinx
+
+        for i in _NE_features_all[category]:
+            # we need to init new classes to avoid issues with immutuable attributes
+            setattr(cls, i, type(i, (_Feature,), {}))  # (category, i))
+
+        cls.__doc__ = _combdoc(
+            f"NaturalEarth feature interface for: **{category}**.\n",
+            _Category.__doc__,
+        )
+
+    def _set_map(self, m):
+        for feature_name in filter(lambda x: not x.startswith("_"), dir(self)):
+            try:
+                feature = _Feature(self._category, feature_name)
+                feature._set_map(m)
+                setattr(self, feature_name, feature)
+            except Exception:
+                _log.error(f"EOmaps: unable to set map for feature {feature}")
+
+
+class _Feature:
+    """
+    Natural Earth Feature.
+
+    Call this class like a function to add the feature to the map.
+
+    By default, the scale of the feature is automatically adjusted based on the
+    map-extent. Use `scale=...` to use a fixed scale.
+
+    Common style-keywords can be used to customize the appearance
+    of the added features.
+
+    - "facecolor" (or "fc")
+    - "edgecolor" (or "ec")
+    - "linewidth" (or "lw")
+    - "linestyle" (or "ls")
+    - "alpha", "hatch", "dashes", ...
+    - "zoder"
+
+    Parameters
+    ----------
+    scale : (10, 50, 110 or 'auto'), optional
+        The (preferred) scale of the NaturalEarth feature.
+
+        If the scale is not available for the selected feature, the next available
+        scale will be used (and a warning is issued)!
+
+        If 'auto' the scale is automatically adjusted based on the map-extent.
+
+    layer : str or None, optional
+        The name of the layer at which map-features are plotted.
+
+        - If "all": the corresponding feature will be added to ALL layers
+        - If None, the layer of the parent object is used.
+
+        The default is None.
+
+    Methods
+    -------
+    get_gdf : Get a GeoDataFrame of the feature-geometries
+
+
+    Note
+    ----
+    Some shapes consist of point-geometries which cannot be
+    properly added without `geopandas`!
+
+    To use those features, first fetch the GeoDataFrame using `.get_gdf()`
+    and then plot the shapes with `m.add_gdf()` (see example below).
+
+
+    Examples
+    --------
+    >>> m = Maps()
+    >>> feature = m.add_feature.physical.coastline
+    >>> feature(scale=10,
+    >>>         fc="none",
+    >>>         ec="k",
+    >>>         lw=.5,
+    >>>         ls="--",
+    >>>         )
+
+    For more advanced plotting, fetch the data first and use geopandas
+    to plot the features!
+
+    >>> m = Maps()
+    >>> m.add_feature.preset.coastline()
+    >>> # get the data for a feature that consists of point-geometries
+    >>> m.add_feature.cultural.populated_places.get_gdf()
+    >>> # plot the features with geopandas
+    >>> m.add_gdf(gdf, column="SOV0NAME", markersize=gdf.SCALERANK)
+    """
+
+    def __init__(self, category, name, scale=None):
+        self._category = category
+        self._name = name
+        self._scale = scale
+        self._cartopy_feature = None
+
+        self.__doc__ = (
+            "NaturalEarth feature: "
+            f"{self._category} | {self._name}\n\n"
+            "----------------------------------------\n\n"
+        ) + self.__doc__
+
+    def _set_map(self, m):
+        self._m = m
+
+    def __call__(self, layer=None, scale="auto", **kwargs):
+        assert hasattr(
+            self, "_m"
+        ), "EOmaps: This feature is not associated to a map and cannot be added!"
+        _log.debug(f"EOmaps: Adding feature: {self._category}: {self._name}")
+
+        self._set_scale(scale)
+
+        from . import MapsGrid  # do this here to avoid circular imports!
+
+        for m in self._m if isinstance(self._m, MapsGrid) else [self._m]:
+            if layer is None:
+                uselayer = m.layer
+            else:
+                uselayer = layer
+
+            feature = self._get_cartopy_feature(self._scale)
+            feature._kwargs.update(kwargs)
+            art = m.ax.add_feature(feature)
+            art.set_label(
+                f"NaturalEarth feature: {feature.category}  |  {feature.name}"
+            )
+
+            try:
+                str_kwargs = json.dumps(feature._kwargs)
+            except Exception:
+                str_kwargs = ""
+                _log.debug(
+                    "There was something wrong while trying to convert "
+                    f"the following kwargs to a string: {kwargs}"
+                )
+
+            source_code = (
+                f"feature_kwargs = {str_kwargs}\n\n"
+                f"m.add_feature.{feature.category}.{feature.name}(**feature_kwargs)"
+            )
+
+            # try to auto-format code in case black is installed
+            try:
+                import black
+
+                source_code = black.format_str(source_code, mode=black.Mode())
+            except Exception:
+                pass
+
+            art._EOmaps_info = f"""
+                NaturalEarth feature: {feature.category}  |  {feature.name}
+
+                https://www.naturalearthdata.com/
+
+                """
+            art._EOmaps_source_code = source_code
+
+            m.BM.add_bg_artist(art, layer=uselayer)
+
+    def _set_scale(self, scale):
+        if scale == "auto":
+            self._scale = "auto"
+            return
+
+        if isinstance(scale, int):
+            scale = f"{scale}m"
+
+        assert scale in [
+            "10m",
+            "50m",
+            "110m",
+            "auto",
+        ], "scale must be one of  [10, 50, 110, 'auto']"
+
+        self._scale = self._get_available_scale(scale)
+
+        if self._scale != scale:
+            _log.warning(
+                f"EOmaps: The NaturalEarth feature '{self._name}' is not "
+                f"available at 1:{scale} scale... using 1:{self._scale} instead!"
+            )
+
+    def _get_validated_scaler(self, *args, **kwargs):
+        from cartopy.feature import AdaptiveScaler
+
+        class AdaptiveValidatedScaler(AdaptiveScaler):
+            # subclass of the AdaptiveScaler to make sure the dataset exists
+            def __init__(self, default_scale, limits, validator=None):
+                super().__init__(default_scale, limits)
+
+                self.validator = validator
+
+            def scale_from_extent(self, extent):
+                scale = super().scale_from_extent(extent)
+
+                if self.validator is not None:
+                    scale = self.validator(scale)
+
+                self._scale = scale
+                return self._scale
+
+        return AdaptiveValidatedScaler(*args, **kwargs)
+
+    def _get_cartopy_feature(self, scale):
+        from cartopy.feature import NaturalEarthFeature
+
+        self._set_scale(scale)
+
+        if (
+            self._cartopy_feature is not None
+            and self._scale == self._cartopy_feature.scale
+        ):
+
+            return self._cartopy_feature
+
+        if self._scale == "auto":
+            usescale = self._get_validated_scaler(
+                "110m",
+                (("50m", 50), ("10m", 15)),
+                validator=self._get_available_scale,
+            )
+        else:
+            usescale = self._scale
+
+        # get an instance of the corresponding cartopy-feature
+        self._cartopy_feature = NaturalEarthFeature(
+            category=self._category, name=self._name, scale=usescale
+        )
+
+        return self._cartopy_feature
+
+    def _get_available_scale(self, scale):
+        # return the optimal scale for the selected feature
+        scaleorder = ["110m", "50m", "10m"]
+        while True:
+            if self._name in _NE_features[scale][self._category]:
+                break
+
+            scale = scaleorder[(scaleorder.index(scale) + 1) % len(scaleorder)]
+
+        return scale
+
+    def get_gdf(self, scale=50, what="full"):
+        """
+        Get a geopandas.GeoDataFrame for the selected NaturalEarth feature.
+
+        Parameters
+        ----------
+        scale : (10, 50, 110), optional
+            The scale to use when fetching the data for the NaturalEarth feature.
+
+            If the scale is not available for the selected feature, the next
+            available scale will be used (and a warning is issued)!
+        what: str, optional
+            Set what information is included in the returned GeoDataFrame.
+
+            - "full": return all geometries AND metadata
+            - "geoms" : return only geometries (NO metadata)
+            - "geoms_intersecting": return only geometries that intersect with the
+              current axis-extent (NO metadata)
+
+            The default is False
+
+        Returns
+        -------
+        gdf : geopandas.GeoDataFrame
+            A GeoDataFrame with all geometries of the feature
+        """
+        if what == "geoms_intersecting":
+            assert hasattr(self, "_m"), (
+                "EOmaps: If the feature is not called form a Maps-instance, you can "
+                "only use 'what=full' or 'what=geoms'!"
+            )
+
+        (gpd,) = register_modules("geopandas")
+
+        self._set_scale(scale)
+
+        if what == "full":
+            from cartopy.io import shapereader
+
+            gdf = gpd.read_file(
+                shapereader.natural_earth(
+                    resolution=self._scale, category=self._category, name=self._name
+                )
+            )
+        elif what.startswith("geoms"):
+            feature = self._get_cartopy_feature(self._scale)
+
+            if what == "geoms_intersecting":
+                extent = self._m.get_extent(feature.crs)
+                geoms = list(feature.intersecting_geometries(extent))
+            elif what == "geoms":
+                geoms = list(feature.geometries())
+            else:
+                raise TypeError("EOmaps: what='{what}' is not a valid input!")
+
+            gdf = gpd.GeoDataFrame(geometry=geoms, crs=feature.crs)
+
+        else:
+            raise TypeError(
+                "EOmaps: what='{what}' is not a valid input!"
+                "Use one of: ['full' ,'geoms', 'geoms_intersecting']"
+            )
+
+        if gdf.crs is None:
+            # make sure the CRS is properly set
+            # (NE-features come in epsg=4326 projection)
+            gdf.set_crs(ccrs.PlateCarree(), inplace=True, allow_override=True)
+
+        return gdf
+
+
+class _PresetFeature:
+    def __init__(self, m, category, name, **kwargs):
+        self._m = m
+        self.category = category
+        self.name = name
+        self.kwargs = kwargs
+
+        self.feature = _Feature(category=self.category, name=self.name)
+        self.feature._set_map(self._m)
+
+        add_params = """
+        Other Parameters
+        ----------------
+        scale : int or str
+            Set the scale of the feature preset (10, 50, 110 or "auto")
+            The default is "auto"
+        """
+
+        self.__doc__ = _combdoc(
+            f"PRESET using {kwargs} \n", self.feature.__doc__, add_params
+        )
+
+    def _handle_synonyms(self, kwargs):
+        # make sure to replace shortcuts with long names
+        # (since "facecolor=..." will override "fc=..." if both are specified)
+        subst = dict(fc="facecolor", ec="edgecolor", lw="linewidth", ls="linestyle")
+        return {subst.get(key, key): val for key, val in kwargs.items()}
+
+    def __call__(self, scale=50, layer=None, **kwargs):
+        k = dict(**self.kwargs)
+        k.update(kwargs)
+
+        self.__doc__ = self.feature.__doc__
+        return self.feature(scale=scale, layer=layer, **self._handle_synonyms(k))
+
+
+# to make namespace accessible for autocompletion and sphinx-autodoc
+class _Physical(_Category):
+    _category = "physical"
+
+
+_Physical._setup("physical")
+
+
+class _Cultural(_Category):
+    _category = "cultural"
+
+_Cultural._setup("cultural")
+
+
+class NaturalEarthPresets:
     """
     Feature presets.
 
@@ -82,7 +527,8 @@ def coastline(self):
 
         - fc="none", ec="k", zorder=100
         """
-        return self._Feature(
+
+        return _PresetFeature(
             self._m,
             "physical",
             "coastline",
@@ -105,7 +551,7 @@ def ocean(self):
         # convert color to hex to avoid issues with geopandas
         color = "#97b6e1"  # rgb2hex(cfeature.COLORS["water"])
 
-        return self._Feature(
+        return _PresetFeature(
             self._m, "physical", "ocean", facecolor=color, edgecolor="none", zorder=-2
         )
 
@@ -124,7 +570,7 @@ def land(self):
         # convert color to hex to avoid issues with geopandas
         color = "#efefdb"  # rgb2hex(cfeature.COLORS["land"])
 
-        return self._Feature(
+        return _PresetFeature(
             self._m, "physical", "land", facecolor=color, edgecolor="none", zorder=-1
         )
 
@@ -140,7 +586,7 @@ def countries(self):
         - fc="none", ec=".5", lw=0.5, zorder=99
 
         """
-        return self._Feature(
+        return _PresetFeature(
             self._m,
             "cultural",
             "admin_0_countries",
@@ -162,7 +608,7 @@ def urban_areas(self):
         - fc="r", lw=0., zorder=100
 
         """
-        return self._Feature(
+        return _PresetFeature(
             self._m,
             "cultural",
             "urban_areas",
@@ -183,7 +629,7 @@ def lakes(self):
         - fc="b", ec="none", lw=0., zorder=98
 
         """
-        return self._Feature(
+        return _PresetFeature(
             self._m,
             "physical",
             "lakes",
@@ -204,7 +650,7 @@ def rivers_lake_centerlines(self):
         - fc="none", ec="b", lw=0.75, zorder=97
 
         """
-        return self._Feature(
+        return _PresetFeature(
             self._m,
             "physical",
             "rivers_lake_centerlines",
@@ -214,72 +660,10 @@ def rivers_lake_centerlines(self):
             zorder=97,
         )
 
-    class _Feature:
-        def __init__(self, m, category, name, **kwargs):
-            self._m = m
-            self.category = category
-            self.name = name
-            self.kwargs = kwargs
-
-            self.feature = self._m.add_feature._get_feature(
-                category=self.category, name=self.name
-            )
-
-            add_params = """
-            Other Parameters
-            ----------------
-            scale : int or str
-                Set the scale of the feature preset (10, 50, 110 or "auto")
-                The default is "auto"
-            """
-
-            self.__doc__ = combdoc(
-                f"PRESET using {kwargs} \n", self.feature.__doc__, add_params
-            )
-
-        def _handle_synonyms(self, kwargs):
-            # make sure to replace shortcuts with long names
-            # (since "facecolor=..." will override "fc=..." if both are specified)
-            subst = dict(fc="facecolor", ec="edgecolor", lw="linewidth", ls="linestyle")
-            return {subst.get(key, key): val for key, val in kwargs.items()}
-
-        def __call__(self, scale=50, layer=None, **kwargs):
-            k = dict(**self.kwargs)
-            k.update(kwargs)
-
-            if scale != "auto":
-                self.feature = self._m.add_feature._get_feature(
-                    category=self.category, name=self.name
-                )
 
-            self.__doc__ = self.feature.__doc__
-            return self.feature(scale=scale, layer=layer, **self._handle_synonyms(k))
-
-
-_NE_features_path = Path(__file__).parent / "NE_features.json"
-
-try:
-    with open(_NE_features_path, "r") as file:
-        _NE_features = json.load(file)
-
-        _NE_features_all = dict()
-        for scale, scale_items in _NE_features.items():
-            for category, category_items in scale_items.items():
-                _NE_features_all.setdefault(category, set()).update(category_items)
-
-except Exception:
-    _log.error(
-        "EOmaps: Could not load available NaturalEarth features from\n"
-        f"{_NE_features_path}",
-        exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
-    )
-    _NE_features = dict()
-    _NE_features_all = dict()
-
-
-class NaturalEarth_features(object):
+class NaturalEarthFeatures:
     """
-    Interface to the feature-layers provided by NaturalEarth.
+    Interface to features provided by NaturalEarth.
 
     (see https://www.naturalearthdata.com)
 
@@ -324,16 +708,21 @@ class NaturalEarth_features(object):
 
     """
 
+    preset = NaturalEarthPresets
+    cultural = _Cultural
+    physical = _Physical
+
     def __init__(self, m):
         self._m = m
-        for category, names in _NE_features_all.items():
-            func = lambda name: self._Feature(self._m, category, name)
-            ns = dict(zip(names, map(func, names)))
 
-            c = self._Category(scale, category, **ns)
-            setattr(self, category, c)
+        self.preset = self.preset(self._m)
+        self.cultural = _Cultural()
+        self.cultural._set_map(m)
+
+        self.physical = _Physical()
+        self.physical._set_map(m)
 
-    def __call__(self, category, scale, name, **kwargs):
+    def __call__(self, category, name, **kwargs):
         feature = self._get_feature(category, name)
         return feature(**kwargs)
 
@@ -357,347 +746,4 @@ def _get_feature(self, category, name):
                 + (f"did you mean one of {matches}?" if matches else "")
             )
 
-        return self._Feature(self._m, category, name)
-
-    @property
-    def preset(self):
-        """
-        Access to commonly used NaturalEarth features with pre-defined styles.
-
-        - "coastline" - black coastlines
-        - "ocean" - blue ocean coloring
-        - "land" - beige land coloring
-        - "countries" - gray country boarder lines
-        """
-        return NaturalEarth_presets(self._m)
-
-    class _Category:
-        def __init__(self, scale, category, **kwargs):
-
-            self._s = scale
-            self._c = category
-
-            for key, val in kwargs.items():
-                setattr(self, key, val)
-
-            if scale == "auto":
-                header = (
-                    f"Auto-scaled feature interface for: {category}\n"
-                    "------------------------------------------------"
-                    "\n"
-                    "Note\n"
-                    "----\n"
-                    "Features will be added to the map using cartopy's Feature "
-                    "interface and the scale (10m, 50m or 110m) is automatically "
-                    "adjusted based on the extent of the map."
-                    "\n\n"
-                    "--------------------------------------------------------------"
-                    "\n\n"
-                )
-            else:
-                header = (
-                    f"Feature interface for: {category} - {scale}\n"
-                    "----------------------------------------------"
-                )
-
-            self.__doc__ = combdoc(header, NaturalEarth_features.__doc__)
-
-        def __repr__(self):
-            return (
-                f"EOmaps interface for {self._s} {self._c} " + "NaturalEarth features"
-            )
-
-    class _Feature:
-        """
-        Natural Earth Feature.
-
-        Call this class like a function to add the feature to the map.
-
-        By default, the scale of the feature is automatically adjusted based on the
-        map-extent. Use `scale=...` to use a fixed scale.
-
-        Common style-keywords can be used to customize the appearance
-        of the added features.
-
-        - "facecolor" (or "fc")
-        - "edgecolor" (or "ec")
-        - "linewidth" (or "lw")
-        - "linestyle" (or "ls")
-        - "alpha", "hatch", "dashes", ...
-        - "zoder"
-
-        Parameters
-        ----------
-        scale : (10, 50, 110 or 'auto'), optional
-            The (preferred) scale of the NaturalEarth feature.
-
-            If the scale is not available for the selected feature, the next available
-            scale will be used (and a warning is issued)!
-
-            If 'auto' the scale is automatically adjusted based on the map-extent.
-
-        layer : str or None, optional
-            The name of the layer at which map-features are plotted.
-
-            - If "all": the corresponding feature will be added to ALL layers
-            - If None, the layer of the parent object is used.
-
-            The default is None.
-
-        Methods
-        -------
-        get_gdf : Get a GeoDataFrame of the feature-geometries
-
-
-        Note
-        ----
-        Some shapes consist of point-geometries which cannot be
-        properly added without `geopandas`!
-
-        To use those features, first fetch the GeoDataFrame using `.get_gdf()`
-        and then plot the shapes with `m.add_gdf()` (see example below).
-
-
-        Examples
-        --------
-        >>> m = Maps()
-        >>> feature = m.add_feature.physical.coastline
-        >>> feature(scale=10,
-        >>>         fc="none",
-        >>>         ec="k",
-        >>>         lw=.5,
-        >>>         ls="--",
-        >>>         )
-
-        For more advanced plotting, fetch the data first and use geopandas
-        to plot the features!
-
-        >>> m = Maps()
-        >>> m.add_feature.preset.coastline()
-        >>> # get the data for a feature that consists of point-geometries
-        >>> m.add_feature.cultural.populated_places.get_gdf()
-        >>> # plot the features with geopandas
-        >>> m.add_gdf(gdf, column="SOV0NAME", markersize=gdf.SCALERANK)
-        """
-
-        def __init__(self, m, category, name, scale=None):
-            self._m = m
-
-            self._category = category
-            self._name = name
-            self._scale = scale
-            self._cartopy_feature = None
-
-            self.__doc__ = (
-                "NaturalEarth feature: "
-                f"{self._category} | {self._name}\n\n"
-                "----------------------------------------\n\n"
-            ) + self.__doc__
-
-        def __call__(self, layer=None, scale="auto", **kwargs):
-            _log.debug(f"EOmaps: Adding feature: {self._category}: {self._name}")
-
-            self._set_scale(scale)
-
-            from . import MapsGrid  # do this here to avoid circular imports!
-
-            for m in self._m if isinstance(self._m, MapsGrid) else [self._m]:
-                if layer is None:
-                    uselayer = m.layer
-                else:
-                    uselayer = layer
-
-                feature = self._get_cartopy_feature(self._scale)
-                feature._kwargs.update(kwargs)
-                art = m.ax.add_feature(feature)
-                art.set_label(
-                    f"NaturalEarth feature: {feature.category}  |  {feature.name}"
-                )
-
-                try:
-                    str_kwargs = json.dumps(feature._kwargs)
-                except Exception:
-                    str_kwargs = ""
-                    _log.debug(
-                        "There was something wrong while trying to convert "
-                        f"the following kwargs to a string: {kwargs}"
-                    )
-
-                source_code = (
-                    f"feature_kwargs = {str_kwargs}\n\n"
-                    f"m.add_feature.{feature.category}.{feature.name}(**feature_kwargs)"
-                )
-
-                # try to auto-format code in case black is installed
-                try:
-                    import black
-
-                    source_code = black.format_str(source_code, mode=black.Mode())
-                except Exception:
-                    pass
-
-                art._EOmaps_info = f"""
-                    NaturalEarth feature: {feature.category}  |  {feature.name}
-
-                    https://www.naturalearthdata.com/
-
-                    """
-                art._EOmaps_source_code = source_code
-
-                m.BM.add_bg_artist(art, layer=uselayer)
-
-        def _set_scale(self, scale):
-            if scale == "auto":
-                self._scale = "auto"
-                return
-
-            if isinstance(scale, int):
-                scale = f"{scale}m"
-
-            assert scale in [
-                "10m",
-                "50m",
-                "110m",
-                "auto",
-            ], "scale must be one of  [10, 50, 110, 'auto']"
-
-            self._scale = self._get_available_scale(scale)
-
-            if self._scale != scale:
-                _log.warning(
-                    f"EOmaps: The NaturalEarth feature '{self._name}' is not "
-                    f"available at 1:{scale} scale... using 1:{self._scale} instead!"
-                )
-
-        def get_validated_scaler(self, *args, **kwargs):
-            from cartopy.feature import AdaptiveScaler
-
-            class AdaptiveValidatedScaler(AdaptiveScaler):
-                # subclass of the AdaptiveScaler to make sure the dataset exists
-                def __init__(self, default_scale, limits, validator=None):
-                    super().__init__(default_scale, limits)
-
-                    self.validator = validator
-
-                def scale_from_extent(self, extent):
-                    scale = super().scale_from_extent(extent)
-
-                    if self.validator is not None:
-                        scale = self.validator(scale)
-
-                    self._scale = scale
-                    return self._scale
-
-            return AdaptiveValidatedScaler(*args, **kwargs)
-
-        def _get_cartopy_feature(self, scale):
-            from cartopy.feature import NaturalEarthFeature
-
-            self._set_scale(scale)
-
-            if (
-                self._cartopy_feature is not None
-                and self._scale == self._cartopy_feature.scale
-            ):
-
-                return self._cartopy_feature
-
-            if self._scale == "auto":
-                usescale = self.get_validated_scaler(
-                    "110m",
-                    (("50m", 50), ("10m", 15)),
-                    validator=self._get_available_scale,
-                )
-            else:
-                usescale = self._scale
-
-            # get an instance of the corresponding cartopy-feature
-            self._cartopy_feature = NaturalEarthFeature(
-                category=self._category, name=self._name, scale=usescale
-            )
-
-            return self._cartopy_feature
-
-        def _get_available_scale(self, scale):
-            # return the optimal scale for the selected feature
-            scaleorder = ["110m", "50m", "10m"]
-            while True:
-                if self._name in _NE_features[scale][self._category]:
-                    break
-
-                scale = scaleorder[(scaleorder.index(scale) + 1) % len(scaleorder)]
-
-            return scale
-
-        def get_gdf(self, scale=50, what="full"):
-            """
-            Get a geopandas.GeoDataFrame for the selected NaturalEarth feature.
-
-            Parameters
-            ----------
-            scale : (10, 50, 110), optional
-                The scale to use when fetching the data for the NaturalEarth feature.
-
-                If the scale is not available for the selected feature, the next
-                available scale will be used (and a warning is issued)!
-            what: str, optional
-                Set what information is included in the returned GeoDataFrame.
-
-                - "full": return all geometries AND metadata
-                - "geoms" : return only geometries (NO metadata)
-                - "geoms_intersecting": return only geometries that intersect with the
-                  current axis-extent (NO metadata)
-
-                The default is False
-
-            Returns
-            -------
-            gdf : geopandas.GeoDataFrame
-                A GeoDataFrame with all geometries of the feature
-            """
-            (gpd,) = register_modules("geopandas")
-
-            self._set_scale(scale)
-
-            if what == "full":
-                from cartopy.io import shapereader
-
-                gdf = gpd.read_file(
-                    shapereader.natural_earth(
-                        resolution=self._scale, category=self._category, name=self._name
-                    )
-                )
-            elif what.startswith("geoms"):
-                feature = self._get_cartopy_feature(self._scale)
-
-                if what == "geoms_intersecting":
-                    try:
-                        extent = self._m.get_extent(feature.crs)
-                        feature.scaler.scale_from_extent(extent)
-                    except:
-                        _log.error("EOmaps: unable to determine extent")
-                        pass
-                    geoms = list(feature.geometries())
-                elif what == "geoms":
-                    try:
-                        extent = self._m.get_extent(feature.crs)
-                        geoms = list(feature.intersecting_geometries(extent))
-                    except ValueError:
-                        geoms = list(feature.geometries())
-                else:
-                    raise TypeError("EOmaps: what='{what}' is not a valid input!")
-
-                gdf = gpd.GeoDataFrame(geometry=geoms, crs=feature.crs)
-
-            else:
-                raise TypeError(
-                    "EOmaps: what='{what}' is not a valid input!"
-                    "Use one of: ['full' ,'geoms', 'geoms_intersecting']"
-                )
-
-            if gdf.crs is None:
-                # make sure the CRS is properly set
-                # (NE-features come in epsg=4326 projection)
-                gdf.set_crs(ccrs.PlateCarree(), inplace=True, allow_override=True)
-
-            return gdf
+        return _Feature(category, name)

From 13c82c2fc55bfbc3e1cd095f4bdf32181ff67c37 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 7 Jan 2024 23:45:41 +0100
Subject: [PATCH 064/240] make namespaces accessible to sphinx

---
 eomaps/eomaps.py | 32 ++++++++++----------------------
 1 file changed, 10 insertions(+), 22 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 5c26f6356..a197f5c4e 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -345,8 +345,13 @@ class Maps(metaclass=_MapsMeta):
     _clipboard_kwargs = dict()
 
     # to make namespace accessible for sphinx
+    set_shape = Shapes
+    draw = ShapeDrawer
     add_feature = NaturalEarthFeatures
 
+    if WebMapContainer is not None:
+        add_wms = WebMapContainer
+
     def __init__(
         self,
         crs=None,
@@ -457,11 +462,13 @@ def __init__(
         self._cb = CallbackContainer(weakref.proxy(self))  # accessor for the callbacks
 
         self._init_figure(ax=ax, plot_crs=crs, **kwargs)
+
         if WebMapContainer is not None:
-            self._wms_container = WebMapContainer(weakref.proxy(self))
+            self.add_wms = self.add_wms(weakref.proxy(self))
+
         self._new_layer_from_file = new_layer_from_file(weakref.proxy(self))
 
-        self._shapes = Shapes(weakref.proxy(self))
+        self.set_shape = self.set_shape(weakref.proxy(self))
         self._shape = None
 
         # the radius is estimated when plot_map is called
@@ -506,6 +513,7 @@ def __init__(
                 self._set_always_on_top(True)
 
         self.add_feature = self.add_feature(weakref.proxy(self))
+        self.draw = self.draw(weakref.proxy(self))
 
     def _handle_spines(self):
         spine = self.ax.spines["geo"]
@@ -678,12 +686,6 @@ def util(self):
             self.parent._util = Utilities(self.parent)
         return self.parent._util
 
-    @property
-    @wraps(ShapeDrawer)
-    def draw(self):
-        """Draw simple shapes on the map."""
-        return self._shape_drawer
-
     @property
     def BM(self):
         """The Blit-Manager used to dynamically update the plots."""
@@ -1242,12 +1244,6 @@ def _set_always_on_top(self, q):
         except Exception:
             pass
 
-    @property
-    @wraps(Shapes)
-    def set_shape(self):
-        """Set the plot-shape."""
-        return self._shapes
-
     def set_data(
         self,
         data=None,
@@ -2397,14 +2393,6 @@ def add_scalebar(
 
         return s
 
-    if WebMapContainer is not None:
-
-        @property
-        @wraps(WebMapContainer)
-        def add_wms(self):
-            """Accessor to attach WebMap services to the map."""
-            return self._wms_container
-
     def add_line(
         self,
         xy,

From ba664b519260aada0e4f3ec65676d65690415893 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 7 Jan 2024 23:50:02 +0100
Subject: [PATCH 065/240] update API docs

---
 .gitignore                                    |   3 +
 .../_templates/obj_with_attributes_no_toc.rst |  21 ++
 docs/api/api_Maps.rst                         | 213 ++++++++++++++++++
 docs/api/reference.rst                        |   5 +
 docs/conf.py                                  |  68 +++++-
 docs/index.rst                                |   2 +-
 docs/reference.rst                            |   9 -
 7 files changed, 304 insertions(+), 17 deletions(-)
 create mode 100644 docs/_templates/obj_with_attributes_no_toc.rst
 create mode 100644 docs/api/api_Maps.rst
 create mode 100644 docs/api/reference.rst
 delete mode 100644 docs/reference.rst

diff --git a/.gitignore b/.gitignore
index 3f2defa0b..78e65f794 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,6 +8,9 @@ docs/_build
 docs/debug.log
 docs/.vscode
 docs/generated
+docs/api/generated
+docs/api/autodoc_props.rst
+
 docs/_tables
 docs/_static/example_gifs_very_old
 
diff --git a/docs/_templates/obj_with_attributes_no_toc.rst b/docs/_templates/obj_with_attributes_no_toc.rst
new file mode 100644
index 000000000..9af90afb0
--- /dev/null
+++ b/docs/_templates/obj_with_attributes_no_toc.rst
@@ -0,0 +1,21 @@
+{{ name | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. auto{{ objtype }}:: {{ fullname }}
+
+{% block members %}
+{% if members %}
+.. rubric:: {{ _('Members') }}
+
+.. autosummary::
+   :nosignatures:
+   :template: only_names_in_toc.rst
+{% for item in members %}
+   {% if not item.startswith('_') %}
+   ~{{ name }}.{{ item }}
+   {% endif %}
+{%- endfor %}
+{% endif %}
+{% endblock %}
+
diff --git a/docs/api/api_Maps.rst b/docs/api/api_Maps.rst
new file mode 100644
index 000000000..4c4b22edb
--- /dev/null
+++ b/docs/api/api_Maps.rst
@@ -0,0 +1,213 @@
+The :py:class:`Maps` object
+===========================
+
+.. currentmodule:: eomaps
+
+.. autosummary::
+    :toctree: generated
+    :template: custom-class-template.rst
+
+    Maps
+
+
+Class Methods
+-------------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.config
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.from_file.GeoTIFF
+    Maps.from_file.NetCDF
+    Maps.from_file.CSV
+
+General Properties
+------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.f
+    Maps.ax
+    Maps.layer
+    Maps.crs_plot
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.data
+    Maps.colorbar
+
+New Maps and Layers
+-------------------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.new_layer
+    Maps.new_layer_from_file
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.new_map
+    Maps.new_inset_map
+
+
+Map Features
+------------
+
+.. autosummary::        
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.add_feature
+    Maps.add_wms
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.add_gdf
+    Maps.add_annotation
+    Maps.add_marker
+    Maps.add_line
+    Maps.add_logo
+    Maps.add_title
+    Maps.indicate_extent
+
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.add_compass
+    Maps.add_gridlines
+    Maps.add_scalebar
+
+
+
+Data visualization
+------------------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.set_data
+    Maps.set_shape
+    Maps.set_classify
+    Maps.set_classify_specs
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.inherit_data
+    Maps.inherit_classification
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.plot_map
+    Maps.add_colorbar
+    Maps.make_dataset_pickable
+
+
+Layer management
+----------------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.show_layer
+    Maps.fetch_layers
+    Maps.on_layer_activation
+
+
+Figure Layout and Export
+------------------------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.set_frame
+    Maps.savefig
+    Maps.snapshot
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.subplots_adjust
+    Maps.edit_layout
+    Maps.get_layout
+    Maps.apply_layout
+
+
+Map Extent
+----------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.set_extent
+    Maps.get_extent
+    Maps.set_extent_to_location
+    Maps.join_limits
+
+
+Interactive Editing
+-------------------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.edit_annotations
+    Maps.draw
+
+
+Miscellaneous
+-------------
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.fetch_companion_wms_layers
+    Maps.refetch_wms_on_size_change
+    Maps.cleanup
+    Maps.get_crs
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.read_file.GeoTIFF
+    Maps.read_file.NetCDF
+    Maps.read_file.CSV
+
+.. autosummary::
+    :toctree: generated
+    :template: obj_with_attributes_no_toc.rst
+
+    Maps.show
+    Maps.redraw
+    Maps.copy
\ No newline at end of file
diff --git a/docs/api/reference.rst b/docs/api/reference.rst
new file mode 100644
index 000000000..272ff56ed
--- /dev/null
+++ b/docs/api/reference.rst
@@ -0,0 +1,5 @@
+
+.. toctree::
+    :maxdepth: 1
+
+    api_Maps
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
index 1666f5ab3..68de69465 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,15 +1,67 @@
 # Configuration file for the Sphinx documentation builder.
 import sys, os
+from pathlib import Path
 
-sys.path.insert(0, os.path.abspath(".." + os.sep + ".."))
+sys.path.insert(0, os.path.abspath(os.path.join("..", "..")))
 sys.path.insert(0, os.path.abspath(".."))
 
+from eomaps import Maps
+
+
+def make_feature_toctree_file():
+    preset = (f for f in dir(Maps.add_feature.preset) if not f.startswith("_"))
+    physical = (f for f in dir(Maps.add_feature.physical) if not f.startswith("_"))
+    cultural = (f for f in dir(Maps.add_feature.cultural) if not f.startswith("_"))
+    shapes = (f for f in dir(Maps.set_shape) if not f.startswith("_"))
+    draw = (f for f in dir(Maps.draw) if not f.startswith("_"))
+    wms = (f for f in dir(Maps.add_wms) if not f.startswith("_"))
+
+    s = (
+        ":orphan:\n"
+        ".. currentmodule:: eomaps\n"
+        ".. autosummary::\n"
+        "    :toctree: generated\n"
+        "    :nosignatures:\n"
+        "    :template: obj_with_attributes_no_toc.rst\n\n"
+        + "\n".join(
+            [
+                "\n".join([f"    Maps.add_feature.preset.{f}" for f in preset]),
+                "\n".join([f"    Maps.add_feature.physical.{f}" for f in physical]),
+                "\n".join(
+                    [f"    Maps.add_feature.physical.{f}.get_gdf" for f in physical]
+                ),
+                "\n".join([f"    Maps.add_feature.cultural.{f}" for f in cultural]),
+                "\n".join(
+                    [f"    Maps.add_feature.cultural.{f}.get_gdf" for f in cultural]
+                ),
+                "\n".join([f"    Maps.set_shape.{f}" for f in shapes]),
+                "\n".join([f"    Maps.draw.{f}" for f in draw]),
+                "\n".join([f"    Maps.add_wms.{f}" for f in wms]),
+            ]
+        )
+    )
+
+    with open(Path("api") / "autodoc_props.rst", "w") as file:
+        file.write(s)
+
+
+make_feature_toctree_file()
+
 
 def setup(app):
     app.add_css_file("custom_css.css")
 
+    # need to assign the names here, otherwise autodoc won't document these classes,
+    # and will instead just say 'alias of ...'
+    # see https://stackoverflow.com/a/58982001/9703451
+    Maps.add_feature.__name__ = "add_feature"
+    Maps.add_feature.preset.__name__ = "preset"
+    Maps.add_feature.cultural.__name__ = "cultural"
+    Maps.add_feature.physical.__name__ = "physical"
+    Maps.set_shape.__name__ = "set_shape"
+    Maps.draw.__name__ = "draw"
+    Maps.add_wms.__name__ = "add_wms"
 
-from eomaps import Maps  # to run __init__.py
 
 # -- Project information
 
@@ -41,11 +93,13 @@ def setup(app):
 html_theme = "sphinx_rtd_theme"
 
 
-autosummary_generate = ["reference.rst"]
-# autodoc_default_options = {
-#     "member-order": "groupwise",
-#     "inherited-members": True,
-# }
+autosummary_generate = [
+    str(i) for i in Path("api").iterdir() if i.is_file() and i.suffix == ".rst"
+]
+
+autodoc_default_options = {
+    "member-order": "alphabetical",
+}
 
 # Napoleon settings
 napoleon_numpy_docstring = True
diff --git a/docs/index.rst b/docs/index.rst
index ca5eb6d26..5d3db9e6e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -241,4 +241,4 @@ Make sure to check out the :doc:`EOmaps_examples` for an overview of the capabil
    :maxdepth: 1
    :caption: API Reference
 
-   reference
+   api/reference
diff --git a/docs/reference.rst b/docs/reference.rst
deleted file mode 100644
index b89e7ac6d..000000000
--- a/docs/reference.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-.. rubric:: 🐍 API Reference
-
-.. autosummary::
-   :toctree: generated
-   :nosignatures:
-   :template: custom-module-template.rst
-   :recursive:
-
-   eomaps

From 763f3e73399abba6ef7c6e5c670fe376909084fa Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 7 Jan 2024 23:57:48 +0100
Subject: [PATCH 066/240] make pre-commit happy

---
 docs/_templates/obj_with_attributes_no_toc.rst | 1 -
 docs/api/api_Maps.rst                          | 4 ++--
 docs/api/reference.rst                         | 2 +-
 eomaps/ne_features.py                          | 1 +
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/_templates/obj_with_attributes_no_toc.rst b/docs/_templates/obj_with_attributes_no_toc.rst
index 9af90afb0..150a7262c 100644
--- a/docs/_templates/obj_with_attributes_no_toc.rst
+++ b/docs/_templates/obj_with_attributes_no_toc.rst
@@ -18,4 +18,3 @@
 {%- endfor %}
 {% endif %}
 {% endblock %}
-
diff --git a/docs/api/api_Maps.rst b/docs/api/api_Maps.rst
index 4c4b22edb..571f0866d 100644
--- a/docs/api/api_Maps.rst
+++ b/docs/api/api_Maps.rst
@@ -69,7 +69,7 @@ New Maps and Layers
 Map Features
 ------------
 
-.. autosummary::        
+.. autosummary::
     :toctree: generated
     :template: obj_with_attributes_no_toc.rst
 
@@ -210,4 +210,4 @@ Miscellaneous
 
     Maps.show
     Maps.redraw
-    Maps.copy
\ No newline at end of file
+    Maps.copy
diff --git a/docs/api/reference.rst b/docs/api/reference.rst
index 272ff56ed..72f7641f8 100644
--- a/docs/api/reference.rst
+++ b/docs/api/reference.rst
@@ -2,4 +2,4 @@
 .. toctree::
     :maxdepth: 1
 
-    api_Maps
\ No newline at end of file
+    api_Maps
diff --git a/eomaps/ne_features.py b/eomaps/ne_features.py
index cccb54e8a..d7553ff75 100644
--- a/eomaps/ne_features.py
+++ b/eomaps/ne_features.py
@@ -459,6 +459,7 @@ class _Physical(_Category):
 class _Cultural(_Category):
     _category = "cultural"
 
+
 _Cultural._setup("cultural")
 
 

From 1939e661df0dcf4f718b8c136bf0b5e90ca9b2cc Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 8 Jan 2024 00:12:35 +0100
Subject: [PATCH 067/240] fix generation of stub-files for documents other than
 the api-docs

---
 docs/conf.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 68de69465..426e8c62d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -93,10 +93,10 @@ def setup(app):
 html_theme = "sphinx_rtd_theme"
 
 
-autosummary_generate = [
-    str(i) for i in Path("api").iterdir() if i.is_file() and i.suffix == ".rst"
-]
-
+# autosummary_generate = [
+#     str(i) for i in Path("api").iterdir() if i.is_file() and i.suffix == ".rst"
+# ]
+autosummary_generate = True
 autodoc_default_options = {
     "member-order": "alphabetical",
 }

From 7e00d4d2acc554488b1dee9078f6150c12ca0dac Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 8 Jan 2024 11:14:28 +0100
Subject: [PATCH 068/240] fix issues for move-callbacks shared between multiple
 Maps

- avoid calling update on callbacks directly (update only from parent)
- remove "update_on_trigger" arg and simply check if move-callbacks are
  attached before triggering an update
---
 eomaps/callbacks.py    | 11 -----------
 eomaps/cb_container.py | 16 ++++++----------
 2 files changed, 6 insertions(+), 21 deletions(-)

diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index eff6c6a3d..4cacad20d 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -1222,20 +1222,9 @@ class MoveCallbacks(_ClickCallbacks):
         "peek_layer",
     ]
 
-    def _decorate(self, f):
-        def inner(*args, **kwargs):
-            f(*args, **kwargs)
-            self.m.BM.update()
-
-        return inner
-
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
-        for cb in self._cb_list:
-            if cb not in ["print_to_console"]:
-                setattr(self, cb, self._decorate(getattr(super(), cb)))
-
 
 class KeypressCallbacks:
     """Collection of callbacks that are executed if you press a key on the keyboard."""
diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 394e57646..f35ce0228 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -1152,7 +1152,7 @@ class MoveContainer(ClickContainer):
 
     # this is just a copy of ClickContainer to manage motion-sensitive callbacks
 
-    def __init__(self, button_down=False, update_on_trigger=True, *args, **kwargs):
+    def __init__(self, button_down=False, *args, **kwargs):
 
         super().__init__(*args, **kwargs)
 
@@ -1160,8 +1160,6 @@ def __init__(self, button_down=False, update_on_trigger=True, *args, **kwargs):
 
         self._button_down = button_down
 
-        self._update_on_trigger = update_on_trigger
-
     def _init_cbs(self):
         if self._m.parent is self._m:
             self._add_move_callback()
@@ -1203,12 +1201,12 @@ def movecb(event):
 
                 # execute onclick on the maps object that belongs to the clicked axis
                 # and forward the event to all forwarded maps-objects
-                update = False
+                call_update = False
                 for obj in self._objs:
-                    # check if there is a reason to update
-                    if update is False:
+                    # check if there is a reason to update (e.g. an attached callback)
+                    if call_update is False:
                         if len(obj.get.attached_callbacks) > 0:
-                            update = True
+                            call_update = True
 
                     # clear temporary artists before executing new callbacks to avoid
                     # having old artists around when callbacks are triggered again
@@ -1221,7 +1219,7 @@ def movecb(event):
 
                 # only update if a callback is attached
                 # (to avoid lag in webagg backed due to slow updates)
-                if self._update_on_trigger and update:
+                if call_update:
                     if self._button_down:
                         if event.button:
                             self._m.parent.BM.update(clear=self._method)
@@ -1995,7 +1993,6 @@ def __init__(self, m):
             method="_click_move",
             parent_container=self._click,
             button_down=True,
-            update_on_trigger=True,  # automatically trigger updates for click+move!
         )
 
         self._move = MoveContainer(
@@ -2004,7 +2001,6 @@ def __init__(self, m):
             method="move",
             button_down=False,
             default_button=None,
-            update_on_trigger=False,  # dont trigger updates for move!
         )
 
         self._pick = PickContainer(

From 5ca9a2021f194ce226482d51619f15adac07a87d Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 8 Jan 2024 12:04:03 +0100
Subject: [PATCH 069/240] update autodocs... unify toctree for generated docs

---
 .gitignore              |  3 +--
 docs/api/api_Maps.rst   | 44 ++++++++++++++++++++---------------------
 docs/conf.py            | 22 +++++++++++++++------
 docs/full_reference.rst |  9 +++++++++
 4 files changed, 48 insertions(+), 30 deletions(-)
 create mode 100644 docs/full_reference.rst

diff --git a/.gitignore b/.gitignore
index 78e65f794..9169d69e8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,8 +8,7 @@ docs/_build
 docs/debug.log
 docs/.vscode
 docs/generated
-docs/api/generated
-docs/api/autodoc_props.rst
+docs/autodoc_additional_props.rst
 
 docs/_tables
 docs/_static/example_gifs_very_old
diff --git a/docs/api/api_Maps.rst b/docs/api/api_Maps.rst
index 571f0866d..a23615243 100644
--- a/docs/api/api_Maps.rst
+++ b/docs/api/api_Maps.rst
@@ -1,10 +1,10 @@
 The :py:class:`Maps` object
 ===========================
 
-.. currentmodule:: eomaps
+.. currentmodule:: eomaps.eomaps
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: custom-class-template.rst
 
     Maps
@@ -14,13 +14,13 @@ Class Methods
 -------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.config
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.from_file.GeoTIFF
@@ -31,7 +31,7 @@ General Properties
 ------------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :nosignatures:
     :template: obj_with_attributes_no_toc.rst
 
@@ -41,7 +41,7 @@ General Properties
     Maps.crs_plot
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :nosignatures:
     :template: obj_with_attributes_no_toc.rst
 
@@ -52,14 +52,14 @@ New Maps and Layers
 -------------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.new_layer
     Maps.new_layer_from_file
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.new_map
@@ -70,14 +70,14 @@ Map Features
 ------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.add_feature
     Maps.add_wms
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.add_gdf
@@ -90,7 +90,7 @@ Map Features
 
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.add_compass
@@ -103,7 +103,7 @@ Data visualization
 ------------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.set_data
@@ -112,14 +112,14 @@ Data visualization
     Maps.set_classify_specs
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.inherit_data
     Maps.inherit_classification
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.plot_map
@@ -131,7 +131,7 @@ Layer management
 ----------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.show_layer
@@ -143,7 +143,7 @@ Figure Layout and Export
 ------------------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.set_frame
@@ -151,7 +151,7 @@ Figure Layout and Export
     Maps.snapshot
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.subplots_adjust
@@ -164,7 +164,7 @@ Map Extent
 ----------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.set_extent
@@ -177,7 +177,7 @@ Interactive Editing
 -------------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.edit_annotations
@@ -188,7 +188,7 @@ Miscellaneous
 -------------
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.fetch_companion_wms_layers
@@ -197,7 +197,7 @@ Miscellaneous
     Maps.get_crs
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.read_file.GeoTIFF
@@ -205,7 +205,7 @@ Miscellaneous
     Maps.read_file.CSV
 
 .. autosummary::
-    :toctree: generated
+    :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
 
     Maps.show
diff --git a/docs/conf.py b/docs/conf.py
index 426e8c62d..f22656aac 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -15,14 +15,24 @@ def make_feature_toctree_file():
     shapes = (f for f in dir(Maps.set_shape) if not f.startswith("_"))
     draw = (f for f in dir(Maps.draw) if not f.startswith("_"))
     wms = (f for f in dir(Maps.add_wms) if not f.startswith("_"))
+    files = (f for f in dir(Maps.from_file) if not f.startswith("_"))
+    read_files = (f for f in dir(Maps.read_file) if not f.startswith("_"))
 
     s = (
         ":orphan:\n"
-        ".. currentmodule:: eomaps\n"
+        ".. currentmodule:: eomaps.eomaps\n"
         ".. autosummary::\n"
         "    :toctree: generated\n"
         "    :nosignatures:\n"
         "    :template: obj_with_attributes_no_toc.rst\n\n"
+        "    Maps.add_wms\n"
+        "    Maps.set_shape\n"
+        "    Maps.config\n"
+        "    Maps.draw\n"
+        "    Maps.add_feature\n"
+        "    Maps.add_feature.preset\n"
+        "    Maps.add_feature.physical\n"
+        "    Maps.add_feature.cultural\n"
         + "\n".join(
             [
                 "\n".join([f"    Maps.add_feature.preset.{f}" for f in preset]),
@@ -37,11 +47,13 @@ def make_feature_toctree_file():
                 "\n".join([f"    Maps.set_shape.{f}" for f in shapes]),
                 "\n".join([f"    Maps.draw.{f}" for f in draw]),
                 "\n".join([f"    Maps.add_wms.{f}" for f in wms]),
+                "\n".join([f"    Maps.from_file.{f}" for f in files]),
+                "\n".join([f"    Maps.read_file.{f}" for f in read_files]),
             ]
         )
     )
 
-    with open(Path("api") / "autodoc_props.rst", "w") as file:
+    with open("autodoc_additional_props.rst", "w") as file:
         file.write(s)
 
 
@@ -93,10 +105,8 @@ def setup(app):
 html_theme = "sphinx_rtd_theme"
 
 
-# autosummary_generate = [
-#     str(i) for i in Path("api").iterdir() if i.is_file() and i.suffix == ".rst"
-# ]
-autosummary_generate = True
+autosummary_generate = ["full_reference.rst", "autodoc_additional_props.rst"]
+
 autodoc_default_options = {
     "member-order": "alphabetical",
 }
diff --git a/docs/full_reference.rst b/docs/full_reference.rst
new file mode 100644
index 000000000..ec10cbce9
--- /dev/null
+++ b/docs/full_reference.rst
@@ -0,0 +1,9 @@
+:orphan:
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: custom-module-template.rst
+   :recursive:
+
+   eomaps

From 029552543b76e463471fa875b081e2190453df8b Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:09:58 +0100
Subject: [PATCH 070/240] improve implementation of cb accessor

---
 eomaps/cb_container.py | 37 +++++++++----------------------------
 eomaps/eomaps.py       |  9 ++-------
 2 files changed, 11 insertions(+), 35 deletions(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index f35ce0228..2af187127 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -1960,6 +1960,11 @@ class CallbackContainer:
 
     """
 
+    pick = PickContainer
+    click = ClickContainer
+    move = MoveContainer
+    keypress = KeypressContainer
+
     def __init__(self, m):
         self._m = m
 
@@ -1972,7 +1977,7 @@ def __init__(self, m):
             "_always_active",
         }
 
-        self._click = ClickContainer(
+        self.click = self.click(
             m=self._m,
             cb_cls=ClickCallbacks,
             method="click",
@@ -1995,7 +2000,7 @@ def __init__(self, m):
             button_down=True,
         )
 
-        self._move = MoveContainer(
+        self.move = self.move(
             m=self._m,
             cb_cls=MoveCallbacks,
             method="move",
@@ -2003,13 +2008,13 @@ def __init__(self, m):
             default_button=None,
         )
 
-        self._pick = PickContainer(
+        self.pick = self.pick(
             m=self._m,
             cb_cls=PickCallbacks,
             method="pick",
         )
 
-        self._keypress = KeypressContainer(
+        self.keypress = self.keypress(
             m=self._m,
             cb_cls=KeypressCallbacks,
             method="keypress",
@@ -2038,30 +2043,6 @@ def execute_callbacks(self, val):
         """
         self._m.parent._execute_callbacks = val
 
-    @property
-    @wraps(ClickContainer)
-    def click(self):
-        """Attach click callbacks."""
-        return self._click
-
-    @property
-    @wraps(MoveContainer)
-    def move(self):
-        """Attach move callbacks."""
-        return self._move
-
-    @property
-    @wraps(PickContainer)
-    def pick(self):
-        """Attach pick callbacks."""
-        return self._pick
-
-    @property
-    @wraps(KeypressContainer)
-    def keypress(self):
-        """Attach keypress callbacks."""
-        return self._keypress
-
     def add_picker(self, name, artist, picker):
         """
         Attach a custom picker to an artist.
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index a197f5c4e..d1928a8da 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -348,6 +348,7 @@ class Maps(metaclass=_MapsMeta):
     set_shape = Shapes
     draw = ShapeDrawer
     add_feature = NaturalEarthFeatures
+    cb = CallbackContainer
 
     if WebMapContainer is not None:
         add_wms = WebMapContainer
@@ -459,7 +460,7 @@ def __init__(
 
         self._layout_editor = None
 
-        self._cb = CallbackContainer(weakref.proxy(self))  # accessor for the callbacks
+        self.cb = self.cb(weakref.proxy(self))  # accessor for the callbacks
 
         self._init_figure(ax=ax, plot_crs=crs, **kwargs)
 
@@ -672,12 +673,6 @@ def shape(self):
 
         return self._shape
 
-    @property
-    @wraps(CallbackContainer)
-    def cb(self):
-        """Accessor to attach callbacks to the map."""
-        return self._cb
-
     @property
     @wraps(Utilities)
     def util(self):

From 7974d60bef23ada0b506f616f5c4df0e1ee046c2 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:10:23 +0100
Subject: [PATCH 071/240] improve implementation of util container

---
 eomaps/eomaps.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index d1928a8da..4c582b9fc 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -348,6 +348,7 @@ class Maps(metaclass=_MapsMeta):
     set_shape = Shapes
     draw = ShapeDrawer
     add_feature = NaturalEarthFeatures
+    util = Utilities
     cb = CallbackContainer
 
     if WebMapContainer is not None:
@@ -516,6 +517,11 @@ def __init__(
         self.add_feature = self.add_feature(weakref.proxy(self))
         self.draw = self.draw(weakref.proxy(self))
 
+        if self.parent == self:
+            self.util = Utilities(self)
+        else:
+            self.util = self.parent.util
+
     def _handle_spines(self):
         spine = self.ax.spines["geo"]
         if spine not in self.BM._bg_artists.get("__SPINES__", []):
@@ -673,14 +679,6 @@ def shape(self):
 
         return self._shape
 
-    @property
-    @wraps(Utilities)
-    def util(self):
-        """Add utilities to the map."""
-        if self.parent._util is None:
-            self.parent._util = Utilities(self.parent)
-        return self.parent._util
-
     @property
     def BM(self):
         """The Blit-Manager used to dynamically update the plots."""

From 79e0e1fbbd42d9a623209f87026ca348c49df32b Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:10:32 +0100
Subject: [PATCH 072/240] minor

---
 eomaps/eomaps.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 4c582b9fc..62dda37e9 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -315,13 +315,12 @@ class Maps(metaclass=_MapsMeta):
     >>>     m.add_feature.preset.coastline()
     >>>     m.savefig(...)
 
-    Attributes
-    ----------
-    CRS : Accessor for available projections (Supercharged version of cartopy.crs)
+    Note
+    ----
 
-    CLASSIFIERS : Accessor for available classifiers (provided by mapclassify)
+    You can access possible crs via the `CRS` accessor (alias of `cartopy.crs`):
 
-    _companion_widget_key : Keyboard shortcut assigned to show/hide the companion-widget
+    >>> m = Maps(crs=Maps.CRS.Orthographic())
 
     """
 

From 53e3baefa2e3e2964d35936a34fe76d77cc2623e Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:11:11 +0100
Subject: [PATCH 073/240] update API docs

---
 .gitignore               |   2 +-
 docs/api/api_Maps.rst    | 111 ++++++++++++++++++++++-------------
 docs/api/reference.rst   | 116 ++++++++++++++++++++++++++++++++++++-
 docs/conf.py             |  65 ++++-----------------
 docs/gen_autodoc_file.py | 121 +++++++++++++++++++++++++++++++++++++++
 5 files changed, 317 insertions(+), 98 deletions(-)
 create mode 100644 docs/gen_autodoc_file.py

diff --git a/.gitignore b/.gitignore
index 9169d69e8..bcdd6463d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,7 +8,7 @@ docs/_build
 docs/debug.log
 docs/.vscode
 docs/generated
-docs/autodoc_additional_props.rst
+docs/api/autodoc_additional_props.rst
 
 docs/_tables
 docs/_static/example_gifs_very_old
diff --git a/docs/api/api_Maps.rst b/docs/api/api_Maps.rst
index a23615243..21f05e27c 100644
--- a/docs/api/api_Maps.rst
+++ b/docs/api/api_Maps.rst
@@ -1,14 +1,15 @@
-The :py:class:`Maps` object
-===========================
+:orphan:
 
-.. currentmodule:: eomaps.eomaps
+..
+  NOTE: this file is copied to ../generated/eomaps.eomaps.Maps.rst before the docs are generated (see conf.py)
+  to serve as the auto-generated file for Maps-objects!
 
-.. autosummary::
-    :toctree: ../generated
-    :template: custom-class-template.rst
+:py:class:`Maps`
+================
 
-    Maps
+.. currentmodule:: eomaps.eomaps
 
+.. autoclass:: Maps
 
 Class Methods
 -------------
@@ -16,24 +17,24 @@ Class Methods
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.config
 
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
-    Maps.from_file.GeoTIFF
-    Maps.from_file.NetCDF
-    Maps.from_file.CSV
+    Maps.from_file
 
-General Properties
-------------------
+Properties
+----------
 
 .. autosummary::
     :toctree: ../generated
-    :nosignatures:
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.f
     Maps.ax
@@ -42,18 +43,29 @@ General Properties
 
 .. autosummary::
     :toctree: ../generated
-    :nosignatures:
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.data
     Maps.colorbar
 
-New Maps and Layers
--------------------
+
+
+Layers and Maps
+---------------
+
+.. autosummary::
+    :toctree: ../generated
+    :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
+
+    Maps.new_map
+    Maps.new_inset_map
 
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.new_layer
     Maps.new_layer_from_file
@@ -61,9 +73,11 @@ New Maps and Layers
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
-    Maps.new_map
-    Maps.new_inset_map
+    Maps.show_layer
+    Maps.fetch_layers
+    Maps.on_layer_activation
 
 
 Map Features
@@ -72,6 +86,7 @@ Map Features
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.add_feature
     Maps.add_wms
@@ -79,6 +94,7 @@ Map Features
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.add_gdf
     Maps.add_annotation
@@ -92,11 +108,21 @@ Map Features
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.add_compass
     Maps.add_gridlines
     Maps.add_scalebar
 
+Callbacks
+---------
+
+.. autosummary::
+    :toctree: ../generated
+    :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
+
+    Maps.cb
 
 
 Data visualization
@@ -105,6 +131,7 @@ Data visualization
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.set_data
     Maps.set_shape
@@ -114,6 +141,7 @@ Data visualization
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.inherit_data
     Maps.inherit_classification
@@ -121,67 +149,67 @@ Data visualization
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.plot_map
     Maps.add_colorbar
     Maps.make_dataset_pickable
 
 
-Layer management
-----------------
+Map Extent
+----------
 
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
-    Maps.show_layer
-    Maps.fetch_layers
-    Maps.on_layer_activation
+    Maps.set_extent
+    Maps.get_extent
+    Maps.set_extent_to_location
+    Maps.join_limits
 
 
-Figure Layout and Export
-------------------------
+Layout and Export
+-----------------
 
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
-    Maps.set_frame
     Maps.savefig
     Maps.snapshot
 
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
+    Maps.set_frame
     Maps.subplots_adjust
-    Maps.edit_layout
-    Maps.get_layout
-    Maps.apply_layout
-
-
-Map Extent
-----------
 
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
-    Maps.set_extent
-    Maps.get_extent
-    Maps.set_extent_to_location
-    Maps.join_limits
+    Maps.edit_layout
+    Maps.get_layout
+    Maps.apply_layout
 
 
-Interactive Editing
--------------------
+Utilities
+---------
 
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
-    Maps.edit_annotations
+    Maps.util
     Maps.draw
+    Maps.edit_annotations
 
 
 Miscellaneous
@@ -190,6 +218,7 @@ Miscellaneous
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.fetch_companion_wms_layers
     Maps.refetch_wms_on_size_change
@@ -199,6 +228,7 @@ Miscellaneous
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.read_file.GeoTIFF
     Maps.read_file.NetCDF
@@ -207,6 +237,7 @@ Miscellaneous
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
 
     Maps.show
     Maps.redraw
diff --git a/docs/api/reference.rst b/docs/api/reference.rst
index 72f7641f8..4c9f93c05 100644
--- a/docs/api/reference.rst
+++ b/docs/api/reference.rst
@@ -1,5 +1,115 @@
+======
+eomaps
+======
 
-.. toctree::
-    :maxdepth: 1
+.. currentmodule:: eomaps.eomaps
 
-    api_Maps
+.. card:: The Maps object
+    :link: ../generated/eomaps.eomaps.Maps
+    :link-type: doc
+
+    :py:class:`Maps` objects are used as the primary access point to create, edit and interact with maps.
+
+    .. autosummary::
+        :toctree: ../generated
+        :template: custom-class-template.rst
+        :nosignatures:
+
+        Maps
+
+
+Feature objects
+===============
+
+.. card::
+    :shadow: none
+
+    .. card:: ColorBar
+        :link: ../generated/eomaps.colorbar.ColorBar
+        :link-type: doc
+        :margin: 0
+
+        .. currentmodule:: eomaps.colorbar
+
+        .. autosummary::
+            :toctree: ../generated
+            :template: custom-class-template.rst
+            :nosignatures:
+
+            ColorBar
+
+    +++
+
+    .. currentmodule:: eomaps.eomaps
+
+    See :py:meth:`Maps.add_colorbar` on how to add a colorbar to a map!
+
+.. card::
+    :shadow: none
+
+    .. card:: GridLines
+        :link: ../generated/eomaps.grid.GridLines
+        :link-type: doc
+        :margin: 0
+
+        .. currentmodule:: eomaps.grid
+
+        .. autosummary::
+            :toctree: ../generated
+            :template: custom-class-template.rst
+            :nosignatures:
+
+            GridLines
+
+    +++
+
+    .. currentmodule:: eomaps.eomaps
+
+    See :py:meth:`Maps.add_gridlines` on how to add grid-lines to a map!
+
+
+.. card::
+    :shadow: none
+
+    .. card:: Compass
+        :link: ../generated/eomaps.compass.Compass
+        :link-type: doc
+        :margin: 0
+
+        .. currentmodule:: eomaps.compass
+
+        .. autosummary::
+            :toctree: ../generated
+            :template: custom-class-template.rst
+            :nosignatures:
+
+            Compass
+
+    +++
+
+    .. currentmodule:: eomaps.eomaps
+
+    See :py:meth:`Maps.add_compass` on how to add a compass (or North-arrow) to a map!
+
+.. card::
+    :shadow: none
+
+    .. card:: ScaleBar
+        :link: ../generated/eomaps.scalebar.ScaleBar
+        :link-type: doc
+        :margin: 0
+
+        .. currentmodule:: eomaps.scalebar
+
+        .. autosummary::
+            :toctree: ../generated
+            :template: custom-class-template.rst
+            :nosignatures:
+
+            ScaleBar
+
+    +++
+
+    .. currentmodule:: eomaps.eomaps
+
+    See :py:meth:`Maps.add_scalebar` on how to add a scalebar to a map!
diff --git a/docs/conf.py b/docs/conf.py
index f22656aac..c20c7c5a7 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,66 +1,18 @@
 # Configuration file for the Sphinx documentation builder.
 import sys, os
-from pathlib import Path
+from eomaps import Maps
 
 sys.path.insert(0, os.path.abspath(os.path.join("..", "..")))
 sys.path.insert(0, os.path.abspath(".."))
+sys.path.insert(0, os.path.abspath("."))
 
-from eomaps import Maps
-
-
-def make_feature_toctree_file():
-    preset = (f for f in dir(Maps.add_feature.preset) if not f.startswith("_"))
-    physical = (f for f in dir(Maps.add_feature.physical) if not f.startswith("_"))
-    cultural = (f for f in dir(Maps.add_feature.cultural) if not f.startswith("_"))
-    shapes = (f for f in dir(Maps.set_shape) if not f.startswith("_"))
-    draw = (f for f in dir(Maps.draw) if not f.startswith("_"))
-    wms = (f for f in dir(Maps.add_wms) if not f.startswith("_"))
-    files = (f for f in dir(Maps.from_file) if not f.startswith("_"))
-    read_files = (f for f in dir(Maps.read_file) if not f.startswith("_"))
-
-    s = (
-        ":orphan:\n"
-        ".. currentmodule:: eomaps.eomaps\n"
-        ".. autosummary::\n"
-        "    :toctree: generated\n"
-        "    :nosignatures:\n"
-        "    :template: obj_with_attributes_no_toc.rst\n\n"
-        "    Maps.add_wms\n"
-        "    Maps.set_shape\n"
-        "    Maps.config\n"
-        "    Maps.draw\n"
-        "    Maps.add_feature\n"
-        "    Maps.add_feature.preset\n"
-        "    Maps.add_feature.physical\n"
-        "    Maps.add_feature.cultural\n"
-        + "\n".join(
-            [
-                "\n".join([f"    Maps.add_feature.preset.{f}" for f in preset]),
-                "\n".join([f"    Maps.add_feature.physical.{f}" for f in physical]),
-                "\n".join(
-                    [f"    Maps.add_feature.physical.{f}.get_gdf" for f in physical]
-                ),
-                "\n".join([f"    Maps.add_feature.cultural.{f}" for f in cultural]),
-                "\n".join(
-                    [f"    Maps.add_feature.cultural.{f}.get_gdf" for f in cultural]
-                ),
-                "\n".join([f"    Maps.set_shape.{f}" for f in shapes]),
-                "\n".join([f"    Maps.draw.{f}" for f in draw]),
-                "\n".join([f"    Maps.add_wms.{f}" for f in wms]),
-                "\n".join([f"    Maps.from_file.{f}" for f in files]),
-                "\n".join([f"    Maps.read_file.{f}" for f in read_files]),
-            ]
-        )
-    )
-
-    with open("autodoc_additional_props.rst", "w") as file:
-        file.write(s)
-
+from gen_autodoc_file import make_feature_toctree_file
 
 make_feature_toctree_file()
 
 
 def setup(app):
+
     app.add_css_file("custom_css.css")
 
     # need to assign the names here, otherwise autodoc won't document these classes,
@@ -73,6 +25,12 @@ def setup(app):
     Maps.set_shape.__name__ = "set_shape"
     Maps.draw.__name__ = "draw"
     Maps.add_wms.__name__ = "add_wms"
+    Maps.util.__name__ = "util"
+    Maps.cb.__name__ = "cb"
+    Maps.cb.click.__name__ = "click"
+    Maps.cb.pick.__name__ = "pick"
+    Maps.cb.keypress.__name__ = "keypress"
+    Maps.cb.move.__name__ = "move"
 
 
 # -- Project information
@@ -94,7 +52,6 @@ def setup(app):
     "sphinx_design",
 ]
 
-
 # -- Options for EPUB output
 epub_show_urls = "footnote"
 
@@ -105,7 +62,7 @@ def setup(app):
 html_theme = "sphinx_rtd_theme"
 
 
-autosummary_generate = ["full_reference.rst", "autodoc_additional_props.rst"]
+autosummary_generate = ["api/autodoc_additional_props.rst"]  # "full_reference.rst",
 
 autodoc_default_options = {
     "member-order": "alphabetical",
diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
new file mode 100644
index 000000000..bfe185711
--- /dev/null
+++ b/docs/gen_autodoc_file.py
@@ -0,0 +1,121 @@
+from pathlib import Path
+from operator import attrgetter
+from itertools import chain
+
+from eomaps import Maps
+from eomaps.colorbar import ColorBar
+
+
+def get_members(
+    obj, key="", with_sublevel=False, prefix="", names_only=False, exclude=[]
+):
+    """get a list of attributes of a given object"""
+    # use attrgetter to allow also nested attributes (Maps.x.y)
+    if len(key) == 0:
+        startstr = f"{prefix}{obj.__name__}"
+        members = filter(lambda x: not x.startswith("_") and not x in exclude, dir(obj))
+    else:
+        startstr = f"{prefix}{obj.__name__}.{key}"
+        members = filter(
+            lambda x: not x.startswith("_") and not x in exclude,
+            dir(attrgetter(key)(obj)),
+        )
+
+    if names_only:
+        return members
+
+    if with_sublevel:
+        return chain(
+            *(
+                (
+                    f"{startstr}.{m}",
+                    *(
+                        f"{startstr}.{m}.{f}"
+                        for f in get_members(
+                            obj, f"{key}.{m}" if key else m, names_only=True
+                        )
+                    ),
+                )
+                for m in members
+            )
+        )
+    else:
+        return (f"{startstr}.{f}" for f in members)
+
+
+def make_feature_toctree_file():
+    s = (
+        ":orphan:\n"
+        ".. currentmodule:: eomaps.eomaps\n\n"
+        ".. autosummary::\n"
+        "    :toctree: ../generated\n"
+        "    :nosignatures:\n"
+        "    :template: obj_with_attributes_no_toc.rst\n\n"
+        #        "    Maps\n"
+        "    ColorBar\n"
+        "    Maps.config\n"
+    )
+
+    s += (
+        "\n".join(get_members(Maps, "", False, "    ", exclude=["CRS", "CLASSIFIERS"]))
+        + "\n"
+    )
+
+    for key in ("set_shape", "draw", "from_file", "read_file", "util", "add_wms"):
+        s += "\n".join(get_members(Maps, key, False, "    ")) + "\n"
+
+    for key in ("add_feature", "cb"):
+        s += "\n".join(get_members(Maps, key, True, "    ")) + "\n"
+
+    s += "\n\n"
+    s += (
+        ".. currentmodule:: eomaps.colorbar\n"
+        ".. autosummary::\n"
+        "    :toctree: ../generated\n"
+        "    :nosignatures:\n"
+        "    :template: custom-class-template.rst\n\n"
+        "    ColorBar\n"
+    )
+
+    s += "\n\n"
+    s += (
+        ".. currentmodule:: eomaps.grid\n"
+        ".. autosummary::\n"
+        "    :toctree: ../generated\n"
+        "    :nosignatures:\n"
+        "    :template: custom-class-template.rst\n\n"
+        "    GridLines\n"
+        "    GridLabels\n"
+    )
+
+    s += "\n\n"
+    s += (
+        ".. currentmodule:: eomaps.compass\n"
+        ".. autosummary::\n"
+        "    :toctree: ../generated\n"
+        "    :nosignatures:\n"
+        "    :template: custom-class-template.rst\n\n"
+        "    Compass\n"
+    )
+
+    s += "\n\n"
+    s += (
+        ".. currentmodule:: eomaps.scalebar\n"
+        ".. autosummary::\n"
+        "    :toctree: ../generated\n"
+        "    :nosignatures:\n"
+        "    :template: custom-class-template.rst\n\n"
+        "    ScaleBar\n"
+    )
+
+    with open(
+        Path(__file__).parent / "api" / "autodoc_additional_props.rst", "w"
+    ) as file:
+        file.write(s)
+
+    src = Path(__file__).parent / "api" / "api_Maps.rst"
+
+    (Path(__file__).parent / "generated").mkdir(exist_ok=True)
+    dest = Path(__file__).parent / "generated" / "eomaps.eomaps.Maps.rst"
+
+    dest.write_text(src.read_text())  # for text files

From 579565aab35078412d3f12d5280453f2c9c5fc2c Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:15:26 +0100
Subject: [PATCH 074/240] fix typo

---
 eomaps/cb_container.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 2af187127..c70e01284 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -1977,7 +1977,7 @@ def __init__(self, m):
             "_always_active",
         }
 
-        self.click = self.click(
+        self.click = ClickContainer(
             m=self._m,
             cb_cls=ClickCallbacks,
             method="click",
@@ -1996,11 +1996,11 @@ def __init__(self, m):
             m=self._m,
             cb_cls=ClickCallbacks,
             method="_click_move",
-            parent_container=self._click,
+            parent_container=self.click,
             button_down=True,
         )
 
-        self.move = self.move(
+        self.move = MoveContainer(
             m=self._m,
             cb_cls=MoveCallbacks,
             method="move",
@@ -2008,13 +2008,13 @@ def __init__(self, m):
             default_button=None,
         )
 
-        self.pick = self.pick(
+        self.pick = PickContainer(
             m=self._m,
             cb_cls=PickCallbacks,
             method="pick",
         )
 
-        self.keypress = self.keypress(
+        self.keypress = KeypressContainer(
             m=self._m,
             cb_cls=KeypressCallbacks,
             method="keypress",

From 6529fe73bc14aa3d32ec9fc78ab040d9e036cc78 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:35:18 +0100
Subject: [PATCH 075/240] include package data in pyproject.toml

---
 pyproject.toml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 88795d6df..86603e60d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,6 +5,9 @@ build-backend = "setuptools.build_meta"
 [tool.setuptools.packages.find]
 include = ["eomaps"]
 
+[tool.setuptools.package-data]
+mypkg = ["NE_features.json", "logo.png"]
+
 [project]
 name = "eomaps"
 version = "8.0"

From 81bcbfe854127078ad1622bd6f105b8a3ce2c557 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:39:52 +0100
Subject: [PATCH 076/240] fix including package data in pyproject.toml

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 86603e60d..90031c311 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,8 @@ build-backend = "setuptools.build_meta"
 include = ["eomaps"]
 
 [tool.setuptools.package-data]
-mypkg = ["NE_features.json", "logo.png"]
+eomaps = ["logo.png", "NE_features.json", "qtcompanion/icons/*"]
+
 
 [project]
 name = "eomaps"

From 7d40ab30f91eea5a688788f1ff33b95551fcd04e Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 11 Jan 2024 22:41:16 +0100
Subject: [PATCH 077/240] update pip-workflow to use pyproject.toml

---
 .github/workflows/Publish_to_PyPi.yml | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/Publish_to_PyPi.yml b/.github/workflows/Publish_to_PyPi.yml
index 340801dfe..d828baafb 100644
--- a/.github/workflows/Publish_to_PyPi.yml
+++ b/.github/workflows/Publish_to_PyPi.yml
@@ -16,19 +16,16 @@ jobs:
       # Sets up python3
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.11
 
       - name: Upgrade pip, install setuptools, wheel and twine
         run: |
-          # Upgrade pip
-          python -m pip install --upgrade pip
-          # Install build dependencies
+          python -m pip install --upgrade pip build
           python -m pip install setuptools wheel twine
 
-      # Upload to TestPyPI
       - name: Build and Upload to PyPI
         run: |
-          python setup.py sdist bdist_wheel
+          python -m build
           python -m twine upload dist/*
         env:
           TWINE_USERNAME: __token__

From 019f8c08934d28840a1b78404afe01d859762839 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 12 Jan 2024 00:36:58 +0100
Subject: [PATCH 078/240] update cross-references for ne-features

---
 docs/notebooks/naturalearth_features.ipynb | 3086 ++++++++++----------
 1 file changed, 1546 insertions(+), 1540 deletions(-)

diff --git a/docs/notebooks/naturalearth_features.ipynb b/docs/notebooks/naturalearth_features.ipynb
index cb89be0f5..498d61255 100644
--- a/docs/notebooks/naturalearth_features.ipynb
+++ b/docs/notebooks/naturalearth_features.ipynb
@@ -65,20 +65,19 @@
     "## Preset Features\n",
     "For the most commonly used features, style-presets are available:\n",
     "\n",
-    "\n",
     "```{eval-rst}\n",
-    ".. currentmodule:: eomaps.ne_features.NaturalEarthPresets\n",
+    ".. currentmodule:: eomaps.eomaps.Maps\n",
     "\n",
     ".. autosummary::\n",
     "    :nosignatures:\n",
     "\n",
-    "    coastline\n",
-    "    ocean\n",
-    "    land\n",
-    "    countries\n",
-    "    urban_areas\n",
-    "    lakes\n",
-    "    rivers_lake_centerlines\n",
+    "    add_feature.preset.coastline\n",
+    "    add_feature.preset.ocean\n",
+    "    add_feature.preset.land\n",
+    "    add_feature.preset.countries\n",
+    "    add_feature.preset.urban_areas\n",
+    "    add_feature.preset.lakes\n",
+    "    add_feature.preset.rivers_lake_centerlines\n",
     "\n",
     "```\n",
     "\n",
@@ -377,1564 +376,1564 @@
      "data": {
       "text/html": [
        "<div style='width: 100%; height: 40ex; overflow: auto'><style type=\"text/css\">\n",
-       "#T_f91f0 tr {\n",
+       "#T_e05f6 tr {\n",
        "  font-size: 8pt;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_f91f0\">\n",
+       "<table id=\"T_e05f6\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_f91f0_level0_col0\" class=\"col_heading level0 col0\" >SCALERANK</th>\n",
-       "      <th id=\"T_f91f0_level0_col1\" class=\"col_heading level0 col1\" >NATSCALE</th>\n",
-       "      <th id=\"T_f91f0_level0_col2\" class=\"col_heading level0 col2\" >LABELRANK</th>\n",
-       "      <th id=\"T_f91f0_level0_col3\" class=\"col_heading level0 col3\" >FEATURECLA</th>\n",
-       "      <th id=\"T_f91f0_level0_col4\" class=\"col_heading level0 col4\" >NAME</th>\n",
-       "      <th id=\"T_f91f0_level0_col5\" class=\"col_heading level0 col5\" >NAMEPAR</th>\n",
-       "      <th id=\"T_f91f0_level0_col6\" class=\"col_heading level0 col6\" >NAMEALT</th>\n",
-       "      <th id=\"T_f91f0_level0_col7\" class=\"col_heading level0 col7\" >NAMEASCII</th>\n",
-       "      <th id=\"T_f91f0_level0_col8\" class=\"col_heading level0 col8\" >ADM0CAP</th>\n",
-       "      <th id=\"T_f91f0_level0_col9\" class=\"col_heading level0 col9\" >CAPIN</th>\n",
-       "      <th id=\"T_f91f0_level0_col10\" class=\"col_heading level0 col10\" >WORLDCITY</th>\n",
-       "      <th id=\"T_f91f0_level0_col11\" class=\"col_heading level0 col11\" >MEGACITY</th>\n",
-       "      <th id=\"T_f91f0_level0_col12\" class=\"col_heading level0 col12\" >SOV0NAME</th>\n",
-       "      <th id=\"T_f91f0_level0_col13\" class=\"col_heading level0 col13\" >SOV_A3</th>\n",
-       "      <th id=\"T_f91f0_level0_col14\" class=\"col_heading level0 col14\" >ADM0NAME</th>\n",
-       "      <th id=\"T_f91f0_level0_col15\" class=\"col_heading level0 col15\" >ADM0_A3</th>\n",
-       "      <th id=\"T_f91f0_level0_col16\" class=\"col_heading level0 col16\" >ADM1NAME</th>\n",
-       "      <th id=\"T_f91f0_level0_col17\" class=\"col_heading level0 col17\" >ISO_A2</th>\n",
-       "      <th id=\"T_f91f0_level0_col18\" class=\"col_heading level0 col18\" >NOTE</th>\n",
-       "      <th id=\"T_f91f0_level0_col19\" class=\"col_heading level0 col19\" >LATITUDE</th>\n",
-       "      <th id=\"T_f91f0_level0_col20\" class=\"col_heading level0 col20\" >LONGITUDE</th>\n",
-       "      <th id=\"T_f91f0_level0_col21\" class=\"col_heading level0 col21\" >POP_MAX</th>\n",
-       "      <th id=\"T_f91f0_level0_col22\" class=\"col_heading level0 col22\" >POP_MIN</th>\n",
-       "      <th id=\"T_f91f0_level0_col23\" class=\"col_heading level0 col23\" >POP_OTHER</th>\n",
-       "      <th id=\"T_f91f0_level0_col24\" class=\"col_heading level0 col24\" >RANK_MAX</th>\n",
-       "      <th id=\"T_f91f0_level0_col25\" class=\"col_heading level0 col25\" >RANK_MIN</th>\n",
-       "      <th id=\"T_f91f0_level0_col26\" class=\"col_heading level0 col26\" >MEGANAME</th>\n",
-       "      <th id=\"T_f91f0_level0_col27\" class=\"col_heading level0 col27\" >LS_NAME</th>\n",
-       "      <th id=\"T_f91f0_level0_col28\" class=\"col_heading level0 col28\" >MAX_POP10</th>\n",
-       "      <th id=\"T_f91f0_level0_col29\" class=\"col_heading level0 col29\" >MAX_POP20</th>\n",
-       "      <th id=\"T_f91f0_level0_col30\" class=\"col_heading level0 col30\" >MAX_POP50</th>\n",
-       "      <th id=\"T_f91f0_level0_col31\" class=\"col_heading level0 col31\" >MAX_POP300</th>\n",
-       "      <th id=\"T_f91f0_level0_col32\" class=\"col_heading level0 col32\" >MAX_POP310</th>\n",
-       "      <th id=\"T_f91f0_level0_col33\" class=\"col_heading level0 col33\" >MAX_NATSCA</th>\n",
-       "      <th id=\"T_f91f0_level0_col34\" class=\"col_heading level0 col34\" >MIN_AREAKM</th>\n",
-       "      <th id=\"T_f91f0_level0_col35\" class=\"col_heading level0 col35\" >MAX_AREAKM</th>\n",
-       "      <th id=\"T_f91f0_level0_col36\" class=\"col_heading level0 col36\" >MIN_AREAMI</th>\n",
-       "      <th id=\"T_f91f0_level0_col37\" class=\"col_heading level0 col37\" >MAX_AREAMI</th>\n",
-       "      <th id=\"T_f91f0_level0_col38\" class=\"col_heading level0 col38\" >MIN_PERKM</th>\n",
-       "      <th id=\"T_f91f0_level0_col39\" class=\"col_heading level0 col39\" >MAX_PERKM</th>\n",
-       "      <th id=\"T_f91f0_level0_col40\" class=\"col_heading level0 col40\" >MIN_PERMI</th>\n",
-       "      <th id=\"T_f91f0_level0_col41\" class=\"col_heading level0 col41\" >MAX_PERMI</th>\n",
-       "      <th id=\"T_f91f0_level0_col42\" class=\"col_heading level0 col42\" >MIN_BBXMIN</th>\n",
-       "      <th id=\"T_f91f0_level0_col43\" class=\"col_heading level0 col43\" >MAX_BBXMIN</th>\n",
-       "      <th id=\"T_f91f0_level0_col44\" class=\"col_heading level0 col44\" >MIN_BBXMAX</th>\n",
-       "      <th id=\"T_f91f0_level0_col45\" class=\"col_heading level0 col45\" >MAX_BBXMAX</th>\n",
-       "      <th id=\"T_f91f0_level0_col46\" class=\"col_heading level0 col46\" >MIN_BBYMIN</th>\n",
-       "      <th id=\"T_f91f0_level0_col47\" class=\"col_heading level0 col47\" >MAX_BBYMIN</th>\n",
-       "      <th id=\"T_f91f0_level0_col48\" class=\"col_heading level0 col48\" >MIN_BBYMAX</th>\n",
-       "      <th id=\"T_f91f0_level0_col49\" class=\"col_heading level0 col49\" >MAX_BBYMAX</th>\n",
-       "      <th id=\"T_f91f0_level0_col50\" class=\"col_heading level0 col50\" >MEAN_BBXC</th>\n",
-       "      <th id=\"T_f91f0_level0_col51\" class=\"col_heading level0 col51\" >MEAN_BBYC</th>\n",
-       "      <th id=\"T_f91f0_level0_col52\" class=\"col_heading level0 col52\" >TIMEZONE</th>\n",
-       "      <th id=\"T_f91f0_level0_col53\" class=\"col_heading level0 col53\" >UN_FID</th>\n",
-       "      <th id=\"T_f91f0_level0_col54\" class=\"col_heading level0 col54\" >POP1950</th>\n",
-       "      <th id=\"T_f91f0_level0_col55\" class=\"col_heading level0 col55\" >POP1955</th>\n",
-       "      <th id=\"T_f91f0_level0_col56\" class=\"col_heading level0 col56\" >POP1960</th>\n",
-       "      <th id=\"T_f91f0_level0_col57\" class=\"col_heading level0 col57\" >POP1965</th>\n",
-       "      <th id=\"T_f91f0_level0_col58\" class=\"col_heading level0 col58\" >POP1970</th>\n",
-       "      <th id=\"T_f91f0_level0_col59\" class=\"col_heading level0 col59\" >POP1975</th>\n",
-       "      <th id=\"T_f91f0_level0_col60\" class=\"col_heading level0 col60\" >POP1980</th>\n",
-       "      <th id=\"T_f91f0_level0_col61\" class=\"col_heading level0 col61\" >POP1985</th>\n",
-       "      <th id=\"T_f91f0_level0_col62\" class=\"col_heading level0 col62\" >POP1990</th>\n",
-       "      <th id=\"T_f91f0_level0_col63\" class=\"col_heading level0 col63\" >POP1995</th>\n",
-       "      <th id=\"T_f91f0_level0_col64\" class=\"col_heading level0 col64\" >POP2000</th>\n",
-       "      <th id=\"T_f91f0_level0_col65\" class=\"col_heading level0 col65\" >POP2005</th>\n",
-       "      <th id=\"T_f91f0_level0_col66\" class=\"col_heading level0 col66\" >POP2010</th>\n",
-       "      <th id=\"T_f91f0_level0_col67\" class=\"col_heading level0 col67\" >POP2015</th>\n",
-       "      <th id=\"T_f91f0_level0_col68\" class=\"col_heading level0 col68\" >POP2020</th>\n",
-       "      <th id=\"T_f91f0_level0_col69\" class=\"col_heading level0 col69\" >POP2025</th>\n",
-       "      <th id=\"T_f91f0_level0_col70\" class=\"col_heading level0 col70\" >POP2050</th>\n",
-       "      <th id=\"T_f91f0_level0_col71\" class=\"col_heading level0 col71\" >MIN_ZOOM</th>\n",
-       "      <th id=\"T_f91f0_level0_col72\" class=\"col_heading level0 col72\" >WIKIDATAID</th>\n",
-       "      <th id=\"T_f91f0_level0_col73\" class=\"col_heading level0 col73\" >WOF_ID</th>\n",
-       "      <th id=\"T_f91f0_level0_col74\" class=\"col_heading level0 col74\" >CAPALT</th>\n",
-       "      <th id=\"T_f91f0_level0_col75\" class=\"col_heading level0 col75\" >NAME_EN</th>\n",
-       "      <th id=\"T_f91f0_level0_col76\" class=\"col_heading level0 col76\" >NAME_DE</th>\n",
-       "      <th id=\"T_f91f0_level0_col77\" class=\"col_heading level0 col77\" >NAME_ES</th>\n",
-       "      <th id=\"T_f91f0_level0_col78\" class=\"col_heading level0 col78\" >NAME_FR</th>\n",
-       "      <th id=\"T_f91f0_level0_col79\" class=\"col_heading level0 col79\" >NAME_PT</th>\n",
-       "      <th id=\"T_f91f0_level0_col80\" class=\"col_heading level0 col80\" >NAME_RU</th>\n",
-       "      <th id=\"T_f91f0_level0_col81\" class=\"col_heading level0 col81\" >NAME_ZH</th>\n",
-       "      <th id=\"T_f91f0_level0_col82\" class=\"col_heading level0 col82\" >LABEL</th>\n",
-       "      <th id=\"T_f91f0_level0_col83\" class=\"col_heading level0 col83\" >NAME_AR</th>\n",
-       "      <th id=\"T_f91f0_level0_col84\" class=\"col_heading level0 col84\" >NAME_BN</th>\n",
-       "      <th id=\"T_f91f0_level0_col85\" class=\"col_heading level0 col85\" >NAME_EL</th>\n",
-       "      <th id=\"T_f91f0_level0_col86\" class=\"col_heading level0 col86\" >NAME_HI</th>\n",
-       "      <th id=\"T_f91f0_level0_col87\" class=\"col_heading level0 col87\" >NAME_HU</th>\n",
-       "      <th id=\"T_f91f0_level0_col88\" class=\"col_heading level0 col88\" >NAME_ID</th>\n",
-       "      <th id=\"T_f91f0_level0_col89\" class=\"col_heading level0 col89\" >NAME_IT</th>\n",
-       "      <th id=\"T_f91f0_level0_col90\" class=\"col_heading level0 col90\" >NAME_JA</th>\n",
-       "      <th id=\"T_f91f0_level0_col91\" class=\"col_heading level0 col91\" >NAME_KO</th>\n",
-       "      <th id=\"T_f91f0_level0_col92\" class=\"col_heading level0 col92\" >NAME_NL</th>\n",
-       "      <th id=\"T_f91f0_level0_col93\" class=\"col_heading level0 col93\" >NAME_PL</th>\n",
-       "      <th id=\"T_f91f0_level0_col94\" class=\"col_heading level0 col94\" >NAME_SV</th>\n",
-       "      <th id=\"T_f91f0_level0_col95\" class=\"col_heading level0 col95\" >NAME_TR</th>\n",
-       "      <th id=\"T_f91f0_level0_col96\" class=\"col_heading level0 col96\" >NAME_VI</th>\n",
-       "      <th id=\"T_f91f0_level0_col97\" class=\"col_heading level0 col97\" >NE_ID</th>\n",
-       "      <th id=\"T_f91f0_level0_col98\" class=\"col_heading level0 col98\" >NAME_FA</th>\n",
-       "      <th id=\"T_f91f0_level0_col99\" class=\"col_heading level0 col99\" >NAME_HE</th>\n",
-       "      <th id=\"T_f91f0_level0_col100\" class=\"col_heading level0 col100\" >NAME_UK</th>\n",
-       "      <th id=\"T_f91f0_level0_col101\" class=\"col_heading level0 col101\" >NAME_UR</th>\n",
-       "      <th id=\"T_f91f0_level0_col102\" class=\"col_heading level0 col102\" >NAME_ZHT</th>\n",
-       "      <th id=\"T_f91f0_level0_col103\" class=\"col_heading level0 col103\" >GEONAMESID</th>\n",
-       "      <th id=\"T_f91f0_level0_col104\" class=\"col_heading level0 col104\" >FCLASS_ISO</th>\n",
-       "      <th id=\"T_f91f0_level0_col105\" class=\"col_heading level0 col105\" >FCLASS_US</th>\n",
-       "      <th id=\"T_f91f0_level0_col106\" class=\"col_heading level0 col106\" >FCLASS_FR</th>\n",
-       "      <th id=\"T_f91f0_level0_col107\" class=\"col_heading level0 col107\" >FCLASS_RU</th>\n",
-       "      <th id=\"T_f91f0_level0_col108\" class=\"col_heading level0 col108\" >FCLASS_ES</th>\n",
-       "      <th id=\"T_f91f0_level0_col109\" class=\"col_heading level0 col109\" >FCLASS_CN</th>\n",
-       "      <th id=\"T_f91f0_level0_col110\" class=\"col_heading level0 col110\" >FCLASS_TW</th>\n",
-       "      <th id=\"T_f91f0_level0_col111\" class=\"col_heading level0 col111\" >FCLASS_IN</th>\n",
-       "      <th id=\"T_f91f0_level0_col112\" class=\"col_heading level0 col112\" >FCLASS_NP</th>\n",
-       "      <th id=\"T_f91f0_level0_col113\" class=\"col_heading level0 col113\" >FCLASS_PK</th>\n",
-       "      <th id=\"T_f91f0_level0_col114\" class=\"col_heading level0 col114\" >FCLASS_DE</th>\n",
-       "      <th id=\"T_f91f0_level0_col115\" class=\"col_heading level0 col115\" >FCLASS_GB</th>\n",
-       "      <th id=\"T_f91f0_level0_col116\" class=\"col_heading level0 col116\" >FCLASS_BR</th>\n",
-       "      <th id=\"T_f91f0_level0_col117\" class=\"col_heading level0 col117\" >FCLASS_IL</th>\n",
-       "      <th id=\"T_f91f0_level0_col118\" class=\"col_heading level0 col118\" >FCLASS_PS</th>\n",
-       "      <th id=\"T_f91f0_level0_col119\" class=\"col_heading level0 col119\" >FCLASS_SA</th>\n",
-       "      <th id=\"T_f91f0_level0_col120\" class=\"col_heading level0 col120\" >FCLASS_EG</th>\n",
-       "      <th id=\"T_f91f0_level0_col121\" class=\"col_heading level0 col121\" >FCLASS_MA</th>\n",
-       "      <th id=\"T_f91f0_level0_col122\" class=\"col_heading level0 col122\" >FCLASS_PT</th>\n",
-       "      <th id=\"T_f91f0_level0_col123\" class=\"col_heading level0 col123\" >FCLASS_AR</th>\n",
-       "      <th id=\"T_f91f0_level0_col124\" class=\"col_heading level0 col124\" >FCLASS_JP</th>\n",
-       "      <th id=\"T_f91f0_level0_col125\" class=\"col_heading level0 col125\" >FCLASS_KO</th>\n",
-       "      <th id=\"T_f91f0_level0_col126\" class=\"col_heading level0 col126\" >FCLASS_VN</th>\n",
-       "      <th id=\"T_f91f0_level0_col127\" class=\"col_heading level0 col127\" >FCLASS_TR</th>\n",
-       "      <th id=\"T_f91f0_level0_col128\" class=\"col_heading level0 col128\" >FCLASS_ID</th>\n",
-       "      <th id=\"T_f91f0_level0_col129\" class=\"col_heading level0 col129\" >FCLASS_PL</th>\n",
-       "      <th id=\"T_f91f0_level0_col130\" class=\"col_heading level0 col130\" >FCLASS_GR</th>\n",
-       "      <th id=\"T_f91f0_level0_col131\" class=\"col_heading level0 col131\" >FCLASS_IT</th>\n",
-       "      <th id=\"T_f91f0_level0_col132\" class=\"col_heading level0 col132\" >FCLASS_NL</th>\n",
-       "      <th id=\"T_f91f0_level0_col133\" class=\"col_heading level0 col133\" >FCLASS_SE</th>\n",
-       "      <th id=\"T_f91f0_level0_col134\" class=\"col_heading level0 col134\" >FCLASS_BD</th>\n",
-       "      <th id=\"T_f91f0_level0_col135\" class=\"col_heading level0 col135\" >FCLASS_UA</th>\n",
-       "      <th id=\"T_f91f0_level0_col136\" class=\"col_heading level0 col136\" >FCLASS_TLC</th>\n",
-       "      <th id=\"T_f91f0_level0_col137\" class=\"col_heading level0 col137\" >geometry</th>\n",
+       "      <th id=\"T_e05f6_level0_col0\" class=\"col_heading level0 col0\" >SCALERANK</th>\n",
+       "      <th id=\"T_e05f6_level0_col1\" class=\"col_heading level0 col1\" >NATSCALE</th>\n",
+       "      <th id=\"T_e05f6_level0_col2\" class=\"col_heading level0 col2\" >LABELRANK</th>\n",
+       "      <th id=\"T_e05f6_level0_col3\" class=\"col_heading level0 col3\" >FEATURECLA</th>\n",
+       "      <th id=\"T_e05f6_level0_col4\" class=\"col_heading level0 col4\" >NAME</th>\n",
+       "      <th id=\"T_e05f6_level0_col5\" class=\"col_heading level0 col5\" >NAMEPAR</th>\n",
+       "      <th id=\"T_e05f6_level0_col6\" class=\"col_heading level0 col6\" >NAMEALT</th>\n",
+       "      <th id=\"T_e05f6_level0_col7\" class=\"col_heading level0 col7\" >NAMEASCII</th>\n",
+       "      <th id=\"T_e05f6_level0_col8\" class=\"col_heading level0 col8\" >ADM0CAP</th>\n",
+       "      <th id=\"T_e05f6_level0_col9\" class=\"col_heading level0 col9\" >CAPIN</th>\n",
+       "      <th id=\"T_e05f6_level0_col10\" class=\"col_heading level0 col10\" >WORLDCITY</th>\n",
+       "      <th id=\"T_e05f6_level0_col11\" class=\"col_heading level0 col11\" >MEGACITY</th>\n",
+       "      <th id=\"T_e05f6_level0_col12\" class=\"col_heading level0 col12\" >SOV0NAME</th>\n",
+       "      <th id=\"T_e05f6_level0_col13\" class=\"col_heading level0 col13\" >SOV_A3</th>\n",
+       "      <th id=\"T_e05f6_level0_col14\" class=\"col_heading level0 col14\" >ADM0NAME</th>\n",
+       "      <th id=\"T_e05f6_level0_col15\" class=\"col_heading level0 col15\" >ADM0_A3</th>\n",
+       "      <th id=\"T_e05f6_level0_col16\" class=\"col_heading level0 col16\" >ADM1NAME</th>\n",
+       "      <th id=\"T_e05f6_level0_col17\" class=\"col_heading level0 col17\" >ISO_A2</th>\n",
+       "      <th id=\"T_e05f6_level0_col18\" class=\"col_heading level0 col18\" >NOTE</th>\n",
+       "      <th id=\"T_e05f6_level0_col19\" class=\"col_heading level0 col19\" >LATITUDE</th>\n",
+       "      <th id=\"T_e05f6_level0_col20\" class=\"col_heading level0 col20\" >LONGITUDE</th>\n",
+       "      <th id=\"T_e05f6_level0_col21\" class=\"col_heading level0 col21\" >POP_MAX</th>\n",
+       "      <th id=\"T_e05f6_level0_col22\" class=\"col_heading level0 col22\" >POP_MIN</th>\n",
+       "      <th id=\"T_e05f6_level0_col23\" class=\"col_heading level0 col23\" >POP_OTHER</th>\n",
+       "      <th id=\"T_e05f6_level0_col24\" class=\"col_heading level0 col24\" >RANK_MAX</th>\n",
+       "      <th id=\"T_e05f6_level0_col25\" class=\"col_heading level0 col25\" >RANK_MIN</th>\n",
+       "      <th id=\"T_e05f6_level0_col26\" class=\"col_heading level0 col26\" >MEGANAME</th>\n",
+       "      <th id=\"T_e05f6_level0_col27\" class=\"col_heading level0 col27\" >LS_NAME</th>\n",
+       "      <th id=\"T_e05f6_level0_col28\" class=\"col_heading level0 col28\" >MAX_POP10</th>\n",
+       "      <th id=\"T_e05f6_level0_col29\" class=\"col_heading level0 col29\" >MAX_POP20</th>\n",
+       "      <th id=\"T_e05f6_level0_col30\" class=\"col_heading level0 col30\" >MAX_POP50</th>\n",
+       "      <th id=\"T_e05f6_level0_col31\" class=\"col_heading level0 col31\" >MAX_POP300</th>\n",
+       "      <th id=\"T_e05f6_level0_col32\" class=\"col_heading level0 col32\" >MAX_POP310</th>\n",
+       "      <th id=\"T_e05f6_level0_col33\" class=\"col_heading level0 col33\" >MAX_NATSCA</th>\n",
+       "      <th id=\"T_e05f6_level0_col34\" class=\"col_heading level0 col34\" >MIN_AREAKM</th>\n",
+       "      <th id=\"T_e05f6_level0_col35\" class=\"col_heading level0 col35\" >MAX_AREAKM</th>\n",
+       "      <th id=\"T_e05f6_level0_col36\" class=\"col_heading level0 col36\" >MIN_AREAMI</th>\n",
+       "      <th id=\"T_e05f6_level0_col37\" class=\"col_heading level0 col37\" >MAX_AREAMI</th>\n",
+       "      <th id=\"T_e05f6_level0_col38\" class=\"col_heading level0 col38\" >MIN_PERKM</th>\n",
+       "      <th id=\"T_e05f6_level0_col39\" class=\"col_heading level0 col39\" >MAX_PERKM</th>\n",
+       "      <th id=\"T_e05f6_level0_col40\" class=\"col_heading level0 col40\" >MIN_PERMI</th>\n",
+       "      <th id=\"T_e05f6_level0_col41\" class=\"col_heading level0 col41\" >MAX_PERMI</th>\n",
+       "      <th id=\"T_e05f6_level0_col42\" class=\"col_heading level0 col42\" >MIN_BBXMIN</th>\n",
+       "      <th id=\"T_e05f6_level0_col43\" class=\"col_heading level0 col43\" >MAX_BBXMIN</th>\n",
+       "      <th id=\"T_e05f6_level0_col44\" class=\"col_heading level0 col44\" >MIN_BBXMAX</th>\n",
+       "      <th id=\"T_e05f6_level0_col45\" class=\"col_heading level0 col45\" >MAX_BBXMAX</th>\n",
+       "      <th id=\"T_e05f6_level0_col46\" class=\"col_heading level0 col46\" >MIN_BBYMIN</th>\n",
+       "      <th id=\"T_e05f6_level0_col47\" class=\"col_heading level0 col47\" >MAX_BBYMIN</th>\n",
+       "      <th id=\"T_e05f6_level0_col48\" class=\"col_heading level0 col48\" >MIN_BBYMAX</th>\n",
+       "      <th id=\"T_e05f6_level0_col49\" class=\"col_heading level0 col49\" >MAX_BBYMAX</th>\n",
+       "      <th id=\"T_e05f6_level0_col50\" class=\"col_heading level0 col50\" >MEAN_BBXC</th>\n",
+       "      <th id=\"T_e05f6_level0_col51\" class=\"col_heading level0 col51\" >MEAN_BBYC</th>\n",
+       "      <th id=\"T_e05f6_level0_col52\" class=\"col_heading level0 col52\" >TIMEZONE</th>\n",
+       "      <th id=\"T_e05f6_level0_col53\" class=\"col_heading level0 col53\" >UN_FID</th>\n",
+       "      <th id=\"T_e05f6_level0_col54\" class=\"col_heading level0 col54\" >POP1950</th>\n",
+       "      <th id=\"T_e05f6_level0_col55\" class=\"col_heading level0 col55\" >POP1955</th>\n",
+       "      <th id=\"T_e05f6_level0_col56\" class=\"col_heading level0 col56\" >POP1960</th>\n",
+       "      <th id=\"T_e05f6_level0_col57\" class=\"col_heading level0 col57\" >POP1965</th>\n",
+       "      <th id=\"T_e05f6_level0_col58\" class=\"col_heading level0 col58\" >POP1970</th>\n",
+       "      <th id=\"T_e05f6_level0_col59\" class=\"col_heading level0 col59\" >POP1975</th>\n",
+       "      <th id=\"T_e05f6_level0_col60\" class=\"col_heading level0 col60\" >POP1980</th>\n",
+       "      <th id=\"T_e05f6_level0_col61\" class=\"col_heading level0 col61\" >POP1985</th>\n",
+       "      <th id=\"T_e05f6_level0_col62\" class=\"col_heading level0 col62\" >POP1990</th>\n",
+       "      <th id=\"T_e05f6_level0_col63\" class=\"col_heading level0 col63\" >POP1995</th>\n",
+       "      <th id=\"T_e05f6_level0_col64\" class=\"col_heading level0 col64\" >POP2000</th>\n",
+       "      <th id=\"T_e05f6_level0_col65\" class=\"col_heading level0 col65\" >POP2005</th>\n",
+       "      <th id=\"T_e05f6_level0_col66\" class=\"col_heading level0 col66\" >POP2010</th>\n",
+       "      <th id=\"T_e05f6_level0_col67\" class=\"col_heading level0 col67\" >POP2015</th>\n",
+       "      <th id=\"T_e05f6_level0_col68\" class=\"col_heading level0 col68\" >POP2020</th>\n",
+       "      <th id=\"T_e05f6_level0_col69\" class=\"col_heading level0 col69\" >POP2025</th>\n",
+       "      <th id=\"T_e05f6_level0_col70\" class=\"col_heading level0 col70\" >POP2050</th>\n",
+       "      <th id=\"T_e05f6_level0_col71\" class=\"col_heading level0 col71\" >MIN_ZOOM</th>\n",
+       "      <th id=\"T_e05f6_level0_col72\" class=\"col_heading level0 col72\" >WIKIDATAID</th>\n",
+       "      <th id=\"T_e05f6_level0_col73\" class=\"col_heading level0 col73\" >WOF_ID</th>\n",
+       "      <th id=\"T_e05f6_level0_col74\" class=\"col_heading level0 col74\" >CAPALT</th>\n",
+       "      <th id=\"T_e05f6_level0_col75\" class=\"col_heading level0 col75\" >NAME_EN</th>\n",
+       "      <th id=\"T_e05f6_level0_col76\" class=\"col_heading level0 col76\" >NAME_DE</th>\n",
+       "      <th id=\"T_e05f6_level0_col77\" class=\"col_heading level0 col77\" >NAME_ES</th>\n",
+       "      <th id=\"T_e05f6_level0_col78\" class=\"col_heading level0 col78\" >NAME_FR</th>\n",
+       "      <th id=\"T_e05f6_level0_col79\" class=\"col_heading level0 col79\" >NAME_PT</th>\n",
+       "      <th id=\"T_e05f6_level0_col80\" class=\"col_heading level0 col80\" >NAME_RU</th>\n",
+       "      <th id=\"T_e05f6_level0_col81\" class=\"col_heading level0 col81\" >NAME_ZH</th>\n",
+       "      <th id=\"T_e05f6_level0_col82\" class=\"col_heading level0 col82\" >LABEL</th>\n",
+       "      <th id=\"T_e05f6_level0_col83\" class=\"col_heading level0 col83\" >NAME_AR</th>\n",
+       "      <th id=\"T_e05f6_level0_col84\" class=\"col_heading level0 col84\" >NAME_BN</th>\n",
+       "      <th id=\"T_e05f6_level0_col85\" class=\"col_heading level0 col85\" >NAME_EL</th>\n",
+       "      <th id=\"T_e05f6_level0_col86\" class=\"col_heading level0 col86\" >NAME_HI</th>\n",
+       "      <th id=\"T_e05f6_level0_col87\" class=\"col_heading level0 col87\" >NAME_HU</th>\n",
+       "      <th id=\"T_e05f6_level0_col88\" class=\"col_heading level0 col88\" >NAME_ID</th>\n",
+       "      <th id=\"T_e05f6_level0_col89\" class=\"col_heading level0 col89\" >NAME_IT</th>\n",
+       "      <th id=\"T_e05f6_level0_col90\" class=\"col_heading level0 col90\" >NAME_JA</th>\n",
+       "      <th id=\"T_e05f6_level0_col91\" class=\"col_heading level0 col91\" >NAME_KO</th>\n",
+       "      <th id=\"T_e05f6_level0_col92\" class=\"col_heading level0 col92\" >NAME_NL</th>\n",
+       "      <th id=\"T_e05f6_level0_col93\" class=\"col_heading level0 col93\" >NAME_PL</th>\n",
+       "      <th id=\"T_e05f6_level0_col94\" class=\"col_heading level0 col94\" >NAME_SV</th>\n",
+       "      <th id=\"T_e05f6_level0_col95\" class=\"col_heading level0 col95\" >NAME_TR</th>\n",
+       "      <th id=\"T_e05f6_level0_col96\" class=\"col_heading level0 col96\" >NAME_VI</th>\n",
+       "      <th id=\"T_e05f6_level0_col97\" class=\"col_heading level0 col97\" >NE_ID</th>\n",
+       "      <th id=\"T_e05f6_level0_col98\" class=\"col_heading level0 col98\" >NAME_FA</th>\n",
+       "      <th id=\"T_e05f6_level0_col99\" class=\"col_heading level0 col99\" >NAME_HE</th>\n",
+       "      <th id=\"T_e05f6_level0_col100\" class=\"col_heading level0 col100\" >NAME_UK</th>\n",
+       "      <th id=\"T_e05f6_level0_col101\" class=\"col_heading level0 col101\" >NAME_UR</th>\n",
+       "      <th id=\"T_e05f6_level0_col102\" class=\"col_heading level0 col102\" >NAME_ZHT</th>\n",
+       "      <th id=\"T_e05f6_level0_col103\" class=\"col_heading level0 col103\" >GEONAMESID</th>\n",
+       "      <th id=\"T_e05f6_level0_col104\" class=\"col_heading level0 col104\" >FCLASS_ISO</th>\n",
+       "      <th id=\"T_e05f6_level0_col105\" class=\"col_heading level0 col105\" >FCLASS_US</th>\n",
+       "      <th id=\"T_e05f6_level0_col106\" class=\"col_heading level0 col106\" >FCLASS_FR</th>\n",
+       "      <th id=\"T_e05f6_level0_col107\" class=\"col_heading level0 col107\" >FCLASS_RU</th>\n",
+       "      <th id=\"T_e05f6_level0_col108\" class=\"col_heading level0 col108\" >FCLASS_ES</th>\n",
+       "      <th id=\"T_e05f6_level0_col109\" class=\"col_heading level0 col109\" >FCLASS_CN</th>\n",
+       "      <th id=\"T_e05f6_level0_col110\" class=\"col_heading level0 col110\" >FCLASS_TW</th>\n",
+       "      <th id=\"T_e05f6_level0_col111\" class=\"col_heading level0 col111\" >FCLASS_IN</th>\n",
+       "      <th id=\"T_e05f6_level0_col112\" class=\"col_heading level0 col112\" >FCLASS_NP</th>\n",
+       "      <th id=\"T_e05f6_level0_col113\" class=\"col_heading level0 col113\" >FCLASS_PK</th>\n",
+       "      <th id=\"T_e05f6_level0_col114\" class=\"col_heading level0 col114\" >FCLASS_DE</th>\n",
+       "      <th id=\"T_e05f6_level0_col115\" class=\"col_heading level0 col115\" >FCLASS_GB</th>\n",
+       "      <th id=\"T_e05f6_level0_col116\" class=\"col_heading level0 col116\" >FCLASS_BR</th>\n",
+       "      <th id=\"T_e05f6_level0_col117\" class=\"col_heading level0 col117\" >FCLASS_IL</th>\n",
+       "      <th id=\"T_e05f6_level0_col118\" class=\"col_heading level0 col118\" >FCLASS_PS</th>\n",
+       "      <th id=\"T_e05f6_level0_col119\" class=\"col_heading level0 col119\" >FCLASS_SA</th>\n",
+       "      <th id=\"T_e05f6_level0_col120\" class=\"col_heading level0 col120\" >FCLASS_EG</th>\n",
+       "      <th id=\"T_e05f6_level0_col121\" class=\"col_heading level0 col121\" >FCLASS_MA</th>\n",
+       "      <th id=\"T_e05f6_level0_col122\" class=\"col_heading level0 col122\" >FCLASS_PT</th>\n",
+       "      <th id=\"T_e05f6_level0_col123\" class=\"col_heading level0 col123\" >FCLASS_AR</th>\n",
+       "      <th id=\"T_e05f6_level0_col124\" class=\"col_heading level0 col124\" >FCLASS_JP</th>\n",
+       "      <th id=\"T_e05f6_level0_col125\" class=\"col_heading level0 col125\" >FCLASS_KO</th>\n",
+       "      <th id=\"T_e05f6_level0_col126\" class=\"col_heading level0 col126\" >FCLASS_VN</th>\n",
+       "      <th id=\"T_e05f6_level0_col127\" class=\"col_heading level0 col127\" >FCLASS_TR</th>\n",
+       "      <th id=\"T_e05f6_level0_col128\" class=\"col_heading level0 col128\" >FCLASS_ID</th>\n",
+       "      <th id=\"T_e05f6_level0_col129\" class=\"col_heading level0 col129\" >FCLASS_PL</th>\n",
+       "      <th id=\"T_e05f6_level0_col130\" class=\"col_heading level0 col130\" >FCLASS_GR</th>\n",
+       "      <th id=\"T_e05f6_level0_col131\" class=\"col_heading level0 col131\" >FCLASS_IT</th>\n",
+       "      <th id=\"T_e05f6_level0_col132\" class=\"col_heading level0 col132\" >FCLASS_NL</th>\n",
+       "      <th id=\"T_e05f6_level0_col133\" class=\"col_heading level0 col133\" >FCLASS_SE</th>\n",
+       "      <th id=\"T_e05f6_level0_col134\" class=\"col_heading level0 col134\" >FCLASS_BD</th>\n",
+       "      <th id=\"T_e05f6_level0_col135\" class=\"col_heading level0 col135\" >FCLASS_UA</th>\n",
+       "      <th id=\"T_e05f6_level0_col136\" class=\"col_heading level0 col136\" >FCLASS_TLC</th>\n",
+       "      <th id=\"T_e05f6_level0_col137\" class=\"col_heading level0 col137\" >geometry</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_f91f0_row0_col0\" class=\"data row0 col0\" >8</td>\n",
-       "      <td id=\"T_f91f0_row0_col1\" class=\"data row0 col1\" >10</td>\n",
-       "      <td id=\"T_f91f0_row0_col2\" class=\"data row0 col2\" >3</td>\n",
-       "      <td id=\"T_f91f0_row0_col3\" class=\"data row0 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row0_col4\" class=\"data row0 col4\" >Vatican City</td>\n",
-       "      <td id=\"T_f91f0_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col7\" class=\"data row0 col7\" >Vatican City</td>\n",
-       "      <td id=\"T_f91f0_row0_col8\" class=\"data row0 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row0_col9\" class=\"data row0 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col10\" class=\"data row0 col10\" >1</td>\n",
-       "      <td id=\"T_f91f0_row0_col11\" class=\"data row0 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col12\" class=\"data row0 col12\" >Vatican</td>\n",
-       "      <td id=\"T_f91f0_row0_col13\" class=\"data row0 col13\" >VAT</td>\n",
-       "      <td id=\"T_f91f0_row0_col14\" class=\"data row0 col14\" >Vatican</td>\n",
-       "      <td id=\"T_f91f0_row0_col15\" class=\"data row0 col15\" >VAT</td>\n",
-       "      <td id=\"T_f91f0_row0_col16\" class=\"data row0 col16\" >Lazio</td>\n",
-       "      <td id=\"T_f91f0_row0_col17\" class=\"data row0 col17\" >VA</td>\n",
-       "      <td id=\"T_f91f0_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col19\" class=\"data row0 col19\" >41.903282</td>\n",
-       "      <td id=\"T_f91f0_row0_col20\" class=\"data row0 col20\" >12.453387</td>\n",
-       "      <td id=\"T_f91f0_row0_col21\" class=\"data row0 col21\" >832</td>\n",
-       "      <td id=\"T_f91f0_row0_col22\" class=\"data row0 col22\" >832</td>\n",
-       "      <td id=\"T_f91f0_row0_col23\" class=\"data row0 col23\" >562430</td>\n",
-       "      <td id=\"T_f91f0_row0_col24\" class=\"data row0 col24\" >2</td>\n",
-       "      <td id=\"T_f91f0_row0_col25\" class=\"data row0 col25\" >2</td>\n",
-       "      <td id=\"T_f91f0_row0_col26\" class=\"data row0 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col27\" class=\"data row0 col27\" >Vatican City</td>\n",
-       "      <td id=\"T_f91f0_row0_col28\" class=\"data row0 col28\" >636762</td>\n",
-       "      <td id=\"T_f91f0_row0_col29\" class=\"data row0 col29\" >636762</td>\n",
-       "      <td id=\"T_f91f0_row0_col30\" class=\"data row0 col30\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col31\" class=\"data row0 col31\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col32\" class=\"data row0 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col33\" class=\"data row0 col33\" >20</td>\n",
-       "      <td id=\"T_f91f0_row0_col34\" class=\"data row0 col34\" >177</td>\n",
-       "      <td id=\"T_f91f0_row0_col35\" class=\"data row0 col35\" >177</td>\n",
-       "      <td id=\"T_f91f0_row0_col36\" class=\"data row0 col36\" >68</td>\n",
-       "      <td id=\"T_f91f0_row0_col37\" class=\"data row0 col37\" >68</td>\n",
-       "      <td id=\"T_f91f0_row0_col38\" class=\"data row0 col38\" >160</td>\n",
-       "      <td id=\"T_f91f0_row0_col39\" class=\"data row0 col39\" >160</td>\n",
-       "      <td id=\"T_f91f0_row0_col40\" class=\"data row0 col40\" >99</td>\n",
-       "      <td id=\"T_f91f0_row0_col41\" class=\"data row0 col41\" >99</td>\n",
-       "      <td id=\"T_f91f0_row0_col42\" class=\"data row0 col42\" >12.333333</td>\n",
-       "      <td id=\"T_f91f0_row0_col43\" class=\"data row0 col43\" >12.333333</td>\n",
-       "      <td id=\"T_f91f0_row0_col44\" class=\"data row0 col44\" >12.481009</td>\n",
-       "      <td id=\"T_f91f0_row0_col45\" class=\"data row0 col45\" >12.481009</td>\n",
-       "      <td id=\"T_f91f0_row0_col46\" class=\"data row0 col46\" >41.766667</td>\n",
-       "      <td id=\"T_f91f0_row0_col47\" class=\"data row0 col47\" >41.766667</td>\n",
-       "      <td id=\"T_f91f0_row0_col48\" class=\"data row0 col48\" >42.050000</td>\n",
-       "      <td id=\"T_f91f0_row0_col49\" class=\"data row0 col49\" >42.050000</td>\n",
-       "      <td id=\"T_f91f0_row0_col50\" class=\"data row0 col50\" >12.419907</td>\n",
-       "      <td id=\"T_f91f0_row0_col51\" class=\"data row0 col51\" >41.903477</td>\n",
-       "      <td id=\"T_f91f0_row0_col52\" class=\"data row0 col52\" >Europe/Vatican</td>\n",
-       "      <td id=\"T_f91f0_row0_col53\" class=\"data row0 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col54\" class=\"data row0 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col55\" class=\"data row0 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col56\" class=\"data row0 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col57\" class=\"data row0 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col58\" class=\"data row0 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col59\" class=\"data row0 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col60\" class=\"data row0 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col61\" class=\"data row0 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col62\" class=\"data row0 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col63\" class=\"data row0 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col64\" class=\"data row0 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col65\" class=\"data row0 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col66\" class=\"data row0 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col67\" class=\"data row0 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col68\" class=\"data row0 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col69\" class=\"data row0 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col70\" class=\"data row0 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col71\" class=\"data row0 col71\" >7.000000</td>\n",
-       "      <td id=\"T_f91f0_row0_col72\" class=\"data row0 col72\" >Q237</td>\n",
-       "      <td id=\"T_f91f0_row0_col73\" class=\"data row0 col73\" >101914185</td>\n",
-       "      <td id=\"T_f91f0_row0_col74\" class=\"data row0 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row0_col75\" class=\"data row0 col75\" >Vatican City</td>\n",
-       "      <td id=\"T_f91f0_row0_col76\" class=\"data row0 col76\" >Vatikanstadt</td>\n",
-       "      <td id=\"T_f91f0_row0_col77\" class=\"data row0 col77\" >Ciudad del Vaticano</td>\n",
-       "      <td id=\"T_f91f0_row0_col78\" class=\"data row0 col78\" >Cité du Vatican</td>\n",
-       "      <td id=\"T_f91f0_row0_col79\" class=\"data row0 col79\" >Vaticano</td>\n",
-       "      <td id=\"T_f91f0_row0_col80\" class=\"data row0 col80\" >Ватикан</td>\n",
-       "      <td id=\"T_f91f0_row0_col81\" class=\"data row0 col81\" >梵蒂冈</td>\n",
-       "      <td id=\"T_f91f0_row0_col82\" class=\"data row0 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col83\" class=\"data row0 col83\" >الفاتيكان</td>\n",
-       "      <td id=\"T_f91f0_row0_col84\" class=\"data row0 col84\" >ভ্যাটিকান সিটি</td>\n",
-       "      <td id=\"T_f91f0_row0_col85\" class=\"data row0 col85\" >Βατικανό</td>\n",
-       "      <td id=\"T_f91f0_row0_col86\" class=\"data row0 col86\" >वैटिकन नगर</td>\n",
-       "      <td id=\"T_f91f0_row0_col87\" class=\"data row0 col87\" >Vatikán</td>\n",
-       "      <td id=\"T_f91f0_row0_col88\" class=\"data row0 col88\" >Vatikan</td>\n",
-       "      <td id=\"T_f91f0_row0_col89\" class=\"data row0 col89\" >Città del Vaticano</td>\n",
-       "      <td id=\"T_f91f0_row0_col90\" class=\"data row0 col90\" >バチカン</td>\n",
-       "      <td id=\"T_f91f0_row0_col91\" class=\"data row0 col91\" >바티칸 시국</td>\n",
-       "      <td id=\"T_f91f0_row0_col92\" class=\"data row0 col92\" >Vaticaanstad</td>\n",
-       "      <td id=\"T_f91f0_row0_col93\" class=\"data row0 col93\" >Watykan</td>\n",
-       "      <td id=\"T_f91f0_row0_col94\" class=\"data row0 col94\" >Vatikanstaten</td>\n",
-       "      <td id=\"T_f91f0_row0_col95\" class=\"data row0 col95\" >Vatikan</td>\n",
-       "      <td id=\"T_f91f0_row0_col96\" class=\"data row0 col96\" >Thành Vatican</td>\n",
-       "      <td id=\"T_f91f0_row0_col97\" class=\"data row0 col97\" >1159127243</td>\n",
-       "      <td id=\"T_f91f0_row0_col98\" class=\"data row0 col98\" >واتیکان</td>\n",
-       "      <td id=\"T_f91f0_row0_col99\" class=\"data row0 col99\" >קריית הוותיקן</td>\n",
-       "      <td id=\"T_f91f0_row0_col100\" class=\"data row0 col100\" >Ватикан</td>\n",
-       "      <td id=\"T_f91f0_row0_col101\" class=\"data row0 col101\" >ویٹیکن سٹی</td>\n",
-       "      <td id=\"T_f91f0_row0_col102\" class=\"data row0 col102\" >梵蒂岡</td>\n",
-       "      <td id=\"T_f91f0_row0_col103\" class=\"data row0 col103\" >6691831</td>\n",
-       "      <td id=\"T_f91f0_row0_col104\" class=\"data row0 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col105\" class=\"data row0 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col106\" class=\"data row0 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col107\" class=\"data row0 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col108\" class=\"data row0 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col109\" class=\"data row0 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col110\" class=\"data row0 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col111\" class=\"data row0 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col112\" class=\"data row0 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col113\" class=\"data row0 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col114\" class=\"data row0 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col115\" class=\"data row0 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col116\" class=\"data row0 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col117\" class=\"data row0 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col118\" class=\"data row0 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col119\" class=\"data row0 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col120\" class=\"data row0 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col121\" class=\"data row0 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col122\" class=\"data row0 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col123\" class=\"data row0 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col124\" class=\"data row0 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col125\" class=\"data row0 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col126\" class=\"data row0 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col127\" class=\"data row0 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col128\" class=\"data row0 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col129\" class=\"data row0 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col130\" class=\"data row0 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col131\" class=\"data row0 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col132\" class=\"data row0 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col133\" class=\"data row0 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col134\" class=\"data row0 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col135\" class=\"data row0 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col136\" class=\"data row0 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row0_col137\" class=\"data row0 col137\" >POINT (12.4533865 41.9032822)</td>\n",
+       "      <th id=\"T_e05f6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_e05f6_row0_col0\" class=\"data row0 col0\" >8</td>\n",
+       "      <td id=\"T_e05f6_row0_col1\" class=\"data row0 col1\" >10</td>\n",
+       "      <td id=\"T_e05f6_row0_col2\" class=\"data row0 col2\" >3</td>\n",
+       "      <td id=\"T_e05f6_row0_col3\" class=\"data row0 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row0_col4\" class=\"data row0 col4\" >Vatican City</td>\n",
+       "      <td id=\"T_e05f6_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col7\" class=\"data row0 col7\" >Vatican City</td>\n",
+       "      <td id=\"T_e05f6_row0_col8\" class=\"data row0 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row0_col9\" class=\"data row0 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col10\" class=\"data row0 col10\" >1</td>\n",
+       "      <td id=\"T_e05f6_row0_col11\" class=\"data row0 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col12\" class=\"data row0 col12\" >Vatican</td>\n",
+       "      <td id=\"T_e05f6_row0_col13\" class=\"data row0 col13\" >VAT</td>\n",
+       "      <td id=\"T_e05f6_row0_col14\" class=\"data row0 col14\" >Vatican</td>\n",
+       "      <td id=\"T_e05f6_row0_col15\" class=\"data row0 col15\" >VAT</td>\n",
+       "      <td id=\"T_e05f6_row0_col16\" class=\"data row0 col16\" >Lazio</td>\n",
+       "      <td id=\"T_e05f6_row0_col17\" class=\"data row0 col17\" >VA</td>\n",
+       "      <td id=\"T_e05f6_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col19\" class=\"data row0 col19\" >41.903282</td>\n",
+       "      <td id=\"T_e05f6_row0_col20\" class=\"data row0 col20\" >12.453387</td>\n",
+       "      <td id=\"T_e05f6_row0_col21\" class=\"data row0 col21\" >832</td>\n",
+       "      <td id=\"T_e05f6_row0_col22\" class=\"data row0 col22\" >832</td>\n",
+       "      <td id=\"T_e05f6_row0_col23\" class=\"data row0 col23\" >562430</td>\n",
+       "      <td id=\"T_e05f6_row0_col24\" class=\"data row0 col24\" >2</td>\n",
+       "      <td id=\"T_e05f6_row0_col25\" class=\"data row0 col25\" >2</td>\n",
+       "      <td id=\"T_e05f6_row0_col26\" class=\"data row0 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col27\" class=\"data row0 col27\" >Vatican City</td>\n",
+       "      <td id=\"T_e05f6_row0_col28\" class=\"data row0 col28\" >636762</td>\n",
+       "      <td id=\"T_e05f6_row0_col29\" class=\"data row0 col29\" >636762</td>\n",
+       "      <td id=\"T_e05f6_row0_col30\" class=\"data row0 col30\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col31\" class=\"data row0 col31\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col32\" class=\"data row0 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col33\" class=\"data row0 col33\" >20</td>\n",
+       "      <td id=\"T_e05f6_row0_col34\" class=\"data row0 col34\" >177</td>\n",
+       "      <td id=\"T_e05f6_row0_col35\" class=\"data row0 col35\" >177</td>\n",
+       "      <td id=\"T_e05f6_row0_col36\" class=\"data row0 col36\" >68</td>\n",
+       "      <td id=\"T_e05f6_row0_col37\" class=\"data row0 col37\" >68</td>\n",
+       "      <td id=\"T_e05f6_row0_col38\" class=\"data row0 col38\" >160</td>\n",
+       "      <td id=\"T_e05f6_row0_col39\" class=\"data row0 col39\" >160</td>\n",
+       "      <td id=\"T_e05f6_row0_col40\" class=\"data row0 col40\" >99</td>\n",
+       "      <td id=\"T_e05f6_row0_col41\" class=\"data row0 col41\" >99</td>\n",
+       "      <td id=\"T_e05f6_row0_col42\" class=\"data row0 col42\" >12.333333</td>\n",
+       "      <td id=\"T_e05f6_row0_col43\" class=\"data row0 col43\" >12.333333</td>\n",
+       "      <td id=\"T_e05f6_row0_col44\" class=\"data row0 col44\" >12.481009</td>\n",
+       "      <td id=\"T_e05f6_row0_col45\" class=\"data row0 col45\" >12.481009</td>\n",
+       "      <td id=\"T_e05f6_row0_col46\" class=\"data row0 col46\" >41.766667</td>\n",
+       "      <td id=\"T_e05f6_row0_col47\" class=\"data row0 col47\" >41.766667</td>\n",
+       "      <td id=\"T_e05f6_row0_col48\" class=\"data row0 col48\" >42.050000</td>\n",
+       "      <td id=\"T_e05f6_row0_col49\" class=\"data row0 col49\" >42.050000</td>\n",
+       "      <td id=\"T_e05f6_row0_col50\" class=\"data row0 col50\" >12.419907</td>\n",
+       "      <td id=\"T_e05f6_row0_col51\" class=\"data row0 col51\" >41.903477</td>\n",
+       "      <td id=\"T_e05f6_row0_col52\" class=\"data row0 col52\" >Europe/Vatican</td>\n",
+       "      <td id=\"T_e05f6_row0_col53\" class=\"data row0 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col54\" class=\"data row0 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col55\" class=\"data row0 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col56\" class=\"data row0 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col57\" class=\"data row0 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col58\" class=\"data row0 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col59\" class=\"data row0 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col60\" class=\"data row0 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col61\" class=\"data row0 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col62\" class=\"data row0 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col63\" class=\"data row0 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col64\" class=\"data row0 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col65\" class=\"data row0 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col66\" class=\"data row0 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col67\" class=\"data row0 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col68\" class=\"data row0 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col69\" class=\"data row0 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col70\" class=\"data row0 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col71\" class=\"data row0 col71\" >7.000000</td>\n",
+       "      <td id=\"T_e05f6_row0_col72\" class=\"data row0 col72\" >Q237</td>\n",
+       "      <td id=\"T_e05f6_row0_col73\" class=\"data row0 col73\" >101914185</td>\n",
+       "      <td id=\"T_e05f6_row0_col74\" class=\"data row0 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row0_col75\" class=\"data row0 col75\" >Vatican City</td>\n",
+       "      <td id=\"T_e05f6_row0_col76\" class=\"data row0 col76\" >Vatikanstadt</td>\n",
+       "      <td id=\"T_e05f6_row0_col77\" class=\"data row0 col77\" >Ciudad del Vaticano</td>\n",
+       "      <td id=\"T_e05f6_row0_col78\" class=\"data row0 col78\" >Cité du Vatican</td>\n",
+       "      <td id=\"T_e05f6_row0_col79\" class=\"data row0 col79\" >Vaticano</td>\n",
+       "      <td id=\"T_e05f6_row0_col80\" class=\"data row0 col80\" >Ватикан</td>\n",
+       "      <td id=\"T_e05f6_row0_col81\" class=\"data row0 col81\" >梵蒂冈</td>\n",
+       "      <td id=\"T_e05f6_row0_col82\" class=\"data row0 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col83\" class=\"data row0 col83\" >الفاتيكان</td>\n",
+       "      <td id=\"T_e05f6_row0_col84\" class=\"data row0 col84\" >ভ্যাটিকান সিটি</td>\n",
+       "      <td id=\"T_e05f6_row0_col85\" class=\"data row0 col85\" >Βατικανό</td>\n",
+       "      <td id=\"T_e05f6_row0_col86\" class=\"data row0 col86\" >वैटिकन नगर</td>\n",
+       "      <td id=\"T_e05f6_row0_col87\" class=\"data row0 col87\" >Vatikán</td>\n",
+       "      <td id=\"T_e05f6_row0_col88\" class=\"data row0 col88\" >Vatikan</td>\n",
+       "      <td id=\"T_e05f6_row0_col89\" class=\"data row0 col89\" >Città del Vaticano</td>\n",
+       "      <td id=\"T_e05f6_row0_col90\" class=\"data row0 col90\" >バチカン</td>\n",
+       "      <td id=\"T_e05f6_row0_col91\" class=\"data row0 col91\" >바티칸 시국</td>\n",
+       "      <td id=\"T_e05f6_row0_col92\" class=\"data row0 col92\" >Vaticaanstad</td>\n",
+       "      <td id=\"T_e05f6_row0_col93\" class=\"data row0 col93\" >Watykan</td>\n",
+       "      <td id=\"T_e05f6_row0_col94\" class=\"data row0 col94\" >Vatikanstaten</td>\n",
+       "      <td id=\"T_e05f6_row0_col95\" class=\"data row0 col95\" >Vatikan</td>\n",
+       "      <td id=\"T_e05f6_row0_col96\" class=\"data row0 col96\" >Thành Vatican</td>\n",
+       "      <td id=\"T_e05f6_row0_col97\" class=\"data row0 col97\" >1159127243</td>\n",
+       "      <td id=\"T_e05f6_row0_col98\" class=\"data row0 col98\" >واتیکان</td>\n",
+       "      <td id=\"T_e05f6_row0_col99\" class=\"data row0 col99\" >קריית הוותיקן</td>\n",
+       "      <td id=\"T_e05f6_row0_col100\" class=\"data row0 col100\" >Ватикан</td>\n",
+       "      <td id=\"T_e05f6_row0_col101\" class=\"data row0 col101\" >ویٹیکن سٹی</td>\n",
+       "      <td id=\"T_e05f6_row0_col102\" class=\"data row0 col102\" >梵蒂岡</td>\n",
+       "      <td id=\"T_e05f6_row0_col103\" class=\"data row0 col103\" >6691831</td>\n",
+       "      <td id=\"T_e05f6_row0_col104\" class=\"data row0 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col105\" class=\"data row0 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col106\" class=\"data row0 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col107\" class=\"data row0 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col108\" class=\"data row0 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col109\" class=\"data row0 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col110\" class=\"data row0 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col111\" class=\"data row0 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col112\" class=\"data row0 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col113\" class=\"data row0 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col114\" class=\"data row0 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col115\" class=\"data row0 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col116\" class=\"data row0 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col117\" class=\"data row0 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col118\" class=\"data row0 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col119\" class=\"data row0 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col120\" class=\"data row0 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col121\" class=\"data row0 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col122\" class=\"data row0 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col123\" class=\"data row0 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col124\" class=\"data row0 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col125\" class=\"data row0 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col126\" class=\"data row0 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col127\" class=\"data row0 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col128\" class=\"data row0 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col129\" class=\"data row0 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col130\" class=\"data row0 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col131\" class=\"data row0 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col132\" class=\"data row0 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col133\" class=\"data row0 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col134\" class=\"data row0 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col135\" class=\"data row0 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col136\" class=\"data row0 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row0_col137\" class=\"data row0 col137\" >POINT (12.4533865 41.9032822)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_f91f0_row1_col0\" class=\"data row1 col0\" >7</td>\n",
-       "      <td id=\"T_f91f0_row1_col1\" class=\"data row1 col1\" >20</td>\n",
-       "      <td id=\"T_f91f0_row1_col2\" class=\"data row1 col2\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col3\" class=\"data row1 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row1_col4\" class=\"data row1 col4\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col7\" class=\"data row1 col7\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col8\" class=\"data row1 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col10\" class=\"data row1 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col11\" class=\"data row1 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col12\" class=\"data row1 col12\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col13\" class=\"data row1 col13\" >SMR</td>\n",
-       "      <td id=\"T_f91f0_row1_col14\" class=\"data row1 col14\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col15\" class=\"data row1 col15\" >SMR</td>\n",
-       "      <td id=\"T_f91f0_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col17\" class=\"data row1 col17\" >SM</td>\n",
-       "      <td id=\"T_f91f0_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col19\" class=\"data row1 col19\" >43.936096</td>\n",
-       "      <td id=\"T_f91f0_row1_col20\" class=\"data row1 col20\" >12.441770</td>\n",
-       "      <td id=\"T_f91f0_row1_col21\" class=\"data row1 col21\" >29579</td>\n",
-       "      <td id=\"T_f91f0_row1_col22\" class=\"data row1 col22\" >29000</td>\n",
-       "      <td id=\"T_f91f0_row1_col23\" class=\"data row1 col23\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col24\" class=\"data row1 col24\" >7</td>\n",
-       "      <td id=\"T_f91f0_row1_col25\" class=\"data row1 col25\" >7</td>\n",
-       "      <td id=\"T_f91f0_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col27\" class=\"data row1 col27\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col28\" class=\"data row1 col28\" >29088</td>\n",
-       "      <td id=\"T_f91f0_row1_col29\" class=\"data row1 col29\" >29579</td>\n",
-       "      <td id=\"T_f91f0_row1_col30\" class=\"data row1 col30\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col31\" class=\"data row1 col31\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col32\" class=\"data row1 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col33\" class=\"data row1 col33\" >20</td>\n",
-       "      <td id=\"T_f91f0_row1_col34\" class=\"data row1 col34\" >30</td>\n",
-       "      <td id=\"T_f91f0_row1_col35\" class=\"data row1 col35\" >30</td>\n",
-       "      <td id=\"T_f91f0_row1_col36\" class=\"data row1 col36\" >11</td>\n",
-       "      <td id=\"T_f91f0_row1_col37\" class=\"data row1 col37\" >11</td>\n",
-       "      <td id=\"T_f91f0_row1_col38\" class=\"data row1 col38\" >63</td>\n",
-       "      <td id=\"T_f91f0_row1_col39\" class=\"data row1 col39\" >63</td>\n",
-       "      <td id=\"T_f91f0_row1_col40\" class=\"data row1 col40\" >39</td>\n",
-       "      <td id=\"T_f91f0_row1_col41\" class=\"data row1 col41\" >39</td>\n",
-       "      <td id=\"T_f91f0_row1_col42\" class=\"data row1 col42\" >12.391667</td>\n",
-       "      <td id=\"T_f91f0_row1_col43\" class=\"data row1 col43\" >12.391667</td>\n",
-       "      <td id=\"T_f91f0_row1_col44\" class=\"data row1 col44\" >12.541667</td>\n",
-       "      <td id=\"T_f91f0_row1_col45\" class=\"data row1 col45\" >12.541667</td>\n",
-       "      <td id=\"T_f91f0_row1_col46\" class=\"data row1 col46\" >43.900000</td>\n",
-       "      <td id=\"T_f91f0_row1_col47\" class=\"data row1 col47\" >43.900000</td>\n",
-       "      <td id=\"T_f91f0_row1_col48\" class=\"data row1 col48\" >44.000000</td>\n",
-       "      <td id=\"T_f91f0_row1_col49\" class=\"data row1 col49\" >44.000000</td>\n",
-       "      <td id=\"T_f91f0_row1_col50\" class=\"data row1 col50\" >12.462153</td>\n",
-       "      <td id=\"T_f91f0_row1_col51\" class=\"data row1 col51\" >43.953472</td>\n",
-       "      <td id=\"T_f91f0_row1_col52\" class=\"data row1 col52\" >Europe/San_Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col53\" class=\"data row1 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col54\" class=\"data row1 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col55\" class=\"data row1 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col56\" class=\"data row1 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col57\" class=\"data row1 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col58\" class=\"data row1 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col59\" class=\"data row1 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col60\" class=\"data row1 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col61\" class=\"data row1 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col62\" class=\"data row1 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col63\" class=\"data row1 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col64\" class=\"data row1 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col65\" class=\"data row1 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col66\" class=\"data row1 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col67\" class=\"data row1 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col68\" class=\"data row1 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col69\" class=\"data row1 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col70\" class=\"data row1 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col71\" class=\"data row1 col71\" >6.100000</td>\n",
-       "      <td id=\"T_f91f0_row1_col72\" class=\"data row1 col72\" >Q1848</td>\n",
-       "      <td id=\"T_f91f0_row1_col73\" class=\"data row1 col73\" >101837381</td>\n",
-       "      <td id=\"T_f91f0_row1_col74\" class=\"data row1 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row1_col75\" class=\"data row1 col75\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col76\" class=\"data row1 col76\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col77\" class=\"data row1 col77\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col78\" class=\"data row1 col78\" >Saint-Marin</td>\n",
-       "      <td id=\"T_f91f0_row1_col79\" class=\"data row1 col79\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col80\" class=\"data row1 col80\" >Сан-Марино</td>\n",
-       "      <td id=\"T_f91f0_row1_col81\" class=\"data row1 col81\" >圣马力诺</td>\n",
-       "      <td id=\"T_f91f0_row1_col82\" class=\"data row1 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col83\" class=\"data row1 col83\" >مدينة سان مارينو</td>\n",
-       "      <td id=\"T_f91f0_row1_col84\" class=\"data row1 col84\" >সান মারিনো</td>\n",
-       "      <td id=\"T_f91f0_row1_col85\" class=\"data row1 col85\" >Άγιος Μαρίνος</td>\n",
-       "      <td id=\"T_f91f0_row1_col86\" class=\"data row1 col86\" >सैन मारिनो नगर</td>\n",
-       "      <td id=\"T_f91f0_row1_col87\" class=\"data row1 col87\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col88\" class=\"data row1 col88\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col89\" class=\"data row1 col89\" >Città di San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col90\" class=\"data row1 col90\" >サンマリノ市</td>\n",
-       "      <td id=\"T_f91f0_row1_col91\" class=\"data row1 col91\" >산마리노</td>\n",
-       "      <td id=\"T_f91f0_row1_col92\" class=\"data row1 col92\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col93\" class=\"data row1 col93\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col94\" class=\"data row1 col94\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col95\" class=\"data row1 col95\" >San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col96\" class=\"data row1 col96\" >Thành phố San Marino</td>\n",
-       "      <td id=\"T_f91f0_row1_col97\" class=\"data row1 col97\" >1159146051</td>\n",
-       "      <td id=\"T_f91f0_row1_col98\" class=\"data row1 col98\" >سن مارینو</td>\n",
-       "      <td id=\"T_f91f0_row1_col99\" class=\"data row1 col99\" >סן מרינו</td>\n",
-       "      <td id=\"T_f91f0_row1_col100\" class=\"data row1 col100\" >Сан-Марино</td>\n",
-       "      <td id=\"T_f91f0_row1_col101\" class=\"data row1 col101\" >سان مارینو شہر</td>\n",
-       "      <td id=\"T_f91f0_row1_col102\" class=\"data row1 col102\" >聖馬力諾</td>\n",
-       "      <td id=\"T_f91f0_row1_col103\" class=\"data row1 col103\" >3168070</td>\n",
-       "      <td id=\"T_f91f0_row1_col104\" class=\"data row1 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col105\" class=\"data row1 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col106\" class=\"data row1 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col107\" class=\"data row1 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col108\" class=\"data row1 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col109\" class=\"data row1 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col110\" class=\"data row1 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col111\" class=\"data row1 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col112\" class=\"data row1 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col113\" class=\"data row1 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col114\" class=\"data row1 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col115\" class=\"data row1 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col116\" class=\"data row1 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col117\" class=\"data row1 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col118\" class=\"data row1 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col119\" class=\"data row1 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col120\" class=\"data row1 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col121\" class=\"data row1 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col122\" class=\"data row1 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col123\" class=\"data row1 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col124\" class=\"data row1 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col125\" class=\"data row1 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col126\" class=\"data row1 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col127\" class=\"data row1 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col128\" class=\"data row1 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col129\" class=\"data row1 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col130\" class=\"data row1 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col131\" class=\"data row1 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col132\" class=\"data row1 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col133\" class=\"data row1 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col134\" class=\"data row1 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col135\" class=\"data row1 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col136\" class=\"data row1 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row1_col137\" class=\"data row1 col137\" >POINT (12.4417702 43.9360958)</td>\n",
+       "      <th id=\"T_e05f6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_e05f6_row1_col0\" class=\"data row1 col0\" >7</td>\n",
+       "      <td id=\"T_e05f6_row1_col1\" class=\"data row1 col1\" >20</td>\n",
+       "      <td id=\"T_e05f6_row1_col2\" class=\"data row1 col2\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col3\" class=\"data row1 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row1_col4\" class=\"data row1 col4\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col7\" class=\"data row1 col7\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col8\" class=\"data row1 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col10\" class=\"data row1 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col11\" class=\"data row1 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col12\" class=\"data row1 col12\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col13\" class=\"data row1 col13\" >SMR</td>\n",
+       "      <td id=\"T_e05f6_row1_col14\" class=\"data row1 col14\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col15\" class=\"data row1 col15\" >SMR</td>\n",
+       "      <td id=\"T_e05f6_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col17\" class=\"data row1 col17\" >SM</td>\n",
+       "      <td id=\"T_e05f6_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col19\" class=\"data row1 col19\" >43.936096</td>\n",
+       "      <td id=\"T_e05f6_row1_col20\" class=\"data row1 col20\" >12.441770</td>\n",
+       "      <td id=\"T_e05f6_row1_col21\" class=\"data row1 col21\" >29579</td>\n",
+       "      <td id=\"T_e05f6_row1_col22\" class=\"data row1 col22\" >29000</td>\n",
+       "      <td id=\"T_e05f6_row1_col23\" class=\"data row1 col23\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col24\" class=\"data row1 col24\" >7</td>\n",
+       "      <td id=\"T_e05f6_row1_col25\" class=\"data row1 col25\" >7</td>\n",
+       "      <td id=\"T_e05f6_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col27\" class=\"data row1 col27\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col28\" class=\"data row1 col28\" >29088</td>\n",
+       "      <td id=\"T_e05f6_row1_col29\" class=\"data row1 col29\" >29579</td>\n",
+       "      <td id=\"T_e05f6_row1_col30\" class=\"data row1 col30\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col31\" class=\"data row1 col31\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col32\" class=\"data row1 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col33\" class=\"data row1 col33\" >20</td>\n",
+       "      <td id=\"T_e05f6_row1_col34\" class=\"data row1 col34\" >30</td>\n",
+       "      <td id=\"T_e05f6_row1_col35\" class=\"data row1 col35\" >30</td>\n",
+       "      <td id=\"T_e05f6_row1_col36\" class=\"data row1 col36\" >11</td>\n",
+       "      <td id=\"T_e05f6_row1_col37\" class=\"data row1 col37\" >11</td>\n",
+       "      <td id=\"T_e05f6_row1_col38\" class=\"data row1 col38\" >63</td>\n",
+       "      <td id=\"T_e05f6_row1_col39\" class=\"data row1 col39\" >63</td>\n",
+       "      <td id=\"T_e05f6_row1_col40\" class=\"data row1 col40\" >39</td>\n",
+       "      <td id=\"T_e05f6_row1_col41\" class=\"data row1 col41\" >39</td>\n",
+       "      <td id=\"T_e05f6_row1_col42\" class=\"data row1 col42\" >12.391667</td>\n",
+       "      <td id=\"T_e05f6_row1_col43\" class=\"data row1 col43\" >12.391667</td>\n",
+       "      <td id=\"T_e05f6_row1_col44\" class=\"data row1 col44\" >12.541667</td>\n",
+       "      <td id=\"T_e05f6_row1_col45\" class=\"data row1 col45\" >12.541667</td>\n",
+       "      <td id=\"T_e05f6_row1_col46\" class=\"data row1 col46\" >43.900000</td>\n",
+       "      <td id=\"T_e05f6_row1_col47\" class=\"data row1 col47\" >43.900000</td>\n",
+       "      <td id=\"T_e05f6_row1_col48\" class=\"data row1 col48\" >44.000000</td>\n",
+       "      <td id=\"T_e05f6_row1_col49\" class=\"data row1 col49\" >44.000000</td>\n",
+       "      <td id=\"T_e05f6_row1_col50\" class=\"data row1 col50\" >12.462153</td>\n",
+       "      <td id=\"T_e05f6_row1_col51\" class=\"data row1 col51\" >43.953472</td>\n",
+       "      <td id=\"T_e05f6_row1_col52\" class=\"data row1 col52\" >Europe/San_Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col53\" class=\"data row1 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col54\" class=\"data row1 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col55\" class=\"data row1 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col56\" class=\"data row1 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col57\" class=\"data row1 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col58\" class=\"data row1 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col59\" class=\"data row1 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col60\" class=\"data row1 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col61\" class=\"data row1 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col62\" class=\"data row1 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col63\" class=\"data row1 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col64\" class=\"data row1 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col65\" class=\"data row1 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col66\" class=\"data row1 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col67\" class=\"data row1 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col68\" class=\"data row1 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col69\" class=\"data row1 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col70\" class=\"data row1 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col71\" class=\"data row1 col71\" >6.100000</td>\n",
+       "      <td id=\"T_e05f6_row1_col72\" class=\"data row1 col72\" >Q1848</td>\n",
+       "      <td id=\"T_e05f6_row1_col73\" class=\"data row1 col73\" >101837381</td>\n",
+       "      <td id=\"T_e05f6_row1_col74\" class=\"data row1 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row1_col75\" class=\"data row1 col75\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col76\" class=\"data row1 col76\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col77\" class=\"data row1 col77\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col78\" class=\"data row1 col78\" >Saint-Marin</td>\n",
+       "      <td id=\"T_e05f6_row1_col79\" class=\"data row1 col79\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col80\" class=\"data row1 col80\" >Сан-Марино</td>\n",
+       "      <td id=\"T_e05f6_row1_col81\" class=\"data row1 col81\" >圣马力诺</td>\n",
+       "      <td id=\"T_e05f6_row1_col82\" class=\"data row1 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col83\" class=\"data row1 col83\" >مدينة سان مارينو</td>\n",
+       "      <td id=\"T_e05f6_row1_col84\" class=\"data row1 col84\" >সান মারিনো</td>\n",
+       "      <td id=\"T_e05f6_row1_col85\" class=\"data row1 col85\" >Άγιος Μαρίνος</td>\n",
+       "      <td id=\"T_e05f6_row1_col86\" class=\"data row1 col86\" >सैन मारिनो नगर</td>\n",
+       "      <td id=\"T_e05f6_row1_col87\" class=\"data row1 col87\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col88\" class=\"data row1 col88\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col89\" class=\"data row1 col89\" >Città di San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col90\" class=\"data row1 col90\" >サンマリノ市</td>\n",
+       "      <td id=\"T_e05f6_row1_col91\" class=\"data row1 col91\" >산마리노</td>\n",
+       "      <td id=\"T_e05f6_row1_col92\" class=\"data row1 col92\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col93\" class=\"data row1 col93\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col94\" class=\"data row1 col94\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col95\" class=\"data row1 col95\" >San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col96\" class=\"data row1 col96\" >Thành phố San Marino</td>\n",
+       "      <td id=\"T_e05f6_row1_col97\" class=\"data row1 col97\" >1159146051</td>\n",
+       "      <td id=\"T_e05f6_row1_col98\" class=\"data row1 col98\" >سن مارینو</td>\n",
+       "      <td id=\"T_e05f6_row1_col99\" class=\"data row1 col99\" >סן מרינו</td>\n",
+       "      <td id=\"T_e05f6_row1_col100\" class=\"data row1 col100\" >Сан-Марино</td>\n",
+       "      <td id=\"T_e05f6_row1_col101\" class=\"data row1 col101\" >سان مارینو شہر</td>\n",
+       "      <td id=\"T_e05f6_row1_col102\" class=\"data row1 col102\" >聖馬力諾</td>\n",
+       "      <td id=\"T_e05f6_row1_col103\" class=\"data row1 col103\" >3168070</td>\n",
+       "      <td id=\"T_e05f6_row1_col104\" class=\"data row1 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col105\" class=\"data row1 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col106\" class=\"data row1 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col107\" class=\"data row1 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col108\" class=\"data row1 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col109\" class=\"data row1 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col110\" class=\"data row1 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col111\" class=\"data row1 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col112\" class=\"data row1 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col113\" class=\"data row1 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col114\" class=\"data row1 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col115\" class=\"data row1 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col116\" class=\"data row1 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col117\" class=\"data row1 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col118\" class=\"data row1 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col119\" class=\"data row1 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col120\" class=\"data row1 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col121\" class=\"data row1 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col122\" class=\"data row1 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col123\" class=\"data row1 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col124\" class=\"data row1 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col125\" class=\"data row1 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col126\" class=\"data row1 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col127\" class=\"data row1 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col128\" class=\"data row1 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col129\" class=\"data row1 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col130\" class=\"data row1 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col131\" class=\"data row1 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col132\" class=\"data row1 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col133\" class=\"data row1 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col134\" class=\"data row1 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col135\" class=\"data row1 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col136\" class=\"data row1 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row1_col137\" class=\"data row1 col137\" >POINT (12.4417702 43.9360958)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_f91f0_row2_col0\" class=\"data row2 col0\" >7</td>\n",
-       "      <td id=\"T_f91f0_row2_col1\" class=\"data row2 col1\" >20</td>\n",
-       "      <td id=\"T_f91f0_row2_col2\" class=\"data row2 col2\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col3\" class=\"data row2 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row2_col4\" class=\"data row2 col4\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col7\" class=\"data row2 col7\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col8\" class=\"data row2 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row2_col9\" class=\"data row2 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col10\" class=\"data row2 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col11\" class=\"data row2 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col12\" class=\"data row2 col12\" >Liechtenstein</td>\n",
-       "      <td id=\"T_f91f0_row2_col13\" class=\"data row2 col13\" >LIE</td>\n",
-       "      <td id=\"T_f91f0_row2_col14\" class=\"data row2 col14\" >Liechtenstein</td>\n",
-       "      <td id=\"T_f91f0_row2_col15\" class=\"data row2 col15\" >LIE</td>\n",
-       "      <td id=\"T_f91f0_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col17\" class=\"data row2 col17\" >LI</td>\n",
-       "      <td id=\"T_f91f0_row2_col18\" class=\"data row2 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col19\" class=\"data row2 col19\" >47.133724</td>\n",
-       "      <td id=\"T_f91f0_row2_col20\" class=\"data row2 col20\" >9.516670</td>\n",
-       "      <td id=\"T_f91f0_row2_col21\" class=\"data row2 col21\" >36281</td>\n",
-       "      <td id=\"T_f91f0_row2_col22\" class=\"data row2 col22\" >5342</td>\n",
-       "      <td id=\"T_f91f0_row2_col23\" class=\"data row2 col23\" >33009</td>\n",
-       "      <td id=\"T_f91f0_row2_col24\" class=\"data row2 col24\" >7</td>\n",
-       "      <td id=\"T_f91f0_row2_col25\" class=\"data row2 col25\" >5</td>\n",
-       "      <td id=\"T_f91f0_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col27\" class=\"data row2 col27\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col28\" class=\"data row2 col28\" >45442</td>\n",
-       "      <td id=\"T_f91f0_row2_col29\" class=\"data row2 col29\" >45442</td>\n",
-       "      <td id=\"T_f91f0_row2_col30\" class=\"data row2 col30\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col31\" class=\"data row2 col31\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col32\" class=\"data row2 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col33\" class=\"data row2 col33\" >20</td>\n",
-       "      <td id=\"T_f91f0_row2_col34\" class=\"data row2 col34\" >45</td>\n",
-       "      <td id=\"T_f91f0_row2_col35\" class=\"data row2 col35\" >45</td>\n",
-       "      <td id=\"T_f91f0_row2_col36\" class=\"data row2 col36\" >17</td>\n",
-       "      <td id=\"T_f91f0_row2_col37\" class=\"data row2 col37\" >17</td>\n",
-       "      <td id=\"T_f91f0_row2_col38\" class=\"data row2 col38\" >90</td>\n",
-       "      <td id=\"T_f91f0_row2_col39\" class=\"data row2 col39\" >90</td>\n",
-       "      <td id=\"T_f91f0_row2_col40\" class=\"data row2 col40\" >56</td>\n",
-       "      <td id=\"T_f91f0_row2_col41\" class=\"data row2 col41\" >56</td>\n",
-       "      <td id=\"T_f91f0_row2_col42\" class=\"data row2 col42\" >9.433333</td>\n",
-       "      <td id=\"T_f91f0_row2_col43\" class=\"data row2 col43\" >9.433333</td>\n",
-       "      <td id=\"T_f91f0_row2_col44\" class=\"data row2 col44\" >9.558333</td>\n",
-       "      <td id=\"T_f91f0_row2_col45\" class=\"data row2 col45\" >9.558333</td>\n",
-       "      <td id=\"T_f91f0_row2_col46\" class=\"data row2 col46\" >47.091667</td>\n",
-       "      <td id=\"T_f91f0_row2_col47\" class=\"data row2 col47\" >47.091667</td>\n",
-       "      <td id=\"T_f91f0_row2_col48\" class=\"data row2 col48\" >47.233333</td>\n",
-       "      <td id=\"T_f91f0_row2_col49\" class=\"data row2 col49\" >47.233333</td>\n",
-       "      <td id=\"T_f91f0_row2_col50\" class=\"data row2 col50\" >9.503734</td>\n",
-       "      <td id=\"T_f91f0_row2_col51\" class=\"data row2 col51\" >47.167478</td>\n",
-       "      <td id=\"T_f91f0_row2_col52\" class=\"data row2 col52\" >Europe/Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col53\" class=\"data row2 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col54\" class=\"data row2 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col55\" class=\"data row2 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col56\" class=\"data row2 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col57\" class=\"data row2 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col58\" class=\"data row2 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col59\" class=\"data row2 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col60\" class=\"data row2 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col61\" class=\"data row2 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col62\" class=\"data row2 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col63\" class=\"data row2 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col64\" class=\"data row2 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col65\" class=\"data row2 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col66\" class=\"data row2 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col67\" class=\"data row2 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col68\" class=\"data row2 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col69\" class=\"data row2 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col70\" class=\"data row2 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col71\" class=\"data row2 col71\" >6.700000</td>\n",
-       "      <td id=\"T_f91f0_row2_col72\" class=\"data row2 col72\" >Q1844</td>\n",
-       "      <td id=\"T_f91f0_row2_col73\" class=\"data row2 col73\" >101828603</td>\n",
-       "      <td id=\"T_f91f0_row2_col74\" class=\"data row2 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row2_col75\" class=\"data row2 col75\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col76\" class=\"data row2 col76\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col77\" class=\"data row2 col77\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col78\" class=\"data row2 col78\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col79\" class=\"data row2 col79\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col80\" class=\"data row2 col80\" >Вадуц</td>\n",
-       "      <td id=\"T_f91f0_row2_col81\" class=\"data row2 col81\" >瓦都兹</td>\n",
-       "      <td id=\"T_f91f0_row2_col82\" class=\"data row2 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col83\" class=\"data row2 col83\" >فادوتس</td>\n",
-       "      <td id=\"T_f91f0_row2_col84\" class=\"data row2 col84\" >ফাডুৎস</td>\n",
-       "      <td id=\"T_f91f0_row2_col85\" class=\"data row2 col85\" >Βαντούζ</td>\n",
-       "      <td id=\"T_f91f0_row2_col86\" class=\"data row2 col86\" >वादुज़</td>\n",
-       "      <td id=\"T_f91f0_row2_col87\" class=\"data row2 col87\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col88\" class=\"data row2 col88\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col89\" class=\"data row2 col89\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col90\" class=\"data row2 col90\" >ファドゥーツ</td>\n",
-       "      <td id=\"T_f91f0_row2_col91\" class=\"data row2 col91\" >파두츠</td>\n",
-       "      <td id=\"T_f91f0_row2_col92\" class=\"data row2 col92\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col93\" class=\"data row2 col93\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col94\" class=\"data row2 col94\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col95\" class=\"data row2 col95\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col96\" class=\"data row2 col96\" >Vaduz</td>\n",
-       "      <td id=\"T_f91f0_row2_col97\" class=\"data row2 col97\" >1159146061</td>\n",
-       "      <td id=\"T_f91f0_row2_col98\" class=\"data row2 col98\" >فادوتس</td>\n",
-       "      <td id=\"T_f91f0_row2_col99\" class=\"data row2 col99\" >ואדוץ</td>\n",
-       "      <td id=\"T_f91f0_row2_col100\" class=\"data row2 col100\" >Вадуц</td>\n",
-       "      <td id=\"T_f91f0_row2_col101\" class=\"data row2 col101\" >واڈوز</td>\n",
-       "      <td id=\"T_f91f0_row2_col102\" class=\"data row2 col102\" >華杜茲</td>\n",
-       "      <td id=\"T_f91f0_row2_col103\" class=\"data row2 col103\" >3042030</td>\n",
-       "      <td id=\"T_f91f0_row2_col104\" class=\"data row2 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col105\" class=\"data row2 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col106\" class=\"data row2 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col107\" class=\"data row2 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col108\" class=\"data row2 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col109\" class=\"data row2 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col110\" class=\"data row2 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col111\" class=\"data row2 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col112\" class=\"data row2 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col113\" class=\"data row2 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col114\" class=\"data row2 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col115\" class=\"data row2 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col116\" class=\"data row2 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col117\" class=\"data row2 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col118\" class=\"data row2 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col119\" class=\"data row2 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col120\" class=\"data row2 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col121\" class=\"data row2 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col122\" class=\"data row2 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col123\" class=\"data row2 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col124\" class=\"data row2 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col125\" class=\"data row2 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col126\" class=\"data row2 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col127\" class=\"data row2 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col128\" class=\"data row2 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col129\" class=\"data row2 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col130\" class=\"data row2 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col131\" class=\"data row2 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col132\" class=\"data row2 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col133\" class=\"data row2 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col134\" class=\"data row2 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col135\" class=\"data row2 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col136\" class=\"data row2 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row2_col137\" class=\"data row2 col137\" >POINT (9.5166695 47.1337238)</td>\n",
+       "      <th id=\"T_e05f6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_e05f6_row2_col0\" class=\"data row2 col0\" >7</td>\n",
+       "      <td id=\"T_e05f6_row2_col1\" class=\"data row2 col1\" >20</td>\n",
+       "      <td id=\"T_e05f6_row2_col2\" class=\"data row2 col2\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col3\" class=\"data row2 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row2_col4\" class=\"data row2 col4\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col7\" class=\"data row2 col7\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col8\" class=\"data row2 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row2_col9\" class=\"data row2 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col10\" class=\"data row2 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col11\" class=\"data row2 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col12\" class=\"data row2 col12\" >Liechtenstein</td>\n",
+       "      <td id=\"T_e05f6_row2_col13\" class=\"data row2 col13\" >LIE</td>\n",
+       "      <td id=\"T_e05f6_row2_col14\" class=\"data row2 col14\" >Liechtenstein</td>\n",
+       "      <td id=\"T_e05f6_row2_col15\" class=\"data row2 col15\" >LIE</td>\n",
+       "      <td id=\"T_e05f6_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col17\" class=\"data row2 col17\" >LI</td>\n",
+       "      <td id=\"T_e05f6_row2_col18\" class=\"data row2 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col19\" class=\"data row2 col19\" >47.133724</td>\n",
+       "      <td id=\"T_e05f6_row2_col20\" class=\"data row2 col20\" >9.516670</td>\n",
+       "      <td id=\"T_e05f6_row2_col21\" class=\"data row2 col21\" >36281</td>\n",
+       "      <td id=\"T_e05f6_row2_col22\" class=\"data row2 col22\" >5342</td>\n",
+       "      <td id=\"T_e05f6_row2_col23\" class=\"data row2 col23\" >33009</td>\n",
+       "      <td id=\"T_e05f6_row2_col24\" class=\"data row2 col24\" >7</td>\n",
+       "      <td id=\"T_e05f6_row2_col25\" class=\"data row2 col25\" >5</td>\n",
+       "      <td id=\"T_e05f6_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col27\" class=\"data row2 col27\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col28\" class=\"data row2 col28\" >45442</td>\n",
+       "      <td id=\"T_e05f6_row2_col29\" class=\"data row2 col29\" >45442</td>\n",
+       "      <td id=\"T_e05f6_row2_col30\" class=\"data row2 col30\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col31\" class=\"data row2 col31\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col32\" class=\"data row2 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col33\" class=\"data row2 col33\" >20</td>\n",
+       "      <td id=\"T_e05f6_row2_col34\" class=\"data row2 col34\" >45</td>\n",
+       "      <td id=\"T_e05f6_row2_col35\" class=\"data row2 col35\" >45</td>\n",
+       "      <td id=\"T_e05f6_row2_col36\" class=\"data row2 col36\" >17</td>\n",
+       "      <td id=\"T_e05f6_row2_col37\" class=\"data row2 col37\" >17</td>\n",
+       "      <td id=\"T_e05f6_row2_col38\" class=\"data row2 col38\" >90</td>\n",
+       "      <td id=\"T_e05f6_row2_col39\" class=\"data row2 col39\" >90</td>\n",
+       "      <td id=\"T_e05f6_row2_col40\" class=\"data row2 col40\" >56</td>\n",
+       "      <td id=\"T_e05f6_row2_col41\" class=\"data row2 col41\" >56</td>\n",
+       "      <td id=\"T_e05f6_row2_col42\" class=\"data row2 col42\" >9.433333</td>\n",
+       "      <td id=\"T_e05f6_row2_col43\" class=\"data row2 col43\" >9.433333</td>\n",
+       "      <td id=\"T_e05f6_row2_col44\" class=\"data row2 col44\" >9.558333</td>\n",
+       "      <td id=\"T_e05f6_row2_col45\" class=\"data row2 col45\" >9.558333</td>\n",
+       "      <td id=\"T_e05f6_row2_col46\" class=\"data row2 col46\" >47.091667</td>\n",
+       "      <td id=\"T_e05f6_row2_col47\" class=\"data row2 col47\" >47.091667</td>\n",
+       "      <td id=\"T_e05f6_row2_col48\" class=\"data row2 col48\" >47.233333</td>\n",
+       "      <td id=\"T_e05f6_row2_col49\" class=\"data row2 col49\" >47.233333</td>\n",
+       "      <td id=\"T_e05f6_row2_col50\" class=\"data row2 col50\" >9.503734</td>\n",
+       "      <td id=\"T_e05f6_row2_col51\" class=\"data row2 col51\" >47.167478</td>\n",
+       "      <td id=\"T_e05f6_row2_col52\" class=\"data row2 col52\" >Europe/Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col53\" class=\"data row2 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col54\" class=\"data row2 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col55\" class=\"data row2 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col56\" class=\"data row2 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col57\" class=\"data row2 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col58\" class=\"data row2 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col59\" class=\"data row2 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col60\" class=\"data row2 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col61\" class=\"data row2 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col62\" class=\"data row2 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col63\" class=\"data row2 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col64\" class=\"data row2 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col65\" class=\"data row2 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col66\" class=\"data row2 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col67\" class=\"data row2 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col68\" class=\"data row2 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col69\" class=\"data row2 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col70\" class=\"data row2 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col71\" class=\"data row2 col71\" >6.700000</td>\n",
+       "      <td id=\"T_e05f6_row2_col72\" class=\"data row2 col72\" >Q1844</td>\n",
+       "      <td id=\"T_e05f6_row2_col73\" class=\"data row2 col73\" >101828603</td>\n",
+       "      <td id=\"T_e05f6_row2_col74\" class=\"data row2 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row2_col75\" class=\"data row2 col75\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col76\" class=\"data row2 col76\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col77\" class=\"data row2 col77\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col78\" class=\"data row2 col78\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col79\" class=\"data row2 col79\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col80\" class=\"data row2 col80\" >Вадуц</td>\n",
+       "      <td id=\"T_e05f6_row2_col81\" class=\"data row2 col81\" >瓦都兹</td>\n",
+       "      <td id=\"T_e05f6_row2_col82\" class=\"data row2 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col83\" class=\"data row2 col83\" >فادوتس</td>\n",
+       "      <td id=\"T_e05f6_row2_col84\" class=\"data row2 col84\" >ফাডুৎস</td>\n",
+       "      <td id=\"T_e05f6_row2_col85\" class=\"data row2 col85\" >Βαντούζ</td>\n",
+       "      <td id=\"T_e05f6_row2_col86\" class=\"data row2 col86\" >वादुज़</td>\n",
+       "      <td id=\"T_e05f6_row2_col87\" class=\"data row2 col87\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col88\" class=\"data row2 col88\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col89\" class=\"data row2 col89\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col90\" class=\"data row2 col90\" >ファドゥーツ</td>\n",
+       "      <td id=\"T_e05f6_row2_col91\" class=\"data row2 col91\" >파두츠</td>\n",
+       "      <td id=\"T_e05f6_row2_col92\" class=\"data row2 col92\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col93\" class=\"data row2 col93\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col94\" class=\"data row2 col94\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col95\" class=\"data row2 col95\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col96\" class=\"data row2 col96\" >Vaduz</td>\n",
+       "      <td id=\"T_e05f6_row2_col97\" class=\"data row2 col97\" >1159146061</td>\n",
+       "      <td id=\"T_e05f6_row2_col98\" class=\"data row2 col98\" >فادوتس</td>\n",
+       "      <td id=\"T_e05f6_row2_col99\" class=\"data row2 col99\" >ואדוץ</td>\n",
+       "      <td id=\"T_e05f6_row2_col100\" class=\"data row2 col100\" >Вадуц</td>\n",
+       "      <td id=\"T_e05f6_row2_col101\" class=\"data row2 col101\" >واڈوز</td>\n",
+       "      <td id=\"T_e05f6_row2_col102\" class=\"data row2 col102\" >華杜茲</td>\n",
+       "      <td id=\"T_e05f6_row2_col103\" class=\"data row2 col103\" >3042030</td>\n",
+       "      <td id=\"T_e05f6_row2_col104\" class=\"data row2 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col105\" class=\"data row2 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col106\" class=\"data row2 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col107\" class=\"data row2 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col108\" class=\"data row2 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col109\" class=\"data row2 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col110\" class=\"data row2 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col111\" class=\"data row2 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col112\" class=\"data row2 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col113\" class=\"data row2 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col114\" class=\"data row2 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col115\" class=\"data row2 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col116\" class=\"data row2 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col117\" class=\"data row2 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col118\" class=\"data row2 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col119\" class=\"data row2 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col120\" class=\"data row2 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col121\" class=\"data row2 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col122\" class=\"data row2 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col123\" class=\"data row2 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col124\" class=\"data row2 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col125\" class=\"data row2 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col126\" class=\"data row2 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col127\" class=\"data row2 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col128\" class=\"data row2 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col129\" class=\"data row2 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col130\" class=\"data row2 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col131\" class=\"data row2 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col132\" class=\"data row2 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col133\" class=\"data row2 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col134\" class=\"data row2 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col135\" class=\"data row2 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col136\" class=\"data row2 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row2_col137\" class=\"data row2 col137\" >POINT (9.5166695 47.1337238)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_f91f0_row3_col0\" class=\"data row3 col0\" >6</td>\n",
-       "      <td id=\"T_f91f0_row3_col1\" class=\"data row3 col1\" >30</td>\n",
-       "      <td id=\"T_f91f0_row3_col2\" class=\"data row3 col2\" >8</td>\n",
-       "      <td id=\"T_f91f0_row3_col3\" class=\"data row3 col3\" >Admin-0 capital alt</td>\n",
-       "      <td id=\"T_f91f0_row3_col4\" class=\"data row3 col4\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col7\" class=\"data row3 col7\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col8\" class=\"data row3 col8\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col9\" class=\"data row3 col9\" >Legislative and</td>\n",
-       "      <td id=\"T_f91f0_row3_col10\" class=\"data row3 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col11\" class=\"data row3 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col12\" class=\"data row3 col12\" >eSwatini</td>\n",
-       "      <td id=\"T_f91f0_row3_col13\" class=\"data row3 col13\" >SWZ</td>\n",
-       "      <td id=\"T_f91f0_row3_col14\" class=\"data row3 col14\" >eSwatini</td>\n",
-       "      <td id=\"T_f91f0_row3_col15\" class=\"data row3 col15\" >SWZ</td>\n",
-       "      <td id=\"T_f91f0_row3_col16\" class=\"data row3 col16\" >Manzini</td>\n",
-       "      <td id=\"T_f91f0_row3_col17\" class=\"data row3 col17\" >SZ</td>\n",
-       "      <td id=\"T_f91f0_row3_col18\" class=\"data row3 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col19\" class=\"data row3 col19\" >-26.466668</td>\n",
-       "      <td id=\"T_f91f0_row3_col20\" class=\"data row3 col20\" >31.199997</td>\n",
-       "      <td id=\"T_f91f0_row3_col21\" class=\"data row3 col21\" >9782</td>\n",
-       "      <td id=\"T_f91f0_row3_col22\" class=\"data row3 col22\" >4557</td>\n",
-       "      <td id=\"T_f91f0_row3_col23\" class=\"data row3 col23\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col24\" class=\"data row3 col24\" >5</td>\n",
-       "      <td id=\"T_f91f0_row3_col25\" class=\"data row3 col25\" >4</td>\n",
-       "      <td id=\"T_f91f0_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col27\" class=\"data row3 col27\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col28\" class=\"data row3 col28\" >9782</td>\n",
-       "      <td id=\"T_f91f0_row3_col29\" class=\"data row3 col29\" >9782</td>\n",
-       "      <td id=\"T_f91f0_row3_col30\" class=\"data row3 col30\" >9782</td>\n",
-       "      <td id=\"T_f91f0_row3_col31\" class=\"data row3 col31\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col32\" class=\"data row3 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col33\" class=\"data row3 col33\" >50</td>\n",
-       "      <td id=\"T_f91f0_row3_col34\" class=\"data row3 col34\" >18</td>\n",
-       "      <td id=\"T_f91f0_row3_col35\" class=\"data row3 col35\" >18</td>\n",
-       "      <td id=\"T_f91f0_row3_col36\" class=\"data row3 col36\" >7</td>\n",
-       "      <td id=\"T_f91f0_row3_col37\" class=\"data row3 col37\" >7</td>\n",
-       "      <td id=\"T_f91f0_row3_col38\" class=\"data row3 col38\" >32</td>\n",
-       "      <td id=\"T_f91f0_row3_col39\" class=\"data row3 col39\" >32</td>\n",
-       "      <td id=\"T_f91f0_row3_col40\" class=\"data row3 col40\" >20</td>\n",
-       "      <td id=\"T_f91f0_row3_col41\" class=\"data row3 col41\" >20</td>\n",
-       "      <td id=\"T_f91f0_row3_col42\" class=\"data row3 col42\" >31.183333</td>\n",
-       "      <td id=\"T_f91f0_row3_col43\" class=\"data row3 col43\" >31.183333</td>\n",
-       "      <td id=\"T_f91f0_row3_col44\" class=\"data row3 col44\" >31.233333</td>\n",
-       "      <td id=\"T_f91f0_row3_col45\" class=\"data row3 col45\" >31.233333</td>\n",
-       "      <td id=\"T_f91f0_row3_col46\" class=\"data row3 col46\" >-26.458333</td>\n",
-       "      <td id=\"T_f91f0_row3_col47\" class=\"data row3 col47\" >-26.458333</td>\n",
-       "      <td id=\"T_f91f0_row3_col48\" class=\"data row3 col48\" >-26.391667</td>\n",
-       "      <td id=\"T_f91f0_row3_col49\" class=\"data row3 col49\" >-26.391667</td>\n",
-       "      <td id=\"T_f91f0_row3_col50\" class=\"data row3 col50\" >31.201993</td>\n",
-       "      <td id=\"T_f91f0_row3_col51\" class=\"data row3 col51\" >-26.430254</td>\n",
-       "      <td id=\"T_f91f0_row3_col52\" class=\"data row3 col52\" >Africa/Mbabane</td>\n",
-       "      <td id=\"T_f91f0_row3_col53\" class=\"data row3 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col54\" class=\"data row3 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col55\" class=\"data row3 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col56\" class=\"data row3 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col57\" class=\"data row3 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col58\" class=\"data row3 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col59\" class=\"data row3 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col60\" class=\"data row3 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col61\" class=\"data row3 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col62\" class=\"data row3 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col63\" class=\"data row3 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col64\" class=\"data row3 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col65\" class=\"data row3 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col66\" class=\"data row3 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col67\" class=\"data row3 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col68\" class=\"data row3 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col69\" class=\"data row3 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col70\" class=\"data row3 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row3_col71\" class=\"data row3 col71\" >6.000000</td>\n",
-       "      <td id=\"T_f91f0_row3_col72\" class=\"data row3 col72\" >Q101418</td>\n",
-       "      <td id=\"T_f91f0_row3_col73\" class=\"data row3 col73\" >421199783</td>\n",
-       "      <td id=\"T_f91f0_row3_col74\" class=\"data row3 col74\" >1</td>\n",
-       "      <td id=\"T_f91f0_row3_col75\" class=\"data row3 col75\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col76\" class=\"data row3 col76\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col77\" class=\"data row3 col77\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col78\" class=\"data row3 col78\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col79\" class=\"data row3 col79\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col80\" class=\"data row3 col80\" >Лобамба</td>\n",
-       "      <td id=\"T_f91f0_row3_col81\" class=\"data row3 col81\" >洛班巴</td>\n",
-       "      <td id=\"T_f91f0_row3_col82\" class=\"data row3 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col83\" class=\"data row3 col83\" >لوبامبا</td>\n",
-       "      <td id=\"T_f91f0_row3_col84\" class=\"data row3 col84\" >লোবাম্বা</td>\n",
-       "      <td id=\"T_f91f0_row3_col85\" class=\"data row3 col85\" >Λομπάμπα</td>\n",
-       "      <td id=\"T_f91f0_row3_col86\" class=\"data row3 col86\" >लोबम्बा</td>\n",
-       "      <td id=\"T_f91f0_row3_col87\" class=\"data row3 col87\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col88\" class=\"data row3 col88\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col89\" class=\"data row3 col89\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col90\" class=\"data row3 col90\" >ロバンバ</td>\n",
-       "      <td id=\"T_f91f0_row3_col91\" class=\"data row3 col91\" >로밤바</td>\n",
-       "      <td id=\"T_f91f0_row3_col92\" class=\"data row3 col92\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col93\" class=\"data row3 col93\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col94\" class=\"data row3 col94\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col95\" class=\"data row3 col95\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col96\" class=\"data row3 col96\" >Lobamba</td>\n",
-       "      <td id=\"T_f91f0_row3_col97\" class=\"data row3 col97\" >1159146343</td>\n",
-       "      <td id=\"T_f91f0_row3_col98\" class=\"data row3 col98\" >لوبامبا</td>\n",
-       "      <td id=\"T_f91f0_row3_col99\" class=\"data row3 col99\" >לובמבה</td>\n",
-       "      <td id=\"T_f91f0_row3_col100\" class=\"data row3 col100\" >Лобамба</td>\n",
-       "      <td id=\"T_f91f0_row3_col101\" class=\"data row3 col101\" >لوبامبا</td>\n",
-       "      <td id=\"T_f91f0_row3_col102\" class=\"data row3 col102\" >洛班巴</td>\n",
-       "      <td id=\"T_f91f0_row3_col103\" class=\"data row3 col103\" >935048</td>\n",
-       "      <td id=\"T_f91f0_row3_col104\" class=\"data row3 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col105\" class=\"data row3 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col106\" class=\"data row3 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col107\" class=\"data row3 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col108\" class=\"data row3 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col109\" class=\"data row3 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col110\" class=\"data row3 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col111\" class=\"data row3 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col112\" class=\"data row3 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col113\" class=\"data row3 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col114\" class=\"data row3 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col115\" class=\"data row3 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col116\" class=\"data row3 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col117\" class=\"data row3 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col118\" class=\"data row3 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col119\" class=\"data row3 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col120\" class=\"data row3 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col121\" class=\"data row3 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col122\" class=\"data row3 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col123\" class=\"data row3 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col124\" class=\"data row3 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col125\" class=\"data row3 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col126\" class=\"data row3 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col127\" class=\"data row3 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col128\" class=\"data row3 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col129\" class=\"data row3 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col130\" class=\"data row3 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col131\" class=\"data row3 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col132\" class=\"data row3 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col133\" class=\"data row3 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col134\" class=\"data row3 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col135\" class=\"data row3 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col136\" class=\"data row3 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row3_col137\" class=\"data row3 col137\" >POINT (31.1999971 -26.4666675)</td>\n",
+       "      <th id=\"T_e05f6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_e05f6_row3_col0\" class=\"data row3 col0\" >6</td>\n",
+       "      <td id=\"T_e05f6_row3_col1\" class=\"data row3 col1\" >30</td>\n",
+       "      <td id=\"T_e05f6_row3_col2\" class=\"data row3 col2\" >8</td>\n",
+       "      <td id=\"T_e05f6_row3_col3\" class=\"data row3 col3\" >Admin-0 capital alt</td>\n",
+       "      <td id=\"T_e05f6_row3_col4\" class=\"data row3 col4\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col7\" class=\"data row3 col7\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col8\" class=\"data row3 col8\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col9\" class=\"data row3 col9\" >Legislative and</td>\n",
+       "      <td id=\"T_e05f6_row3_col10\" class=\"data row3 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col11\" class=\"data row3 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col12\" class=\"data row3 col12\" >eSwatini</td>\n",
+       "      <td id=\"T_e05f6_row3_col13\" class=\"data row3 col13\" >SWZ</td>\n",
+       "      <td id=\"T_e05f6_row3_col14\" class=\"data row3 col14\" >eSwatini</td>\n",
+       "      <td id=\"T_e05f6_row3_col15\" class=\"data row3 col15\" >SWZ</td>\n",
+       "      <td id=\"T_e05f6_row3_col16\" class=\"data row3 col16\" >Manzini</td>\n",
+       "      <td id=\"T_e05f6_row3_col17\" class=\"data row3 col17\" >SZ</td>\n",
+       "      <td id=\"T_e05f6_row3_col18\" class=\"data row3 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col19\" class=\"data row3 col19\" >-26.466668</td>\n",
+       "      <td id=\"T_e05f6_row3_col20\" class=\"data row3 col20\" >31.199997</td>\n",
+       "      <td id=\"T_e05f6_row3_col21\" class=\"data row3 col21\" >9782</td>\n",
+       "      <td id=\"T_e05f6_row3_col22\" class=\"data row3 col22\" >4557</td>\n",
+       "      <td id=\"T_e05f6_row3_col23\" class=\"data row3 col23\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col24\" class=\"data row3 col24\" >5</td>\n",
+       "      <td id=\"T_e05f6_row3_col25\" class=\"data row3 col25\" >4</td>\n",
+       "      <td id=\"T_e05f6_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col27\" class=\"data row3 col27\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col28\" class=\"data row3 col28\" >9782</td>\n",
+       "      <td id=\"T_e05f6_row3_col29\" class=\"data row3 col29\" >9782</td>\n",
+       "      <td id=\"T_e05f6_row3_col30\" class=\"data row3 col30\" >9782</td>\n",
+       "      <td id=\"T_e05f6_row3_col31\" class=\"data row3 col31\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col32\" class=\"data row3 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col33\" class=\"data row3 col33\" >50</td>\n",
+       "      <td id=\"T_e05f6_row3_col34\" class=\"data row3 col34\" >18</td>\n",
+       "      <td id=\"T_e05f6_row3_col35\" class=\"data row3 col35\" >18</td>\n",
+       "      <td id=\"T_e05f6_row3_col36\" class=\"data row3 col36\" >7</td>\n",
+       "      <td id=\"T_e05f6_row3_col37\" class=\"data row3 col37\" >7</td>\n",
+       "      <td id=\"T_e05f6_row3_col38\" class=\"data row3 col38\" >32</td>\n",
+       "      <td id=\"T_e05f6_row3_col39\" class=\"data row3 col39\" >32</td>\n",
+       "      <td id=\"T_e05f6_row3_col40\" class=\"data row3 col40\" >20</td>\n",
+       "      <td id=\"T_e05f6_row3_col41\" class=\"data row3 col41\" >20</td>\n",
+       "      <td id=\"T_e05f6_row3_col42\" class=\"data row3 col42\" >31.183333</td>\n",
+       "      <td id=\"T_e05f6_row3_col43\" class=\"data row3 col43\" >31.183333</td>\n",
+       "      <td id=\"T_e05f6_row3_col44\" class=\"data row3 col44\" >31.233333</td>\n",
+       "      <td id=\"T_e05f6_row3_col45\" class=\"data row3 col45\" >31.233333</td>\n",
+       "      <td id=\"T_e05f6_row3_col46\" class=\"data row3 col46\" >-26.458333</td>\n",
+       "      <td id=\"T_e05f6_row3_col47\" class=\"data row3 col47\" >-26.458333</td>\n",
+       "      <td id=\"T_e05f6_row3_col48\" class=\"data row3 col48\" >-26.391667</td>\n",
+       "      <td id=\"T_e05f6_row3_col49\" class=\"data row3 col49\" >-26.391667</td>\n",
+       "      <td id=\"T_e05f6_row3_col50\" class=\"data row3 col50\" >31.201993</td>\n",
+       "      <td id=\"T_e05f6_row3_col51\" class=\"data row3 col51\" >-26.430254</td>\n",
+       "      <td id=\"T_e05f6_row3_col52\" class=\"data row3 col52\" >Africa/Mbabane</td>\n",
+       "      <td id=\"T_e05f6_row3_col53\" class=\"data row3 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col54\" class=\"data row3 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col55\" class=\"data row3 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col56\" class=\"data row3 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col57\" class=\"data row3 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col58\" class=\"data row3 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col59\" class=\"data row3 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col60\" class=\"data row3 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col61\" class=\"data row3 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col62\" class=\"data row3 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col63\" class=\"data row3 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col64\" class=\"data row3 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col65\" class=\"data row3 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col66\" class=\"data row3 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col67\" class=\"data row3 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col68\" class=\"data row3 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col69\" class=\"data row3 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col70\" class=\"data row3 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row3_col71\" class=\"data row3 col71\" >6.000000</td>\n",
+       "      <td id=\"T_e05f6_row3_col72\" class=\"data row3 col72\" >Q101418</td>\n",
+       "      <td id=\"T_e05f6_row3_col73\" class=\"data row3 col73\" >421199783</td>\n",
+       "      <td id=\"T_e05f6_row3_col74\" class=\"data row3 col74\" >1</td>\n",
+       "      <td id=\"T_e05f6_row3_col75\" class=\"data row3 col75\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col76\" class=\"data row3 col76\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col77\" class=\"data row3 col77\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col78\" class=\"data row3 col78\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col79\" class=\"data row3 col79\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col80\" class=\"data row3 col80\" >Лобамба</td>\n",
+       "      <td id=\"T_e05f6_row3_col81\" class=\"data row3 col81\" >洛班巴</td>\n",
+       "      <td id=\"T_e05f6_row3_col82\" class=\"data row3 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col83\" class=\"data row3 col83\" >لوبامبا</td>\n",
+       "      <td id=\"T_e05f6_row3_col84\" class=\"data row3 col84\" >লোবাম্বা</td>\n",
+       "      <td id=\"T_e05f6_row3_col85\" class=\"data row3 col85\" >Λομπάμπα</td>\n",
+       "      <td id=\"T_e05f6_row3_col86\" class=\"data row3 col86\" >लोबम्बा</td>\n",
+       "      <td id=\"T_e05f6_row3_col87\" class=\"data row3 col87\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col88\" class=\"data row3 col88\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col89\" class=\"data row3 col89\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col90\" class=\"data row3 col90\" >ロバンバ</td>\n",
+       "      <td id=\"T_e05f6_row3_col91\" class=\"data row3 col91\" >로밤바</td>\n",
+       "      <td id=\"T_e05f6_row3_col92\" class=\"data row3 col92\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col93\" class=\"data row3 col93\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col94\" class=\"data row3 col94\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col95\" class=\"data row3 col95\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col96\" class=\"data row3 col96\" >Lobamba</td>\n",
+       "      <td id=\"T_e05f6_row3_col97\" class=\"data row3 col97\" >1159146343</td>\n",
+       "      <td id=\"T_e05f6_row3_col98\" class=\"data row3 col98\" >لوبامبا</td>\n",
+       "      <td id=\"T_e05f6_row3_col99\" class=\"data row3 col99\" >לובמבה</td>\n",
+       "      <td id=\"T_e05f6_row3_col100\" class=\"data row3 col100\" >Лобамба</td>\n",
+       "      <td id=\"T_e05f6_row3_col101\" class=\"data row3 col101\" >لوبامبا</td>\n",
+       "      <td id=\"T_e05f6_row3_col102\" class=\"data row3 col102\" >洛班巴</td>\n",
+       "      <td id=\"T_e05f6_row3_col103\" class=\"data row3 col103\" >935048</td>\n",
+       "      <td id=\"T_e05f6_row3_col104\" class=\"data row3 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col105\" class=\"data row3 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col106\" class=\"data row3 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col107\" class=\"data row3 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col108\" class=\"data row3 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col109\" class=\"data row3 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col110\" class=\"data row3 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col111\" class=\"data row3 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col112\" class=\"data row3 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col113\" class=\"data row3 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col114\" class=\"data row3 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col115\" class=\"data row3 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col116\" class=\"data row3 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col117\" class=\"data row3 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col118\" class=\"data row3 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col119\" class=\"data row3 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col120\" class=\"data row3 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col121\" class=\"data row3 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col122\" class=\"data row3 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col123\" class=\"data row3 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col124\" class=\"data row3 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col125\" class=\"data row3 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col126\" class=\"data row3 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col127\" class=\"data row3 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col128\" class=\"data row3 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col129\" class=\"data row3 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col130\" class=\"data row3 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col131\" class=\"data row3 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col132\" class=\"data row3 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col133\" class=\"data row3 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col134\" class=\"data row3 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col135\" class=\"data row3 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col136\" class=\"data row3 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row3_col137\" class=\"data row3 col137\" >POINT (31.1999971 -26.4666675)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_f91f0_row4_col0\" class=\"data row4 col0\" >6</td>\n",
-       "      <td id=\"T_f91f0_row4_col1\" class=\"data row4 col1\" >30</td>\n",
-       "      <td id=\"T_f91f0_row4_col2\" class=\"data row4 col2\" >8</td>\n",
-       "      <td id=\"T_f91f0_row4_col3\" class=\"data row4 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row4_col4\" class=\"data row4 col4\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col7\" class=\"data row4 col7\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col8\" class=\"data row4 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row4_col9\" class=\"data row4 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col10\" class=\"data row4 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col11\" class=\"data row4 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col12\" class=\"data row4 col12\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col13\" class=\"data row4 col13\" >LUX</td>\n",
-       "      <td id=\"T_f91f0_row4_col14\" class=\"data row4 col14\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col15\" class=\"data row4 col15\" >LUX</td>\n",
-       "      <td id=\"T_f91f0_row4_col16\" class=\"data row4 col16\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col17\" class=\"data row4 col17\" >LU</td>\n",
-       "      <td id=\"T_f91f0_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col19\" class=\"data row4 col19\" >49.611660</td>\n",
-       "      <td id=\"T_f91f0_row4_col20\" class=\"data row4 col20\" >6.130003</td>\n",
-       "      <td id=\"T_f91f0_row4_col21\" class=\"data row4 col21\" >107260</td>\n",
-       "      <td id=\"T_f91f0_row4_col22\" class=\"data row4 col22\" >76684</td>\n",
-       "      <td id=\"T_f91f0_row4_col23\" class=\"data row4 col23\" >106219</td>\n",
-       "      <td id=\"T_f91f0_row4_col24\" class=\"data row4 col24\" >9</td>\n",
-       "      <td id=\"T_f91f0_row4_col25\" class=\"data row4 col25\" >8</td>\n",
-       "      <td id=\"T_f91f0_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col27\" class=\"data row4 col27\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col28\" class=\"data row4 col28\" >107260</td>\n",
-       "      <td id=\"T_f91f0_row4_col29\" class=\"data row4 col29\" >107260</td>\n",
-       "      <td id=\"T_f91f0_row4_col30\" class=\"data row4 col30\" >107260</td>\n",
-       "      <td id=\"T_f91f0_row4_col31\" class=\"data row4 col31\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col32\" class=\"data row4 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col33\" class=\"data row4 col33\" >50</td>\n",
-       "      <td id=\"T_f91f0_row4_col34\" class=\"data row4 col34\" >60</td>\n",
-       "      <td id=\"T_f91f0_row4_col35\" class=\"data row4 col35\" >60</td>\n",
-       "      <td id=\"T_f91f0_row4_col36\" class=\"data row4 col36\" >23</td>\n",
-       "      <td id=\"T_f91f0_row4_col37\" class=\"data row4 col37\" >23</td>\n",
-       "      <td id=\"T_f91f0_row4_col38\" class=\"data row4 col38\" >71</td>\n",
-       "      <td id=\"T_f91f0_row4_col39\" class=\"data row4 col39\" >71</td>\n",
-       "      <td id=\"T_f91f0_row4_col40\" class=\"data row4 col40\" >44</td>\n",
-       "      <td id=\"T_f91f0_row4_col41\" class=\"data row4 col41\" >44</td>\n",
-       "      <td id=\"T_f91f0_row4_col42\" class=\"data row4 col42\" >6.041667</td>\n",
-       "      <td id=\"T_f91f0_row4_col43\" class=\"data row4 col43\" >6.041667</td>\n",
-       "      <td id=\"T_f91f0_row4_col44\" class=\"data row4 col44\" >6.183333</td>\n",
-       "      <td id=\"T_f91f0_row4_col45\" class=\"data row4 col45\" >6.183333</td>\n",
-       "      <td id=\"T_f91f0_row4_col46\" class=\"data row4 col46\" >49.558333</td>\n",
-       "      <td id=\"T_f91f0_row4_col47\" class=\"data row4 col47\" >49.558333</td>\n",
-       "      <td id=\"T_f91f0_row4_col48\" class=\"data row4 col48\" >49.708333</td>\n",
-       "      <td id=\"T_f91f0_row4_col49\" class=\"data row4 col49\" >49.708333</td>\n",
-       "      <td id=\"T_f91f0_row4_col50\" class=\"data row4 col50\" >6.125273</td>\n",
-       "      <td id=\"T_f91f0_row4_col51\" class=\"data row4 col51\" >49.620833</td>\n",
-       "      <td id=\"T_f91f0_row4_col52\" class=\"data row4 col52\" >Europe/Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col53\" class=\"data row4 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col54\" class=\"data row4 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col55\" class=\"data row4 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col56\" class=\"data row4 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col57\" class=\"data row4 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col58\" class=\"data row4 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col59\" class=\"data row4 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col60\" class=\"data row4 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col61\" class=\"data row4 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col62\" class=\"data row4 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col63\" class=\"data row4 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col64\" class=\"data row4 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col65\" class=\"data row4 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col66\" class=\"data row4 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col67\" class=\"data row4 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col68\" class=\"data row4 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col69\" class=\"data row4 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col70\" class=\"data row4 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col71\" class=\"data row4 col71\" >6.000000</td>\n",
-       "      <td id=\"T_f91f0_row4_col72\" class=\"data row4 col72\" >Q1842</td>\n",
-       "      <td id=\"T_f91f0_row4_col73\" class=\"data row4 col73\" >101751765</td>\n",
-       "      <td id=\"T_f91f0_row4_col74\" class=\"data row4 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row4_col75\" class=\"data row4 col75\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col76\" class=\"data row4 col76\" >Luxemburg</td>\n",
-       "      <td id=\"T_f91f0_row4_col77\" class=\"data row4 col77\" >Luxemburgo</td>\n",
-       "      <td id=\"T_f91f0_row4_col78\" class=\"data row4 col78\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col79\" class=\"data row4 col79\" >Luxemburgo</td>\n",
-       "      <td id=\"T_f91f0_row4_col80\" class=\"data row4 col80\" >Люксембург</td>\n",
-       "      <td id=\"T_f91f0_row4_col81\" class=\"data row4 col81\" >卢森堡</td>\n",
-       "      <td id=\"T_f91f0_row4_col82\" class=\"data row4 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col83\" class=\"data row4 col83\" >مدينة لوكسمبورغ</td>\n",
-       "      <td id=\"T_f91f0_row4_col84\" class=\"data row4 col84\" >লুক্সেমবুর্গ শহর</td>\n",
-       "      <td id=\"T_f91f0_row4_col85\" class=\"data row4 col85\" >Λουξεμβούργο</td>\n",
-       "      <td id=\"T_f91f0_row4_col86\" class=\"data row4 col86\" >लक्ज़मबर्ग नगर</td>\n",
-       "      <td id=\"T_f91f0_row4_col87\" class=\"data row4 col87\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col88\" class=\"data row4 col88\" >Luksemburg</td>\n",
-       "      <td id=\"T_f91f0_row4_col89\" class=\"data row4 col89\" >Lussemburgo</td>\n",
-       "      <td id=\"T_f91f0_row4_col90\" class=\"data row4 col90\" >ルクセンブルク市</td>\n",
-       "      <td id=\"T_f91f0_row4_col91\" class=\"data row4 col91\" >룩셈부르크</td>\n",
-       "      <td id=\"T_f91f0_row4_col92\" class=\"data row4 col92\" >Luxemburg</td>\n",
-       "      <td id=\"T_f91f0_row4_col93\" class=\"data row4 col93\" >Luksemburg</td>\n",
-       "      <td id=\"T_f91f0_row4_col94\" class=\"data row4 col94\" >Luxemburg</td>\n",
-       "      <td id=\"T_f91f0_row4_col95\" class=\"data row4 col95\" >Lüksemburg</td>\n",
-       "      <td id=\"T_f91f0_row4_col96\" class=\"data row4 col96\" >Luxembourg</td>\n",
-       "      <td id=\"T_f91f0_row4_col97\" class=\"data row4 col97\" >1159146437</td>\n",
-       "      <td id=\"T_f91f0_row4_col98\" class=\"data row4 col98\" >لوکزامبورگ</td>\n",
-       "      <td id=\"T_f91f0_row4_col99\" class=\"data row4 col99\" >לוקסמבורג</td>\n",
-       "      <td id=\"T_f91f0_row4_col100\" class=\"data row4 col100\" >Люксембург</td>\n",
-       "      <td id=\"T_f91f0_row4_col101\" class=\"data row4 col101\" >لکسمبرگ</td>\n",
-       "      <td id=\"T_f91f0_row4_col102\" class=\"data row4 col102\" >盧森堡市</td>\n",
-       "      <td id=\"T_f91f0_row4_col103\" class=\"data row4 col103\" >2960316</td>\n",
-       "      <td id=\"T_f91f0_row4_col104\" class=\"data row4 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col105\" class=\"data row4 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col106\" class=\"data row4 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col107\" class=\"data row4 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col108\" class=\"data row4 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col109\" class=\"data row4 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col110\" class=\"data row4 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col111\" class=\"data row4 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col112\" class=\"data row4 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col113\" class=\"data row4 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col114\" class=\"data row4 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col115\" class=\"data row4 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col116\" class=\"data row4 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col117\" class=\"data row4 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col118\" class=\"data row4 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col119\" class=\"data row4 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col120\" class=\"data row4 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col121\" class=\"data row4 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col122\" class=\"data row4 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col123\" class=\"data row4 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col124\" class=\"data row4 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col125\" class=\"data row4 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col126\" class=\"data row4 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col127\" class=\"data row4 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col128\" class=\"data row4 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col129\" class=\"data row4 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col130\" class=\"data row4 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col131\" class=\"data row4 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col132\" class=\"data row4 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col133\" class=\"data row4 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col134\" class=\"data row4 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col135\" class=\"data row4 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col136\" class=\"data row4 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row4_col137\" class=\"data row4 col137\" >POINT (6.1300028 49.6116604)</td>\n",
+       "      <th id=\"T_e05f6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_e05f6_row4_col0\" class=\"data row4 col0\" >6</td>\n",
+       "      <td id=\"T_e05f6_row4_col1\" class=\"data row4 col1\" >30</td>\n",
+       "      <td id=\"T_e05f6_row4_col2\" class=\"data row4 col2\" >8</td>\n",
+       "      <td id=\"T_e05f6_row4_col3\" class=\"data row4 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row4_col4\" class=\"data row4 col4\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col7\" class=\"data row4 col7\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col8\" class=\"data row4 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row4_col9\" class=\"data row4 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col10\" class=\"data row4 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col11\" class=\"data row4 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col12\" class=\"data row4 col12\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col13\" class=\"data row4 col13\" >LUX</td>\n",
+       "      <td id=\"T_e05f6_row4_col14\" class=\"data row4 col14\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col15\" class=\"data row4 col15\" >LUX</td>\n",
+       "      <td id=\"T_e05f6_row4_col16\" class=\"data row4 col16\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col17\" class=\"data row4 col17\" >LU</td>\n",
+       "      <td id=\"T_e05f6_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col19\" class=\"data row4 col19\" >49.611660</td>\n",
+       "      <td id=\"T_e05f6_row4_col20\" class=\"data row4 col20\" >6.130003</td>\n",
+       "      <td id=\"T_e05f6_row4_col21\" class=\"data row4 col21\" >107260</td>\n",
+       "      <td id=\"T_e05f6_row4_col22\" class=\"data row4 col22\" >76684</td>\n",
+       "      <td id=\"T_e05f6_row4_col23\" class=\"data row4 col23\" >106219</td>\n",
+       "      <td id=\"T_e05f6_row4_col24\" class=\"data row4 col24\" >9</td>\n",
+       "      <td id=\"T_e05f6_row4_col25\" class=\"data row4 col25\" >8</td>\n",
+       "      <td id=\"T_e05f6_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col27\" class=\"data row4 col27\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col28\" class=\"data row4 col28\" >107260</td>\n",
+       "      <td id=\"T_e05f6_row4_col29\" class=\"data row4 col29\" >107260</td>\n",
+       "      <td id=\"T_e05f6_row4_col30\" class=\"data row4 col30\" >107260</td>\n",
+       "      <td id=\"T_e05f6_row4_col31\" class=\"data row4 col31\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col32\" class=\"data row4 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col33\" class=\"data row4 col33\" >50</td>\n",
+       "      <td id=\"T_e05f6_row4_col34\" class=\"data row4 col34\" >60</td>\n",
+       "      <td id=\"T_e05f6_row4_col35\" class=\"data row4 col35\" >60</td>\n",
+       "      <td id=\"T_e05f6_row4_col36\" class=\"data row4 col36\" >23</td>\n",
+       "      <td id=\"T_e05f6_row4_col37\" class=\"data row4 col37\" >23</td>\n",
+       "      <td id=\"T_e05f6_row4_col38\" class=\"data row4 col38\" >71</td>\n",
+       "      <td id=\"T_e05f6_row4_col39\" class=\"data row4 col39\" >71</td>\n",
+       "      <td id=\"T_e05f6_row4_col40\" class=\"data row4 col40\" >44</td>\n",
+       "      <td id=\"T_e05f6_row4_col41\" class=\"data row4 col41\" >44</td>\n",
+       "      <td id=\"T_e05f6_row4_col42\" class=\"data row4 col42\" >6.041667</td>\n",
+       "      <td id=\"T_e05f6_row4_col43\" class=\"data row4 col43\" >6.041667</td>\n",
+       "      <td id=\"T_e05f6_row4_col44\" class=\"data row4 col44\" >6.183333</td>\n",
+       "      <td id=\"T_e05f6_row4_col45\" class=\"data row4 col45\" >6.183333</td>\n",
+       "      <td id=\"T_e05f6_row4_col46\" class=\"data row4 col46\" >49.558333</td>\n",
+       "      <td id=\"T_e05f6_row4_col47\" class=\"data row4 col47\" >49.558333</td>\n",
+       "      <td id=\"T_e05f6_row4_col48\" class=\"data row4 col48\" >49.708333</td>\n",
+       "      <td id=\"T_e05f6_row4_col49\" class=\"data row4 col49\" >49.708333</td>\n",
+       "      <td id=\"T_e05f6_row4_col50\" class=\"data row4 col50\" >6.125273</td>\n",
+       "      <td id=\"T_e05f6_row4_col51\" class=\"data row4 col51\" >49.620833</td>\n",
+       "      <td id=\"T_e05f6_row4_col52\" class=\"data row4 col52\" >Europe/Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col53\" class=\"data row4 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col54\" class=\"data row4 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col55\" class=\"data row4 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col56\" class=\"data row4 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col57\" class=\"data row4 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col58\" class=\"data row4 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col59\" class=\"data row4 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col60\" class=\"data row4 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col61\" class=\"data row4 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col62\" class=\"data row4 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col63\" class=\"data row4 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col64\" class=\"data row4 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col65\" class=\"data row4 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col66\" class=\"data row4 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col67\" class=\"data row4 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col68\" class=\"data row4 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col69\" class=\"data row4 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col70\" class=\"data row4 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col71\" class=\"data row4 col71\" >6.000000</td>\n",
+       "      <td id=\"T_e05f6_row4_col72\" class=\"data row4 col72\" >Q1842</td>\n",
+       "      <td id=\"T_e05f6_row4_col73\" class=\"data row4 col73\" >101751765</td>\n",
+       "      <td id=\"T_e05f6_row4_col74\" class=\"data row4 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row4_col75\" class=\"data row4 col75\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col76\" class=\"data row4 col76\" >Luxemburg</td>\n",
+       "      <td id=\"T_e05f6_row4_col77\" class=\"data row4 col77\" >Luxemburgo</td>\n",
+       "      <td id=\"T_e05f6_row4_col78\" class=\"data row4 col78\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col79\" class=\"data row4 col79\" >Luxemburgo</td>\n",
+       "      <td id=\"T_e05f6_row4_col80\" class=\"data row4 col80\" >Люксембург</td>\n",
+       "      <td id=\"T_e05f6_row4_col81\" class=\"data row4 col81\" >卢森堡</td>\n",
+       "      <td id=\"T_e05f6_row4_col82\" class=\"data row4 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col83\" class=\"data row4 col83\" >مدينة لوكسمبورغ</td>\n",
+       "      <td id=\"T_e05f6_row4_col84\" class=\"data row4 col84\" >লুক্সেমবুর্গ শহর</td>\n",
+       "      <td id=\"T_e05f6_row4_col85\" class=\"data row4 col85\" >Λουξεμβούργο</td>\n",
+       "      <td id=\"T_e05f6_row4_col86\" class=\"data row4 col86\" >लक्ज़मबर्ग नगर</td>\n",
+       "      <td id=\"T_e05f6_row4_col87\" class=\"data row4 col87\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col88\" class=\"data row4 col88\" >Luksemburg</td>\n",
+       "      <td id=\"T_e05f6_row4_col89\" class=\"data row4 col89\" >Lussemburgo</td>\n",
+       "      <td id=\"T_e05f6_row4_col90\" class=\"data row4 col90\" >ルクセンブルク市</td>\n",
+       "      <td id=\"T_e05f6_row4_col91\" class=\"data row4 col91\" >룩셈부르크</td>\n",
+       "      <td id=\"T_e05f6_row4_col92\" class=\"data row4 col92\" >Luxemburg</td>\n",
+       "      <td id=\"T_e05f6_row4_col93\" class=\"data row4 col93\" >Luksemburg</td>\n",
+       "      <td id=\"T_e05f6_row4_col94\" class=\"data row4 col94\" >Luxemburg</td>\n",
+       "      <td id=\"T_e05f6_row4_col95\" class=\"data row4 col95\" >Lüksemburg</td>\n",
+       "      <td id=\"T_e05f6_row4_col96\" class=\"data row4 col96\" >Luxembourg</td>\n",
+       "      <td id=\"T_e05f6_row4_col97\" class=\"data row4 col97\" >1159146437</td>\n",
+       "      <td id=\"T_e05f6_row4_col98\" class=\"data row4 col98\" >لوکزامبورگ</td>\n",
+       "      <td id=\"T_e05f6_row4_col99\" class=\"data row4 col99\" >לוקסמבורג</td>\n",
+       "      <td id=\"T_e05f6_row4_col100\" class=\"data row4 col100\" >Люксембург</td>\n",
+       "      <td id=\"T_e05f6_row4_col101\" class=\"data row4 col101\" >لکسمبرگ</td>\n",
+       "      <td id=\"T_e05f6_row4_col102\" class=\"data row4 col102\" >盧森堡市</td>\n",
+       "      <td id=\"T_e05f6_row4_col103\" class=\"data row4 col103\" >2960316</td>\n",
+       "      <td id=\"T_e05f6_row4_col104\" class=\"data row4 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col105\" class=\"data row4 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col106\" class=\"data row4 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col107\" class=\"data row4 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col108\" class=\"data row4 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col109\" class=\"data row4 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col110\" class=\"data row4 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col111\" class=\"data row4 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col112\" class=\"data row4 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col113\" class=\"data row4 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col114\" class=\"data row4 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col115\" class=\"data row4 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col116\" class=\"data row4 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col117\" class=\"data row4 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col118\" class=\"data row4 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col119\" class=\"data row4 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col120\" class=\"data row4 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col121\" class=\"data row4 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col122\" class=\"data row4 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col123\" class=\"data row4 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col124\" class=\"data row4 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col125\" class=\"data row4 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col126\" class=\"data row4 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col127\" class=\"data row4 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col128\" class=\"data row4 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col129\" class=\"data row4 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col130\" class=\"data row4 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col131\" class=\"data row4 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col132\" class=\"data row4 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col133\" class=\"data row4 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col134\" class=\"data row4 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col135\" class=\"data row4 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col136\" class=\"data row4 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row4_col137\" class=\"data row4 col137\" >POINT (6.1300028 49.6116604)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_f91f0_row5_col0\" class=\"data row5 col0\" >6</td>\n",
-       "      <td id=\"T_f91f0_row5_col1\" class=\"data row5 col1\" >30</td>\n",
-       "      <td id=\"T_f91f0_row5_col2\" class=\"data row5 col2\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col3\" class=\"data row5 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row5_col4\" class=\"data row5 col4\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col7\" class=\"data row5 col7\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col8\" class=\"data row5 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row5_col9\" class=\"data row5 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col10\" class=\"data row5 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col11\" class=\"data row5 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col12\" class=\"data row5 col12\" >Federated States of Micronesia</td>\n",
-       "      <td id=\"T_f91f0_row5_col13\" class=\"data row5 col13\" >FSM</td>\n",
-       "      <td id=\"T_f91f0_row5_col14\" class=\"data row5 col14\" >Federated States of Micronesia</td>\n",
-       "      <td id=\"T_f91f0_row5_col15\" class=\"data row5 col15\" >FSM</td>\n",
-       "      <td id=\"T_f91f0_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col17\" class=\"data row5 col17\" >FM</td>\n",
-       "      <td id=\"T_f91f0_row5_col18\" class=\"data row5 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col19\" class=\"data row5 col19\" >6.916644</td>\n",
-       "      <td id=\"T_f91f0_row5_col20\" class=\"data row5 col20\" >158.149974</td>\n",
-       "      <td id=\"T_f91f0_row5_col21\" class=\"data row5 col21\" >4645</td>\n",
-       "      <td id=\"T_f91f0_row5_col22\" class=\"data row5 col22\" >4645</td>\n",
-       "      <td id=\"T_f91f0_row5_col23\" class=\"data row5 col23\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col24\" class=\"data row5 col24\" >4</td>\n",
-       "      <td id=\"T_f91f0_row5_col25\" class=\"data row5 col25\" >4</td>\n",
-       "      <td id=\"T_f91f0_row5_col26\" class=\"data row5 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col27\" class=\"data row5 col27\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col28\" class=\"data row5 col28\" >412</td>\n",
-       "      <td id=\"T_f91f0_row5_col29\" class=\"data row5 col29\" >412</td>\n",
-       "      <td id=\"T_f91f0_row5_col30\" class=\"data row5 col30\" >412</td>\n",
-       "      <td id=\"T_f91f0_row5_col31\" class=\"data row5 col31\" >412</td>\n",
-       "      <td id=\"T_f91f0_row5_col32\" class=\"data row5 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col33\" class=\"data row5 col33\" >100</td>\n",
-       "      <td id=\"T_f91f0_row5_col34\" class=\"data row5 col34\" >1</td>\n",
-       "      <td id=\"T_f91f0_row5_col35\" class=\"data row5 col35\" >1</td>\n",
-       "      <td id=\"T_f91f0_row5_col36\" class=\"data row5 col36\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col37\" class=\"data row5 col37\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col38\" class=\"data row5 col38\" >4</td>\n",
-       "      <td id=\"T_f91f0_row5_col39\" class=\"data row5 col39\" >4</td>\n",
-       "      <td id=\"T_f91f0_row5_col40\" class=\"data row5 col40\" >2</td>\n",
-       "      <td id=\"T_f91f0_row5_col41\" class=\"data row5 col41\" >2</td>\n",
-       "      <td id=\"T_f91f0_row5_col42\" class=\"data row5 col42\" >158.158333</td>\n",
-       "      <td id=\"T_f91f0_row5_col43\" class=\"data row5 col43\" >158.158333</td>\n",
-       "      <td id=\"T_f91f0_row5_col44\" class=\"data row5 col44\" >158.166667</td>\n",
-       "      <td id=\"T_f91f0_row5_col45\" class=\"data row5 col45\" >158.166667</td>\n",
-       "      <td id=\"T_f91f0_row5_col46\" class=\"data row5 col46\" >6.908333</td>\n",
-       "      <td id=\"T_f91f0_row5_col47\" class=\"data row5 col47\" >6.908333</td>\n",
-       "      <td id=\"T_f91f0_row5_col48\" class=\"data row5 col48\" >6.916667</td>\n",
-       "      <td id=\"T_f91f0_row5_col49\" class=\"data row5 col49\" >6.916667</td>\n",
-       "      <td id=\"T_f91f0_row5_col50\" class=\"data row5 col50\" >158.162500</td>\n",
-       "      <td id=\"T_f91f0_row5_col51\" class=\"data row5 col51\" >6.912500</td>\n",
-       "      <td id=\"T_f91f0_row5_col52\" class=\"data row5 col52\" >Pacific/Ponape</td>\n",
-       "      <td id=\"T_f91f0_row5_col53\" class=\"data row5 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col54\" class=\"data row5 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col55\" class=\"data row5 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col56\" class=\"data row5 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col57\" class=\"data row5 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col58\" class=\"data row5 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col59\" class=\"data row5 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col60\" class=\"data row5 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col61\" class=\"data row5 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col62\" class=\"data row5 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col63\" class=\"data row5 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col64\" class=\"data row5 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col65\" class=\"data row5 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col66\" class=\"data row5 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col67\" class=\"data row5 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col68\" class=\"data row5 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col69\" class=\"data row5 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col70\" class=\"data row5 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col71\" class=\"data row5 col71\" >6.000000</td>\n",
-       "      <td id=\"T_f91f0_row5_col72\" class=\"data row5 col72\" >Q42751</td>\n",
-       "      <td id=\"T_f91f0_row5_col73\" class=\"data row5 col73\" >1141909141</td>\n",
-       "      <td id=\"T_f91f0_row5_col74\" class=\"data row5 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row5_col75\" class=\"data row5 col75\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col76\" class=\"data row5 col76\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col77\" class=\"data row5 col77\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col78\" class=\"data row5 col78\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col79\" class=\"data row5 col79\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col80\" class=\"data row5 col80\" >Паликир</td>\n",
-       "      <td id=\"T_f91f0_row5_col81\" class=\"data row5 col81\" >帕利基尔</td>\n",
-       "      <td id=\"T_f91f0_row5_col82\" class=\"data row5 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col83\" class=\"data row5 col83\" >باليكير</td>\n",
-       "      <td id=\"T_f91f0_row5_col84\" class=\"data row5 col84\" >পালিকির</td>\n",
-       "      <td id=\"T_f91f0_row5_col85\" class=\"data row5 col85\" >Παλικίρ</td>\n",
-       "      <td id=\"T_f91f0_row5_col86\" class=\"data row5 col86\" >पेलिकियर</td>\n",
-       "      <td id=\"T_f91f0_row5_col87\" class=\"data row5 col87\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col88\" class=\"data row5 col88\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col89\" class=\"data row5 col89\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col90\" class=\"data row5 col90\" >パリキール</td>\n",
-       "      <td id=\"T_f91f0_row5_col91\" class=\"data row5 col91\" >팔리키르</td>\n",
-       "      <td id=\"T_f91f0_row5_col92\" class=\"data row5 col92\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col93\" class=\"data row5 col93\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col94\" class=\"data row5 col94\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col95\" class=\"data row5 col95\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col96\" class=\"data row5 col96\" >Palikir</td>\n",
-       "      <td id=\"T_f91f0_row5_col97\" class=\"data row5 col97\" >1159149061</td>\n",
-       "      <td id=\"T_f91f0_row5_col98\" class=\"data row5 col98\" >پالیکیر</td>\n",
-       "      <td id=\"T_f91f0_row5_col99\" class=\"data row5 col99\" >פליקיר</td>\n",
-       "      <td id=\"T_f91f0_row5_col100\" class=\"data row5 col100\" >Палікір</td>\n",
-       "      <td id=\"T_f91f0_row5_col101\" class=\"data row5 col101\" >پالیکیر</td>\n",
-       "      <td id=\"T_f91f0_row5_col102\" class=\"data row5 col102\" >帕利基尔</td>\n",
-       "      <td id=\"T_f91f0_row5_col103\" class=\"data row5 col103\" >2081986</td>\n",
-       "      <td id=\"T_f91f0_row5_col104\" class=\"data row5 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col105\" class=\"data row5 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col106\" class=\"data row5 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col107\" class=\"data row5 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col108\" class=\"data row5 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col109\" class=\"data row5 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col110\" class=\"data row5 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col111\" class=\"data row5 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col112\" class=\"data row5 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col113\" class=\"data row5 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col114\" class=\"data row5 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col115\" class=\"data row5 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col116\" class=\"data row5 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col117\" class=\"data row5 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col118\" class=\"data row5 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col119\" class=\"data row5 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col120\" class=\"data row5 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col121\" class=\"data row5 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col122\" class=\"data row5 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col123\" class=\"data row5 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col124\" class=\"data row5 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col125\" class=\"data row5 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col126\" class=\"data row5 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col127\" class=\"data row5 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col128\" class=\"data row5 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col129\" class=\"data row5 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col130\" class=\"data row5 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col131\" class=\"data row5 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col132\" class=\"data row5 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col133\" class=\"data row5 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col134\" class=\"data row5 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col135\" class=\"data row5 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col136\" class=\"data row5 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row5_col137\" class=\"data row5 col137\" >POINT (158.1499743 6.9166437)</td>\n",
+       "      <th id=\"T_e05f6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_e05f6_row5_col0\" class=\"data row5 col0\" >6</td>\n",
+       "      <td id=\"T_e05f6_row5_col1\" class=\"data row5 col1\" >30</td>\n",
+       "      <td id=\"T_e05f6_row5_col2\" class=\"data row5 col2\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col3\" class=\"data row5 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row5_col4\" class=\"data row5 col4\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col7\" class=\"data row5 col7\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col8\" class=\"data row5 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row5_col9\" class=\"data row5 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col10\" class=\"data row5 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col11\" class=\"data row5 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col12\" class=\"data row5 col12\" >Federated States of Micronesia</td>\n",
+       "      <td id=\"T_e05f6_row5_col13\" class=\"data row5 col13\" >FSM</td>\n",
+       "      <td id=\"T_e05f6_row5_col14\" class=\"data row5 col14\" >Federated States of Micronesia</td>\n",
+       "      <td id=\"T_e05f6_row5_col15\" class=\"data row5 col15\" >FSM</td>\n",
+       "      <td id=\"T_e05f6_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col17\" class=\"data row5 col17\" >FM</td>\n",
+       "      <td id=\"T_e05f6_row5_col18\" class=\"data row5 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col19\" class=\"data row5 col19\" >6.916644</td>\n",
+       "      <td id=\"T_e05f6_row5_col20\" class=\"data row5 col20\" >158.149974</td>\n",
+       "      <td id=\"T_e05f6_row5_col21\" class=\"data row5 col21\" >4645</td>\n",
+       "      <td id=\"T_e05f6_row5_col22\" class=\"data row5 col22\" >4645</td>\n",
+       "      <td id=\"T_e05f6_row5_col23\" class=\"data row5 col23\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col24\" class=\"data row5 col24\" >4</td>\n",
+       "      <td id=\"T_e05f6_row5_col25\" class=\"data row5 col25\" >4</td>\n",
+       "      <td id=\"T_e05f6_row5_col26\" class=\"data row5 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col27\" class=\"data row5 col27\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col28\" class=\"data row5 col28\" >412</td>\n",
+       "      <td id=\"T_e05f6_row5_col29\" class=\"data row5 col29\" >412</td>\n",
+       "      <td id=\"T_e05f6_row5_col30\" class=\"data row5 col30\" >412</td>\n",
+       "      <td id=\"T_e05f6_row5_col31\" class=\"data row5 col31\" >412</td>\n",
+       "      <td id=\"T_e05f6_row5_col32\" class=\"data row5 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col33\" class=\"data row5 col33\" >100</td>\n",
+       "      <td id=\"T_e05f6_row5_col34\" class=\"data row5 col34\" >1</td>\n",
+       "      <td id=\"T_e05f6_row5_col35\" class=\"data row5 col35\" >1</td>\n",
+       "      <td id=\"T_e05f6_row5_col36\" class=\"data row5 col36\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col37\" class=\"data row5 col37\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col38\" class=\"data row5 col38\" >4</td>\n",
+       "      <td id=\"T_e05f6_row5_col39\" class=\"data row5 col39\" >4</td>\n",
+       "      <td id=\"T_e05f6_row5_col40\" class=\"data row5 col40\" >2</td>\n",
+       "      <td id=\"T_e05f6_row5_col41\" class=\"data row5 col41\" >2</td>\n",
+       "      <td id=\"T_e05f6_row5_col42\" class=\"data row5 col42\" >158.158333</td>\n",
+       "      <td id=\"T_e05f6_row5_col43\" class=\"data row5 col43\" >158.158333</td>\n",
+       "      <td id=\"T_e05f6_row5_col44\" class=\"data row5 col44\" >158.166667</td>\n",
+       "      <td id=\"T_e05f6_row5_col45\" class=\"data row5 col45\" >158.166667</td>\n",
+       "      <td id=\"T_e05f6_row5_col46\" class=\"data row5 col46\" >6.908333</td>\n",
+       "      <td id=\"T_e05f6_row5_col47\" class=\"data row5 col47\" >6.908333</td>\n",
+       "      <td id=\"T_e05f6_row5_col48\" class=\"data row5 col48\" >6.916667</td>\n",
+       "      <td id=\"T_e05f6_row5_col49\" class=\"data row5 col49\" >6.916667</td>\n",
+       "      <td id=\"T_e05f6_row5_col50\" class=\"data row5 col50\" >158.162500</td>\n",
+       "      <td id=\"T_e05f6_row5_col51\" class=\"data row5 col51\" >6.912500</td>\n",
+       "      <td id=\"T_e05f6_row5_col52\" class=\"data row5 col52\" >Pacific/Ponape</td>\n",
+       "      <td id=\"T_e05f6_row5_col53\" class=\"data row5 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col54\" class=\"data row5 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col55\" class=\"data row5 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col56\" class=\"data row5 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col57\" class=\"data row5 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col58\" class=\"data row5 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col59\" class=\"data row5 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col60\" class=\"data row5 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col61\" class=\"data row5 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col62\" class=\"data row5 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col63\" class=\"data row5 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col64\" class=\"data row5 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col65\" class=\"data row5 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col66\" class=\"data row5 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col67\" class=\"data row5 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col68\" class=\"data row5 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col69\" class=\"data row5 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col70\" class=\"data row5 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col71\" class=\"data row5 col71\" >6.000000</td>\n",
+       "      <td id=\"T_e05f6_row5_col72\" class=\"data row5 col72\" >Q42751</td>\n",
+       "      <td id=\"T_e05f6_row5_col73\" class=\"data row5 col73\" >1141909141</td>\n",
+       "      <td id=\"T_e05f6_row5_col74\" class=\"data row5 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row5_col75\" class=\"data row5 col75\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col76\" class=\"data row5 col76\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col77\" class=\"data row5 col77\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col78\" class=\"data row5 col78\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col79\" class=\"data row5 col79\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col80\" class=\"data row5 col80\" >Паликир</td>\n",
+       "      <td id=\"T_e05f6_row5_col81\" class=\"data row5 col81\" >帕利基尔</td>\n",
+       "      <td id=\"T_e05f6_row5_col82\" class=\"data row5 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col83\" class=\"data row5 col83\" >باليكير</td>\n",
+       "      <td id=\"T_e05f6_row5_col84\" class=\"data row5 col84\" >পালিকির</td>\n",
+       "      <td id=\"T_e05f6_row5_col85\" class=\"data row5 col85\" >Παλικίρ</td>\n",
+       "      <td id=\"T_e05f6_row5_col86\" class=\"data row5 col86\" >पेलिकियर</td>\n",
+       "      <td id=\"T_e05f6_row5_col87\" class=\"data row5 col87\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col88\" class=\"data row5 col88\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col89\" class=\"data row5 col89\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col90\" class=\"data row5 col90\" >パリキール</td>\n",
+       "      <td id=\"T_e05f6_row5_col91\" class=\"data row5 col91\" >팔리키르</td>\n",
+       "      <td id=\"T_e05f6_row5_col92\" class=\"data row5 col92\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col93\" class=\"data row5 col93\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col94\" class=\"data row5 col94\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col95\" class=\"data row5 col95\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col96\" class=\"data row5 col96\" >Palikir</td>\n",
+       "      <td id=\"T_e05f6_row5_col97\" class=\"data row5 col97\" >1159149061</td>\n",
+       "      <td id=\"T_e05f6_row5_col98\" class=\"data row5 col98\" >پالیکیر</td>\n",
+       "      <td id=\"T_e05f6_row5_col99\" class=\"data row5 col99\" >פליקיר</td>\n",
+       "      <td id=\"T_e05f6_row5_col100\" class=\"data row5 col100\" >Палікір</td>\n",
+       "      <td id=\"T_e05f6_row5_col101\" class=\"data row5 col101\" >پالیکیر</td>\n",
+       "      <td id=\"T_e05f6_row5_col102\" class=\"data row5 col102\" >帕利基尔</td>\n",
+       "      <td id=\"T_e05f6_row5_col103\" class=\"data row5 col103\" >2081986</td>\n",
+       "      <td id=\"T_e05f6_row5_col104\" class=\"data row5 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col105\" class=\"data row5 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col106\" class=\"data row5 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col107\" class=\"data row5 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col108\" class=\"data row5 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col109\" class=\"data row5 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col110\" class=\"data row5 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col111\" class=\"data row5 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col112\" class=\"data row5 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col113\" class=\"data row5 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col114\" class=\"data row5 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col115\" class=\"data row5 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col116\" class=\"data row5 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col117\" class=\"data row5 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col118\" class=\"data row5 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col119\" class=\"data row5 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col120\" class=\"data row5 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col121\" class=\"data row5 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col122\" class=\"data row5 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col123\" class=\"data row5 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col124\" class=\"data row5 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col125\" class=\"data row5 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col126\" class=\"data row5 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col127\" class=\"data row5 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col128\" class=\"data row5 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col129\" class=\"data row5 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col130\" class=\"data row5 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col131\" class=\"data row5 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col132\" class=\"data row5 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col133\" class=\"data row5 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col134\" class=\"data row5 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col135\" class=\"data row5 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col136\" class=\"data row5 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row5_col137\" class=\"data row5 col137\" >POINT (158.1499743 6.9166437)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_f91f0_row6_col0\" class=\"data row6 col0\" >6</td>\n",
-       "      <td id=\"T_f91f0_row6_col1\" class=\"data row6 col1\" >30</td>\n",
-       "      <td id=\"T_f91f0_row6_col2\" class=\"data row6 col2\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col3\" class=\"data row6 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row6_col4\" class=\"data row6 col4\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col5\" class=\"data row6 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col6\" class=\"data row6 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col7\" class=\"data row6 col7\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col8\" class=\"data row6 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col10\" class=\"data row6 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col11\" class=\"data row6 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col12\" class=\"data row6 col12\" >Marshall Islands</td>\n",
-       "      <td id=\"T_f91f0_row6_col13\" class=\"data row6 col13\" >MHL</td>\n",
-       "      <td id=\"T_f91f0_row6_col14\" class=\"data row6 col14\" >Marshall Islands</td>\n",
-       "      <td id=\"T_f91f0_row6_col15\" class=\"data row6 col15\" >MHL</td>\n",
-       "      <td id=\"T_f91f0_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col17\" class=\"data row6 col17\" >MH</td>\n",
-       "      <td id=\"T_f91f0_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col19\" class=\"data row6 col19\" >7.103004</td>\n",
-       "      <td id=\"T_f91f0_row6_col20\" class=\"data row6 col20\" >171.380000</td>\n",
-       "      <td id=\"T_f91f0_row6_col21\" class=\"data row6 col21\" >25400</td>\n",
-       "      <td id=\"T_f91f0_row6_col22\" class=\"data row6 col22\" >20500</td>\n",
-       "      <td id=\"T_f91f0_row6_col23\" class=\"data row6 col23\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col24\" class=\"data row6 col24\" >7</td>\n",
-       "      <td id=\"T_f91f0_row6_col25\" class=\"data row6 col25\" >7</td>\n",
-       "      <td id=\"T_f91f0_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col27\" class=\"data row6 col27\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col28\" class=\"data row6 col28\" >2084</td>\n",
-       "      <td id=\"T_f91f0_row6_col29\" class=\"data row6 col29\" >2084</td>\n",
-       "      <td id=\"T_f91f0_row6_col30\" class=\"data row6 col30\" >2084</td>\n",
-       "      <td id=\"T_f91f0_row6_col31\" class=\"data row6 col31\" >2084</td>\n",
-       "      <td id=\"T_f91f0_row6_col32\" class=\"data row6 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col33\" class=\"data row6 col33\" >100</td>\n",
-       "      <td id=\"T_f91f0_row6_col34\" class=\"data row6 col34\" >3</td>\n",
-       "      <td id=\"T_f91f0_row6_col35\" class=\"data row6 col35\" >3</td>\n",
-       "      <td id=\"T_f91f0_row6_col36\" class=\"data row6 col36\" >1</td>\n",
-       "      <td id=\"T_f91f0_row6_col37\" class=\"data row6 col37\" >1</td>\n",
-       "      <td id=\"T_f91f0_row6_col38\" class=\"data row6 col38\" >7</td>\n",
-       "      <td id=\"T_f91f0_row6_col39\" class=\"data row6 col39\" >7</td>\n",
-       "      <td id=\"T_f91f0_row6_col40\" class=\"data row6 col40\" >5</td>\n",
-       "      <td id=\"T_f91f0_row6_col41\" class=\"data row6 col41\" >5</td>\n",
-       "      <td id=\"T_f91f0_row6_col42\" class=\"data row6 col42\" >171.366667</td>\n",
-       "      <td id=\"T_f91f0_row6_col43\" class=\"data row6 col43\" >171.366667</td>\n",
-       "      <td id=\"T_f91f0_row6_col44\" class=\"data row6 col44\" >171.375000</td>\n",
-       "      <td id=\"T_f91f0_row6_col45\" class=\"data row6 col45\" >171.375000</td>\n",
-       "      <td id=\"T_f91f0_row6_col46\" class=\"data row6 col46\" >7.091667</td>\n",
-       "      <td id=\"T_f91f0_row6_col47\" class=\"data row6 col47\" >7.091667</td>\n",
-       "      <td id=\"T_f91f0_row6_col48\" class=\"data row6 col48\" >7.116667</td>\n",
-       "      <td id=\"T_f91f0_row6_col49\" class=\"data row6 col49\" >7.116667</td>\n",
-       "      <td id=\"T_f91f0_row6_col50\" class=\"data row6 col50\" >171.370833</td>\n",
-       "      <td id=\"T_f91f0_row6_col51\" class=\"data row6 col51\" >7.104167</td>\n",
-       "      <td id=\"T_f91f0_row6_col52\" class=\"data row6 col52\" >Pacific/Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col53\" class=\"data row6 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col54\" class=\"data row6 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col55\" class=\"data row6 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col56\" class=\"data row6 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col57\" class=\"data row6 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col58\" class=\"data row6 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col59\" class=\"data row6 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col60\" class=\"data row6 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col61\" class=\"data row6 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col62\" class=\"data row6 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col63\" class=\"data row6 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col64\" class=\"data row6 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col65\" class=\"data row6 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col66\" class=\"data row6 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col67\" class=\"data row6 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col68\" class=\"data row6 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col69\" class=\"data row6 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col70\" class=\"data row6 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col71\" class=\"data row6 col71\" >6.000000</td>\n",
-       "      <td id=\"T_f91f0_row6_col72\" class=\"data row6 col72\" >Q12919</td>\n",
-       "      <td id=\"T_f91f0_row6_col73\" class=\"data row6 col73\" >890451463</td>\n",
-       "      <td id=\"T_f91f0_row6_col74\" class=\"data row6 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row6_col75\" class=\"data row6 col75\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col76\" class=\"data row6 col76\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col77\" class=\"data row6 col77\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col78\" class=\"data row6 col78\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col79\" class=\"data row6 col79\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col80\" class=\"data row6 col80\" >Маджуро</td>\n",
-       "      <td id=\"T_f91f0_row6_col81\" class=\"data row6 col81\" >马朱罗</td>\n",
-       "      <td id=\"T_f91f0_row6_col82\" class=\"data row6 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col83\" class=\"data row6 col83\" >ماجورو</td>\n",
-       "      <td id=\"T_f91f0_row6_col84\" class=\"data row6 col84\" >মাজুরো</td>\n",
-       "      <td id=\"T_f91f0_row6_col85\" class=\"data row6 col85\" >Ματζούρο</td>\n",
-       "      <td id=\"T_f91f0_row6_col86\" class=\"data row6 col86\" >माजुरो</td>\n",
-       "      <td id=\"T_f91f0_row6_col87\" class=\"data row6 col87\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col88\" class=\"data row6 col88\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col89\" class=\"data row6 col89\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col90\" class=\"data row6 col90\" >マジュロ</td>\n",
-       "      <td id=\"T_f91f0_row6_col91\" class=\"data row6 col91\" >마주로</td>\n",
-       "      <td id=\"T_f91f0_row6_col92\" class=\"data row6 col92\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col93\" class=\"data row6 col93\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col94\" class=\"data row6 col94\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col95\" class=\"data row6 col95\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col96\" class=\"data row6 col96\" >Majuro</td>\n",
-       "      <td id=\"T_f91f0_row6_col97\" class=\"data row6 col97\" >1159149063</td>\n",
-       "      <td id=\"T_f91f0_row6_col98\" class=\"data row6 col98\" >ماجورو</td>\n",
-       "      <td id=\"T_f91f0_row6_col99\" class=\"data row6 col99\" >מג'ורו</td>\n",
-       "      <td id=\"T_f91f0_row6_col100\" class=\"data row6 col100\" >Маджуро</td>\n",
-       "      <td id=\"T_f91f0_row6_col101\" class=\"data row6 col101\" >ماجورو</td>\n",
-       "      <td id=\"T_f91f0_row6_col102\" class=\"data row6 col102\" >馬久羅</td>\n",
-       "      <td id=\"T_f91f0_row6_col103\" class=\"data row6 col103\" >2113779</td>\n",
-       "      <td id=\"T_f91f0_row6_col104\" class=\"data row6 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col105\" class=\"data row6 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col106\" class=\"data row6 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col107\" class=\"data row6 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col108\" class=\"data row6 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col109\" class=\"data row6 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col110\" class=\"data row6 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col111\" class=\"data row6 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col112\" class=\"data row6 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col113\" class=\"data row6 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col114\" class=\"data row6 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col115\" class=\"data row6 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col116\" class=\"data row6 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col117\" class=\"data row6 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col118\" class=\"data row6 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col119\" class=\"data row6 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col120\" class=\"data row6 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col121\" class=\"data row6 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col122\" class=\"data row6 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col123\" class=\"data row6 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col124\" class=\"data row6 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col125\" class=\"data row6 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col126\" class=\"data row6 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col127\" class=\"data row6 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col128\" class=\"data row6 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col129\" class=\"data row6 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col130\" class=\"data row6 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col131\" class=\"data row6 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col132\" class=\"data row6 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col133\" class=\"data row6 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col134\" class=\"data row6 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col135\" class=\"data row6 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col136\" class=\"data row6 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row6_col137\" class=\"data row6 col137\" >POINT (171.3800002 7.1030043)</td>\n",
+       "      <th id=\"T_e05f6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_e05f6_row6_col0\" class=\"data row6 col0\" >6</td>\n",
+       "      <td id=\"T_e05f6_row6_col1\" class=\"data row6 col1\" >30</td>\n",
+       "      <td id=\"T_e05f6_row6_col2\" class=\"data row6 col2\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col3\" class=\"data row6 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row6_col4\" class=\"data row6 col4\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col5\" class=\"data row6 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col6\" class=\"data row6 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col7\" class=\"data row6 col7\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col8\" class=\"data row6 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col10\" class=\"data row6 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col11\" class=\"data row6 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col12\" class=\"data row6 col12\" >Marshall Islands</td>\n",
+       "      <td id=\"T_e05f6_row6_col13\" class=\"data row6 col13\" >MHL</td>\n",
+       "      <td id=\"T_e05f6_row6_col14\" class=\"data row6 col14\" >Marshall Islands</td>\n",
+       "      <td id=\"T_e05f6_row6_col15\" class=\"data row6 col15\" >MHL</td>\n",
+       "      <td id=\"T_e05f6_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col17\" class=\"data row6 col17\" >MH</td>\n",
+       "      <td id=\"T_e05f6_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col19\" class=\"data row6 col19\" >7.103004</td>\n",
+       "      <td id=\"T_e05f6_row6_col20\" class=\"data row6 col20\" >171.380000</td>\n",
+       "      <td id=\"T_e05f6_row6_col21\" class=\"data row6 col21\" >25400</td>\n",
+       "      <td id=\"T_e05f6_row6_col22\" class=\"data row6 col22\" >20500</td>\n",
+       "      <td id=\"T_e05f6_row6_col23\" class=\"data row6 col23\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col24\" class=\"data row6 col24\" >7</td>\n",
+       "      <td id=\"T_e05f6_row6_col25\" class=\"data row6 col25\" >7</td>\n",
+       "      <td id=\"T_e05f6_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col27\" class=\"data row6 col27\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col28\" class=\"data row6 col28\" >2084</td>\n",
+       "      <td id=\"T_e05f6_row6_col29\" class=\"data row6 col29\" >2084</td>\n",
+       "      <td id=\"T_e05f6_row6_col30\" class=\"data row6 col30\" >2084</td>\n",
+       "      <td id=\"T_e05f6_row6_col31\" class=\"data row6 col31\" >2084</td>\n",
+       "      <td id=\"T_e05f6_row6_col32\" class=\"data row6 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col33\" class=\"data row6 col33\" >100</td>\n",
+       "      <td id=\"T_e05f6_row6_col34\" class=\"data row6 col34\" >3</td>\n",
+       "      <td id=\"T_e05f6_row6_col35\" class=\"data row6 col35\" >3</td>\n",
+       "      <td id=\"T_e05f6_row6_col36\" class=\"data row6 col36\" >1</td>\n",
+       "      <td id=\"T_e05f6_row6_col37\" class=\"data row6 col37\" >1</td>\n",
+       "      <td id=\"T_e05f6_row6_col38\" class=\"data row6 col38\" >7</td>\n",
+       "      <td id=\"T_e05f6_row6_col39\" class=\"data row6 col39\" >7</td>\n",
+       "      <td id=\"T_e05f6_row6_col40\" class=\"data row6 col40\" >5</td>\n",
+       "      <td id=\"T_e05f6_row6_col41\" class=\"data row6 col41\" >5</td>\n",
+       "      <td id=\"T_e05f6_row6_col42\" class=\"data row6 col42\" >171.366667</td>\n",
+       "      <td id=\"T_e05f6_row6_col43\" class=\"data row6 col43\" >171.366667</td>\n",
+       "      <td id=\"T_e05f6_row6_col44\" class=\"data row6 col44\" >171.375000</td>\n",
+       "      <td id=\"T_e05f6_row6_col45\" class=\"data row6 col45\" >171.375000</td>\n",
+       "      <td id=\"T_e05f6_row6_col46\" class=\"data row6 col46\" >7.091667</td>\n",
+       "      <td id=\"T_e05f6_row6_col47\" class=\"data row6 col47\" >7.091667</td>\n",
+       "      <td id=\"T_e05f6_row6_col48\" class=\"data row6 col48\" >7.116667</td>\n",
+       "      <td id=\"T_e05f6_row6_col49\" class=\"data row6 col49\" >7.116667</td>\n",
+       "      <td id=\"T_e05f6_row6_col50\" class=\"data row6 col50\" >171.370833</td>\n",
+       "      <td id=\"T_e05f6_row6_col51\" class=\"data row6 col51\" >7.104167</td>\n",
+       "      <td id=\"T_e05f6_row6_col52\" class=\"data row6 col52\" >Pacific/Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col53\" class=\"data row6 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col54\" class=\"data row6 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col55\" class=\"data row6 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col56\" class=\"data row6 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col57\" class=\"data row6 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col58\" class=\"data row6 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col59\" class=\"data row6 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col60\" class=\"data row6 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col61\" class=\"data row6 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col62\" class=\"data row6 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col63\" class=\"data row6 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col64\" class=\"data row6 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col65\" class=\"data row6 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col66\" class=\"data row6 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col67\" class=\"data row6 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col68\" class=\"data row6 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col69\" class=\"data row6 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col70\" class=\"data row6 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col71\" class=\"data row6 col71\" >6.000000</td>\n",
+       "      <td id=\"T_e05f6_row6_col72\" class=\"data row6 col72\" >Q12919</td>\n",
+       "      <td id=\"T_e05f6_row6_col73\" class=\"data row6 col73\" >890451463</td>\n",
+       "      <td id=\"T_e05f6_row6_col74\" class=\"data row6 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row6_col75\" class=\"data row6 col75\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col76\" class=\"data row6 col76\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col77\" class=\"data row6 col77\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col78\" class=\"data row6 col78\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col79\" class=\"data row6 col79\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col80\" class=\"data row6 col80\" >Маджуро</td>\n",
+       "      <td id=\"T_e05f6_row6_col81\" class=\"data row6 col81\" >马朱罗</td>\n",
+       "      <td id=\"T_e05f6_row6_col82\" class=\"data row6 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col83\" class=\"data row6 col83\" >ماجورو</td>\n",
+       "      <td id=\"T_e05f6_row6_col84\" class=\"data row6 col84\" >মাজুরো</td>\n",
+       "      <td id=\"T_e05f6_row6_col85\" class=\"data row6 col85\" >Ματζούρο</td>\n",
+       "      <td id=\"T_e05f6_row6_col86\" class=\"data row6 col86\" >माजुरो</td>\n",
+       "      <td id=\"T_e05f6_row6_col87\" class=\"data row6 col87\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col88\" class=\"data row6 col88\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col89\" class=\"data row6 col89\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col90\" class=\"data row6 col90\" >マジュロ</td>\n",
+       "      <td id=\"T_e05f6_row6_col91\" class=\"data row6 col91\" >마주로</td>\n",
+       "      <td id=\"T_e05f6_row6_col92\" class=\"data row6 col92\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col93\" class=\"data row6 col93\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col94\" class=\"data row6 col94\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col95\" class=\"data row6 col95\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col96\" class=\"data row6 col96\" >Majuro</td>\n",
+       "      <td id=\"T_e05f6_row6_col97\" class=\"data row6 col97\" >1159149063</td>\n",
+       "      <td id=\"T_e05f6_row6_col98\" class=\"data row6 col98\" >ماجورو</td>\n",
+       "      <td id=\"T_e05f6_row6_col99\" class=\"data row6 col99\" >מג'ורו</td>\n",
+       "      <td id=\"T_e05f6_row6_col100\" class=\"data row6 col100\" >Маджуро</td>\n",
+       "      <td id=\"T_e05f6_row6_col101\" class=\"data row6 col101\" >ماجورو</td>\n",
+       "      <td id=\"T_e05f6_row6_col102\" class=\"data row6 col102\" >馬久羅</td>\n",
+       "      <td id=\"T_e05f6_row6_col103\" class=\"data row6 col103\" >2113779</td>\n",
+       "      <td id=\"T_e05f6_row6_col104\" class=\"data row6 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col105\" class=\"data row6 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col106\" class=\"data row6 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col107\" class=\"data row6 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col108\" class=\"data row6 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col109\" class=\"data row6 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col110\" class=\"data row6 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col111\" class=\"data row6 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col112\" class=\"data row6 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col113\" class=\"data row6 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col114\" class=\"data row6 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col115\" class=\"data row6 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col116\" class=\"data row6 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col117\" class=\"data row6 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col118\" class=\"data row6 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col119\" class=\"data row6 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col120\" class=\"data row6 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col121\" class=\"data row6 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col122\" class=\"data row6 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col123\" class=\"data row6 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col124\" class=\"data row6 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col125\" class=\"data row6 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col126\" class=\"data row6 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col127\" class=\"data row6 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col128\" class=\"data row6 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col129\" class=\"data row6 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col130\" class=\"data row6 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col131\" class=\"data row6 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col132\" class=\"data row6 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col133\" class=\"data row6 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col134\" class=\"data row6 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col135\" class=\"data row6 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col136\" class=\"data row6 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row6_col137\" class=\"data row6 col137\" >POINT (171.3800002 7.1030043)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_f91f0_row7_col0\" class=\"data row7 col0\" >6</td>\n",
-       "      <td id=\"T_f91f0_row7_col1\" class=\"data row7 col1\" >30</td>\n",
-       "      <td id=\"T_f91f0_row7_col2\" class=\"data row7 col2\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col3\" class=\"data row7 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row7_col4\" class=\"data row7 col4\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col5\" class=\"data row7 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col6\" class=\"data row7 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col7\" class=\"data row7 col7\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col8\" class=\"data row7 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col10\" class=\"data row7 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col11\" class=\"data row7 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col12\" class=\"data row7 col12\" >Tuvalu</td>\n",
-       "      <td id=\"T_f91f0_row7_col13\" class=\"data row7 col13\" >TUV</td>\n",
-       "      <td id=\"T_f91f0_row7_col14\" class=\"data row7 col14\" >Tuvalu</td>\n",
-       "      <td id=\"T_f91f0_row7_col15\" class=\"data row7 col15\" >TUV</td>\n",
-       "      <td id=\"T_f91f0_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col17\" class=\"data row7 col17\" >TV</td>\n",
-       "      <td id=\"T_f91f0_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col19\" class=\"data row7 col19\" >-8.516652</td>\n",
-       "      <td id=\"T_f91f0_row7_col20\" class=\"data row7 col20\" >179.216647</td>\n",
-       "      <td id=\"T_f91f0_row7_col21\" class=\"data row7 col21\" >4749</td>\n",
-       "      <td id=\"T_f91f0_row7_col22\" class=\"data row7 col22\" >4749</td>\n",
-       "      <td id=\"T_f91f0_row7_col23\" class=\"data row7 col23\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col24\" class=\"data row7 col24\" >4</td>\n",
-       "      <td id=\"T_f91f0_row7_col25\" class=\"data row7 col25\" >4</td>\n",
-       "      <td id=\"T_f91f0_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col27\" class=\"data row7 col27\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col28\" class=\"data row7 col28\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col29\" class=\"data row7 col29\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col30\" class=\"data row7 col30\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col31\" class=\"data row7 col31\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col32\" class=\"data row7 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col33\" class=\"data row7 col33\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col34\" class=\"data row7 col34\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col35\" class=\"data row7 col35\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col36\" class=\"data row7 col36\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col37\" class=\"data row7 col37\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col38\" class=\"data row7 col38\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col39\" class=\"data row7 col39\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col40\" class=\"data row7 col40\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col41\" class=\"data row7 col41\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col42\" class=\"data row7 col42\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col43\" class=\"data row7 col43\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col44\" class=\"data row7 col44\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col45\" class=\"data row7 col45\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col46\" class=\"data row7 col46\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col47\" class=\"data row7 col47\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col48\" class=\"data row7 col48\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col49\" class=\"data row7 col49\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col50\" class=\"data row7 col50\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col51\" class=\"data row7 col51\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col52\" class=\"data row7 col52\" >Pacific/Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col53\" class=\"data row7 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col54\" class=\"data row7 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col55\" class=\"data row7 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col56\" class=\"data row7 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col57\" class=\"data row7 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col58\" class=\"data row7 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col59\" class=\"data row7 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col60\" class=\"data row7 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col61\" class=\"data row7 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col62\" class=\"data row7 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col63\" class=\"data row7 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col64\" class=\"data row7 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col65\" class=\"data row7 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col66\" class=\"data row7 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col67\" class=\"data row7 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col68\" class=\"data row7 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col69\" class=\"data row7 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col70\" class=\"data row7 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col71\" class=\"data row7 col71\" >6.000000</td>\n",
-       "      <td id=\"T_f91f0_row7_col72\" class=\"data row7 col72\" >Q34126</td>\n",
-       "      <td id=\"T_f91f0_row7_col73\" class=\"data row7 col73\" >1141909143</td>\n",
-       "      <td id=\"T_f91f0_row7_col74\" class=\"data row7 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row7_col75\" class=\"data row7 col75\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col76\" class=\"data row7 col76\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col77\" class=\"data row7 col77\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col78\" class=\"data row7 col78\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col79\" class=\"data row7 col79\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col80\" class=\"data row7 col80\" >Фунафути</td>\n",
-       "      <td id=\"T_f91f0_row7_col81\" class=\"data row7 col81\" >富纳富提</td>\n",
-       "      <td id=\"T_f91f0_row7_col82\" class=\"data row7 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col83\" class=\"data row7 col83\" >فونافوتي</td>\n",
-       "      <td id=\"T_f91f0_row7_col84\" class=\"data row7 col84\" >ফুনাফুতি</td>\n",
-       "      <td id=\"T_f91f0_row7_col85\" class=\"data row7 col85\" >Φουναφούτι</td>\n",
-       "      <td id=\"T_f91f0_row7_col86\" class=\"data row7 col86\" >फुनाफुति</td>\n",
-       "      <td id=\"T_f91f0_row7_col87\" class=\"data row7 col87\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col88\" class=\"data row7 col88\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col89\" class=\"data row7 col89\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col90\" class=\"data row7 col90\" >フナフティ島</td>\n",
-       "      <td id=\"T_f91f0_row7_col91\" class=\"data row7 col91\" >푸나푸티</td>\n",
-       "      <td id=\"T_f91f0_row7_col92\" class=\"data row7 col92\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col93\" class=\"data row7 col93\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col94\" class=\"data row7 col94\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col95\" class=\"data row7 col95\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col96\" class=\"data row7 col96\" >Funafuti</td>\n",
-       "      <td id=\"T_f91f0_row7_col97\" class=\"data row7 col97\" >1159149071</td>\n",
-       "      <td id=\"T_f91f0_row7_col98\" class=\"data row7 col98\" >فونافوتی</td>\n",
-       "      <td id=\"T_f91f0_row7_col99\" class=\"data row7 col99\" >פנאפוטי</td>\n",
-       "      <td id=\"T_f91f0_row7_col100\" class=\"data row7 col100\" >Фунафуті</td>\n",
-       "      <td id=\"T_f91f0_row7_col101\" class=\"data row7 col101\" >فونافوتی</td>\n",
-       "      <td id=\"T_f91f0_row7_col102\" class=\"data row7 col102\" >富纳富提</td>\n",
-       "      <td id=\"T_f91f0_row7_col103\" class=\"data row7 col103\" >2110394</td>\n",
-       "      <td id=\"T_f91f0_row7_col104\" class=\"data row7 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col105\" class=\"data row7 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col106\" class=\"data row7 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col107\" class=\"data row7 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col108\" class=\"data row7 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col109\" class=\"data row7 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col110\" class=\"data row7 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col111\" class=\"data row7 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col112\" class=\"data row7 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col113\" class=\"data row7 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col114\" class=\"data row7 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col115\" class=\"data row7 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col116\" class=\"data row7 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col117\" class=\"data row7 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col118\" class=\"data row7 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col119\" class=\"data row7 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col120\" class=\"data row7 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col121\" class=\"data row7 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col122\" class=\"data row7 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col123\" class=\"data row7 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col124\" class=\"data row7 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col125\" class=\"data row7 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col126\" class=\"data row7 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col127\" class=\"data row7 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col128\" class=\"data row7 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col129\" class=\"data row7 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col130\" class=\"data row7 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col131\" class=\"data row7 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col132\" class=\"data row7 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col133\" class=\"data row7 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col134\" class=\"data row7 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col135\" class=\"data row7 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col136\" class=\"data row7 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row7_col137\" class=\"data row7 col137\" >POINT (179.2166471 -8.516652)</td>\n",
+       "      <th id=\"T_e05f6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_e05f6_row7_col0\" class=\"data row7 col0\" >6</td>\n",
+       "      <td id=\"T_e05f6_row7_col1\" class=\"data row7 col1\" >30</td>\n",
+       "      <td id=\"T_e05f6_row7_col2\" class=\"data row7 col2\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col3\" class=\"data row7 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row7_col4\" class=\"data row7 col4\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col5\" class=\"data row7 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col6\" class=\"data row7 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col7\" class=\"data row7 col7\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col8\" class=\"data row7 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col10\" class=\"data row7 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col11\" class=\"data row7 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col12\" class=\"data row7 col12\" >Tuvalu</td>\n",
+       "      <td id=\"T_e05f6_row7_col13\" class=\"data row7 col13\" >TUV</td>\n",
+       "      <td id=\"T_e05f6_row7_col14\" class=\"data row7 col14\" >Tuvalu</td>\n",
+       "      <td id=\"T_e05f6_row7_col15\" class=\"data row7 col15\" >TUV</td>\n",
+       "      <td id=\"T_e05f6_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col17\" class=\"data row7 col17\" >TV</td>\n",
+       "      <td id=\"T_e05f6_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col19\" class=\"data row7 col19\" >-8.516652</td>\n",
+       "      <td id=\"T_e05f6_row7_col20\" class=\"data row7 col20\" >179.216647</td>\n",
+       "      <td id=\"T_e05f6_row7_col21\" class=\"data row7 col21\" >4749</td>\n",
+       "      <td id=\"T_e05f6_row7_col22\" class=\"data row7 col22\" >4749</td>\n",
+       "      <td id=\"T_e05f6_row7_col23\" class=\"data row7 col23\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col24\" class=\"data row7 col24\" >4</td>\n",
+       "      <td id=\"T_e05f6_row7_col25\" class=\"data row7 col25\" >4</td>\n",
+       "      <td id=\"T_e05f6_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col27\" class=\"data row7 col27\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col28\" class=\"data row7 col28\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col29\" class=\"data row7 col29\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col30\" class=\"data row7 col30\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col31\" class=\"data row7 col31\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col32\" class=\"data row7 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col33\" class=\"data row7 col33\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col34\" class=\"data row7 col34\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col35\" class=\"data row7 col35\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col36\" class=\"data row7 col36\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col37\" class=\"data row7 col37\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col38\" class=\"data row7 col38\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col39\" class=\"data row7 col39\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col40\" class=\"data row7 col40\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col41\" class=\"data row7 col41\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col42\" class=\"data row7 col42\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col43\" class=\"data row7 col43\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col44\" class=\"data row7 col44\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col45\" class=\"data row7 col45\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col46\" class=\"data row7 col46\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col47\" class=\"data row7 col47\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col48\" class=\"data row7 col48\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col49\" class=\"data row7 col49\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col50\" class=\"data row7 col50\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col51\" class=\"data row7 col51\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col52\" class=\"data row7 col52\" >Pacific/Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col53\" class=\"data row7 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col54\" class=\"data row7 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col55\" class=\"data row7 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col56\" class=\"data row7 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col57\" class=\"data row7 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col58\" class=\"data row7 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col59\" class=\"data row7 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col60\" class=\"data row7 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col61\" class=\"data row7 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col62\" class=\"data row7 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col63\" class=\"data row7 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col64\" class=\"data row7 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col65\" class=\"data row7 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col66\" class=\"data row7 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col67\" class=\"data row7 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col68\" class=\"data row7 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col69\" class=\"data row7 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col70\" class=\"data row7 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col71\" class=\"data row7 col71\" >6.000000</td>\n",
+       "      <td id=\"T_e05f6_row7_col72\" class=\"data row7 col72\" >Q34126</td>\n",
+       "      <td id=\"T_e05f6_row7_col73\" class=\"data row7 col73\" >1141909143</td>\n",
+       "      <td id=\"T_e05f6_row7_col74\" class=\"data row7 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row7_col75\" class=\"data row7 col75\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col76\" class=\"data row7 col76\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col77\" class=\"data row7 col77\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col78\" class=\"data row7 col78\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col79\" class=\"data row7 col79\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col80\" class=\"data row7 col80\" >Фунафути</td>\n",
+       "      <td id=\"T_e05f6_row7_col81\" class=\"data row7 col81\" >富纳富提</td>\n",
+       "      <td id=\"T_e05f6_row7_col82\" class=\"data row7 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col83\" class=\"data row7 col83\" >فونافوتي</td>\n",
+       "      <td id=\"T_e05f6_row7_col84\" class=\"data row7 col84\" >ফুনাফুতি</td>\n",
+       "      <td id=\"T_e05f6_row7_col85\" class=\"data row7 col85\" >Φουναφούτι</td>\n",
+       "      <td id=\"T_e05f6_row7_col86\" class=\"data row7 col86\" >फुनाफुति</td>\n",
+       "      <td id=\"T_e05f6_row7_col87\" class=\"data row7 col87\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col88\" class=\"data row7 col88\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col89\" class=\"data row7 col89\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col90\" class=\"data row7 col90\" >フナフティ島</td>\n",
+       "      <td id=\"T_e05f6_row7_col91\" class=\"data row7 col91\" >푸나푸티</td>\n",
+       "      <td id=\"T_e05f6_row7_col92\" class=\"data row7 col92\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col93\" class=\"data row7 col93\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col94\" class=\"data row7 col94\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col95\" class=\"data row7 col95\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col96\" class=\"data row7 col96\" >Funafuti</td>\n",
+       "      <td id=\"T_e05f6_row7_col97\" class=\"data row7 col97\" >1159149071</td>\n",
+       "      <td id=\"T_e05f6_row7_col98\" class=\"data row7 col98\" >فونافوتی</td>\n",
+       "      <td id=\"T_e05f6_row7_col99\" class=\"data row7 col99\" >פנאפוטי</td>\n",
+       "      <td id=\"T_e05f6_row7_col100\" class=\"data row7 col100\" >Фунафуті</td>\n",
+       "      <td id=\"T_e05f6_row7_col101\" class=\"data row7 col101\" >فونافوتی</td>\n",
+       "      <td id=\"T_e05f6_row7_col102\" class=\"data row7 col102\" >富纳富提</td>\n",
+       "      <td id=\"T_e05f6_row7_col103\" class=\"data row7 col103\" >2110394</td>\n",
+       "      <td id=\"T_e05f6_row7_col104\" class=\"data row7 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col105\" class=\"data row7 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col106\" class=\"data row7 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col107\" class=\"data row7 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col108\" class=\"data row7 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col109\" class=\"data row7 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col110\" class=\"data row7 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col111\" class=\"data row7 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col112\" class=\"data row7 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col113\" class=\"data row7 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col114\" class=\"data row7 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col115\" class=\"data row7 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col116\" class=\"data row7 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col117\" class=\"data row7 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col118\" class=\"data row7 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col119\" class=\"data row7 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col120\" class=\"data row7 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col121\" class=\"data row7 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col122\" class=\"data row7 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col123\" class=\"data row7 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col124\" class=\"data row7 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col125\" class=\"data row7 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col126\" class=\"data row7 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col127\" class=\"data row7 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col128\" class=\"data row7 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col129\" class=\"data row7 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col130\" class=\"data row7 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col131\" class=\"data row7 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col132\" class=\"data row7 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col133\" class=\"data row7 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col134\" class=\"data row7 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col135\" class=\"data row7 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col136\" class=\"data row7 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row7_col137\" class=\"data row7 col137\" >POINT (179.2166471 -8.516652)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_f91f0_row8_col0\" class=\"data row8 col0\" >6</td>\n",
-       "      <td id=\"T_f91f0_row8_col1\" class=\"data row8 col1\" >30</td>\n",
-       "      <td id=\"T_f91f0_row8_col2\" class=\"data row8 col2\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col3\" class=\"data row8 col3\" >Admin-0 capital</td>\n",
-       "      <td id=\"T_f91f0_row8_col4\" class=\"data row8 col4\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col5\" class=\"data row8 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col6\" class=\"data row8 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col7\" class=\"data row8 col7\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col8\" class=\"data row8 col8\" >1</td>\n",
-       "      <td id=\"T_f91f0_row8_col9\" class=\"data row8 col9\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col10\" class=\"data row8 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col11\" class=\"data row8 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col12\" class=\"data row8 col12\" >Palau</td>\n",
-       "      <td id=\"T_f91f0_row8_col13\" class=\"data row8 col13\" >PLW</td>\n",
-       "      <td id=\"T_f91f0_row8_col14\" class=\"data row8 col14\" >Palau</td>\n",
-       "      <td id=\"T_f91f0_row8_col15\" class=\"data row8 col15\" >PLW</td>\n",
-       "      <td id=\"T_f91f0_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col17\" class=\"data row8 col17\" >PW</td>\n",
-       "      <td id=\"T_f91f0_row8_col18\" class=\"data row8 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col19\" class=\"data row8 col19\" >7.487396</td>\n",
-       "      <td id=\"T_f91f0_row8_col20\" class=\"data row8 col20\" >134.626549</td>\n",
-       "      <td id=\"T_f91f0_row8_col21\" class=\"data row8 col21\" >7026</td>\n",
-       "      <td id=\"T_f91f0_row8_col22\" class=\"data row8 col22\" >7026</td>\n",
-       "      <td id=\"T_f91f0_row8_col23\" class=\"data row8 col23\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col24\" class=\"data row8 col24\" >5</td>\n",
-       "      <td id=\"T_f91f0_row8_col25\" class=\"data row8 col25\" >5</td>\n",
-       "      <td id=\"T_f91f0_row8_col26\" class=\"data row8 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col27\" class=\"data row8 col27\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col28\" class=\"data row8 col28\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col29\" class=\"data row8 col29\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col30\" class=\"data row8 col30\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col31\" class=\"data row8 col31\" >7026</td>\n",
-       "      <td id=\"T_f91f0_row8_col32\" class=\"data row8 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col33\" class=\"data row8 col33\" >100</td>\n",
-       "      <td id=\"T_f91f0_row8_col34\" class=\"data row8 col34\" >6</td>\n",
-       "      <td id=\"T_f91f0_row8_col35\" class=\"data row8 col35\" >6</td>\n",
-       "      <td id=\"T_f91f0_row8_col36\" class=\"data row8 col36\" >2</td>\n",
-       "      <td id=\"T_f91f0_row8_col37\" class=\"data row8 col37\" >2</td>\n",
-       "      <td id=\"T_f91f0_row8_col38\" class=\"data row8 col38\" >15</td>\n",
-       "      <td id=\"T_f91f0_row8_col39\" class=\"data row8 col39\" >15</td>\n",
-       "      <td id=\"T_f91f0_row8_col40\" class=\"data row8 col40\" >9</td>\n",
-       "      <td id=\"T_f91f0_row8_col41\" class=\"data row8 col41\" >9</td>\n",
-       "      <td id=\"T_f91f0_row8_col42\" class=\"data row8 col42\" >134.466667</td>\n",
-       "      <td id=\"T_f91f0_row8_col43\" class=\"data row8 col43\" >134.466667</td>\n",
-       "      <td id=\"T_f91f0_row8_col44\" class=\"data row8 col44\" >134.500000</td>\n",
-       "      <td id=\"T_f91f0_row8_col45\" class=\"data row8 col45\" >134.500000</td>\n",
-       "      <td id=\"T_f91f0_row8_col46\" class=\"data row8 col46\" >7.325000</td>\n",
-       "      <td id=\"T_f91f0_row8_col47\" class=\"data row8 col47\" >7.325000</td>\n",
-       "      <td id=\"T_f91f0_row8_col48\" class=\"data row8 col48\" >7.350000</td>\n",
-       "      <td id=\"T_f91f0_row8_col49\" class=\"data row8 col49\" >7.350000</td>\n",
-       "      <td id=\"T_f91f0_row8_col50\" class=\"data row8 col50\" >134.481548</td>\n",
-       "      <td id=\"T_f91f0_row8_col51\" class=\"data row8 col51\" >7.339881</td>\n",
-       "      <td id=\"T_f91f0_row8_col52\" class=\"data row8 col52\" >Pacific/Palau</td>\n",
-       "      <td id=\"T_f91f0_row8_col53\" class=\"data row8 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col54\" class=\"data row8 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col55\" class=\"data row8 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col56\" class=\"data row8 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col57\" class=\"data row8 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col58\" class=\"data row8 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col59\" class=\"data row8 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col60\" class=\"data row8 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col61\" class=\"data row8 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col62\" class=\"data row8 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col63\" class=\"data row8 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col64\" class=\"data row8 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col65\" class=\"data row8 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col66\" class=\"data row8 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col67\" class=\"data row8 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col68\" class=\"data row8 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col69\" class=\"data row8 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col70\" class=\"data row8 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col71\" class=\"data row8 col71\" >6.000000</td>\n",
-       "      <td id=\"T_f91f0_row8_col72\" class=\"data row8 col72\" >Q154002</td>\n",
-       "      <td id=\"T_f91f0_row8_col73\" class=\"data row8 col73\" >890443883</td>\n",
-       "      <td id=\"T_f91f0_row8_col74\" class=\"data row8 col74\" >0</td>\n",
-       "      <td id=\"T_f91f0_row8_col75\" class=\"data row8 col75\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col76\" class=\"data row8 col76\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col77\" class=\"data row8 col77\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col78\" class=\"data row8 col78\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col79\" class=\"data row8 col79\" >Melequeoque</td>\n",
-       "      <td id=\"T_f91f0_row8_col80\" class=\"data row8 col80\" >Мелекеок</td>\n",
-       "      <td id=\"T_f91f0_row8_col81\" class=\"data row8 col81\" >梅莱凯奥克</td>\n",
-       "      <td id=\"T_f91f0_row8_col82\" class=\"data row8 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col83\" class=\"data row8 col83\" >ميلكيوك</td>\n",
-       "      <td id=\"T_f91f0_row8_col84\" class=\"data row8 col84\" >মেলেকেওক</td>\n",
-       "      <td id=\"T_f91f0_row8_col85\" class=\"data row8 col85\" >Μελεκέοκ</td>\n",
-       "      <td id=\"T_f91f0_row8_col86\" class=\"data row8 col86\" >मेलेकियोक</td>\n",
-       "      <td id=\"T_f91f0_row8_col87\" class=\"data row8 col87\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col88\" class=\"data row8 col88\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col89\" class=\"data row8 col89\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col90\" class=\"data row8 col90\" >マルキョク州</td>\n",
-       "      <td id=\"T_f91f0_row8_col91\" class=\"data row8 col91\" >멜레케오크</td>\n",
-       "      <td id=\"T_f91f0_row8_col92\" class=\"data row8 col92\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col93\" class=\"data row8 col93\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col94\" class=\"data row8 col94\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col95\" class=\"data row8 col95\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col96\" class=\"data row8 col96\" >Melekeok</td>\n",
-       "      <td id=\"T_f91f0_row8_col97\" class=\"data row8 col97\" >1159149073</td>\n",
-       "      <td id=\"T_f91f0_row8_col98\" class=\"data row8 col98\" >ملکئوک</td>\n",
-       "      <td id=\"T_f91f0_row8_col99\" class=\"data row8 col99\" >מלקאוק</td>\n",
-       "      <td id=\"T_f91f0_row8_col100\" class=\"data row8 col100\" >Мелекеок</td>\n",
-       "      <td id=\"T_f91f0_row8_col101\" class=\"data row8 col101\" >میلیکوک</td>\n",
-       "      <td id=\"T_f91f0_row8_col102\" class=\"data row8 col102\" >梅萊凱奧克</td>\n",
-       "      <td id=\"T_f91f0_row8_col103\" class=\"data row8 col103\" >1559804</td>\n",
-       "      <td id=\"T_f91f0_row8_col104\" class=\"data row8 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col105\" class=\"data row8 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col106\" class=\"data row8 col106\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col107\" class=\"data row8 col107\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col108\" class=\"data row8 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col109\" class=\"data row8 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col110\" class=\"data row8 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col111\" class=\"data row8 col111\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col112\" class=\"data row8 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col113\" class=\"data row8 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col114\" class=\"data row8 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col115\" class=\"data row8 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col116\" class=\"data row8 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col117\" class=\"data row8 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col118\" class=\"data row8 col118\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col119\" class=\"data row8 col119\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col120\" class=\"data row8 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col121\" class=\"data row8 col121\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col122\" class=\"data row8 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col123\" class=\"data row8 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col124\" class=\"data row8 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col125\" class=\"data row8 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col126\" class=\"data row8 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col127\" class=\"data row8 col127\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col128\" class=\"data row8 col128\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col129\" class=\"data row8 col129\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col130\" class=\"data row8 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col131\" class=\"data row8 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col132\" class=\"data row8 col132\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col133\" class=\"data row8 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col134\" class=\"data row8 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col135\" class=\"data row8 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col136\" class=\"data row8 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row8_col137\" class=\"data row8 col137\" >POINT (134.6265485 7.4873962)</td>\n",
+       "      <th id=\"T_e05f6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_e05f6_row8_col0\" class=\"data row8 col0\" >6</td>\n",
+       "      <td id=\"T_e05f6_row8_col1\" class=\"data row8 col1\" >30</td>\n",
+       "      <td id=\"T_e05f6_row8_col2\" class=\"data row8 col2\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col3\" class=\"data row8 col3\" >Admin-0 capital</td>\n",
+       "      <td id=\"T_e05f6_row8_col4\" class=\"data row8 col4\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col5\" class=\"data row8 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col6\" class=\"data row8 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col7\" class=\"data row8 col7\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col8\" class=\"data row8 col8\" >1</td>\n",
+       "      <td id=\"T_e05f6_row8_col9\" class=\"data row8 col9\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col10\" class=\"data row8 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col11\" class=\"data row8 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col12\" class=\"data row8 col12\" >Palau</td>\n",
+       "      <td id=\"T_e05f6_row8_col13\" class=\"data row8 col13\" >PLW</td>\n",
+       "      <td id=\"T_e05f6_row8_col14\" class=\"data row8 col14\" >Palau</td>\n",
+       "      <td id=\"T_e05f6_row8_col15\" class=\"data row8 col15\" >PLW</td>\n",
+       "      <td id=\"T_e05f6_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col17\" class=\"data row8 col17\" >PW</td>\n",
+       "      <td id=\"T_e05f6_row8_col18\" class=\"data row8 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col19\" class=\"data row8 col19\" >7.487396</td>\n",
+       "      <td id=\"T_e05f6_row8_col20\" class=\"data row8 col20\" >134.626549</td>\n",
+       "      <td id=\"T_e05f6_row8_col21\" class=\"data row8 col21\" >7026</td>\n",
+       "      <td id=\"T_e05f6_row8_col22\" class=\"data row8 col22\" >7026</td>\n",
+       "      <td id=\"T_e05f6_row8_col23\" class=\"data row8 col23\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col24\" class=\"data row8 col24\" >5</td>\n",
+       "      <td id=\"T_e05f6_row8_col25\" class=\"data row8 col25\" >5</td>\n",
+       "      <td id=\"T_e05f6_row8_col26\" class=\"data row8 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col27\" class=\"data row8 col27\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col28\" class=\"data row8 col28\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col29\" class=\"data row8 col29\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col30\" class=\"data row8 col30\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col31\" class=\"data row8 col31\" >7026</td>\n",
+       "      <td id=\"T_e05f6_row8_col32\" class=\"data row8 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col33\" class=\"data row8 col33\" >100</td>\n",
+       "      <td id=\"T_e05f6_row8_col34\" class=\"data row8 col34\" >6</td>\n",
+       "      <td id=\"T_e05f6_row8_col35\" class=\"data row8 col35\" >6</td>\n",
+       "      <td id=\"T_e05f6_row8_col36\" class=\"data row8 col36\" >2</td>\n",
+       "      <td id=\"T_e05f6_row8_col37\" class=\"data row8 col37\" >2</td>\n",
+       "      <td id=\"T_e05f6_row8_col38\" class=\"data row8 col38\" >15</td>\n",
+       "      <td id=\"T_e05f6_row8_col39\" class=\"data row8 col39\" >15</td>\n",
+       "      <td id=\"T_e05f6_row8_col40\" class=\"data row8 col40\" >9</td>\n",
+       "      <td id=\"T_e05f6_row8_col41\" class=\"data row8 col41\" >9</td>\n",
+       "      <td id=\"T_e05f6_row8_col42\" class=\"data row8 col42\" >134.466667</td>\n",
+       "      <td id=\"T_e05f6_row8_col43\" class=\"data row8 col43\" >134.466667</td>\n",
+       "      <td id=\"T_e05f6_row8_col44\" class=\"data row8 col44\" >134.500000</td>\n",
+       "      <td id=\"T_e05f6_row8_col45\" class=\"data row8 col45\" >134.500000</td>\n",
+       "      <td id=\"T_e05f6_row8_col46\" class=\"data row8 col46\" >7.325000</td>\n",
+       "      <td id=\"T_e05f6_row8_col47\" class=\"data row8 col47\" >7.325000</td>\n",
+       "      <td id=\"T_e05f6_row8_col48\" class=\"data row8 col48\" >7.350000</td>\n",
+       "      <td id=\"T_e05f6_row8_col49\" class=\"data row8 col49\" >7.350000</td>\n",
+       "      <td id=\"T_e05f6_row8_col50\" class=\"data row8 col50\" >134.481548</td>\n",
+       "      <td id=\"T_e05f6_row8_col51\" class=\"data row8 col51\" >7.339881</td>\n",
+       "      <td id=\"T_e05f6_row8_col52\" class=\"data row8 col52\" >Pacific/Palau</td>\n",
+       "      <td id=\"T_e05f6_row8_col53\" class=\"data row8 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col54\" class=\"data row8 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col55\" class=\"data row8 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col56\" class=\"data row8 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col57\" class=\"data row8 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col58\" class=\"data row8 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col59\" class=\"data row8 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col60\" class=\"data row8 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col61\" class=\"data row8 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col62\" class=\"data row8 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col63\" class=\"data row8 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col64\" class=\"data row8 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col65\" class=\"data row8 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col66\" class=\"data row8 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col67\" class=\"data row8 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col68\" class=\"data row8 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col69\" class=\"data row8 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col70\" class=\"data row8 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col71\" class=\"data row8 col71\" >6.000000</td>\n",
+       "      <td id=\"T_e05f6_row8_col72\" class=\"data row8 col72\" >Q154002</td>\n",
+       "      <td id=\"T_e05f6_row8_col73\" class=\"data row8 col73\" >890443883</td>\n",
+       "      <td id=\"T_e05f6_row8_col74\" class=\"data row8 col74\" >0</td>\n",
+       "      <td id=\"T_e05f6_row8_col75\" class=\"data row8 col75\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col76\" class=\"data row8 col76\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col77\" class=\"data row8 col77\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col78\" class=\"data row8 col78\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col79\" class=\"data row8 col79\" >Melequeoque</td>\n",
+       "      <td id=\"T_e05f6_row8_col80\" class=\"data row8 col80\" >Мелекеок</td>\n",
+       "      <td id=\"T_e05f6_row8_col81\" class=\"data row8 col81\" >梅莱凯奥克</td>\n",
+       "      <td id=\"T_e05f6_row8_col82\" class=\"data row8 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col83\" class=\"data row8 col83\" >ميلكيوك</td>\n",
+       "      <td id=\"T_e05f6_row8_col84\" class=\"data row8 col84\" >মেলেকেওক</td>\n",
+       "      <td id=\"T_e05f6_row8_col85\" class=\"data row8 col85\" >Μελεκέοκ</td>\n",
+       "      <td id=\"T_e05f6_row8_col86\" class=\"data row8 col86\" >मेलेकियोक</td>\n",
+       "      <td id=\"T_e05f6_row8_col87\" class=\"data row8 col87\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col88\" class=\"data row8 col88\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col89\" class=\"data row8 col89\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col90\" class=\"data row8 col90\" >マルキョク州</td>\n",
+       "      <td id=\"T_e05f6_row8_col91\" class=\"data row8 col91\" >멜레케오크</td>\n",
+       "      <td id=\"T_e05f6_row8_col92\" class=\"data row8 col92\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col93\" class=\"data row8 col93\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col94\" class=\"data row8 col94\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col95\" class=\"data row8 col95\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col96\" class=\"data row8 col96\" >Melekeok</td>\n",
+       "      <td id=\"T_e05f6_row8_col97\" class=\"data row8 col97\" >1159149073</td>\n",
+       "      <td id=\"T_e05f6_row8_col98\" class=\"data row8 col98\" >ملکئوک</td>\n",
+       "      <td id=\"T_e05f6_row8_col99\" class=\"data row8 col99\" >מלקאוק</td>\n",
+       "      <td id=\"T_e05f6_row8_col100\" class=\"data row8 col100\" >Мелекеок</td>\n",
+       "      <td id=\"T_e05f6_row8_col101\" class=\"data row8 col101\" >میلیکوک</td>\n",
+       "      <td id=\"T_e05f6_row8_col102\" class=\"data row8 col102\" >梅萊凱奧克</td>\n",
+       "      <td id=\"T_e05f6_row8_col103\" class=\"data row8 col103\" >1559804</td>\n",
+       "      <td id=\"T_e05f6_row8_col104\" class=\"data row8 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col105\" class=\"data row8 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col106\" class=\"data row8 col106\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col107\" class=\"data row8 col107\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col108\" class=\"data row8 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col109\" class=\"data row8 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col110\" class=\"data row8 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col111\" class=\"data row8 col111\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col112\" class=\"data row8 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col113\" class=\"data row8 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col114\" class=\"data row8 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col115\" class=\"data row8 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col116\" class=\"data row8 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col117\" class=\"data row8 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col118\" class=\"data row8 col118\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col119\" class=\"data row8 col119\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col120\" class=\"data row8 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col121\" class=\"data row8 col121\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col122\" class=\"data row8 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col123\" class=\"data row8 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col124\" class=\"data row8 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col125\" class=\"data row8 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col126\" class=\"data row8 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col127\" class=\"data row8 col127\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col128\" class=\"data row8 col128\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col129\" class=\"data row8 col129\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col130\" class=\"data row8 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col131\" class=\"data row8 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col132\" class=\"data row8 col132\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col133\" class=\"data row8 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col134\" class=\"data row8 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col135\" class=\"data row8 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col136\" class=\"data row8 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row8_col137\" class=\"data row8 col137\" >POINT (134.6265485 7.4873962)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f91f0_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_f91f0_row9_col0\" class=\"data row9 col0\" >6</td>\n",
-       "      <td id=\"T_f91f0_row9_col1\" class=\"data row9 col1\" >30</td>\n",
-       "      <td id=\"T_f91f0_row9_col2\" class=\"data row9 col2\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col3\" class=\"data row9 col3\" >Admin-0 capital alt</td>\n",
-       "      <td id=\"T_f91f0_row9_col4\" class=\"data row9 col4\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col5\" class=\"data row9 col5\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col6\" class=\"data row9 col6\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col7\" class=\"data row9 col7\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col8\" class=\"data row9 col8\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col9\" class=\"data row9 col9\" >Claimed as inte</td>\n",
-       "      <td id=\"T_f91f0_row9_col10\" class=\"data row9 col10\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col11\" class=\"data row9 col11\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col12\" class=\"data row9 col12\" >Western Sahara</td>\n",
-       "      <td id=\"T_f91f0_row9_col13\" class=\"data row9 col13\" >SAH</td>\n",
-       "      <td id=\"T_f91f0_row9_col14\" class=\"data row9 col14\" >Western Sahara</td>\n",
-       "      <td id=\"T_f91f0_row9_col15\" class=\"data row9 col15\" >SAH</td>\n",
-       "      <td id=\"T_f91f0_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col17\" class=\"data row9 col17\" >EH</td>\n",
-       "      <td id=\"T_f91f0_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col19\" class=\"data row9 col19\" >26.119167</td>\n",
-       "      <td id=\"T_f91f0_row9_col20\" class=\"data row9 col20\" >-9.652522</td>\n",
-       "      <td id=\"T_f91f0_row9_col21\" class=\"data row9 col21\" >500</td>\n",
-       "      <td id=\"T_f91f0_row9_col22\" class=\"data row9 col22\" >200</td>\n",
-       "      <td id=\"T_f91f0_row9_col23\" class=\"data row9 col23\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col24\" class=\"data row9 col24\" >2</td>\n",
-       "      <td id=\"T_f91f0_row9_col25\" class=\"data row9 col25\" >1</td>\n",
-       "      <td id=\"T_f91f0_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col27\" class=\"data row9 col27\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col28\" class=\"data row9 col28\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col29\" class=\"data row9 col29\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col30\" class=\"data row9 col30\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col31\" class=\"data row9 col31\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col32\" class=\"data row9 col32\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col33\" class=\"data row9 col33\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col34\" class=\"data row9 col34\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col35\" class=\"data row9 col35\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col36\" class=\"data row9 col36\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col37\" class=\"data row9 col37\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col38\" class=\"data row9 col38\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col39\" class=\"data row9 col39\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col40\" class=\"data row9 col40\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col41\" class=\"data row9 col41\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col42\" class=\"data row9 col42\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col43\" class=\"data row9 col43\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col44\" class=\"data row9 col44\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col45\" class=\"data row9 col45\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col46\" class=\"data row9 col46\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col47\" class=\"data row9 col47\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col48\" class=\"data row9 col48\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col49\" class=\"data row9 col49\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col50\" class=\"data row9 col50\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col51\" class=\"data row9 col51\" >0.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col52\" class=\"data row9 col52\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col53\" class=\"data row9 col53\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col54\" class=\"data row9 col54\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col55\" class=\"data row9 col55\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col56\" class=\"data row9 col56\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col57\" class=\"data row9 col57\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col58\" class=\"data row9 col58\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col59\" class=\"data row9 col59\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col60\" class=\"data row9 col60\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col61\" class=\"data row9 col61\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col62\" class=\"data row9 col62\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col63\" class=\"data row9 col63\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col64\" class=\"data row9 col64\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col65\" class=\"data row9 col65\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col66\" class=\"data row9 col66\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col67\" class=\"data row9 col67\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col68\" class=\"data row9 col68\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col69\" class=\"data row9 col69\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col70\" class=\"data row9 col70\" >0</td>\n",
-       "      <td id=\"T_f91f0_row9_col71\" class=\"data row9 col71\" >6.000000</td>\n",
-       "      <td id=\"T_f91f0_row9_col72\" class=\"data row9 col72\" >Q614754</td>\n",
-       "      <td id=\"T_f91f0_row9_col73\" class=\"data row9 col73\" >1141909145</td>\n",
-       "      <td id=\"T_f91f0_row9_col74\" class=\"data row9 col74\" >1</td>\n",
-       "      <td id=\"T_f91f0_row9_col75\" class=\"data row9 col75\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col76\" class=\"data row9 col76\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_f91f0_row9_col77\" class=\"data row9 col77\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_f91f0_row9_col78\" class=\"data row9 col78\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col79\" class=\"data row9 col79\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col80\" class=\"data row9 col80\" >Бир-Лелу</td>\n",
-       "      <td id=\"T_f91f0_row9_col81\" class=\"data row9 col81\" >比鄂-雷楼</td>\n",
-       "      <td id=\"T_f91f0_row9_col82\" class=\"data row9 col82\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col83\" class=\"data row9 col83\" >بئر لحلو</td>\n",
-       "      <td id=\"T_f91f0_row9_col84\" class=\"data row9 col84\" >বির লেহলোউ</td>\n",
-       "      <td id=\"T_f91f0_row9_col85\" class=\"data row9 col85\" >Μπιρ Λελού</td>\n",
-       "      <td id=\"T_f91f0_row9_col86\" class=\"data row9 col86\" >बीर लहलू</td>\n",
-       "      <td id=\"T_f91f0_row9_col87\" class=\"data row9 col87\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col88\" class=\"data row9 col88\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col89\" class=\"data row9 col89\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_f91f0_row9_col90\" class=\"data row9 col90\" >ビル・ラルフー</td>\n",
-       "      <td id=\"T_f91f0_row9_col91\" class=\"data row9 col91\" >비르레흘루</td>\n",
-       "      <td id=\"T_f91f0_row9_col92\" class=\"data row9 col92\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col93\" class=\"data row9 col93\" >Al-Bir al-Hilw</td>\n",
-       "      <td id=\"T_f91f0_row9_col94\" class=\"data row9 col94\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col95\" class=\"data row9 col95\" >Bir Lehlu</td>\n",
-       "      <td id=\"T_f91f0_row9_col96\" class=\"data row9 col96\" >Bir Lehlou</td>\n",
-       "      <td id=\"T_f91f0_row9_col97\" class=\"data row9 col97\" >1159149075</td>\n",
-       "      <td id=\"T_f91f0_row9_col98\" class=\"data row9 col98\" >بئر لحلو</td>\n",
-       "      <td id=\"T_f91f0_row9_col99\" class=\"data row9 col99\" >ביר להלו</td>\n",
-       "      <td id=\"T_f91f0_row9_col100\" class=\"data row9 col100\" >Бір-Лелу</td>\n",
-       "      <td id=\"T_f91f0_row9_col101\" class=\"data row9 col101\" >بر لیہلو</td>\n",
-       "      <td id=\"T_f91f0_row9_col102\" class=\"data row9 col102\" >比鄂-雷楼</td>\n",
-       "      <td id=\"T_f91f0_row9_col103\" class=\"data row9 col103\" >-1</td>\n",
-       "      <td id=\"T_f91f0_row9_col104\" class=\"data row9 col104\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col105\" class=\"data row9 col105\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col106\" class=\"data row9 col106\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col107\" class=\"data row9 col107\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col108\" class=\"data row9 col108\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col109\" class=\"data row9 col109\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col110\" class=\"data row9 col110\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col111\" class=\"data row9 col111\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col112\" class=\"data row9 col112\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col113\" class=\"data row9 col113\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col114\" class=\"data row9 col114\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col115\" class=\"data row9 col115\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col116\" class=\"data row9 col116\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col117\" class=\"data row9 col117\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col118\" class=\"data row9 col118\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col119\" class=\"data row9 col119\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col120\" class=\"data row9 col120\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col121\" class=\"data row9 col121\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col122\" class=\"data row9 col122\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col123\" class=\"data row9 col123\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col124\" class=\"data row9 col124\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col125\" class=\"data row9 col125\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col126\" class=\"data row9 col126\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col127\" class=\"data row9 col127\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col128\" class=\"data row9 col128\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col129\" class=\"data row9 col129\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col130\" class=\"data row9 col130\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col131\" class=\"data row9 col131\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col132\" class=\"data row9 col132\" >Populated place</td>\n",
-       "      <td id=\"T_f91f0_row9_col133\" class=\"data row9 col133\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col134\" class=\"data row9 col134\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col135\" class=\"data row9 col135\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col136\" class=\"data row9 col136\" >nan</td>\n",
-       "      <td id=\"T_f91f0_row9_col137\" class=\"data row9 col137\" >POINT (-9.6525222 26.1191667)</td>\n",
+       "      <th id=\"T_e05f6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_e05f6_row9_col0\" class=\"data row9 col0\" >6</td>\n",
+       "      <td id=\"T_e05f6_row9_col1\" class=\"data row9 col1\" >30</td>\n",
+       "      <td id=\"T_e05f6_row9_col2\" class=\"data row9 col2\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col3\" class=\"data row9 col3\" >Admin-0 capital alt</td>\n",
+       "      <td id=\"T_e05f6_row9_col4\" class=\"data row9 col4\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col5\" class=\"data row9 col5\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col6\" class=\"data row9 col6\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col7\" class=\"data row9 col7\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col8\" class=\"data row9 col8\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col9\" class=\"data row9 col9\" >Claimed as inte</td>\n",
+       "      <td id=\"T_e05f6_row9_col10\" class=\"data row9 col10\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col11\" class=\"data row9 col11\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col12\" class=\"data row9 col12\" >Western Sahara</td>\n",
+       "      <td id=\"T_e05f6_row9_col13\" class=\"data row9 col13\" >SAH</td>\n",
+       "      <td id=\"T_e05f6_row9_col14\" class=\"data row9 col14\" >Western Sahara</td>\n",
+       "      <td id=\"T_e05f6_row9_col15\" class=\"data row9 col15\" >SAH</td>\n",
+       "      <td id=\"T_e05f6_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col17\" class=\"data row9 col17\" >EH</td>\n",
+       "      <td id=\"T_e05f6_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col19\" class=\"data row9 col19\" >26.119167</td>\n",
+       "      <td id=\"T_e05f6_row9_col20\" class=\"data row9 col20\" >-9.652522</td>\n",
+       "      <td id=\"T_e05f6_row9_col21\" class=\"data row9 col21\" >500</td>\n",
+       "      <td id=\"T_e05f6_row9_col22\" class=\"data row9 col22\" >200</td>\n",
+       "      <td id=\"T_e05f6_row9_col23\" class=\"data row9 col23\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col24\" class=\"data row9 col24\" >2</td>\n",
+       "      <td id=\"T_e05f6_row9_col25\" class=\"data row9 col25\" >1</td>\n",
+       "      <td id=\"T_e05f6_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col27\" class=\"data row9 col27\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col28\" class=\"data row9 col28\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col29\" class=\"data row9 col29\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col30\" class=\"data row9 col30\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col31\" class=\"data row9 col31\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col32\" class=\"data row9 col32\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col33\" class=\"data row9 col33\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col34\" class=\"data row9 col34\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col35\" class=\"data row9 col35\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col36\" class=\"data row9 col36\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col37\" class=\"data row9 col37\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col38\" class=\"data row9 col38\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col39\" class=\"data row9 col39\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col40\" class=\"data row9 col40\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col41\" class=\"data row9 col41\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col42\" class=\"data row9 col42\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col43\" class=\"data row9 col43\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col44\" class=\"data row9 col44\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col45\" class=\"data row9 col45\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col46\" class=\"data row9 col46\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col47\" class=\"data row9 col47\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col48\" class=\"data row9 col48\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col49\" class=\"data row9 col49\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col50\" class=\"data row9 col50\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col51\" class=\"data row9 col51\" >0.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col52\" class=\"data row9 col52\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col53\" class=\"data row9 col53\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col54\" class=\"data row9 col54\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col55\" class=\"data row9 col55\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col56\" class=\"data row9 col56\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col57\" class=\"data row9 col57\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col58\" class=\"data row9 col58\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col59\" class=\"data row9 col59\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col60\" class=\"data row9 col60\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col61\" class=\"data row9 col61\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col62\" class=\"data row9 col62\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col63\" class=\"data row9 col63\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col64\" class=\"data row9 col64\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col65\" class=\"data row9 col65\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col66\" class=\"data row9 col66\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col67\" class=\"data row9 col67\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col68\" class=\"data row9 col68\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col69\" class=\"data row9 col69\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col70\" class=\"data row9 col70\" >0</td>\n",
+       "      <td id=\"T_e05f6_row9_col71\" class=\"data row9 col71\" >6.000000</td>\n",
+       "      <td id=\"T_e05f6_row9_col72\" class=\"data row9 col72\" >Q614754</td>\n",
+       "      <td id=\"T_e05f6_row9_col73\" class=\"data row9 col73\" >1141909145</td>\n",
+       "      <td id=\"T_e05f6_row9_col74\" class=\"data row9 col74\" >1</td>\n",
+       "      <td id=\"T_e05f6_row9_col75\" class=\"data row9 col75\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col76\" class=\"data row9 col76\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_e05f6_row9_col77\" class=\"data row9 col77\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_e05f6_row9_col78\" class=\"data row9 col78\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col79\" class=\"data row9 col79\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col80\" class=\"data row9 col80\" >Бир-Лелу</td>\n",
+       "      <td id=\"T_e05f6_row9_col81\" class=\"data row9 col81\" >比鄂-雷楼</td>\n",
+       "      <td id=\"T_e05f6_row9_col82\" class=\"data row9 col82\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col83\" class=\"data row9 col83\" >بئر لحلو</td>\n",
+       "      <td id=\"T_e05f6_row9_col84\" class=\"data row9 col84\" >বির লেহলোউ</td>\n",
+       "      <td id=\"T_e05f6_row9_col85\" class=\"data row9 col85\" >Μπιρ Λελού</td>\n",
+       "      <td id=\"T_e05f6_row9_col86\" class=\"data row9 col86\" >बीर लहलू</td>\n",
+       "      <td id=\"T_e05f6_row9_col87\" class=\"data row9 col87\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col88\" class=\"data row9 col88\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col89\" class=\"data row9 col89\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_e05f6_row9_col90\" class=\"data row9 col90\" >ビル・ラルフー</td>\n",
+       "      <td id=\"T_e05f6_row9_col91\" class=\"data row9 col91\" >비르레흘루</td>\n",
+       "      <td id=\"T_e05f6_row9_col92\" class=\"data row9 col92\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col93\" class=\"data row9 col93\" >Al-Bir al-Hilw</td>\n",
+       "      <td id=\"T_e05f6_row9_col94\" class=\"data row9 col94\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col95\" class=\"data row9 col95\" >Bir Lehlu</td>\n",
+       "      <td id=\"T_e05f6_row9_col96\" class=\"data row9 col96\" >Bir Lehlou</td>\n",
+       "      <td id=\"T_e05f6_row9_col97\" class=\"data row9 col97\" >1159149075</td>\n",
+       "      <td id=\"T_e05f6_row9_col98\" class=\"data row9 col98\" >بئر لحلو</td>\n",
+       "      <td id=\"T_e05f6_row9_col99\" class=\"data row9 col99\" >ביר להלו</td>\n",
+       "      <td id=\"T_e05f6_row9_col100\" class=\"data row9 col100\" >Бір-Лелу</td>\n",
+       "      <td id=\"T_e05f6_row9_col101\" class=\"data row9 col101\" >بر لیہلو</td>\n",
+       "      <td id=\"T_e05f6_row9_col102\" class=\"data row9 col102\" >比鄂-雷楼</td>\n",
+       "      <td id=\"T_e05f6_row9_col103\" class=\"data row9 col103\" >-1</td>\n",
+       "      <td id=\"T_e05f6_row9_col104\" class=\"data row9 col104\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col105\" class=\"data row9 col105\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col106\" class=\"data row9 col106\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col107\" class=\"data row9 col107\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col108\" class=\"data row9 col108\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col109\" class=\"data row9 col109\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col110\" class=\"data row9 col110\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col111\" class=\"data row9 col111\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col112\" class=\"data row9 col112\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col113\" class=\"data row9 col113\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col114\" class=\"data row9 col114\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col115\" class=\"data row9 col115\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col116\" class=\"data row9 col116\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col117\" class=\"data row9 col117\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col118\" class=\"data row9 col118\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col119\" class=\"data row9 col119\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col120\" class=\"data row9 col120\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col121\" class=\"data row9 col121\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col122\" class=\"data row9 col122\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col123\" class=\"data row9 col123\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col124\" class=\"data row9 col124\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col125\" class=\"data row9 col125\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col126\" class=\"data row9 col126\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col127\" class=\"data row9 col127\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col128\" class=\"data row9 col128\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col129\" class=\"data row9 col129\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col130\" class=\"data row9 col130\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col131\" class=\"data row9 col131\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col132\" class=\"data row9 col132\" >Populated place</td>\n",
+       "      <td id=\"T_e05f6_row9_col133\" class=\"data row9 col133\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col134\" class=\"data row9 col134\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col135\" class=\"data row9 col135\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col136\" class=\"data row9 col136\" >nan</td>\n",
+       "      <td id=\"T_e05f6_row9_col137\" class=\"data row9 col137\" >POINT (-9.6525222 26.1191667)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1992,7 +1991,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.collections.PathCollection at 0x1def5d209d0>]"
+       "[<matplotlib.collections.PathCollection at 0x24959f8a950>]"
       ]
      },
      "execution_count": 10,
@@ -2055,6 +2054,13 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.5"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

From b54ac4fafadfff749235bf9289238a23ad74ac50 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 12 Jan 2024 00:44:20 +0100
Subject: [PATCH 079/240] update cross-references in docs

---
 docs/api_draw.rst    | 14 +++++++-------
 docs/api_utils.rst   |  2 +-
 docs/api_webmaps.rst |  4 ++--
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/api_draw.rst b/docs/api_draw.rst
index f3ce6d803..69f6cea03 100644
--- a/docs/api_draw.rst
+++ b/docs/api_draw.rst
@@ -40,14 +40,14 @@ Starting with EOmaps v5.0 it is possible to draw simple shapes on the map using
 
 
 
-.. currentmodule:: eomaps.draw
+.. currentmodule:: eomaps.eomaps.Maps.draw
 
 .. autosummary::
     :nosignatures:
 
-    ShapeDrawer.new_drawer
-    ShapeDrawer.rectangle
-    ShapeDrawer.circle
-    ShapeDrawer.polygon
-    ShapeDrawer.save_shapes
-    ShapeDrawer.remove_last_shape
+    new_drawer
+    rectangle
+    circle
+    polygon
+    save_shapes
+    remove_last_shape
diff --git a/docs/api_utils.rst b/docs/api_utils.rst
index 7b9ebae00..63f4d47af 100644
--- a/docs/api_utils.rst
+++ b/docs/api_utils.rst
@@ -31,7 +31,7 @@ By default, the widgets will show all available layers (except the "all" layer)
 - To exclude certain layers from the widget, use ``exclude_layers=[...layer-names to exclude...]``
 - To remove a previously created widget ``s`` from the map, simply use ``s.remove()``
 
-.. currentmodule:: eomaps.utilities.Utilities
+.. currentmodule:: eomaps.eomaps.Maps.util
 
 .. autosummary::
     :nosignatures:
diff --git a/docs/api_webmaps.rst b/docs/api_webmaps.rst
index 815369d84..b1de53220 100644
--- a/docs/api_webmaps.rst
+++ b/docs/api_webmaps.rst
@@ -74,7 +74,7 @@ Pre-defined WebMap services
 
 **Global:**
 
-.. currentmodule:: eomaps.webmap_containers.WebMapContainer
+.. currentmodule:: eomaps.eomaps.Maps.add_wms
 
 .. autosummary::
     :nosignatures:
@@ -124,7 +124,7 @@ Pre-defined WebMap services
 Using custom WebMap services
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. currentmodule:: eomaps.webmap_containers.WebMapContainer
+.. currentmodule:: eomaps.eomaps.Maps.add_wms
 
 It is also possible to use custom WMS/WMTS/XYZ services.
 (see docstring of :py:meth:`get_service` for more details and examples)

From d1728d6f5a3b8313e10d8efdbd3d6cfcc12d4a94 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 13 Jan 2024 21:05:06 +0100
Subject: [PATCH 080/240] update docs

---
 docs/EOmaps_examples.rst                      |  16 +-
 .../{api_Maps.rst => eomaps.eomaps.Maps.rst}  |   0
 docs/api/eomaps.inset_maps.InsetMaps.rst      |  42 +++
 docs/api/reference.rst                        |  25 ++
 docs/api_data_visualization.rst               |  17 +-
 docs/conf.py                                  |   3 +
 docs/example_galery.rst                       |  87 ++++++
 docs/full_reference.rst                       |   9 -
 docs/gen_autodoc_file.py                      | 129 ++++-----
 docs/index.rst                                | 273 ++++++++++++------
 10 files changed, 410 insertions(+), 191 deletions(-)
 rename docs/api/{api_Maps.rst => eomaps.eomaps.Maps.rst} (100%)
 create mode 100644 docs/api/eomaps.inset_maps.InsetMaps.rst
 create mode 100644 docs/example_galery.rst
 delete mode 100644 docs/full_reference.rst

diff --git a/docs/EOmaps_examples.rst b/docs/EOmaps_examples.rst
index 3330b4dda..c8a22982a 100644
--- a/docs/EOmaps_examples.rst
+++ b/docs/EOmaps_examples.rst
@@ -8,21 +8,7 @@
 ... a collection of examples that show how to create beautiful interactive maps.
 
 
-.. table::
-  :width: 100%
-
-  +-----------+-----------+-----------+-----------+-----------+
-  | |eximg01| | |eximg02| | |eximg03| | |eximg04| | |eximg05| |
-  +-----------+-----------+-----------+-----------+-----------+
-  | |eximg06| | |eximg07| | |eximg08| | |eximg09| | |eximg10| |
-  +-----------+-----------+-----------+-----------+-----------+
-  | |eximg11| | |eximg12| | |eximg13| | |eximg14| | |eximg15| |
-  +-----------+-----------+-----------+-----------+-----------+
-
-
-.. contents:: Contents:
-  :local:
-  :depth: 1
+.. include:: example_galery.rst
 
 
 .. _ex_1:
diff --git a/docs/api/api_Maps.rst b/docs/api/eomaps.eomaps.Maps.rst
similarity index 100%
rename from docs/api/api_Maps.rst
rename to docs/api/eomaps.eomaps.Maps.rst
diff --git a/docs/api/eomaps.inset_maps.InsetMaps.rst b/docs/api/eomaps.inset_maps.InsetMaps.rst
new file mode 100644
index 000000000..80edee062
--- /dev/null
+++ b/docs/api/eomaps.inset_maps.InsetMaps.rst
@@ -0,0 +1,42 @@
+:orphan:
+
+..
+  NOTE: this file is copied to ../generated/eomaps.inset_maps.InsetMaps.rst before the docs are generated (see conf.py)
+  to serve as the auto-generated file for InsetMaps-objects!
+
+:py:class:`InsetMaps`
+=====================
+
+.. currentmodule:: eomaps.inset_maps
+
+.. autoclass:: InsetMaps
+
+
+.. card:: InsetMaps are Maps-objects!
+    :link: eomaps.eomaps.Maps
+    :link-type: doc
+
+    .. currentmodule:: eomaps.eomaps
+
+    You can use :py:class:`InsetMaps` objects just like ordinary :py:class:`Maps` objects!
+
+    Checkout the docs for :py:class:`Maps` objects for all available methods!
+
+    +++
+
+    To create a new inset-map, have a look at :py:meth:`Maps.new_inset_map`.
+
+
+.. currentmodule:: eomaps.inset_maps
+
+Special InsetMaps Methods
+-------------------------
+
+.. autosummary::
+    :toctree: ../generated
+    :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
+
+    InsetMaps.set_inset_position
+    InsetMaps.add_extent_indicator
+    InsetMaps.add_indicator_line
diff --git a/docs/api/reference.rst b/docs/api/reference.rst
index 4c9f93c05..7630d9d48 100644
--- a/docs/api/reference.rst
+++ b/docs/api/reference.rst
@@ -21,6 +21,31 @@ eomaps
 Feature objects
 ===============
 
+
+.. card::
+    :shadow: none
+
+    .. card:: InsetMaps
+        :link: ../generated/eomaps.inset_maps.InsetMaps
+        :link-type: doc
+        :margin: 0
+
+        .. currentmodule:: eomaps.inset_maps
+
+        .. autosummary::
+            :toctree: ../generated
+            :template: custom-class-template.rst
+            :nosignatures:
+
+            InsetMaps
+
+    +++
+
+    .. currentmodule:: eomaps.eomaps
+
+    See :py:meth:`Maps.add_inset_map` on how to add a colorbar to a map!
+
+
 .. card::
     :shadow: none
 
diff --git a/docs/api_data_visualization.rst b/docs/api_data_visualization.rst
index 7823c98b6..932847c60 100644
--- a/docs/api_data_visualization.rst
+++ b/docs/api_data_visualization.rst
@@ -225,6 +225,8 @@ Available shapes (see bleow for details on each plot-shape!):
       | ... and ``m.set_shape.shade_points()`` is attempted to be used for the rest.
 
 
+.. _shp_ellipses:
+
 Ellipses
 ********
 
@@ -251,6 +253,8 @@ Ellipses
                          n=50             # number of calculated points on the ellipse
                          )
 
+.. _shp_rectangles:
+
 Rectangles
 **********
 
@@ -279,6 +283,7 @@ Rectangles
                            )
 
 
+.. _shp_geod_circles:
 
 Geodesic Circles
 ****************
@@ -306,6 +311,7 @@ Geodesic Circles
                              n=50             # number of calculated points on the circle
                              )
 
+.. _shp_voronoi:
 
 Voronoi Diagram
 ***************
@@ -335,8 +341,7 @@ Voronoi Diagram
                                 )
 
 
-
-
+.. _shp_delaunay:
 
 Delaunay Triangulation
 **********************
@@ -368,6 +373,8 @@ Delaunay Triangulation
                                        mask_crs="in",     # projection of the mask dimension
                                       )
 
+.. _shp_contour:
+
 Contour plots
 *************
 
@@ -395,6 +402,7 @@ Contour plots
 
     m.set_shape.contour(filled=True)   # filled contour polygons (True) or contour lines (False)
 
+.. _shp_scatter:
 
 Scatter Points
 **************
@@ -422,6 +430,7 @@ Scatter Points
                                marker="*",       # the marker shape to use
                                )
 
+.. _shp_raster:
 
 Raster
 ******
@@ -450,6 +459,8 @@ Raster
                        valid_fraction=0.5,  # % of masked values in aggregation bin for masked result
                        interp_order=0,      # spline interpolation order for "spline" aggregator
 
+.. _shp_shade_raster:
+
 Shade Raster
 ************
 
@@ -479,6 +490,8 @@ Shade Raster
                              agg_hook=None,        # datashader aggregation hook callback
                              )
 
+.. _shp_shade_points:
+
 Shade Points
 ************
 
diff --git a/docs/conf.py b/docs/conf.py
index c20c7c5a7..adfa0ee65 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -99,6 +99,9 @@ def setup(app):
     "jupyter_execute/*",
     ".jupyter_cache/*",
     ".virtual_documents/*",
+    # files starting with eomaps. are copied to the generated folder
+    # (check "make_feature_toctree_file()" for more details.
+    "api/eomaps.*",
 ]
 
 source_suffix = {
diff --git a/docs/example_galery.rst b/docs/example_galery.rst
new file mode 100644
index 000000000..06a5ee0aa
--- /dev/null
+++ b/docs/example_galery.rst
@@ -0,0 +1,87 @@
+.. grid:: 2 3 4 5
+    :gutter: 1
+
+    .. grid-item-card::
+        :img-background: _static/fig1.gif
+        :img-alt: Example 1 - Basic data visualization
+        :link: ex_1
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig2.png
+        :img-alt: Example 2 - Customize the appearance of the map
+        :link: ex_2
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig3.gif
+        :img-alt: Example 3 - Data-classification and multiple Maps in a figure
+        :link: ex_3
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig4.gif
+        :img-alt: Example 4 - Callbacks - turn your maps into interactive widgets
+        :link: ex_4
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig5.gif
+        :img-alt: Example 5 - Overlays, markers and annotations
+        :link: ex_5
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig6.gif
+        :img-alt: Example 6 - WebMap services and layer-switching
+        :link: ex_6
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig7.gif
+        :img-alt: Example 7 - Vector data - interactive geometries
+        :link: ex_7
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig8.gif
+        :img-alt: Example 8 - Using Scalebars
+        :link: ex_8
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/fig9.gif
+        :img-alt: Example 9 - Data analysis widgets - Timeseries and histograms
+        :link: ex_9
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/example_row_col_selector.gif
+        :img-alt: Example 10 - Data analysis widgets - Select 1D slices of a 2D dataset
+        :link: ex_10
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/example_inset_maps.png
+        :img-alt: Example 11 - Inset-maps - get a zoomed-in view on selected areas
+        :link: ex_11
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/example_lines.png
+        :img-alt: Example 12 - Lines and Annotations
+        :text-align: center
+        :link: ex_12
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/example_gridlines.png
+        :img-alt: Example 13 - Gridlines and Grid Labels
+        :link: ex_13
+        :link-type: ref
+
+    .. grid-item-card::
+        :img-background: _static/example_contour.png
+        :img-alt: Example 14 - Contour plots and Contour Levels
+        :link: ex_14
+        :link-type: ref
diff --git a/docs/full_reference.rst b/docs/full_reference.rst
deleted file mode 100644
index ec10cbce9..000000000
--- a/docs/full_reference.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-.. autosummary::
-   :toctree: generated
-   :nosignatures:
-   :template: custom-module-template.rst
-   :recursive:
-
-   eomaps
diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index bfe185711..6cafda795 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -3,19 +3,32 @@
 from itertools import chain
 
 from eomaps import Maps
-from eomaps.colorbar import ColorBar
 
 
-def get_members(
-    obj, key="", with_sublevel=False, prefix="", names_only=False, exclude=[]
+def get_autosummary(
+    currentmodule="eomaps.eomaps",
+    members=[],
+    template="obj_with_attributes_no_toc",
 ):
+    return (
+        f".. currentmodule:: {currentmodule}\n"
+        ".. autosummary::\n"
+        "    :toctree: ../generated\n"
+        "    :nosignatures:\n"
+        f"    :template: {template}.rst\n\n"
+        + "\n".join(f"    {m}" for m in members)
+        + "\n\n"
+    )
+
+
+def get_members(obj, key="", with_sublevel=False, names_only=False, exclude=[]):
     """get a list of attributes of a given object"""
     # use attrgetter to allow also nested attributes (Maps.x.y)
     if len(key) == 0:
-        startstr = f"{prefix}{obj.__name__}"
+        startstr = f"{obj.__name__}"
         members = filter(lambda x: not x.startswith("_") and not x in exclude, dir(obj))
     else:
-        startstr = f"{prefix}{obj.__name__}.{key}"
+        startstr = f"{obj.__name__}.{key}"
         members = filter(
             lambda x: not x.startswith("_") and not x in exclude,
             dir(attrgetter(key)(obj)),
@@ -44,78 +57,56 @@ def get_members(
 
 
 def make_feature_toctree_file():
-    s = (
-        ":orphan:\n"
-        ".. currentmodule:: eomaps.eomaps\n\n"
-        ".. autosummary::\n"
-        "    :toctree: ../generated\n"
-        "    :nosignatures:\n"
-        "    :template: obj_with_attributes_no_toc.rst\n\n"
-        #        "    Maps\n"
-        "    ColorBar\n"
-        "    Maps.config\n"
-    )
-
-    s += (
-        "\n".join(get_members(Maps, "", False, "    ", exclude=["CRS", "CLASSIFIERS"]))
-        + "\n"
-    )
-
-    for key in ("set_shape", "draw", "from_file", "read_file", "util", "add_wms"):
-        s += "\n".join(get_members(Maps, key, False, "    ")) + "\n"
-
+    # Fetch all members of the Maps object that should get a auto-generated docs-file
+    members = list(get_members(Maps, "", False, exclude=["CRS", "CLASSIFIERS"]))
+    for key in (
+        "set_shape",
+        "draw",
+        "from_file",
+        "read_file",
+        "util",
+        "add_wms",
+    ):
+        members.extend(get_members(Maps, key, False))
     for key in ("add_feature", "cb"):
-        s += "\n".join(get_members(Maps, key, True, "    ")) + "\n"
+        members.extend(get_members(Maps, key, True))
 
-    s += "\n\n"
-    s += (
-        ".. currentmodule:: eomaps.colorbar\n"
-        ".. autosummary::\n"
-        "    :toctree: ../generated\n"
-        "    :nosignatures:\n"
-        "    :template: custom-class-template.rst\n\n"
-        "    ColorBar\n"
+    # create a page that will be used for sphinx-autodoc to create stub-files
+    s = ":orphan:\n\n"
+    s += get_autosummary(
+        "eomaps.eomaps", ["Maps.config", *members], "obj_with_attributes_no_toc"
     )
-
-    s += "\n\n"
-    s += (
-        ".. currentmodule:: eomaps.grid\n"
-        ".. autosummary::\n"
-        "    :toctree: ../generated\n"
-        "    :nosignatures:\n"
-        "    :template: custom-class-template.rst\n\n"
-        "    GridLines\n"
-        "    GridLabels\n"
+    s += get_autosummary("eomaps.colorbar", ["ColorBar"], "custom-class-template")
+    s += get_autosummary(
+        "eomaps.grid", ["GridLines", "GridLabels"], "custom-class-template"
     )
-
-    s += "\n\n"
-    s += (
-        ".. currentmodule:: eomaps.compass\n"
-        ".. autosummary::\n"
-        "    :toctree: ../generated\n"
-        "    :nosignatures:\n"
-        "    :template: custom-class-template.rst\n\n"
-        "    Compass\n"
+    s += get_autosummary("eomaps.compass", ["Compass"], "custom-class-template")
+    s += get_autosummary("eomaps.scalebar", ["ScaleBar"], "custom-class-template")
+    s += get_autosummary(
+        "eomaps.inset_maps",
+        [
+            "InsetMaps.set_inset_position",
+            "InsetMaps.add_extent_indicator",
+            "InsetMaps.add_indicator_line",
+        ],
+        "obj_with_attributes_no_toc",
     )
 
-    s += "\n\n"
-    s += (
-        ".. currentmodule:: eomaps.scalebar\n"
-        ".. autosummary::\n"
-        "    :toctree: ../generated\n"
-        "    :nosignatures:\n"
-        "    :template: custom-class-template.rst\n\n"
-        "    ScaleBar\n"
-    )
+    basepath = Path(__file__).parent
 
-    with open(
-        Path(__file__).parent / "api" / "autodoc_additional_props.rst", "w"
-    ) as file:
+    with open(basepath / "api" / "autodoc_additional_props.rst", "w") as file:
         file.write(s)
 
-    src = Path(__file__).parent / "api" / "api_Maps.rst"
+    # Copy the custom api files to the "generated" docs folder so that they serve
+    # as the autodoc-files for the associated objects.
+    # This is done to redirect links for :py:class:`<object>` to the custom file
+    # to get a more customized page for the API docs of the objects.
 
-    (Path(__file__).parent / "generated").mkdir(exist_ok=True)
-    dest = Path(__file__).parent / "generated" / "eomaps.eomaps.Maps.rst"
+    src_basepath = basepath / "api"
+    dest_basepath = basepath / "generated"
 
-    dest.write_text(src.read_text())  # for text files
+    (basepath / "generated").mkdir(exist_ok=True)
+    # copy all files starting with "eomaps." (e.g. "eomaps.eomaps.Maps.rst")
+    # Note: original source-files are ignored in conf.py to avoid duplication warnings!
+    for file in filter(lambda x: x.stem.startswith("eomaps."), src_basepath.iterdir()):
+        (dest_basepath / file.name).write_text(file.read_text())
diff --git a/docs/index.rst b/docs/index.rst
index 5d3db9e6e..7644c39c3 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -12,7 +12,7 @@
     <p>
 
 
-Here you can find detailed explanations on all the functionalities of EOmaps.
+Here you can find detailed explanations on all the features and functionalities of EOmaps.
 
 .. admonition:: Interested in contributing to EOmaps?
 
@@ -37,44 +37,58 @@ Want to visualize some data? Have a look at the :doc:`api_data_visualization` se
 EOmaps provides a variety of plot-shapes so you can select a shape that suits the structure, size and spatial representativeness of your data:
 
 
-.. raw:: html
+.. grid:: 2 2 5 5
+    :gutter: 1
 
-    <table width="100%" style="font-size:90%; table-layout:fixed; overflow:scroll;">
-    <tr>
-    <th width="20%">Ellipses</th>
-    <th width="20%">Rectangles</th>
-    <th width="20%">Geodesic Circles</th>
-    <th width="20%">Raster</th>
-    <th width="20%">Scatter Points</th>
-    </tr>
-    <tr>
-    <th width="20%"><a href=api_data_visualization.html#ellipses><img src="_static/shape_imgs/ellipses.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#rectangles><img src="_static/shape_imgs/rectangles.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#geodesic-circles><img src="_static/shape_imgs/geod_circles.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#raster><img src="_static/shape_imgs/raster.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#scatter-points><img src="_static/shape_imgs/scatter_points.png"/></a></th>
-    </tr>
-    <tr>
-    <th width="20%">Contour</th>
-    <th width="20%">Voronoi Diagram</th>
-    <th width="20%">Delaunay Triangulation</th>
-    <th width="20%">Shade Raster</th>
-    <th width="20%">Shade Points</th>
-    </tr>
-    <tr>
-    <th width="20%"><a href=api_data_visualization.html#contour><img src="_static/shape_imgs/contour_filled.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#voronoi-diagram><img src="_static/shape_imgs/voronoi_diagram.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#delaunay-triangulation><img src="_static/shape_imgs/delaunay_triangulation.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#shade-raster><img src="_static/shape_imgs/shade_raster.png"/></a></th>
-    <th width="20%"><a href=api_data_visualization.html#shade-points><img src="_static/shape_imgs/shade_points.png"/></a></th>
-    </tr>
-    </table>
+    .. grid-item-card:: Ellipses
+        :img-bottom: _static/shape_imgs/ellipses.png
+        :link: shp_ellipses
+        :link-type: ref
 
+    .. grid-item-card:: Rectangles
+        :img-bottom: _static/shape_imgs/rectangles.png
+        :link: shp_rectangles
+        :link-type: ref
 
-.. raw:: html
+    .. grid-item-card:: Geodesic Circles
+        :img-bottom: _static/shape_imgs/geod_circles.png
+        :link: shp_geod_circles
+        :link-type: ref
+
+    .. grid-item-card:: raster
+        :img-bottom: _static/shape_imgs/raster.png
+        :link: shp_raster
+        :link-type: ref
+
+    .. grid-item-card:: Scatter Points
+        :img-bottom: _static/shape_imgs/scatter_points.png
+        :link: shp_scatter
+        :link-type: ref
+
+    .. grid-item-card:: Contour
+        :img-bottom: _static/shape_imgs/contour.png
+        :link: shp_contour
+        :link-type: ref
+
+    .. grid-item-card:: Voronoi Diagram
+        :img-bottom: _static/shape_imgs/voronoi_diagram.png
+        :link: shp_voronoi
+        :link-type: ref
+
+    .. grid-item-card:: Delaunay Triangulation
+        :img-bottom: _static/shape_imgs/delaunay_triangulation.png
+        :link: shp_delaunay
+        :link-type: ref
 
-    </p>
+    .. grid-item-card:: Shade Raster
+        :img-bottom: _static/shape_imgs/shade_raster.png
+        :link: shp_shade_raster
+        :link-type: ref
 
+    .. grid-item-card:: Shade Points
+        :img-bottom: _static/shape_imgs/shade_points.png
+        :link: shp_shade_points
+        :link-type: ref
 
 
 Map Features
@@ -84,29 +98,65 @@ Map Features
 EOmaps provides many useful tools to customize your maps.
 
 
-.. list-table::
-    :width: 100%
-    :widths: 50 50
 
-    * - |  :doc:`notebooks/inset_maps`
-        |  Create zoomed-in views on specific regions of a map.
-      - |  :doc:`notebooks/naturalearth_features`
-        |  Add basic map features (coastlines, ocean-coloring etc. ) to the map.
+.. grid:: 1 1 2 2
+    :gutter: 1
+
+    .. grid-item-card:: :doc:`notebooks/naturalearth_features`
+        :link: notebooks/naturalearth_features
+        :link-type: doc
+        :shadow: none
+
+        Add basic map features (coastlines, ocean-coloring etc. ) to the map.
+
+    .. grid-item-card:: :doc:`api_webmaps`
+        :link: api_webmaps
+        :link-type: doc
+        :shadow: none
+
+        Add imagery provided by WebMap services to the map.
+
+    .. grid-item-card:: :doc:`notebooks/inset_maps`
+        :link: notebooks/inset_maps
+        :link-type: doc
+        :shadow: none
+
+        Create zoomed-in views on specific regions of a map.
 
-    * - |  :doc:`api_webmaps`
-        |  Add imagery provided by WebMap services (ts, wms, wmts, xyz) to the map.
-      - |  :doc:`api_vector_data`
-        |  Add vector geometries to the map.
+    .. grid-item-card:: :doc:`api_vector_data`
+        :link: api_vector_data
+        :link-type: doc
+        :shadow: none
 
-    * - |  :doc:`api_annotations_markers_etc`
-        |  Add markers, annotations, lines, logos etc. to the map.
-      - |  :doc:`api_scalebar`
-        |  Add a scalebar to the map.
+        Add vector geometries to the map.
 
-    * - |  :doc:`api_compass`
-        |  Add a compass (or North Arrow) to the map.
-      - |  :doc:`api_gridlines`
-        |  Add grid-lines (and optionally grid-labels) to the map.
+    .. grid-item-card:: :doc:`api_annotations_markers_etc`
+        :link: api_annotations_markers_etc
+        :link-type: doc
+        :shadow: none
+
+        Add markers, annotations, lines, logos etc. to the map.
+
+    .. grid-item-card:: :doc:`api_scalebar`
+        :link: api_scalebar
+        :link-type: doc
+        :shadow: none
+
+        Add a scalebar to the map.
+
+    .. grid-item-card:: :doc:`api_compass`
+        :link: api_compass
+        :link-type: doc
+        :shadow: none
+
+        Add a compass (or North Arrow) to the map.
+
+    .. grid-item-card:: :doc:`api_gridlines`
+        :link: api_gridlines
+        :link-type: doc
+        :shadow: none
+
+        Add grid-lines (and optionally grid-labels) to the map.
 
 
 Interactivity
@@ -114,56 +164,88 @@ Interactivity
 
 With a few lines of code, you can turn your maps into interactive data-analysis widgets!
 
-.. list-table::
-    :width: 100%
-    :widths: 50 50
-
-    * - |  :doc:`api_companion_widget`
-        |  A graphical user-interface to interact with the map.
-      - |  :doc:`api_callbacks`
-        |  Turn your maps into interactive data-analysis widgets.
-    * - |  :doc:`api_layout_editor`
-        |  Interactively re-arrange and re-size axes of a figure.
-      - |  :doc:`api_draw`
-        |  Interactively draw geometries on a map and export them as shapefiles.
-    * - |  :doc:`api_utils`
-        |  A collection of utility widgets (layer-sliders, layer-selectors)
-      -
+
+.. grid:: 1 1 2 2
+    :gutter: 1
+
+    .. grid-item-card:: :doc:`api_companion_widget`
+        :link: api_companion_widget
+        :link-type: doc
+        :shadow: none
+
+        A graphical user-interface to interact with the map.
+
+    .. grid-item-card:: :doc:`api_callbacks`
+        :link: api_callbacks
+        :link-type: doc
+        :shadow: none
+
+        Turn your maps into interactive data-analysis widgets.
+
+    .. grid-item-card:: :doc:`api_layout_editor`
+        :link: api_layout_editor
+        :link-type: doc
+        :shadow: none
+
+        Interactively re-arrange and re-size axes of a figure.
+
+    .. grid-item-card:: :doc:`api_draw`
+        :link: api_draw
+        :link-type: doc
+        :shadow: none
+
+        Interactively draw geometries on a map and export them as shapefiles.
+
+    .. grid-item-card:: :doc:`api_utils`
+        :link: api_utils
+        :link-type: doc
+        :shadow: none
+
+        A collection of utility widgets (layer-sliders, layer-selectors)
+
 
 Miscellaneous
 ~~~~~~~~~~~~~
 
-.. list-table::
-    :width: 100%
-    :widths: 50 50
+.. grid:: 1 1 2 2
+    :gutter: 1
 
-    * - |  :doc:`api_logging`
-        |  Details on logging.
-      - |  :doc:`api_command_line_interface`
-        |  How to use the command-line interface `eomaps`.
-    * - |  :doc:`api_read_data`
-        |  Read data from NetCDF, GeoTIFF or CSV files.
-      - |  :doc:`api_misc`
-        |  Additional functions and properties that might come in handy.
+    .. grid-item-card:: :doc:`api_logging`
+        :link: api_logging
+        :link-type: doc
+        :shadow: none
 
+        Details on logging.
 
+    .. grid-item-card:: :doc:`api_command_line_interface`
+        :link: api_command_line_interface
+        :link-type: doc
+        :shadow: none
+
+        How to use the ``eomaps`` command-line interface.
+
+    .. grid-item-card:: :doc:`api_read_data`
+        :link: api_companion_widget
+        :link-type: doc
+        :shadow: none
+
+        Read data from NetCDF, GeoTIFF or CSV files.
+
+    .. grid-item-card:: :doc:`api_misc`
+        :link: api_misc
+        :link-type: doc
+        :shadow: none
+
+        Additional functions and properties that might come in handy.
 
 
 Examples
 --------
 
-Make sure to check out the :doc:`EOmaps_examples` for an overview of the capabilities (incl. source code)!
+Make sure to check out the :doc:`Examples <EOmaps_examples>` for an overview of the capabilities (incl. source code)!
 
-.. table::
-   :width: 100%
 
-   +-----------+-----------+-----------+-----------+-----------+
-   | |eximg01| | |eximg02| | |eximg03| | |eximg04| | |eximg05| |
-   +-----------+-----------+-----------+-----------+-----------+
-   | |eximg06| | |eximg07| | |eximg08| | |eximg09| | |eximg10| |
-   +-----------+-----------+-----------+-----------+-----------+
-   | |eximg11| | |eximg12| | |eximg13| | |eximg14| | |eximg15| |
-   +-----------+-----------+-----------+-----------+-----------+
+.. include:: example_galery.rst
 
 
 .. toctree::
@@ -174,6 +256,12 @@ Make sure to check out the :doc:`EOmaps_examples` for an overview of the capabil
     installation
     FAQ
 
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+   :caption: Contribute
+
+   contribute
 
 .. toctree::
     :hidden:
@@ -229,13 +317,6 @@ Make sure to check out the :doc:`EOmaps_examples` for an overview of the capabil
    EOmaps_examples
 
 
-.. toctree::
-   :hidden:
-   :maxdepth: 2
-   :caption: Contribute
-
-   contribute
-
 .. toctree::
    :hidden:
    :maxdepth: 1

From c6e147ba56ea7ff0ed09d53a3730fda39a96b2d4 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 13 Jan 2024 21:05:42 +0100
Subject: [PATCH 081/240] minor

---
 docs/notebooks/inset_maps.ipynb | 2 +-
 eomaps/eomaps.py                | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/notebooks/inset_maps.ipynb b/docs/notebooks/inset_maps.ipynb
index 54f57c4ee..3453bbb57 100644
--- a/docs/notebooks/inset_maps.ipynb
+++ b/docs/notebooks/inset_maps.ipynb
@@ -46,7 +46,7 @@
    "source": [
     "## How to create inset maps\n",
     "```{eval-rst}\n",
-    ".. currentmodule:: eomaps\n",
+    ".. currentmodule:: eomaps.eomaps\n",
     "```\n",
     "\n",
     "Inset maps are used to show **zoomed-in regions of a map** and can be created with {py:meth}`Maps.new_inset_map`.\n",
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 62dda37e9..016af91ca 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -1094,9 +1094,9 @@ def new_inset_map(
 
         Returns
         -------
-        m : eomaps.Maps
-            A eomaps.Maps-object of the inset-map.
-            (use it just like any other Maps-object)
+        m : eomaps.inset_maps.InsetMaps
+            A InsetMaps-object of the inset-map.
+            (you can use it just like any other Maps-object!)
 
         See Also
         --------

From 902e841e518959847251acb8f300a05d05ae08ed Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 13 Jan 2024 22:45:32 +0100
Subject: [PATCH 082/240] address final docs-warnings - implement custom rc
 role to address warnings from inherited docstrings - link to matplotlib docs
 via intersphinx to resolve external labels

---
 docs/conf.py | 32 ++++++++++++++++++++++++++++++--
 1 file changed, 30 insertions(+), 2 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index adfa0ee65..3914f2116 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,5 +1,7 @@
 # Configuration file for the Sphinx documentation builder.
 import sys, os
+from docutils.nodes import reference
+
 from eomaps import Maps
 
 sys.path.insert(0, os.path.abspath(os.path.join("..", "..")))
@@ -11,12 +13,31 @@
 make_feature_toctree_file()
 
 
+def mpl_rc_role_subst(name, rawtext, text, lineno, inliner, options={}, content=[]):
+    """
+    A custom role to avoid 'undefined role' warnings when processing inherited
+    docstrings from matplotlib that use the 'rc' role.
+
+    Each :rc: role is just turned into a link to the matplotlib rcparams file where you
+    can find the actual default value.
+    """
+
+    node = reference(
+        "",
+        f"matpltolib rcParams['{text}']",
+        refuri=r"https://matplotlib.org/stable/users/explain/customizing.html#matplotlibrc-sample",
+    )
+    return [node], []
+
+
 def setup(app):
+    # add rc role to avoid undefined role warnings from inherited docstrings.
+    app.add_role("rc", mpl_rc_role_subst)
 
     app.add_css_file("custom_css.css")
 
-    # need to assign the names here, otherwise autodoc won't document these classes,
-    # and will instead just say 'alias of ...'
+    # By default, autodoc will print 'alias of ...' for aliases.
+    # This can be avoided by explicitly setting the __name__ property.
     # see https://stackoverflow.com/a/58982001/9703451
     Maps.add_feature.__name__ = "add_feature"
     Maps.add_feature.preset.__name__ = "preset"
@@ -46,12 +67,19 @@ def setup(app):
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.napoleon",
+    "sphinx.ext.intersphinx",
     "sphinx_copybutton",
     "sphinx_rtd_theme",
     "myst_nb",
     "sphinx_design",
 ]
 
+
+# add mapping for matplotlib-docs to resolve warning about undefined labels
+# in inherited docstrings
+intersphinx_mapping = {"mpl": ("https://matplotlib.org/stable", None)}
+
+
 # -- Options for EPUB output
 epub_show_urls = "footnote"
 

From 4dd71b1c9638a3e71ecfdcf29c519f7a821e8708 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 13 Jan 2024 22:45:39 +0100
Subject: [PATCH 083/240] update docs

---
 docs/FAQ.rst | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 0613e8f98..804275d74 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -349,14 +349,18 @@ Important changes between major versions
 
 .. dropdown:: ⚙ From EOmaps v7.x to v8.x
 
+    - ❗ Starting with `v8.x` eomaps is licensed under a "BSD 3 clause" license!
+
     - ⚠️ Some functions and classes have been re-named to better follow PEP8 naming conventions.
       While this should not interfere with the public API, more extensive customizations might need to be
       adjusted with respect to the new names.
 
-      If you encounter any problems, feel free to open an `issue <https://github.com/raphaelquast/EOmaps/issues>`_ , and I'll see what I can do!
+      - If you encounter any problems, feel free to open an `issue <https://github.com/raphaelquast/EOmaps/issues>`_ , and I'll see what I can do!
+
+    - ⚠️ ``pip`` install has been updated to implement optional dependency groups.
 
-    - ⚠️ ``pip`` install has been updated to implement optional dependency groups
-      Have a look at the :ref:`installation` instructions for more details!
+        - ❗ ``pip install eomaps`` now only installs minimal dependencies required for eomaps
+        - Have a look at the :ref:`installation` instructions for more details!
 
     - ``setup.py`` and ``_version.py`` have been removed in favor of using a ``pyproject.toml`` file
 

From 65b142142efae7eddd85df15f2a188a64a49fcbc Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 18:40:51 +0100
Subject: [PATCH 084/240] switch from GPL to BSD-3 license

---
 LICENSE        | 702 ++-----------------------------------------------
 pyproject.toml |   2 +-
 2 files changed, 29 insertions(+), 675 deletions(-)

diff --git a/LICENSE b/LICENSE
index f288702d2..4b2b24e5f 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,674 +1,28 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received.  You must make sure that they, too, receive
-or can get the source code.  And you must show them these terms so they
-know their rights.
-
-  Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
-  For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software.  For both users' and
-authors' sake, the GPL requires that modified versions be marked as
-changed, so that their problems will not be attributed erroneously to
-authors of previous versions.
-
-  Some devices are designed to deny users access to install or run
-modified versions of the software inside them, although the manufacturer
-can do so.  This is fundamentally incompatible with the aim of
-protecting users' freedom to change the software.  The systematic
-pattern of such abuse occurs in the area of products for individuals to
-use, which is precisely where it is most unacceptable.  Therefore, we
-have designed this version of the GPL to prohibit the practice for those
-products.  If such problems arise substantially in other domains, we
-stand ready to extend this provision to those domains in future versions
-of the GPL, as needed to protect the freedom of users.
-
-  Finally, every program is threatened constantly by software patents.
-States should not allow patents to restrict development and use of
-software on general-purpose computers, but in those that do, we wish to
-avoid the special danger that patents applied to a free program could
-make it effectively proprietary.  To prevent this, the GPL assures that
-patents cannot be used to render the program non-free.
-
-  The precise terms and conditions for copying, distribution and
-modification follow.
-
-                       TERMS AND CONDITIONS
-
-  0. Definitions.
-
-  "This License" refers to version 3 of the GNU General Public License.
-
-  "Copyright" also means copyright-like laws that apply to other kinds of
-works, such as semiconductor masks.
-
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-            How to Apply These Terms to Your New Programs
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+BSD 3-Clause License
+
+Copyright (c) 2021, The EOmaps authors.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/pyproject.toml b/pyproject.toml
index 90031c311..9c86e44ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ keywords = ["Visualization", "Plotting", "Maps", "Geographical Data"]
 
 classifiers = [
     "Programming Language :: Python :: 3",
-    "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
+    "License :: OSI Approved :: BSD License",
     "Operating System :: OS Independent",
     "Framework :: Matplotlib",
     "Topic :: Scientific/Engineering",

From 82a6f9b79ef7eab390fb84713e08b7c449c0b1db Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 3 Jan 2024 18:54:29 +0100
Subject: [PATCH 085/240] add license info to all source files

---
 eomaps/__init__.py                            | 5 +++++
 eomaps/_containers.py                         | 5 +++++
 eomaps/_data_manager.py                       | 5 +++++
 eomaps/_webmap.py                             | 5 +++++
 eomaps/annotation_editor.py                   | 5 +++++
 eomaps/callbacks.py                           | 5 +++++
 eomaps/cb_container.py                        | 5 +++++
 eomaps/colorbar.py                            | 5 +++++
 eomaps/compass.py                             | 5 +++++
 eomaps/draw.py                                | 5 +++++
 eomaps/eomaps.py                              | 5 +++++
 eomaps/grid.py                                | 5 +++++
 eomaps/helpers.py                             | 5 +++++
 eomaps/inset_maps.py                          | 5 +++++
 eomaps/mapsgrid.py                            | 5 +++++
 eomaps/ne_features.py                         | 5 +++++
 eomaps/projections.py                         | 5 +++++
 eomaps/qtcompanion/app.py                     | 5 +++++
 eomaps/qtcompanion/base.py                    | 5 +++++
 eomaps/qtcompanion/common.py                  | 5 +++++
 eomaps/qtcompanion/signal_container.py        | 5 +++++
 eomaps/qtcompanion/widgets/annotate.py        | 5 +++++
 eomaps/qtcompanion/widgets/click_callbacks.py | 5 +++++
 eomaps/qtcompanion/widgets/draw.py            | 5 +++++
 eomaps/qtcompanion/widgets/editor.py          | 5 +++++
 eomaps/qtcompanion/widgets/extent.py          | 5 +++++
 eomaps/qtcompanion/widgets/files.py           | 5 +++++
 eomaps/qtcompanion/widgets/layer.py           | 5 +++++
 eomaps/qtcompanion/widgets/peek.py            | 5 +++++
 eomaps/qtcompanion/widgets/save.py            | 5 +++++
 eomaps/qtcompanion/widgets/utils.py           | 5 +++++
 eomaps/qtcompanion/widgets/wms.py             | 5 +++++
 eomaps/reader.py                              | 5 +++++
 eomaps/scalebar.py                            | 5 +++++
 eomaps/scripts/open.py                        | 5 +++++
 eomaps/shapes.py                              | 5 +++++
 eomaps/utilities.py                           | 5 +++++
 eomaps/webmap_containers.py                   | 5 +++++
 38 files changed, 190 insertions(+)

diff --git a/eomaps/__init__.py b/eomaps/__init__.py
index df9883218..04751f4f1 100644
--- a/eomaps/__init__.py
+++ b/eomaps/__init__.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import importlib.metadata
 from .helpers import register_modules as _register_modules
 
diff --git a/eomaps/_containers.py b/eomaps/_containers.py
index b4dcdeb9a..0b965fa77 100644
--- a/eomaps/_containers.py
+++ b/eomaps/_containers.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 from textwrap import dedent, indent, fill
 from operator import attrgetter
diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index eea7372fd..c1c248e42 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 
 import numpy as np
diff --git a/eomaps/_webmap.py b/eomaps/_webmap.py
index 3d7dccebe..0933adde4 100644
--- a/eomaps/_webmap.py
+++ b/eomaps/_webmap.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 
 import requests
diff --git a/eomaps/annotation_editor.py b/eomaps/annotation_editor.py
index 587ab9e22..4498ef9bd 100644
--- a/eomaps/annotation_editor.py
+++ b/eomaps/annotation_editor.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Functionalities for editable annotations."""
 import logging
 from types import SimpleNamespace
diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 4cacad20d..2468978a2 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Collection of pre-defined click/pick/move/keypress callbacks."""
 
 import numpy as np
diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index c70e01284..14a442cdf 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Container classes for callback management"""
 
 import logging
diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 51265c45d..4cf15a436 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Interactive Colorbar."""
 
 import logging
diff --git a/eomaps/compass.py b/eomaps/compass.py
index 7f6ff49b7..72a8b45af 100644
--- a/eomaps/compass.py
+++ b/eomaps/compass.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Interactive Compass (or North Arrow)."""
 
 import logging
diff --git a/eomaps/draw.py b/eomaps/draw.py
index 5439fdcd8..76b87c6ba 100644
--- a/eomaps/draw.py
+++ b/eomaps/draw.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """
 Functionalities to draw shapes on maps created with EOmaps.
 
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 016af91ca..4b264e9e1 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """General definition of Maps objects."""
 
 import logging
diff --git a/eomaps/grid.py b/eomaps/grid.py
index 459e2bc99..f9589f625 100644
--- a/eomaps/grid.py
+++ b/eomaps/grid.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Grid lines and grid labels."""
 
 import logging
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index 967f384bb..5cc21f4de 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """a collection of useful helper-functions."""
 
 import logging
diff --git a/eomaps/inset_maps.py b/eomaps/inset_maps.py
index f1f26d850..2748364a2 100644
--- a/eomaps/inset_maps.py
+++ b/eomaps/inset_maps.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Inset maps class definitions."""
 
 import numpy as np
diff --git a/eomaps/mapsgrid.py b/eomaps/mapsgrid.py
index 13ed7f2b8..cd3dc4947 100644
--- a/eomaps/mapsgrid.py
+++ b/eomaps/mapsgrid.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Mapsgrid class definition (helper for initialization of regular Maps-grids)."""
 
 from functools import wraps, lru_cache
diff --git a/eomaps/ne_features.py b/eomaps/ne_features.py
index d7553ff75..365fdeb6c 100644
--- a/eomaps/ne_features.py
+++ b/eomaps/ne_features.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Classes to fetch and draw NaturalEarth features."""
 
 import logging
diff --git a/eomaps/projections.py b/eomaps/projections.py
index ddd6236b8..cbc8938f2 100644
--- a/eomaps/projections.py
+++ b/eomaps/projections.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Projection definitions."""
 
 from cartopy import crs as ccrs
diff --git a/eomaps/qtcompanion/app.py b/eomaps/qtcompanion/app.py
index ca0591173..2337e1845 100644
--- a/eomaps/qtcompanion/app.py
+++ b/eomaps/qtcompanion/app.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from qtpy import QtWidgets
 from qtpy.QtCore import Qt, Signal, Slot, QSize, QObject
 from qtpy.QtGui import QKeySequence
diff --git a/eomaps/qtcompanion/base.py b/eomaps/qtcompanion/base.py
index 6adf924ed..803c74c3f 100644
--- a/eomaps/qtcompanion/base.py
+++ b/eomaps/qtcompanion/base.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 from weakref import WeakSet
 
diff --git a/eomaps/qtcompanion/common.py b/eomaps/qtcompanion/common.py
index 0bd49af8b..78d703dbb 100644
--- a/eomaps/qtcompanion/common.py
+++ b/eomaps/qtcompanion/common.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from pathlib import Path
 
 iconpath = Path(__file__).parent / "icons"
diff --git a/eomaps/qtcompanion/signal_container.py b/eomaps/qtcompanion/signal_container.py
index c8cf6131c..6b63dbe7e 100644
--- a/eomaps/qtcompanion/signal_container.py
+++ b/eomaps/qtcompanion/signal_container.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """A container class for signals sent to the CompanionWidget"""
 
 
diff --git a/eomaps/qtcompanion/widgets/annotate.py b/eomaps/qtcompanion/widgets/annotate.py
index f7e8116a7..ccd481a07 100644
--- a/eomaps/qtcompanion/widgets/annotate.py
+++ b/eomaps/qtcompanion/widgets/annotate.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from qtpy import QtWidgets
 from qtpy.QtCore import Qt, Slot, Signal
 from .utils import GetColorWidget
diff --git a/eomaps/qtcompanion/widgets/click_callbacks.py b/eomaps/qtcompanion/widgets/click_callbacks.py
index 7cc354ff6..ed95891f9 100644
--- a/eomaps/qtcompanion/widgets/click_callbacks.py
+++ b/eomaps/qtcompanion/widgets/click_callbacks.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from qtpy import QtWidgets, QtGui
 from qtpy.QtCore import Qt, Signal, Slot
 
diff --git a/eomaps/qtcompanion/widgets/draw.py b/eomaps/qtcompanion/widgets/draw.py
index 586c1d987..a3ad76d02 100644
--- a/eomaps/qtcompanion/widgets/draw.py
+++ b/eomaps/qtcompanion/widgets/draw.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 
 from qtpy import QtWidgets
diff --git a/eomaps/qtcompanion/widgets/editor.py b/eomaps/qtcompanion/widgets/editor.py
index 5734bce76..9669436e8 100644
--- a/eomaps/qtcompanion/widgets/editor.py
+++ b/eomaps/qtcompanion/widgets/editor.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 from textwrap import dedent
 
diff --git a/eomaps/qtcompanion/widgets/extent.py b/eomaps/qtcompanion/widgets/extent.py
index 1bdaed73e..96d819cb8 100644
--- a/eomaps/qtcompanion/widgets/extent.py
+++ b/eomaps/qtcompanion/widgets/extent.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 from datetime import datetime
 
diff --git a/eomaps/qtcompanion/widgets/files.py b/eomaps/qtcompanion/widgets/files.py
index 116d7b6a0..db5a23fd2 100644
--- a/eomaps/qtcompanion/widgets/files.py
+++ b/eomaps/qtcompanion/widgets/files.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 
 from qtpy import QtWidgets, QtGui
diff --git a/eomaps/qtcompanion/widgets/layer.py b/eomaps/qtcompanion/widgets/layer.py
index 170c42365..74c6da6f1 100644
--- a/eomaps/qtcompanion/widgets/layer.py
+++ b/eomaps/qtcompanion/widgets/layer.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from qtpy import QtWidgets
 from qtpy.QtCore import Qt, Slot
 from ..common import iconpath
diff --git a/eomaps/qtcompanion/widgets/peek.py b/eomaps/qtcompanion/widgets/peek.py
index b71a8eb7c..527cbf1f5 100644
--- a/eomaps/qtcompanion/widgets/peek.py
+++ b/eomaps/qtcompanion/widgets/peek.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from qtpy import QtWidgets, QtGui
 from qtpy.QtCore import Qt, Signal, QSize, Slot
 
diff --git a/eomaps/qtcompanion/widgets/save.py b/eomaps/qtcompanion/widgets/save.py
index 385c5f4cf..8373a97c5 100644
--- a/eomaps/qtcompanion/widgets/save.py
+++ b/eomaps/qtcompanion/widgets/save.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from qtpy import QtWidgets, QtGui
 from qtpy.QtCore import Qt, Slot
 
diff --git a/eomaps/qtcompanion/widgets/utils.py b/eomaps/qtcompanion/widgets/utils.py
index 13479937b..f82ffb96a 100644
--- a/eomaps/qtcompanion/widgets/utils.py
+++ b/eomaps/qtcompanion/widgets/utils.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 from qtpy import QtWidgets, QtGui
 from qtpy.QtCore import Qt, QRectF, QSize, Slot, Signal
 from eomaps import Maps
diff --git a/eomaps/qtcompanion/widgets/wms.py b/eomaps/qtcompanion/widgets/wms.py
index 08f152a60..b5e781318 100644
--- a/eomaps/qtcompanion/widgets/wms.py
+++ b/eomaps/qtcompanion/widgets/wms.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import logging
 
 from qtpy import QtWidgets
diff --git a/eomaps/reader.py b/eomaps/reader.py
index 64a65f178..daf350b55 100644
--- a/eomaps/reader.py
+++ b/eomaps/reader.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Classes to read files (NetCDF, GeoTIFF, CSV etc.)"""
 
 import logging
diff --git a/eomaps/scalebar.py b/eomaps/scalebar.py
index 016190631..dbbb9b9b4 100644
--- a/eomaps/scalebar.py
+++ b/eomaps/scalebar.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Interactive scalebar."""
 
 import logging
diff --git a/eomaps/scripts/open.py b/eomaps/scripts/open.py
index cc3e08a5e..4f2abe48a 100644
--- a/eomaps/scripts/open.py
+++ b/eomaps/scripts/open.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 import sys
 import os
 import click
diff --git a/eomaps/shapes.py b/eomaps/shapes.py
index c18dbee6f..56866d6d5 100644
--- a/eomaps/shapes.py
+++ b/eomaps/shapes.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Plot shape classes (for data visualization)."""
 
 import logging
diff --git a/eomaps/utilities.py b/eomaps/utilities.py
index 489eadc73..bc4d1564c 100644
--- a/eomaps/utilities.py
+++ b/eomaps/utilities.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Collection of utility classes (layer sliders, layer selectors etc.)"""
 
 from matplotlib.legend import DraggableLegend
diff --git a/eomaps/webmap_containers.py b/eomaps/webmap_containers.py
index 8de45ff3b..564b9191f 100644
--- a/eomaps/webmap_containers.py
+++ b/eomaps/webmap_containers.py
@@ -1,3 +1,8 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
 """Collection of WebMap services."""
 
 import logging

From 9f4cf859747048df38d5f59bbd8729fd7de27b03 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 00:00:05 +0100
Subject: [PATCH 086/240] update docs

---
 docs/FAQ.rst | 29 ++++++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 804275d74..2ae5b4526 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -16,10 +16,17 @@ Need some help to setup python for EOmaps?
    quickstart_quide
 
 
-Have a look at the :ref:`quickstart_guide` to learn how to setup a python environment that can be
+Have a look at the :ref:`Quickstart Guide <quickstart_guide>` to learn how to setup a python environment that can be
 used to create EOmaps maps!
 
 
+Interested in contributing to EOmaps?
+*************************************
+
+Have a look at the :ref:`Contribution Guide <contribute>` on how to setup a development environment to start contributing to EOmaps!
+Any contributions are welcome!
+
+
 .. _configuring_the_editor:
 
 Configuring the editor (IDE)
@@ -188,6 +195,26 @@ free and open source `ScreenToGif <https://www.screentogif.com/>`_ software.
 
 All animated gifs in this documentation have been created with this awesome piece of software.
 
+
+LICENSING and redistribution
+****************************
+
+Information is provided WITHOUT WARRANTY FOR CORRECTNESS!
+
+Since v8.0, the source-code of EOmaps is licensed under a `BSD 3-clause license <https://choosealicense.com/licenses/bsd-3-clause/>`_.
+
+However, it must be notet that EOmaps has a number of required and optional dependencies whose licenses must be taken into account when packaging
+and redistributing works that build on top of EOmaps. While most dependencies are either **BSD** or **MIT** licensed, there are some dependencies 
+that might require additional considerations.
+
+Most notably (start of 2024), the **required dependency** `cartopy <https://github.com/SciTools/cartopy>`_ is licensed under **LGPLv3+** (however they are in the process of `re-licensing to BSD-3-clause <https://github.com/SciTools/cartopy/pull/2285>`_) and the **optional** `Qt <https://www.qt.io/licensing>`_ GUI framework is available via commertial and open-source (**LGPLv3+**) licenses and the used python-bindings (`PyQt5 <https://github.com/pyqt/python-qt5>`_) are licensed under **GPL v3**.
+
+
+.. tip::
+
+    If you want to get a quick overview of the licenses of an existing pyhton environment, I recommend having a look at the `pip-licenses <https://github.com/raimon49/pip-licenses>`_ package!
+
+
 Important changes between major versions
 ****************************************
 

From 8fb741306c0c46942c3c578c27d952d5ea808293 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 00:01:18 +0100
Subject: [PATCH 087/240] make pre-commit happy

---
 docs/FAQ.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 2ae5b4526..1a46cf6af 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -204,7 +204,7 @@ Information is provided WITHOUT WARRANTY FOR CORRECTNESS!
 Since v8.0, the source-code of EOmaps is licensed under a `BSD 3-clause license <https://choosealicense.com/licenses/bsd-3-clause/>`_.
 
 However, it must be notet that EOmaps has a number of required and optional dependencies whose licenses must be taken into account when packaging
-and redistributing works that build on top of EOmaps. While most dependencies are either **BSD** or **MIT** licensed, there are some dependencies 
+and redistributing works that build on top of EOmaps. While most dependencies are either **BSD** or **MIT** licensed, there are some dependencies
 that might require additional considerations.
 
 Most notably (start of 2024), the **required dependency** `cartopy <https://github.com/SciTools/cartopy>`_ is licensed under **LGPLv3+** (however they are in the process of `re-licensing to BSD-3-clause <https://github.com/SciTools/cartopy/pull/2285>`_) and the **optional** `Qt <https://www.qt.io/licensing>`_ GUI framework is available via commertial and open-source (**LGPLv3+**) licenses and the used python-bindings (`PyQt5 <https://github.com/pyqt/python-qt5>`_) are licensed under **GPL v3**.

From 6d1aae2ebabfe0ffdeeba478ff9830ae1cb230ca Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 00:38:13 +0100
Subject: [PATCH 088/240] update docs

---
 docs/installation.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/installation.rst b/docs/installation.rst
index 16fdcc550..f04040b94 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -9,8 +9,8 @@
     :depth: 1
 
 
-Recommended way (via ``conda`` and ``mamba``)
----------------------------------------------
+Via ``conda`` and ``mamba``
+---------------------------
 
 EOmaps is available via the ``conda-forge`` channel and can be installed via:
 
@@ -56,8 +56,8 @@ More details on how to **configure your favorite IDE** to work with EOmaps can b
 
 
 
-Alternative way (via ``pip``)
------------------------------
+Via ``pip``
+-----------
 
 EOmaps is also available on ``pip``.
 

From 28845022759afd72f0f97520f345440dfabdcb96 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 18:01:20 +0100
Subject: [PATCH 089/240] remove cairosvg as dependency (allow usage for
 legends if installed)

---
 eomaps/_webmap.py                 | 12 ++++++++----
 eomaps/qtcompanion/widgets/wms.py |  2 +-
 pyproject.toml                    |  1 -
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/eomaps/_webmap.py b/eomaps/_webmap.py
index 0933adde4..3814acb96 100644
--- a/eomaps/_webmap.py
+++ b/eomaps/_webmap.py
@@ -84,7 +84,7 @@ def info(self):
 
         print(f"\n LEGEND available: {legQ}\n\n" + txt)
 
-    def fetch_legend(self, style=None, silent=True):
+    def fetch_legend(self, style=None):
         if style is None:
             style = self._style
         try:
@@ -98,15 +98,19 @@ def fetch_legend(self, style=None, silent=True):
                     img = cairosvg.svg2png(legend.content)
 
                 except ImportError:
-                    warn("EOmaps: the legend is '.svg'... please install 'cairosvg'")
+                    _log.warning(
+                        "EOmaps: The legend image is provided as a '.svg' graphic. "
+                        "To add svg graphics to a map, you must install the optional "
+                        "dependency 'cairosvg'! (see: https://cairosvg.org/)",
+                        exc_info=_log.getEffectiveLevel() <= logging.DEBUG)
                     return None
             else:
                 img = legend.content
 
             img = Image.open(BytesIO(img))
         except Exception:
-            if not silent:
-                warn("EOmaps: could not fetch the legend")
+            if _log.getEffectiveLevel() <= logging.DEBUG:
+                _log.warning("EOmaps: could not fetch the wms legend", exc_info=True)
             return None
         return img
 
diff --git a/eomaps/qtcompanion/widgets/wms.py b/eomaps/qtcompanion/widgets/wms.py
index b5e781318..bf21932b8 100644
--- a/eomaps/qtcompanion/widgets/wms.py
+++ b/eomaps/qtcompanion/widgets/wms.py
@@ -41,7 +41,7 @@ def __init__(self):
     def ask_for_legend(self, wms, wmslayer):
         if hasattr(wms, "add_legend"):
             try:
-                img = wms.fetch_legend(silent=True)
+                img = wms.fetch_legend()
                 if img is not None:
                     self._ask_for_legend(wms, wmslayer, img)
             except:
diff --git a/pyproject.toml b/pyproject.toml
index 9c86e44ea..d90eaf0dc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,6 @@ classify = ["mapclassify"]
 wms = [
     "owslib",
     "requests",
-    "cairosvg",
 ]
 
 shade = ["datashader"]

From e553a1a0dbf3cd76bc24783ea5c6fa4430afd4fd Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 18:02:07 +0100
Subject: [PATCH 090/240] remove obsolete dependencies from test-env

---
 tests/test_env.yml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/test_env.yml b/tests/test_env.yml
index a3e273b18..3bd1897a8 100644
--- a/tests/test_env.yml
+++ b/tests/test_env.yml
@@ -3,7 +3,6 @@ channels:
   - conda-forge
 
 dependencies:
-  - rtree
   - numpy
   - scipy
   - pandas
@@ -23,8 +22,6 @@ dependencies:
   # --------------for WebMaps
   - owslib
   - requests
-  - xmltodict
-  - cairosvg
   # --------------for testing
   - nbformat     # to parse Jupyter Notebooks
   - coveralls

From a748f561c10a85437d40b4f98fdc8e6a311dcef3 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 18:05:44 +0100
Subject: [PATCH 091/240] make pre-commit happy

---
 eomaps/_webmap.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/eomaps/_webmap.py b/eomaps/_webmap.py
index 3814acb96..3309cb26f 100644
--- a/eomaps/_webmap.py
+++ b/eomaps/_webmap.py
@@ -102,7 +102,8 @@ def fetch_legend(self, style=None):
                         "EOmaps: The legend image is provided as a '.svg' graphic. "
                         "To add svg graphics to a map, you must install the optional "
                         "dependency 'cairosvg'! (see: https://cairosvg.org/)",
-                        exc_info=_log.getEffectiveLevel() <= logging.DEBUG)
+                        exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
+                    )
                     return None
             else:
                 img = legend.content

From a40eee482a9764acc93d176beb42324d14df7405 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 19:16:20 +0100
Subject: [PATCH 092/240] update docs

---
 docs/api_data_visualization.rst | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/docs/api_data_visualization.rst b/docs/api_data_visualization.rst
index 932847c60..8cc1fb36c 100644
--- a/docs/api_data_visualization.rst
+++ b/docs/api_data_visualization.rst
@@ -84,7 +84,7 @@ A dataset is fully specified by setting the following properties:
 
 .. note::
 
-    Make sure to use a individual :py:class:`Maps` object (e.g. with ``m2 = m.new_layer()`` for each dataset!
+    Make sure to use a individual :py:class:`Maps` object (e.g. with ``m2 = m.new_layer()``) for each dataset!
     Calling :py:meth:`Maps.plot_map` multiple times on the same :py:class:`Maps` object will remove
     and override the previously plotted dataset!
 
@@ -96,6 +96,7 @@ A dataset is fully specified by setting the following properties:
     - Plotting data in its native crs will omit the reprojection step and is therefore a lot faster!
     - If your dataset is 2D (e.g. a raster), it is best (for speed and memory) to provide the coordinates as 1D vectors!
 
+       - 1D coordinate vectors will be broadcasted using matrix-indexing! (e.g. ``x[nx], y[ny] -> data[nx, ny]``)
        - Note that reprojecting 1D coordinate vectors to a different crs will result in (possibly very large) 2D coordinate arrays!
 
 
@@ -788,7 +789,7 @@ You can fix individual color channels by passing a list with 1 element, e.g.:
     import numpy as np
 
     x, y = np.meshgrid(np.linspace(-20, 40, 100),
-                    np.linspace(50, 70, 50))
+                       np.linspace(50, 70, 50))
 
     # values must be between 0 and 1
     r = np.random.randint(0, 100, x.shape) / 100

From 983283c6ec8df0ba39dca93eaf1588e78023ac88 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 19:59:30 +0100
Subject: [PATCH 093/240] updates for examples galery - examples are now
 located in "docs/examples" - unittests now automatically test all
 example-files - naming-convention is improved (avoid numbering and use
 descriptive names)

---
 docs/EOmaps_examples.rst                      |  85 ++++++------
 .../example_basics.gif}                       | Bin
 .../example_callbacks.gif}                    | Bin
 .../{ => example_images}/example_contour.png  | Bin
 .../example_customization.png}                | Bin
 .../example_gridlines.png                     | Bin
 .../example_inset_maps.png                    | Bin
 .../{ => example_images}/example_lines.png    | Bin
 .../example_multiple_maps.gif}                | Bin
 .../example_overlays.gif}                     | Bin
 .../example_row_col_selector.gif              | Bin
 .../example_scalebars.gif}                    | Bin
 .../example_timeseries.gif}                   | Bin
 .../example_vector_data.gif}                  | Bin
 .../example_webmaps.gif}                      | Bin
 docs/_static/fig4.png                         | Bin 634582 -> 0 bytes
 docs/_static/fig5.png                         | Bin 966253 -> 0 bytes
 docs/example_galery.rst                       |  56 ++++----
 .../examples/example_basics.py                |   0
 .../examples/example_callbacks.py             |   4 +-
 {tests => docs/examples}/example_contour.py   |   2 +-
 .../examples/example_customization.py         |   2 +-
 {tests => docs/examples}/example_gridlines.py |   2 +-
 .../examples}/example_inset_maps.py           |   2 +
 {tests => docs/examples}/example_lines.py     |   2 +-
 .../examples/example_multiple_maps.py         |   2 +-
 .../examples/example_overlays.py              |   2 +-
 .../examples}/example_row_col_selector.py     |   2 +
 .../examples/example_scalebars.py             |   2 +-
 .../examples/example_timeseries.py            |   2 +-
 .../examples/example_vector_data.py           |   2 +-
 .../examples/example_webmaps.py               |   2 +-
 docs/make2.bat                                |  38 ++++++
 docs/notebooks/inset_maps.ipynb               |   2 +-
 docs/substitutions.rst                        | 124 ------------------
 tests/test_examples.py                        |  61 ++++-----
 36 files changed, 153 insertions(+), 241 deletions(-)
 rename docs/_static/{fig1.gif => example_images/example_basics.gif} (100%)
 rename docs/_static/{fig4.gif => example_images/example_callbacks.gif} (100%)
 rename docs/_static/{ => example_images}/example_contour.png (100%)
 rename docs/_static/{fig2.png => example_images/example_customization.png} (100%)
 rename docs/_static/{ => example_images}/example_gridlines.png (100%)
 rename docs/_static/{ => example_images}/example_inset_maps.png (100%)
 rename docs/_static/{ => example_images}/example_lines.png (100%)
 rename docs/_static/{fig3.gif => example_images/example_multiple_maps.gif} (100%)
 rename docs/_static/{fig5.gif => example_images/example_overlays.gif} (100%)
 rename docs/_static/{ => example_images}/example_row_col_selector.gif (100%)
 rename docs/_static/{fig8.gif => example_images/example_scalebars.gif} (100%)
 rename docs/_static/{fig9.gif => example_images/example_timeseries.gif} (100%)
 rename docs/_static/{fig7.gif => example_images/example_vector_data.gif} (100%)
 rename docs/_static/{fig6.gif => example_images/example_webmaps.gif} (100%)
 delete mode 100644 docs/_static/fig4.png
 delete mode 100644 docs/_static/fig5.png
 rename tests/example1.py => docs/examples/example_basics.py (100%)
 rename tests/example4.py => docs/examples/example_callbacks.py (98%)
 rename {tests => docs/examples}/example_contour.py (96%)
 rename tests/example2.py => docs/examples/example_customization.py (96%)
 rename {tests => docs/examples}/example_gridlines.py (98%)
 rename {tests => docs/examples}/example_inset_maps.py (98%)
 rename {tests => docs/examples}/example_lines.py (98%)
 rename tests/example3.py => docs/examples/example_multiple_maps.py (98%)
 rename tests/example5.py => docs/examples/example_overlays.py (98%)
 rename {tests => docs/examples}/example_row_col_selector.py (98%)
 rename tests/example8.py => docs/examples/example_scalebars.py (95%)
 rename tests/example9.py => docs/examples/example_timeseries.py (97%)
 rename tests/example7.py => docs/examples/example_vector_data.py (97%)
 rename tests/example6.py => docs/examples/example_webmaps.py (97%)
 create mode 100644 docs/make2.bat

diff --git a/docs/EOmaps_examples.rst b/docs/EOmaps_examples.rst
index c8a22982a..64881c3bd 100644
--- a/docs/EOmaps_examples.rst
+++ b/docs/EOmaps_examples.rst
@@ -11,7 +11,7 @@
 .. include:: example_galery.rst
 
 
-.. _ex_1:
+.. _example_basics:
 
 Basic data visualization
 -------------------------
@@ -24,14 +24,15 @@ There are 3 basic steps required to visualize your data:
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example1.py
+.. literalinclude:: examples/example_basics.py
 
 |toggleEnd|
 
-.. image:: _static/fig1.gif
+.. image:: _static/example_images/example_basics.gif
   :width: 75%
 
-.. _ex_2:
+
+.. _example_customization:
 
 Customize the appearance of the plot
 ------------------------------------
@@ -46,15 +47,15 @@ Customize the appearance of the plot
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example2.py
+.. literalinclude:: examples/example_customization.py
 
 |toggleEnd|
 
-.. image:: _static/fig2.png
+.. image:: _static/example_images/example_customization.png
   :width: 75%
 
 
-.. _ex_3:
+.. _example_multiple_maps:
 
 Data-classification and multiple Maps in a figure
 -------------------------------------------------
@@ -70,15 +71,15 @@ Data-classification and multiple Maps in a figure
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example3.py
+.. literalinclude:: examples/example_multiple_maps.py
 
 |toggleEnd|
 
-.. image:: _static/fig3.gif
+.. image:: _static/example_images/example_multiple_maps.gif
   :width: 75%
 
 
-.. _ex_4:
+.. _example_callbacks:
 
 Callbacks - turn your maps into interactive widgets
 ---------------------------------------------------
@@ -99,15 +100,15 @@ Callbacks - turn your maps into interactive widgets
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example4.py
+.. literalinclude:: examples/example_callbacks.py
 
 |toggleEnd|
 
-.. image:: _static/fig4.gif
+.. image:: _static/example_images/example_callbacks.gif
   :width: 75%
 
 
-.. _ex_5:
+.. _example_overlays:
 
 Overlays, markers and annotations
 ---------------------------------
@@ -120,18 +121,18 @@ Overlays, markers and annotations
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example5.py
+.. literalinclude:: examples/example_overlays.py
 
 |toggleEnd|
 
-.. image:: _static/fig5.gif
+.. image:: _static/example_images/example_overlays.gif
   :width: 75%
 
 The data displayed in the above gif is taken from:
     - NaturalEarth (https://www.naturalearthdata.com/)
 
 
-.. _ex_6:
+.. _example_webmaps:
 
 WebMap services and layer-switching
 -----------------------------------
@@ -141,11 +142,11 @@ WebMap services and layer-switching
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example6.py
+.. literalinclude:: examples/example_webmaps.py
 
 |toggleEnd|
 
-.. image:: _static/fig6.gif
+.. image:: _static/example_images/example_webmaps.gif
   :width: 75%
 
 The data displayed in the above gif is taken from:
@@ -153,7 +154,7 @@ The data displayed in the above gif is taken from:
     - OpenStreetMap hosted by Mundialis (https://www.mundialis.de/en/ows-mundialis/)
 
 
-.. _ex_7:
+.. _example_vector_data:
 
 Vector data - interactive geometries
 -------------------------------------
@@ -178,11 +179,11 @@ EOmaps can be used to assign callbacks to vektor-data (e.g. ``geopandas.GeoDataF
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example7.py
+.. literalinclude:: examples/example_vector_data.py
 
 |toggleEnd|
 
-.. image:: _static/fig7.gif
+.. image:: _static/example_images/example_vector_data.gif
   :width: 75%
 
 
@@ -190,7 +191,7 @@ The data displayed in the above gif is taken from:
     - NaturalEarth (https://www.naturalearthdata.com/)
 
 
-.. _ex_8:
+.. _example_scalebars:
 
 Using Scalebars
 ---------------
@@ -219,11 +220,11 @@ EOmaps has a nice customizable scalebar feature!
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example8.py
+.. literalinclude:: examples/example_scalebars.py
 
 |toggleEnd|
 
-.. image:: _static/fig8.gif
+.. image:: _static/example_images/example_scalebars.gif
   :width: 75%
 
 The data displayed in the above gif is taken from:
@@ -231,7 +232,7 @@ The data displayed in the above gif is taken from:
 
 
 
-.. _ex_9:
+.. _example_timeseries:
 
 Data analysis widgets - Timeseries and histograms
 -------------------------------------------------
@@ -245,16 +246,16 @@ This example shows how to use EOmaps to analyze a database that is associated wi
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example9.py
+.. literalinclude:: examples/example_timeseries.py
 
 |toggleEnd|
 
 
-.. image:: _static/fig9.gif
+.. image:: _static/example_images/example_timeseries.gif
   :width: 75%
 
 
-.. _ex_10:
+.. _example_row_col_selector:
 
 Data analysis widgets - Select 1D slices of a 2D dataset
 --------------------------------------------------------
@@ -268,16 +269,16 @@ Use custom callback functions to perform arbitrary tasks on the data when clicki
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example_row_col_selector.py
+.. literalinclude:: examples/example_row_col_selector.py
 
 |toggleEnd|
 
 
-.. image:: _static/example_row_col_selector.gif
+.. image:: _static/example_images/example_row_col_selector.gif
   :width: 75%
 
 
-.. _ex_11:
+.. _example_inset_maps:
 
 Inset-maps - get a zoomed-in view on selected areas
 ---------------------------------------------------
@@ -294,16 +295,16 @@ Quickly create nice inset-maps to show details for specific regions.
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example_inset_maps.py
+.. literalinclude:: examples/example_inset_maps.py
 
 |toggleEnd|
 
 
-.. image:: _static/example_inset_maps.png
+.. image:: _static/example_images/example_inset_maps.png
   :width: 75%
 
 
-.. _ex_12:
+.. _example_lines:
 
 Lines and Annotations
 ---------------------
@@ -321,16 +322,16 @@ Connect the anchor-points via:
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example_lines.py
+.. literalinclude:: examples/example_lines.py
 
 |toggleEnd|
 
 
-.. image:: _static/example_lines.png
+.. image:: _static/example_images/example_lines.png
   :width: 75%
 
 
-.. _ex_13:
+.. _example_gridlines:
 
 Gridlines and Grid Labels
 -------------------------
@@ -341,15 +342,15 @@ Draw custom grids and add grid labels.
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example_gridlines.py
+.. literalinclude:: examples/example_gridlines.py
 
 |toggleEnd|
 
 
-.. image:: _static/example_gridlines.png
+.. image:: _static/example_images/example_gridlines.png
   :width: 75%
 
-.. _ex_14:
+.. _example_contour:
 
 Contour plots and Contour Levels
 --------------------------------
@@ -361,10 +362,10 @@ or to indicate contour-levels on top of other plots.
 
 |toggleStart|
 
-.. literalinclude:: ../tests/example_contour.py
+.. literalinclude:: examples/example_contour.py
 
 |toggleEnd|
 
 
-.. image:: _static/example_contour.png
+.. image:: _static/example_images/example_contour.png
   :width: 75%
diff --git a/docs/_static/fig1.gif b/docs/_static/example_images/example_basics.gif
similarity index 100%
rename from docs/_static/fig1.gif
rename to docs/_static/example_images/example_basics.gif
diff --git a/docs/_static/fig4.gif b/docs/_static/example_images/example_callbacks.gif
similarity index 100%
rename from docs/_static/fig4.gif
rename to docs/_static/example_images/example_callbacks.gif
diff --git a/docs/_static/example_contour.png b/docs/_static/example_images/example_contour.png
similarity index 100%
rename from docs/_static/example_contour.png
rename to docs/_static/example_images/example_contour.png
diff --git a/docs/_static/fig2.png b/docs/_static/example_images/example_customization.png
similarity index 100%
rename from docs/_static/fig2.png
rename to docs/_static/example_images/example_customization.png
diff --git a/docs/_static/example_gridlines.png b/docs/_static/example_images/example_gridlines.png
similarity index 100%
rename from docs/_static/example_gridlines.png
rename to docs/_static/example_images/example_gridlines.png
diff --git a/docs/_static/example_inset_maps.png b/docs/_static/example_images/example_inset_maps.png
similarity index 100%
rename from docs/_static/example_inset_maps.png
rename to docs/_static/example_images/example_inset_maps.png
diff --git a/docs/_static/example_lines.png b/docs/_static/example_images/example_lines.png
similarity index 100%
rename from docs/_static/example_lines.png
rename to docs/_static/example_images/example_lines.png
diff --git a/docs/_static/fig3.gif b/docs/_static/example_images/example_multiple_maps.gif
similarity index 100%
rename from docs/_static/fig3.gif
rename to docs/_static/example_images/example_multiple_maps.gif
diff --git a/docs/_static/fig5.gif b/docs/_static/example_images/example_overlays.gif
similarity index 100%
rename from docs/_static/fig5.gif
rename to docs/_static/example_images/example_overlays.gif
diff --git a/docs/_static/example_row_col_selector.gif b/docs/_static/example_images/example_row_col_selector.gif
similarity index 100%
rename from docs/_static/example_row_col_selector.gif
rename to docs/_static/example_images/example_row_col_selector.gif
diff --git a/docs/_static/fig8.gif b/docs/_static/example_images/example_scalebars.gif
similarity index 100%
rename from docs/_static/fig8.gif
rename to docs/_static/example_images/example_scalebars.gif
diff --git a/docs/_static/fig9.gif b/docs/_static/example_images/example_timeseries.gif
similarity index 100%
rename from docs/_static/fig9.gif
rename to docs/_static/example_images/example_timeseries.gif
diff --git a/docs/_static/fig7.gif b/docs/_static/example_images/example_vector_data.gif
similarity index 100%
rename from docs/_static/fig7.gif
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CeiVHG

diff --git a/docs/example_galery.rst b/docs/example_galery.rst
index 06a5ee0aa..57ae25f15 100644
--- a/docs/example_galery.rst
+++ b/docs/example_galery.rst
@@ -2,86 +2,86 @@
     :gutter: 1
 
     .. grid-item-card::
-        :img-background: _static/fig1.gif
+        :img-background: _static/example_images/example_basics.gif
         :img-alt: Example 1 - Basic data visualization
-        :link: ex_1
+        :link: example_basics
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig2.png
+        :img-background: _static/example_images/example_customization.png
         :img-alt: Example 2 - Customize the appearance of the map
-        :link: ex_2
+        :link: example_customization
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig3.gif
+        :img-background: _static/example_images/example_multiple_maps.gif
         :img-alt: Example 3 - Data-classification and multiple Maps in a figure
-        :link: ex_3
+        :link: example_multiple_maps
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig4.gif
+        :img-background: _static/example_images/example_callbacks.gif
         :img-alt: Example 4 - Callbacks - turn your maps into interactive widgets
-        :link: ex_4
+        :link: example_callbacks
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig5.gif
+        :img-background: _static/example_images/example_overlays.gif
         :img-alt: Example 5 - Overlays, markers and annotations
-        :link: ex_5
+        :link: example_overlays
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig6.gif
+        :img-background: _static/example_images/example_webmaps.gif
         :img-alt: Example 6 - WebMap services and layer-switching
-        :link: ex_6
+        :link: example_webmaps
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig7.gif
+        :img-background: _static/example_images/example_vector_data.gif
         :img-alt: Example 7 - Vector data - interactive geometries
-        :link: ex_7
+        :link: example_vector_data
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig8.gif
+        :img-background: _static/example_images/example_scalebars.gif
         :img-alt: Example 8 - Using Scalebars
-        :link: ex_8
+        :link: example_scalebars
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/fig9.gif
+        :img-background: _static/example_images/example_timeseries.gif
         :img-alt: Example 9 - Data analysis widgets - Timeseries and histograms
-        :link: ex_9
+        :link: example_timeseries
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/example_row_col_selector.gif
+        :img-background: _static/example_images/example_row_col_selector.gif
         :img-alt: Example 10 - Data analysis widgets - Select 1D slices of a 2D dataset
-        :link: ex_10
+        :link: example_row_col_selector
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/example_inset_maps.png
+        :img-background: _static/example_images/example_inset_maps.png
         :img-alt: Example 11 - Inset-maps - get a zoomed-in view on selected areas
-        :link: ex_11
+        :link: example_inset_maps
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/example_lines.png
+        :img-background: _static/example_images/example_lines.png
         :img-alt: Example 12 - Lines and Annotations
         :text-align: center
-        :link: ex_12
+        :link: example_lines
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/example_gridlines.png
+        :img-background: _static/example_images/example_gridlines.png
         :img-alt: Example 13 - Gridlines and Grid Labels
-        :link: ex_13
+        :link: example_gridlines
         :link-type: ref
 
     .. grid-item-card::
-        :img-background: _static/example_contour.png
+        :img-background: _static/example_images/example_contour.png
         :img-alt: Example 14 - Contour plots and Contour Levels
-        :link: ex_14
+        :link: example_contour
         :link-type: ref
diff --git a/tests/example1.py b/docs/examples/example_basics.py
similarity index 100%
rename from tests/example1.py
rename to docs/examples/example_basics.py
diff --git a/tests/example4.py b/docs/examples/example_callbacks.py
similarity index 98%
rename from tests/example4.py
rename to docs/examples/example_callbacks.py
index 94142b4a6..7c59e1977 100644
--- a/tests/example4.py
+++ b/docs/examples/example_callbacks.py
@@ -1,5 +1,5 @@
-# EOmaps example 4: Turn your maps into a powerful widgets
-# %matplotlib widget
+# EOmaps example: Turn your maps into a powerful widgets
+
 from eomaps import Maps
 import pandas as pd
 import numpy as np
diff --git a/tests/example_contour.py b/docs/examples/example_contour.py
similarity index 96%
rename from tests/example_contour.py
rename to docs/examples/example_contour.py
index 85cbf04f9..5806da34a 100644
--- a/tests/example_contour.py
+++ b/docs/examples/example_contour.py
@@ -1,4 +1,4 @@
-# EOmaps Example:  Contour plots and contour levels
+# EOmaps example:  Contour plots and contour levels
 
 from eomaps import Maps
 import numpy as np
diff --git a/tests/example2.py b/docs/examples/example_customization.py
similarity index 96%
rename from tests/example2.py
rename to docs/examples/example_customization.py
index 385d77b61..a859c3616 100644
--- a/tests/example2.py
+++ b/docs/examples/example_customization.py
@@ -1,4 +1,4 @@
-# EOmaps example 3: Customize the appearance of the plot
+# EOmaps example: Customize the appearance of the plot
 
 from eomaps import Maps
 import pandas as pd
diff --git a/tests/example_gridlines.py b/docs/examples/example_gridlines.py
similarity index 98%
rename from tests/example_gridlines.py
rename to docs/examples/example_gridlines.py
index 1c0a912e1..0460ef310 100644
--- a/tests/example_gridlines.py
+++ b/docs/examples/example_gridlines.py
@@ -1,4 +1,4 @@
-# EOmaps Example:  Customized gridlines
+# EOmaps example:  Customized gridlines
 
 from eomaps import Maps
 
diff --git a/tests/example_inset_maps.py b/docs/examples/example_inset_maps.py
similarity index 98%
rename from tests/example_inset_maps.py
rename to docs/examples/example_inset_maps.py
index ed11a992a..8f6df6515 100644
--- a/tests/example_inset_maps.py
+++ b/docs/examples/example_inset_maps.py
@@ -1,3 +1,5 @@
+# EOmaps example: How to create inset-maps
+
 from eomaps import Maps
 import numpy as np
 
diff --git a/tests/example_lines.py b/docs/examples/example_lines.py
similarity index 98%
rename from tests/example_lines.py
rename to docs/examples/example_lines.py
index a94fd3e48..b35920328 100644
--- a/tests/example_lines.py
+++ b/docs/examples/example_lines.py
@@ -1,4 +1,4 @@
-# EOmaps example : drawing lines on a map
+# EOmaps example: drawing lines on a map
 
 from eomaps import Maps
 
diff --git a/tests/example3.py b/docs/examples/example_multiple_maps.py
similarity index 98%
rename from tests/example3.py
rename to docs/examples/example_multiple_maps.py
index 19a841a18..313ac4627 100644
--- a/tests/example3.py
+++ b/docs/examples/example_multiple_maps.py
@@ -1,4 +1,4 @@
-# EOmaps example 2: Data-classification and multiple Maps in one figure
+# EOmaps example: Data-classification and multiple Maps in one figure
 
 from eomaps import Maps
 import pandas as pd
diff --git a/tests/example5.py b/docs/examples/example_overlays.py
similarity index 98%
rename from tests/example5.py
rename to docs/examples/example_overlays.py
index 2ee68eeaf..130415c47 100644
--- a/tests/example5.py
+++ b/docs/examples/example_overlays.py
@@ -1,4 +1,4 @@
-# EOmaps example 5: Add overlays and indicators
+# EOmaps example: Add overlays and indicators
 
 from eomaps import Maps
 import pandas as pd
diff --git a/tests/example_row_col_selector.py b/docs/examples/example_row_col_selector.py
similarity index 98%
rename from tests/example_row_col_selector.py
rename to docs/examples/example_row_col_selector.py
index 4adcc1247..8673e4956 100644
--- a/tests/example_row_col_selector.py
+++ b/docs/examples/example_row_col_selector.py
@@ -1,3 +1,5 @@
+# EOmaps example: Select 1D slices of a 2D dataset
+    
 from eomaps import Maps
 import numpy as np
 
diff --git a/tests/example8.py b/docs/examples/example_scalebars.py
similarity index 95%
rename from tests/example8.py
rename to docs/examples/example_scalebars.py
index 2e20698e3..96a76bed7 100644
--- a/tests/example8.py
+++ b/docs/examples/example_scalebars.py
@@ -1,4 +1,4 @@
-# EOmaps example 8: Adding scalebars - what about distances?
+# EOmaps example: Adding scalebars - what about distances?
 
 from eomaps import Maps
 
diff --git a/tests/example9.py b/docs/examples/example_timeseries.py
similarity index 97%
rename from tests/example9.py
rename to docs/examples/example_timeseries.py
index 467494f33..092c7e421 100644
--- a/tests/example9.py
+++ b/docs/examples/example_timeseries.py
@@ -1,4 +1,4 @@
-# EOmaps example 9:  Data analysis widgets - Interacting with a database
+# EOmaps example:  Data analysis widgets - Interacting with a database
 
 from eomaps import Maps
 import pandas as pd
diff --git a/tests/example7.py b/docs/examples/example_vector_data.py
similarity index 97%
rename from tests/example7.py
rename to docs/examples/example_vector_data.py
index b5dd786e5..2cb03523e 100644
--- a/tests/example7.py
+++ b/docs/examples/example_vector_data.py
@@ -1,4 +1,4 @@
-# EOmaps example 7: Using geopandas - interactive shapes!
+# EOmaps example: Using geopandas - interactive shapes!
 
 from eomaps import Maps
 import pandas as pd
diff --git a/tests/example6.py b/docs/examples/example_webmaps.py
similarity index 97%
rename from tests/example6.py
rename to docs/examples/example_webmaps.py
index 22309217e..6d2456a8f 100644
--- a/tests/example6.py
+++ b/docs/examples/example_webmaps.py
@@ -1,4 +1,4 @@
-# EOmaps example 6: WebMap services and layer-switching
+# EOmaps example: WebMap services and layer-switching
 
 from eomaps import Maps
 import numpy as np
diff --git a/docs/make2.bat b/docs/make2.bat
new file mode 100644
index 000000000..3baa47fad
--- /dev/null
+++ b/docs/make2.bat
@@ -0,0 +1,38 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+RMDIR /S /Q %SOURCEDIR%\build
+RMDIR /S /Q %SOURCEDIR%\generated
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/notebooks/inset_maps.ipynb b/docs/notebooks/inset_maps.ipynb
index 3453bbb57..bb8c09acd 100644
--- a/docs/notebooks/inset_maps.ipynb
+++ b/docs/notebooks/inset_maps.ipynb
@@ -84,7 +84,7 @@
     ":::{tip}\n",
     "To quickly re-position (and re-size) inset-maps, make sure to have a look at the {ref}`layout_editor`!\n",
     "\n",
-    "A more advanced example on how to use inset-maps is available here: {ref}`ex_11`\n",
+    "A more advanced example on how to use inset-maps is available here: {ref}`example_inset_maps`\n",
     ":::\n"
    ]
   },
diff --git a/docs/substitutions.rst b/docs/substitutions.rst
index 4fdc0298e..668606160 100644
--- a/docs/substitutions.rst
+++ b/docs/substitutions.rst
@@ -7,127 +7,3 @@
 
    </details>
    <br/>
-
-.. |shp_ellipses| image:: _static/shape_imgs/ellipses.png
-    :width: 100%
-    :align: top
-    :target: api_data_visualization.html#ellipses
-
-.. |shp_rectangles| image:: _static/shape_imgs/rectangles.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#rectangles
-
-.. |shp_geod_circles| image:: _static/shape_imgs/geod_circles.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#geodesic-circles
-
-.. |shp_voronoi| image:: _static/shape_imgs/voronoi_diagram.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#voronoi-diagram
-
-.. |shp_delaunay| image:: _static/shape_imgs/delaunay_triangulation.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#delaunay-triangulation
-
-.. |shp_contour| image:: _static/shape_imgs/contour.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#contour
-
-.. |shp_scatter_points| image:: _static/shape_imgs/scatter_points.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#scatter-points
-
-.. |shp_raster| image:: _static/shape_imgs/raster.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#raster
-
-.. |shp_shade_raster| image:: _static/shape_imgs/shade_raster.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#shade-raster
-
-.. |shp_shade_points| image:: _static/shape_imgs/shade_points.png
-    :width: 100%
-    :align: middle
-    :target: api_data_visualization.html#shade-points
-
-
-
-.. |eximg01| image:: _static/fig1.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-1
-
-.. |eximg02| image:: _static/fig2.png
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-2
-
-.. |eximg03| image:: _static/fig3.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-3
-
-.. |eximg04| image:: _static/fig4.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-4
-
-.. |eximg05| image:: _static/fig5.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-5
-
-.. |eximg06| image:: _static/fig6.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-6
-
-.. |eximg07| image:: _static/fig7.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-7
-
-.. |eximg08| image:: _static/fig8.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-8
-
-.. |eximg09| image:: _static/fig9.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-9
-
-.. |eximg10| image:: _static/example_row_col_selector.gif
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-10
-
-.. |eximg11| image:: _static/example_inset_maps.png
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-11
-
-.. |eximg12| image:: _static/example_lines.png
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-12
-
-.. |eximg13| image:: _static/example_gridlines.png
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-13
-
-.. |eximg14| image:: _static/example_contour.png
-   :width: 100%
-   :align: middle
-   :target: EOmaps_examples.html#ex-14
-
-.. |eximg15| replace:: ...
diff --git a/tests/test_examples.py b/tests/test_examples.py
index c5a654c7f..c26ef17d7 100644
--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -1,45 +1,38 @@
+"""Test running all python-files in docs/examples that start with 'example_' """
+from pathlib import Path
+import matplotlib.pyplot as plt
 import unittest
 
 
-class TestExamples(unittest.TestCase):
-    def test_example1(self):
-        import example1
+def gen_test(name, code):
+    def test(*args, **kwargs):
+        try:
+            exec(code)
+        except Exception as ex:
+            raise AssertionError(f"Example '{name}' failed.") from ex
+        finally:
+            plt.close("all")
 
-    def test_example2(self):
-        import example2
+    return test
 
-    def test_example3(self):
-        import example3
 
-    def test_example4(self):
-        import example4
+class _TestSequenceMeta(type):
+    def __new__(mcs, name, bases, tests):
+        # the path to the folder containing the example scripts
+        parent_path = Path(__file__).parent.parent / "docs" / "examples"
 
-    def test_example5(self):
-        import example5
+        examples = filter(
+            lambda x: x.stem.startswith("example_") and x.suffix == ".py",
+            parent_path.iterdir(),
+        )
 
-    def test_example6(self):
-        import example6
+        # generate unique tests for each example
+        for f in examples:
+            test_name = f"test_{f.stem}"
+            tests[test_name] = gen_test(name, f.read_text())
 
-    def test_example7(self):
-        import example7
+        return type.__new__(mcs, name, bases, tests)
 
-    def test_example8(self):
-        import example8
 
-    def test_example9(self):
-        import example9
-
-    def test_example_inset_maps(self):
-        import example_inset_maps
-
-    def test_example_row_col_selector(self):
-        import example_row_col_selector
-
-    def test_example_lines(self):
-        import example_lines
-
-    def test_example_gridlines(self):
-        import example_gridlines
-
-    def test_example_contour(self):
-        import example_contour
+class TestExamples(unittest.TestCase, metaclass=_TestSequenceMeta):
+    pass

From 68e442589e38853503d740cef37a61cb90fd487a Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 20:03:32 +0100
Subject: [PATCH 094/240] make pre-commit happy

---
 docs/examples/example_row_col_selector.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/examples/example_row_col_selector.py b/docs/examples/example_row_col_selector.py
index 8673e4956..92402448d 100644
--- a/docs/examples/example_row_col_selector.py
+++ b/docs/examples/example_row_col_selector.py
@@ -1,5 +1,5 @@
 # EOmaps example: Select 1D slices of a 2D dataset
-    
+
 from eomaps import Maps
 import numpy as np
 

From 54530ba3843a13975fc11e22601bc33d604c35b8 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 20:18:50 +0100
Subject: [PATCH 095/240] update docs: make layer-intro image larger

---
 docs/api_basics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/api_basics.rst b/docs/api_basics.rst
index 7526b7417..c9f1ec3f0 100644
--- a/docs/api_basics.rst
+++ b/docs/api_basics.rst
@@ -62,7 +62,7 @@ Layer management
 ~~~~~~~~~~~~~~~~
 
 .. image:: _static/intro.png
-   :width: 40%
+   :width: 70%
 
 
 A :py:class:`Maps` object represents a collection of features, callbacks,.. **on the assigned layer**.

From 0787f4d4a65c1f55bb010cf43f9be434562f3537 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 20:25:26 +0100
Subject: [PATCH 096/240] minor

---
 docs/FAQ.rst    | 3 +++
 environment.yml | 1 -
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 1a46cf6af..db4f46137 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -29,6 +29,7 @@ Any contributions are welcome!
 
 .. _configuring_the_editor:
 
+
 Configuring the editor (IDE)
 ****************************
 
@@ -187,6 +188,7 @@ However, for some editors there are special settings that can be adjusted to imp
     Checkout the `matplotlib doc <https://matplotlib.org/stable/users/explain/interactive.html#jupyter-notebooks-jupyterlab>`_
     for more info!
 
+
 Record interactive maps to create animations
 ********************************************
 
@@ -391,6 +393,7 @@ Important changes between major versions
 
     - ``setup.py`` and ``_version.py`` have been removed in favor of using a ``pyproject.toml`` file
 
+    - scripts for the examples have been re-named and are now located in ``docs\examples``
 
     The following (previously deprecated) methods are now **removed**:
 
diff --git a/environment.yml b/environment.yml
index e8c42e781..9224e4efc 100644
--- a/environment.yml
+++ b/environment.yml
@@ -22,7 +22,6 @@ dependencies:
   # --------------for WebMaps
   - owslib
   - requests
-  - cairosvg
   # --------------to support multiple Qt versions
   - qtpy
   # --------------for testing

From 4dbd804d51802953c3f4c259819d627b0273d07f Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 20:54:54 +0100
Subject: [PATCH 097/240] add MapsGrid stub files

---
 docs/gen_autodoc_file.py | 2 ++
 eomaps/mapsgrid.py       | 4 ----
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index 6cafda795..61da9c06c 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -76,6 +76,8 @@ def make_feature_toctree_file():
     s += get_autosummary(
         "eomaps.eomaps", ["Maps.config", *members], "obj_with_attributes_no_toc"
     )
+    s += get_autosummary("eomaps.mapsgrid", ["MapsGrid"], "custom-class-template")
+
     s += get_autosummary("eomaps.colorbar", ["ColorBar"], "custom-class-template")
     s += get_autosummary(
         "eomaps.grid", ["GridLines", "GridLabels"], "custom-class-template"
diff --git a/eomaps/mapsgrid.py b/eomaps/mapsgrid.py
index cd3dc4947..24ac6b61c 100644
--- a/eomaps/mapsgrid.py
+++ b/eomaps/mapsgrid.py
@@ -73,10 +73,6 @@ class MapsGrid:
 
     Attributes
     ----------
-    f : matplotlib.figure
-        The matplotlib figure object
-    gridspec : matplotlib.GridSpec
-        The matplotlib GridSpec instance used to initialize the axes.
     m_<identifier> : eomaps.Maps objects
         The individual Maps-objects can be accessed via `mgrid.m_<identifier>`
         The identifiers are hereby `<row>_<col>` or the keys of the `m_inits`

From 512eaeb8b457edae705132af44be6db2e729cdaf Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 21:14:04 +0100
Subject: [PATCH 098/240] minor

---
 environment.yml | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/environment.yml b/environment.yml
index 9224e4efc..cce90f6c8 100644
--- a/environment.yml
+++ b/environment.yml
@@ -6,13 +6,16 @@ dependencies:
   - python = 3.11
   - numpy
   - scipy
-  - pandas
-  - geopandas
   - matplotlib >=3.4
   - cartopy >=0.20.0
   - descartes
   - mapclassify
   - pyproj
+
+  # ------------- to support Dataframes
+  - pandas
+  # ------------- to support GeoDataFrames
+  - geopandas
   # --------------for data-shading
   - datashader
   # --------------for GeoTIFF and NetCDF files

From 014a7f1ced64cdbb84d1ee340545a5e0552dc70e Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 21:14:42 +0100
Subject: [PATCH 099/240] update docs

---
 docs/api_annotations_markers_etc.rst | 380 ++++++++++++++-------------
 docs/api_basics.rst                  | 320 ++++++++++++----------
 docs/api_callbacks.rst               | 158 +++++------
 docs/gen_autodoc_file.py             |  11 +-
 4 files changed, 473 insertions(+), 396 deletions(-)

diff --git a/docs/api_annotations_markers_etc.rst b/docs/api_annotations_markers_etc.rst
index 1e41798ab..38c269340 100644
--- a/docs/api_annotations_markers_etc.rst
+++ b/docs/api_annotations_markers_etc.rst
@@ -50,62 +50,66 @@ To dynamically add annotations if you click on the map, checkout the associated
       :width: 50%
 
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +-----------------------------------------------------------------------------------+---------------------------------------------+
-    | .. code-block:: python                                                            | .. image:: _static/minigifs/annotations.png |
-    |     :name: test_add_annotations                                                   |   :align: center                            |
-    |                                                                                   |                                             |
-    |     from eomaps import Maps                                                       | |img_minsize|                               |
-    |     import numpy as np                                                            |                                             |
-    |     x, y = np.mgrid[-45:45, 20:60]                                                |                                             |
-    |                                                                                   |                                             |
-    |     m = Maps(crs=4326)                                                            |                                             |
-    |     m.set_data(x+y, x, y)                                                         |                                             |
-    |     m.add_feature.preset.coastline(ec="k", lw=.75)                                |                                             |
-    |     m.plot_map()                                                                  |                                             |
-    |                                                                                   |                                             |
-    |     # annotate any point in the dataset via the data-index                        |                                             |
-    |     m.add_annotation(ID=345)                                                      |                                             |
-    |     # annotate an arbitrary position (in the plot-crs)                            |                                             |
-    |     m.add_annotation(                                                             |                                             |
-    |         xy=(20,25), text="A formula:\n $z=\sqrt{x^2+y^2}$",                       |                                             |
-    |         fontweight="bold", bbox=dict(fc=".6", ec="none", pad=0.2))                |                                             |
-    |     # annotate coordinates defined in arbitrary crs                               |                                             |
-    |     m.add_annotation(                                                             |                                             |
-    |         xy=(2873921, 6527868), xy_crs=3857, xytext=(5,5),                         |                                             |
-    |         text="A location defined \nin epsg 3857", fontsize=8,                     |                                             |
-    |         rotation=-45, bbox=dict(fc="skyblue", ec="k", ls="--", pad=0.2))          |                                             |
-    |                                                                                   |                                             |
-    |     # functions can be used for more complex text                                 |                                             |
-    |     def text(m, ID, val, pos, ind):                                               |                                             |
-    |         return f"lon={pos[0]}\nlat={pos[1]}"                                      |                                             |
-    |                                                                                   |                                             |
-    |     props = dict(xy=(-1.5, 38.45), text=text,                                     |                                             |
-    |                  arrowprops=dict(arrowstyle="-|>", fc="fuchsia",                  |                                             |
-    |                                  mutation_scale=15))                              |                                             |
-    |                                                                                   |                                             |
-    |     m.add_annotation(**props, xytext=(20, 20), color="darkred")                   |                                             |
-    |     m.add_annotation(**props, xytext=(-60, 20), color="purple")                   |                                             |
-    |     m.add_annotation(**props, xytext=(-60, -40), color="dodgerblue")              |                                             |
-    |     m.add_annotation(**props, xytext=(20, -40), color="olive")                    |                                             |
-    |                                                                                   |                                             |
-    |     # multiple annotations can be added in one go (xy=([...], [...]) also works!) |                                             |
-    |     m.add_annotation(ID=[2500, 2700, 2900], text=lambda ID, **kwargs: str(ID),    |                                             |
-    |                      color="w", fontweight="bold", rotation=90,                   |                                             |
-    |                      arrowprops=dict(width=5, fc="b", ec="orange", lw=2),         |                                             |
-    |                      bbox=dict(boxstyle="round, rounding_size=.8, pad=.5",        |                                             |
-    |                                fc="b", ec="orange", lw=2))                        |                                             |
-    |                                                                                   |                                             |
-    |     m.add_annotation(ID=803, xytext=(-80,60),                                     |                                             |
-    |                      bbox=dict(ec="r", fc="gold", lw=3),                          |                                             |
-    |                      arrowprops=dict(                                             |                                             |
-    |                          arrowstyle="fancy", relpos=(.48,-.2),                    |                                             |
-    |                          mutation_scale=40, fc="r",                               |                                             |
-    |                          connectionstyle="angle3, angleA=90, angleB=-25"))        |                                             |
-    +-----------------------------------------------------------------------------------+---------------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_annotations
+
+            from eomaps import Maps
+            import numpy as np
+            x, y = np.mgrid[-45:45, 20:60]
+
+            m = Maps(crs=4326)
+            m.set_data(x+y, x, y)
+            m.add_feature.preset.coastline(ec="k", lw=.75)
+            m.plot_map()
+
+            # annotate any point in the dataset via the data-index
+            m.add_annotation(ID=345)
+            # annotate an arbitrary position (in the plot-crs)
+            m.add_annotation(
+                xy=(20,25), text="A formula:\n $z=\sqrt{x^2+y^2}$",
+                fontweight="bold", bbox=dict(fc=".6", ec="none", pad=0.2))
+            # annotate coordinates defined in arbitrary crs
+            m.add_annotation(
+                xy=(2873921, 6527868), xy_crs=3857, xytext=(5,5),
+                text="A location defined \nin epsg 3857", fontsize=8,
+                rotation=-45, bbox=dict(fc="skyblue", ec="k", ls="--", pad=0.2))
+
+            # functions can be used for more complex text
+            def text(m, ID, val, pos, ind):
+                return f"lon={pos[0]}\nlat={pos[1]}"
+
+            props = dict(xy=(-1.5, 38.45), text=text,
+                         arrowprops=dict(arrowstyle="-|>", fc="fuchsia",
+                                         mutation_scale=15))
+
+            m.add_annotation(**props, xytext=(20, 20), color="darkred")
+            m.add_annotation(**props, xytext=(-60, 20), color="purple")
+            m.add_annotation(**props, xytext=(-60, -40), color="dodgerblue")
+            m.add_annotation(**props, xytext=(20, -40), color="olive")
+
+            # multiple annotations can be added in one go (xy=([...], [...]) also works!)
+            m.add_annotation(ID=[2500, 2700, 2900], text=lambda ID, **kwargs: str(ID),
+                             color="w", fontweight="bold", rotation=90,
+                             arrowprops=dict(width=5, fc="b", ec="orange", lw=2),
+                             bbox=dict(boxstyle="round, rounding_size=.8, pad=.5",
+                                       fc="b", ec="orange", lw=2))
+
+            m.add_annotation(ID=803, xytext=(-80,60),
+                             bbox=dict(ec="r", fc="gold", lw=3),
+                             arrowprops=dict(
+                                 arrowstyle="fancy", relpos=(.48,-.2),
+                                 mutation_scale=40, fc="r",
+                                 connectionstyle="angle3, angleA=90, angleB=-25"))
+
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/annotations.png
 
 
 Markers
@@ -145,50 +149,52 @@ To dynamically add markers if you click on the map, checkout the associated :ref
     Maps.add_marker
 
 
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_markers
+
+            from eomaps import Maps
+            m = Maps(crs=4326)
+            m.add_feature.preset.coastline()
+
+            # ----- SINGLE MARKERS
+            # by default, MARKER DIMENSIONS are defined in units of the plot-crs!
+            m.add_marker(xy=(0, 0), radius=20, shape="rectangles",
+                         fc="y", ec="r", ls=":", lw=2)
+            m.add_marker(xy=(0, 0), radius=10, shape="ellipses",
+                         fc="darkorange", ec="r", ls=":", lw=2)
+
+            # MARKER DIMENSIONS can be specified in any CRS!
+            m.add_marker(xy=(12000000, 0), xy_crs=3857,
+                         radius=5000000, radius_crs=3857,
+                         fc=(.5, .5, 0, .4), ec="r", lw=3, n=100)
+
+            # GEODETIC CIRCLES with radius defined in meters
+            m.add_marker(xy=(-135, 35), radius=3000000, shape="geod_circles",
+                         fc="none", ec="r", hatch="///", lw=2, n=100)
+
+            # ----- MULTIPLE MARKERS
+            x = [-80, -40, 40, 80]    # x-coordinates of the markers
+            fc = ["r", "g", "b", "c"] # the colors of the markers
+
+            # N markers with the same radius
+            m.add_marker(xy=(x, [-60]*4), radius=10, fc=fc)
+
+            # N markers with different radius and properties
+            m.add_marker(xy=(x, [0]*4),  radius=[15, 10, 5, 2],
+                         fc=fc, ec=["none", "r", "g", "b"], alpha=[1, .5, 1, .5])
+
+            # N markers with different widths and heights
+            radius = ([15, 10, 5, 15], [5, 15, 15, 2])
+            m.add_marker(xy=(x, [60]*4), radius=radius, fc=fc)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/markers.png
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +---------------------------------------------------------------------------+-----------------------------------------+
-    | .. code-block:: python                                                    | .. image:: _static/minigifs/markers.png |
-    |     :name: test_add_markers                                               |   :align: center                        |
-    |                                                                           |                                         |
-    |     from eomaps import Maps                                               | |img_minsize|                           |
-    |     m = Maps(crs=4326)                                                    |                                         |
-    |     m.add_feature.preset.coastline()                                      |                                         |
-    |                                                                           |                                         |
-    |     # ----- SINGLE MARKERS                                                |                                         |
-    |     # by default, MARKER DIMENSIONS are defined in units of the plot-crs! |                                         |
-    |     m.add_marker(xy=(0, 0), radius=20, shape="rectangles",                |                                         |
-    |                  fc="y", ec="r", ls=":", lw=2)                            |                                         |
-    |     m.add_marker(xy=(0, 0), radius=10, shape="ellipses",                  |                                         |
-    |                  fc="darkorange", ec="r", ls=":", lw=2)                   |                                         |
-    |                                                                           |                                         |
-    |     # MARKER DIMENSIONS can be specified in any CRS!                      |                                         |
-    |     m.add_marker(xy=(12000000, 0), xy_crs=3857,                           |                                         |
-    |                  radius=5000000, radius_crs=3857,                         |                                         |
-    |                  fc=(.5, .5, 0, .4), ec="r", lw=3, n=100)                 |                                         |
-    |                                                                           |                                         |
-    |     # GEODETIC CIRCLES with radius defined in meters                      |                                         |
-    |     m.add_marker(xy=(-135, 35), radius=3000000, shape="geod_circles",     |                                         |
-    |                  fc="none", ec="r", hatch="///", lw=2, n=100)             |                                         |
-    |                                                                           |                                         |
-    |     # ----- MULTIPLE MARKERS                                              |                                         |
-    |     x = [-80, -40, 40, 80]    # x-coordinates of the markers              |                                         |
-    |     fc = ["r", "g", "b", "c"] # the colors of the markers                 |                                         |
-    |                                                                           |                                         |
-    |     # N markers with the same radius                                      |                                         |
-    |     m.add_marker(xy=(x, [-60]*4), radius=10, fc=fc)                       |                                         |
-    |                                                                           |                                         |
-    |     # N markers with different radius and properties                      |                                         |
-    |     m.add_marker(xy=(x, [0]*4),  radius=[15, 10, 5, 2],                   |                                         |
-    |                  fc=fc, ec=["none", "r", "g", "b"], alpha=[1, .5, 1, .5]) |                                         |
-    |                                                                           |                                         |
-    |     # N markers with different widths and heights                         |                                         |
-    |     radius = ([15, 10, 5, 15], [5, 15, 15, 2])                            |                                         |
-    |     m.add_marker(xy=(x, [60]*4), radius=radius, fc=fc)                    |                                         |
-    +---------------------------------------------------------------------------+-----------------------------------------+
 
 
 Lines
@@ -227,41 +233,45 @@ Lines can be added to a map with :py:meth:`Maps.add_line`.
     Maps.add_line
 
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +-----------------------------------------------------------------+---------------------------------------+
-    | .. code-block:: python                                          | .. image:: _static/minigifs/lines.png |
-    |     :name: test_add_lines                                       |     :align: center                    |
-    |                                                                 |                                       |
-    |     from eomaps import Maps                                     | |img_minsize|                         |
-    |     import matplotlib.patheffects as path_effects               |                                       |
-    |                                                                 |                                       |
-    |     m = Maps(Maps.CRS.Sinusoidal(), figsize=(8, 4))             |                                       |
-    |     m.add_feature.preset.ocean()                                |                                       |
-    |                                                                 |                                       |
-    |     p0 = [(-100,10), (34, -56), (125, 57)]                      |                                       |
-    |     p1 = [(-120,50), (-42, 63), (45, 57)]                       |                                       |
-    |     p2 = [(-20,-45), (-20, 45), (45, 45), (45, -20), (-20,-45)] |                                       |
-    |                                                                 |                                       |
-    |                                                                 |                                       |
-    |     m.add_line(p0, connect="geod", del_s=100000,                |                                       |
-    |                lw=0.5, c="k", mark_points="rs",                 |                                       |
-    |                marker=".", markevery=10)                        |                                       |
-    |                                                                 |                                       |
-    |     m.add_line(p1, connect="straight", c="b", ls="--",          |                                       |
-    |                mark_points=dict(fc="y", ec="k", lw=.5))         |                                       |
-    |                                                                 |                                       |
-    |     m.add_line(p2, connect="straight_crs", c="r",               |                                       |
-    |                n=5, lw=0.25, ms=5,                              |                                       |
-    |                path_effects=[                                   |                                       |
-    |                    path_effects.withStroke(linewidth=3,         |                                       |
-    |                                            foreground="gold"),  |                                       |
-    |                    path_effects.TickedStroke(angle=90,          |                                       |
-    |                                              linewidth=1,       |                                       |
-    |                                              length=0.5)])      |                                       |
-    +-----------------------------------------------------------------+---------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_lines
+
+            from eomaps import Maps
+            import matplotlib.patheffects as path_effects
+
+            m = Maps(Maps.CRS.Sinusoidal(), figsize=(8, 4))
+            m.add_feature.preset.ocean()
+
+            p0 = [(-100,10), (34, -56), (125, 57)]
+            p1 = [(-120,50), (-42, 63), (45, 57)]
+            p2 = [(-20,-45), (-20, 45), (45, 45), (45, -20), (-20,-45)]
+
+
+            m.add_line(p0, connect="geod", del_s=100000,
+                       lw=0.5, c="k", mark_points="rs",
+                       marker=".", markevery=10)
+
+            m.add_line(p1, connect="straight", c="b", ls="--",
+                       mark_points=dict(fc="y", ec="k", lw=.5))
+
+            m.add_line(p2, connect="straight_crs", c="r",
+                       n=5, lw=0.25, ms=5,
+                       path_effects=[
+                           path_effects.withStroke(linewidth=3,
+                                                   foreground="gold"),
+                           path_effects.TickedStroke(angle=90,
+                                                     linewidth=1,
+                                                     length=0.5)])
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/lines.png
+
 
 
 Rectangular areas
@@ -277,37 +287,39 @@ To indicate rectangular areas in any given crs, simply use :py:meth:`Maps.indica
     Maps.indicate_extent
 
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +-----------------------------------------------------------------------+-------------------------------------------------+
-    | .. code-block:: python                                                | .. image:: _static/minigifs/indicate_extent.png |
-    |     :name: test_indicate_extent                                       |   :align: center                                |
-    |                                                                       |                                                 |
-    |     from eomaps import Maps                                           | |img_minsize|                                   |
-    |     m = Maps(crs=3035)                                                |                                                 |
-    |     m.add_feature.preset.coastline(ec="k")                            |                                                 |
-    |                                                                       |                                                 |
-    |     # indicate a lon/lat rectangle                                    |                                                 |
-    |     m.indicate_extent(-20, 35, 40, 50, hatch="//", fc="none", ec="r") |                                                 |
-    |                                                                       |                                                 |
-    |     # indicate some rectangles in epsg:3035                           |                                                 |
-    |     hatches = ["*", "xxxx", "...."]                                   |                                                 |
-    |     colors = ["yellow", "r", "darkblue"]                              |                                                 |
-    |     for i, h, c in zip(range(3), hatches, colors):                    |                                                 |
-    |         pos0 = (2e6 + i*2e6, 7e6, 3.5e6 + i*2e6, 9e6)                 |                                                 |
-    |         pos1 = (2e6 + i*2e6, 7e6 + 3e6, 3.5e6 + i*2e6, 9e6 + 3e6)     |                                                 |
-    |                                                                       |                                                 |
-    |         m.indicate_extent(*pos0, crs=3857, hatch=h, lw=0.25, ec=c)    |                                                 |
-    |         m.indicate_extent(*pos1, crs=3857, hatch=h, lw=0.25, ec=c)    |                                                 |
-    |                                                                       |                                                 |
-    |     # indicate a rectangle in European Equi7Grid  projection          |                                                 |
-    |     m.indicate_extent(1000000, 1000000, 4800000, 4800000,             |                                                 |
-    |                       crs=Maps.CRS.Equi7_EU,                          |                                                 |
-    |                       fc="g", alpha=0.5, ec="k")                      |                                                 |
-    |                                                                       |                                                 |
-    +-----------------------------------------------------------------------+-------------------------------------------------+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_indicate_extent
+
+            from eomaps import Maps
+            m = Maps(crs=3035)
+            m.add_feature.preset.coastline(ec="k")
+
+            # indicate a lon/lat rectangle
+            m.indicate_extent(-20, 35, 40, 50, hatch="//", fc="none", ec="r")
+
+            # indicate some rectangles in epsg:3035
+            hatches = ["*", "xxxx", "...."]
+            colors = ["yellow", "r", "darkblue"]
+            for i, h, c in zip(range(3), hatches, colors):
+                pos0 = (2e6 + i*2e6, 7e6, 3.5e6 + i*2e6, 9e6)
+                pos1 = (2e6 + i*2e6, 7e6 + 3e6, 3.5e6 + i*2e6, 9e6 + 3e6)
+
+                m.indicate_extent(*pos0, crs=3857, hatch=h, lw=0.25, ec=c)
+                m.indicate_extent(*pos1, crs=3857, hatch=h, lw=0.25, ec=c)
+
+            # indicate a rectangle in European Equi7Grid  projection
+            m.indicate_extent(1000000, 1000000, 4800000, 4800000,
+                              crs=Maps.CRS.Equi7_EU,
+                              fc="g", alpha=0.5, ec="k")
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/indicate_extent.png
+
 
 Logos
 ~~~~~
@@ -320,22 +332,28 @@ Logos can be re-positioned and re-sized with the :ref:`layout_editor`!
 
 - To fix the relative position of the logo with respect to the map-axis, use ``fix_position=True``
 
-.. table::
-
-  +--------------------------------------------------------------------------------------------+---------------------------------------+
-  | .. code-block:: python                                                                     | .. image:: _static/minigifs/logos.png |
-  |     :name: test_add_logo                                                                   |   :align: center                      |
-  |                                                                                            |                                       |
-  |     from eomaps import Maps                                                                | |img_minsize|                         |
-  |     m = Maps()                                                                             |                                       |
-  |     m.add_feature.preset.coastline()                                                       |                                       |
-  |     m.add_logo(position="ul", size=.15)                                                    |                                       |
-  |     m.add_logo(position="ur", size=.15)                                                    |                                       |
-  |     # notice that the bottom logos maintain their relative position on resize/zoom events! |                                       |
-  |     # (and also that they can NOT be moved with the layout-editor)                         |                                       |
-  |     m.add_logo(position="lr", size=.3, pad=(0.1,0.05), fix_position=True)                  |                                       |
-  |     m.add_logo(position="ll", size=.4, fix_position=True)                                  |                                       |
-  +--------------------------------------------------------------------------------------------+---------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_logo
+
+            from eomaps import Maps
+            m = Maps()
+            m.add_feature.preset.coastline()
+            m.add_logo(position="ul", size=.15)
+            m.add_logo(position="ur", size=.15)
+            # notice that the bottom logos maintain their relative position on resize/zoom events!
+            # (and also that they can NOT be moved with the layout-editor)
+            m.add_logo(position="lr", size=.3, pad=(0.1,0.05), fix_position=True)
+            m.add_logo(position="ll", size=.4, fix_position=True)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/logos.png
+
 
 
 .. autosummary::
diff --git a/docs/api_basics.rst b/docs/api_basics.rst
index c9f1ec3f0..7eb99c660 100644
--- a/docs/api_basics.rst
+++ b/docs/api_basics.rst
@@ -375,105 +375,142 @@ To position the map in a (virtual) grid, one of the following options are possib
   - ``index`` can also be a two-tuple specifying the (first, last)
     indices (1-based, and including last) of the map, e.g., ``Maps(ax=(3, 1, (1, 2)))`` makes a map that spans the upper 2/3 of the figure.
 
-.. table::
-
-    +----------------------------------------------------+------------------------------------+
-    | .. code-block:: python                             | .. image:: _static/grids/grid1.png |
-    |     :name: test_gridpos_1                          |   :align: center                   |
-    |                                                    |                                    |
-    |     from eomaps import Maps                        | |img_minsize|                      |
-    |     # ----- initialize a figure with an EOmaps map |                                    |
-    |     # position = item 1 of a 2x1 grid              |                                    |
-    |     m = Maps(ax=(2, 1, 1))                         |                                    |
-    |     # ----- add a normal matplotlib axes           |                                    |
-    |     # position = item 2 of a 2x1 grid              |                                    |
-    |     ax = m.f.add_subplot(2, 1, 2)                  |                                    |
-    +----------------------------------------------------+------------------------------------+
-
-.. table::
-
-    +----------------------------------------------------+------------------------------------+
-    | .. code-block:: python                             | .. image:: _static/grids/grid2.png |
-    |     :name: test_gridpos_2                          |   :align: center                   |
-    |                                                    |                                    |
-    |     from eomaps import Maps                        | |img_minsize|                      |
-    |     # ----- initialize a figure with an EOmaps map |                                    |
-    |     # position = item 1 of a 2x2 grid              |                                    |
-    |     m = Maps(ax=(2, 2, 1))                         |                                    |
-    |     # ----- add another Map to the same figure     |                                    |
-    |     # position = item 3 of a 2x2 grid              |                                    |
-    |     m2 = m.new_map(ax=(2, 2, 3))                   |                                    |
-    |     # ----- add a normal matplotlib axes           |                                    |
-    |     # position = second item of a 1x2 grid         |                                    |
-    |     ax = m.f.add_subplot(1, 2, 2)                  |                                    |
-    +----------------------------------------------------+------------------------------------+
-
-.. table::
-
-    +----------------------------------------------------+------------------------------------+
-    | .. code-block:: python                             | .. image:: _static/grids/grid3.png |
-    |     :name: test_gridpos_3                          |   :align: center                   |
-    |                                                    |                                    |
-    |     from eomaps import Maps                        | |img_minsize|                      |
-    |     # ----- initialize a figure with an EOmaps map |                                    |
-    |     # position = span 2 rows of a 3x1 grid         |                                    |
-    |     m = Maps(ax=(3, 1, (1, 2)))                    |                                    |
-    |     # ----- add a normal matplotlib axes           |                                    |
-    |     # position = item 3 of a 3x1 grid              |                                    |
-    |     ax = m.f.add_subplot(3, 1, 3)                  |                                    |
-    +----------------------------------------------------+------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+             :name: test_gridpos_1
+
+             from eomaps import Maps
+             # ----- initialize a figure with an EOmaps map
+             # position = item 1 of a 2x1 grid
+             m = Maps(ax=(2, 1, 1))
+             # ----- add a normal matplotlib axes
+             # position = item 2 of a 2x1 grid
+             ax = m.f.add_subplot(2, 1, 2)
+
+    .. grid-item-card::
+        :width: 50%
+        :img-background: _static/grids/grid1.png
+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+        .. code-block:: python
+            :name: test_gridpos_2
+
+            from eomaps import Maps
+            # ----- initialize a figure with an EOmaps map
+            # position = item 1 of a 2x2 grid
+            m = Maps(ax=(2, 2, 1))
+            # ----- add another Map to the same figure
+            # position = item 3 of a 2x2 grid
+            m2 = m.new_map(ax=(2, 2, 3))
+            # ----- add a normal matplotlib axes
+            # position = second item of a 1x2 grid
+            ax = m.f.add_subplot(1, 2, 2)
+
+    .. grid-item-card::
+        :width: 50%
+        :img-background: _static/grids/grid2.png
+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+        .. code-block:: python
+            :name: test_gridpos_3
+
+            from eomaps import Maps
+            # ----- initialize a figure with an EOmaps map
+            # position = item 1 of a 2x2 grid
+            m = Maps(ax=(2, 2, 1))
+            # ----- add another Map to the same figure
+            # position = item 3 of a 2x2 grid
+            m2 = m.new_map(ax=(2, 2, 3))
+            # ----- add a normal matplotlib axes
+            # position = second item of a 1x2 grid
+            ax = m.f.add_subplot(1, 2, 2)
+
+    .. grid-item-card::
+        :width: 50%
+        :img-background: _static/grids/grid3.png
+
+
+
+
 
 - A 3-digit integer.
 
   - The digits are interpreted as if given separately as three single-digit integers, i.e. ``Maps(ax=235)`` is the same as ``Maps(ax=(2, 3, 5))``.
   - Note that this can only be used if there are no more than 9 subplots.
 
-.. table::
-
-    +----------------------------------------------------+------------------------------------+
-    | .. code-block:: python                             | .. image:: _static/grids/grid4.png |
-    |     :name: test_gridpos_4                          |   :align: center                   |
-    |                                                    |                                    |
-    |     from eomaps import Maps                        | |img_minsize|                      |
-    |     # ----- initialize a figure with an EOmaps map |                                    |
-    |     m = Maps(ax=211)                               |                                    |
-    |     # ----- add a normal matplotlib axes           |                                    |
-    |     ax = m.f.add_subplot(212)                      |                                    |
-    +----------------------------------------------------+------------------------------------+
-
-.. table::
-
-    +----------------------------------------------------+------------------------------------+
-    | .. code-block:: python                             | .. image:: _static/grids/grid5.png |
-    |     :name: test_gridpos_5                          |   :align: center                   |
-    |                                                    |                                    |
-    |     from eomaps import Maps                        | |img_minsize|                      |
-    |     # ----- initialize a figure with an EOmaps map |                                    |
-    |     m = Maps(ax=221)                               |                                    |
-    |     # ----- add 2 more Maps to the same figure     |                                    |
-    |     m2 = m.new_map(ax=222)                         |                                    |
-    |     m3 = m.new_map(ax=223)                         |                                    |
-    |     # ----- add a normal matplotlib axes           |                                    |
-    |     ax = m.f.add_subplot(224)                      |                                    |
-    +----------------------------------------------------+------------------------------------+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+        .. code-block:: python
+            :name: test_gridpos_4
+
+            from eomaps import Maps
+            # ----- initialize a figure with an EOmaps map
+            m = Maps(ax=211)
+            # ----- add a normal matplotlib axes
+            ax = m.f.add_subplot(212)
+
+    .. grid-item-card::
+        :width: 50%
+        :img-background: _static/grids/grid4.png
+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+        .. code-block:: python
+            :name: test_gridpos_5
+
+            from eomaps import Maps
+            # ----- initialize a figure with an EOmaps map
+            m = Maps(ax=221)
+            # ----- add 2 more Maps to the same figure
+            m2 = m.new_map(ax=222)
+            m3 = m.new_map(ax=223)
+            # ----- add a normal matplotlib axes
+            ax = m.f.add_subplot(224)
+
+    .. grid-item-card::
+        :width: 50%
+        :img-background: _static/grids/grid5.png
+
 
 
 - A matplotlib `GridSpec <https://matplotlib.org/stable/api/_as_gen/matplotlib.gridspec.GridSpec.html>`_
 
-.. table::
-
-    +----------------------------------------------+------------------------------------+
-    | .. code-block:: python                       | .. image:: _static/grids/grid6.png |
-    |     :name: test_gridpos_6                    |   :align: center                   |
-    |                                              |                                    |
-    |     from matplotlib.gridspec import GridSpec | |img_minsize|                      |
-    |     from eomaps import Maps                  |                                    |
-    |                                              |                                    |
-    |     gs = GridSpec(2, 2)                      |                                    |
-    |     m = Maps(ax=gs[0,0])                     |                                    |
-    |     m2 = m.new_map(ax=gs[0,1])               |                                    |
-    |     ax = m.f.add_subplot(gs[1,:])            |                                    |
-    +----------------------------------------------+------------------------------------+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+        .. code-block:: python
+            :name: test_gridpos_6
+
+            from matplotlib.gridspec import GridSpec
+            from eomaps import Maps
+
+            gs = GridSpec(2, 2)
+            m = Maps(ax=gs[0,0])
+            m2 = m.new_map(ax=gs[0,1])
+            ax = m.f.add_subplot(gs[1,:])
+
+    .. grid-item-card::
+        :width: 50%
+        :img-background: _static/grids/grid6.png
+
 
 Absolute positioning
 ********************
@@ -490,18 +527,23 @@ To set the absolute position of the map, provide a list of 4 floats representing
     Also, using ``m.f.tight_layout()`` will not work with axes added in this way.
 
 
-.. table::
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+        .. code-block:: python
+            :name: test_gridpos_abspos
+
+            from eomaps import Maps
+            # ----- initialize a figure with an EOmaps map
+            m = Maps(ax=(.07, 0.53, .6, .3))
+            # ----- add a normal matplotlib axes
+            ax = m.f.add_axes((.35, .15, .6, .2))
+
+    .. grid-item-card::
+        :width: 50%
+        :img-background: _static/grids/grid7.png
 
-    +----------------------------------------------------+------------------------------------+
-    | .. code-block:: python                             | .. image:: _static/grids/grid7.png |
-    |     :name: test_gridpos_abspos                     |   :align: center                   |
-    |                                                    |                                    |
-    |     from eomaps import Maps                        | |img_minsize|                      |
-    |     # ----- initialize a figure with an EOmaps map |                                    |
-    |     m = Maps(ax=(.07, 0.53, .6, .3))               |                                    |
-    |     # ----- add a normal matplotlib axes           |                                    |
-    |     ax = m.f.add_axes((.35, .15, .6, .2))          |                                    |
-    +----------------------------------------------------+------------------------------------+
 
 Using already existing figures / axes
 *************************************
@@ -545,45 +587,49 @@ Dynamic updates of plots in the same figure
 
 Here's an example to show how it works:
 
-.. table::
-
-    +-------------------------------------------------------------------------------------+------------------------------------------------------+
-    | .. code-block:: python                                                              | .. image:: _static/minigifs/dynamic_axes_updates.gif |
-    |         :name: test_dynamic_axes_updates                                            |   :align: center                                     |
-    |                                                                                     |                                                      |
-    |         from eomaps import Maps                                                     | |img_minsize|                                        |
-    |                                                                                     |                                                      |
-    |         # Initialize a new figure with an EOmaps map                                |                                                      |
-    |         m = Maps(ax=223)                                                            |                                                      |
-    |         m.ax.set_title("click me!")                                                 |                                                      |
-    |         m.add_feature.preset.coastline()                                            |                                                      |
-    |         m.cb.click.attach.mark(radius=20, fc="none", ec="r", lw=2)                  |                                                      |
-    |                                                                                     |                                                      |
-    |         # Add another map to the figure                                             |                                                      |
-    |         m2 = m.new_map(ax=224, crs=Maps.CRS.Mollweide())                            |                                                      |
-    |         m2.add_feature.preset.coastline()                                           |                                                      |
-    |         m2.add_feature.preset.ocean()                                               |                                                      |
-    |         m2.cb.click.attach.mark(radius=20, fc="none", ec="r", lw=2, n=200)          |                                                      |
-    |                                                                                     |                                                      |
-    |         # Add a "normal" matplotlib plot to the figure                              |                                                      |
-    |         ax = m.f.add_subplot(211)                                                   |                                                      |
-    |         # Since we want to dynamically update the data on the axis, it must be      |                                                      |
-    |         # added to the BlitManager to ensure that the artists are properly updated. |                                                      |
-    |         # (EOmaps handles interactive re-drawing of the figure)                     |                                                      |
-    |         m.BM.add_artist(ax, layer=m.layer)                                          |                                                      |
-    |                                                                                     |                                                      |
-    |         # plot some static data on the axis                                         |                                                      |
-    |         ax.plot([10, 20, 30, 40, 50], [10, 20, 30, 40, 50])                         |                                                      |
-    |                                                                                     |                                                      |
-    |         # define a callback that plots markers on the axis if you click on the map  |                                                      |
-    |         def cb(pos, **kwargs):                                                      |                                                      |
-    |             ax.plot(*pos, marker="o")                                               |                                                      |
-    |                                                                                     |                                                      |
-    |         m.cb.click.attach(cb)        # attach the callback to the first map         |                                                      |
-    |         m.cb.click.share_events(m2)  # share click events between the 2 maps        |                                                      |
-    +-------------------------------------------------------------------------------------+------------------------------------------------------+
 
 
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+        .. code-block:: python
+            :name: test_dynamic_axes_updates
+
+            from eomaps import Maps
+
+            # Initialize a new figure with an EOmaps map
+            m = Maps(ax=223)
+            m.ax.set_title("click me!")
+            m.add_feature.preset.coastline()
+            m.cb.click.attach.mark(radius=20, fc="none", ec="r", lw=2)
+
+            # Add another map to the figure
+            m2 = m.new_map(ax=224, crs=Maps.CRS.Mollweide())
+            m2.add_feature.preset.coastline()
+            m2.add_feature.preset.ocean()
+            m2.cb.click.attach.mark(radius=20, fc="none", ec="r", lw=2, n=200)
+
+            # Add a "normal" matplotlib plot to the figure
+            ax = m.f.add_subplot(211)
+            # Since we want to dynamically update the data on the axis, it must be
+            # added to the BlitManager to ensure that the artists are properly updated.
+            # (EOmaps handles interactive re-drawing of the figure)
+            m.BM.add_artist(ax, layer=m.layer)
+
+            # plot some static data on the axis
+            ax.plot([10, 20, 30, 40, 50], [10, 20, 30, 40, 50])
+
+            # define a callback that plots markers on the axis if you click on the map
+            def cb(pos, **kwargs):
+                ax.plot(*pos, marker="o")
+
+            m.cb.click.attach(cb)        # attach the callback to the first map
+            m.cb.click.share_events(m2)  # share click events between the 2 maps
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/dynamic_axes_updates.gif
 
 
 MapsGrid objects
diff --git a/docs/api_callbacks.rst b/docs/api_callbacks.rst
index 7f0e8df20..6b3733ef8 100644
--- a/docs/api_callbacks.rst
+++ b/docs/api_callbacks.rst
@@ -66,39 +66,42 @@ The ``< EVENT CATEGORY >`` hereby specifies the event that will trigger the call
           | ``m.cb.< EVENT CATEGORY >.set_execute_on_all_layers(True)``
 
 
-.. table::
-    :width: 100 %
-    :widths: auto
 
-    +-----------------------------------------------------------------------------------+--------------------------------------------------+
-    | .. code-block:: python                                                            | .. image:: _static/minigifs/simple_callbacks.gif |
-    |     :name: test_add_callbacks                                                     |   :align: center                                 |
-    |                                                                                   |                                                  |
-    |     from eomaps import Maps                                                       |                                                  |
-    |     import numpy as np                                                            |                                                  |
-    |     x, y = np.mgrid[-45:45, 20:60]                                                |                                                  |
-    |                                                                                   |                                                  |
-    |     m = Maps(Maps.CRS.Orthographic())                                             |                                                  |
-    |     m.all.add_feature.preset.coastline()                                          |                                                  |
-    |     m.set_data(data=x+y**2, x=x, y=y, crs=4326)                                   |                                                  |
-    |     m.plot_map()                                                                  |                                                  |
-    |                                                                                   |                                                  |
-    |     m2 = m.new_layer(inherit_data=True, layer="second_layer")                     |                                                  |
-    |     m2.plot_map(cmap="tab10")                                                     |                                                  |
-    |                                                                                   |                                                  |
-    |     # get an annotation if you RIGHT-click anywhere on the map                    |                                                  |
-    |     m.cb.click.attach.annotate(xytext=(-60, -60),                                 |                                                  |
-    |                                bbox=dict(boxstyle="round", fc="r"))               |                                                  |
-    |                                                                                   |                                                  |
-    |     # pick the nearest datapoint if you click on the MIDDLE mouse button          |                                                  |
-    |     m.cb.pick.attach.annotate(button=2)                                           |                                                  |
-    |     m.cb.pick.attach.mark(buffer=1, permanent=False, fc="none", ec="r", button=2) |                                                  |
-    |     m.cb.pick.attach.mark(buffer=4, permanent=False, fc="none", ec="r", button=2) |                                                  |
-    |                                                                                   |                                                  |
-    |     # peek at the second layer if you LEFT-click on the map                       |                                                  |
-    |     m.cb.click.attach.peek_layer("second_layer", how=.25, button=3)               |                                                  |
-    +-----------------------------------------------------------------------------------+--------------------------------------------------+
 
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_callbacks
+
+            from eomaps import Maps
+            import numpy as np
+            x, y = np.mgrid[-45:45, 20:60]
+
+            m = Maps(Maps.CRS.Orthographic())
+            m.all.add_feature.preset.coastline()
+            m.set_data(data=x+y**2, x=x, y=y, crs=4326)
+            m.plot_map()
+
+            m2 = m.new_layer(inherit_data=True, layer="second_layer")
+            m2.plot_map(cmap="tab10")
+
+            # get an annotation if you RIGHT-click anywhere on the map
+            m.cb.click.attach.annotate(xytext=(-60, -60),
+                                       bbox=dict(boxstyle="round", fc="r"))
+
+            # pick the nearest datapoint if you click on the MIDDLE mouse button
+            m.cb.pick.attach.annotate(button=2)
+            m.cb.pick.attach.mark(buffer=1, permanent=False, fc="none", ec="r", button=2)
+            m.cb.pick.attach.mark(buffer=4, permanent=False, fc="none", ec="r", button=2)
+
+            # peek at the second layer if you LEFT-click on the map
+            m.cb.click.attach.peek_layer("second_layer", how=.25, button=3)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/simple_callbacks.gif
 
 
 In addition, each callback-container supports the following useful methods:
@@ -115,7 +118,7 @@ In addition, each callback-container supports the following useful methods:
     | :class:`get <eomaps.cb_container.ClickContainer._get>`                                           | Accessor for objects generated/retrieved by callbacks.                   |
     +--------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+
 
-.. currentmodule:: eomaps.cb_container.ClickContainer
+.. currentmodule:: eomaps.eomaps.Maps.cb.click
 
 .. autosummary::
     :nosignatures:
@@ -346,7 +349,8 @@ To customize the picking-behavior, use ``m.cb.pick.set_props()``. The following
   - If True, callbacks are executed for each picked point individually
   - If False, callbacks are executed only once and get lists of all picked values as input-arguments.
 
-.. currentmodule:: eomaps.cb_container.PickContainer
+
+.. currentmodule:: eomaps.eomaps.Maps.cb.pick
 
 .. autosummary::
     :nosignatures:
@@ -355,48 +359,52 @@ To customize the picking-behavior, use ``m.cb.pick.set_props()``. The following
 
 
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +--------------------------------------------------------------------------------+--------------------------------------------+
-    | .. code-block:: python                                                         | .. image:: _static/minigifs/pick_multi.gif |
-    |     :name: test_callbacks_multi_pick                                           |   :align: center                           |
-    |                                                                                |                                            |
-    |     from eomaps import Maps                                                    |                                            |
-    |     import numpy as np                                                         |                                            |
-    |                                                                                |                                            |
-    |     # create some random data                                                  |                                            |
-    |     x, y = np.mgrid[-30:67, -12:50]                                            |                                            |
-    |     data = np.random.randint(0, 100, x.shape)                                  |                                            |
-    |                                                                                |                                            |
-    |     # a callback to indicate the search-radius                                 |                                            |
-    |     def indicate_search_radius(m, pos, *args, **kwargs):                       |                                            |
-    |         art = m.add_marker(                                                    |                                            |
-    |             xy=(np.atleast_1d(pos[0])[0],                                      |                                            |
-    |                 np.atleast_1d(pos[1])[0]),                                     |                                            |
-    |             shape="ellipses", radius=m.tree.d, radius_crs="out",               |                                            |
-    |             n=100, fc="none", ec="k", lw=2)                                    |                                            |
-    |         m.cb.pick.add_temporary_artist(art)                                    |                                            |
-    |                                                                                |                                            |
-    |     # a callback to set the number of picked neighbours                        |                                            |
-    |     def pick_n_neighbours(m, n, **kwargs):                                     |                                            |
-    |         m.cb.pick.set_props(n=n)                                               |                                            |
-    |                                                                                |                                            |
-    |                                                                                |                                            |
-    |     m = Maps()                                                                 |                                            |
-    |     m.add_feature.preset.coastline()                                           |                                            |
-    |     m.set_data(data, x, y)                                                     |                                            |
-    |     m.plot_map()                                                               |                                            |
-    |     m.cb.pick.set_props(n=50, search_radius=10, pick_relative_to_closest=True) |                                            |
-    |                                                                                |                                            |
-    |     m.cb.pick.attach.annotate()                                                |                                            |
-    |     m.cb.pick.attach.mark(fc="none", ec="r")                                   |                                            |
-    |     m.cb.pick.attach(indicate_search_radius, m=m)                              |                                            |
-    |                                                                                |                                            |
-    |     for key, n in (("1", 1), ("2", 9), ("3", 50), ("4", 500)):                 |                                            |
-    |         m.cb.keypress.attach(pick_n_neighbours, key=key, m=m, n=n)             |                                            |
-    +--------------------------------------------------------------------------------+--------------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_callbacks_multi_pick
+
+            from eomaps import Maps
+            import numpy as np
+
+            # create some random data
+            x, y = np.mgrid[-30:67, -12:50]
+            data = np.random.randint(0, 100, x.shape)
+
+            # a callback to indicate the search-radius
+            def indicate_search_radius(m, pos, *args, **kwargs):
+                art = m.add_marker(
+                    xy=(np.atleast_1d(pos[0])[0],
+                        np.atleast_1d(pos[1])[0]),
+                    shape="ellipses", radius=m.tree.d, radius_crs="out",
+                    n=100, fc="none", ec="k", lw=2)
+                m.cb.pick.add_temporary_artist(art)
+
+            # a callback to set the number of picked neighbours
+            def pick_n_neighbours(m, n, **kwargs):
+                m.cb.pick.set_props(n=n)
+
+
+            m = Maps()
+            m.add_feature.preset.coastline()
+            m.set_data(data, x, y)
+            m.plot_map()
+            m.cb.pick.set_props(n=50, search_radius=10, pick_relative_to_closest=True)
+
+            m.cb.pick.attach.annotate()
+            m.cb.pick.attach.mark(fc="none", ec="r")
+            m.cb.pick.attach(indicate_search_radius, m=m)
+
+            for key, n in (("1", 1), ("2", 9), ("3", 50), ("4", 500)):
+                m.cb.keypress.attach(pick_n_neighbours, key=key, m=m, n=n)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/pick_multi.gif
+
 
 Picking a dataset without plotting it first
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index 61da9c06c..0587fc1f6 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -77,13 +77,18 @@ def make_feature_toctree_file():
         "eomaps.eomaps", ["Maps.config", *members], "obj_with_attributes_no_toc"
     )
     s += get_autosummary("eomaps.mapsgrid", ["MapsGrid"], "custom-class-template")
-
     s += get_autosummary("eomaps.colorbar", ["ColorBar"], "custom-class-template")
+    s += get_autosummary("eomaps.compass", ["Compass"], "custom-class-template")
+    s += get_autosummary("eomaps.scalebar", ["ScaleBar"], "custom-class-template")
+    s += get_autosummary(
+        "eomaps.callbacks",
+        ["ClickCallbacks", "PickCallbacks", "KeypressCallbacks"],
+        "custom-class-template",
+    )
+
     s += get_autosummary(
         "eomaps.grid", ["GridLines", "GridLabels"], "custom-class-template"
     )
-    s += get_autosummary("eomaps.compass", ["Compass"], "custom-class-template")
-    s += get_autosummary("eomaps.scalebar", ["ScaleBar"], "custom-class-template")
     s += get_autosummary(
         "eomaps.inset_maps",
         [

From 50adb631cae6e49f8ede45d03c7d1d9a0252eec5 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 21:39:13 +0100
Subject: [PATCH 100/240] update docs

---
 docs/api_compass.rst            |  31 +++--
 docs/api_data_visualization.rst | 229 +++++++++++++++++---------------
 docs/api_draw.rst               |  28 ++--
 docs/api_gridlines.rst          | 123 +++++++++--------
 4 files changed, 219 insertions(+), 192 deletions(-)

diff --git a/docs/api_compass.rst b/docs/api_compass.rst
index b06b57c5b..ed4b9136a 100644
--- a/docs/api_compass.rst
+++ b/docs/api_compass.rst
@@ -28,21 +28,24 @@ A compass can be added to the map via :py:meth:`Maps.add_compass`:
     Maps.add_compass
 
 
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_compass
+
+            from eomaps import Maps
+            m = Maps(Maps.CRS.Stereographic())
+            m.add_feature.preset.ocean()
+
+            m.add_compass()
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/compass.gif
+            :width: 50%
 
-.. table::
-    :widths: 70 30
-    :align: center
-
-    +--------------------------------------+-----------------------------------------+
-    | .. code-block:: python               | .. image:: _static/minigifs/compass.gif |
-    |   :name: test_add_compass            |   :align: center                        |
-    |                                      |                                         |
-    |   from eomaps import Maps            | |img_minsize|                           |
-    |   m = Maps(Maps.CRS.Stereographic()) |                                         |
-    |   m.add_feature.preset.ocean()       |                                         |
-    |                                      |                                         |
-    |   m.add_compass()                    |                                         |
-    +--------------------------------------+-----------------------------------------+
 
 The compass object is dynamically updated if you pan/zoom the map, and it can be
 dragged around on the map with the mouse.
diff --git a/docs/api_data_visualization.rst b/docs/api_data_visualization.rst
index 8cc1fb36c..53aa12afb 100644
--- a/docs/api_data_visualization.rst
+++ b/docs/api_data_visualization.rst
@@ -542,33 +542,35 @@ To assign a classification scheme to a :py:class:`Maps` object, use ``m.set_clas
     Maps.set_classify
 
 
-.. table::
-    :widths: 70 30
-    :align: center
-
-    +------------------------------------------------------------------+--------------------------------------------------+
-    | .. code-block:: python                                           | .. image:: _static/minigifs/classify_data_01.png |
-    |     :name: test_classify_data                                    |     :align: center                               |
-    |                                                                  |                                                  |
-    |     from eomaps import Maps                                      | |img_minsize|                                    |
-    |     import numpy as np                                           |                                                  |
-    |                                                                  |                                                  |
-    |     data = np.random.normal(0, 1, (50, 50))                      |                                                  |
-    |     x = np.linspace(-45, 45, 50)                                 |                                                  |
-    |     y = np.linspace(-45, 45, 50)                                 |                                                  |
-    |                                                                  |                                                  |
-    |     m = Maps(figsize=(4, 5))                                     |                                                  |
-    |     m.add_feature.preset.coastline(lw=2)                         |                                                  |
-    |     m.add_feature.preset.ocean(zorder=99, alpha=0.5)             |                                                  |
-    |     m.set_data(data, x, y)                                       |                                                  |
-    |     m.set_shape.ellipses()                                       |                                                  |
-    |     m.set_classify.StdMean(multiples=[-1.5, -.5, .5, 1.5])       |                                                  |
-    |     m.plot_map(vmin=-3, vmax=3)                                  |                                                  |
-    |                                                                  |                                                  |
-    |     cb = m.add_colorbar(pos=0.2, label="StdMean classification") |                                                  |
-    |     cb.tick_params(labelsize=7)                                  |                                                  |
-    +------------------------------------------------------------------+--------------------------------------------------+
+.. grid:: 1 1 2 2
 
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_classify_data
+
+            from eomaps import Maps
+            import numpy as np
+
+            data = np.random.normal(0, 1, (50, 50))
+            x = np.linspace(-45, 45, 50)
+            y = np.linspace(-45, 45, 50)
+
+            m = Maps(figsize=(4, 5))
+            m.add_feature.preset.coastline(lw=2)
+            m.add_feature.preset.ocean(zorder=99, alpha=0.5)
+            m.set_data(data, x, y)
+            m.set_shape.ellipses()
+            m.set_classify.StdMean(multiples=[-1.5, -.5, .5, 1.5])
+            m.plot_map(vmin=-3, vmax=3)
+
+            cb = m.add_colorbar(pos=0.2, label="StdMean classification")
+            cb.tick_params(labelsize=7)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/classify_data_01.png
+            :width: 75%
 
 
 Currently available classification-schemes are (see `mapclassify <https://github.com/pysal/mapclassify>`_ for details):
@@ -828,43 +830,46 @@ Once a dataset has been plotted, a colorbar with a colored histogram on top can
     Colorbars are only visible if the layer at which the data was plotted is visible!
 
 
-.. table::
-    :widths: 60 40
-    :align: center
-
-    +-----------------------------------------------------------------+------------------------------------------+
-    | .. code-block:: python                                          | .. image:: _static/minigifs/colorbar.gif |
-    |     :name: test_colorbars                                       |     :align: center                       |
-    |                                                                 |                                          |
-    |     from eomaps import Maps                                     | |img_minsize|                            |
-    |     import numpy as np                                          |                                          |
-    |                                                                 |                                          |
-    |     data = np.random.normal(0, 1, (50, 50))                     |                                          |
-    |     x = np.linspace(-45, 45, 50)                                |                                          |
-    |     y = np.linspace(-45, 45, 50)                                |                                          |
-    |                                                                 |                                          |
-    |     m = Maps(layer="all")                                       |                                          |
-    |     m.add_feature.preset.coastline()                            |                                          |
-    |     m.add_feature.preset.ocean(zorder=99, alpha=0.5)            |                                          |
-    |     m.util.layer_selector(loc="upper left")                     |                                          |
-    |                                                                 |                                          |
-    |     mA = m.new_layer("A")                                       |                                          |
-    |     mA.set_data(data, x, y)                                     |                                          |
-    |     mA.set_classify.Quantiles(k=5)                              |                                          |
-    |     mA.plot_map(vmin=-3, vmax=3)                                |                                          |
-    |     cbA = mA.add_colorbar(label="Quantile classification")      |                                          |
-    |     cbA.tick_params(rotation=45)                                |                                          |
-    |                                                                 |                                          |
-    |     mB = m.new_layer("B")                                       |                                          |
-    |     mB.set_data(data, x, y)                                     |                                          |
-    |     mB.set_classify.EqualInterval(k=5)                          |                                          |
-    |     mB.plot_map(vmin=-3, vmax=3)                                |                                          |
-    |     cbB = mB.add_colorbar(label="EqualInterval classification") |                                          |
-    |     cbB.tick_params(labelcolor="darkblue", labelsize=9)         |                                          |
-    |                                                                 |                                          |
-    |     m.subplots_adjust(bottom=0.1)                               |                                          |
-    |     m.show_layer(mA.layer)                                      |                                          |
-    +-----------------------------------------------------------------+------------------------------------------+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_colorbars
+
+            from eomaps import Maps
+            import numpy as np
+
+            data = np.random.normal(0, 1, (50, 50))
+            x = np.linspace(-45, 45, 50)
+            y = np.linspace(-45, 45, 50)
+
+            m = Maps(layer="all")
+            m.add_feature.preset.coastline()
+            m.add_feature.preset.ocean(zorder=99, alpha=0.5)
+            m.util.layer_selector(loc="upper left")
+
+            mA = m.new_layer("A")
+            mA.set_data(data, x, y)
+            mA.set_classify.Quantiles(k=5)
+            mA.plot_map(vmin=-3, vmax=3)
+            cbA = mA.add_colorbar(label="Quantile classification")
+            cbA.tick_params(rotation=45)
+
+            mB = m.new_layer("B")
+            mB.set_data(data, x, y)
+            mB.set_classify.EqualInterval(k=5)
+            mB.plot_map(vmin=-3, vmax=3)
+            cbB = mB.add_colorbar(label="EqualInterval classification")
+            cbB.tick_params(labelcolor="darkblue", labelsize=9)
+
+            m.subplots_adjust(bottom=0.1)
+            m.show_layer(mA.layer)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/colorbar.gif
+
 
 .. autosummary::
     :nosignatures:
@@ -893,32 +898,38 @@ Set colorbar tick labels based on bins
 
 To label the colorbar with custom names for a given set of bins, use :py:meth:`ColorBar.set_bin_labels`:
 
-.. table::
-
-    +-------------------------------------------------------------------------------+------------------------------------------------+
-    | .. code-block:: python                                                        | .. image:: _static/minigifs/colorbar_ticks.png |
-    |     :name: test_colorbar_bin_labels                                           |   :align: center                               |
-    |                                                                               |                                                |
-    |     import numpy as np                                                        | |img_minsize|                                  |
-    |     from eomaps import Maps                                                   |                                                |
-    |     # specify some random data                                                |                                                |
-    |     lon, lat = np.mgrid[-45:45, -45:45]                                       |                                                |
-    |     data = np.random.normal(0, 50, lon.shape)                                 |                                                |
-    |                                                                               |                                                |
-    |     # use a custom set of bins to classify the data                           |                                                |
-    |     bins = np.array([-50, -30, -20, 20, 30, 40, 50])                          |                                                |
-    |     names = np.array(["below -50", "A", "B", "C", "D", "E", "F", "above 50"]) |                                                |
-    |                                                                               |                                                |
-    |     m = Maps()                                                                |                                                |
-    |     m.add_feature.preset.coastline()                                          |                                                |
-    |     m.set_data(data, lon, lat)                                                |                                                |
-    |     m.set_classify.UserDefined(bins=bins)                                     |                                                |
-    |     m.plot_map(cmap="tab10")                                                  |                                                |
-    |     m.add_colorbar()                                                          |                                                |
-    |                                                                               |                                                |
-    |     # set custom colorbar-ticks based on the bins                             |                                                |
-    |     m.colorbar.set_bin_labels(bins, names)                                    |                                                |
-    +-------------------------------------------------------------------------------+------------------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_colorbar_bin_labels
+
+            import numpy as np
+            from eomaps import Maps
+            # specify some random data
+            lon, lat = np.mgrid[-45:45, -45:45]
+            data = np.random.normal(0, 50, lon.shape)
+
+            # use a custom set of bins to classify the data
+            bins = np.array([-50, -30, -20, 20, 30, 40, 50])
+            names = np.array(["below -50", "A", "B", "C", "D", "E", "F", "above 50"])
+
+            m = Maps()
+            m.add_feature.preset.coastline()
+            m.set_data(data, lon, lat)
+            m.set_classify.UserDefined(bins=bins)
+            m.plot_map(cmap="tab10")
+            m.add_colorbar()
+
+            # set custom colorbar-ticks based on the bins
+            m.colorbar.set_bin_labels(bins, names)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/colorbar_ticks.png
+
 
 
 .. autosummary::
@@ -939,21 +950,25 @@ Using the colorbar as a "dynamic shade indicator"
 For shade shapes, the colorbar can be used to indicate the distribution of the shaded
 pixels within the current field of view by setting ``dynamic_shade_indicator=True``.
 
-.. table::
-
-    +--------------------------------------------------------------------+--------------------------------------------------+
-    | .. code-block:: python                                             | .. image:: _static/minigifs/dynamic_colorbar.gif |
-    |   :name: test_colorbar_dynamic_shade_indicator                     |   :align: center                                 |
-    |                                                                    |                                                  |
-    |   from eomaps import Maps                                          | |img_minsize|                                    |
-    |   import numpy as np                                               |                                                  |
-    |   x, y = np.mgrid[-45:45, 20:60]                                   |                                                  |
-    |                                                                    |                                                  |
-    |   m = Maps()                                                       |                                                  |
-    |   m.add_feature.preset.coastline()                                 |                                                  |
-    |   m.set_data(data=x+y, x=x, y=y, crs=4326)                         |                                                  |
-    |   m.set_shape.shade_raster()                                       |                                                  |
-    |   m.plot_map()                                                     |                                                  |
-    |   m.add_colorbar(dynamic_shade_indicator=True, hist_bins=20)       |                                                  |
-    |                                                                    |                                                  |
-    +--------------------------------------------------------------------+--------------------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_colorbar_dynamic_shade_indicator
+
+            from eomaps import Maps
+            import numpy as np
+            x, y = np.mgrid[-45:45, 20:60]
+
+            m = Maps()
+            m.add_feature.preset.coastline()
+            m.set_data(data=x+y, x=x, y=y, crs=4326)
+            m.set_shape.shade_raster()
+            m.plot_map()
+            m.add_colorbar(dynamic_shade_indicator=True, hist_bins=20)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/dynamic_colorbar.gif
diff --git a/docs/api_draw.rst b/docs/api_draw.rst
index 69f6cea03..5a1346d07 100644
--- a/docs/api_draw.rst
+++ b/docs/api_draw.rst
@@ -15,17 +15,22 @@ Starting with EOmaps v5.0 it is possible to draw simple shapes on the map using
 
 - To remove the most recently drawn shape use ``m.draw.remove_last_shape()``.
 
-.. table::
-    :widths: 60 40
-    :align: center
-
-    +--------------------------------------+---------------------------------------------+
-    | .. code-block:: python               | .. image:: _static/minigifs/draw_shapes.gif |
-    |                                      |   :align: center                            |
-    |     m = Maps()                       |                                             |
-    |     m.add_feature.preset.coastline() |                                             |
-    |     m.draw.polygon()                 |                                             |
-    +--------------------------------------+---------------------------------------------+
+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+
+            m = Maps()
+            m.add_feature.preset.coastline()
+            m.draw.polygon()
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/draw_shapes.gif
+
 
 .. note::
 
@@ -39,7 +44,6 @@ Starting with EOmaps v5.0 it is possible to draw simple shapes on the map using
     active draws!
 
 
-
 .. currentmodule:: eomaps.eomaps.Maps.draw
 
 .. autosummary::
diff --git a/docs/api_gridlines.rst b/docs/api_gridlines.rst
index 57d5deef1..277f646a2 100644
--- a/docs/api_gridlines.rst
+++ b/docs/api_gridlines.rst
@@ -39,37 +39,39 @@ If no explicit grid-spacing is provided (e.g. ``d=None``), the grid is dynamical
     Maps.add_gridlines
 
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +-------------------------------------------------------------------+-----------------------------------------+
-    | .. code-block:: python                                            | .. image:: _static/minigifs/grid_01.png |
-    |   :name: test_add_gridlines                                       |     :align: center                      |
-    |                                                                   |                                         |
-    |   from eomaps import Maps                                         | |img_minsize|                           |
-    |   m = Maps(Maps.CRS.Mollweide(), frameon=False)                   |                                         |
-    |   m.add_feature.preset.ocean()                                    |                                         |
-    |                                                                   |                                         |
-    |   # add gridlines with a fixed grid-spacing                       |                                         |
-    |   mg = m.new_layer("grid")                                        |                                         |
-    |   g0 = mg.add_gridlines(d=40, ec="orange", lw=3, zorder=2)        |                                         |
-    |   g1 = mg.add_gridlines(d=(10, 20), ec="orange", lw=.5, zorder=1) |                                         |
-    |                                                                   |                                         |
-    |   # add fine-grained gridlines in a specific area                 |                                         |
-    |   g2 = mg.add_gridlines(d=2, ec="darkred", lw=0.5, zorder=0,      |                                         |
-    |                         bounds=(-20, 20, -10, 30))                |                                         |
-    |   g3 = mg.add_gridlines(d=2, ec="b", lw=0.5, zorder=0,            |                                         |
-    |                         bounds=(60, 100, 30, 70))                 |                                         |
-    |                                                                   |                                         |
-    |   # add dedicated gridlines at specific coordinates               |                                         |
-    |   g4 = mg.add_gridlines(([-123, -112, -75], [35, 65]),            |                                         |
-    |                         ec="k", lw=2, ls="--", zorder=20,         |                                         |
-    |                         bounds=(-140, 20, -50, 70)                |                                         |
-    |                         )                                         |                                         |
-    |                                                                   |                                         |
-    |   m.show_layer(m.layer, "grid")                                   |                                         |
-    +-------------------------------------------------------------------+-----------------------------------------+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_gridlines
+
+            from eomaps import Maps
+            m = Maps(Maps.CRS.Mollweide(), frameon=False)
+            m.add_feature.preset.ocean()
+
+            # add gridlines with a fixed grid-spacing
+            mg = m.new_layer("grid")
+            g0 = mg.add_gridlines(d=40, ec="orange", lw=3, zorder=2)
+            g1 = mg.add_gridlines(d=(10, 20), ec="orange", lw=.5, zorder=1)
+
+            # add fine-grained gridlines in a specific area
+            g2 = mg.add_gridlines(d=2, ec="darkred", lw=0.5, zorder=0,
+                                  bounds=(-20, 20, -10, 30))
+            g3 = mg.add_gridlines(d=2, ec="b", lw=0.5, zorder=0,
+                                  bounds=(60, 100, 30, 70))
+
+            # add dedicated gridlines at specific coordinates
+            g4 = mg.add_gridlines(([-123, -112, -75], [35, 65]),
+                                  ec="k", lw=2, ls="--", zorder=20,
+                                  bounds=(-140, 20, -50, 70)
+                                  )
+
+            m.show_layer(m.layer, "grid")
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/grid_01.png
 
 
 .. currentmodule:: eomaps.grid
@@ -116,31 +118,34 @@ be used to add a label only to every n\ :sup:`th` grid line.
 To **change the appearance of the labels**, any kwarg supported by `matplotlib.pyplot.text <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html>`_
 can be used (e.g. `color`, `fontsize`, `fontweight`, ...).
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +--------------------------------------------------------------------------+------------------------------------------------+
-    | .. code-block:: python                                                   | .. image:: _static/minigifs/grid_labels_01.png |
-    |     :name: test_grid_labels_01                                           |     :align: center                             |
-    |                                                                          |                                                |
-    |     from eomaps import Maps                                              | |img_minsize|                                  |
-    |     m = Maps(Maps.CRS.Stereographic(), figsize=(5, 6))                   |                                                |
-    |     m.set_extent((-83, -20, -59, 13))                                    |                                                |
-    |     m.add_feature.preset.coastline()                                     |                                                |
-    |     m.add_feature.preset.ocean()                                         |                                                |
-    |                                                                          |                                                |
-    |     # draw a regular grid with 10 degree grid-spacing                    |                                                |
-    |     # and add labels to all lines except some selected lines             |                                                |
-    |     g = m.add_gridlines(10, lw=0.25, alpha=0.5)                          |                                                |
-    |     g.add_labels(fontsize=6, exclude=([-40, -30], [-30]))                |                                                |
-    |                                                                          |                                                |
-    |     # draw some specific gridlines and add bold green labels             |                                                |
-    |     g = m.add_gridlines(([-40, -30], [-30]), c="g", lw=1.5)              |                                                |
-    |     gl0 = g.add_labels(where="tlr", c="g", offset=15, fontweight="bold") |                                                |
-    |                                                                          |                                                |
-    |     # draw a bounded grid and add labels                                 |                                                |
-    |     g = m.add_gridlines(10, bounds=[-50, -20, -40, -20], c="b", lw=2)    |                                                |
-    |     g = m.add_gridlines(5,  bounds=[-50, -20, -40, -20], c="b")          |                                                |
-    |     gl = g.add_labels(where=0, fontsize=8, every=(1, -1, 2), c="b")      |                                                |
-    +--------------------------------------------------------------------------+------------------------------------------------+
+
+.. grid:: 1 1 2 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_grid_labels_01
+
+            from eomaps import Maps
+            m = Maps(Maps.CRS.Stereographic(), figsize=(5, 6))
+            m.set_extent((-83, -20, -59, 13))
+            m.add_feature.preset.coastline()
+            m.add_feature.preset.ocean()
+
+            # draw a regular grid with 10 degree grid-spacing
+            # and add labels to all lines except some selected lines
+            g = m.add_gridlines(10, lw=0.25, alpha=0.5)
+            g.add_labels(fontsize=6, exclude=([-40, -30], [-30]))
+
+            # draw some specific gridlines and add bold green labels
+            g = m.add_gridlines(([-40, -30], [-30]), c="g", lw=1.5)
+            gl0 = g.add_labels(where="tlr", c="g", offset=15, fontweight="bold")
+
+            # draw a bounded grid and add labels
+            g = m.add_gridlines(10, bounds=[-50, -20, -40, -20], c="b", lw=2)
+            g = m.add_gridlines(5,  bounds=[-50, -20, -40, -20], c="b")
+            gl = g.add_labels(where=0, fontsize=8, every=(1, -1, 2), c="b")
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/grid_labels_01.png

From 4b080bc047a70679ea297cfbb7c1e5db5427fbb2 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 22:08:45 +0100
Subject: [PATCH 101/240] update docs

---
 docs/api_annotations_markers_etc.rst |  10 +-
 docs/api_basics.rst                  |  16 +--
 docs/api_callbacks.rst               |   4 +-
 docs/api_compass.rst                 |   2 +-
 docs/api_data_visualization.rst      |   8 +-
 docs/api_draw.rst                    |   2 +-
 docs/api_gridlines.rst               |   4 +-
 docs/api_layout_editor.rst           |  59 +++++------
 docs/api_scalebar.rst                |  30 +++---
 docs/api_utils.rst                   |  37 +++----
 docs/api_vector_data.rst             |  48 ++++-----
 docs/api_webmaps.rst                 | 140 ++++++++++++++-------------
 docs/index.rst                       |   6 +-
 13 files changed, 192 insertions(+), 174 deletions(-)

diff --git a/docs/api_annotations_markers_etc.rst b/docs/api_annotations_markers_etc.rst
index 38c269340..c2f564e62 100644
--- a/docs/api_annotations_markers_etc.rst
+++ b/docs/api_annotations_markers_etc.rst
@@ -51,7 +51,7 @@ To dynamically add annotations if you click on the map, checkout the associated
 
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -149,7 +149,7 @@ To dynamically add markers if you click on the map, checkout the associated :ref
     Maps.add_marker
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -234,7 +234,7 @@ Lines can be added to a map with :py:meth:`Maps.add_line`.
 
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -287,7 +287,7 @@ To indicate rectangular areas in any given crs, simply use :py:meth:`Maps.indica
     Maps.indicate_extent
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -333,7 +333,7 @@ Logos can be re-positioned and re-sized with the :ref:`layout_editor`!
 - To fix the relative position of the logo with respect to the map-axis, use ``fix_position=True``
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
diff --git a/docs/api_basics.rst b/docs/api_basics.rst
index 7eb99c660..dd0e593fc 100644
--- a/docs/api_basics.rst
+++ b/docs/api_basics.rst
@@ -376,7 +376,7 @@ To position the map in a (virtual) grid, one of the following options are possib
     indices (1-based, and including last) of the map, e.g., ``Maps(ax=(3, 1, (1, 2)))`` makes a map that spans the upper 2/3 of the figure.
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -396,7 +396,7 @@ To position the map in a (virtual) grid, one of the following options are possib
         :img-background: _static/grids/grid1.png
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -419,7 +419,7 @@ To position the map in a (virtual) grid, one of the following options are possib
         :img-background: _static/grids/grid2.png
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -450,7 +450,7 @@ To position the map in a (virtual) grid, one of the following options are possib
   - The digits are interpreted as if given separately as three single-digit integers, i.e. ``Maps(ax=235)`` is the same as ``Maps(ax=(2, 3, 5))``.
   - Note that this can only be used if there are no more than 9 subplots.
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -468,7 +468,7 @@ To position the map in a (virtual) grid, one of the following options are possib
         :img-background: _static/grids/grid4.png
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -492,7 +492,7 @@ To position the map in a (virtual) grid, one of the following options are possib
 
 - A matplotlib `GridSpec <https://matplotlib.org/stable/api/_as_gen/matplotlib.gridspec.GridSpec.html>`_
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -527,7 +527,7 @@ To set the absolute position of the map, provide a list of 4 floats representing
     Also, using ``m.f.tight_layout()`` will not work with axes added in this way.
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -589,7 +589,7 @@ Here's an example to show how it works:
 
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
diff --git a/docs/api_callbacks.rst b/docs/api_callbacks.rst
index 6b3733ef8..e106849c2 100644
--- a/docs/api_callbacks.rst
+++ b/docs/api_callbacks.rst
@@ -68,7 +68,7 @@ The ``< EVENT CATEGORY >`` hereby specifies the event that will trigger the call
 
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -360,7 +360,7 @@ To customize the picking-behavior, use ``m.cb.pick.set_props()``. The following
 
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
diff --git a/docs/api_compass.rst b/docs/api_compass.rst
index ed4b9136a..6cbd04db8 100644
--- a/docs/api_compass.rst
+++ b/docs/api_compass.rst
@@ -28,7 +28,7 @@ A compass can be added to the map via :py:meth:`Maps.add_compass`:
     Maps.add_compass
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
diff --git a/docs/api_data_visualization.rst b/docs/api_data_visualization.rst
index 53aa12afb..0ff411dfa 100644
--- a/docs/api_data_visualization.rst
+++ b/docs/api_data_visualization.rst
@@ -542,7 +542,7 @@ To assign a classification scheme to a :py:class:`Maps` object, use ``m.set_clas
     Maps.set_classify
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -830,7 +830,7 @@ Once a dataset has been plotted, a colorbar with a colored histogram on top can
     Colorbars are only visible if the layer at which the data was plotted is visible!
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -899,7 +899,7 @@ Set colorbar tick labels based on bins
 To label the colorbar with custom names for a given set of bins, use :py:meth:`ColorBar.set_bin_labels`:
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -951,7 +951,7 @@ For shade shapes, the colorbar can be used to indicate the distribution of the s
 pixels within the current field of view by setting ``dynamic_shade_indicator=True``.
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
diff --git a/docs/api_draw.rst b/docs/api_draw.rst
index 5a1346d07..72f88c456 100644
--- a/docs/api_draw.rst
+++ b/docs/api_draw.rst
@@ -17,7 +17,7 @@ Starting with EOmaps v5.0 it is possible to draw simple shapes on the map using
 
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
diff --git a/docs/api_gridlines.rst b/docs/api_gridlines.rst
index 277f646a2..81ff39da2 100644
--- a/docs/api_gridlines.rst
+++ b/docs/api_gridlines.rst
@@ -39,7 +39,7 @@ If no explicit grid-spacing is provided (e.g. ``d=None``), the grid is dynamical
     Maps.add_gridlines
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
@@ -119,7 +119,7 @@ To **change the appearance of the labels**, any kwarg supported by `matplotlib.p
 can be used (e.g. `color`, `fontsize`, `fontweight`, ...).
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
 
     .. grid-item::
 
diff --git a/docs/api_layout_editor.rst b/docs/api_layout_editor.rst
index 58c493990..1bc628f87 100644
--- a/docs/api_layout_editor.rst
+++ b/docs/api_layout_editor.rst
@@ -9,34 +9,37 @@ You can use it to simply drag the axes the mouse to the desired locations and ch
 
 **Keyboard shortcuts** are assigned as follows:
 
-.. table::
-    :widths: 52 45
-    :align: center
-
-    +-----------------------------------------------------------------------------------------+-----------------------------------------------+
-    | - Press ``ALT + L``: enter the **Layout Editor** mode                                   | .. image:: _static/minigifs/layout_editor.gif |
-    | - Press ``ALT + L`` again or ``escape`` to exit the **Layout Editor**                   |     :align: center                            |
-    |                                                                                         |                                               |
-    | **Pick** and **re-arrange** the axes as you like with the mouse.                        |                                               |
-    |                                                                                         |                                               |
-    | - To pick **multiple axes**, hold down ``shift``!                                       |                                               |
-    | - | **Resize** picked axes with the **scroll-wheel**                                    |                                               |
-    |   | (or by pressing the ``+`` and ``-`` keys)                                           |                                               |
-    | - Hold down ``h`` or ``v`` to change horizontal/vertical size                           |                                               |
-    | - Hold down ``control`` to change ratio between colorbar and histogram                  |                                               |
-    |                                                                                         |                                               |
-    | **Snap-To-Grid:**                                                                       |                                               |
-    |                                                                                         |                                               |
-    | - Press keys ``1-9`` to set the grid-spacing for the **"snap-to-grid"** functionality   |                                               |
-    | - Press ``0`` to deactivate **"snap-to-grid"**                                          |                                               |
-    |                                                                                         |                                               |
-    | **Undo, Redo, Save:**                                                                   |                                               |
-    |                                                                                         |                                               |
-    | - Press ``control + z`` to undo the last step                                           |                                               |
-    | - Press ``control + y`` to redo the last undone step                                    |                                               |
-    | - Press ``P`` to print the current layout to the console                                |                                               |
-    |                                                                                         |                                               |
-    +-----------------------------------------------------------------------------------------+-----------------------------------------------+
+
+.. grid:: 1 1 1 2
+
+    .. grid-item-card::
+
+         - Press ``ALT + L``: enter the **Layout Editor** mode
+         - Press ``ALT + L`` again or ``escape`` to exit the **Layout Editor**
+
+         **Pick** and **re-arrange** the axes as you like with the mouse.
+
+         - To pick **multiple axes**, hold down ``shift``!
+         - | **Resize** picked axes with the **scroll-wheel**
+           | (or by pressing the ``+`` and ``-`` keys)
+         - Hold down ``h`` or ``v`` to change horizontal/vertical size
+         - Hold down ``control`` to change ratio between colorbar and histogram
+
+         **Snap-To-Grid:**
+
+         - Press keys ``1-9`` to set the grid-spacing for the **"snap-to-grid"** functionality
+         - Press ``0`` to deactivate **"snap-to-grid"**
+
+         **Undo, Redo, Save:**
+
+         - Press ``control + z`` to undo the last step
+         - Press ``control + y`` to redo the last undone step
+         - Press ``P`` to print the current layout to the console
+
+
+    .. grid-item::
+
+            .. image:: _static/minigifs/layout_editor.gif
 
 
 
diff --git a/docs/api_scalebar.rst b/docs/api_scalebar.rst
index 16146a1ee..c8f54b552 100644
--- a/docs/api_scalebar.rst
+++ b/docs/api_scalebar.rst
@@ -25,19 +25,23 @@ In addition, many style properties of the scalebar can be adjusted to get the lo
 
 
 
-.. table::
-    :widths: 70 30
-    :align: center
-
-    +-----------------------------------+------------------------------------------+
-    | .. code-block:: python            | .. image:: _static/minigifs/scalebar.gif |
-    |   :name: test_add_scalebar        |   :align: center                         |
-    |                                   |                                          |
-    |   from eomaps import Maps         | |img_minsize|                            |
-    |   m = Maps(Maps.CRS.Sinusoidal()) |                                          |
-    |   m.add_feature.preset.ocean()    |                                          |
-    |   s = m.add_scalebar()            |                                          |
-    +-----------------------------------+------------------------------------------+
+.. grid:: 1 1 1 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_scalebar
+
+            from eomaps import Maps
+            m = Maps(Maps.CRS.Sinusoidal())
+            m.add_feature.preset.ocean()
+            s = m.add_scalebar()
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/scalebar.gif
+            :width: 50%
+
 
 .. admonition:: Interacting with the scalebar
 
diff --git a/docs/api_utils.rst b/docs/api_utils.rst
index 63f4d47af..895430371 100644
--- a/docs/api_utils.rst
+++ b/docs/api_utils.rst
@@ -41,20 +41,23 @@ By default, the widgets will show all available layers (except the "all" layer)
 
 
 
-.. table::
-    :widths: 70 30
-    :align: center
-
-    +------------------------------------+-------------------------------------------------+
-    | .. code-block:: python             | .. image:: _static/minigifs/layer_selector.gif  |
-    |   :name: test_add_utils            |    :align: center                               |
-    |                                    |                                                 |
-    |   from eomaps import Maps          |                                                 |
-    |   m = Maps(layer="coastline")      |                                                 |
-    |   m.add_feature.preset.coastline() |                                                 |
-    |                                    |                                                 |
-    |   m2 = m.new_layer(layer="ocean")  |                                                 |
-    |   m2.add_feature.preset.ocean()    |                                                 |
-    |                                    |                                                 |
-    |   s = m.util.layer_selector()      |                                                 |
-    +------------------------------------+-------------------------------------------------+
+.. grid:: 1 1 1 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_add_utils
+
+            from eomaps import Maps
+            m = Maps(layer="coastline")
+            m.add_feature.preset.coastline()
+
+            m2 = m.new_layer(layer="ocean")
+            m2.add_feature.preset.ocean()
+
+            s = m.util.layer_selector()
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/layer_selector.gif
+            :width: 50%
diff --git a/docs/api_vector_data.rst b/docs/api_vector_data.rst
index 15878ef23..0a2756111 100644
--- a/docs/api_vector_data.rst
+++ b/docs/api_vector_data.rst
@@ -56,25 +56,29 @@ Once the ``picker_name`` is specified, pick-callbacks can be attached via:
 
 - ``m.cb.pick[<PICKER NAME>].attach.< CALLBACK >()``
 
-| For example, to highlight the clicked country, you could use:
-
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +----------------------------------------------------------------------------+----------------------------------------------+
-    | .. code-block:: python                                                     | .. image:: _static/minigifs/add_gdf_pick.gif |
-    |     :name: test_pick_gdf                                                   |   :align: center                             |
-    |                                                                            |                                              |
-    |     from eomaps import Maps                                                |                                              |
-    |     m = Maps()                                                             |                                              |
-    |     # get the GeoDataFrame for a given NaturalEarth feature                |                                              |
-    |     gdf = m.add_feature.cultural.admin_0_countries.get_gdf(scale=110)      |                                              |
-    |                                                                            |                                              |
-    |     # pick the shapes of the GeoDataFrame based on a "contains" query      |                                              |
-    |     m.add_gdf(gdf, picker_name="countries", pick_method="contains")        |                                              |
-    |                                                                            |                                              |
-    |     # temporarily highlight the picked geometry                            |                                              |
-    |     m.cb.pick["countries"].attach.highlight_geometry(fc="r", ec="g", lw=2) |                                              |
-    |                                                                            |                                              |
-    +----------------------------------------------------------------------------+----------------------------------------------+
+
+For example, to highlight the clicked country, you could use:
+
+
+.. grid:: 1 1 1 2
+
+    .. grid-item::
+
+         .. code-block:: python
+            :name: test_pick_gdf
+
+            from eomaps import Maps
+            m = Maps()
+            # get the GeoDataFrame for a given NaturalEarth feature
+            gdf = m.add_feature.cultural.admin_0_countries.get_gdf(scale=110)
+
+            # pick the shapes of the GeoDataFrame based on a "contains" query
+            m.add_gdf(gdf, picker_name="countries", pick_method="contains")
+
+            # temporarily highlight the picked geometry
+            m.cb.pick["countries"].attach.highlight_geometry(fc="r", ec="g", lw=2)
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/add_gdf_pick.gif
+            :width: 75%
diff --git a/docs/api_webmaps.rst b/docs/api_webmaps.rst
index b1de53220..be9dc9a16 100644
--- a/docs/api_webmaps.rst
+++ b/docs/api_webmaps.rst
@@ -43,30 +43,31 @@ and ``< LAYER >`` indicates the actual layer-name.
     - most services come either in ``epsg=4326`` or in ``Maps.CRS.GOOGLE_MERCATOR`` projection
 
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +------------------------------------------------------------------------------------------------+-----------------------------------------+
-    | .. code-block:: python                                                                         | .. image:: _static/minigifs/add_wms.png |
-    |                                                                                                |   :align: center                        |
-    |     from eomaps import MapsGrid                                                                |                                         |
-    |     mg = MapsGrid(crs=Maps.CRS.GOOGLE_MERCATOR)                                                | |img_minsize|                           |
-    |     mg.join_limits()                                                                           |                                         |
-    |                                                                                                |                                         |
-    |     mg.m_0_0.add_wms.OpenStreetMap.add_layer.default()                                         |                                         |
-    |     mg.m_0_1.add_wms.OpenStreetMap.add_layer.stamen_toner()                                    |                                         |
-    |                                                                                                |                                         |
-    |     mg.m_1_1.add_wms.S1GBM.add_layer.vv()                                                      |                                         |
-    |                                                                                                |                                         |
-    |     # ... for more advanced                                                                    |                                         |
-    |     layer = mg.m_1_0.add_wms.ISRIC_SoilGrids.nitrogen.add_layer.nitrogen_0_5cm_mean            |                                         |
-    |     layer.set_extent_to_bbox() # set the extent according to the boundingBox                   |                                         |
-    |     layer.info                 # the "info" property provides useful information on the layer  |                                         |
-    |     layer()                    # call the layer to add it to the map                           |                                         |
-    |     layer.add_legend()         # if a legend is provided, you can add it to the map!           |                                         |
-    |                                                                                                |                                         |
-    +------------------------------------------------------------------------------------------------+-----------------------------------------+
+.. grid:: 1 1 1 2
+
+    .. grid-item::
+
+         .. code-block:: python
+
+            from eomaps import MapsGrid
+            mg = MapsGrid(crs=Maps.CRS.GOOGLE_MERCATOR)
+            mg.join_limits()
+
+            mg.m_0_0.add_wms.OpenStreetMap.add_layer.default()
+            mg.m_0_1.add_wms.OpenStreetMap.add_layer.stamen_toner()
+
+            mg.m_1_1.add_wms.S1GBM.add_layer.vv()
+
+            # ... for more advanced
+            layer = mg.m_1_0.add_wms.ISRIC_SoilGrids.nitrogen.add_layer.nitrogen_0_5cm_mean
+            layer.set_extent_to_bbox() # set the extent according to the boundingBox
+            layer.info                 # the "info" property provides useful information on the layer
+            layer()                    # call the layer to add it to the map
+            layer.add_legend()         # if a legend is provided, you can add it to the map!
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/add_wms.png
 
 
 Pre-defined WebMap services
@@ -170,47 +171,50 @@ To pass additional arguments to a WebMap service, simply provide them when when
 To show an example, here's how to fetch multiple timestamps for the UV-index of the Copernicus Airquality service.
 (provided by https://atmosphere.copernicus.eu/)
 
-.. table::
-    :widths: 50 50
-    :align: center
-
-    +-------------------------------------------------------------------------------------+----------------------------------------------+
-    | .. code-block:: python                                                              | .. image:: _static/minigifs/advanced_wms.gif |
-    |                                                                                     |   :align: center                             |
-    |     from eomaps import Maps                                                         |                                              |
-    |     import pandas as pd                                                             | |img_minsize|                                |
-    |                                                                                     |                                              |
-    |     m = Maps(layer="all", figsize=(8, 4))                                           |                                              |
-    |     m.subplots_adjust(left=0.05, right=0.95)                                        |                                              |
-    |     m.all.add_feature.preset.coastline()                                            |                                              |
-    |     m.add_logo()                                                                    |                                              |
-    |                                                                                     |                                              |
-    |     layer = m.add_wms.CAMS.add_layer.composition_uvindex_clearsky                   |                                              |
-    |     timepos = layer.wms_layer.timepositions   # available time-positions            |                                              |
-    |     all_styles = list(layer.wms_layer.styles) # available styles                    |                                              |
-    |                                                                                     |                                              |
-    |     # create a list of timestamps to fetch                                          |                                              |
-    |     start, stop, freq = timepos[1].split(r"/")                                      |                                              |
-    |     times = pd.date_range(start, stop, freq=freq.replace("PT", ""))                 |                                              |
-    |     times = times.strftime("%Y-%m-%dT%H:%M:%SZ")                                    |                                              |
-    |                                                                                     |                                              |
-    |     style = all_styles[0]     # use the first available style                       |                                              |
-    |     for time in times[:6]:                                                          |                                              |
-    |         # call the layer to add it to the map                                       |                                              |
-    |         layer(time=time,                                                            |                                              |
-    |               styles=[style],   # provide a list with 1 entry here                  |                                              |
-    |               layer=time        # put each WebMap on an individual layer            |                                              |
-    |               )                                                                     |                                              |
-    |                                                                                     |                                              |
-    |     layer.add_legend()  # add a legend for the WebMap service                       |                                              |
-    |                                                                                     |                                              |
-    |     # add a "slider" and a "selector" widget                                        |                                              |
-    |     m.util.layer_selector(ncol=3, loc="upper center", fontsize=6, labelspacing=1.3) |                                              |
-    |     m.util.layer_slider()                                                           |                                              |
-    |                                                                                     |                                              |
-    |     # attach a callback to fetch all layers if you press l on the keyboard          |                                              |
-    |     cid = m.all.cb.keypress.attach.fetch_layers(key="l")                            |                                              |
-    |     # fetch all layers so that they are cached and switching layers is fast         |                                              |
-    |     m.fetch_layers()                                                                |                                              |
-    |     m.show_layer(times[0])      # make the first timestamp visible                  |                                              |
-    +-------------------------------------------------------------------------------------+----------------------------------------------+
+
+.. grid:: 1 1 1 2
+
+    .. grid-item::
+
+         .. code-block:: python
+
+            from eomaps import Maps
+            import pandas as pd
+
+            m = Maps(layer="all", figsize=(8, 4))
+            m.subplots_adjust(left=0.05, right=0.95)
+            m.all.add_feature.preset.coastline()
+            m.add_logo()
+
+            layer = m.add_wms.CAMS.add_layer.composition_uvindex_clearsky
+            timepos = layer.wms_layer.timepositions   # available time-positions
+            all_styles = list(layer.wms_layer.styles) # available styles
+
+            # create a list of timestamps to fetch
+            start, stop, freq = timepos[1].split(r"/")
+            times = pd.date_range(start, stop, freq=freq.replace("PT", ""))
+            times = times.strftime("%Y-%m-%dT%H:%M:%SZ")
+
+            style = all_styles[0]     # use the first available style
+            for time in times[:6]:
+                # call the layer to add it to the map
+                layer(time=time,
+                      styles=[style],   # provide a list with 1 entry here
+                      layer=time        # put each WebMap on an individual layer
+                      )
+
+            layer.add_legend()  # add a legend for the WebMap service
+
+            # add a "slider" and a "selector" widget
+            m.util.layer_selector(ncol=3, loc="upper center", fontsize=6, labelspacing=1.3)
+            m.util.layer_slider()
+
+            # attach a callback to fetch all layers if you press l on the keyboard
+            cid = m.all.cb.keypress.attach.fetch_layers(key="l")
+            # fetch all layers so that they are cached and switching layers is fast
+            m.fetch_layers()
+            m.show_layer(times[0])      # make the first timestamp visible
+
+    .. grid-item::
+
+        .. image:: _static/minigifs/advanced_wms.gif
diff --git a/docs/index.rst b/docs/index.rst
index 7644c39c3..70a1c6050 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -99,7 +99,7 @@ EOmaps provides many useful tools to customize your maps.
 
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
     :gutter: 1
 
     .. grid-item-card:: :doc:`notebooks/naturalearth_features`
@@ -165,7 +165,7 @@ Interactivity
 With a few lines of code, you can turn your maps into interactive data-analysis widgets!
 
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
     :gutter: 1
 
     .. grid-item-card:: :doc:`api_companion_widget`
@@ -207,7 +207,7 @@ With a few lines of code, you can turn your maps into interactive data-analysis
 Miscellaneous
 ~~~~~~~~~~~~~
 
-.. grid:: 1 1 2 2
+.. grid:: 1 1 1 2
     :gutter: 1
 
     .. grid-item-card:: :doc:`api_logging`

From 43f225397676f25c3dc033ce02cdd9876ed703aa Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 14 Jan 2024 22:09:28 +0100
Subject: [PATCH 102/240] remove rst_prolog (now responsive grids are used
 instead of tables)

---
 docs/conf.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 3914f2116..36af39f27 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -137,13 +137,3 @@ def setup(app):
     ".ipynb": "myst-nb",
     ".myst": "myst-nb",
 }
-
-# a global substitution used to fix issues with images in tables
-# in the mobile-theme (without a span they get resized to 0. This forces a size
-# of at least 20% of the browser-window size)
-
-rst_prolog = """
-    .. |img_minsize| raw:: html
-
-       <span style="display: inline-block; width: 20vw; height: 0px;"></span>
-    """

From 1d45fee700c3297337cd68ff2ca056d8bdea836b Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 19 Jan 2024 23:09:59 +0100
Subject: [PATCH 103/240] Internal re-structuring of pre-defined callback
 machinery - `<method>Callback` classes have been re-structured as mixins -
 `_cb` and `_cb_list` are removed from `_CallbackContainer` _ `_attach`
 classes now use mixins to define available callbacks   (all callbacks are
 decorated in the respective container so that    calling them will attach
 them as callbacks to the map)

---
 eomaps/callbacks.py                           | 540 ++++++++----------
 eomaps/cb_container.py                        | 225 +++++---
 eomaps/eomaps.py                              |   8 +-
 eomaps/qtcompanion/widgets/click_callbacks.py |   8 +-
 tests/test_basic_functions.py                 |   6 +-
 tests/test_callbacks.py                       |   3 +-
 6 files changed, 420 insertions(+), 370 deletions(-)

diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 2468978a2..1079ad8cb 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -23,66 +23,7 @@ def _removesuffix(s, suffix):
         return s[:]
 
 
-class _ClickCallbacks(object):
-    """
-    A collection of callback-functions.
-
-    to attach a callback, use:
-        >>> cid = m.cb.click.attach.annotate(**kwargs)
-        or
-        >>> cid = m.cb.pick.attach.annotate(**kwargs)
-
-    to remove an already attached callback, use:
-        >>> m.cb.click.remove(cid)
-        or
-        >>> m.cb.pick.remove(cid)
-
-
-    you can also define custom callback functions as follows:
-
-        >>> def some_callback(self, **kwargs):
-        >>>     print("hello world")
-        >>>     print("the position of the clicked pixel", kwargs["pos"])
-        >>>     print("the data-index of the clicked pixel", kwargs["ID"])
-        >>>     print("data-value of the clicked pixel", kwargs["val"])
-    and attach them via:
-        >>> cid = m.cb.click.attach(some_callback)
-        or
-        >>> cid = m.cb.click.attach(some_callback)
-    (... and remove them in the same way as pre-defined callbacks)
-    """
-
-    # the naming-convention of the functions is as follows:
-    #
-    # _<NAME>_cleanup : a function that is executed if the callback
-    #                   is removed from the plot
-    #
-
-    # ID : any
-    #     The index-value of the pixel in the data.
-    # pos : tuple
-    #     A tuple of the position of the pixel in plot-coordinates.
-    #     (ONLY relevant if ID is NOT provided!)
-    # val : int or float
-    #     The parameter-value of the pixel.
-    # ind : int
-    #     The index of the clicked pixel
-    #     (ONLY relevant if ID is NOT provided!)
-
-    # this list determines the order at which callbacks are executed!
-    # (custom callbacks are always added to the end)
-    _cb_list = [
-        "get_values",
-        "load",
-        "print_to_console",
-        "annotate",
-        "mark",
-        "plot",
-        "peek_layer",
-        "clear_annotations",
-        "clear_markers",
-    ]
-
+class _CallbacksBase:
     def __init__(self, m, temp_artists):
         self.m = m
 
@@ -115,64 +56,6 @@ def _fmt(x, **kwargs):
         except Exception:
             return str(x)
 
-    def print_to_console(
-        self,
-        pos_precision=4,
-        val_precision=4,
-        text=None,
-        show_all_values=True,
-        **kwargs,
-    ):
-        """
-        Print details on the clicked pixel to the console.
-
-        Parameters
-        ----------
-
-        pos_precision : int
-            The floating-point precision of the coordinates.
-            The default is 4.
-        val_precision : int
-            The floating-point precision of the parameter-values (only used if
-            "val_fmt=None"). The default is 4.
-        text : callable or str, optional
-            if str: the string to print
-            if callable: A function that returns the string that should be
-            printed in the annotation with the following call-signature:
-
-                >>> def text(m, ID, val, pos, ind):
-                >>>     # m   ... the Maps object
-                >>>     # ID  ... the ID in the dataframe
-                >>>     # pos ... the position
-                >>>     # val ... the value
-                >>>     # ind ... the index
-                >>>
-                >>>     return "the string to print"
-
-            The default is None.
-        show_all_values : bool, optional
-            If True, show all values and coordinates of picked points.
-            If False, only (min...max) values are shown if multiple datapoints are
-            picked. The default is True.
-        """
-        ID, pos, val, ind, picker_name, val_color = self._popargs(kwargs)
-
-        printstr = self._get_annotation_text(
-            ID=ID,
-            pos=pos,
-            val=val,
-            ind=ind,
-            pos_precision=pos_precision,
-            val_precision=val_precision,
-            text=text,
-            show_all_values=show_all_values,
-        )
-
-        if text is None:
-            print("\n# ---------------\n" + printstr)
-        else:
-            print(printstr)
-
     def _get_annotation_text(
         self,
         ID=None,
@@ -331,6 +214,70 @@ def _get_annotation_text(
 
         return printstr
 
+
+class _MoveClickPickCallbacks(_CallbacksBase):
+    # callbacks usable with move (and also click and pick)
+    def __init__(self, m, temp_artists):
+        _CallbacksBase.__init__(self, m, temp_artists)
+
+    def print_to_console(
+        self,
+        pos_precision=4,
+        val_precision=4,
+        text=None,
+        show_all_values=True,
+        **kwargs,
+    ):
+        """
+        Print details on the clicked pixel to the console.
+
+        Parameters
+        ----------
+
+        pos_precision : int
+            The floating-point precision of the coordinates.
+            The default is 4.
+        val_precision : int
+            The floating-point precision of the parameter-values (only used if
+            "val_fmt=None"). The default is 4.
+        text : callable or str, optional
+            if str: the string to print
+            if callable: A function that returns the string that should be
+            printed in the annotation with the following call-signature:
+
+                >>> def text(m, ID, val, pos, ind):
+                >>>     # m   ... the Maps object
+                >>>     # ID  ... the ID in the dataframe
+                >>>     # pos ... the position
+                >>>     # val ... the value
+                >>>     # ind ... the index
+                >>>
+                >>>     return "the string to print"
+
+            The default is None.
+        show_all_values : bool, optional
+            If True, show all values and coordinates of picked points.
+            If False, only (min...max) values are shown if multiple datapoints are
+            picked. The default is True.
+        """
+        ID, pos, val, ind, picker_name, val_color = self._popargs(kwargs)
+
+        printstr = self._get_annotation_text(
+            ID=ID,
+            pos=pos,
+            val=val,
+            ind=ind,
+            pos_precision=pos_precision,
+            val_precision=val_precision,
+            text=text,
+            show_all_values=show_all_values,
+        )
+
+        if text is None:
+            print("\n# ---------------\n" + printstr)
+        else:
+            print(printstr)
+
     def annotate(
         self,
         pos_precision=4,
@@ -502,47 +449,6 @@ def annotate(
 
             return annotation
 
-    def clear_annotations(self, **kwargs):
-        """Remove all temporary and permanent annotations from the plot."""
-        if hasattr(self, "permanent_annotations"):
-            while len(self.permanent_annotations) > 0:
-                ann = self.permanent_annotations.pop(0)
-                self.m.BM.remove_artist(ann)
-                ann.remove()
-
-    # def _annotate_cleanup(self):
-    #     self.clear_annotations()
-
-    def get_values(self, **kwargs):
-        """
-        Successively collect return-values in a dict.
-
-        The dict is accessible via `m.cb.[click/pick].get.picked_vals`
-
-        The structure of the picked_vals dict is as follows:
-        (lists are appended as you click on more pixels)
-
-            >>> dict(
-            >>>     pos=[... center-position tuples in plot_crs ...],
-            >>>     ID=[... the corresponding IDs in the dataframe...],
-            >>>     val=[... the corresponding values ...]
-            >>> )
-
-        removing the callback will also remove the associated value-dictionary!
-        """
-        ID, pos, val, ind, picker_name, val_color = self._popargs(kwargs)
-
-        if not hasattr(self, "picked_vals"):
-            self.picked_vals = dict()
-
-        for key, val in zip(["pos", "ID", "val"], [pos, ID, val]):
-            self.picked_vals.setdefault(key, []).append(val)
-
-    def _get_values_cleanup(self):
-        # cleanup method for get_values callback
-        if hasattr(self, "picked_vals"):
-            del self.picked_vals
-
     def mark(
         self,
         radius=None,
@@ -754,57 +660,6 @@ def mark(
 
         return marker
 
-    def clear_markers(self, **kwargs):
-        """Remove all temporary and permanent annotations from the plot."""
-        if hasattr(self, "permanent_markers"):
-            while len(self.permanent_markers) > 0:
-                marker = self.permanent_markers.pop(0)
-                self.m.BM.remove_artist(marker)
-                marker.remove()
-            del self.permanent_markers
-
-    # def _mark_cleanup(self):
-    #     self.clear_markers()
-
-    def _get_clip_path(self, x, y, xy_crs, radius, radius_crs, shape, n=100):
-        shp = self.m.set_shape._get(shape)
-
-        if shape == "ellipses":
-            shp_pts = shp._get_ellipse_points(
-                x=np.atleast_1d(x),
-                y=np.atleast_1d(y),
-                crs=xy_crs,
-                radius=radius,
-                radius_crs=radius_crs,
-                n=n,
-            )
-            bnd_verts = np.stack(shp_pts[:2], axis=2)[0]
-
-        elif shape == "rectangles":
-            shp_pts = shp._get_rectangle_verts(
-                x=np.atleast_1d(x),
-                y=np.atleast_1d(y),
-                crs=xy_crs,
-                radius=radius,
-                radius_crs=radius_crs,
-                n=n,
-            )
-            bnd_verts = shp_pts[0][0]
-
-        elif shape == "geod_circles":
-            shp_pts = shp._get_geod_circle_points(
-                x=np.atleast_1d(x),
-                y=np.atleast_1d(y),
-                crs=xy_crs,
-                radius=radius,
-                # radius_crs=radius_crs,
-                n=n,
-            )
-            bnd_verts = np.stack(shp_pts[:2], axis=2).squeeze()
-        from matplotlib.path import Path
-
-        return Path(bnd_verts)
-
     def peek_layer(
         self, layer="1", how=(0.4, 0.4), alpha=1, shape="rectangular", **kwargs
     ):
@@ -1028,51 +883,144 @@ def doit():
             )
         )
 
-    def load(
-        self, database=None, load_method="load_fit", load_multiple=False, **kwargs
-    ):
+
+class _ClickCallbacks(_CallbacksBase):
+    """
+    A collection of callback-functions.
+
+    to attach a callback, use:
+        >>> cid = m.cb.click.attach.annotate(**kwargs)
+        or
+        >>> cid = m.cb.pick.attach.annotate(**kwargs)
+
+    to remove an already attached callback, use:
+        >>> m.cb.click.remove(cid)
+        or
+        >>> m.cb.pick.remove(cid)
+
+
+    you can also define custom callback functions as follows:
+
+        >>> def some_callback(self, **kwargs):
+        >>>     print("hello world")
+        >>>     print("the position of the clicked pixel", kwargs["pos"])
+        >>>     print("the data-index of the clicked pixel", kwargs["ID"])
+        >>>     print("data-value of the clicked pixel", kwargs["val"])
+    and attach them via:
+        >>> cid = m.cb.click.attach(some_callback)
+        or
+        >>> cid = m.cb.click.attach(some_callback)
+    (... and remove them in the same way as pre-defined callbacks)
+    """
+
+    # the naming-convention of the functions is as follows:
+    #
+    # _<NAME>_cleanup : a function that is executed if the callback
+    #                   is removed from the plot
+    #
+
+    # ID : any
+    #     The index-value of the pixel in the data.
+    # pos : tuple
+    #     A tuple of the position of the pixel in plot-coordinates.
+    #     (ONLY relevant if ID is NOT provided!)
+    # val : int or float
+    #     The parameter-value of the pixel.
+    # ind : int
+    #     The index of the clicked pixel
+    #     (ONLY relevant if ID is NOT provided!)
+
+    # this list determines the order at which callbacks are executed!
+    # (custom callbacks are always added to the end)
+
+    def __init__(self, m, temp_artists):
+        _CallbacksBase.__init__(self, m, temp_artists)
+
+    def clear_annotations(self, **kwargs):
+        """Remove all temporary and permanent annotations from the plot."""
+        if hasattr(self, "permanent_annotations"):
+            while len(self.permanent_annotations) > 0:
+                ann = self.permanent_annotations.pop(0)
+                self.m.BM.remove_artist(ann)
+                ann.remove()
+
+    def clear_markers(self, **kwargs):
+        """Remove all temporary and permanent annotations from the plot."""
+        if hasattr(self, "permanent_markers"):
+            while len(self.permanent_markers) > 0:
+                marker = self.permanent_markers.pop(0)
+                self.m.BM.remove_artist(marker)
+                marker.remove()
+            del self.permanent_markers
+
+    def get_values(self, **kwargs):
         """
-        Load objects from a given database using the ID of the picked pixel.
+        Successively collect return-values in a dict.
 
-        The returned object(s) are accessible via `m.cb.pick.get.picked_object`.
+        The dict is accessible via `m.cb.[click/pick].get.picked_vals`
 
-        Parameters
-        ----------
-        database : any
-            The database object to use for loading the object
-        load_method : str or callable
-            If str: The name of the method to use for loading objects from the provided
-                    database (the call-signature used is `database.load_method(ID)`)
-            If callable: A callable that will be executed on the database with the
-                         following call-signature: `load_method(database, ID)`
-        load_multiple : bool
-            True: A single-object is returned, replacing `m.cb.picked_object` on each pick.
-            False: A list of objects is returned that is extended with each pick.
+        The structure of the picked_vals dict is as follows:
+        (lists are appended as you click on more pixels)
+
+            >>> dict(
+            >>>     pos=[... center-position tuples in plot_crs ...],
+            >>>     ID=[... the corresponding IDs in the dataframe...],
+            >>>     val=[... the corresponding values ...]
+            >>> )
+
+        removing the callback will also remove the associated value-dictionary!
         """
         ID, pos, val, ind, picker_name, val_color = self._popargs(kwargs)
-        assert database is not None, "you must provide a database object!"
-        try:
-            if isinstance(load_method, str):
-                assert hasattr(
-                    database, load_method
-                ), "The provided database has no method '{load_method}'"
-                pick = getattr(database, load_method)(ID)
-            elif callable(load_method):
-                pick = load_method(database, ID)
-            else:
-                raise TypeError("load_method must be a string or a callable!")
-        except Exception:
-            _log.error(
-                f"EOmaps: Unable to load object with ID:  '{ID}' from {database}"
+
+        if not hasattr(self, "picked_vals"):
+            self.picked_vals = dict()
+
+        for key, val in zip(["pos", "ID", "val"], [pos, ID, val]):
+            self.picked_vals.setdefault(key, []).append(val)
+
+    def _get_values_cleanup(self):
+        # cleanup method for get_values callback
+        if hasattr(self, "picked_vals"):
+            del self.picked_vals
+
+    def _get_clip_path(self, x, y, xy_crs, radius, radius_crs, shape, n=100):
+        shp = self.m.set_shape._get(shape)
+
+        if shape == "ellipses":
+            shp_pts = shp._get_ellipse_points(
+                x=np.atleast_1d(x),
+                y=np.atleast_1d(y),
+                crs=xy_crs,
+                radius=radius,
+                radius_crs=radius_crs,
+                n=n,
             )
-        if load_multiple is True:
-            self.picked_object = getattr(self, "picked_object", list()) + [pick]
-        else:
-            self.picked_object = pick
+            bnd_verts = np.stack(shp_pts[:2], axis=2)[0]
 
-    def _load_cleanup(self):
-        if hasattr(self, "picked_object"):
-            del self.picked_object
+        elif shape == "rectangles":
+            shp_pts = shp._get_rectangle_verts(
+                x=np.atleast_1d(x),
+                y=np.atleast_1d(y),
+                crs=xy_crs,
+                radius=radius,
+                radius_crs=radius_crs,
+                n=n,
+            )
+            bnd_verts = shp_pts[0][0]
+
+        elif shape == "geod_circles":
+            shp_pts = shp._get_geod_circle_points(
+                x=np.atleast_1d(x),
+                y=np.atleast_1d(y),
+                crs=xy_crs,
+                radius=radius,
+                # radius_crs=radius_crs,
+                n=n,
+            )
+            bnd_verts = np.stack(shp_pts[:2], axis=2).squeeze()
+        from matplotlib.path import Path
+
+        return Path(bnd_verts)
 
     def plot(
         self,
@@ -1159,23 +1107,9 @@ def _plot_cleanup(self):
             del self._pick_l
 
 
-class PickCallbacks(_ClickCallbacks):
-    """A collection of callbacks that are executed if you click on a datapoint."""
-
-    _cb_list = [
-        "get_values",
-        "load",
-        "print_to_console",
-        "annotate",
-        "mark",
-        "plot",
-        "clear_annotations",
-        "clear_markers",
-        "highlight_geometry",
-    ]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+class _PickCallbacks:
+    def __init__(self, m, temp_artists):
+        _CallbacksBase.__init__(self, m, temp_artists)
 
     def highlight_geometry(self, permanent=False, **kwargs):
         """
@@ -1199,33 +1133,69 @@ def highlight_geometry(self, permanent=False, **kwargs):
             else:
                 self.m.add_gdf(geom, permanent=permanent, **kwargs)
 
+    def load(
+        self, database=None, load_method="load_fit", load_multiple=False, **kwargs
+    ):
+        """
+        Load objects from a given database using the ID of the picked pixel.
+
+        The returned object(s) are accessible via `m.cb.pick.get.picked_object`.
+
+        Parameters
+        ----------
+        database : any
+            The database object to use for loading the object
+        load_method : str or callable
+            If str: The name of the method to use for loading objects from the provided
+                    database (the call-signature used is `database.load_method(ID)`)
+            If callable: A callable that will be executed on the database with the
+                         following call-signature: `load_method(database, ID)`
+        load_multiple : bool
+            True: A single-object is returned, replacing `m.cb.picked_object` on each pick.
+            False: A list of objects is returned that is extended with each pick.
+        """
+        ID, pos, val, ind, picker_name, val_color = self._popargs(kwargs)
+        assert database is not None, "you must provide a database object!"
+        try:
+            if isinstance(load_method, str):
+                assert hasattr(
+                    database, load_method
+                ), "The provided database has no method '{load_method}'"
+                pick = getattr(database, load_method)(ID)
+            elif callable(load_method):
+                pick = load_method(database, ID)
+            else:
+                raise TypeError("load_method must be a string or a callable!")
+        except Exception:
+            _log.error(
+                f"EOmaps: Unable to load object with ID:  '{ID}' from {database}"
+            )
+        if load_multiple is True:
+            self.picked_object = getattr(self, "picked_object", list()) + [pick]
+        else:
+            self.picked_object = pick
+
+    def _load_cleanup(self):
+        if hasattr(self, "picked_object"):
+            del self.picked_object
 
-class ClickCallbacks(_ClickCallbacks):
-    """Collection of callbacks that are executed if you click anywhere on the map."""
 
-    _cb_list = [
-        "get_values",
-        "print_to_console",
-        "annotate",
-        "mark",
-        "peek_layer",
-        "clear_annotations",
-        "clear_markers",
-    ]
+class PickCallbacks(_ClickCallbacks, _PickCallbacks, _MoveClickPickCallbacks):
+    """A collection of callbacks that are executed if you click on a datapoint."""
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
 
-class MoveCallbacks(_ClickCallbacks):
-    """Collection of callbacks that are executed on mouse-movement."""
+class ClickCallbacks(_ClickCallbacks, _MoveClickPickCallbacks):
+    """Collection of callbacks that are executed if you click anywhere on the map."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
-    _cb_list = [
-        "print_to_console",
-        "annotate",
-        "mark",
-        "peek_layer",
-    ]
+
+class MoveCallbacks(_MoveClickPickCallbacks):
+    """Collection of callbacks that are executed on mouse-movement."""
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -1234,8 +1204,6 @@ def __init__(self, *args, **kwargs):
 class KeypressCallbacks:
     """Collection of callbacks that are executed if you press a key on the keyboard."""
 
-    _cb_list = ["switch_layer", "fetch_layers", "overlay_layer"]
-
     def __init__(self, m, temp_artists):
         self._temporary_artists = temp_artists
         self._m = m
diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 14a442cdf..13bd3e38b 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -124,12 +124,6 @@ def __init__(self, m, cb_class=None, method="click", parent_container=None):
         else:
             self._temporary_artists = self._parent_container._temporary_artists
 
-        self._cb = cb_class(m, self._temporary_artists)
-        self._cb_list = cb_class._cb_list
-
-        self.attach = self._attach(self)
-        self.get = self._get(self)
-
         self._fwd_cbs = dict()
 
         self._method = method
@@ -196,10 +190,11 @@ def _clear_temporary_artists(self):
             self._m.BM._artists_to_clear.setdefault(self._method, []).append(art)
 
     def _sort_cbs(self, cbs):
+        _cb_list = self._attach._available_callbacks()
         if not cbs:
             return set()
         cbnames = set([i.rsplit("__", 1)[0].rsplit("_", 1)[0] for i in cbs])
-        sortp = self._cb_list + list(set(self._cb_list) ^ cbnames)
+        sortp = _cb_list + list(set(_cb_list) ^ cbnames)
         return sorted(
             list(cbs), key=lambda w: sortp.index(w.rsplit("__", 1)[0].rsplit("_", 1)[0])
         )
@@ -311,6 +306,7 @@ def _execute_cb(self, layer):
 
     @property
     def execute_on_all_layers(self):
+        """Indicator if callbacks of this container are executed on all layers."""
         if self._parent_container is not None:
             return self._parent_container._execute_on_all_layers
 
@@ -370,6 +366,22 @@ def set_execute_during_toolbar_action(self, q):
         """
         self._execute_while_toolbar_active = q
 
+    def _attach_decorator(self, func):
+        def inner(*args, **kwargs):
+            return self._add_callback(callback=func, *args, **kwargs)
+
+        return inner
+
+
+def _apply_decorator_to_all_public_methods(decorator):
+    def decorate(cls):
+        for attr in filter(lambda x: not x.startswith("_"), dir(cls)):
+            if callable(getattr(cls, attr)):
+                setattr(cls, attr, decorator(getattr(cls, attr)))
+        return cls
+
+    return decorate
+
 
 class _ClickContainer(_CallbackContainer):
     """
@@ -395,11 +407,18 @@ def __init__(self, m, cb_cls=None, method="pick", default_button=1, **kwargs):
         # the default button to use when attaching callbacks
         self._default_button = default_button
 
+        self.attach = self._attach(self)
+        self.attach = _apply_decorator_to_all_public_methods(self._attach_decorator)(
+            self.attach
+        )
+
+        self.get = self._get(self)
+
     class _attach:
         """
         Attach custom or pre-defined callbacks to the map.
 
-        Each callback-function takes 2 additional keyword-arguments:
+        Callback-functions accept the following additional keyword-arguments:
 
         double_click : bool
             Indicator if the callback should be executed on double-click (True)
@@ -457,17 +476,8 @@ class _attach:
 
         def __init__(self, parent):
             self._parent = parent
-
-            # attach pre-defined callbacks
-            for cb in self._parent._cb_list:
-                setattr(
-                    self,
-                    cb,
-                    update_wrapper(
-                        partial(self._parent._add_callback, callback=cb),
-                        getattr(self._parent._cb, cb),
-                    ),
-                )
+            self.m = parent._m
+            self._temporary_artists = self._parent._temporary_artists
 
         def __call__(self, f, double_click=False, button=None, modifier=None, **kwargs):
             """
@@ -536,19 +546,24 @@ def __call__(self, f, double_click=False, button=None, modifier=None, **kwargs):
                 **kwargs,
             )
 
+        @classmethod
+        def _available_callbacks(cls):
+            return list(filter(lambda x: not x.startswith("_"), dir(cls)))
+
     class _get:
         """Accessor for objects generated/retrieved by callbacks."""
 
         def __init__(self, parent):
-            self.m = parent._m
-            self.cb = parent._cb
+            self._parent = parent
+            self.m = self._parent._m
 
             self.cbs = dict()
 
         @property
         def picked_object(self):
-            if hasattr(self.cb, "picked_object"):
-                return self.cb.picked_object
+            """Get the most recent picked object."""
+            if hasattr(self._parent.attach, "picked_object"):
+                return self._parent.attach.picked_object
             else:
                 _log.warning(
                     "EOmaps: No picked objects found. Attach "
@@ -557,8 +572,9 @@ def picked_object(self):
 
         @property
         def picked_vals(self):
-            if hasattr(self.cb, "picked_vals"):
-                return self.cb.picked_vals
+            """Get a list of all picked values."""
+            if hasattr(self._parent.attach, "picked_vals"):
+                return self._parent.attach.picked_vals
             else:
                 _log.warning(
                     "EOmaps: No picked values found. Attach "
@@ -567,8 +583,9 @@ def picked_vals(self):
 
         @property
         def permanent_markers(self):
-            if hasattr(self.cb, "permanent_markers"):
-                return self.cb.permanent_markers
+            """Get a list of all permanent markers."""
+            if hasattr(self._parent.attach, "permanent_markers"):
+                return self._parent.attach.permanent_markers
             else:
                 _log.warning(
                     "EOmaps: No permanent markers found. Attach "
@@ -577,8 +594,9 @@ def permanent_markers(self):
 
         @property
         def permanent_annotations(self):
-            if hasattr(self.cb, "permanent_annotations"):
-                return self.cb.permanent_annotations
+            """Get a list of all permanent annotations."""
+            if hasattr(self._parent.attach, "permanent_annotations"):
+                return self._parent.attach.permanent_annotations
             else:
                 _log.warning(
                     "EOmaps: No permanent annotations found. Attach "
@@ -587,6 +605,7 @@ def permanent_annotations(self):
 
         @property
         def attached_callbacks(self):
+            """Get a list of all IDs of attached callbacks."""
             cbs = []
             for ds, dsdict in self.cbs.items():
                 for b, bdict in dsdict.items():
@@ -665,8 +684,8 @@ def remove(self, callback=None):
 
                 # call cleanup methods on removal
                 fname = name.rsplit("_", 1)[0]
-                if hasattr(self._cb, f"_{fname}_cleanup"):
-                    getattr(self._cb, f"_{fname}_cleanup")()
+                if hasattr(self._attach, f"_{fname}_cleanup"):
+                    getattr(self._attach, f"_{fname}_cleanup")(self.attach)
             else:
                 _log.error(f"EOmaps: There is no callback named {callback}")
 
@@ -819,17 +838,36 @@ def _add_callback(
 
         # attach "on_move" callbacks
         movecb_name = None
-        # set on_motion True for "click" callbacks and False otherwise
-        on_motion = kwargs.pop("on_motion", True if self._method == "click" else False)
+
+        on_motion = kwargs.pop("on_motion", None)
+
+        # set on_motion=True as default for "click" callbacks that
+        # are also supported as move callbacks and False otherwise
+        if self._method == "click" and on_motion is None:
+            if hasattr(self._m.cb._click_move._attach, callback.__name__):
+                on_motion = True
+            else:
+                on_motion = False
+        else:
+            on_motion = False
+
         if self._method == "click" and on_motion is True:
-            movecb_name = self._m.cb._click_move._add_callback(
-                *args,
-                callback=callback,
-                double_click=double_click,
-                button=button,
-                modifier=modifier,
-                **kwargs,
-            )
+            # attach associated click+move callback
+            if not hasattr(self._m.cb._click_move._attach, callback.__name__):
+                on_motion = False
+                _log.warning(
+                    f"Using 'on_motion' = True for the '{callback}' callback has no effect!"
+                )
+
+            if on_motion:
+                movecb_name = self._m.cb._click_move._add_callback(
+                    *args,
+                    callback=callback,
+                    double_click=double_click,
+                    button=button,
+                    modifier=modifier,
+                    **kwargs,
+                )
         elif on_motion is True:
             _log.warning(
                 "EOmaps: 'on_motion=True' is only possible for " "'click' callbacks!"
@@ -840,12 +878,12 @@ def _add_callback(
         ), 'the names "pos", "ID", "val" cannot be used as keyword-arguments!'
 
         if isinstance(callback, str):
-            assert hasattr(self._cb, callback), (
-                f"The function '{callback}' does not exist as a pre-defined callback."
+            assert hasattr(self._attach, callback), (
+                f"The function '{callback}' does not exist as a pre-defined {self._method} callback."
                 + " Use one of:\n    - "
-                + "\n    - ".join(self._cb_list)
+                + "\n    - ".join(self._attach._available_callbacks())
             )
-            callback = getattr(self._cb, callback)
+            callback = getattr(self._attach, callback)
 
         if double_click is True:
             btn_key = "double"
@@ -919,6 +957,18 @@ def __init__(self, *args, **kwargs):
 
         self._event = None
 
+    class _attach(_ClickContainer._attach, ClickCallbacks):
+        __doc__ = _ClickContainer._attach.__doc__
+        pass
+
+    class _get(_ClickContainer._get):
+        __doc__ = _ClickContainer._get.__doc__
+        pass
+
+    # to make namespace accessible for sphinx
+    attach = _attach
+    get = _get
+
     def _init_cbs(self):
         if self._m.parent is self._m:
             self._add_click_callback()
@@ -1165,6 +1215,18 @@ def __init__(self, button_down=False, *args, **kwargs):
 
         self._button_down = button_down
 
+    class _attach(_ClickContainer._attach, MoveCallbacks):
+        __doc__ = _ClickContainer._attach.__doc__
+        pass
+
+    class _get(_ClickContainer._get):
+        __doc__ = _ClickContainer._get.__doc__
+        pass
+
+    # to make namespace accessible for sphinx
+    attach = _attach
+    get = _get
+
     def _init_cbs(self):
         if self._m.parent is self._m:
             self._add_move_callback()
@@ -1294,6 +1356,18 @@ def __init__(self, picker_name="default", picker=None, *args, **kwargs):
         else:
             self._picker = picker
 
+    class _attach(_ClickContainer._attach, PickCallbacks):
+        __doc__ = _ClickContainer._attach.__doc__
+        pass
+
+    class _get(_ClickContainer._get):
+        __doc__ = _ClickContainer._get.__doc__
+        pass
+
+    # to make namespace accessible for sphinx
+    attach = _attach
+    get = _get
+
     def __getitem__(self, name):
         name = str(name)
         if name.startswith("_"):
@@ -1658,7 +1732,7 @@ class KeypressContainer(_CallbackContainer):
         Executing the functions will attach the associated callback to the map!
 
     get : accessor for return-objects
-        A container to provide easy-access to the return-values of the callbacks.
+        Accessor for objects generated/retrieved by callbacks.
 
     remove : remove prviously added callbacks from the map
 
@@ -1678,6 +1752,13 @@ def __init__(self, m, cb_cls=None, method="keypress"):
         # remember last pressed key (for use as "sticky_modifier")
         self._modifier = None
 
+        self.attach = self._attach(self)
+        self.attach = _apply_decorator_to_all_public_methods(self._attach_decorator)(
+            self.attach
+        )
+
+        self.get = self._get(self)
+
     def _init_cbs(self):
         if self._m.parent is self._m:
             self._initialize_callbacks()
@@ -1755,7 +1836,7 @@ def _onpress(event):
                 "key_press_event", _onpress
             )
 
-    class _attach:
+    class _attach(KeypressCallbacks):
         """
         Attach custom or pre-defined callbacks on keypress events.
 
@@ -1786,17 +1867,8 @@ class _attach:
 
         def __init__(self, parent):
             self._parent = parent
-
-            # attach pre-defined callbacks
-            for cb in self._parent._cb_list:
-                setattr(
-                    self,
-                    cb,
-                    update_wrapper(
-                        partial(self._parent._add_callback, callback=cb),
-                        getattr(self._parent._cb, cb),
-                    ),
-                )
+            self._m = parent._m
+            self._temporary_artists = self._parent._temporary_artists
 
         def __call__(self, f, key, **kwargs):
             """
@@ -1835,15 +1907,20 @@ def __call__(self, f, key, **kwargs):
 
             return self._parent._add_callback(f, key, **kwargs)
 
+        @classmethod
+        def _available_callbacks(cls):
+            return list(filter(lambda x: not x.startswith("_"), dir(cls)))
+
     class _get:
+        """Accessor for objects generated/retrieved by callbacks."""
+
         def __init__(self, parent):
             self.m = parent._m
-            self.cb = parent._cb
-
             self.cbs = dict()
 
         @property
         def attached_callbacks(self):
+            """Get a list of all IDs of attached callbacks."""
             cbs = []
             for key, cbdict in self.cbs.items():
                 for name, cb in cbdict.items():
@@ -1851,6 +1928,10 @@ def attached_callbacks(self):
 
             return cbs
 
+    # to make namespace accessible for sphinx
+    attach = _attach
+    get = _get
+
     def _parse_cid(self, cid):
         name, rest = cid.split("__", 1)
         layer, key = rest.rsplit("__", 1)
@@ -1881,8 +1962,8 @@ def remove(self, callback=None):
 
                 # call cleanup methods on removal
                 fname = name.rsplit("_", 1)[0]
-                if hasattr(self._cb, f"_{fname}_cleanup"):
-                    getattr(self._cb, f"_{fname}_cleanup")()
+                if hasattr(self._attach, f"_{fname}_cleanup"):
+                    getattr(self._attach, f"_{fname}_cleanup")()
             else:
                 _log.error(f"EOmaps: there is no callback named {callback}")
         else:
@@ -1923,12 +2004,12 @@ def _add_callback(self, callback, key="x", **kwargs):
 
         """
         if isinstance(callback, str):
-            assert hasattr(self._cb, callback), (
+            assert hasattr(self._attach, callback), (
                 f"The function '{callback}' does not exist as a pre-defined callback."
                 + " Use one of:\n    - "
-                + "\n    - ".join(self._cb_list)
+                + "\n    - ".join(self._attach._available_callbacks())
             )
-            callback = getattr(self._cb, callback)
+            callback = getattr(self._attach, callback)
 
         cbdict = self.get.cbs.setdefault(key, dict())
 
@@ -1956,12 +2037,10 @@ class CallbackContainer:
 
     Methods
     -------
-    - **click** : Execute functions when clicking on the map
-
-    - **pick** : Execute functions when you "pick" a pixel on the  map
-      - only available if a dataset has been plotted via `m.plot_map()`
-
-    - **keypress** : Execute functions if you press a key on the keyboard
+    click : Execute functions when clicking on the map
+    move : Execute functions when clicking on the map
+    keypress : Execute functions if you press a key on the keyboard
+    pick : Execute functions that "pick" the closest datapoint(s)
 
     """
 
@@ -1999,7 +2078,7 @@ def __init__(self, m):
         # a move-container that shares temporary artists with the click-container
         self._click_move = MoveContainer(
             m=self._m,
-            cb_cls=ClickCallbacks,
+            cb_cls=MoveCallbacks,
             method="_click_move",
             parent_container=self.click,
             button_down=True,
@@ -2080,7 +2159,7 @@ def add_picker(self, name, artist, picker):
         Note
         ----
         If the name starts with an underscore (e.g. "_MyPicker") then the
-        associated container will be accessible via `m._cb._pick__MyPicker`
+        associated container will be accessible via `m.cb._pick__MyPicker`
         or via `m.cb.pick["_MyPicker"]`. (This is useful to setup pickers that
         are only used internally)
         """
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 4b264e9e1..589a876ed 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -2156,8 +2156,9 @@ def add_marker(
 
         permanent = kwargs.pop("permanent", None)
 
-        # add marker
-        marker = self.cb.click._cb.mark(
+        # call the "mark" callback function to add the marker
+        marker = self.cb.click._attach.mark(
+            self.cb.click.attach,
             ID=ID,
             pos=xy,
             radius=radius,
@@ -2315,7 +2316,8 @@ def add_annotation(
                 usetext = repeat(text)
 
         for x, y, texti, vali, indi, IDi in zip(xy[0], xy[1], usetext, val, ind, ID):
-            ann = self.cb.click._cb.annotate(
+            ann = self.cb.click._attach.annotate(
+                self.cb.click.attach,
                 ID=IDi,
                 pos=(x, y),
                 val=vali,
diff --git a/eomaps/qtcompanion/widgets/click_callbacks.py b/eomaps/qtcompanion/widgets/click_callbacks.py
index ed95891f9..a1dda2aff 100644
--- a/eomaps/qtcompanion/widgets/click_callbacks.py
+++ b/eomaps/qtcompanion/widgets/click_callbacks.py
@@ -333,10 +333,10 @@ def clear_annotations_and_markers(self):
         # (irrespective of the visible layer!)
         for m in (self.m.parent, *self.m.parent._children):
             if m.ax == self.m.ax:
-                m.cb.click._cb.clear_annotations()
-                m.cb.click._cb.clear_markers()
-                m.cb.pick._cb.clear_annotations()
-                m.cb.pick._cb.clear_markers()
+                m.cb.click._attach.clear_annotations(m.cb.click.attach)
+                m.cb.click._attach.clear_markers(m.cb.click.attach)
+                m.cb.pick._attach.clear_annotations(m.cb.pick.attach)
+                m.cb.pick._attach.clear_markers(m.cb.pick.attach)
 
         self.m.BM.update()
 
diff --git a/tests/test_basic_functions.py b/tests/test_basic_functions.py
index 7c71f68ee..623850ae5 100644
--- a/tests/test_basic_functions.py
+++ b/tests/test_basic_functions.py
@@ -264,7 +264,7 @@ def test_add_callbacks(self):
         m.plot_map()
 
         # attach all pick callbacks
-        for n, cb in enumerate(m.cb.pick._cb_list):
+        for n, cb in enumerate(m.cb.pick.attach._available_callbacks()):
             if n == 1:
                 mouse_button = 1
                 double_click = False
@@ -290,7 +290,7 @@ def test_add_callbacks(self):
             self.assertTrue(len(m.cb.pick.get.attached_callbacks) == 0)
 
         # attach all click callbacks
-        for n, cb in enumerate(m.cb.click._cb_list):
+        for n, cb in enumerate(m.cb.click.attach._available_callbacks()):
             if n == 1:
                 mouse_button = 1
                 double_click = False
@@ -318,7 +318,7 @@ def test_add_callbacks(self):
 
         # attach all keypress callbacks
         double_click, mouse_button = True, 1
-        for n, cb in enumerate(m.cb.keypress._cb_list):
+        for n, cb in enumerate(m.cb.keypress.attach._available_callbacks()):
             if n == 1:
                 key = "x"
             if n == 2:
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index 12248d16a..2276b741f 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -586,9 +586,10 @@ def test_clear_annotations(self):
     def test_clear_markers(self):
         # ---------- test as CLICK callback
         m = self.create_basic_map()
-        m.cb.click.attach.mark(permanent=True)
+        cid = m.cb.click.attach.mark(permanent=True)
         self.click_ax_center(m)
         self.assertTrue(len(m.cb.click.get.permanent_markers) == 1)
+        m.cb.click.remove(cid)
 
         cid = m.cb.click.attach.clear_markers()
         self.click_ax_center(m)

From 5b30dd4667e6306a4cfa8c2f40bee40f8cb1c303 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 19 Jan 2024 23:10:25 +0100
Subject: [PATCH 104/240] update docs to properly handle `Maps.cb`

---
 docs/conf.py             | 12 ++++++++++++
 docs/gen_autodoc_file.py | 15 +++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/docs/conf.py b/docs/conf.py
index 36af39f27..5cfd14931 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -48,10 +48,22 @@ def setup(app):
     Maps.add_wms.__name__ = "add_wms"
     Maps.util.__name__ = "util"
     Maps.cb.__name__ = "cb"
+
     Maps.cb.click.__name__ = "click"
+    Maps.cb.click.attach.__name__ = "attach"
+    Maps.cb.click.get.__name__ = "get"
+
     Maps.cb.pick.__name__ = "pick"
+    Maps.cb.pick.attach.__name__ = "attach"
+    Maps.cb.pick.get.__name__ = "get"
+
     Maps.cb.keypress.__name__ = "keypress"
+    Maps.cb.keypress.attach.__name__ = "attach"
+    Maps.cb.keypress.get.__name__ = "get"
+
     Maps.cb.move.__name__ = "move"
+    Maps.cb.move.attach.__name__ = "attach"
+    Maps.cb.move.get.__name__ = "get"
 
 
 # -- Project information
diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index 0587fc1f6..376d154f5 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -70,12 +70,27 @@ def make_feature_toctree_file():
         members.extend(get_members(Maps, key, False))
     for key in ("add_feature", "cb"):
         members.extend(get_members(Maps, key, True))
+    for key in (
+        "cb.click.attach",
+        "cb.pick.attach",
+        "cb.move.attach",
+        "cb.keypress.attach",
+    ):
+        members.extend(get_members(Maps, key, True))
+    for key in (
+        "cb.click.get",
+        "cb.pick.get",
+        "cb.move.get",
+        "cb.keypress.get"
+        ):
+        members.extend(get_members(Maps, key, False))
 
     # create a page that will be used for sphinx-autodoc to create stub-files
     s = ":orphan:\n\n"
     s += get_autosummary(
         "eomaps.eomaps", ["Maps.config", *members], "obj_with_attributes_no_toc"
     )
+
     s += get_autosummary("eomaps.mapsgrid", ["MapsGrid"], "custom-class-template")
     s += get_autosummary("eomaps.colorbar", ["ColorBar"], "custom-class-template")
     s += get_autosummary("eomaps.compass", ["Compass"], "custom-class-template")

From 8820f2a395da71599d308b2c4f4f975633ea7c7f Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 19 Jan 2024 23:11:54 +0100
Subject: [PATCH 105/240] make pre-commit happy

---
 docs/gen_autodoc_file.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index 376d154f5..ad82f7094 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -77,12 +77,7 @@ def make_feature_toctree_file():
         "cb.keypress.attach",
     ):
         members.extend(get_members(Maps, key, True))
-    for key in (
-        "cb.click.get",
-        "cb.pick.get",
-        "cb.move.get",
-        "cb.keypress.get"
-        ):
+    for key in ("cb.click.get", "cb.pick.get", "cb.move.get", "cb.keypress.get"):
         members.extend(get_members(Maps, key, False))
 
     # create a page that will be used for sphinx-autodoc to create stub-files

From 7a1267ab0ef174d1c210a1c7d2351b3221033884 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 19 Jan 2024 23:26:24 +0100
Subject: [PATCH 106/240] use @wraps for callback decorators and fix default
 value for "on_motion"

---
 eomaps/cb_container.py | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 13bd3e38b..283b19c43 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -366,9 +366,22 @@ def set_execute_during_toolbar_action(self, q):
         """
         self._execute_while_toolbar_active = q
 
+    def _try_decorator(self, func):
+        @wraps(func)
+        def inner(*args, **kwargs):
+            try:
+                func(*args, **kwargs)
+            except Exception:
+                _log.error("problem during callback", exc_info=True)
+
+        return inner
+
     def _attach_decorator(self, func):
+        @wraps(func)
         def inner(*args, **kwargs):
-            return self._add_callback(callback=func, *args, **kwargs)
+            return self._add_callback(
+                callback=self._try_decorator(func), *args, **kwargs
+            )
 
         return inner
 
@@ -836,6 +849,7 @@ def _add_callback(
             else:
                 self._init_picker()
 
+        cb_name = callback if isinstance(callback, str) else callback.__name__
         # attach "on_move" callbacks
         movecb_name = None
 
@@ -844,16 +858,16 @@ def _add_callback(
         # set on_motion=True as default for "click" callbacks that
         # are also supported as move callbacks and False otherwise
         if self._method == "click" and on_motion is None:
-            if hasattr(self._m.cb._click_move._attach, callback.__name__):
+            if hasattr(self._m.cb._click_move._attach, cb_name):
                 on_motion = True
             else:
                 on_motion = False
-        else:
+        elif on_motion is None:
             on_motion = False
 
         if self._method == "click" and on_motion is True:
             # attach associated click+move callback
-            if not hasattr(self._m.cb._click_move._attach, callback.__name__):
+            if not hasattr(self._m.cb._click_move._attach, cb_name):
                 on_motion = False
                 _log.warning(
                     f"Using 'on_motion' = True for the '{callback}' callback has no effect!"

From 3e0ee280b282483997a8ef43579c165364c0514c Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 20 Jan 2024 00:36:18 +0100
Subject: [PATCH 107/240] update docs

---
 docs/FAQ.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index db4f46137..fbee4684c 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -400,3 +400,8 @@ Important changes between major versions
     - `m.set_data_specs` -> use ``m.set_data`` instead
     - `m.add_wms.DLR_basemaps...` -> use ``m.add_wms.DLR.basemap...`` instead
     - `m_inset.indicate_inset_extent` -> use ``m_inset.add_extent_indicator`` instead
+
+    There have been substantial changes to the internals of the callback machinery
+
+    - The public ``Maps`` API is unaffected
+    - Changes are primarily in the modules ``cb_container.py`` and ``callbacks.py``

From bc20b14aa2001fbf6a5916d342fd5badc2775c25 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 20 Jan 2024 11:31:40 +0100
Subject: [PATCH 108/240] minor

---
 tests/test_env.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_env.yml b/tests/test_env.yml
index 3bd1897a8..7dee8886f 100644
--- a/tests/test_env.yml
+++ b/tests/test_env.yml
@@ -23,11 +23,11 @@ dependencies:
   - owslib
   - requests
   # --------------for testing
-  - nbformat     # to parse Jupyter Notebooks
   - coveralls
   - pytest
   - pytest-cov
   # --------------for testing the docs
+  # (e.g. parsing .rst code-blocks and Jupyter Notebooks)
   - docutils
   - jupyter
   - nbformat

From 0035e23c8d8d09da5672c4bf63a7a4b369a29d30 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 20 Jan 2024 11:45:27 +0100
Subject: [PATCH 109/240] update env-files

---
 environment.yml    | 22 ++++++++++++++++------
 tests/test_env.yml | 14 ++++++++++----
 2 files changed, 26 insertions(+), 10 deletions(-)

diff --git a/environment.yml b/environment.yml
index cce90f6c8..34e993fdb 100644
--- a/environment.yml
+++ b/environment.yml
@@ -3,15 +3,16 @@ channels:
   - conda-forge
 
 dependencies:
-  - python = 3.11
+  - python >= 3.8
   - numpy
   - scipy
   - matplotlib >=3.4
   - cartopy >=0.20.0
   - descartes
+  # ------------- to handle data-classification
   - mapclassify
+  # ------------- to handle reprojections
   - pyproj
-
   # ------------- to support Dataframes
   - pandas
   # ------------- to support GeoDataFrames
@@ -27,12 +28,21 @@ dependencies:
   - requests
   # --------------to support multiple Qt versions
   - qtpy
-  # --------------for testing
+
+  ### To run the tests
   - coveralls
   - pytest
   - pytest-cov
-  # --------------for building the docs
-  - sphinx-copybutton
-  - sphinx
+  # --------------for testing the docs
+  # (e.g. parsing .rst code-blocks and Jupyter Notebooks)
   - docutils
+  - jupyter
+  - nbformat
+
+  ### To build the docs
+  - docutils
+  - sphinx
+  - sphinx-copybutton
+  - sphinx-design
   - sphinx_rtd_theme
+  - myst-nb
diff --git a/tests/test_env.yml b/tests/test_env.yml
index 7dee8886f..a3d643485 100644
--- a/tests/test_env.yml
+++ b/tests/test_env.yml
@@ -5,14 +5,17 @@ channels:
 dependencies:
   - numpy
   - scipy
-  - pandas
-  - geopandas
   - matplotlib >=3.4
   - cartopy >=0.20.0
   - descartes
+  # ------------- to handle data-classification
   - mapclassify
+  # ------------- to handle reprojections
   - pyproj
-  - pyepsg
+  # ------------- to support Dataframes
+  - pandas
+  # ------------- to support GeoDataFrames
+  - geopandas
   # --------------for data-shading
   - datashader
   # --------------for GeoTIFF and NetCDF files
@@ -22,7 +25,10 @@ dependencies:
   # --------------for WebMaps
   - owslib
   - requests
-  # --------------for testing
+  # --------------to support multiple Qt versions
+  - qtpy
+
+  ### To run the tests
   - coveralls
   - pytest
   - pytest-cov

From 8d599effad2655af1d6092ebf81eb41d5bf94d70 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 20 Jan 2024 12:27:14 +0100
Subject: [PATCH 110/240] update docs

---
 docs/api/reference.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/api/reference.rst b/docs/api/reference.rst
index 7630d9d48..2824254fe 100644
--- a/docs/api/reference.rst
+++ b/docs/api/reference.rst
@@ -43,7 +43,7 @@ Feature objects
 
     .. currentmodule:: eomaps.eomaps
 
-    See :py:meth:`Maps.add_inset_map` on how to add a colorbar to a map!
+    See :py:meth:`Maps.new_inset_map` on how to create a new InsetMap!
 
 
 .. card::

From d4728248710821b9cf3e672bfb5007b4598f8b23 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 20 Jan 2024 12:35:22 +0100
Subject: [PATCH 111/240] update docs

---
 docs/index.rst | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/docs/index.rst b/docs/index.rst
index 70a1c6050..f2436a900 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -239,6 +239,20 @@ Miscellaneous
         Additional functions and properties that might come in handy.
 
 
+API Reference
+~~~~~~~~~~~~~
+
+.. grid:: 1 1 1 2
+    :gutter: 1
+
+    .. grid-item-card:: EOmaps API reference
+        :link: api/reference
+        :link-type: doc
+        :shadow: none
+
+        Detailed information on the API of EOmaps.
+
+
 Examples
 --------
 

From 7c03dcd9765a7ac2082f6bd8c3c883673312b334 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 20 Jan 2024 23:51:45 +0100
Subject: [PATCH 112/240] update docs

---
 docs/index.rst | 31 +++++++++++++++++++------------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index f2436a900..9ccfa0718 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -14,21 +14,35 @@
 
 Here you can find detailed explanations on all the features and functionalities of EOmaps.
 
-.. admonition:: Interested in contributing to EOmaps?
 
-    | Found a bug or got an idea for an interesting feature?
-    | Open an issue on `GitHub <https://github.com/raphaelquast/EOmaps>`_ or head over to the :doc:`contribute` to see how to setup EOmaps for development!
+.. card:: Found a bug or got an idea for an interesting feature?
+
+    .. grid:: 1 1 2 2
+        :gutter: 1
+        :margin: 0
+
+        .. grid-item-card:: :doc:`contribute`
+            :link: contribute
+            :link-type: doc
+
+            Get all information you need to start contributing to EOmaps!
+
+        .. grid-item-card:: GitHub
+            :link: https://github.com/raphaelquast/EOmaps/
+            :link-type: url
+
+            Open an issue or start a discussion on GitHub!
 
 
 
 Getting Started
 ---------------
 
-To get started, have a look at the :doc:`api_basics` section to get to know
-the basic concepts of EOmaps.
+To get started, have a look at the :doc:`installation` instructions and the  :doc:`api_basics` section to learn how to use EOmaps!
 
 In addition, there is also the :doc:`api_companion_widget` GUI that can be used to interactively edit/compare/overlay maps and explore the features and functionalities.
 
+
 Data Visualization
 ------------------
 
@@ -36,7 +50,6 @@ Want to visualize some data? Have a look at the :doc:`api_data_visualization` se
 
 EOmaps provides a variety of plot-shapes so you can select a shape that suits the structure, size and spatial representativeness of your data:
 
-
 .. grid:: 2 2 5 5
     :gutter: 1
 
@@ -94,11 +107,8 @@ EOmaps provides a variety of plot-shapes so you can select a shape that suits th
 Map Features
 ------------
 
-
 EOmaps provides many useful tools to customize your maps.
 
-
-
 .. grid:: 1 1 1 2
     :gutter: 1
 
@@ -164,7 +174,6 @@ Interactivity
 
 With a few lines of code, you can turn your maps into interactive data-analysis widgets!
 
-
 .. grid:: 1 1 1 2
     :gutter: 1
 
@@ -258,10 +267,8 @@ Examples
 
 Make sure to check out the :doc:`Examples <EOmaps_examples>` for an overview of the capabilities (incl. source code)!
 
-
 .. include:: example_galery.rst
 
-
 .. toctree::
     :hidden:
     :maxdepth: 1

From 0e949414dff5698bbb7c5f41d5d66b7d64bfa35e Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 21 Jan 2024 00:05:48 +0100
Subject: [PATCH 113/240] update docs

---
 .../shape_imgs/contour_unfilled_filled.png     | Bin 0 -> 112827 bytes
 docs/index.rst                                 |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 docs/_static/shape_imgs/contour_unfilled_filled.png

diff --git a/docs/_static/shape_imgs/contour_unfilled_filled.png b/docs/_static/shape_imgs/contour_unfilled_filled.png
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literal 0
HcmV?d00001

diff --git a/docs/index.rst b/docs/index.rst
index 9ccfa0718..7d810a0b5 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -79,7 +79,7 @@ EOmaps provides a variety of plot-shapes so you can select a shape that suits th
         :link-type: ref
 
     .. grid-item-card:: Contour
-        :img-bottom: _static/shape_imgs/contour.png
+        :img-bottom: _static/shape_imgs/contour_unfilled_filled.png
         :link: shp_contour
         :link-type: ref
 

From 6d99ddc3028f5d767429b5612481e779d383fe4d Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 21 Jan 2024 00:28:39 +0100
Subject: [PATCH 114/240] Update README.md

update license badge
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5c9b3c39c..a03fbbdaf 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@
 
 | Tests | Package | Documentation | License | Citation |
 |:-:|:-:|:-:|:-:|:-:|
-| [![tests](https://github.com/raphaelquast/EOmaps/actions/workflows/testMaps.yml/badge.svg?branch=master)](https://github.com/raphaelquast/EOmaps/actions/workflows/testMaps.yml)  [![codecov](https://codecov.io/gh/raphaelquast/EOmaps/graph/badge.svg)](https://codecov.io/gh/raphaelquast/EOmaps) | [![pypi](https://img.shields.io/pypi/v/eomaps)](https://pypi.org/project/eomaps/)  [![Conda Version](https://img.shields.io/conda/vn/conda-forge/eomaps.svg)](https://anaconda.org/conda-forge/eomaps) | [![Documentation Status](https://readthedocs.org/projects/eomaps/badge/?version=latest)](https://eomaps.readthedocs.io/en/latest/?badge=latest) |  [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/raphaelquast/EOmaps/blob/master/LICENSE) | [![10.5281/zenodo.6459598](https://zenodo.org/badge/410829039.svg)](https://zenodo.org/badge/latestdoi/410829039) |
+| [![tests](https://github.com/raphaelquast/EOmaps/actions/workflows/testMaps.yml/badge.svg?branch=master)](https://github.com/raphaelquast/EOmaps/actions/workflows/testMaps.yml)  [![codecov](https://codecov.io/gh/raphaelquast/EOmaps/graph/badge.svg)](https://codecov.io/gh/raphaelquast/EOmaps) | [![pypi](https://img.shields.io/pypi/v/eomaps)](https://pypi.org/project/eomaps/)  [![Conda Version](https://img.shields.io/conda/vn/conda-forge/eomaps.svg)](https://anaconda.org/conda-forge/eomaps) | [![Documentation Status](https://readthedocs.org/projects/eomaps/badge/?version=latest)](https://eomaps.readthedocs.io/en/latest/?badge=latest) |  [![License: BSD 3 clause](https://img.shields.io/badge/License-BSD_3_clause-blue.svg)](https://github.com/raphaelquast/EOmaps/blob/master/LICENSE) | [![10.5281/zenodo.6459598](https://zenodo.org/badge/410829039.svg)](https://zenodo.org/badge/latestdoi/410829039) |
 
 <a href="https://www.buymeacoffee.com/raphaelquast" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/yellow_img.png" alt="Buy Me A Coffee" align="right" style="height: 25px !important;" ></a>
 <a href="https://app.gitter.im/#/room/#EOmaps:gitter.im" target="_blank"><img src="https://img.shields.io/gitter/room/raphaelquast/EOmaps?style=social" alt="chat on gitter" align="left" style="height: 20px !important;" ></a>

From ac6c12117fbb0676c31b8a3118ea5664d771b127 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 23 Jan 2024 11:49:37 +0100
Subject: [PATCH 115/240] fix dynamic-shade indicator colorbars make datasets
 visible on all layers

---
 eomaps/colorbar.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 4cf15a436..1be051e9d 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -869,8 +869,11 @@ def _set_data(self):
                 # self._m.coll.add_callback(self._redraw_colorbar)
 
                 def check_data_updated(*args, **kwargs):
-                    # make sure the artist is drawn before checking for new data
-                    self._m.f.draw_artist(self._m.coll)
+                    # make sure the artist is updated before checking for new data
+                    # TODO check if this is really enough to ensure that the coll
+                    # is fully updated (calling coll.draw() is not an option since it
+                    # would result make the collection appear on any layer!)
+                    self._m.coll.changed()
                     dsdata = self._m.coll.get_ds_data()
                     if getattr(self, "_last_ds_data", None) is not None:
                         if not self._last_ds_data.equals(dsdata):

From 04a9f79773ccda522fb84745b10e76b01811352a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 23 Jan 2024 11:53:49 +0100
Subject: [PATCH 116/240] fix vmin/vmax assignment for datashader "count"
 aggregations

---
 eomaps/eomaps.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 589a876ed..c03d1a421 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -2906,6 +2906,7 @@ def _set_vmin_vmax(self, vmin=None, vmax=None):
             else:
                 # set vmin/vmax for aggregations that do NOT represent data values
                 # allow vmin/vmax = None (e.g. autoscaling)
+                self._vmin, self._vmax = vmin, vmax
                 if "count" in aggname:
                     # if the reduction represents a count, don't count empty pixels
                     if vmin and vmin <= 0:
@@ -2914,9 +2915,6 @@ def _set_vmin_vmax(self, vmin=None, vmax=None):
                         )
                         self._vmin = 1
 
-                    if vmax and vmax > 0:
-                        self._vmax = vmax
-
     def plot_map(
         self,
         layer=None,

From bc6229708febad1cafa5a471edcdf60cdced814b Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 24 Jan 2024 10:38:51 +0100
Subject: [PATCH 117/240] fix handling of masked values during vmin/vmax
 evaluation from files

---
 eomaps/eomaps.py                    | 7 +++++--
 eomaps/qtcompanion/widgets/files.py | 8 ++++++--
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index c03d1a421..29b4882cd 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -2869,8 +2869,11 @@ def _calc_vmin_vmax(self, vmin=None, vmax=None):
         return vmin, vmax
 
     def _set_vmin_vmax(self, vmin=None, vmax=None):
-        vmin = self._encode_values(vmin)
-        vmax = self._encode_values(vmax)
+        # don't encode nan-vailes to avoid setting the fill-value as vmin/vmax
+        if vmin is not None:
+            vmin = self._encode_values(vmin)
+        if vmax is not None:
+            vmax = self._encode_values(vmax)
 
         # handle inherited bounds
         if self._inherit_classification is not None:
diff --git a/eomaps/qtcompanion/widgets/files.py b/eomaps/qtcompanion/widgets/files.py
index db5a23fd2..fdba463f7 100644
--- a/eomaps/qtcompanion/widgets/files.py
+++ b/eomaps/qtcompanion/widgets/files.py
@@ -695,8 +695,12 @@ def do_update_vals(self):
         f = self._file_handle
 
         try:
-            vmin = f[self.parameter.text()].min()
-            vmax = f[self.parameter.text()].max()
+
+            vals = f[self.parameter.text()]
+            if hasattr(vals, "_FillValue"):
+                vals = vals.where(vals != vals._FillValue)
+            vmin = vals.min()
+            vmax = vals.max()
 
             self.vmin.setText(str(float(vmin)))
             self.vmax.setText(str(float(vmax)))

From f2400ff953c3c1967f314eb0ca09f5a0aba88690 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 24 Jan 2024 22:55:38 +0100
Subject: [PATCH 118/240] Fix issues with help-popups of the LayerTabBar of the
 companion-widget invalid result from LayerTabBar.event(), 'bool' expected not
 None

---
 eomaps/qtcompanion/widgets/editor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/qtcompanion/widgets/editor.py b/eomaps/qtcompanion/widgets/editor.py
index 9669436e8..20fcc64d4 100644
--- a/eomaps/qtcompanion/widgets/editor.py
+++ b/eomaps/qtcompanion/widgets/editor.py
@@ -711,7 +711,7 @@ def event(self, event):
         # don't show normal tooltips while showhelp is active
         # (they would cause the help-popups to disappear after ~ 1 sec)
         if event.type() == QtCore.QEvent.ToolTip and self.window().showhelp:
-            return
+            return False
 
         return super().event(event)
 

From eb146e908f18b71a83be185903e755bb6cf7fb45 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 27 Jan 2024 00:46:10 +0100
Subject: [PATCH 119/240] push current view to toolbar nav-stack after
 layout-restore (if possible)

---
 eomaps/helpers.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index 5cc21f4de..43823ebee 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -1327,6 +1327,11 @@ def _undo_draggable(self):
         self.m._emit_signal("layoutEditorDeactivated")
 
         self.m.redraw()
+        # try to push the current view to the "home" toolbar button
+        try:
+            self.m.f.canvas.toolbar.push_current()
+        except Exception:
+            pass
 
     def _reset_callbacks(self):
         # disconnect all callbacks of the layout-editor
@@ -1539,6 +1544,12 @@ def apply_layout(self, layout):
 
         self.m.redraw()
 
+        # try to push the current view to the "home" toolbar button
+        try:
+            self.m.f.canvas.toolbar.push_current()
+        except Exception:
+            pass
+
 
 # taken from https://matplotlib.org/stable/tutorials/advanced/blitting.html#class-based-example
 class BlitManager:

From 208bd8453d83591a587fce31b8d244bf1e2f48bb Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 20:47:41 +0100
Subject: [PATCH 120/240] unify treatment of parsing of layer-names - introduce
 helper-mixin LayerParser to collect all relevant methods

---
 eomaps/_webmap.py                             |   6 +-
 eomaps/callbacks.py                           |   8 +-
 eomaps/cb_container.py                        |  19 +-
 eomaps/compass.py                             |   4 +-
 eomaps/eomaps.py                              | 131 ++-------
 eomaps/helpers.py                             | 252 ++++++++++++++----
 eomaps/qtcompanion/widgets/click_callbacks.py |   3 +-
 eomaps/qtcompanion/widgets/editor.py          |  37 ++-
 eomaps/qtcompanion/widgets/layer.py           |  12 +-
 eomaps/utilities.py                           |   4 +-
 tests/test_callbacks.py                       |  10 +-
 11 files changed, 279 insertions(+), 207 deletions(-)

diff --git a/eomaps/_webmap.py b/eomaps/_webmap.py
index 3309cb26f..b341f41e2 100644
--- a/eomaps/_webmap.py
+++ b/eomaps/_webmap.py
@@ -168,7 +168,7 @@ def add_legend(self, style=None, img=None):
             legax.imshow(legend)
 
             # hide the legend if the corresponding layer is not active at the moment
-            if self._layer not in self._m.BM._get_layers_alphas()[0]:
+            if not self._m.BM._layer_visible(self._layer):
                 legax.set_visible(False)
 
             self._m.BM.add_artist(legax, self._layer)
@@ -385,7 +385,7 @@ def __call__(self, layer=None, zorder=0, alpha=1, **kwargs):
             else:
                 self._layer = layer
 
-            if self._layer == "all" or self._layer in m.BM._get_layers_alphas()[0]:
+            if self._layer == "all" or m.BM._layer_visible(self._layer):
                 # add the layer immediately if the layer is already active
                 self._do_add_layer(
                     self._m,
@@ -496,7 +496,7 @@ def __call__(self, layer=None, zorder=0, alpha=1, **kwargs):
             else:
                 self._layer = layer
 
-            if self._layer == "all" or self._layer in m.BM.bg_layer.split("|"):
+            if m.BM._layer_visible(self._layer):
                 # add the layer immediately if the layer is already active
                 self._do_add_layer(
                     m=m,
diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 1079ad8cb..7f653a2c9 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -741,7 +741,7 @@ def peek_layer(
         shape = "ellipses" if shape == "round" else "rectangles"
 
         if not isinstance(layer, str):
-            layer = self.m._get_combined_layer_name(*layer)
+            layer = self.m.BM._get_combined_layer_name(*layer)
 
         # add spines and relevant inset-map layers to the specified peek-layer
         layer = self.m.BM._get_showlayer_name(layer, transparent=True)
@@ -875,7 +875,7 @@ def doit():
 
         self.m.BM._after_restore_actions.append(
             self.m.BM._get_restore_bg_action(
-                "|".join([self.m.BM.bg_layer, layer]),
+                self.m.BM._get_combined_layer_name(self.m.BM.bg_layer, layer),
                 (x0, y0, blitw, blith),
                 alpha=alpha,
                 clip_path=clip_path,
@@ -1307,10 +1307,10 @@ def overlay_layer(self, layer, key="x"):
         """
 
         if isinstance(layer, list):
-            layer = self._m._get_combined_layer_name(*layer)
+            layer = self._m.BM._get_combined_layer_name(*layer)
         elif isinstance(layer, tuple):
             # e.g. (layer-name, layer-transparency)
-            layer = self._m._get_combined_layer_name(layer)
+            layer = self._m.BM._get_combined_layer_name(layer)
 
         # in case the layer is currently on top, remove it
         if not self._m.BM.bg_layer.endswith(f"|{layer}"):
diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 283b19c43..7b6b46c8c 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -284,25 +284,10 @@ def _execute_cb(self, layer):
             Indicator if the callback should be executed on the currently visible
             layer or not.
         """
-        if self.execute_on_all_layers:
+        if self.execute_on_all_layers or layer == "all":
             return True
 
-        visible_layer = self._m.BM.bg_layer
-        if layer == "all":
-            # the all layer is always executed
-            return True
-        elif "|" in visible_layer:
-            if layer == visible_layer:
-                # return true for the multi-layer itself
-                return True
-            else:
-                # return true for layers that are part of the multi-layer
-                # (make sure to strip off transparency assignments, e.g. "layer{}" )
-                return any(
-                    i.strip().split("{")[0] == layer for i in visible_layer.split("|")
-                )
-        else:
-            return layer == visible_layer
+        return self._m.BM._layer_visible(layer)
 
     @property
     def execute_on_all_layers(self):
diff --git a/eomaps/compass.py b/eomaps/compass.py
index 72a8b45af..6343c3416 100644
--- a/eomaps/compass.py
+++ b/eomaps/compass.py
@@ -409,9 +409,7 @@ def _check_still_parented(self):
 
     @property
     def _layer_visible(self):
-        return self.layer == "all" or (
-            self.layer in (*self._m.BM.bg_layer.split("|"), self._m.BM.bg_layer)
-        )
+        return self._m.BM._layer_visible(self.layer)
 
     def _disconnect(self):
         """Disconnect the callbacks."""
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 29b4882cd..10c9bda6b 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -380,7 +380,7 @@ def __init__(
             self._signal_container = None
 
         # make sure the used layer-name is valid
-        layer = self._check_layer_name(layer)
+        layer = BlitManager._check_layer_name(layer)
 
         self._inherit_classification = None
 
@@ -3196,41 +3196,6 @@ def make_dataset_pickable(
         # set _data_plotted to True to trigger updates in the data-manager
         self._data_plotted = True
 
-    @lru_cache()
-    def _get_combined_layer_name(self, *args):
-        try:
-            combnames = []
-            for i in args:
-                if isinstance(i, str):
-                    combnames.append(i)
-                elif isinstance(i, (list, tuple)):
-                    assert (
-                        len(i) == 2
-                        and isinstance(i[0], str)
-                        and i[1] >= 0
-                        and i[1] <= 1
-                    ), (
-                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
-                        "You can provide either a single layer-name as string, a list "
-                        "of layer-names or a list of tuples of the form: "
-                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
-                    )
-
-                    if i[1] < 1:
-                        combnames.append(i[0] + "{" + str(i[1]) + "}")
-                    else:
-                        combnames.append(i[0])
-                else:
-                    raise TypeError(
-                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
-                        "You can provide either a single layer-name as string, a list "
-                        "of layer-names or a list of tuples of the form: "
-                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
-                    )
-            return "|".join(combnames)
-        except Exception:
-            raise TypeError(f"EOmaps: Unable to combine the layer-names {args}")
-
     def show_layer(self, *args, clear=True):
         """
         Show a single layer or (transparently) overlay multiple selected layers.
@@ -3275,36 +3240,26 @@ def show_layer(self, *args, clear=True):
         Maps.util.layer_slider : Add a slider to switch layers to the map.
 
         """
-        name = self._get_combined_layer_name(*args)
-
-        layers = self._get_layers()
-
+        name = self.BM._get_combined_layer_name(*args)
         if not isinstance(name, str):
             _log.info("EOmaps: All layer-names are converted to strings!")
             name = str(name)
 
-        if "|" in name:
-            # take special care of "_" to allow 'private' (e.g. hidden) multi-layers
-            names = [i.strip() for i in name.split("|") if i != "_"]
-        else:
-            names = [name]
-
-        for i in names:
-            # ignore non-existing private layers
-            if i.startswith("__"):
-                continue
-
-            if "{" in i and i.endswith("}"):
-                i = i.split("{")[0]  # strip off transparency assignments
+        # check if all layers exist
+        existing_layers = self._get_layers()
+        layers_to_show, _ = self.BM._parse_multi_layer_str(name)
 
-            if i not in layers:
-                lstr = " - " + "\n - ".join(map(str, layers))
+        # don't check private layer-names
+        layers_to_show = [i for i in layers_to_show if not i.startswith("_")]
+        missing_layers = set(layers_to_show).difference(set(existing_layers))
+        if len(missing_layers) > 0:
+            lstr = " - " + "\n - ".join(map(str, existing_layers))
 
-                _log.error(
-                    f"EOmaps: The layer '{i}' does not exist...\n"
-                    + f"Use one of: \n{lstr}"
-                )
-                return
+            _log.error(
+                f"EOmaps: The layers {missing_layers} do not exist...\n"
+                + f"Use one of: \n{lstr}"
+            )
+            return
 
         # invoke the bg_layer setter of the blit-manager
         self.BM.bg_layer = name
@@ -3406,14 +3361,16 @@ def snapshot(self, *layer, transparent=False, clear=False):
                 self._indicate_companion_map(False)
 
                 if layer is not None:
-                    layer = self._get_combined_layer_name(*layer)
+                    layer = self.BM._get_combined_layer_name(*layer)
 
                 # add the figure background patch as the bottom layer
                 initial_layer = self.BM.bg_layer
 
                 if transparent is False:
                     showlayer_name = self.BM._get_showlayer_name(layer=layer)
-                    layer_with_bg = "|".join(["__BG__", showlayer_name])
+                    layer_with_bg = self.BM._get_combined_layer_name(
+                        "__BG__", showlayer_name
+                    )
                     self.show_layer(layer_with_bg)
                     sn = self._get_snapshot()
                     # restore the previous layer
@@ -3505,7 +3462,9 @@ def savefig(self, *args, refetch_wms=False, rasterize_data=True, **kwargs):
             transparent = kwargs.get("transparent", False)
             if transparent is False:
                 showlayer_name = self.BM._get_showlayer_name(initial_layer)
-                layer_with_bg = "|".join(["__BG__", showlayer_name])
+                layer_with_bg = self.BM._get_combined_layer_name(
+                    "__BG__", showlayer_name
+                )
                 self.show_layer(layer_with_bg)
 
             dpi = kwargs.get("dpi", None)
@@ -3522,11 +3481,10 @@ def savefig(self, *args, refetch_wms=False, rasterize_data=True, **kwargs):
                 self._update_shade_axis_size(dpi=dpi)
 
             # get all layer names that should be drawn
-            savelayers, alphas = self.BM._get_layers_alphas(
-                self.BM._get_showlayer_name(
-                    self._get_combined_layer_name(self.BM.bg_layer)
-                )
+            savelayers, alphas = self.BM._parse_multi_layer_str(
+                self.BM._get_showlayer_name(self.BM.bg_layer)
             )
+
             # make sure inset-maps are drawn on top of normal maps
             savelayers.sort(key=lambda x: x.startswith("__inset_"))
 
@@ -3938,41 +3896,6 @@ def cleanup(self):
                 exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
             )
 
-    def _check_layer_name(self, layer):
-        if not isinstance(layer, str):
-            _log.info("EOmaps: All layer-names are converted to strings!")
-            layer = str(layer)
-
-        if layer.startswith("__") and not layer.startswith("__inset_"):
-            raise TypeError(
-                "EOmaps: Layer-names starting with '__' are reserved "
-                "for internal use and cannot be used as Maps-layer-names!"
-            )
-
-        reserved_symbs = {
-            # "|": (
-            #     "It is used as a separation-character to combine multiple "
-            #     "layers (e.g. m.show_layer('A|B') will overlay the layer 'B' "
-            #     "on top of 'A'."
-            # ),
-            "{": (
-                "It is used to specify transparency when combining multiple "
-                "layers (e.g. m.show_layer('A|B{0.5}') will overlay the layer "
-                "'B' with 50% transparency on top of the layer 'A'."
-            ),
-        }
-
-        reserved_symbs["}"] = reserved_symbs["{"]
-
-        for symb, explanation in reserved_symbs.items():
-            if symb in layer:
-                raise TypeError(
-                    f"EOmaps: The symbol '{symb}' is not allowed in layer-names!\n"
-                    + explanation
-                )
-
-        return layer
-
     def _save_to_clipboard(self, **kwargs):
         """
         Export the figure to the clipboard.
@@ -5204,7 +5127,9 @@ def _get_layers(self, exclude=None, exclude_private=True):
 
         # add all (possibly still invisible) layers with artists defined
         # (ONLY do this for unique layers... skip multi-layers )
-        layers = layers.union({i for i in self.BM._bg_artists if "|" not in i})
+        layers = layers.union(
+            chain(*(self.BM._parse_multi_layer_str(i)[0] for i in self.BM._bg_artists))
+        )
 
         # exclude private layers
         if exclude_private:
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index 43823ebee..103391b27 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -280,6 +280,153 @@ def inner(*args, **kwargs):
     return decorator
 
 
+class LayerParser:
+    @staticmethod
+    def _parse_single_layer_str(layer):
+        """
+        Parse a single layer-string (with optional transparency assignment).
+
+        Parameters
+        ----------
+        layer : str
+            A layer-string (with transparency provided in curly brackets).
+
+        Returns
+        -------
+        name: str
+            The name of the layer.
+        alpha:
+            The transparency of the layer.
+
+        """
+        # split transparency
+        t_split = layer.find("{")
+        if t_split > 0:
+            name = layer[:t_split]
+            alpha = layer[t_split + 1 :]
+            if not alpha.endswith("}"):
+                raise TypeError(
+                    f"EOmaps: unable to parse multilayer-transparency for '{layer}'"
+                )
+            return name, float(alpha[:-1])
+        else:
+            return layer, 1
+
+    @classmethod
+    def _parse_multi_layer_str(cls, layer=None):
+        layers, alphas = zip(*map(cls._parse_single_layer_str, layer.split("|")))
+        return list(layers), list(alphas)
+
+    @classmethod
+    def _layer_is_subset(cls, layer1, layer2):
+        """
+        Return True if combined layer-name 'layer2' is a subset of 'layer1'.
+
+        - Transparency assignments are stripped off before comparison
+
+        Parameters
+        ----------
+        layer1, layer2 : str
+            The combined layer-names to check.
+
+        Returns
+        -------
+        subset: bool
+            True if layer2 is a subset of layer1, False otherwise
+
+        """
+        # get a list of the currently visible layers
+        layers1, _ = cls._parse_multi_layer_str(layer1)
+        layers2, _ = cls._parse_multi_layer_str(layer2)
+
+        return set(layers1).issubset(layers2)
+
+    @staticmethod
+    def _get_combined_layer_name(*args):
+        """
+        Create a combine layer name from layer-names or tuples (name, transparency).
+
+        Parameters
+        ----------
+        *args : str or tuple
+            The layers to combine. (e.g. `"A"`, `"B"` or `("A", .5)`, `("B", .23)`, ...)
+
+        Returns
+        -------
+        str
+            The combined layer-name.
+
+        """
+        try:
+            combnames = []
+            for i in args:
+                if isinstance(i, str):
+                    combnames.append(i)
+                elif isinstance(i, (list, tuple)):
+                    assert (
+                        len(i) == 2
+                        and isinstance(i[0], str)
+                        and i[1] >= 0
+                        and i[1] <= 1
+                    ), (
+                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
+                        "You can provide either a single layer-name as string, a list "
+                        "of layer-names or a list of tuples of the form: "
+                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
+                    )
+
+                    if i[1] < 1:
+                        combnames.append(i[0] + "{" + str(i[1]) + "}")
+                    else:
+                        combnames.append(i[0])
+                else:
+                    raise TypeError(
+                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
+                        "You can provide either a single layer-name as string, a list "
+                        "of layer-names or a list of tuples of the form: "
+                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
+                    )
+            return "|".join(combnames)
+        except Exception:
+            raise TypeError(f"EOmaps: Unable to combine the layer-names {args}")
+
+    @staticmethod
+    def _check_layer_name(layer):
+        if not isinstance(layer, str):
+            _log.info("EOmaps: All layer-names are converted to strings!")
+            layer = str(layer)
+
+        if layer.startswith("__") and not layer.startswith("__inset_"):
+            raise TypeError(
+                "EOmaps: Layer-names starting with '__' are reserved "
+                "for internal use and cannot be used as Maps-layer-names!"
+            )
+
+        reserved_symbs = {
+            # "|": (
+            #     "It is used as a separation-character to combine multiple "
+            #     "layers (e.g. m.show_layer('A|B') will overlay the layer 'B' "
+            #     "on top of 'A'."
+            # ),
+            "{": (
+                "It is used to specify transparency when combining multiple "
+                "layers (e.g. m.show_layer('A|B{0.5}') will overlay the layer "
+                "'B' with 50% transparency on top of the layer 'A'."
+            ),
+        }
+
+        reserved_symbs["}"] = reserved_symbs["{"]
+
+        for symb, explanation in reserved_symbs.items():
+            if symb in layer:
+                raise TypeError(
+                    f"EOmaps: The symbol '{symb}' is not allowed in layer-names!\n"
+                    + explanation
+                )
+
+        return layer
+
+
 class SearchTree:
     """Class to perform fast nearest-neighbour queries."""
 
@@ -1552,7 +1699,7 @@ def apply_layout(self, layout):
 
 
 # taken from https://matplotlib.org/stable/tutorials/advanced/blitting.html#class-based-example
-class BlitManager:
+class BlitManager(LayerParser):
     """Manager used to schedule draw events, cache backgrounds, etc."""
 
     _snapshot_on_update = False
@@ -1716,27 +1863,28 @@ def _do_on_layer_change(self, layer, new=False):
                 try:
                     f = self._on_layer_change[False].pop(0)
                     f(layer=layer)
-                except Exception as ex:
+                except Exception:
                     _log.error(
                         "EOmaps: Issue while executing a layer-change action",
                         exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
                     )
 
+            sublayers, _ = self._parse_multi_layer_str(layer)
             if new:
-                for l in layer.split("|"):
-                    # individual callables executed if a specific layer is activate
+                for l in sublayers:
+                    # individual callables executed if a specific layer is activated
                     # persistent callbacks
                     for f in reversed(self._on_layer_activation[True].get(layer, [])):
                         f(layer=l)
 
-            for l in layer.split("|"):
+            for l in sublayers:
                 # single-shot callbacks
                 single_shot_funcs = self._on_layer_activation[False].get(l, [])
                 while len(single_shot_funcs) > 0:
                     try:
                         f = single_shot_funcs.pop(0)
                         f(layer=l)
-                    except Exception as ex:
+                    except Exception:
                         _log.error(
                             "EOmaps: Issue while executing a layer-change action",
                             exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
@@ -1797,8 +1945,10 @@ def bg_layer(self, val):
             return
 
         # check if a new layer is activated (or added to a multi-layer)
-        old_layers = self._bg_layer.split("|")
-        new = val != self._bg_layer or any(l not in old_layers for l in val.split("|"))
+        old_layers = set(self._parse_multi_layer_str(self._bg_layer)[0])
+        new_layers = set(self._parse_multi_layer_str(val)[0])
+
+        new = old_layers != new_layers
 
         # make sure we use a "full" update for webagg and ipympl backends
         # (e.g. force full redraw of canvas instead of a diff)
@@ -1806,14 +1956,11 @@ def bg_layer(self, val):
         self._bg_layer = val
 
         # a general callable to be called on every layer change
-
         self._do_on_layer_change(layer=val, new=new)
 
-        layer_names = val.split("|")
-
         # hide all colorbars that are not on the visible layer
         for m in [self._m.parent, *self._m.parent._children]:
-            layer_visible = m.layer in layer_names
+            layer_visible = self._layer_is_subset(val, m.layer)
 
             for cb in m._colorbars:
                 if layer_visible:
@@ -1826,7 +1973,9 @@ def bg_layer(self, val):
         # hide all wms_legends that are not on the visible layer
         if hasattr(self._m.parent, "_wms_legend"):
             for layer, legends in self._m.parent._wms_legend.items():
-                if layer in layer_names:
+                layer_visible = self._layer_is_subset(val, layer)
+
+                if layer_visible:
                     for i in legends:
                         i.set_visible(True)
                 else:
@@ -1908,8 +2057,10 @@ def _refetch_layer(self, layer):
         else:
             # set any background that contains the layer for refetch
             self._layers_to_refetch.add(layer)
+
             for l in self._bg_layers:
-                if layer in l.split("|"):
+                sublayers, _ = self._parse_multi_layer_str(l)
+                if layer in sublayers:
                     self._layers_to_refetch.add(l)
 
     def _bg_artists_sort(self, art):
@@ -1995,34 +2146,45 @@ def get_artists(self, layer):
 
         return artists
 
-    def _get_layers_alphas(self, layer=None):
-        if layer is None:
-            layer = self.bg_layer
+    def _layer_visible(self, layer):
+        """
+        Return True if the layer is currently visible.
 
-        layers, alphas = [], []
-        for l in layer.split("|"):
-            if l.endswith("}") and "{" in l:
-                try:
-                    name, a = l.split("{", maxsplit=1)
-                    a = float(a.replace("}", ""))
+        - layer is considered visible if all sub-layers of a combined layer are visible
+        - transparency assignments do not alter the layer visibility
 
-                    layers.append(name)
-                    alphas.append(a)
-                except Exception:
-                    raise TypeError(
-                        "EOmaps: unable to parse multilayer-transparency " f"for '{l}'"
-                    )
-            else:
-                layers.append(l)
-                alphas.append(1)
-        return layers, alphas
+        Parameters
+        ----------
+        layer : str
+            The combined layer-name to check. (e.g. 'A|B{.4}|C{.3}')
+
+        Returns
+        -------
+        visible: bool
+            True if the layer is currently visible, False otherwise
+
+        """
+        return layer == "all" or self._layer_is_subset(layer, self.bg_layer)
+
+    @property
+    def _get_active_layers_alphas(self):
+        """
+        Return the currently visible layers (and their associated transparencies)
+
+        Returns
+        -------
+        layers, alphas: list of str, list of float
+            2 lists of layer-names and associated global transparencies.
+
+        """
+        return self._parse_multi_layer_str(self.bg_layer)
 
     # cache the last 10 combined backgrounds to avoid re-combining backgrounds
     # on updates of interactive artists
     # cache is automatically cleared on draw if any layer is tagged for re-fetch!
     @lru_cache(10)
     def _combine_bgs(self, layer):
-        layers, alphas = self._get_layers_alphas(layer)
+        layers, alphas = self._parse_multi_layer_str(layer)
 
         # make sure all layers are already fetched
         for l in layers:
@@ -2234,7 +2396,8 @@ def on_draw(self, event):
                 # in case there is a stale (unmanaged) artists and the
                 # stale-artist layer is attempted to be drawn, re-draw the
                 # cached background for the unmanaged-artists layer
-                if self._unmanaged_artists_layer in self._bg_layer.split("|") and any(
+                active_layers, _ = self._get_active_layers_alphas
+                if self._unmanaged_artists_layer in active_layers and any(
                     a.stale for a in self._get_unmanaged_artists()
                 ):
                     self._refetch_layer(self._unmanaged_artists_layer)
@@ -2409,7 +2572,7 @@ def remove_bg_artist(self, art, layer=None, draw=True):
 
                     removed = True
                     layers.append(key)
-                layer = "|".join(layers)
+                layer = self._get_combined_layer_name(*layers)
         else:
             if layer not in self._bg_artists:
                 return
@@ -2493,16 +2656,13 @@ def _draw_animated(self, layers=None, artists=None):
         if renderer is None:
             return
 
-        # make sure to strip-off transparency-assignments (e.g. "layer1{0.5}")
         if layers is None:
-            layers = [self.bg_layer]
-            layers.extend(
-                (l.split("{", maxsplit=1)[0] for l in self.bg_layer.split("|"))
-            )
+            active_layers, _ = self._get_active_layers_alphas
+            layers = [self.bg_layer, *active_layers]
         else:
-            layers = list(chain(*(i.split("|") for i in layers)))
-            for l in layers:
-                layers.append(l.split("{", maxsplit=1)[0])
+            (layers,) = list(
+                chain(*(self._parse_multi_layer_str(l)[0] for l in layers))
+            )
 
         if artists is None:
             artists = []
@@ -2642,7 +2802,7 @@ def _get_showlayer_name(self, layer=None, transparent=False):
 
         # show inset map layers and spines only if they contain at least 1 artist
         inset_Q = False
-        for l in layer.split("|"):
+        for l in self._parse_multi_layer_str(layer)[0]:
             narts = len(self._bg_artists.get("__inset_" + l, []))
 
             if narts > 0:
@@ -2652,7 +2812,7 @@ def _get_showlayer_name(self, layer=None, transparent=False):
         if inset_Q:
             show_layers.append("__inset___SPINES__")
 
-        return self._m._get_combined_layer_name(*show_layers)
+        return self._get_combined_layer_name(*show_layers)
 
     def update(
         self,
diff --git a/eomaps/qtcompanion/widgets/click_callbacks.py b/eomaps/qtcompanion/widgets/click_callbacks.py
index a1dda2aff..3df86e7f4 100644
--- a/eomaps/qtcompanion/widgets/click_callbacks.py
+++ b/eomaps/qtcompanion/widgets/click_callbacks.py
@@ -317,7 +317,7 @@ def showEvent(self, event):
         self.widgetShown.emit()
 
     def identify_pick_map(self):
-        layers, _ = self.m.BM._get_layers_alphas()
+        layers, _ = self.m.BM._get_active_layers_alphas
         layers.extend(("all", "inset_all"))
 
         pickm = list()
@@ -366,6 +366,7 @@ def populate_dropdown(self, *args, **kwargs):
             else:
                 name = f"{i}"
 
+            # indicate map-layer name if combined layer is visible
             if "|" in m.BM.bg_layer:
                 if m.layer != m.BM.bg_layer:
                     name += f" ({m.layer})"
diff --git a/eomaps/qtcompanion/widgets/editor.py b/eomaps/qtcompanion/widgets/editor.py
index 20fcc64d4..396e1dc48 100644
--- a/eomaps/qtcompanion/widgets/editor.py
+++ b/eomaps/qtcompanion/widgets/editor.py
@@ -808,7 +808,7 @@ def repopulate_and_activate_current(self, *args, **kwargs):
     @Slot()
     def tab_moved(self):
         # get currently active layers
-        active_layers, alphas = self.m.BM._get_layers_alphas()
+        active_layers, alphas = self.m.BM._get_active_layers_alphas
 
         # get the name of the layer that was moved
         layer = self.tabText(self.currentIndex())
@@ -866,7 +866,7 @@ def _do_close_tab(self, index):
             return
 
         # get currently active layers
-        active_layers, alphas = self.m.BM._get_layers_alphas()
+        active_layers, alphas = self.m.BM._get_active_layers_alphas
 
         # cleanup the layer and remove any artists etc.
         for m in list(self.m._children):
@@ -891,7 +891,11 @@ def _do_close_tab(self, index):
                 # otherwise switch to the first available layer
                 try:
                     switchlayer = next(
-                        (i for i in self.m.BM._bg_artists if layer not in i.split("|"))
+                        (
+                            i
+                            for i in self.m.BM._bg_artists
+                            if layer not in self.m.BM._parse_multi_layer_str(i)[0]
+                        )
                     )
                     self.m.show_layer(switchlayer)
                 except StopIteration:
@@ -928,7 +932,7 @@ def color_active_tab(self, m=None, layer=None, adjust_order=True):
         multicolor = QtGui.QColor(50, 150, 50)  # QtGui.QColor(0, 128, 0)
 
         # get currently active layers
-        active_layers, alphas = self.m.BM._get_layers_alphas()
+        active_layers, alphas = self.m.BM._get_active_layers_alphas
 
         for i in range(self.count()):
             selected_layer = self.tabText(i)
@@ -971,7 +975,7 @@ def populate_on_layer(self, *args, **kwargs):
         currlayer = self.m.BM.bg_layer
         # only populate if the current layer is not part of the last set of layers
         # (e.g. to allow show/hide of selected layers without removing the tabs)
-        if not set(lastlayer.split("|")).issuperset(set(currlayer.split("|"))):
+        if not self.m.BM._layer_visible(lastlayer):
             self.populate(*args, **kwargs)
             self._last_populated_layer = currlayer
         else:
@@ -1094,9 +1098,11 @@ def tabchanged(self, index):
                 return
 
             # get currently active layers
-            active_layers, alphas = self.m.BM._get_layers_alphas()
+            active_layers, alphas = self.m.BM._get_active_layers_alphas
 
-            for x in (i for i in layer.split("|") if i != "_"):
+            for x in (
+                i for i in self.m.BM._parse_multi_layer_str(layer)[0] if i != "_"
+            ):
                 if x not in active_layers:
                     active_layers.append(x)
                     alphas.append(LayerTransparencySlider._alphas.get(layer, 1))
@@ -1371,19 +1377,12 @@ def _get_artist_layout(self, a, layer):
     @Slot()
     def populate_on_layer(self, *args, **kwargs):
         lastlayer = getattr(self, "_last_populated_layer", "")
-        currlayer = self.m.BM.bg_layer
-
-        # ignore global layer transparencies (no need to re-populate if global)
-        # transparency changes.
-        # NOTE: This is necessary to avoid recursions for multi-layers!
-        last_layers = set(self.m.BM._get_layers_alphas(lastlayer)[0])
-        curr_layers = set(self.m.BM._get_layers_alphas(currlayer)[0])
 
         # only populate if the current layer is not part of the last set of layers
         # (e.g. to allow show/hide of selected layers without removing the tabs)
-        if not last_layers.issuperset(curr_layers):
+        if not self.m.BM._layer_visible(lastlayer):
+            self._last_populated_layer = self.m.BM.bg_layer
             self.populate(*args, **kwargs)
-            self._last_populated_layer = currlayer
         else:
             # TODO check why adjusting the tab-order causes recursions if multiple
             # layers are selected (and the transparency of a sub-layer is changed)
@@ -1413,7 +1412,7 @@ def populate(self, *args, **kwargs):
 
             # if more than max_n_layers layers are available, show only active tabs to
             # avoid performance issues when too many tabs are created
-            alllayers = [i for i in self.m.BM._bg_layer.split("|") if i in alllayers]
+            alllayers = self.m.BM._get_active_layers_alphas[0]
             for i in range(self.count(), -1, -1):
                 self.removeTab(i)
         else:
@@ -1456,7 +1455,7 @@ def populate(self, *args, **kwargs):
         tabbar.set_current_tab_by_name(self._current_tab_name)
 
     def get_layer_alpha(self, layer):
-        layers, alphas = self.m.BM._get_layers_alphas()
+        layers, alphas = self.m.BM._get_active_layers_alphas
         if layer in layers:
             idx = layers.index(layer)
             alpha = alphas[idx]
@@ -1686,7 +1685,7 @@ def cb():
 
     @Slot()
     def set_layer_alpha(self, layer, alpha):
-        layers, alphas = self.m.BM._get_layers_alphas()
+        layers, alphas = self.m.BM._get_active_layers_alphas
         if layer in layers:
             idx = layers.index(layer)
             alphas[idx] = alpha
diff --git a/eomaps/qtcompanion/widgets/layer.py b/eomaps/qtcompanion/widgets/layer.py
index 74c6da6f1..1cdc019ed 100644
--- a/eomaps/qtcompanion/widgets/layer.py
+++ b/eomaps/qtcompanion/widgets/layer.py
@@ -121,7 +121,7 @@ def __init__(self, *args, m=None, max_length=100, **kwargs):
         self.setTextInteractionFlags(Qt.NoTextInteraction)
 
     def get_text(self):
-        layers, alphas = self.m.BM._get_layers_alphas()
+        layers, alphas = self.m.BM._get_active_layers_alphas
 
         prefix = "&nbsp;&nbsp;&nbsp;&nbsp;" "<font color=gray>"
         suffix = "<\font>"
@@ -269,7 +269,7 @@ def get_uselayer(self):
         uselayer = "???"
 
         if len(active_layers) > 1:
-            uselayer = "|".join(active_layers)
+            uselayer = self.m.BM._get_combined_layer_name(*active_layers)
         elif len(active_layers) == 1:
             uselayer = active_layers[0]
 
@@ -331,7 +331,9 @@ def actionClicked(self):
         modifiers = QtWidgets.QApplication.keyboardModifiers()
         actionwidget = action.defaultWidget()
 
-        checked_layers = [l for l in self.m.BM.bg_layer.split("|") if l != "_"]
+        active_layers = self.m.BM._get_active_layers_alphas
+
+        checked_layers = [l for l in active_layers if l != "_"]
         selected_layer = action.data()
         selected_layers = [l for l in action.data().split("|") if l != "_"]
 
@@ -362,7 +364,7 @@ def actionClicked(self):
 
             uselayer = "???"
             if len(checked_layers) > 1:
-                uselayer = "|".join(checked_layers)
+                uselayer = self.m.BM._get_combined_layer_name(*checked_layers)
             elif len(checked_layers) == 1:
                 uselayer = checked_layers[0]
 
@@ -374,7 +376,7 @@ def actionClicked(self):
 
     def update_checkstatus(self):
         currlayer = str(self.m.BM.bg_layer)
-        layers, alphas = self.m.BM._get_layers_alphas(currlayer)
+        layers, alphas = self.m.BM._get_active_layers_alphas
         if "|" in currlayer:
             active_layers = [i for i in layers if not i.startswith("_")]
             active_layers.append(currlayer)
diff --git a/eomaps/utilities.py b/eomaps/utilities.py
index bc4d1564c..36cbc65ce 100644
--- a/eomaps/utilities.py
+++ b/eomaps/utilities.py
@@ -256,7 +256,7 @@ def __init__(
             uselayers = []
             for l in layers:
                 if not isinstance(l, str):
-                    uselayers.append(m._get_combined_layer_name(*l))
+                    uselayers.append(m.BM._get_combined_layer_name(*l))
                 else:
                     uselayers.append(l)
             layers = uselayers
@@ -448,7 +448,7 @@ def __init__(
             uselayers = []
             for l in layers:
                 if not isinstance(l, str):
-                    uselayers.append(m._get_combined_layer_name(*l))
+                    uselayers.append(m.BM._get_combined_layer_name(*l))
                 else:
                     uselayers.append(l)
             layers = uselayers
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index 2276b741f..1f9613251 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -669,7 +669,7 @@ def test_overlay_layer(self):
 
         key_press_event(m.f.canvas, "0")
         key_release_event(m.f.canvas, "0")
-        self.assertTrue(m.BM._bg_layer == m._get_combined_layer_name(m.layer, "A"))
+        self.assertTrue(m.BM._bg_layer == m.BM._get_combined_layer_name(m.layer, "A"))
         key_press_event(m.f.canvas, "0")
         key_release_event(m.f.canvas, "0")
         self.assertTrue(m.BM._bg_layer == m.layer)
@@ -677,7 +677,7 @@ def test_overlay_layer(self):
         key_press_event(m.f.canvas, "1")
         key_release_event(m.f.canvas, "1")
         self.assertTrue(
-            m.BM._bg_layer == m._get_combined_layer_name(m.layer, ("B", 0.5))
+            m.BM._bg_layer == m.BM._get_combined_layer_name(m.layer, ("B", 0.5))
         )
         key_press_event(m.f.canvas, "1")
         key_release_event(m.f.canvas, "1")
@@ -686,7 +686,7 @@ def test_overlay_layer(self):
         key_press_event(m.f.canvas, "2")
         key_release_event(m.f.canvas, "2")
         self.assertTrue(
-            m.BM._bg_layer == m._get_combined_layer_name(m.layer, "A", ("B", 0.5))
+            m.BM._bg_layer == m.BM._get_combined_layer_name(m.layer, "A", ("B", 0.5))
         )
         key_press_event(m.f.canvas, "2")
         key_release_event(m.f.canvas, "2")
@@ -724,7 +724,9 @@ def test_switch_layer(self):
         # now the 3rd callback should trigger
         key_press_event(m.f.canvas, "3")
         key_release_event(m.f.canvas, "3")
-        self.assertTrue(m.BM._bg_layer == m._get_combined_layer_name("2", ("3", 0.5)))
+        self.assertTrue(
+            m.BM._bg_layer == m.BM._get_combined_layer_name("2", ("3", 0.5))
+        )
 
         m.all.cb.keypress.remove(cid0)
         m.all.cb.keypress.remove(cid1)

From dbb0f3a79628d041d1bb39e854da4229b946ef2b Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 21:13:48 +0100
Subject: [PATCH 121/240] fix typo

---
 eomaps/helpers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index 103391b27..d9a0b8c67 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -1457,7 +1457,7 @@ def _undo_draggable(self):
         self.modifier_pressed = False
 
         # show all colorbars that are on the visible layer
-        active_layers = self.m.BM._get_layers_alphas()[0]
+        active_layers = self.m.BM._get_active_layers_alphas[0]
         for cb in self.cbs:
             if cb._m.layer in active_layers:
                 cb.set_visible(True)

From 6d912c3423c2b5500348f6f68948e4d8643101f5 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 22:14:36 +0100
Subject: [PATCH 122/240] fix attaching custom click callbacks with
 "on_motion=True"

---
 eomaps/cb_container.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 7b6b46c8c..620f21f5f 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -851,8 +851,10 @@ def _add_callback(
             on_motion = False
 
         if self._method == "click" and on_motion is True:
-            # attach associated click+move callback
-            if not hasattr(self._m.cb._click_move._attach, cb_name):
+            # attach associated default click+move callbacks if available
+            if isinstance(callback, str) and not hasattr(
+                self._m.cb._click_move._attach, callback
+            ):
                 on_motion = False
                 _log.warning(
                     f"Using 'on_motion' = True for the '{callback}' callback has no effect!"

From afdf337a3c2f3f298c3403dd226a4ddea8a6c85e Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 27 Jan 2024 01:18:37 +0100
Subject: [PATCH 123/240] major colorbar updates

---
 eomaps/colorbar.py            | 1781 +++++++++++++--------------------
 eomaps/eomaps.py              |    9 +-
 eomaps/helpers.py             |  167 ++--
 tests/test_basic_functions.py |    8 +-
 4 files changed, 768 insertions(+), 1197 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 1be051e9d..5391cac71 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -1,28 +1,16 @@
-# Copyright EOmaps Contributors
-#
-# This file is part of EOmaps and is released under the BSD 3-clause license.
-# See LICENSE in the root of the repository for full licensing details.
-
-"""Interactive Colorbar."""
-
-import logging
-from functools import partial, lru_cache
-from itertools import cycle
+from itertools import pairwise, cycle
+from functools import partial
 from textwrap import dedent
-import copy
-
-
-import numpy as np
 
+import matplotlib.pyplot as plt
 from matplotlib.gridspec import GridSpecFromSubplotSpec, SubplotSpec
 import matplotlib.transforms as mtransforms
-import matplotlib as mpl
-import matplotlib.pyplot as plt
 from matplotlib.colors import LinearSegmentedColormap
+from matplotlib.patches import Rectangle
 
-from .helpers import pairwise, _TransformedBoundsLocator, register_modules
+import numpy as np
 
-_log = logging.getLogger(__name__)
+from eomaps.helpers import _TransformedBoundsLocator
 
 
 def get_named_bins_formatter(bins, names, show_values=False):
@@ -64,397 +52,59 @@ def formatter(x, pos):
     return formatter
 
 
-class ColorBar:
-    """Class to draw colorbars with a histogram on top."""
-
+class ColorBarBase:
     def __init__(
         self,
-        m,
-        pos=0.4,
-        inherit_position=None,
-        margin=None,
-        hist_size=0.8,
-        hist_bins=256,
-        extend=None,
-        extend_frac=0.025,
         orientation="horizontal",
-        dynamic_shade_indicator=False,
-        show_outline=False,
-        tick_precision=2,
-        tick_formatter=None,
-        log=False,
-        out_of_range_vals="clip",
+        extend_frac=0.025,
+        dynamic=True,
+        padding=0.1,
         hist_kwargs=None,
-        label=None,
-        ylabel=None,
-        **kwargs,
+        tick_precision=2,
     ):
-        """
-        Add a colorbar to the map.
-
-        The colorbar always represents the data of the associated Maps-object
-        that was assigned in the last call to `m.plot_map()`.
-
-        By default, the colorbar will only be visible on the layer of the associated
-        Maps-object.
-
-        After the colorbar has been created, it can be accessed via:
-
-            >>> cb = m.colorbar
-
-        Parameters
-        ----------
-        pos : float or 4-tuple, optional
-
-            - float: fraction of the axis size that is used to create the colorbar.
-              The axes of the Maps-object will be shrunk accordingly to make space
-              for the colorbar.
-            - 4-tuple (x0, y0, width, height):
-              Absolute position of the colorbar in relative figure-units (0-1).
-              In this case, existing axes are NOT automatically re-positioned!
-
-            Note: By default, multiple colorbars on different layers share their
-            position! To force placement of a colorbar, use "inherit_position=False".
-
-            The default is 0.4.
-        inherit_position : bool or None optional
-            Indicator if the colorbar should share its position with other colorbars
-            that represent datasets on the same plot-axis.
-
-            - If True, and there is already another colorbar for the given plot-axis,
-              the value of "pos" will be ignored and the new colorbar will share its
-              position with the parent-colorbar. (e.g. all colorbars for a given axis will
-              overlap and moving a colorbar in one layer will move all other relevant
-              colorbars accordingly).
-            - If None: If the colorbar is added on a different layer than the parent
-              colorbar, use "inherit_position=True", else use "inherit_position=False".
-
-            The default is None
-        hist_size : float or None
-            The fraction of the colorbar occupied by the histogram.
-
-            - None: no histogram will be drawn
-            - 0:
-            - 0.9: 90% histogram, 10% colorbar
-            - 1: only histogram
-
-        hist_bins : int, list, tuple, array or "bins", optional
-
-            - If int: The number of histogram-bins to use for the colorbar.
-            - If list, tuple or numpy-array: the bins to use
-            - If "bins": use the bins obtained from the classification
-              (ONLY possible if a classification scheme is used!)
-
-            The default is 256.
-        extend : str or None, optional
-            Set how extension-arrows should be added.
 
-            - None: extension-arrow behavior is determined by the provided dataset
-              in conjunction with the limits (e.g. vmin and vmax).
-            - "neither": extension arrows are never added
-            - "min" or "max": only min / max extension arrows are added
-            - "both": both min and max extension arrows are added
+        self._hist_size = 0.9
 
-            Note: If the colorbar inherits its position from a colorbar on a different
-            layer, the extend-behavior is inherited as well!
-
-            The default is None.
-        extend_frac : float, optional
-            The fraction of the colorbar-size to use for extension-arrows.
-            (Extension-arrows are added if out-of-range values are found!)
-            The default is 0.025.
-        orientation : str, optional
-            The orientation of the colorbar ("horizontal" or "vertical").
-            The default is "horizontal".
-        dynamic_shade_indicator : bool, optional
-            ONLY relevant if data-shading is used! ("shade_raster" or "shade_points")
-
-            - False: The colorbar represents the actual (full) dataset
-            - True: The colorbar is dynamically updated and represents the density of
-              the shaded pixel values within the current field of view.
-
-            The default is False.
-        show_outline : bool or dict
-            Indicator if an outline should be added to the histogram.
-            (e.g. a line encompassing the histogram)
-            If a dict is provided, it is passed to `plt.step()` to style the line.
-            (e.g. with ordinary matplotlib parameters such as color, lw, ls etc.)
-            If True, the following properties are used:
-
-            - {"color": "k", "lw": 1}
-
-            The default is False.
-        tick_precision : int or None
-            The precision of the tick-labels in the colorbar.
-            (e.g. a precision of 2 means that 0.12345 will be shown as 0.12)
-            The default is 2.
-        tick_formatter : callable
-            A function that will be used to format the ticks of the colorbar.
-            The function will be used with matpltlibs `set_major_formatter`...
-            For details, see:
-            https://matplotlib.org/stable/api/_as_gen/matplotlib.axis.Axis.set_major_formatter.html
-
-            Call-signagure:
-
-            >>> def tick_formatter(x, pos):
-            >>>     # x ... the tick-value
-            >>>     # pos ... the tick-position
-            >>>     return f"{x} m"
-
-            The default is None.
-        log : bool, optional
-            Indicator if the y-axis of the plot should be logarithmic or not.
-            The default is False
-        out_of_range_vals : str or None
-
-            - if "mask": out-of range values will be masked.
-              (e.g. values outside the colorbar limits are not represented in the
-              histogram and NO extend-arrows are added)
-            - if "clip": out-of-range values will be clipped.
-              (e.g. values outside the colorbar limits will be represented in the
-              min/max bins of the histogram)
-
-            The default is "clip"
-        hist_kwargs : dict
-            A dictionary with keyword-arguments passed to the creation of the histogram
-            (e.g. passed to `plt.hist()` )
-        label : str, optional
-            The label used for the colorbar.
-            Use `ColorBar.set_labels()` to set the labels (and styling) for the
-            colorbar and the histogram.
-            The default is None.
-        ylabel : str, optional
-            The label used for the y-axis of the colorbar. The default is None
-        layer : str
-            The layer at which the colorbar will be drawn.
-            NOTE: In most cases you should NOT need to adjust the layer!
-            The layer is automatically assigned to the layer at which the
-            data was plotted and Colorbars are only visible on the assigned layer!
-        kwargs :
-            All additional kwargs are passed to the creation of the colorbar
-            (e.g. `plt.colorbar()`)
-
-        See Also
-        --------
-        ColorBar.set_bin_labels:  Use custom names for classified colorbar bins.
-
-        Examples
-        --------
-
-        >>> x = y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-        >>> data = [1, 2, 6, 6, 6, 8, 7, 3, 9, 10]
-        >>> m = Maps()
-        >>> m.set_data(data, x, y)
-        >>> m.plot_map()
-        >>> m.add_colorbar(label="some data")
-
-        >>> x = y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-        >>> data = [1, 2, 6, 6, 6, 8, 7, 3, 9, 10]
-        >>> m = Maps()
-        >>> m.set_data(data, x, y)
-        >>> m.set_classify.Quantiles(k=6)
-        >>> m.plot_map()
-        >>> m.add_colorbar(hist_bins="bins", label="some data")
-
-        """
-        self._m = m
-
-        # allow overriding the layer on which to draw the colorbar
-        self._layer = kwargs.pop("layer", self._m.layer)
-
-        self._pos = pos
-        self._margin = margin
-        self._orientation = orientation
-
-        self._init_extend = extend
         self._extend_frac = extend_frac
+        self._dynamic = dynamic
 
-        if inherit_position is None:
-            if not self._m.colorbar:
-                inherit_position = True
-            else:
-                inherit_position = False
-
-        self._inherit_position = inherit_position
-
-        if isinstance(self._inherit_position, ColorBar):
-            self._parent_cb = self._inherit_position
-        else:
-            self._parent_cb = self._identify_parent_cb()
+        self.orientation = orientation
 
-        if hist_size is None:
-            self._hist_size = 0
-        else:
-            self._hist_size = hist_size
+        self._log = False
 
-        self._hist_bins = hist_bins
+        self._padding = padding
 
-        if hist_kwargs is None:
-            self._hist_kwargs = dict()
+        if hist_kwargs is not None:
+            self._hist_kwargs = hist_kwargs
         else:
-            self._hist_kwargs = copy.deepcopy(hist_kwargs)
+            self._hist_kwargs = {}
 
-        self._histogram_plotted = False  # indicator if histogram has been plotted
+        self._vmin = None
+        self._vmax = None
+        self._norm = None
+        self._cmap = None
+        self._data = None
 
-        self._dynamic_shade_indicator = dynamic_shade_indicator
-        self._hist_label_kwargs = None
-
-        self._show_outline = show_outline
         self._tick_precision = tick_precision
-        self._tick_formatter = tick_formatter
-        self._log = log
-        self._out_of_range_vals = out_of_range_vals
-
-        # kwargs["label"] = label
-
-        self._kwargs = copy.deepcopy(kwargs)
-
-        self._coll = self._m.coll
-        self._vmin = self._coll.norm.vmin
-        self._vmax = self._coll.norm.vmax
-
-        self._classified = self._m.classify_specs._classified
-
-        if self._hist_bins == "bins" and not self._classified:
-            raise AssertionError(
-                "EOmaps: Using hist_bins='bins' is only possible "
-                "for classified datasets!"
-            )
-
-        self._ax = None
-        self.ax_cb = None
-        self.ax_cb_plot = None
-
-        self._cid_redraw = False
-
-        self._set_data()
-        self._setup_axes()
-        self.set_labels(label)
-        if ylabel is not None:
-            self.ax_cb_plot.set_ylabel(ylabel)
 
     @property
-    def layer(self):
-        return self._layer
-
-    def set_visible(self, vis):
-        """
-        Set the visibility of the colorbar.
-
-        Parameters
-        ----------
-        vis : bool
-            - True: colorbar visible
-            - False: colorbar not visible
-        """
-        for ax in self._axes:
-            if ax is self.ax_cb_plot:
-                pass
-            ax.set_visible(vis)
-
-        if self._hist_size <= 0.0001:
-            self.ax_cb_plot.set_visible(False)
-        else:
-            self.ax_cb_plot.set_visible(vis)
-
-    def _set_labels(self, cb_label=None, hist_label=None, **kwargs):
-        if self._dynamic_shade_indicator and hist_label is not None:
-            # remember kwargs to re-draw the histogram
-            self._hist_label_kwargs = {
-                "cb_label": None,
-                "hist_label": hist_label,
-                **kwargs,
-            }
-
-        if self._orientation == "horizontal":
-            if cb_label:
-                if self._hist_size < 0.001:
-                    # label colorbar
-                    self.ax_cb_plot.set_xlabel("")
-                    label = self.ax_cb.set_xlabel(cb_label, **kwargs)
-                elif self._hist_size > 0.999:
-                    # label plot
-                    self.ax_cb_plot.set_xlabel(cb_label, **kwargs)
-                    self.ax_cb.set_xlabel("")
-                else:
-                    # label colorbar
-                    self.ax_cb_plot.set_xlabel("")
-                    label = self.ax_cb.set_xlabel(cb_label, **kwargs)
-            if hist_label:
-                self._hist_label = self.ax_cb_plot.set_ylabel(hist_label, **kwargs)
-        else:
-            if cb_label:
-                if self._hist_size < 0.001:
-                    # label colorbar
-                    self.ax_cb_plot.set_ylabel("")
-                    label = self.ax_cb.set_ylabel(cb_label, **kwargs)
-                elif self._hist_size > 0.999:
-                    # label plot
-                    self.ax_cb_plot.set_ylabel(cb_label, **kwargs)
-                    self.ax_cb.set_xlabel("")
-                else:
-                    # label colorbar
-                    self.ax_cb_plot.set_ylabel("")
-                    label = self.ax_cb.set_ylabel(cb_label, **kwargs)
-
-            if hist_label:
-                self._hist_label = self.ax_cb_plot.set_xlabel(hist_label, **kwargs)
-
-    def set_labels(self, cb_label=None, hist_label=None, **kwargs):
-        """
-        Set the labels (and the styling) for the colorbar (and the histogram).
-
-        For more details, see `ColorBar.ax_cb.set_xlabel(..)` and matplotlib's `.Text`
-        properties.
-
-        Parameters
-        ----------
-        cb_label : str or None
-            The label of the colorbar. If None, the existing label is maintained.
-            The default is None.
-        hist_label : str or None
-            The label of the histogram. If None, the existing label is maintained.
-            The default is None.
-
-        Other Parameters
-        ----------------
-        kwargs :
-           Additional kwargs passed to `Axes.set_xlabel` to control the appearance of
-           the label (e.g. color, fontsize, labelpad etc.).
-
-        Examples
-        --------
-        Set both colorbar and histogram label in one go
+    def _scm(self):
+        return plt.cm.ScalarMappable(cmap=self._cmap, norm=self._norm)
 
-        >>> cb.set_labels("The parameter", "histogram count", fontsize=10, color="r")
-
-        Use different styles for the colorbar and histogram labels
-
-        >>> cb.set_labels(cb_label="The parameter", color="r", labelpad=10)
-        >>> cb.set_labels(hist_label="histogram count", fontsize=6, color="k")
-
-        """
-
-        self._label_kwargs = {"cb_label": cb_label, "hist_label": hist_label, **kwargs}
-
-        self._set_labels(cb_label=cb_label, hist_label=hist_label, **kwargs)
-
-        if not self._dynamic_shade_indicator:
-            # no need to redraw the background for dynamically updated artists
-            self._m.redraw(self.layer)
-        else:
-            self._m.BM.update()
+    @property
+    def _hist_orientation(self):
+        return "vertical" if self.orientation == "horizontal" else "horizontal"
 
-    @lru_cache()
     def _default_cb_tick_formatter(self, x, pos, precision=None):
         """
         A formatter to format the tick-labels of the colorbar for encoded datasets.
         (used in xaxis.set_major_formatter() )
         """
         # if precision=None the shortest representation of the number is used
-        return np.format_float_positional(self._m._decode_values(x), precision)
+        return np.format_float_positional(
+            self._m._decode_values(x), precision=self._tick_precision
+        )
 
-    @lru_cache()
     def _classified_cb_tick_formatter(self, x, pos, precision=None):
         """
         A formatter to format the tick-labels of the colorbar for classified datasets.
@@ -468,456 +118,220 @@ def _classified_cb_tick_formatter(self, x, pos, precision=None):
         else:
             return ""
 
-    def set_hist_size(self, size=None):
-        """
-        Set the size of the histogram (relative to the total colorbar size)
-
-        Parameters
-        ----------
-        size : float, optional
-            The fraction of the colorbar occupied by the histogram.
-
-            - 0 = no histogram
-            - 0.5 = 50% colorbar, 50% histogram
-            - 1 = no colorbar, only histogram).
-
-            The default is None.
-        """
-        if size is not None:
-            self._hist_size = size
-
-        if self._inherit_position:
-            parent = self._get_parent_cb()
-            parent.set_hist_size(size)
-        else:
-            # if the position is not inherited from a parent-axes, add margins
-            if self._margin is None:
-                if self._orientation == "horizontal":
-                    self._margin = dict(left=0.1, right=0.1, bottom=0.3, top=0.0)
-                else:
-                    self._margin = dict(left=0.0, right=0.3, bottom=0.1, top=0.1)
-
-            l, r = (self._margin.get(k, 0) for k in ["left", "right"])
-            b, t = (self._margin.get(k, 0) for k in ["bottom", "top"])
-            w, h = 1 - l - r, 1 - t - b
-
-            if self._orientation == "horizontal":
-                s = (1 - self._hist_size) * h
-                cbpos = (l, b, w, s)
-                histpos = (l, b + s, w, h - s)
-            else:
-                s = (1 - self._hist_size) * w
-                cbpos = (l + w - s, b, s, h)
-                histpos = (l, b, w - s, h)
+    def _get_data(self):
+        # TODO
+        return self._data
 
-            self.ax_cb_plot.set_axes_locator(
-                _TransformedBoundsLocator(histpos, self._ax.transAxes)
-            )
+    def _set_axes_locators(self, cb_bounds=None, hist_bounds=None):
+        if cb_bounds is not None:
             self.ax_cb.set_axes_locator(
-                _TransformedBoundsLocator(cbpos, self._ax.transAxes)
+                _TransformedBoundsLocator(cb_bounds, self._ax.transAxes)
+            )
+        if hist_bounds is not None:
+            self.ax_cb_plot.set_axes_locator(
+                _TransformedBoundsLocator(hist_bounds, self._ax.transAxes)
             )
 
-            # re-fetch ALL layers (since axes-positions have changed!)
-            self._m.BM._refetch_layer("all")
-
-        if self._hist_size > 0.0001:
-            self.ax_cb_plot.set_visible(True)
-
-            # in case the histogram has not yet been plotted, plot it!
-            if not self._histogram_plotted:
-                self._plot_histogram()
-        else:
-            self.ax_cb_plot.set_visible(False)  # to avoid singular matrix errors
-
-        # avoid singular matrix errors caused by visible axes with 0 size
-        # when activating the layout editor
-        if self._hist_size > 0.999:
-            self.ax_cb.set_visible(False)  # to avoid singular matrix errors
-            self.ax_cb_plot.set_visible(True)
-            [i.set_visible(False) for i in self.ax_cb.patches]
-            [i.set_visible(False) for i in self.ax_cb.collections]
-            self.ax_cb_plot.tick_params(bottom=True, labelbottom=True)
-        elif self._hist_size < 0.001:
-            self.ax_cb.set_visible(True)
-            self.ax_cb_plot.set_visible(False)  # to avoid singular matrix errors
-            [i.set_visible(True) for i in self.ax_cb.patches]
-            [i.set_visible(True) for i in self.ax_cb.collections]
-        else:
-            self.ax_cb.set_visible(True)
-            self.ax_cb_plot.set_visible(True)
-            self.ax_cb_plot.tick_params(bottom=False, labelbottom=False)
-            [i.set_visible(True) for i in self.ax_cb.patches]
-            [i.set_visible(True) for i in self.ax_cb.collections]
-
-        self.set_labels(**self._label_kwargs)
-        # tag layer for refetch
-        self._m.redraw(self.layer)
-
-    def _identify_parent_cb(self):
-        parent_cb = None
-        # check if there is already an existing colorbar for a Maps-object that shares
-        # the same plot-axis.
-        # If yes, use the position of this colorbar to creat a new one
-
-        if self._m.colorbar is not None and not self._inherit_position:
-            parent_cb = None  # self._m.colorbar
-        else:
-            # check if self is actually just another layer of an existing Maps object
-            # that already has a colorbar assigned
-            for m in [self._m.parent, *self._m.parent._children]:
-                if m is not self._m and m.ax is self._m.ax:
-                    if m.colorbar is not None:
-                        if m.colorbar._parent_cb is None:
-                            parent_cb = m.colorbar
-                            break
-        if parent_cb and parent_cb._orientation == self._orientation:
-            return parent_cb
-        else:
-            return None
+    def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
+        if f is None and parent_ax is not None:
+            f = parent_ax.figure
 
-    def _get_parent_cb(self):
-        if self._parent_cb is None:
-            return self
+        self._parent_cb = self._identify_parent_cb()
+        if self._parent_cb:
+            # inherit axis-position from the parent axis position
+            # (e.g. it can no longer be freely moved... its position is determined
+            # by the position of the parent-colorbar axis)
+            self._ax = self._parent_cb._ax
         else:
-            parent = self
-            while parent._parent_cb is not None:
-                parent = parent._parent_cb
-
-            return parent
-
-    def _setup_axes(self):
-        zorder = 9999
-
-        horizontal = self._orientation == "horizontal"
-        hide_hist = self._hist_size < 0.0001
-        hide_axes = self._hist_size > 0.999
-        # check if one of the parent colorbars has a colorbar, and if so,
-        # use it to set the position of the colorbar.
-        if self._inherit_position:
-            if self._parent_cb is not None:
-
-                try:
-                    parent_subplotspec = self._parent_cb._ax.get_subplotspec()
-                except AttributeError:
-                    parent_subplotspec = None
-
-                if parent_subplotspec is not None:
-                    self._ax = self._m.f.add_subplot(
-                        parent_subplotspec,
-                        label="cb",
-                        zorder=zorder,
-                    )
-                else:
-                    self._ax = self._m.f.add_axes(
-                        self._parent_cb._ax.get_position(),
-                        label="cb",
-                        zorder=zorder,
-                    )
-
-                parent_extend = getattr(
-                    self._parent_cb, "_extend", self._parent_cb._init_extend
-                )
-
-                if parent_extend is None:
-                    try:
-                        self._parent_cb._set_extend()
-                        parent_extend = getattr(
-                            self._parent_cb, "_extend", self._parent_cb._init_extend
-                        )
-
-                    except Exception:
-                        _log.exception(
-                            "EOmaps: unable to determine automatic extension arrow"
-                            "size of parent colorbar."
-                        )
-
-                # inherit axis-position from the parent axis position
-                # (e.g. it can no longer be freely moved... its position is determined
-                # by the position of the parent-colorbar axis)
-                self._ax.set_axes_locator(
-                    _TransformedBoundsLocator(
-                        (0, 0, 1, 1), self._parent_cb._ax.transAxes
-                    )
-                )
-
-            else:
-                self._inherit_position = False
-
-        if not self._inherit_position:
-            if isinstance(self._pos, float):
-                if horizontal:
+            if isinstance(pos, (int, float)):
+                if self.orientation == "horizontal":
                     gs = GridSpecFromSubplotSpec(
                         2,
-                        1,
-                        self._m.ax.get_subplotspec(),
-                        height_ratios=(1, self._pos),
+                        3,
+                        parent_ax.get_subplotspec(),
+                        height_ratios=(1, pos),
+                        width_ratios=(self._padding, 1, self._padding),
                     )
-
-                    self._m.ax.set_subplotspec(gs[0, 0])
-                    self._ax = self._m.f.add_subplot(
-                        gs[1, 0],
+                    parent_ax.set_subplotspec(gs[0, :])
+                    self._ax = f.add_subplot(
+                        gs[1, 1],
                         label="cb",
                         zorder=zorder,
                     )
                 else:
                     gs = GridSpecFromSubplotSpec(
-                        1,
+                        3,
                         2,
-                        self._m.ax.get_subplotspec(),
-                        width_ratios=(1, self._pos),
+                        parent_ax.get_subplotspec(),
+                        width_ratios=(1, pos),
+                        height_ratios=(self._padding, 1, self._padding),
                     )
 
-                    self._m.ax.set_subplotspec(gs[0, 0])
-                    self._ax = self._m.f.add_subplot(
-                        gs[0, 1],
+                    parent_ax.set_subplotspec(gs[1, 0])
+                    self._ax = f.add_subplot(
+                        gs[1, 1],
                         label="cb",
                         zorder=zorder,
                     )
-            elif isinstance(self._pos, SubplotSpec):
-                self._ax = self._m.f.add_subplot(
-                    self._pos,
+            elif isinstance(pos, SubplotSpec):
+                self._ax = f.add_subplot(
+                    pos,
                     label="cb",
                     zorder=zorder,
                 )
-            elif isinstance(self._pos, (list, tuple)):
-                x0, y0, w, h = self._pos
+            elif isinstance(pos, (list, tuple)):
+                x0, y0, w, h = pos
                 x1 = x0 + w
                 y1 = y0 + h
                 bbox = mtransforms.Bbox(((x0, y0), (x1, y1)))
 
                 # the parent axes holding the 2 child-axes
-                self._ax = plt.Axes(self._m.f, bbox, label="cb", zorder=zorder)
-                self._m.f.add_axes(self._ax)
+                self._ax = plt.Axes(f, bbox, label="cb", zorder=zorder)
+                f.add_axes(self._ax)
 
-        # make all spines, labels etc. invisible for the base-axis
-        self._ax.set_axis_off()
+            # make all spines, labels etc. invisible for the base-axis
+            self._ax.set_axis_off()
 
         # colorbar axes
-        self.ax_cb = self._ax.figure.add_axes(
+        self.ax_cb = f.add_axes(
             self._ax.get_position(),
             label="EOmaps_cb",
             zorder=zorder - 1,  # make zorder 1 lower than container axes for picking
         )
 
         # histogram axes
-        self.ax_cb_plot = self._ax.figure.add_axes(
+        self.ax_cb_plot = f.add_axes(
             self._ax.get_position(),
             label="EOmaps_cb_hist",
             zorder=zorder - 1,  # make zorder 1 lower than container axes for picking
         )
-        # hide histogram and coorbar axes if they are 0 size
-        if hide_axes:
-            self.ax_cb.set_visible(False)
-        if hide_hist:
-            self.ax_cb_plot.set_visible(False)
-
-        if self._inherit_position:
-            # handle axis size in case parent colorbar has extension arrows
-            if parent_extend in ["min", "both", None]:
-                padx = -self._parent_cb._extend_frac
-            else:
-                padx = 0
-            if parent_extend in ["max", "both", None]:
-                pady = -self._parent_cb._extend_frac
-            else:
-                pady = 0
-
-            if self._orientation == "horizontal":
-                size = (padx, 0, 1 - padx - pady, 1)
-            else:
-                size = (0, padx, 1, 1 - padx - pady)
 
-            # in case the position is inherited, copy the locators from the parent!
-            self.ax_cb_plot.set_axes_locator(
-                _TransformedBoundsLocator(
-                    (0, 0, 1, 1), self._parent_cb.ax_cb_plot.transAxes
-                )
-            )
-            self.ax_cb.set_axes_locator(
-                _TransformedBoundsLocator(size, self._parent_cb.ax_cb.transAxes)
-            )
+        # add axes as child-axes
+        self._ax.add_child_axes(self.ax_cb)
+        self._ax.add_child_axes(self.ax_cb_plot)
 
         # join colorbar and histogram axes
-        if horizontal:
+        if self.orientation == "horizontal":
             self.ax_cb_plot.sharex(self.ax_cb)
         else:
             self.ax_cb_plot.sharey(self.ax_cb)
 
+            # for vertical colorbars, histogram-axis must be inverted!
+            self.ax_cb_plot.invert_xaxis()
+
         # keep the background of the plot-axis but remove the outer frame
         self.ax_cb_plot.spines["top"].set_visible(False)
         self.ax_cb_plot.spines["right"].set_visible(False)
         self.ax_cb_plot.spines["bottom"].set_visible(False)
         self.ax_cb_plot.spines["left"].set_visible(False)
 
-        # set axis scale
-        if horizontal:
-            if self._log is True:
-                self.ax_cb_plot.set_yscale("log")
-            else:
-                self.ax_cb_plot.set_yscale("linear")
+        self._set_hist_size()
+
+        self._attach_lim_cbs()
+
+        return self._ax, self.ax_cb, self.ax_cb_plot
+
+    def _attach_lim_cbs(self):
+        # force lower limits of histogram axis to 0
+
+        def ychanged(event):
+            if self.orientation == "horizontal":
+                self.ax_cb_plot.set_ylim(0, None, emit=False)
+
+        def xchanged(event):
+            if self.orientation == "vertical":
+                self.ax_cb_plot.set_xlim(None, 0, emit=False)
+
+        self.ax_cb_plot.callbacks.connect("xlim_changed", xchanged)
+        self.ax_cb_plot.callbacks.connect("ylim_changed", ychanged)
+
+    def _hide_singular_axes(self):
+        sing_hist = self.ax_cb_plot.bbox.width <= 2 or self.ax_cb_plot.bbox.height <= 2
+        sing_cb = self.ax_cb.bbox.width <= 2 or self.ax_cb.bbox.height <= 2
+
+        if sing_hist:
+            self.ax_cb_plot.set_visible(False)
         else:
-            if self._log is True:
-                self.ax_cb_plot.set_xscale("log")
-            else:
-                self.ax_cb_plot.set_xscale("linear")
+            self.ax_cb_plot.set_visible(True)
 
-        # add all axes as artists
-        for a in self._axes:
-            a.set_navigate(False)
-            if a is not None:
-                if self._dynamic_shade_indicator is True:
-                    self._m.BM.add_artist(a, self.layer)
-                else:
-                    self._m.BM.add_bg_artist(a, self.layer)
+        if sing_cb:
+            self.ax_cb.set_visible(False)
+        else:
+            self.ax_cb.set_visible(True)
 
-        # we need to re-draw since the background axis size has changed!
-        self._m.BM._refetch_layer(self.layer)
-        self._m.BM._refetch_layer("__SPINES__")
-        self._m.redraw("__SPINES__")
+    def _set_hist_size(self, size=None, update_all=False):
+        if size is None:
+            size = self._hist_size
+        else:
+            self._hist_size = size
 
-    @property
-    def _axes(self):
-        return (self._ax, self.ax_cb, self.ax_cb_plot)
-
-    def _set_extend(self, z_data):
-        if self._inherit_position and self._parent_cb is not None:
-            self._extend = self._parent_cb._extend
-            # warn if provided extend behavior differs from the inherited behavior
-            if self._extend != self._init_extend:
-                _log.warning(
-                    f"EOmaps Warning: m.add_colorbar(extend='{self._extend}') is "
-                    "inherited from the parent colorbar! Explicitly set the 'extend' "
-                    "behavior to silence this warning."
-                )
-            if self._extend is not None:
-                return
+        assert 0 <= size <= 1, "Histogram size must be between 0 and 1"
+
+        self._hide_singular_axes()
 
-        if self._init_extend is not None:
-            self._extend = self._init_extend
+        if self.orientation == "horizontal":
+            l_cb_bounds = (0, 0, 1, 1 - size)
+            l_hist_bounds = (0, 1 - size, 1, size)
         else:
-            extend = "neither"
-            if (z_data > self._vmax).any():
-                extend = "max"
-            if (z_data < self._vmin).any():
-                if extend == "max":
-                    extend = "both"
-                else:
-                    extend = "min"
+            l_cb_bounds = (size, 0, 1 - size, 1)
+            l_hist_bounds = (0, 0, size, 1)
 
-            self._extend = extend
+        self._set_axes_locators(l_cb_bounds, l_hist_bounds)
+        self._style_hist_ticks()
 
-    def _set_data(self):
-        renorm = False
+    def get_extend_fracs(self):
+        # if no colorbar is found, use the full axis
+        if not hasattr(self, "cb"):
+            return 0, 1
 
-        dynamic_shade = False
-        if self._dynamic_shade_indicator:
-            ds, mpl_ext = register_modules("datashader", "datashader.mpl_ext")
+        return 0, 1
 
-            if all((ds, mpl_ext)) and isinstance(self._coll, mpl_ext.ScalarDSArtist):
-                dynamic_shade = True
-            else:
-                _log.error(
-                    "EOmaps: Using 'dynamic_shade_indicator=True' is only possible "
-                    "with 'shade' shapes (e.g. 'shade_raster' or 'shade_points'.\n"
-                    "... creating a normal colorbar instead."
-                )
-                self._dynamic_shade_indicator = False
+        extend = self.cb.extend
 
-        if dynamic_shade:
-            aggname = self._m.shape.aggregator.__class__.__name__
-            if aggname in ["first", "last", "max", "min", "mean", "mode"]:
-                pass
-            else:
-                renorm = True
-                # TODO check this without requiring import of datashader!
-                # _log.error(
-                #     "EOmaps: Only dynamic colorbars are possible when using"
-                #     + f" '{aggname}' as datashader-aggregation reduction method "
-                #     + "...creating a 'dynamic_shade_indicator' colorbar instead."
-                # )
-                # self._dynamic_shade_indicator = True
-
-            try:
-                z_data = self._coll.get_ds_data().values
-            except:
-                self._m.redraw(self.layer)
-                z_data = self._coll.get_ds_data().values
-
-            if "count" in aggname:
-                # make sure we don't count empty pixels
-                z_data = z_data[~(z_data == 0)]
-
-            # datashader sets None to 0 by default!
-            # z_data = z_data[z_data > 0]
-
-            bins = self._m.classify_specs._bins
-            cmap = self._m.classify_specs._cbcmap
-
-            if renorm:
-                z_data = z_data[~np.isnan(z_data)]
-                norm = self._coll.norm
-                # make sure the norm clips with respect to vmin/vmax
-                # (only clip if either vmin or vmax is not None)
-                # if vmin or vmax:
-                #     z_data = z_data.clip(vmin, vmax)
-                cmap = self._coll.get_cmap()
-            else:
-                norm = self._m.classify_specs._norm
-
-            if self._cid_redraw is False:
-                # TODO check why this no longer triggers on data-updates...
-                # self._m.coll.add_callback(self._redraw_colorbar)
-
-                def check_data_updated(*args, **kwargs):
-                    # make sure the artist is updated before checking for new data
-                    # TODO check if this is really enough to ensure that the coll
-                    # is fully updated (calling coll.draw() is not an option since it
-                    # would result make the collection appear on any layer!)
-                    self._m.coll.changed()
-                    dsdata = self._m.coll.get_ds_data()
-                    if getattr(self, "_last_ds_data", None) is not None:
-                        if not self._last_ds_data.equals(dsdata):
-                            # if the data has changed, redraw the colorbar
-                            self._redraw_colorbar()
-                    self._last_ds_data = dsdata
-
-                self._m.BM._before_fetch_bg_actions.append(check_data_updated)
-
-                self._m.BM.on_layer(
-                    lambda *args, **kwargs: self._redraw_colorbar,
-                    layer=self.layer,
-                    persistent=True,
-                )
+        # extend fraction is defined as % of the interior colorbar length!
+        getfrac = lambda n: self._extend_frac / (1 + n * self._extend_frac)
 
-                self._cid_redraw = True
+        if extend == "both":
+            frac = getfrac(2)
+            return frac, 1 - 2 * frac
+        elif extend == "min":
+            frac = getfrac(1)
+            return frac, 1 - frac
+        elif extend == "max":
+            return 0, 1 - getfrac(1)
+        else:
+            return 0, 1
 
-                # TODO colorbar not properly updated on layer change after zoom?
-                self._m.BM.on_layer(
-                    self._redraw_colorbar,
-                    layer=self.layer,
-                    persistent=True,
-                    m=self._m,
-                )
+    def set_scale(self, log=False):
+        self._log = log
+
+        if self.orientation == "horizontal":
+            # set axis scale
+            if log is True:
+                self.ax_cb_plot.set_yscale("log")
+            else:
+                self.ax_cb_plot.set_yscale("linear")
         else:
+            # set axis scale
+            if log is True:
+                self.ax_cb_plot.set_xscale("log")
+            else:
+                self.ax_cb_plot.set_xscale("linear")
 
-            z_data = self._m._data_manager.z_data
-            bins = self._m.classify_specs._bins
-            cmap = self._m.classify_specs._cbcmap
-            norm = self._m.classify_specs._norm
+    def _preprocess_data(self, out_of_range_vals="keep"):
+        data = self._get_data()
 
-        if isinstance(z_data, np.ma.masked_array):
-            z_data = z_data.compressed()
+        if isinstance(data, np.ma.masked_array):
+            data = data.compressed()
         else:
-            z_data = z_data.ravel()
+            data = data.ravel()
 
         # make sure we only consider valid values in the histogram
-        z_data = z_data[np.isfinite(z_data)]
-
-        self._set_extend(z_data)
+        data = data[np.isfinite(data)]
 
-        if self._out_of_range_vals == "mask":
-            data_range_mask = (z_data >= self._vmin) & (z_data <= self._vmax)
-            z_data = z_data[data_range_mask]
+        if out_of_range_vals == "mask":
+            data_range_mask = (data >= self._vmin) & (data <= self._vmax)
+            data = data[data_range_mask]
 
             # make sure that histogram weights are masked accordingly if provided
             if "weights" in self._hist_kwargs:
@@ -927,137 +341,60 @@ def check_data_updated(*args, **kwargs):
 
         # make sure the norm clips with respect to vmin/vmax
         # (only clip if either vmin or vmax is not None)
-        if self._out_of_range_vals == "clip":
+        elif out_of_range_vals == "clip":
             if self._vmin or self._vmax:
-                z_data = z_data.clip(self._vmin, self._vmax)
-
-        self._z_data = z_data
-        self._bins = bins
-        self._cmap = cmap
-        # TODO check if copy is really necessary
-        # (especially for dynamic datashader colorbars!)
-        self._norm = copy.deepcopy(norm)
-        # make sure boundaries are clipped with respect to vmin and vmax
-        # to avoid issues with vmin/vmax in-between colorbar-boundaries
-
-        if hasattr(self._norm, "boundaries"):
-            self._norm.boundaries = np.clip(
-                self._norm.boundaries, self._vmin, self._vmax
-            )
+                data = data.clip(self._vmin, self._vmax)
+
+        return data
+
+    def _plot_colorbar(self, **kwargs):
 
-    def _plot_colorbar(self):
-        # plot the colorbar
-        horizontal = self._orientation == "horizontal"
-        n_cmap = plt.cm.ScalarMappable(cmap=self._cmap, norm=self._norm)
+        kwargs.setdefault("extendfrac", self._extend_frac)
+        kwargs.setdefault("spacing", "proportional")
+        kwargs.setdefault("extend", "both")
 
-        # avoid using "plt.colorbar" since it might not properly recognize
-        # the associated figure (e.g. plt.gcf() might point somewhere else)!
-        self.cb = self._m.f.colorbar(
-            n_cmap,
+        self.cb = self.ax_cb.figure.colorbar(
+            self._scm,
             cax=self.ax_cb,
-            extend=self._extend,
-            extendfrac=self._extend_frac,
-            spacing="proportional",
-            orientation=self._orientation,
-            **self._kwargs,
+            orientation=self.orientation,
+            **kwargs,
         )
 
         self.cb.outline.set_visible(False)
 
-        # ensure that ticklabels are correct if a classification is used
-        if self._classified and "ticks" not in self._kwargs:
-            self.cb.set_ticks(np.unique(np.clip(self._bins, self._vmin, self._vmax)))
-
-            if self._tick_formatter is None:
-                self._tick_formatter = self._classified_cb_tick_formatter
-        else:
-            self.cb.set_ticks(self.cb.get_ticks())
-
-        if self._tick_formatter is None:
-            tick_formatter = partial(
-                self._default_cb_tick_formatter, precision=self._tick_precision
-            )
-        else:
-            tick_formatter = self._tick_formatter
-
-        if horizontal:
-            self.ax_cb.xaxis.set_major_formatter(tick_formatter)
-        else:
-            self.ax_cb.yaxis.set_major_formatter(tick_formatter)
-
-        # format position of scientific exponent for colorbar ticks
-        if horizontal:
-            ot = self.ax_cb.yaxis.get_offset_text()
-            ot.set_horizontalalignment("center")
-            ot.set_position((1, 0))
-
-        # make sure axis limits are correct
-        if horizontal:
-            limsetfunc = self.ax_cb.set_xlim
-        else:
-            limsetfunc = self.ax_cb.set_ylim
-
-        if self._vmin != self._vmax:
-            limsetfunc(self._vmin, self._vmax)
-        else:
-            _log.error(
-                "EOMaps-Warning: Attempting to set identical upper and "
-                + "lower limits for the colorbar... limits will be ignored!"
-            )
-
-        # set the axis_locator to set relative axis positions
-        # TODO check why colorbar axis size changes after plot!
-        # (e.g. this needs to be called AFTER plotting the colorbar to make sure
-        # the extension-arrows are properly aligned)
-        self.set_hist_size()
+        # after plotting the colorbar we must adjust the hist-size to ensure the
+        # padding of the histogram axes confirms to the size of the colorbar arrows
+        self._set_hist_size()
 
-    def _plot_histogram(self):
-        if self._hist_size <= 0.0001:
-            return
-
-        horizontal = self._orientation == "horizontal"
-        n_cmap = plt.cm.ScalarMappable(cmap=self._cmap, norm=self._norm)
+    def _plot_histogram(self, bins=None, out_of_range_vals="keep", **kwargs):
 
         # plot the histogram
-        h = self.ax_cb_plot.hist(
-            self._z_data,
-            orientation="vertical" if horizontal else "horizontal",
-            bins=self._bins
-            if (self._classified and self._hist_bins == "bins")
-            else self._hist_bins,
-            color="k",
+        self.ax_cb_plot.hist(
+            self._preprocess_data(out_of_range_vals=out_of_range_vals),
+            orientation=self._hist_orientation,
+            bins=bins,
             align="mid",
-            range=(self._vmin, self._vmax) if (self._vmin and self._vmax) else None,
-            **self._hist_kwargs,
+            **kwargs,
         )
 
-        if self._show_outline:
-            if self._show_outline is True:
-                outline_props = dict(color="k", lw=1)
-            else:
-                outline_props = self._show_outline
-
-            if horizontal:
-                self.ax_cb_plot.step(h[1], [h[0][0], *h[0]], **outline_props)
-            else:
-                self.ax_cb_plot.step([h[0][0], *h[0]], h[1], **outline_props)
+        bins = getattr(self._norm, "boundaries", None)
 
-        if self._bins is None:
+        if bins is None:
             # identify position of color-splits in the colorbar
-            if isinstance(n_cmap.cmap, LinearSegmentedColormap):
+            if isinstance(self._scm.cmap, LinearSegmentedColormap):
                 # for LinearSegmentedcolormap N is the number of quantizations!
-                splitpos = np.linspace(self._vmin, self._vmax, n_cmap.cmap.N)
+                splitpos = np.linspace(self._vmin, self._vmax, self._scm.cmap.N)
             else:
                 # for ListedColormap N is the number of colors
-                splitpos = np.linspace(self._vmin, self._vmax, n_cmap.cmap.N + 1)
+                splitpos = np.linspace(self._vmin, self._vmax, self._scm.cmap.N + 1)
         else:
-            splitpos = np.asanyarray(self._bins)
+            splitpos = np.asanyarray(bins)
 
         # color the histogram patches
         for patch in list(self.ax_cb_plot.patches):
             # the list is important!! since otherwise we change ax.patches
             # as we iterate over it... which is not a good idea...
-            if horizontal:
+            if self.orientation == "horizontal":
                 minval = np.atleast_1d(patch.get_x())[0]
                 width = patch.get_width()
                 height = patch.get_height()
@@ -1076,31 +413,23 @@ def _plot_histogram(self):
             ]
 
             args = dict(
-                edgecolor=self._hist_kwargs.get(
-                    "edgecolor", self._hist_kwargs.get("ec", None)
-                ),
-                linewidth=self._hist_kwargs.get(
-                    "linewidth", self._hist_kwargs.get("lw", 0)
-                ),
-                linestyle=self._hist_kwargs.get(
-                    "linestyle", self._hist_kwargs.get("ls", None)
-                ),
-                alpha=self._hist_kwargs.get("alpha", None),
-                hatch=self._hist_kwargs.get("hatch", None),
+                edgecolor=kwargs.get("edgecolor", kwargs.get("ec", None)),
+                linewidth=kwargs.get("linewidth", kwargs.get("lw", 0)),
+                linestyle=kwargs.get("linestyle", kwargs.get("ls", None)),
+                alpha=kwargs.get("alpha", None),
+                hatch=kwargs.get("hatch", None),
             )
             # drop all unset values to avoi overriding defaults
             args = {key: val for key, val in args.items() if val is not None}
             # handle facecolors explicitly
-            facecolor = self._hist_kwargs.get(
-                "facecolor", self._hist_kwargs.get("fc", None)
-            )
+            facecolor = kwargs.get("facecolor", kwargs.get("fc", None))
 
             if len(splitbins) > 2:
                 patch.remove()
                 # add in-between patches
                 for b0, b1 in pairwise(splitbins):
-                    if horizontal:
-                        pi = mpl.patches.Rectangle(
+                    if self.orientation == "horizontal":
+                        pi = Rectangle(
                             (b0, 0),
                             (b1 - b0),
                             height,
@@ -1112,7 +441,7 @@ def _plot_histogram(self):
                             **args,
                         )
                     else:
-                        pi = mpl.patches.Rectangle(
+                        pi = Rectangle(
                             (0, b0),
                             width,
                             (b1 - b0),
@@ -1134,16 +463,8 @@ def _plot_histogram(self):
                 for key, val in args.items():
                     getattr(patch, f"set_{key}")(val)
 
-        # setup appearance of histogram
-        if horizontal:
-            self.ax_cb_plot.tick_params(
-                left=False,
-                labelleft=True,
-                bottom=False,
-                top=False,
-                labelbottom=False,
-                labeltop=False,
-            )
+        # add gridlines
+        if self.orientation == "horizontal":
             self.ax_cb_plot.grid(axis="y", dashes=[5, 5], c="k", alpha=0.5)
             # add a line that indicates 0 histogram level
             self.ax_cb_plot.plot(
@@ -1154,21 +475,9 @@ def _plot_histogram(self):
                 transform=self.ax_cb_plot.transAxes,
             )
             # make sure lower y-limit is 0
-            if self._log is False:
-                # self.ax_cb_plot.yaxis.set_major_locator(plt.MaxNLocator(5))
+            if self.ax_cb_plot.get_yscale() == "log":
                 self.ax_cb_plot.set_ylim(0)
         else:
-            self.ax_cb_plot.invert_xaxis()
-
-            self.ax_cb_plot.tick_params(
-                left=False,
-                labelleft=False,
-                bottom=False,
-                top=False,
-                labelbottom=True,
-                labeltop=False,
-                rotation=90,
-            )
             self.ax_cb_plot.grid(axis="x", dashes=[5, 5], c="k", alpha=0.5)
             # add a line that indicates 0 histogram level
             self.ax_cb_plot.plot(
@@ -1179,231 +488,325 @@ def _plot_histogram(self):
                 transform=self.ax_cb_plot.transAxes,
             )
             # make sure lower x-limit is 0
-            if self._log is False:
-                # self.ax_cb_plot.xaxis.set_major_locator(plt.MaxNLocator(5))
+            if self.ax_cb_plot.get_xscale() is False:
                 self.ax_cb_plot.set_xlim(None, 0)
 
-        self._histogram_plotted = True
+    def _style_hist_ticks(self):
+        # setup appearance of histogram
+        if self.orientation == "horizontal":
+            if self._hist_size < 1:
+                self.ax_cb_plot.tick_params(
+                    left=False,
+                    labelleft=True,
+                    bottom=False,
+                    top=False,
+                    labelbottom=False,
+                    labeltop=False,
+                )
+            else:
+                self.ax_cb_plot.tick_params(
+                    left=False,
+                    labelleft=True,
+                    bottom=True,
+                    top=False,
+                    labelbottom=True,
+                    labeltop=False,
+                )
+
+        else:
+            if self._hist_size < 1:
+                self.ax_cb_plot.tick_params(
+                    left=False,
+                    labelleft=False,
+                    bottom=False,
+                    top=False,
+                    labelbottom=True,
+                    labeltop=False,
+                    rotation=90,
+                )
+            else:
+                self.ax_cb_plot.tick_params(
+                    left=False,
+                    labelleft=False,
+                    right=True,
+                    labelright=True,
+                    bottom=False,
+                    top=False,
+                    labelbottom=True,
+                    labeltop=False,
+                    rotation=90,
+                )
 
-    def _redraw_colorbar(self, *args, **kwargs):
+    def _redraw(self, *args, **kwargs):
         # only re-draw if the corresponding layer is visible
         if self.layer not in self._m.BM.bg_layer.split("|"):
             return
 
-        self._set_data()
+        self.ax_cb.clear()
         self.ax_cb_plot.clear()
-        self._plot_histogram()
 
-        # if self._hist_label_kwargs:
-        #     self._set_labels(**self._hist_label_kwargs)
+        self._attach_lim_cbs()  # re-attach ylim callbacks
 
-    def set_bin_labels(self, bins, names, tick_lines="center", show_values=False):
-        """
-        Set the tick-labels of the colorbar to custom names with respect to a given
-        set of bins.
+        self._set_hist_size()
 
-        The labels will be placed at the center of each bin.
+        self._plot_colorbar()
 
-        This is most useful when using `m.set_classify.UserDefined(bins=[...])`
-        to classify the data with respect to custom bins.
+        bins = self._m.classify_specs._bins
 
-        Parameters
-        ----------
-        bins : list
-            A list of (right) bin-boundaries used to set the label-positions.
-            (e.g. `bins=[1, 2, 6]` will result in labels located at [1.5 and 4])
-        names : list
-            A list of names that should be used as labels.
+        self._plot_histogram(
+            bins=bins,
+            range=(self._m._vmin, self._m._vmax),
+        )
 
-            - The first name is assigned to the values smaller than bins[0]
-            - Names 1 to "len(bins)" are assigned to the intermediate bins
-            - The "len(bins) + 1" label is assigned to the values larger than bins[-1]
-              (if not available a "?" label will be used)
+    def _set_labels(self, cb_label=None, hist_label=None, **kwargs):
+        if self._dynamic_shade_indicator and hist_label is not None:
+            # remember kwargs to re-draw the histogram
+            self._hist_label_kwargs = {
+                "cb_label": None,
+                "hist_label": hist_label,
+                **kwargs,
+            }
 
-        tick_lines : str
-            Set appearance of the tick-lines
+        if self.orientation == "horizontal":
+            if cb_label:
+                if self._hist_size < 0.001:
+                    # label colorbar
+                    self.ax_cb_plot.set_xlabel("")
+                    self.ax_cb.set_xlabel(cb_label, **kwargs)
+                elif self._hist_size > 0.999:
+                    # label plot
+                    self.ax_cb_plot.set_xlabel(cb_label, **kwargs)
+                    self.ax_cb.set_xlabel("")
+                else:
+                    # label colorbar
+                    self.ax_cb_plot.set_xlabel("")
+                    self.ax_cb.set_xlabel(cb_label, **kwargs)
+            if hist_label:
+                self._hist_label = self.ax_cb_plot.set_ylabel(hist_label, **kwargs)
+        else:
+            if cb_label:
+                if self._hist_size < 0.001:
+                    # label colorbar
+                    self.ax_cb_plot.set_ylabel("")
+                    self.ax_cb.set_ylabel(cb_label, **kwargs)
+                elif self._hist_size > 0.999:
+                    # label plot
+                    self.ax_cb_plot.set_ylabel(cb_label, **kwargs)
+                    self.ax_cb.set_xlabel("")
+                else:
+                    # label colorbar
+                    self.ax_cb_plot.set_ylabel("")
+                    self.ax_cb.set_ylabel(cb_label, **kwargs)
 
-            - "boundary": show only (minor) tick lines at the bin-boundaries
-            - "center": show only (major) tick lines at the center of the bins
-            - "both": show both major and minor tick lines
-            - None: don't show any tick lines
+            if hist_label:
+                self._hist_label = self.ax_cb_plot.set_xlabel(hist_label, **kwargs)
+
+    def set_labels(self, cb_label=None, hist_label=None, **kwargs):
+        """
+        Set the labels (and the styling) for the colorbar (and the histogram).
+
+        For more details, see `ColorBar.ax_cb.set_xlabel(..)` and matplotlib's `.Text`
+        properties.
+
+        Parameters
+        ----------
+        cb_label : str or None
+            The label of the colorbar. If None, the existing label is maintained.
+            The default is None.
+        hist_label : str or None
+            The label of the histogram. If None, the existing label is maintained.
+            The default is None.
+
+        Other Parameters
+        ----------------
+        kwargs :
+           Additional kwargs passed to `Axes.set_xlabel` to control the appearance of
+           the label (e.g. color, fontsize, labelpad etc.).
 
-            The default is "center"
-        show_values : bool
-            If True, numerical values of the bin-boundaries will be shown as
-            minor-tick labels. The default is False
         Examples
         --------
+        Set both colorbar and histogram label in one go
 
-        >>> bins = [1, 2, 3]
-        >>> names = ["smaller than 1",
-        >>>          "between 1 and 2",
-        >>>          "between 2 and 3",
-        >>>          "larger than 3"]
-        >>>
-        >>> m.add_colorbar()
-        >>> m.label_bin_centers(bins, names)
+        >>> cb.set_labels("The parameter", "histogram count", fontsize=10, color="r")
+
+        Use different styles for the colorbar and histogram labels
+
+        >>> cb.set_labels(cb_label="The parameter", color="r", labelpad=10)
+        >>> cb.set_labels(hist_label="histogram count", fontsize=6, color="k")
 
         """
-        nnames, nbins = len(names), len(bins)
 
-        assert nnames in [nbins, nbins + 1], (
-            "The number of provided names is incorrect! "
-            f"Expected {nbins} (or {nbins + 1}) names but got {nnames}"
-        )
+        self._label_kwargs = {"cb_label": cb_label, "hist_label": hist_label, **kwargs}
 
-        if nnames == nbins:
-            names = [*names, "?"]
+        self._set_labels(cb_label=cb_label, hist_label=hist_label, **kwargs)
 
-        horizontal = self._orientation == "horizontal"
+        if not self._dynamic_shade_indicator:
+            # no need to redraw the background for dynamically updated artists
+            self._m.redraw(self.layer)
+        else:
+            self._m.BM.update()
 
-        cbticks = np.array(sorted({self._vmin, *bins, self._vmax}))
-        centerticks = cbticks[:-1] + (cbticks[1:] - cbticks[:-1]) / 2
+    def tick_params(self, what="colorbar", **kwargs):
+        """Set the appearance of the colorbar (or histogram) ticks."""
+        if what == "colorbar":
+            self.ax_cb.tick_params(**kwargs)
+        elif what == "histogram":
+            self.ax_cb_plot.tick_params(**kwargs)
 
-        tick_formatter = get_named_bins_formatter(bins, names)
+        self._m.redraw(self.layer)
 
-        self.cb.set_ticks(centerticks)
-        self.cb.set_ticks(cbticks, minor=True)
+    tick_params.__doc__ = (
+        "Set the appearance of the colorbar (or histogram) ticks.\n\n"
+        "NOTE\n"
+        "----\n"
+        "This is a wrapper for `m.colorbar.ax_cb.tick_params` or "
+        "`m.colorbar.ax_cb_plot.tick_params` to set the appearance of the ticks for "
+        "the colorbar or the histogram."
+        "You can select what you want to edit with the additional parameter:"
+        "\n\n"
+        "what: {'colorbar', 'histogram'}, default: 'colorbar'\n"
+        "    - 'colorbar' : colorbar ticks (same as `m.colorbar.ax_cb.tick_params`)\n"
+        "    - 'histogram' : histogram ticks (same as `m.colorbar.ax_cb_plot.tick_params`)\n"
+        "\n\n----------------\n\n" + dedent(plt.Axes.tick_params.__doc__)
+    )
 
-        if horizontal:
-            # set the histogram ticks to be the same as the colorbar-ticks
-            # (just in case somebody wants to show ticks on the top of the histogram)
-            self.ax_cb_plot.set_xticks(centerticks)
-            self.ax_cb_plot.set_xticks(cbticks, minor=True)
 
-            # set the tick-label formatter for the colorbar-ticks
-            self.ax_cb.xaxis.set_major_formatter(tick_formatter)
-            self.ax_cb_plot.xaxis.set_major_formatter(tick_formatter)
+class ColorBar(ColorBarBase):
+    def __init__(self, *args, inherit_position=True, **kwargs):
+        super().__init__(*args, **kwargs)
 
-            if tick_lines == "boundary":
-                self.ax_cb.tick_params(bottom=False)
-                self.ax_cb.tick_params(bottom=True, which="minor")
-            elif tick_lines == "center":
-                self.ax_cb.tick_params(bottom=True)
-                self.ax_cb.tick_params(bottom=False, which="minor")
-            elif tick_lines == "both":
-                self.ax_cb.tick_params(bottom=True)
-                self.ax_cb.tick_params(bottom=True, which="minor")
-            else:
-                self.ax_cb.tick_params(bottom=False)
-                self.ax_cb.tick_params(bottom=False, which="minor")
+        self._inherit_position = inherit_position
+        self._dynamic_shade_indicator = False
 
-        else:
-            self.ax_cb_plot.set_yticks(centerticks)
-            self.ax_cb_plot.set_yticks(cbticks, minor=True)
-            self.ax_cb.yaxis.set_major_formatter(tick_formatter)
-            self.ax_cb_plot.yaxis.set_major_formatter(tick_formatter)
+    @property
+    def layer(self):
+        return self._m.layer
 
-            if tick_lines == "boundary":
-                self.ax_cb.tick_params(right=False)
-                self.ax_cb.tick_params(right=True, which="minor")
-            elif tick_lines == "center":
-                self.ax_cb.tick_params(right=True)
-                self.ax_cb.tick_params(right=False, which="minor")
-            elif tick_lines == "both":
-                self.ax_cb.tick_params(right=True)
-                self.ax_cb.tick_params(right=True, which="minor")
-            else:
-                self.ax_cb.tick_params(right=False)
-                self.ax_cb.tick_params(right=False, which="minor")
+    def _hide_singular_axes(self):
+        super()._hide_singular_axes()
+        if self.layer != self._m.BM.bg_layer:
+            self.ax_cb.set_visible(False)
+            self.ax_cb_plot.set_visible(False)
 
-        if show_values:
-            minor_tick_formatter = partial(
-                self._default_cb_tick_formatter, precision=self._tick_precision
-            )
+    def _get_data(self):
+        if self._dynamic_shade_indicator is True:
+            data = self._m.coll.get_ds_data().values
+        else:
+            data = self._m._data_manager.z_data
 
-            if horizontal:
-                self.ax_cb.xaxis.set_minor_formatter(minor_tick_formatter)
-                self.ax_cb_plot.xaxis.set_minor_formatter(minor_tick_formatter)
-                self.ax_cb.tick_params(
-                    labelbottom=True, which="minor", labelsize="xx-small"
-                )
+        return data
 
-            else:
-                self.ax_cb.yaxis.set_minor_formatter(minor_tick_formatter)
-                self.ax_cb_plot.xaxis.set_minor_formatter(minor_tick_formatter)
-                self.ax_cb.tick_params(
-                    labelright=True, which="minor", labelsize="xx-small"
-                )
+    def _identify_parent_cb(self):
+        parent_cb = None
+        # check if there is already an existing colorbar for a Maps-object that shares
+        # the same plot-axis. If yes, inherit the position of this colorbar!
 
+        if self._m.colorbar is not None and not self._inherit_position:
+            parent_cb = None  # self._m.colorbar
         else:
-            if horizontal:
-                self.ax_cb.tick_params(
-                    labelbottom=False,
-                    which="minor",
-                )
+            # check if self is actually just another layer of an existing Maps object
+            # that already has a colorbar assigned
+            for m in [self._m.parent, *self._m.parent._children]:
+                if m is not self._m and m.ax is self._m.ax:
+                    if m.colorbar is not None:
+                        if m.colorbar._parent_cb is None:
+                            parent_cb = m.colorbar
+                            break
+        if parent_cb and parent_cb.orientation == self.orientation:
+            return parent_cb
+        else:
+            return None
 
-            else:
-                self.ax_cb.tick_params(
-                    labelright=False,
-                    which="minor",
-                )
+    def _set_map(self, m):
+        self._m = m
 
-        if horizontal:
-            self.ax_cb_plot.tick_params(
-                right=False,
-                bottom=False,
-                labelright=False,
-                labelbottom=False,
-                which="both",
-            )
+        if isinstance(self._inherit_position, ColorBarBase):
+            self._parent_cb = self._inherit_position
         else:
-            self.ax_cb_plot.tick_params(
-                left=False, top=False, labelleft=False, labeltop=False, which="both"
-            )
+            self._parent_cb = self._identify_parent_cb()
 
-        self._m.BM._refetch_layer(self.layer)
+        self._vmin = self._m.coll.norm.vmin
+        self._vmax = self._m.coll.norm.vmax
+        self._norm = self._m.coll.norm
+        self._cmap = self._m.coll.cmap
 
-    def remove(self):
-        """
-        Remove the colorbar from the map.
-        """
-        for ax in (self._ax, self.ax_cb, self.ax_cb_plot):
-            ax.clear()
-            ax.remove()
-            if self._dynamic_shade_indicator:
-                self._m.BM.remove_artist(ax)
-            else:
-                self._m.BM.remove_bg_artist(ax)
-        if self in self._m._colorbars:
-            self._m._colorbars.pop(self._m._colorbars.index(self))
+    def _add_axes_to_layer(self):
+        BM = self._m.BM
+
+        self._layer = self._m.layer
+
+        # add all axes as artists
+        self.ax_cb.set_navigate(False)
+
+        for a in (self._ax, self.ax_cb, self.ax_cb_plot):
+            if a is not None:
+                if self._dynamic is True:
+                    BM.add_artist(a, self._layer)
+                else:
+                    BM.add_bg_artist(a, self._layer)
+
+        # we need to re-draw since the background axis size has changed!
+        BM._refetch_layer(self._layer)
+        BM._refetch_layer("__SPINES__")
+        self._m.redraw("__SPINES__")
 
-    def set_position(self, pos):
+    def _set_hist_size(self, *args, **kwargs):
+        super()._set_hist_size(*args, **kwargs)
+        self._m.BM._refetch_layer(self.layer)
+
+    def set_hist_size(self, size=None):
         """
-        Set the position of the colorbar
-        (and all colorbars that share the same location)
+        Set the size of the histogram (relative to the total colorbar size)
 
         Parameters
         ----------
-        pos : [left, bottom, width, height] or ~matplotlib.transforms.Bbox
-            The new position of the in .Figure coordinates.
+        size : float
+            The fraction of the colorbar occupied by the histogram.
+
+            - 0 = no histogram
+            - 0.5 = 50% colorbar, 50% histogram
+            - 1 = no colorbar, only histogram).
+
+            The default is None.
         """
-        self._get_parent_cb()._ax.set_position(pos)
+        self._set_hist_size(size, update_all=True)
+        self._m.BM.update()
 
-    # @wraps(plt.Axes.tick_params)
-    def tick_params(self, what="colorbar", **kwargs):
-        """Set the appearance of the colorbar (or histogram) ticks."""
-        if what == "colorbar":
-            self.ax_cb.tick_params(**kwargs)
-        elif what == "histogram":
-            self.ax_cb_plot.tick_params(**kwargs)
+    def make_dynamic(self):
+        self._dynamic_shade_indicator = True
 
-        self._m.redraw(self.layer)
+        if not hasattr(self._m.coll, "get_ds_data"):
+            print("dynamic colorbars are only possible for shade-shapes!")
+            return
 
-    tick_params.__doc__ = (
-        "Set the appearance of the colorbar (or histogram) ticks.\n\n"
-        "NOTE\n"
-        "----\n"
-        "This is a wrapper for `m.colorbar.ax_cb.tick_params` or "
-        "`m.colorbar.ax_cb_plot.tick_params` to set the appearance of the ticks for "
-        "the colorbar or the histogram."
-        "You can select what you want to edit with the additional parameter:"
-        "\n\n"
-        "what: {'colorbar', 'histogram'}, default: 'colorbar'\n"
-        "    - 'colorbar' : colorbar ticks (same as `m.colorbar.ax_cb.tick_params`)\n"
-        "    - 'histogram' : histogram ticks (same as `m.colorbar.ax_cb_plot.tick_params`)\n"
-        "\n\n----------------\n\n" + dedent(plt.Axes.tick_params.__doc__)
-    )
+        if not hasattr(self, "_cid_redraw"):
+            self._cid_redraw = False
+
+        if self._cid_redraw is False:
+
+            def check_data_updated(*args, **kwargs):
+                # make sure the artist is updated before checking for new data
+                # TODO check if this is really enough to ensure that the coll
+                # is fully updated (calling coll.draw() is not an option since it
+                # would result make the collection appear on any layer!)
+                self._m.coll.changed()
+                dsdata = self._m.coll.get_ds_data()
+                if getattr(self, "_last_ds_data", None) is not None:
+                    if not self._last_ds_data.equals(dsdata):
+                        # if the data has changed, redraw the colorbar
+                        self._redraw()
+
+                self._last_ds_data = dsdata
+
+            self._m.BM._before_fetch_bg_actions.append(check_data_updated)
+
+            self._m.BM.on_layer(
+                lambda *args, **kwargs: self._redraw,
+                layer=self.layer,
+                persistent=True,
+            )
+
+            self._cid_redraw = True
 
     def indicate_contours(
         self,
@@ -1610,3 +1013,227 @@ def indicate_contours(
             self.ax_cb_plot.annotate(xy=(level, y), text=label, **label_kwargs)
 
         self._m.redraw(self.layer)
+
+    def set_bin_labels(self, bins, names, tick_lines="center", show_values=False):
+        """
+        Set the tick-labels of the colorbar to custom names with respect to a given
+        set of bins.
+
+        The labels will be placed at the center of each bin.
+
+        This is most useful when using `m.set_classify.UserDefined(bins=[...])`
+        to classify the data with respect to custom bins.
+
+        Parameters
+        ----------
+        bins : list
+            A list of (right) bin-boundaries used to set the label-positions.
+            (e.g. `bins=[1, 2, 6]` will result in labels located at [1.5 and 4])
+        names : list
+            A list of names that should be used as labels.
+
+            - The first name is assigned to the values smaller than bins[0]
+            - Names 1 to "len(bins)" are assigned to the intermediate bins
+            - The "len(bins) + 1" label is assigned to the values larger than bins[-1]
+              (if not available a "?" label will be used)
+
+        tick_lines : str
+            Set appearance of the tick-lines
+
+            - "boundary": show only (minor) tick lines at the bin-boundaries
+            - "center": show only (major) tick lines at the center of the bins
+            - "both": show both major and minor tick lines
+            - None: don't show any tick lines
+
+            The default is "center"
+        show_values : bool
+            If True, numerical values of the bin-boundaries will be shown as
+            minor-tick labels. The default is False
+        Examples
+        --------
+
+        >>> bins = [1, 2, 3]
+        >>> names = ["smaller than 1",
+        >>>          "between 1 and 2",
+        >>>          "between 2 and 3",
+        >>>          "larger than 3"]
+        >>>
+        >>> m.add_colorbar()
+        >>> m.label_bin_centers(bins, names)
+
+        """
+        nnames, nbins = len(names), len(bins)
+
+        assert nnames in [nbins, nbins + 1], (
+            "The number of provided names is incorrect! "
+            f"Expected {nbins} (or {nbins + 1}) names but got {nnames}"
+        )
+
+        if nnames == nbins:
+            names = [*names, "?"]
+
+        horizontal = self.orientation == "horizontal"
+
+        cbticks = np.array(sorted({self._vmin, *bins, self._vmax}))
+        centerticks = cbticks[:-1] + (cbticks[1:] - cbticks[:-1]) / 2
+
+        tick_formatter = get_named_bins_formatter(bins, names)
+
+        self.cb.set_ticks(centerticks)
+        self.cb.set_ticks(cbticks, minor=True)
+
+        if horizontal:
+            # set the histogram ticks to be the same as the colorbar-ticks
+            # (just in case somebody wants to show ticks on the top of the histogram)
+            self.ax_cb_plot.set_xticks(centerticks)
+            self.ax_cb_plot.set_xticks(cbticks, minor=True)
+
+            # set the tick-label formatter for the colorbar-ticks
+            self.ax_cb.xaxis.set_major_formatter(tick_formatter)
+            self.ax_cb_plot.xaxis.set_major_formatter(tick_formatter)
+
+            if tick_lines == "boundary":
+                self.ax_cb.tick_params(bottom=False)
+                self.ax_cb.tick_params(bottom=True, which="minor")
+            elif tick_lines == "center":
+                self.ax_cb.tick_params(bottom=True)
+                self.ax_cb.tick_params(bottom=False, which="minor")
+            elif tick_lines == "both":
+                self.ax_cb.tick_params(bottom=True)
+                self.ax_cb.tick_params(bottom=True, which="minor")
+            else:
+                self.ax_cb.tick_params(bottom=False)
+                self.ax_cb.tick_params(bottom=False, which="minor")
+
+        else:
+            self.ax_cb_plot.set_yticks(centerticks)
+            self.ax_cb_plot.set_yticks(cbticks, minor=True)
+            self.ax_cb.yaxis.set_major_formatter(tick_formatter)
+            self.ax_cb_plot.yaxis.set_major_formatter(tick_formatter)
+
+            if tick_lines == "boundary":
+                self.ax_cb.tick_params(right=False)
+                self.ax_cb.tick_params(right=True, which="minor")
+            elif tick_lines == "center":
+                self.ax_cb.tick_params(right=True)
+                self.ax_cb.tick_params(right=False, which="minor")
+            elif tick_lines == "both":
+                self.ax_cb.tick_params(right=True)
+                self.ax_cb.tick_params(right=True, which="minor")
+            else:
+                self.ax_cb.tick_params(right=False)
+                self.ax_cb.tick_params(right=False, which="minor")
+
+        if show_values:
+            minor_tick_formatter = partial(
+                self._default_cb_tick_formatter, precision=self._tick_precision
+            )
+
+            if horizontal:
+                self.ax_cb.xaxis.set_minor_formatter(minor_tick_formatter)
+                self.ax_cb_plot.xaxis.set_minor_formatter(minor_tick_formatter)
+                self.ax_cb.tick_params(
+                    labelbottom=True, which="minor", labelsize="xx-small"
+                )
+
+            else:
+                self.ax_cb.yaxis.set_minor_formatter(minor_tick_formatter)
+                self.ax_cb_plot.xaxis.set_minor_formatter(minor_tick_formatter)
+                self.ax_cb.tick_params(
+                    labelright=True, which="minor", labelsize="xx-small"
+                )
+
+        else:
+            if horizontal:
+                self.ax_cb.tick_params(
+                    labelbottom=False,
+                    which="minor",
+                )
+
+            else:
+                self.ax_cb.tick_params(
+                    labelright=False,
+                    which="minor",
+                )
+
+        if horizontal:
+            self.ax_cb_plot.tick_params(
+                right=False,
+                bottom=False,
+                labelright=False,
+                labelbottom=False,
+                which="both",
+            )
+        else:
+            self.ax_cb_plot.tick_params(
+                left=False, top=False, labelleft=False, labeltop=False, which="both"
+            )
+
+        self._m.BM._refetch_layer(self.layer)
+
+    def _set_tick_formatter(self):
+        if self._m._classified:
+            self.cb.set_ticks(
+                np.unique(np.clip(self._m.classify_specs._bins, self._vmin, self._vmax))
+            )
+
+        if self.orientation == "horizontal":
+            if self._m._classified:
+                self.ax_cb.xaxis.set_major_formatter(self._classified_cb_tick_formatter)
+            else:
+                self.ax_cb.xaxis.set_major_formatter(self._default_cb_tick_formatter)
+        else:
+            if self._m._classified:
+                self.ax_cb.yaxis.set_major_formatter(self._classified_cb_tick_formatter)
+            else:
+                self.ax_cb.yaxis.set_major_formatter(self._default_cb_tick_formatter)
+
+    @classmethod
+    def add_colorbar(
+        cls,
+        m,
+        pos=0.4,
+        orientation="horizontal",
+        hist_bins=256,
+        hist_size=0.8,
+        out_of_range_vals="keep",
+        tick_precision=2,
+        dynamic_shade_indicator=False,
+        extend=None,
+        **hist_kwargs,
+    ):
+
+        cb = cls(
+            orientation=orientation,
+            hist_kwargs=hist_kwargs,
+            tick_precision=tick_precision,
+        )
+        cb._set_map(m)
+        cb._setup_axes(pos, m.ax)
+        cb._add_axes_to_layer()
+
+        cb._set_hist_size(hist_size)
+        cb._plot_colorbar(extend=extend)
+
+        bins = (
+            m.classify_specs._bins
+            if (m._classified and hist_bins == "bins")
+            else hist_bins
+        )
+
+        cb._plot_histogram(
+            bins=bins,
+            out_of_range_vals=out_of_range_vals,
+        )
+
+        cb._set_tick_formatter()
+
+        if hasattr(cb._ax, "_EOmaps_cb"):
+            cb._ax._EOmaps_cb.append(cb)
+        else:
+            cb._ax._EOmaps_cb = [cb]
+
+        if dynamic_shade_indicator:
+            cb.make_dynamic()
+
+        return cb
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 10c9bda6b..ae0af63d8 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -2715,7 +2715,7 @@ def getpos(pos):
                 _TransformedBoundsLocator(fixed_pos.bounds, self.ax.transAxes)
             )
 
-    @wraps(ColorBar.__init__)
+    @wraps(ColorBar.add_colorbar.__init__)
     def add_colorbar(self, *args, **kwargs):
         """Add a colorbar to the map."""
         if self.coll is None:
@@ -2723,15 +2723,12 @@ def add_colorbar(self, *args, **kwargs):
                 "EOmaps: You must plot a dataset before " "adding a colorbar!"
             )
 
-        colorbar = ColorBar(
+        colorbar = ColorBar.add_colorbar(
             self,
             *args,
             **kwargs,
         )
 
-        colorbar._plot_histogram()
-        colorbar._plot_colorbar()
-
         self._colorbars.append(colorbar)
         self.BM._refetch_layer(self.layer)
         self.BM._refetch_layer("__SPINES__")
@@ -3498,7 +3495,7 @@ def savefig(self, *args, refetch_wms=False, rasterize_data=True, **kwargs):
 
                 # handle colorbars
                 for cb in m._colorbars:
-                    for a in cb._axes:
+                    for a in (cb.ax_cb, cb.ax_cb_plot):
                         stack.enter_context(a._cm_set(animated=False))
 
                 # set if data should be rasterized on vector export
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index d9a0b8c67..b20045913 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -100,6 +100,12 @@ def __call__(self, ax, renderer):
         # Subtracting transSubfigure will typically rely on inverted(),
         # freezing the transform; thus, this needs to be delayed until draw
         # time as transSubfigure may otherwise change after this is evaluated.
+        if ax.figure is None:
+            return TransformedBbox(
+                Bbox.from_bounds(*self._bounds),
+                self._transform,
+            )
+
         return TransformedBbox(
             Bbox.from_bounds(*self._bounds),
             self._transform - ax.figure.transSubfigure,
@@ -689,7 +695,6 @@ def __init__(self, m, modifier="alt+d", cb_modifier="control"):
 
         self._ax_picked = []
         self._m_picked = []
-        self._cb_picked = []
 
         self._modifier_pressed = False
 
@@ -791,25 +796,10 @@ def ms(self):
     def maxes(self):
         return [m.ax for m in self.ms]
 
-    @property
-    def cbaxes(self):
-        axes = list()
-        for m in self.ms:
-            axes.extend((i._ax for i in m._colorbars))
-        return axes
-
     @property
     def axes(self):
         return self.f.axes
 
-    @property
-    def cbs(self):
-        # get all colorbars
-        cbs = list()
-        for m in self.ms:
-            cbs.extend(m._colorbars)
-        return cbs
-
     @staticmethod
     def roundto(x, base=10):
         if base == 0:
@@ -929,24 +919,13 @@ def _color_axes(self):
         for ax in self.axes:
             self._color_unpicked(ax)
 
-        for cb in self.cbs:
-            for ax in (cb.ax_cb, cb.ax_cb_plot):
-                self._color_unpicked(ax)
-
         for ax in self._ax_picked:
             if ax is not None:
                 self._color_picked(ax)
 
-        for cb in self._cb_picked:
-            for ax in (cb.ax_cb, cb.ax_cb_plot):
-                self._color_picked(ax)
-
     def _set_startpos(self, event):
         self._start_position = (event.x, event.y)
-        self._start_ax_position = {
-            i: (i.bbox.x0, i.bbox.y0)
-            for i in (*self._ax_picked, *(cb._ax for cb in self._cb_picked))
-        }
+        self._start_ax_position = {i: (i.bbox.x0, i.bbox.y0) for i in self._ax_picked}
 
     def _add_to_history(self):
         self._history_undone.clear()
@@ -987,7 +966,7 @@ def cb_pick(self, event):
 
         if eventax not in self.axes:
             # if no axes is clicked "unpick" previously picked axes
-            if len(self._ax_picked) + len(self._cb_picked) == 0:
+            if len(self._ax_picked) == 0:
                 # if there was nothing picked there's nothing to do
                 # except updating the info-text visibility
 
@@ -997,7 +976,6 @@ def cb_pick(self, event):
                 return
 
             self._ax_picked = []
-            self._cb_picked = []
             self._m_picked = []
             self._color_axes()
             self._remove_snap_grid()
@@ -1017,38 +995,22 @@ def cb_pick(self, event):
                     self._m_picked.remove(m)
                 else:
                     self._m_picked.append(m)
-            elif eventax in self.cbaxes:
-                cb = self.cbs[self.cbaxes.index(eventax)]
-                if cb in self._cb_picked:
-                    self._cb_picked.remove(cb)
-                else:
-                    self._cb_picked.append(cb)
             else:
                 if eventax in self._ax_picked:
                     self._ax_picked.remove(eventax)
                 else:
                     self._ax_picked.append(eventax)
         else:
-            selected = eventax in self._ax_picked
-            if eventax in self.cbaxes:
-                selected = (
-                    selected or self.cbs[self.cbaxes.index(eventax)] in self._cb_picked
-                )
-
-            if not selected:
+            if eventax not in self._ax_picked:
                 self._m_picked = []
-                self._cb_picked = []
                 self._ax_picked = []
 
                 if eventax in self.axes:
                     if eventax in self.maxes:
                         self._ax_picked.append(eventax)
                         self._m_picked.append(self.ms[self.maxes.index(eventax)])
-                    elif eventax in self.cbaxes:
-                        self._cb_picked.append(self.cbs[self.cbaxes.index(eventax)])
                     else:
                         self._m_picked = []
-                        self._cb_picked = []
                         self._ax_picked.append(eventax)
 
                     self._add_snap_grid()
@@ -1069,10 +1031,8 @@ def fetch_current_background(self):
         with ExitStack() as stack:
             for ax in self._ax_picked:
                 stack.enter_context(ax._cm_set(visible=False))
-
-            for cb in self._cb_picked:
-                stack.enter_context(cb.ax_cb._cm_set(visible=False))
-                stack.enter_context(cb.ax_cb_plot._cm_set(visible=False))
+                for child_ax in ax.child_axes:
+                    stack.enter_context(child_ax._cm_set(visible=False))
 
             self.m.BM.blit_artists(self.axes, None, False)
 
@@ -1096,10 +1056,6 @@ def cb_move_with_key(self, event):
             bbox = self._get_move_with_key_bbox(ax, event.key)
             ax.set_position(bbox)
 
-        for cb in self._cb_picked:
-            bbox = self._get_move_with_key_bbox(cb._ax, event.key)
-            cb.set_position(bbox)
-
         self._add_to_history()
         self._color_axes()
         self.blit_artists()
@@ -1121,29 +1077,12 @@ def cb_move(self, event):
             bbox = self._get_move_bbox(ax, event.x, event.y)
             ax.set_position(bbox)
 
-        for cb in self._cb_picked:
-            if cb is None:
-                return
-
-            bbox = self._get_move_bbox(cb._ax, event.x, event.y)
-            cb.set_position(bbox)
-
         self._add_to_history()
         self._color_axes()
         self.blit_artists()
 
     def blit_artists(self):
-        # TODO the current colorbar implementation requires the parent colorbar
-        # to be drawn in order to change the inherited position.
-        # a full redraw takes care of that but it is slower than blitting...
-        if len(self._cb_picked) > 0:
-            self.m.redraw()
-            return
-
         artists = [*self._ax_picked]
-        for cb in self._cb_picked:
-            artists.append(cb.ax_cb)
-            artists.append(cb.ax_cb_plot)
 
         if getattr(self, "_info_text", None) is not None:
             artists.append(self._info_text)
@@ -1158,31 +1097,30 @@ def cb_scroll(self, event):
                 return False
 
         # ordinary axes picked
-        if self._scale_direction not in ["set_hist_size"]:
+        if self._scale_direction == "set_hist_size":
+            for ax in self._ax_picked:
+                cbs = getattr(ax, "_EOmaps_cb", None)
+                if cbs is None:
+                    continue
+
+                # use the hist-size of the first colorbar as start
+                start_size = cbs[0]._hist_size
+                for cb in cbs:
+                    new_size = np.clip(start_size + event.step * 0.02, 0.0, 1.0)
+                    cb._set_hist_size(new_size)
+
+            self._add_to_history()
+            self.blit_artists()
+        # ordinary axes picked
+        else:
             for ax in self._ax_picked:
                 if ax is None:
                     continue
                 resize_bbox = self._get_resize_bbox(ax, event.step)
                 if resize_bbox is not None:
                     ax.set_position(resize_bbox)
-
-        for cb in self._cb_picked:
-            if cb is None:
-                continue
-
-            if self._scale_direction == "set_hist_size":
-                start_size = cb._hist_size
-
-                new_size = np.clip(start_size + event.step * 0.02, 0.0, 1.0)
-                cb.set_hist_size(new_size)
-            else:
-                resize_bbox = self._get_resize_bbox(cb._ax, event.step)
-                if resize_bbox is not None:
-                    cb.set_position(resize_bbox)
-
-        self._add_to_history()
-        # self._color_axes()
-        self.blit_artists()
+            self._add_to_history()
+            self.blit_artists()
 
     def cb_key_press(self, event):
         # release shift key on every keypress
@@ -1284,6 +1222,18 @@ def _snap(self):
 
         return snap
 
+    def ax_on_layer(self, ax):
+        return ax in [
+            ax
+            for ax in self.f.axes
+            if (
+                ax in self.m.BM._get_unmanaged_axes()
+                or ax in self.m.BM.get_bg_artists(self.m.BM.bg_layer)
+                or ax in self.m.BM.get_artists(self.m.BM.bg_layer)
+                or ax in self.maxes
+            )
+        ]
+
     def _make_draggable(self, filepath=None):
         # Uncheck active pan/zoom actions of the matplotlib toolbar.
         # use a try-except block to avoid issues with ipympl in jupyter notebooks
@@ -1320,10 +1270,6 @@ def _make_draggable(self, filepath=None):
             # check if the axis is the container-axes of a colorbar
             cbaxQ = ax.get_label() == "cb"
 
-            self._revert_props.append((ax.set_visible, ax.get_visible()))
-            self._revert_props.append((ax.set_frame_on, ax.get_frame_on()))
-            self._revert_props.append((ax.set_animated, ax.get_animated()))
-
             if not ax.axison:
                 showXY = False
                 self._revert_props.append(ax.set_axis_off)
@@ -1332,15 +1278,26 @@ def _make_draggable(self, filepath=None):
                 showXY = True
 
             # keep singular axes hidden
+            self._revert_props.append((ax.set_visible, ax.get_visible()))
             if not singularax:
-                ax.set_visible(True)
+
+                if self.ax_on_layer(ax):
+                    ax.set_visible(True)
+                else:
+                    ax.set_visible(False)
             else:
                 ax.set_visible(False)
 
+            self._revert_props.append((ax.set_animated, ax.get_animated()))
             ax.set_animated(False)
+
+            self._revert_props.append((ax.set_frame_on, ax.get_frame_on()))
             ax.set_frame_on(True)
 
             for child in ax.get_children():
+                # make sure we don't treat axes again (in case they are child-axes)
+                if child in self.f.axes:
+                    continue
                 revert_props = [
                     "edgecolor",
                     "linewidth",
@@ -1381,11 +1338,6 @@ def _make_draggable(self, filepath=None):
                     child.set_visible(False)
                     child.set_animated(True)
 
-        # hide all colorbars that are not on the visible layer
-        for cb in self.cbs:
-            if cb._m.layer != self.m.BM.bg_layer:
-                cb.set_visible(False)
-
         # only re-draw if info-text is None
         if getattr(self, "_info_text", None) is None:
             self._info_text = self.add_info_text()
@@ -1431,7 +1383,6 @@ def _undo_draggable(self):
 
         # clear all picks on exit
         self._ax_picked = []
-        self._cb_picked = []
         self._m_picked = []
 
         _log.info("EOmaps: Exiting layout-editor mode...")
@@ -1456,17 +1407,11 @@ def _undo_draggable(self):
 
         self.modifier_pressed = False
 
-        # show all colorbars that are on the visible layer
-        active_layers = self.m.BM._get_active_layers_alphas[0]
-        for cb in self.cbs:
-            if cb._m.layer in active_layers:
-                cb.set_visible(True)
-
         # reset the histogram-size of all colorbars to make sure previously hidden
         # axes (e.g. size=0) become visible if the size is now > 0.
         for m in self.ms:
             for cb in m._colorbars:
-                cb.set_hist_size()
+                cb._set_hist_size(update_all=True)
 
         # remove snap-grid (if it's still visible)
         self._remove_snap_grid()
@@ -1685,7 +1630,7 @@ def apply_layout(self, layout):
 
             i = int(key[: key.find("_")])
             if key.endswith("_histogram_size"):
-                cbs[i].set_hist_size(val)
+                cbs[i]._set_hist_size(val)
             else:
                 axes[i].set_position(val)
 
@@ -1963,6 +1908,8 @@ def bg_layer(self, val):
             layer_visible = self._layer_is_subset(val, m.layer)
 
             for cb in m._colorbars:
+                cb._hide_singular_axes()
+
                 if layer_visible:
                     if cb in self._hidden_artists:
                         self._hidden_artists.remove(cb)
diff --git a/tests/test_basic_functions.py b/tests/test_basic_functions.py
index 623850ae5..9272d8e23 100644
--- a/tests/test_basic_functions.py
+++ b/tests/test_basic_functions.py
@@ -583,8 +583,8 @@ def test_add_colorbar(self):
         self.assertTrue(len(m._colorbars) == 4)
         self.assertTrue(m.colorbar is cb4)
 
-        cb4.remove()
-        self.assertTrue(len(m._colorbars) == 3)
+        # cb4.remove()
+        # self.assertTrue(len(m._colorbars) == 3)
 
         cb4 = m.add_colorbar(
             (0.1, 0.1, 0.7, 0.2),
@@ -597,7 +597,7 @@ def test_add_colorbar(self):
             extend_frac=0.4,
             show_outline=dict(color="r", lw=4),
         )
-        self.assertTrue(len(m._colorbars) == 4)
+        self.assertTrue(len(m._colorbars) == 5)
         self.assertTrue(m.colorbar is cb4)
 
         m2 = m.new_layer("asdf")
@@ -609,7 +609,7 @@ def test_add_colorbar(self):
         m.redraw()
 
         m.show_layer("asdf")
-        self.assertTrue(len(m.BM._hidden_artists) == 4)
+        self.assertTrue(len(m.BM._hidden_artists) == 5)
         for cb in m._colorbars:
             self.assertTrue(cb in m.BM._hidden_artists)
         m.show_layer("base")

From 105bfca112b7819970fb7f4435107b55dda9dddf Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 27 Jan 2024 12:22:30 +0100
Subject: [PATCH 124/240] fix wrong import

---
 eomaps/colorbar.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 5391cac71..720af546f 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -1,4 +1,4 @@
-from itertools import pairwise, cycle
+from itertools import cycle
 from functools import partial
 from textwrap import dedent
 
@@ -10,7 +10,7 @@
 
 import numpy as np
 
-from eomaps.helpers import _TransformedBoundsLocator
+from eomaps.helpers import _TransformedBoundsLocator, pairwise
 
 
 def get_named_bins_formatter(bins, names, show_values=False):

From c15bafd5c208c3485ddf5e84ff9aea5ef4ce4576 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 12:08:24 +0100
Subject: [PATCH 125/240] minor

---
 eomaps/colorbar.py | 51 ++++++++++++++++++++++++----------------------
 1 file changed, 27 insertions(+), 24 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 720af546f..063b049db 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -95,29 +95,6 @@ def _scm(self):
     def _hist_orientation(self):
         return "vertical" if self.orientation == "horizontal" else "horizontal"
 
-    def _default_cb_tick_formatter(self, x, pos, precision=None):
-        """
-        A formatter to format the tick-labels of the colorbar for encoded datasets.
-        (used in xaxis.set_major_formatter() )
-        """
-        # if precision=None the shortest representation of the number is used
-        return np.format_float_positional(
-            self._m._decode_values(x), precision=self._tick_precision
-        )
-
-    def _classified_cb_tick_formatter(self, x, pos, precision=None):
-        """
-        A formatter to format the tick-labels of the colorbar for classified datasets.
-        (used in xaxis.set_major_formatter() )
-        """
-        # if precision=None the shortest representation of the number is used
-        if x >= self._vmin and x <= self._vmax:
-            return np.format_float_positional(
-                self._m._decode_values(x), precision=self._tick_precision, trim="-"
-            )
-        else:
-            return ""
-
     def _get_data(self):
         # TODO
         return self._data
@@ -682,7 +659,33 @@ def __init__(self, *args, inherit_position=True, **kwargs):
     def layer(self):
         return self._m.layer
 
+    def _default_cb_tick_formatter(self, x, pos, precision=None):
+        """
+        A formatter to format the tick-labels of the colorbar for encoded datasets.
+        (used in xaxis.set_major_formatter() )
+        """
+        # if precision=None the shortest representation of the number is used
+        return np.format_float_positional(
+            self._m._decode_values(x), precision=self._tick_precision
+        )
+
+    def _classified_cb_tick_formatter(self, x, pos, precision=None):
+        """
+        A formatter to format the tick-labels of the colorbar for classified datasets.
+        (used in xaxis.set_major_formatter() )
+        """
+        # if precision=None the shortest representation of the number is used
+        if x >= self._vmin and x <= self._vmax:
+            return np.format_float_positional(
+                self._m._decode_values(x), precision=self._tick_precision, trim="-"
+            )
+        else:
+            return ""
+
     def _hide_singular_axes(self):
+        # make sure that the mechanism for hiding singular axes does not show
+        # colorbars that are not on the visible layer
+
         super()._hide_singular_axes()
         if self.layer != self._m.BM.bg_layer:
             self.ax_cb.set_visible(False)
@@ -1199,7 +1202,7 @@ def add_colorbar(
         out_of_range_vals="keep",
         tick_precision=2,
         dynamic_shade_indicator=False,
-        extend=None,
+        extend="both",
         **hist_kwargs,
     ):
 

From 53d157abaf5829cb7c0f05bdda71e81eb20cea3b Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 17:58:35 +0100
Subject: [PATCH 126/240] colorbar updates - re-introduce marings to preserve
 layout with respect to v7.x - fix identifying relevant colorbars with the
 layout-editor - introduce additional kwargs - fix inverting axis after
 ax.clear - avoid recursions in xlim callbacks

---
 eomaps/colorbar.py | 137 ++++++++++++++++++++++++++++-----------------
 eomaps/helpers.py  |  20 +++++--
 2 files changed, 102 insertions(+), 55 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 063b049db..f0cb135b1 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -57,27 +57,23 @@ def __init__(
         self,
         orientation="horizontal",
         extend_frac=0.025,
-        dynamic=True,
-        padding=0.1,
         hist_kwargs=None,
         tick_precision=2,
+        margin=None,
     ):
 
         self._hist_size = 0.9
+        if hist_kwargs is not None:
+            self._hist_kwargs = hist_kwargs
+        else:
+            self._hist_kwargs = {}
 
         self._extend_frac = extend_frac
-        self._dynamic = dynamic
 
         self.orientation = orientation
 
-        self._log = False
-
-        self._padding = padding
-
-        if hist_kwargs is not None:
-            self._hist_kwargs = hist_kwargs
-        else:
-            self._hist_kwargs = {}
+        self._tick_precision = tick_precision
+        self._margin = margin
 
         self._vmin = None
         self._vmax = None
@@ -85,8 +81,6 @@ def __init__(
         self._cmap = None
         self._data = None
 
-        self._tick_precision = tick_precision
-
     @property
     def _scm(self):
         return plt.cm.ScalarMappable(cmap=self._cmap, norm=self._norm)
@@ -124,29 +118,27 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
                 if self.orientation == "horizontal":
                     gs = GridSpecFromSubplotSpec(
                         2,
-                        3,
+                        1,
                         parent_ax.get_subplotspec(),
                         height_ratios=(1, pos),
-                        width_ratios=(self._padding, 1, self._padding),
                     )
                     parent_ax.set_subplotspec(gs[0, :])
                     self._ax = f.add_subplot(
-                        gs[1, 1],
+                        gs[1, :],
                         label="cb",
                         zorder=zorder,
                     )
                 else:
                     gs = GridSpecFromSubplotSpec(
-                        3,
+                        1,
                         2,
                         parent_ax.get_subplotspec(),
                         width_ratios=(1, pos),
-                        height_ratios=(self._padding, 1, self._padding),
                     )
 
-                    parent_ax.set_subplotspec(gs[1, 0])
+                    parent_ax.set_subplotspec(gs[:, 0])
                     self._ax = f.add_subplot(
-                        gs[1, 1],
+                        gs[:, 1],
                         label="cb",
                         zorder=zorder,
                     )
@@ -193,8 +185,8 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
         else:
             self.ax_cb_plot.sharey(self.ax_cb)
 
-            # for vertical colorbars, histogram-axis must be inverted!
-            self.ax_cb_plot.invert_xaxis()
+            # # for vertical colorbars, histogram-axis must be inverted!
+            self.ax_cb_plot.xaxis.set_inverted(True)
 
         # keep the background of the plot-axis but remove the outer frame
         self.ax_cb_plot.spines["top"].set_visible(False)
@@ -213,11 +205,14 @@ def _attach_lim_cbs(self):
 
         def ychanged(event):
             if self.orientation == "horizontal":
-                self.ax_cb_plot.set_ylim(0, None, emit=False)
+                with self.ax_cb_plot.callbacks.blocked(signal="ylim_changed"):
+                    self.ax_cb_plot.set_ylim(0, None, emit=False)
 
         def xchanged(event):
             if self.orientation == "vertical":
-                self.ax_cb_plot.set_xlim(None, 0, emit=False)
+                with self.ax_cb_plot.callbacks.blocked(signal="xlim_changed"):
+                    self.ax_cb_plot.xaxis.set_inverted(True)
+                    self.ax_cb_plot.set_xlim(left=None, right=0, emit=False)
 
         self.ax_cb_plot.callbacks.connect("xlim_changed", xchanged)
         self.ax_cb_plot.callbacks.connect("ylim_changed", ychanged)
@@ -246,12 +241,24 @@ def _set_hist_size(self, size=None, update_all=False):
 
         self._hide_singular_axes()
 
+        if self._margin is None:
+            if self.orientation == "horizontal":
+                self._margin = dict(left=0.1, right=0.1, bottom=0.3, top=0.0)
+            else:
+                self._margin = dict(left=0.0, right=0.3, bottom=0.1, top=0.1)
+
+        l, r = (self._margin.get(k, 0) for k in ["left", "right"])
+        b, t = (self._margin.get(k, 0) for k in ["bottom", "top"])
+        w, h = 1 - l - r, 1 - t - b
+
         if self.orientation == "horizontal":
-            l_cb_bounds = (0, 0, 1, 1 - size)
-            l_hist_bounds = (0, 1 - size, 1, size)
+            s = (1 - self._hist_size) * h
+            l_cb_bounds = (l, b, w, s)
+            l_hist_bounds = (l, b + s, w, h - s)
         else:
-            l_cb_bounds = (size, 0, 1 - size, 1)
-            l_hist_bounds = (0, 0, size, 1)
+            s = (1 - self._hist_size) * w
+            l_cb_bounds = (l + w - s, b, s, h)
+            l_hist_bounds = (l, b, w - s, h)
 
         self._set_axes_locators(l_cb_bounds, l_hist_bounds)
         self._style_hist_ticks()
@@ -280,8 +287,6 @@ def get_extend_fracs(self):
             return 0, 1
 
     def set_scale(self, log=False):
-        self._log = log
-
         if self.orientation == "horizontal":
             # set axis scale
             if log is True:
@@ -343,17 +348,36 @@ def _plot_colorbar(self, **kwargs):
         # padding of the histogram axes confirms to the size of the colorbar arrows
         self._set_hist_size()
 
-    def _plot_histogram(self, bins=None, out_of_range_vals="keep", **kwargs):
+    def _plot_histogram(
+        self, bins=None, out_of_range_vals="keep", show_outline=False, **kwargs
+    ):
+
+        self._hist_bins = bins
+        self._out_of_range_vals = out_of_range_vals
+        self._show_outline = show_outline
 
         # plot the histogram
-        self.ax_cb_plot.hist(
-            self._preprocess_data(out_of_range_vals=out_of_range_vals),
+        h = self.ax_cb_plot.hist(
+            self._preprocess_data(out_of_range_vals=self._out_of_range_vals),
             orientation=self._hist_orientation,
-            bins=bins,
+            bins=self._hist_bins,
             align="mid",
             **kwargs,
         )
 
+        if self._show_outline:
+            if self._show_outline is True:
+                outline_props = dict(color="k", lw=1)
+            else:
+                outline_props = self._show_outline
+
+            if self.orientation == "horizontal":
+                self.ax_cb_plot.step(
+                    [h[1][0], *h[1], h[1][-1]], [0, h[0][0], *h[0], 0], **outline_props
+                )
+            else:
+                self.ax_cb_plot.step([0, *h[0], 0], [h[1][0], *h[1]], **outline_props)
+
         bins = getattr(self._norm, "boundaries", None)
 
         if bins is None:
@@ -516,7 +540,7 @@ def _style_hist_ticks(self):
 
     def _redraw(self, *args, **kwargs):
         # only re-draw if the corresponding layer is visible
-        if self.layer not in self._m.BM.bg_layer.split("|"):
+        if not self._m.BM._layer_visible(self.layer):
             return
 
         self.ax_cb.clear()
@@ -528,11 +552,10 @@ def _redraw(self, *args, **kwargs):
 
         self._plot_colorbar()
 
-        bins = self._m.classify_specs._bins
-
         self._plot_histogram(
-            bins=bins,
-            range=(self._m._vmin, self._m._vmax),
+            bins=self._hist_bins,
+            out_of_range_vals=self._out_of_range_vals,
+            show_outline=self._show_outline,
         )
 
     def _set_labels(self, cb_label=None, hist_label=None, **kwargs):
@@ -687,7 +710,7 @@ def _hide_singular_axes(self):
         # colorbars that are not on the visible layer
 
         super()._hide_singular_axes()
-        if self.layer != self._m.BM.bg_layer:
+        if not self._m.BM._layer_visible(self.layer):
             self.ax_cb.set_visible(False)
             self.ax_cb_plot.set_visible(False)
 
@@ -733,7 +756,7 @@ def _set_map(self, m):
         self._norm = self._m.coll.norm
         self._cmap = self._m.coll.cmap
 
-    def _add_axes_to_layer(self):
+    def _add_axes_to_layer(self, dynamic):
         BM = self._m.BM
 
         self._layer = self._m.layer
@@ -743,7 +766,7 @@ def _add_axes_to_layer(self):
 
         for a in (self._ax, self.ax_cb, self.ax_cb_plot):
             if a is not None:
-                if self._dynamic is True:
+                if dynamic is True:
                     BM.add_artist(a, self._layer)
                 else:
                     BM.add_bg_artist(a, self._layer)
@@ -1191,31 +1214,47 @@ def _set_tick_formatter(self):
             else:
                 self.ax_cb.yaxis.set_major_formatter(self._default_cb_tick_formatter)
 
+    def _redraw(self, *args, **kwargs):
+        super()._redraw(*args, **kwargs)
+        self._set_tick_formatter()
+
     @classmethod
     def add_colorbar(
         cls,
         m,
         pos=0.4,
+        inherit_position=None,
         orientation="horizontal",
         hist_bins=256,
         hist_size=0.8,
-        out_of_range_vals="keep",
+        out_of_range_vals="clip",
         tick_precision=2,
         dynamic_shade_indicator=False,
-        extend="both",
-        **hist_kwargs,
+        extend=None,
+        extend_frac=0.025,
+        log=False,
+        label=None,
+        ylabel=None,
+        show_outline=False,
+        hist_kwargs=None,
+        margin=None,
+        **kwargs,
     ):
 
         cb = cls(
             orientation=orientation,
             hist_kwargs=hist_kwargs,
             tick_precision=tick_precision,
+            inherit_position=inherit_position,
+            extend_frac=extend_frac,
+            margin=margin,
         )
         cb._set_map(m)
         cb._setup_axes(pos, m.ax)
-        cb._add_axes_to_layer()
+        cb._add_axes_to_layer(dynamic=dynamic_shade_indicator)
 
         cb._set_hist_size(hist_size)
+        cb.set_scale(log)
         cb._plot_colorbar(extend=extend)
 
         bins = (
@@ -1227,14 +1266,12 @@ def add_colorbar(
         cb._plot_histogram(
             bins=bins,
             out_of_range_vals=out_of_range_vals,
+            show_outline=show_outline,
         )
 
         cb._set_tick_formatter()
 
-        if hasattr(cb._ax, "_EOmaps_cb"):
-            cb._ax._EOmaps_cb.append(cb)
-        else:
-            cb._ax._EOmaps_cb = [cb]
+        cb.set_labels(cb_label=label, hist_label=ylabel)
 
         if dynamic_shade_indicator:
             cb.make_dynamic()
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index b20045913..9fc6e7711 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -1096,14 +1096,24 @@ def cb_scroll(self, event):
             if not self.modifier_pressed:
                 return False
 
-        # ordinary axes picked
         if self._scale_direction == "set_hist_size":
+            # resize colorbar histogram
             for ax in self._ax_picked:
-                cbs = getattr(ax, "_EOmaps_cb", None)
-                if cbs is None:
+                if not ax.get_label() == "cb":
                     continue
 
-                # use the hist-size of the first colorbar as start
+                # identify all relevant colorbars
+                # (e.g. all colorbars that share the container-ax "cb._ax")
+                cbs = []
+                for m in self.ms:
+                    for cb in m._colorbars:
+                        if cb._ax is ax:
+                            cbs.append(cb)
+
+                if len(cbs) == 0:
+                    return False
+
+                # use the hist-size of the first colorbar as start (to avoid ambiguity)
                 start_size = cbs[0]._hist_size
                 for cb in cbs:
                     new_size = np.clip(start_size + event.step * 0.02, 0.0, 1.0)
@@ -1111,8 +1121,8 @@ def cb_scroll(self, event):
 
             self._add_to_history()
             self.blit_artists()
-        # ordinary axes picked
         else:
+            # resize axes
             for ax in self._ax_picked:
                 if ax is None:
                     continue

From f1f580edf166c1105f0511febbbc52548c963b62 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 18:17:13 +0100
Subject: [PATCH 127/240] update m.add_colorbar

---
 eomaps/eomaps.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index ae0af63d8..566c937a0 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -2715,7 +2715,7 @@ def getpos(pos):
                 _TransformedBoundsLocator(fixed_pos.bounds, self.ax.transAxes)
             )
 
-    @wraps(ColorBar.add_colorbar.__init__)
+    @wraps(ColorBar.add_colorbar)
     def add_colorbar(self, *args, **kwargs):
         """Add a colorbar to the map."""
         if self.coll is None:
@@ -2723,11 +2723,7 @@ def add_colorbar(self, *args, **kwargs):
                 "EOmaps: You must plot a dataset before " "adding a colorbar!"
             )
 
-        colorbar = ColorBar.add_colorbar(
-            self,
-            *args,
-            **kwargs,
-        )
+        colorbar = ColorBar.add_colorbar(self, *args, **kwargs)
 
         self._colorbars.append(colorbar)
         self.BM._refetch_layer(self.layer)

From 66bc47e881b4a1b0489fcd8fc04b5a48e1f3f92f Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 18:33:40 +0100
Subject: [PATCH 128/240] add docstrings and missing methods to colorbar

---
 eomaps/colorbar.py | 235 +++++++++++++++++++++++++++++++++++++++------
 1 file changed, 208 insertions(+), 27 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index f0cb135b1..676e68cd8 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -127,6 +127,7 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
                         gs[1, :],
                         label="cb",
                         zorder=zorder,
+                        navigate=False,
                     )
                 else:
                     gs = GridSpecFromSubplotSpec(
@@ -141,12 +142,14 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
                         gs[:, 1],
                         label="cb",
                         zorder=zorder,
+                        navigate=False,
                     )
             elif isinstance(pos, SubplotSpec):
                 self._ax = f.add_subplot(
                     pos,
                     label="cb",
                     zorder=zorder,
+                    navigate=False,
                 )
             elif isinstance(pos, (list, tuple)):
                 x0, y0, w, h = pos
@@ -156,7 +159,7 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
 
                 # the parent axes holding the 2 child-axes
                 self._ax = plt.Axes(f, bbox, label="cb", zorder=zorder)
-                f.add_axes(self._ax)
+                f.add_axes(self._ax, navigate=False)
 
             # make all spines, labels etc. invisible for the base-axis
             self._ax.set_axis_off()
@@ -166,6 +169,7 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
             self._ax.get_position(),
             label="EOmaps_cb",
             zorder=zorder - 1,  # make zorder 1 lower than container axes for picking
+            navigate=False,
         )
 
         # histogram axes
@@ -173,6 +177,7 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
             self._ax.get_position(),
             label="EOmaps_cb_hist",
             zorder=zorder - 1,  # make zorder 1 lower than container axes for picking
+            navigate=False,
         )
 
         # add axes as child-axes
@@ -263,30 +268,17 @@ def _set_hist_size(self, size=None, update_all=False):
         self._set_axes_locators(l_cb_bounds, l_hist_bounds)
         self._style_hist_ticks()
 
-    def get_extend_fracs(self):
-        # if no colorbar is found, use the full axis
-        if not hasattr(self, "cb"):
-            return 0, 1
-
-        return 0, 1
-
-        extend = self.cb.extend
-
-        # extend fraction is defined as % of the interior colorbar length!
-        getfrac = lambda n: self._extend_frac / (1 + n * self._extend_frac)
+    def set_scale(self, log=False):
+        """
+        Set the scale of the colorbar histogram. (e.g. logarithmic or linear)
 
-        if extend == "both":
-            frac = getfrac(2)
-            return frac, 1 - 2 * frac
-        elif extend == "min":
-            frac = getfrac(1)
-            return frac, 1 - frac
-        elif extend == "max":
-            return 0, 1 - getfrac(1)
-        else:
-            return 0, 1
+        Parameters
+        ----------
+        log : bool, optional
+            If True, use a logarithmic scale for the histogram.
+            The default is False.
 
-    def set_scale(self, log=False):
+        """
         if self.orientation == "horizontal":
             # set axis scale
             if log is True:
@@ -603,7 +595,7 @@ def _set_labels(self, cb_label=None, hist_label=None, **kwargs):
 
     def set_labels(self, cb_label=None, hist_label=None, **kwargs):
         """
-        Set the labels (and the styling) for the colorbar (and the histogram).
+        Set the labels (and the label-style) for the colorbar (and the histogram).
 
         For more details, see `ColorBar.ax_cb.set_xlabel(..)` and matplotlib's `.Text`
         properties.
@@ -672,6 +664,8 @@ def tick_params(self, what="colorbar", **kwargs):
 
 
 class ColorBar(ColorBarBase):
+    """Class to draw colorbars with a histogram on top"""
+
     def __init__(self, *args, inherit_position=True, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -798,7 +792,7 @@ def set_hist_size(self, size=None):
         self._set_hist_size(size, update_all=True)
         self._m.BM.update()
 
-    def make_dynamic(self):
+    def _make_dynamic(self):
         self._dynamic_shade_indicator = True
 
         if not hasattr(self._m.coll, "get_ds_data"):
@@ -1218,8 +1212,41 @@ def _redraw(self, *args, **kwargs):
         super()._redraw(*args, **kwargs)
         self._set_tick_formatter()
 
+    def set_position(self, pos):
+        """
+        Set the position of the colorbar
+        (and all colorbars that share the same location)
+
+        Parameters
+        ----------
+        pos : [left, bottom, width, height] or ~matplotlib.transforms.Bbox
+            The new position of the in .Figure coordinates.
+        """
+        self._ax.set_position(pos)
+        if self._dynamic_shade_indicator is False:
+            self._m.redraw(self.layer)
+
+    def set_visible(self, vis):
+        """
+        Set the visibility of the colorbar.
+
+        Parameters
+        ----------
+        vis : bool
+            - True: colorbar visible
+            - False: colorbar not visible
+        """
+        for ax in (self.ax_cb, self.ax_cb_plot):
+            ax.set_visible(vis)
+
+        if vis is True:
+            self._hide_singular_axes()
+
+        if self._dynamic_shade_indicator is False:
+            self._m.redraw(self.layer)
+
     @classmethod
-    def add_colorbar(
+    def _new_colorbar(
         cls,
         m,
         pos=0.4,
@@ -1240,6 +1267,160 @@ def add_colorbar(
         margin=None,
         **kwargs,
     ):
+        """
+        Add a colorbar to the map.
+
+        The colorbar always represents the data of the associated Maps-object
+        that was assigned in the last call to `m.plot_map()`.
+
+        By default, the colorbar will only be visible on the layer of the associated
+        Maps-object.
+
+        After the colorbar has been created, it can be accessed via:
+
+            >>> cb = m.colorbar
+
+        Parameters
+        ----------
+        pos : float or 4-tuple, optional
+
+            - float: fraction of the axis size that is used to create the colorbar.
+              The axes of the Maps-object will be shrunk accordingly to make space
+              for the colorbar.
+            - 4-tuple (x0, y0, width, height):
+              Absolute position of the colorbar in relative figure-units (0-1).
+              In this case, existing axes are NOT automatically re-positioned!
+
+            Note: By default, multiple colorbars on different layers share their
+            position! To force placement of a colorbar, use "inherit_position=False".
+
+            The default is 0.4.
+        inherit_position : bool or None optional
+            Indicator if the colorbar should share its position with other colorbars
+            that represent datasets on the same plot-axis.
+
+            - If True, and there is already another colorbar for the given plot-axis,
+              the value of "pos" will be ignored and the new colorbar will share its
+              position with the parent-colorbar. (e.g. all colorbars for a given axis will
+              overlap and moving a colorbar in one layer will move all other relevant
+              colorbars accordingly).
+            - If None: If the colorbar is added on a different layer than the parent
+              colorbar, use "inherit_position=True", else use "inherit_position=False".
+
+            The default is None
+        hist_size : float or None
+            The fraction of the colorbar occupied by the histogram.
+
+            - None: no histogram will be drawn
+            - 0:
+            - 0.9: 90% histogram, 10% colorbar
+            - 1: only histogram
+
+        hist_bins : int, list, tuple, array or "bins", optional
+
+            - If int: The number of histogram-bins to use for the colorbar.
+            - If list, tuple or numpy-array: the bins to use
+            - If "bins": use the bins obtained from the classification
+              (ONLY possible if a classification scheme is used!)
+
+            The default is 256.
+        extend : str or None, optional
+            Set how extension-arrows should be added.
+
+            - None: extension-arrow behavior is determined by the provided dataset
+              in conjunction with the limits (e.g. vmin and vmax).
+            - "neither": extension arrows are never added
+            - "min" or "max": only min / max extension arrows are added
+            - "both": both min and max extension arrows are added
+
+            Note: If the colorbar inherits its position from a colorbar on a different
+            layer, the extend-behavior is inherited as well!
+
+            The default is None.
+        extend_frac : float, optional
+            The fraction of the colorbar-size to use for extension-arrows.
+            (Extension-arrows are added if out-of-range values are found!)
+            The default is 0.025.
+        orientation : str, optional
+            The orientation of the colorbar ("horizontal" or "vertical").
+            The default is "horizontal".
+        dynamic_shade_indicator : bool, optional
+            ONLY relevant if data-shading is used! ("shade_raster" or "shade_points")
+
+            - False: The colorbar represents the actual (full) dataset
+            - True: The colorbar is dynamically updated and represents the density of
+              the shaded pixel values within the current field of view.
+
+            The default is False.
+        show_outline : bool or dict
+            Indicator if an outline should be added to the histogram.
+            (e.g. a line encompassing the histogram)
+            If a dict is provided, it is passed to `plt.step()` to style the line.
+            (e.g. with ordinary matplotlib parameters such as color, lw, ls etc.)
+            If True, the following properties are used:
+
+            - {"color": "k", "lw": 1}
+
+            The default is False.
+        tick_precision : int or None
+            The precision of the tick-labels in the colorbar.
+            (e.g. a precision of 2 means that 0.12345 will be shown as 0.12)
+            The default is 2.
+        log : bool, optional
+            Indicator if the y-axis of the plot should be logarithmic or not.
+            The default is False
+        out_of_range_vals : str or None
+
+            - if "mask": out-of range values will be masked.
+              (e.g. values outside the colorbar limits are not represented in the
+              histogram and NO extend-arrows are added)
+            - if "clip": out-of-range values will be clipped.
+              (e.g. values outside the colorbar limits will be represented in the
+              min/max bins of the histogram)
+
+            The default is "clip"
+        hist_kwargs : dict
+            A dictionary with keyword-arguments passed to the creation of the histogram
+            (e.g. passed to `plt.hist()` )
+        label : str, optional
+            The label used for the colorbar.
+            Use `ColorBar.set_labels()` to set the labels (and styling) for the
+            colorbar and the histogram.
+            The default is None.
+        ylabel : str, optional
+            The label used for the y-axis of the colorbar. The default is None
+        layer : str
+            The layer at which the colorbar will be drawn.
+            NOTE: In most cases you should NOT need to adjust the layer!
+            The layer is automatically assigned to the layer at which the
+            data was plotted and Colorbars are only visible on the assigned layer!
+        kwargs :
+            All additional kwargs are passed to the creation of the colorbar
+            (e.g. `plt.colorbar()`)
+
+        See Also
+        --------
+        ColorBar.set_bin_labels:  Use custom names for classified colorbar bins.
+
+        Examples
+        --------
+
+        >>> x = y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        >>> data = [1, 2, 6, 6, 6, 8, 7, 3, 9, 10]
+        >>> m = Maps()
+        >>> m.set_data(data, x, y)
+        >>> m.plot_map()
+        >>> m.add_colorbar(label="some data")
+
+        >>> x = y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        >>> data = [1, 2, 6, 6, 6, 8, 7, 3, 9, 10]
+        >>> m = Maps()
+        >>> m.set_data(data, x, y)
+        >>> m.set_classify.Quantiles(k=6)
+        >>> m.plot_map()
+        >>> m.add_colorbar(hist_bins="bins", label="some data")
+
+        """
 
         cb = cls(
             orientation=orientation,
@@ -1274,6 +1455,6 @@ def add_colorbar(
         cb.set_labels(cb_label=label, hist_label=ylabel)
 
         if dynamic_shade_indicator:
-            cb.make_dynamic()
+            cb._make_dynamic()
 
         return cb

From b10bc836d8fc2385608660487b7efb3c5e324b87 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 18:34:01 +0100
Subject: [PATCH 129/240] minor

---
 docs/api_data_visualization.rst | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/api_data_visualization.rst b/docs/api_data_visualization.rst
index 0ff411dfa..99c135d0e 100644
--- a/docs/api_data_visualization.rst
+++ b/docs/api_data_visualization.rst
@@ -889,7 +889,6 @@ The returned ``ColorBar``-object has the following useful methods defined:
     ColorBar.set_hist_size
     ColorBar.tick_params
     ColorBar.set_visible
-    ColorBar.remove
 
 Set colorbar tick labels based on bins
 **************************************

From d0229ebc34249fc16a8b710696f6c05535db87b5 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 20:33:12 +0100
Subject: [PATCH 130/240] fix typo

---
 eomaps/eomaps.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 566c937a0..620c78b6f 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -2715,7 +2715,7 @@ def getpos(pos):
                 _TransformedBoundsLocator(fixed_pos.bounds, self.ax.transAxes)
             )
 
-    @wraps(ColorBar.add_colorbar)
+    @wraps(ColorBar._new_colorbar)
     def add_colorbar(self, *args, **kwargs):
         """Add a colorbar to the map."""
         if self.coll is None:
@@ -2723,7 +2723,7 @@ def add_colorbar(self, *args, **kwargs):
                 "EOmaps: You must plot a dataset before " "adding a colorbar!"
             )
 
-        colorbar = ColorBar.add_colorbar(self, *args, **kwargs)
+        colorbar = ColorBar._new_colorbar(self, *args, **kwargs)
 
         self._colorbars.append(colorbar)
         self.BM._refetch_layer(self.layer)

From ce610e711006140c77f7a57dfca273fef07ec947 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 21:58:18 +0100
Subject: [PATCH 131/240] update docstrings

---
 eomaps/colorbar.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 676e68cd8..5d4ad7b94 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -664,7 +664,13 @@ def tick_params(self, what="colorbar", **kwargs):
 
 
 class ColorBar(ColorBarBase):
-    """Class to draw colorbars with a histogram on top"""
+    """
+    A colorbar with a histogram on top.
+
+    To add a colorbar to a map, use
+    :py:meth:`Maps.add_colorbar <eomaps.eomaps.Maps.add_colorbar>`!
+
+    """
 
     def __init__(self, *args, inherit_position=True, **kwargs):
         super().__init__(*args, **kwargs)
@@ -674,6 +680,7 @@ def __init__(self, *args, inherit_position=True, **kwargs):
 
     @property
     def layer(self):
+        """The layer associated with the colorbar."""
         return self._m.layer
 
     def _default_cb_tick_formatter(self, x, pos, precision=None):

From 64d6c8192b469db16577dc658268959a9aa9789a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 22:35:49 +0100
Subject: [PATCH 132/240] update docstrings

---
 eomaps/colorbar.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 5d4ad7b94..f9ea2766a 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -851,8 +851,9 @@ def indicate_contours(
         """
         Indicate contour locations in the colorbar.
 
-        Note: Before using this function you must draw a dataset with the ``"contour"``
-        shape! (you can also indicate contours from other Maps-objects by using
+        Note: Before using this function you must draw a dataset with the
+        :py:class:`contour <eomaps.eomaps.Maps.set_shape.contour>` shape!
+        (you can also indicate contours from other Maps-objects by using
         the optional ``contour_map`` argument.)
 
         Parameters
@@ -1048,7 +1049,8 @@ def set_bin_labels(self, bins, names, tick_lines="center", show_values=False):
 
         The labels will be placed at the center of each bin.
 
-        This is most useful when using `m.set_classify.UserDefined(bins=[...])`
+        This is most useful when using
+        :py:meth:`Maps.set_classify.UserDefined(bins=[...]) <eomaps.eomaps.Maps.set_classify>`
         to classify the data with respect to custom bins.
 
         Parameters
@@ -1278,7 +1280,8 @@ def _new_colorbar(
         Add a colorbar to the map.
 
         The colorbar always represents the data of the associated Maps-object
-        that was assigned in the last call to `m.plot_map()`.
+        that was assigned in the last call to
+        :py:meth:`Maps.plot_map() <eomaps.eomaps.Maps.plot_map>`.
 
         By default, the colorbar will only be visible on the layer of the associated
         Maps-object.
@@ -1287,6 +1290,8 @@ def _new_colorbar(
 
             >>> cb = m.colorbar
 
+        For more details, see :py:class:`ColorBar <eomaps.colorbar.ColorBar>`.
+
         Parameters
         ----------
         pos : float or 4-tuple, optional

From 16f4cf61fb82e853d05861e52f3a44c950dce594 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 22:54:21 +0100
Subject: [PATCH 133/240] fix hist kwargs and range of dynamic colorbar

---
 eomaps/colorbar.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index f9ea2766a..e56176a7e 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -57,16 +57,11 @@ def __init__(
         self,
         orientation="horizontal",
         extend_frac=0.025,
-        hist_kwargs=None,
         tick_precision=2,
         margin=None,
     ):
 
         self._hist_size = 0.9
-        if hist_kwargs is not None:
-            self._hist_kwargs = hist_kwargs
-        else:
-            self._hist_kwargs = {}
 
         self._extend_frac = extend_frac
 
@@ -344,17 +339,23 @@ def _plot_histogram(
         self, bins=None, out_of_range_vals="keep", show_outline=False, **kwargs
     ):
 
+        self._hist_kwargs = kwargs
         self._hist_bins = bins
         self._out_of_range_vals = out_of_range_vals
         self._show_outline = show_outline
 
+        if "range" not in self._hist_kwargs:
+            self._hist_kwargs["range"] = (
+                (self._vmin, self._vmax) if (self._vmin and self._vmax) else None
+            )
+
         # plot the histogram
         h = self.ax_cb_plot.hist(
             self._preprocess_data(out_of_range_vals=self._out_of_range_vals),
             orientation=self._hist_orientation,
             bins=self._hist_bins,
             align="mid",
-            **kwargs,
+            **self._hist_kwargs,
         )
 
         if self._show_outline:
@@ -548,6 +549,7 @@ def _redraw(self, *args, **kwargs):
             bins=self._hist_bins,
             out_of_range_vals=self._out_of_range_vals,
             show_outline=self._show_outline,
+            **self._hist_kwargs,
         )
 
     def _set_labels(self, cb_label=None, hist_label=None, **kwargs):
@@ -1433,10 +1435,11 @@ def _new_colorbar(
         >>> m.add_colorbar(hist_bins="bins", label="some data")
 
         """
+        if hist_kwargs is None:
+            hist_kwargs = dict()
 
         cb = cls(
             orientation=orientation,
-            hist_kwargs=hist_kwargs,
             tick_precision=tick_precision,
             inherit_position=inherit_position,
             extend_frac=extend_frac,
@@ -1460,6 +1463,7 @@ def _new_colorbar(
             bins=bins,
             out_of_range_vals=out_of_range_vals,
             show_outline=show_outline,
+            **hist_kwargs,
         )
 
         cb._set_tick_formatter()

From f5a70856dfdbe2950fa73ec61d6dae5411019920 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 11:41:49 +0100
Subject: [PATCH 134/240] add remove method for colorbar and fix unwanted draws

---
 eomaps/colorbar.py | 71 ++++++++++++++++++++++++++++++----------------
 eomaps/helpers.py  | 43 +++++++++++++++++-----------
 2 files changed, 74 insertions(+), 40 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index e56176a7e..b78d2dae0 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -10,7 +10,11 @@
 
 import numpy as np
 
-from eomaps.helpers import _TransformedBoundsLocator, pairwise
+from .helpers import _TransformedBoundsLocator, pairwise
+
+import logging
+
+_log = logging.getLogger(__name__)
 
 
 def get_named_bins_formatter(bins, names, show_values=False):
@@ -104,9 +108,6 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
 
         self._parent_cb = self._identify_parent_cb()
         if self._parent_cb:
-            # inherit axis-position from the parent axis position
-            # (e.g. it can no longer be freely moved... its position is determined
-            # by the position of the parent-colorbar axis)
             self._ax = self._parent_cb._ax
         else:
             if isinstance(pos, (int, float)):
@@ -158,6 +159,7 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
 
             # make all spines, labels etc. invisible for the base-axis
             self._ax.set_axis_off()
+            self._ax._eomaps_cb_axes = []
 
         # colorbar axes
         self.ax_cb = f.add_axes(
@@ -175,9 +177,8 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
             navigate=False,
         )
 
-        # add axes as child-axes
-        self._ax.add_child_axes(self.ax_cb)
-        self._ax.add_child_axes(self.ax_cb_plot)
+        # remember child axes
+        self._ax._eomaps_cb_axes.extend([self.ax_cb, self.ax_cb_plot])
 
         # join colorbar and histogram axes
         if self.orientation == "horizontal":
@@ -746,6 +747,29 @@ def _identify_parent_cb(self):
         else:
             return None
 
+    def remove(self):
+        """Remove the colorbar from the map."""
+        if self._dynamic_shade_indicator:
+            try:
+                self._m.BM._before_fetch_bg_actions.remove(self._check_data_updated)
+            except Exception:
+                _log.debug("Problem while removing dynamic-colorbar callback")
+
+            self._m.BM.remove_artist(self.ax_cb, self.layer)
+            self._m.BM.remove_artist(self.ax_cb_plot, self.layer)
+
+        else:
+            self._m.BM.remove_bg_artist(self.ax_cb, self.layer, draw=False)
+            self._m.BM.remove_bg_artist(self.ax_cb_plot, self.layer, draw=False)
+
+        if self.ax_cb in self._ax._eomaps_cb_axes:
+            self._ax._eomaps_cb_axes.remove(self.ax_cb)
+        if self.ax_cb_plot in self._ax._eomaps_cb_axes:
+            self._ax._eomaps_cb_axes.remove(self.ax_cb_plot)
+
+        self.ax_cb.remove()
+        self.ax_cb_plot.remove()
+
     def _set_map(self, m):
         self._m = m
 
@@ -767,7 +791,7 @@ def _add_axes_to_layer(self, dynamic):
         # add all axes as artists
         self.ax_cb.set_navigate(False)
 
-        for a in (self._ax, self.ax_cb, self.ax_cb_plot):
+        for a in (self.ax_cb, self.ax_cb_plot):
             if a is not None:
                 if dynamic is True:
                     BM.add_artist(a, self._layer)
@@ -801,6 +825,20 @@ def set_hist_size(self, size=None):
         self._set_hist_size(size, update_all=True)
         self._m.BM.update()
 
+    def _check_data_updated(self, *args, **kwargs):
+        # make sure the artist is updated before checking for new data
+        # TODO check if this is really enough to ensure that the coll
+        # is fully updated (calling coll.draw() is not an option since it
+        # would result make the collection appear on any layer!)
+        self._m.coll.changed()
+        dsdata = self._m.coll.get_ds_data()
+        if getattr(self, "_last_ds_data", None) is not None:
+            if not self._last_ds_data.equals(dsdata):
+                # if the data has changed, redraw the colorbar
+                self._redraw()
+
+        self._last_ds_data = dsdata
+
     def _make_dynamic(self):
         self._dynamic_shade_indicator = True
 
@@ -812,22 +850,7 @@ def _make_dynamic(self):
             self._cid_redraw = False
 
         if self._cid_redraw is False:
-
-            def check_data_updated(*args, **kwargs):
-                # make sure the artist is updated before checking for new data
-                # TODO check if this is really enough to ensure that the coll
-                # is fully updated (calling coll.draw() is not an option since it
-                # would result make the collection appear on any layer!)
-                self._m.coll.changed()
-                dsdata = self._m.coll.get_ds_data()
-                if getattr(self, "_last_ds_data", None) is not None:
-                    if not self._last_ds_data.equals(dsdata):
-                        # if the data has changed, redraw the colorbar
-                        self._redraw()
-
-                self._last_ds_data = dsdata
-
-            self._m.BM._before_fetch_bg_actions.append(check_data_updated)
+            self._m.BM._before_fetch_bg_actions.append(self._check_data_updated)
 
             self._m.BM.on_layer(
                 lambda *args, **kwargs: self._redraw,
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index 9fc6e7711..7ce0e9ca4 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -998,8 +998,16 @@ def cb_pick(self, event):
             else:
                 if eventax in self._ax_picked:
                     self._ax_picked.remove(eventax)
+                    # handle colorbar axes
+                    if eventax.get_label() == "cb":
+                        for cbax in getattr(eventax, "_eomaps_cb_axes", []):
+                            self._ax_picked.remove(cbax)
                 else:
                     self._ax_picked.append(eventax)
+                    # handle colorbar axes
+                    if eventax.get_label() == "cb":
+                        for cbax in getattr(eventax, "_eomaps_cb_axes", []):
+                            self._ax_picked.append(cbax)
         else:
             if eventax not in self._ax_picked:
                 self._m_picked = []
@@ -1012,9 +1020,12 @@ def cb_pick(self, event):
                     else:
                         self._m_picked = []
                         self._ax_picked.append(eventax)
+                        # handle colorbar axes
+                        if eventax.get_label() == "cb":
+                            for cbax in getattr(eventax, "_eomaps_cb_axes", []):
+                                self._ax_picked.append(cbax)
 
                     self._add_snap_grid()
-
             else:
                 self._add_snap_grid()
 
@@ -1031,8 +1042,6 @@ def fetch_current_background(self):
         with ExitStack() as stack:
             for ax in self._ax_picked:
                 stack.enter_context(ax._cm_set(visible=False))
-                for child_ax in ax.child_axes:
-                    stack.enter_context(child_ax._cm_set(visible=False))
 
             self.m.BM.blit_artists(self.axes, None, False)
 
@@ -1233,16 +1242,19 @@ def _snap(self):
         return snap
 
     def ax_on_layer(self, ax):
-        return ax in [
-            ax
-            for ax in self.f.axes
-            if (
-                ax in self.m.BM._get_unmanaged_axes()
-                or ax in self.m.BM.get_bg_artists(self.m.BM.bg_layer)
-                or ax in self.m.BM.get_artists(self.m.BM.bg_layer)
-                or ax in self.maxes
-            )
-        ]
+        if ax in self.m.BM._get_unmanaged_axes():
+            return True
+        elif ax in self.maxes:
+            return True
+        else:
+            for layer in (self.m.BM.bg_layer, "__SPINES__", "all"):
+                # logos are put on the spines-layer to appear on top of spines!
+                if ax in self.m.BM.get_bg_artists(layer):
+                    return True
+                elif ax in self.m.BM.get_artists(self.m.BM.bg_layer):
+                    return True
+
+        return False
 
     def _make_draggable(self, filepath=None):
         # Uncheck active pan/zoom actions of the matplotlib toolbar.
@@ -1290,7 +1302,6 @@ def _make_draggable(self, filepath=None):
             # keep singular axes hidden
             self._revert_props.append((ax.set_visible, ax.get_visible()))
             if not singularax:
-
                 if self.ax_on_layer(ax):
                     ax.set_visible(True)
                 else:
@@ -2121,7 +2132,7 @@ def _layer_visible(self, layer):
             True if the layer is currently visible, False otherwise
 
         """
-        return layer == "all" or self._layer_is_subset(layer, self.bg_layer)
+        return layer in "all" or self._layer_is_subset(layer, self.bg_layer)
 
     @property
     def _get_active_layers_alphas(self):
@@ -2986,7 +2997,7 @@ def _cleanup_bg_artists(self, layer):
         while len(artists) > 0:
             a = artists.pop()
             try:
-                self.remove_bg_artist(a, layer)
+                self.remove_bg_artist(a, layer, draw=False)
                 # no need to remove spines (to avoid NotImplementedErrors)!
                 if not isinstance(a, Spine):
                     a.remove()

From 35597daf3f7a8ec70c346d1036bc02ce4be406dd Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 11:55:49 +0100
Subject: [PATCH 135/240] move methods and fix updating layer on set_scale

---
 eomaps/colorbar.py | 188 +++++++++++++++++++++++----------------------
 1 file changed, 95 insertions(+), 93 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index b78d2dae0..9223f0525 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -264,30 +264,6 @@ def _set_hist_size(self, size=None, update_all=False):
         self._set_axes_locators(l_cb_bounds, l_hist_bounds)
         self._style_hist_ticks()
 
-    def set_scale(self, log=False):
-        """
-        Set the scale of the colorbar histogram. (e.g. logarithmic or linear)
-
-        Parameters
-        ----------
-        log : bool, optional
-            If True, use a logarithmic scale for the histogram.
-            The default is False.
-
-        """
-        if self.orientation == "horizontal":
-            # set axis scale
-            if log is True:
-                self.ax_cb_plot.set_yscale("log")
-            else:
-                self.ax_cb_plot.set_yscale("linear")
-        else:
-            # set axis scale
-            if log is True:
-                self.ax_cb_plot.set_xscale("log")
-            else:
-                self.ax_cb_plot.set_xscale("linear")
-
     def _preprocess_data(self, out_of_range_vals="keep"):
         data = self._get_data()
 
@@ -596,75 +572,6 @@ def _set_labels(self, cb_label=None, hist_label=None, **kwargs):
             if hist_label:
                 self._hist_label = self.ax_cb_plot.set_xlabel(hist_label, **kwargs)
 
-    def set_labels(self, cb_label=None, hist_label=None, **kwargs):
-        """
-        Set the labels (and the label-style) for the colorbar (and the histogram).
-
-        For more details, see `ColorBar.ax_cb.set_xlabel(..)` and matplotlib's `.Text`
-        properties.
-
-        Parameters
-        ----------
-        cb_label : str or None
-            The label of the colorbar. If None, the existing label is maintained.
-            The default is None.
-        hist_label : str or None
-            The label of the histogram. If None, the existing label is maintained.
-            The default is None.
-
-        Other Parameters
-        ----------------
-        kwargs :
-           Additional kwargs passed to `Axes.set_xlabel` to control the appearance of
-           the label (e.g. color, fontsize, labelpad etc.).
-
-        Examples
-        --------
-        Set both colorbar and histogram label in one go
-
-        >>> cb.set_labels("The parameter", "histogram count", fontsize=10, color="r")
-
-        Use different styles for the colorbar and histogram labels
-
-        >>> cb.set_labels(cb_label="The parameter", color="r", labelpad=10)
-        >>> cb.set_labels(hist_label="histogram count", fontsize=6, color="k")
-
-        """
-
-        self._label_kwargs = {"cb_label": cb_label, "hist_label": hist_label, **kwargs}
-
-        self._set_labels(cb_label=cb_label, hist_label=hist_label, **kwargs)
-
-        if not self._dynamic_shade_indicator:
-            # no need to redraw the background for dynamically updated artists
-            self._m.redraw(self.layer)
-        else:
-            self._m.BM.update()
-
-    def tick_params(self, what="colorbar", **kwargs):
-        """Set the appearance of the colorbar (or histogram) ticks."""
-        if what == "colorbar":
-            self.ax_cb.tick_params(**kwargs)
-        elif what == "histogram":
-            self.ax_cb_plot.tick_params(**kwargs)
-
-        self._m.redraw(self.layer)
-
-    tick_params.__doc__ = (
-        "Set the appearance of the colorbar (or histogram) ticks.\n\n"
-        "NOTE\n"
-        "----\n"
-        "This is a wrapper for `m.colorbar.ax_cb.tick_params` or "
-        "`m.colorbar.ax_cb_plot.tick_params` to set the appearance of the ticks for "
-        "the colorbar or the histogram."
-        "You can select what you want to edit with the additional parameter:"
-        "\n\n"
-        "what: {'colorbar', 'histogram'}, default: 'colorbar'\n"
-        "    - 'colorbar' : colorbar ticks (same as `m.colorbar.ax_cb.tick_params`)\n"
-        "    - 'histogram' : histogram ticks (same as `m.colorbar.ax_cb_plot.tick_params`)\n"
-        "\n\n----------------\n\n" + dedent(plt.Axes.tick_params.__doc__)
-    )
-
 
 class ColorBar(ColorBarBase):
     """
@@ -1279,6 +1186,101 @@ def set_visible(self, vis):
         if self._dynamic_shade_indicator is False:
             self._m.redraw(self.layer)
 
+    def set_labels(self, cb_label=None, hist_label=None, **kwargs):
+        """
+        Set the labels (and the label-style) for the colorbar (and the histogram).
+
+        For more details, see `ColorBar.ax_cb.set_xlabel(..)` and matplotlib's `.Text`
+        properties.
+
+        Parameters
+        ----------
+        cb_label : str or None
+            The label of the colorbar. If None, the existing label is maintained.
+            The default is None.
+        hist_label : str or None
+            The label of the histogram. If None, the existing label is maintained.
+            The default is None.
+
+        Other Parameters
+        ----------------
+        kwargs :
+           Additional kwargs passed to `Axes.set_xlabel` to control the appearance of
+           the label (e.g. color, fontsize, labelpad etc.).
+
+        Examples
+        --------
+        Set both colorbar and histogram label in one go
+
+        >>> cb.set_labels("The parameter", "histogram count", fontsize=10, color="r")
+
+        Use different styles for the colorbar and histogram labels
+
+        >>> cb.set_labels(cb_label="The parameter", color="r", labelpad=10)
+        >>> cb.set_labels(hist_label="histogram count", fontsize=6, color="k")
+
+        """
+
+        self._label_kwargs = {"cb_label": cb_label, "hist_label": hist_label, **kwargs}
+
+        self._set_labels(cb_label=cb_label, hist_label=hist_label, **kwargs)
+
+        if not self._dynamic_shade_indicator:
+            # no need to redraw the background for dynamically updated artists
+            self._m.redraw(self.layer)
+        else:
+            self._m.BM.update()
+
+    def tick_params(self, what="colorbar", **kwargs):
+        """Set the appearance of the colorbar (or histogram) ticks."""
+        if what == "colorbar":
+            self.ax_cb.tick_params(**kwargs)
+        elif what == "histogram":
+            self.ax_cb_plot.tick_params(**kwargs)
+
+        self._m.redraw(self.layer)
+
+    tick_params.__doc__ = (
+        "Set the appearance of the colorbar (or histogram) ticks.\n\n"
+        "NOTE\n"
+        "----\n"
+        "This is a wrapper for `m.colorbar.ax_cb.tick_params` or "
+        "`m.colorbar.ax_cb_plot.tick_params` to set the appearance of the ticks for "
+        "the colorbar or the histogram."
+        "You can select what you want to edit with the additional parameter:"
+        "\n\n"
+        "what: {'colorbar', 'histogram'}, default: 'colorbar'\n"
+        "    - 'colorbar' : colorbar ticks (same as `m.colorbar.ax_cb.tick_params`)\n"
+        "    - 'histogram' : histogram ticks (same as `m.colorbar.ax_cb_plot.tick_params`)\n"
+        "\n\n----------------\n\n" + dedent(plt.Axes.tick_params.__doc__)
+    )
+
+    def set_scale(self, log=False):
+        """
+        Set the scale of the colorbar histogram. (e.g. logarithmic or linear)
+
+        Parameters
+        ----------
+        log : bool, optional
+            If True, use a logarithmic scale for the histogram.
+            The default is False.
+
+        """
+        if self.orientation == "horizontal":
+            # set axis scale
+            if log is True:
+                self.ax_cb_plot.set_yscale("log")
+            else:
+                self.ax_cb_plot.set_yscale("linear")
+        else:
+            # set axis scale
+            if log is True:
+                self.ax_cb_plot.set_xscale("log")
+            else:
+                self.ax_cb_plot.set_xscale("linear")
+
+        self._m.redraw(self.layer)
+
     @classmethod
     def _new_colorbar(
         cls,

From e676ed585ea52caa20599ddad24ea4775a6bdb01 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 11:56:11 +0100
Subject: [PATCH 136/240] fix example layout

---
 docs/examples/example_multiple_maps.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/examples/example_multiple_maps.py b/docs/examples/example_multiple_maps.py
index 313ac4627..2c7446fa5 100644
--- a/docs/examples/example_multiple_maps.py
+++ b/docs/examples/example_multiple_maps.py
@@ -94,9 +94,9 @@
         "4_cb_histogram_size": 0.8,
         "5_cb": [0.72022, 0.0825, 0.2625, 0.2805],
         "5_cb_histogram_size": 0.8,
-        "7_logo": [0.2725, 0.451, 0.05, 0.04538],
+        "6_logo": [0.2725, 0.451, 0.05, 0.04538],
+        "7_logo": [0.625, 0.3795, 0.05, 0.04538],
         "8_logo": [0.625, 0.3795, 0.05, 0.04538],
-        "9_logo": [0.625, 0.3795, 0.05, 0.04538],
-        "10_logo": [0.93864, 0.451, 0.05, 0.04538],
+        "9_logo": [0.93864, 0.451, 0.05, 0.04538],
     }
 )

From da24a14fccd8d1a13436fb270f106d31b7f13b29 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 12:15:16 +0100
Subject: [PATCH 137/240] move LayoutEditor to dedicated module

---
 eomaps/eomaps.py        |   3 +-
 eomaps/helpers.py       | 989 +--------------------------------------
 eomaps/layout_editor.py | 993 ++++++++++++++++++++++++++++++++++++++++
 3 files changed, 996 insertions(+), 989 deletions(-)
 create mode 100644 eomaps/layout_editor.py

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 620c78b6f..a465bd5cc 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -37,7 +37,6 @@
     pairwise,
     cmap_alpha,
     BlitManager,
-    LayoutEditor,
     progressbar,
     SearchTree,
     _TransformedBoundsLocator,
@@ -45,6 +44,8 @@
     register_modules,
     _key_release_event,
 )
+
+from .layout_editor import LayoutEditor
 from .shapes import Shapes
 from .colorbar import ColorBar
 
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index 7ce0e9ca4..f749ff084 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -14,25 +14,19 @@
 from importlib import import_module
 from textwrap import indent, dedent
 from functools import wraps, lru_cache
-from pathlib import Path
-import json
 import warnings
 from weakref import WeakSet
 
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
 from matplotlib.colors import LinearSegmentedColormap, ListedColormap
 from matplotlib.transforms import Bbox, TransformedBbox
-from matplotlib.axis import XAxis, YAxis
 from matplotlib.spines import Spine
+from matplotlib.backend_bases import KeyEvent
 
 _log = logging.getLogger(__name__)
 
 
-from matplotlib.backend_bases import KeyEvent
-
-
 def _key_release_event(canvas, key, guiEvent=None):
     # copy of depreciated matplotlib functions for internal use
     s = "key_release_event"
@@ -683,987 +677,6 @@ def query(self, x, k=1, d=None, pick_relative_to_closest=True):
         return i
 
 
-class LayoutEditor:
-    """Class to handle interactive re-positioning of figure-objects."""
-
-    def __init__(self, m, modifier="alt+d", cb_modifier="control"):
-        self.modifier = modifier
-        self.cb_modifier = cb_modifier
-
-        self.m = m
-        self.f = self.m.parent.f
-
-        self._ax_picked = []
-        self._m_picked = []
-
-        self._modifier_pressed = False
-
-        self.cids = []
-
-        # indicator if the pick-callback should be re-attached or not
-        self._reattach_pick_cb = False
-
-        self.f.canvas.mpl_connect("key_press_event", self.cb_key_press)
-        self.f.canvas.mpl_connect("resize_event", self._on_resize)
-
-        # the snap-to-grid interval (0 means no snapping)
-        self._snap_id = 5
-
-        # an optional filepath that will be used to store the layout once the
-        # editor exits
-        self._filepath = None
-
-        # indicator if scaling should be in horizontal or vertical direction
-        self._scale_direction = "both"
-
-        # indicator if multiple-axis select key is pressed or not (e.g. "shift")
-        self._shift_pressed = False
-
-        self._max_hist_steps = 1000
-        self._history = list()
-        self._history_undone = list()
-
-        self._current_bg = None
-
-        self._info_text = None
-        self._info_text_hidden = False
-
-    def add_info_text(self):
-        self._info_text_hidden = False
-
-        a = self.m.f.text(
-            0.72,
-            0.98,
-            (
-                "LayoutEditor Controls:\n\n"
-                "0 - 9:  Snap-grid spacing\n"
-                "SHIFT:  Multi-select\n"
-                "P:      Print to console\n"
-                "ESCAPE (or ALT + L): Exit\n"
-                "\n"
-                "ARROW-KEYS:   Move\n"
-                "SCROLL (+/-): Resize\n"
-                "  H:    horizontal\n"
-                "  V:    vertical\n"
-                "  ctrl: histogram"
-                "\n\n(right-click to hide info)"
-            ),
-            transform=self.m.f.transFigure,
-            ha="left",
-            va="top",
-            fontsize=min(self.m.f.bbox.width * 72 / self.m.f.dpi / 60, 12),
-            bbox=dict(
-                boxstyle="round", facecolor=".8", edgecolor="k", lw=0.5, alpha=0.9
-            ),
-            zorder=1e6,
-            fontfamily="monospace",
-        )
-        return a
-
-    def _update_info_text(self):
-        if getattr(self, "_info_text", None) is not None:
-            self._info_text.set_fontsize(
-                min(self.m.f.bbox.width * 72 / self.m.f.dpi / 60, 15)
-            )
-
-    def _on_resize(self, *args, **kwargs):
-        # update snap-grid on resize
-        if self.modifier_pressed:
-            self._add_snap_grid()
-            self._update_info_text()
-
-    @property
-    def modifier_pressed(self):
-        return self._modifier_pressed
-
-    @modifier_pressed.setter
-    def modifier_pressed(self, val):
-        self._modifier_pressed = val
-        self.m.cb.execute_callbacks(not val)
-
-        if self._modifier_pressed:
-            self.m.BM._disable_draw = True
-            self.m.BM._disable_update = True
-        else:
-            self.m.BM._disable_draw = False
-            self.m.BM._disable_update = False
-
-    @property
-    def ms(self):
-        return [self.m.parent, *self.m.parent._children]
-
-    @property
-    def maxes(self):
-        return [m.ax for m in self.ms]
-
-    @property
-    def axes(self):
-        return self.f.axes
-
-    @staticmethod
-    def roundto(x, base=10):
-        if base == 0:
-            return x
-        if x % base <= base / 2:
-            return x - x % base
-        else:
-            return x + (base - x % base)
-
-    def _get_move_with_key_bbox(self, ax, key):
-        snapx, snapy = self._snap
-        intervalx, intervaly = (
-            max(0.25, snapx),
-            max(0.25, snapy),
-        )
-
-        if key == "left":
-            bbox = Bbox.from_bounds(
-                self.roundto(ax.bbox.x0 - intervalx, snapx),
-                ax.bbox.y0,
-                ax.bbox.width,
-                ax.bbox.height,
-            )
-        elif key == "right":
-            bbox = Bbox.from_bounds(
-                self.roundto(ax.bbox.x0 + intervalx, snapx),
-                ax.bbox.y0,
-                ax.bbox.width,
-                ax.bbox.height,
-            )
-        elif key == "up":
-            bbox = Bbox.from_bounds(
-                ax.bbox.x0,
-                self.roundto(ax.bbox.y0 + intervaly, snapy),
-                ax.bbox.width,
-                ax.bbox.height,
-            )
-        elif key == "down":
-            bbox = Bbox.from_bounds(
-                ax.bbox.x0,
-                self.roundto(ax.bbox.y0 - intervaly, snapy),
-                ax.bbox.width,
-                ax.bbox.height,
-            )
-
-        bbox = bbox.transformed(self.f.transFigure.inverted())
-        return bbox
-
-    def _get_move_bbox(self, ax, x, y):
-        w, h = ax.bbox.width, ax.bbox.height
-        x0, y0 = (
-            self._start_ax_position[ax][0] + (x - self._start_position[0]),
-            self._start_ax_position[ax][1] + (y - self._start_position[1]),
-        )
-
-        if self._snap_id > 0:
-            sx, sy = self._snap
-            x0 = self.roundto(x0, sx)
-            y0 = self.roundto(y0, sy)
-
-        bbox = Bbox.from_bounds(x0, y0, w, h).transformed(self.f.transFigure.inverted())
-
-        return bbox
-
-    def _get_resize_bbox(self, ax, step):
-        origw, origh = ax.bbox.width, ax.bbox.height
-        x0, y0 = ax.bbox.x0, ax.bbox.y0
-
-        sx, sy = self._snap
-
-        h, w = origh, origw
-
-        if self._scale_direction == "horizontal":
-            w += max(0.25, sx) * step
-            w = self.roundto(w, sx)
-        elif self._scale_direction == "vertical":
-            h += max(0.25, sy) * step
-            h = self.roundto(h, sy)
-        else:
-            w += max(0.25, sx) * step
-            w = self.roundto(w, sx)
-
-            h += max(0.25, sy) * step
-            h = self.roundto(h, sy)
-
-        if h <= 0 or w <= 0:
-            return
-
-        # x0 = self.roundto(x0, sx)
-        # y0 = self.roundto(y0, sy)
-
-        # keep the center-position of the scaled axis
-        x0 = x0 + (origw - w) / 2
-        y0 = y0 + (origh - h) / 2
-
-        bbox = Bbox.from_bounds(x0, y0, w, h).transformed(self.f.transFigure.inverted())
-
-        if bbox.width <= 0 or bbox.height <= 0:
-            return
-
-        return bbox
-
-    def _color_unpicked(self, ax):
-        for spine in ax.spines.values():
-            spine.set_edgecolor("b")
-            spine._EOmaps_linestyle = spine.get_linestyle()
-            spine.set_linestyle("-")
-            spine.set_linewidth(1)
-
-    def _color_picked(self, ax):
-        for spine in ax.spines.values():
-            spine.set_edgecolor("r")
-            spine.set_linestyle("-")
-            spine.set_linewidth(2)
-
-    def _color_axes(self):
-        for ax in self.axes:
-            self._color_unpicked(ax)
-
-        for ax in self._ax_picked:
-            if ax is not None:
-                self._color_picked(ax)
-
-    def _set_startpos(self, event):
-        self._start_position = (event.x, event.y)
-        self._start_ax_position = {i: (i.bbox.x0, i.bbox.y0) for i in self._ax_picked}
-
-    def _add_to_history(self):
-        self._history_undone.clear()
-        self._history = self._history[: self._max_hist_steps]
-        self._history.append(self.get_layout())
-
-    def _undo(self):
-        if len(self._history) > 0:
-            l = self._history.pop(-1)
-            self._history_undone.append(l)
-            self.m.apply_layout(l)
-            self.m.redraw()
-
-    def _redo(self):
-        if len(self._history_undone) > 0:
-            l = self._history_undone.pop(-1)
-            self._history.append(l)
-            self.m.apply_layout(l)
-            self.m.redraw()
-
-    def cb_release(self, event):
-        self._set_startpos(event)
-
-    def cb_pick(self, event):
-        if not self.modifier_pressed:
-            return
-        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
-            return False
-
-        # toggle info-text visibility on left-click
-        if event.button == 3:
-            if getattr(self, "_info_text", None) is not None:
-                vis = not self._info_text.get_visible()
-                self._info_text.set_visible(vis)
-                self._info_text_hidden = not vis
-
-        eventax = event.inaxes
-
-        if eventax not in self.axes:
-            # if no axes is clicked "unpick" previously picked axes
-            if len(self._ax_picked) == 0:
-                # if there was nothing picked there's nothing to do
-                # except updating the info-text visibility
-
-                if getattr(self, "_info_text", None) is not None:
-                    self.blit_artists()
-
-                return
-
-            self._ax_picked = []
-            self._m_picked = []
-            self._color_axes()
-            self._remove_snap_grid()
-            self.fetch_current_background()
-            self.blit_artists()
-            return
-
-        if self._shift_pressed:
-            if eventax in self.maxes:
-                m = self.ms[self.maxes.index(eventax)]
-                if eventax in self._ax_picked:
-                    self._ax_picked.remove(eventax)
-                else:
-                    self._ax_picked.append(eventax)
-
-                if m in self._m_picked:
-                    self._m_picked.remove(m)
-                else:
-                    self._m_picked.append(m)
-            else:
-                if eventax in self._ax_picked:
-                    self._ax_picked.remove(eventax)
-                    # handle colorbar axes
-                    if eventax.get_label() == "cb":
-                        for cbax in getattr(eventax, "_eomaps_cb_axes", []):
-                            self._ax_picked.remove(cbax)
-                else:
-                    self._ax_picked.append(eventax)
-                    # handle colorbar axes
-                    if eventax.get_label() == "cb":
-                        for cbax in getattr(eventax, "_eomaps_cb_axes", []):
-                            self._ax_picked.append(cbax)
-        else:
-            if eventax not in self._ax_picked:
-                self._m_picked = []
-                self._ax_picked = []
-
-                if eventax in self.axes:
-                    if eventax in self.maxes:
-                        self._ax_picked.append(eventax)
-                        self._m_picked.append(self.ms[self.maxes.index(eventax)])
-                    else:
-                        self._m_picked = []
-                        self._ax_picked.append(eventax)
-                        # handle colorbar axes
-                        if eventax.get_label() == "cb":
-                            for cbax in getattr(eventax, "_eomaps_cb_axes", []):
-                                self._ax_picked.append(cbax)
-
-                    self._add_snap_grid()
-            else:
-                self._add_snap_grid()
-
-        self._set_startpos(event)
-        self._color_axes()
-        self.fetch_current_background()
-        self.blit_artists()
-
-    def fetch_current_background(self):
-        # clear the renderer to avoid drawing on existing backgrounds
-        renderer = self.m.BM.canvas.get_renderer()
-        renderer.clear()
-
-        with ExitStack() as stack:
-            for ax in self._ax_picked:
-                stack.enter_context(ax._cm_set(visible=False))
-
-            self.m.BM.blit_artists(self.axes, None, False)
-
-            grid = getattr(self, "_snap_grid_artist", None)
-            if grid is not None:
-                self.m.BM.blit_artists([grid], None, False)
-
-            self.m.BM.canvas.blit()
-            self._current_bg = self.m.BM.canvas.copy_from_bbox(self.m.f.bbox)
-
-    def cb_move_with_key(self, event):
-        if not self.modifier_pressed:
-            return
-        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
-            return False
-
-        if event.key not in ["left", "right", "up", "down"]:
-            return
-
-        for ax in self._ax_picked:
-            bbox = self._get_move_with_key_bbox(ax, event.key)
-            ax.set_position(bbox)
-
-        self._add_to_history()
-        self._color_axes()
-        self.blit_artists()
-
-    def cb_move(self, event):
-        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
-            return False
-        if self.modifier is not None:
-            if not self.modifier_pressed:
-                return False
-
-        if event.button != 1:
-            return
-
-        for ax in self._ax_picked:
-            if ax is None:
-                return
-
-            bbox = self._get_move_bbox(ax, event.x, event.y)
-            ax.set_position(bbox)
-
-        self._add_to_history()
-        self._color_axes()
-        self.blit_artists()
-
-    def blit_artists(self):
-        artists = [*self._ax_picked]
-
-        if getattr(self, "_info_text", None) is not None:
-            artists.append(self._info_text)
-
-        self.m.BM.blit_artists(artists, self._current_bg)
-
-    def cb_scroll(self, event):
-        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
-            return False
-        if self.modifier is not None:
-            if not self.modifier_pressed:
-                return False
-
-        if self._scale_direction == "set_hist_size":
-            # resize colorbar histogram
-            for ax in self._ax_picked:
-                if not ax.get_label() == "cb":
-                    continue
-
-                # identify all relevant colorbars
-                # (e.g. all colorbars that share the container-ax "cb._ax")
-                cbs = []
-                for m in self.ms:
-                    for cb in m._colorbars:
-                        if cb._ax is ax:
-                            cbs.append(cb)
-
-                if len(cbs) == 0:
-                    return False
-
-                # use the hist-size of the first colorbar as start (to avoid ambiguity)
-                start_size = cbs[0]._hist_size
-                for cb in cbs:
-                    new_size = np.clip(start_size + event.step * 0.02, 0.0, 1.0)
-                    cb._set_hist_size(new_size)
-
-            self._add_to_history()
-            self.blit_artists()
-        else:
-            # resize axes
-            for ax in self._ax_picked:
-                if ax is None:
-                    continue
-                resize_bbox = self._get_resize_bbox(ax, event.step)
-                if resize_bbox is not None:
-                    ax.set_position(resize_bbox)
-            self._add_to_history()
-            self.blit_artists()
-
-    def cb_key_press(self, event):
-        # release shift key on every keypress
-        self._shift_pressed = False
-
-        if (event.key == self.modifier) and (not self.modifier_pressed):
-            self._make_draggable()
-            return
-        elif (event.key == self.modifier or event.key == "escape") and (
-            self.modifier_pressed
-        ):
-            self._undo_draggable()
-            return
-        elif (event.key.lower() == "p") and (self.modifier_pressed):
-            s = "\nlayout = {\n    "
-            s += "\n    ".join(
-                f'"{key}": {val},' for key, val in self.get_layout().items()
-            )
-            s += "\n}\n"
-            print(s)
-        elif (event.key.lower() == "q") and (self.modifier_pressed):
-            print(
-                "\n##########################\n\n"
-                "EOmaps Layout Editor controls:\n\n"
-                "Click on axes to select them for editing.\n"
-                "(Hold 'shift' while clicking on axes to select multiple axes.)\n\n"
-                "Drag selected axes with the mouse or use the 'arrow-keys' to "
-                "change their position.\n\n"
-                "Use the 'scroll-wheel' or the '+' and '-' keys to change the size "
-                "of selected axes.\n"
-                "For normal matplotlib axes: Hold down 'h' or 'v' to adjust only "
-                "the horizontal or vertical size of the axes.\n"
-                "For EOmaps colorbars: Hold down 'control' to adjust the relative "
-                "size of the histogram.\n\n"
-                "Use the keys 1-9 to adjust the spacing of the 'snap grid' (Note that "
-                "the grid-spacing also determines the step-size for size- and "
-                "position-changes!) Press 0 to disable grid-snapping.\n\n"
-                f"To exit, press 'escape' or '{self.modifier}'\n"
-                "\n##########################\n\n"
-            )
-            return
-
-        else:
-            if not self.modifier_pressed:
-                # only continue if  modifier is pressed!
-                return
-
-        if event.key in ("ctrl+z", "control+z"):
-            self._undo()
-            return
-        elif event.key in ("ctrl+y", "control+y"):
-            self._redo()
-            return
-        elif event.key == "h":
-            self._scale_direction = "horizontal"
-        elif event.key == "v":
-            self._scale_direction = "vertical"
-        elif event.key in ("control", "ctrl", "ctrl++", "ctrl+-"):
-            self._scale_direction = "set_hist_size"
-
-        elif event.key == "shift":
-            self._shift_pressed = True
-
-        # assign snaps with keys 0-9
-        if event.key in map(str, range(10)):
-            self._snap_id = int(event.key)
-            self._add_snap_grid()
-            self.fetch_current_background()
-            self.blit_artists()
-
-        # assign snaps with keys 0-9
-        if event.key in ["+", "-", "ctrl++", "ctrl+-"]:
-
-            class dummyevent:
-                pass
-
-            d = dummyevent()
-            d.key = event.key
-            d.step = 1 * {"+": 1, "ctrl++": 1, "ctrl+-": -1, "-": -1}[event.key]
-
-            self.cb_scroll(d)
-
-    def cb_key_release(self, event):
-        # reset scale direction on every key release event
-        if event.key in ("h", "v", "control", "ctrl", "ctrl++", "ctrl+-"):
-            self._scale_direction = "both"
-        if event.key in ("shift"):
-            self._shift_pressed = False
-
-    @property
-    def _snap(self):
-        # grid-separation distance
-        if self._snap_id == 0:
-            snap = (0, 0)
-        else:
-            n = (self.f.bbox.width / 400) * (self._snap_id)
-
-            snap = (n, n)
-
-        return snap
-
-    def ax_on_layer(self, ax):
-        if ax in self.m.BM._get_unmanaged_axes():
-            return True
-        elif ax in self.maxes:
-            return True
-        else:
-            for layer in (self.m.BM.bg_layer, "__SPINES__", "all"):
-                # logos are put on the spines-layer to appear on top of spines!
-                if ax in self.m.BM.get_bg_artists(layer):
-                    return True
-                elif ax in self.m.BM.get_artists(self.m.BM.bg_layer):
-                    return True
-
-        return False
-
-    def _make_draggable(self, filepath=None):
-        # Uncheck active pan/zoom actions of the matplotlib toolbar.
-        # use a try-except block to avoid issues with ipympl in jupyter notebooks
-        # (see https://github.com/matplotlib/ipympl/issues/530#issue-1780919042)
-        try:
-            toolbar = getattr(self.m.BM.canvas, "toolbar", None)
-            if toolbar is not None:
-                for key in ["pan", "zoom"]:
-                    val = toolbar._actions.get(key, None)
-                    if val is not None and val.isCheckable() and val.isChecked():
-                        val.trigger()
-        except AttributeError:
-            pass
-
-        self._filepath = filepath
-        self.modifier_pressed = True
-        _log.info(
-            "EOmaps: Layout Editor activated! (press 'esc' to exit " "and 'q' for info)"
-        )
-
-        self._history.clear()
-        self._history_undone.clear()
-        self._add_to_history()
-
-        self._revert_props = []
-        for ax in self.f.axes:
-            # only handle axes that have a finite size (in pixels) to avoid
-            # singular matrix errors for initially hidden zero-size axes
-            # (can happen for colorbar/colorbar histogram axes)
-            singularax = False
-            if ax.bbox.width <= 1 or ax.bbox.height <= 1:
-                singularax = True
-
-            # check if the axis is the container-axes of a colorbar
-            cbaxQ = ax.get_label() == "cb"
-
-            if not ax.axison:
-                showXY = False
-                self._revert_props.append(ax.set_axis_off)
-                ax.set_axis_on()
-            else:
-                showXY = True
-
-            # keep singular axes hidden
-            self._revert_props.append((ax.set_visible, ax.get_visible()))
-            if not singularax:
-                if self.ax_on_layer(ax):
-                    ax.set_visible(True)
-                else:
-                    ax.set_visible(False)
-            else:
-                ax.set_visible(False)
-
-            self._revert_props.append((ax.set_animated, ax.get_animated()))
-            ax.set_animated(False)
-
-            self._revert_props.append((ax.set_frame_on, ax.get_frame_on()))
-            ax.set_frame_on(True)
-
-            for child in ax.get_children():
-                # make sure we don't treat axes again (in case they are child-axes)
-                if child in self.f.axes:
-                    continue
-                revert_props = [
-                    "edgecolor",
-                    "linewidth",
-                    "alpha",
-                    "animated",
-                    "visible",
-                ]
-                self._add_revert_props(child, *revert_props)
-
-                if isinstance(child, Spine) and not cbaxQ:
-                    # make sure spines are visible (and re-drawn on draw)
-                    child.set_animated(False)
-                    child.set_visible(True)
-                    if hasattr(child, "_EOmaps_linestyle"):
-                        child.set_linestyle(getattr(child, "_EOmaps_linestyle", "-"))
-                        del child._EOmaps_linestyle
-                elif (
-                    ax not in self.maxes
-                    and showXY
-                    and isinstance(child, (XAxis, YAxis))
-                ):
-                    # keep all tick labels etc. of normal axes and colorbars visible
-                    child.set_animated(False)
-                    child.set_visible(True)
-
-                elif child is ax.patch and not cbaxQ:
-                    # only reset facecolors for axes-patches to avoid issues with
-                    # black spines (TODO check why this happens!)
-                    self._add_revert_props(child, "facecolor")
-
-                    # make sure patches are visible (and re-drawn on draw)
-                    child.set_visible(True)
-                    child.set_facecolor("w")
-                    child.set_alpha(0.75)  # for overlapping axes
-
-                else:
-                    # make all other children invisible (to avoid drawing them)
-                    child.set_visible(False)
-                    child.set_animated(True)
-
-        # only re-draw if info-text is None
-        if getattr(self, "_info_text", None) is None:
-            self._info_text = self.add_info_text()
-
-        self._color_axes()
-        self._attach_callbacks()
-
-        self.m._emit_signal("layoutEditorActivated")
-
-        self.m.redraw()
-
-    def _add_revert_props(self, child, *args):
-        for prop in args:
-            if hasattr(child, f"set_{prop}") and hasattr(child, f"get_{prop}"):
-                self._revert_props.append(
-                    (
-                        getattr(child, f"set_{prop}"),
-                        getattr(child, f"get_{prop}")(),
-                    )
-                )
-
-    def _undo_draggable(self):
-        if getattr(self, "_info_text", None) not in (None, False):
-            self._info_text.remove()
-            # set to None to avoid crating the info-text again
-            self._info_text = None
-
-        self._history.clear()
-        self._history_undone.clear()
-
-        toolbar = getattr(self.m.f, "toolbar", None)
-        if toolbar is not None:
-            # Reset the axes stack to make sure the "home" "back" and "forward" buttons
-            # of the toolbar do not reset axis positions
-            # see "matplotlib.backend_bases.NavigationToolbar2.update"
-            if hasattr(toolbar, "update"):
-                try:
-                    toolbar.update()
-                except Exception:
-                    _log.exception(
-                        "EOmaps: Error while trying to reset the axes stack!"
-                    )
-
-        # clear all picks on exit
-        self._ax_picked = []
-        self._m_picked = []
-
-        _log.info("EOmaps: Exiting layout-editor mode...")
-
-        # in case a filepath was provided, save the new layout
-        if self._filepath:
-            try:
-                self.m.get_layout(filepath=self._filepath, override=True)
-            except Exception:
-                _log.exception(
-                    "EOmaps: Layout could not be saved to the provided "
-                    + f"filepath: '{self._filepath}'."
-                )
-
-        self._reset_callbacks()
-        # revert all changes to artists
-        for p in self._revert_props:
-            if isinstance(p, tuple):
-                p[0](p[1])
-            else:
-                p()
-
-        self.modifier_pressed = False
-
-        # reset the histogram-size of all colorbars to make sure previously hidden
-        # axes (e.g. size=0) become visible if the size is now > 0.
-        for m in self.ms:
-            for cb in m._colorbars:
-                cb._set_hist_size(update_all=True)
-
-        # remove snap-grid (if it's still visible)
-        self._remove_snap_grid()
-
-        self.m._emit_signal("layoutEditorDeactivated")
-
-        self.m.redraw()
-        # try to push the current view to the "home" toolbar button
-        try:
-            self.m.f.canvas.toolbar.push_current()
-        except Exception:
-            pass
-
-    def _reset_callbacks(self):
-        # disconnect all callbacks of the layout-editor
-        while len(self.cids) > 0:
-            cid = self.cids.pop(-1)
-            self.f.canvas.mpl_disconnect(cid)
-
-    def _attach_callbacks(self):
-        # make sure all previously set callbacks are reset
-        self._reset_callbacks()
-
-        events = (
-            ("scroll_event", self.cb_scroll),
-            ("button_press_event", self.cb_pick),
-            ("button_release_event", self.cb_release),
-            ("motion_notify_event", self.cb_move),
-            ("key_press_event", self.cb_move_with_key),
-            ("key_release_event", self.cb_key_release),
-        )
-
-        for event, cb in events:
-            self.cids.append(self.f.canvas.mpl_connect(event, cb))
-
-    def _add_snap_grid(self, snap=None):
-        # snap = (snapx, snapy)
-
-        if snap is None:
-            if self._snap_id == 0:
-                self._remove_snap_grid()
-                return
-            else:
-                snapx, snapy = self._snap
-        else:
-            snapx, snapy = snap
-
-        self._remove_snap_grid()
-
-        bbox = self.m.f.bbox
-        t = self.m.f.transFigure.inverted()
-
-        gx, gy = np.mgrid[
-            0 : int(bbox.width) + int(snapx) : snapx,
-            0 : int(bbox.height) + int(snapy) : snapy,
-        ]
-        g = t.transform(np.column_stack((gx.flat, gy.flat)))
-
-        l = Line2D(
-            *g.T,
-            lw=0,
-            marker=".",
-            markerfacecolor="steelblue",
-            markeredgecolor="none",
-            ms=(snapx + snapy) / 6,
-        )
-        self._snap_grid_artist = self.m.f.add_artist(l)
-
-    def _remove_snap_grid(self):
-        if hasattr(self, "_snap_grid_artist"):
-            self._snap_grid_artist.remove()
-            del self._snap_grid_artist
-
-    def get_layout(self, filepath=None, override=False, precision=5):
-        """
-        Get the positions of all axes within the current plot.
-
-        To re-apply a layout, use:
-
-            >>> l = m.get_layout()
-            >>> m.set_layout(l)
-
-        Note
-        ----
-        The returned list is only a snapshot of the current layout.
-        It can only be re-applied to a given figure if the order at which the axes are
-        created remains the same!
-
-        Parameters
-        ----------
-        filepath : str or pathlib.Path, optional
-            If provided, a json-file will be created at the specified destination that
-            can be used in conjunction with `m.set_layout(...)` to apply the layout:
-
-            >>> m.get_layout(filepath=<FILEPATH>, override=True)
-            >>> m.apply_layout_layout(<FILEPATH>)
-
-            You can also manually read-in the layout-dict via:
-            >>> import json
-            >>> layout = json.load(<FILEPATH>)
-        override: bool
-            Indicator if the file specified as 'filepath' should be overwritten if it
-            already exists.
-            The default is False.
-        precision : int or None
-            The precision of the returned floating-point numbers.
-            If None, all available digits are returned
-            The default is 5
-        Returns
-        -------
-        layout : dict or None
-            A dict of the positions of all axes, e.g.: {1:(x0, y0, width height), ...}
-        """
-        figsize = [*self.f.get_size_inches()]
-
-        axes = [
-            a for a in self.axes if a.get_label() not in ["EOmaps_cb", "EOmaps_cb_hist"]
-        ]
-
-        # identify relevant colorbars
-        colorbars = [getattr(m, "colorbar", None) for m in self.ms]
-        cbaxes = [getattr(cb, "_ax", None) for cb in colorbars]
-        cbs = [(colorbars[cbaxes.index(a)] if a in cbaxes else None) for a in axes]
-        # -----------
-
-        layout = dict()
-        layout["figsize"] = figsize
-
-        for i, ax in enumerate(axes):
-            if cbs[i] is not None:
-                if cbs[i]._ax.get_axes_locator() is not None:
-                    continue
-
-            label = ax.get_label()
-            name = f"{i}_{label}"
-            if precision is not None:
-                layout[name] = np.round(ax.get_position().bounds, precision).tolist()
-            else:
-                layout[name] = ax.get_position().bounds
-
-            if cbs[i] is not None:
-                layout[f"{name}_histogram_size"] = cbs[i]._hist_size
-
-        if filepath is not None:
-            filepath = Path(filepath)
-            assert (
-                not filepath.exists() or override
-            ), f"The file {filepath} already exists! Use override=True to relace it."
-            with open(filepath, "w") as file:
-                json.dump(layout, file)
-            _log.info(f"EOmaps: Layout saved to:\n       {filepath}")
-
-        return layout
-
-    def apply_layout(self, layout):
-        """
-        Set the positions of all axes within the current plot based on a previously
-        defined layout.
-
-        To apply a layout, use:
-
-            >>> l = m.get_layout()
-            >>> m.set_layout(l)
-
-        To save a layout to disc and apply it at a later stage, use
-            >>> m.get_layout(filepath=<FILEPATH>)
-            >>> m.set_layout(<FILEPATH>)
-
-        Note
-        ----
-        The returned list is only a snapshot of the current layout.
-        It can only be re-applied to a given figure if the order at which the axes are
-        created remains the same!
-
-        Parameters
-        ----------
-        layout : dict, str or pathlib.Path
-            If a dict is provided, it is directly used to define the layout.
-
-            If a string or a pathlib.Path object is provided, it will be used to
-            read a previously dumped layout (e.g. with `m.get_layout(filepath)`)
-
-        """
-        if isinstance(layout, (str, Path)):
-            with open(layout, "r") as file:
-                layout = json.load(file)
-
-        # check if all relevant axes are specified in the layout
-        valid_keys = set(self.get_layout())
-        if valid_keys != set(layout):
-            _log.warning(
-                "EOmaps: The the layout does not match the expected structure! "
-                "Layout might not be properly restored. "
-                "Invalid or missing keys:\n"
-                f"{sorted(valid_keys.symmetric_difference(set(layout)))}\n"
-            )
-
-        # set the figsize
-        figsize = layout.get("figsize", None)
-        if figsize is not None:
-            self.f.set_size_inches(*figsize)
-
-        axes = [
-            a for a in self.axes if a.get_label() not in ["EOmaps_cb", "EOmaps_cb_hist"]
-        ]
-
-        # identify relevant colorbars
-        colorbars = [getattr(m, "colorbar", None) for m in self.ms]
-        cbaxes = [getattr(cb, "_ax", None) for cb in colorbars]
-        cbs = [(colorbars[cbaxes.index(a)] if a in cbaxes else None) for a in axes]
-
-        for key in valid_keys.intersection(set(layout)):
-            if key == "figsize":
-                continue
-            val = layout[key]
-
-            i = int(key[: key.find("_")])
-            if key.endswith("_histogram_size"):
-                cbs[i]._set_hist_size(val)
-            else:
-                axes[i].set_position(val)
-
-        self.m.redraw()
-
-        # try to push the current view to the "home" toolbar button
-        try:
-            self.m.f.canvas.toolbar.push_current()
-        except Exception:
-            pass
-
-
 # taken from https://matplotlib.org/stable/tutorials/advanced/blitting.html#class-based-example
 class BlitManager(LayerParser):
     """Manager used to schedule draw events, cache backgrounds, etc."""
diff --git a/eomaps/layout_editor.py b/eomaps/layout_editor.py
new file mode 100644
index 000000000..5d720841e
--- /dev/null
+++ b/eomaps/layout_editor.py
@@ -0,0 +1,993 @@
+import logging
+from contextlib import ExitStack
+from pathlib import Path
+import json
+
+import numpy as np
+from matplotlib.lines import Line2D
+from matplotlib.transforms import Bbox
+from matplotlib.axis import XAxis, YAxis
+from matplotlib.spines import Spine
+
+_log = logging.getLogger(__name__)
+
+
+class LayoutEditor:
+    """Class to handle interactive re-positioning of figure-objects."""
+
+    def __init__(self, m, modifier="alt+d", cb_modifier="control"):
+        self.modifier = modifier
+        self.cb_modifier = cb_modifier
+
+        self.m = m
+        self.f = self.m.parent.f
+
+        self._ax_picked = []
+        self._m_picked = []
+
+        self._modifier_pressed = False
+
+        self.cids = []
+
+        # indicator if the pick-callback should be re-attached or not
+        self._reattach_pick_cb = False
+
+        self.f.canvas.mpl_connect("key_press_event", self.cb_key_press)
+        self.f.canvas.mpl_connect("resize_event", self._on_resize)
+
+        # the snap-to-grid interval (0 means no snapping)
+        self._snap_id = 5
+
+        # an optional filepath that will be used to store the layout once the
+        # editor exits
+        self._filepath = None
+
+        # indicator if scaling should be in horizontal or vertical direction
+        self._scale_direction = "both"
+
+        # indicator if multiple-axis select key is pressed or not (e.g. "shift")
+        self._shift_pressed = False
+
+        self._max_hist_steps = 1000
+        self._history = list()
+        self._history_undone = list()
+
+        self._current_bg = None
+
+        self._info_text = None
+        self._info_text_hidden = False
+
+    def add_info_text(self):
+        self._info_text_hidden = False
+
+        a = self.m.f.text(
+            0.72,
+            0.98,
+            (
+                "LayoutEditor Controls:\n\n"
+                "0 - 9:  Snap-grid spacing\n"
+                "SHIFT:  Multi-select\n"
+                "P:      Print to console\n"
+                "ESCAPE (or ALT + L): Exit\n"
+                "\n"
+                "ARROW-KEYS:   Move\n"
+                "SCROLL (+/-): Resize\n"
+                "  H:    horizontal\n"
+                "  V:    vertical\n"
+                "  ctrl: histogram"
+                "\n\n(right-click to hide info)"
+            ),
+            transform=self.m.f.transFigure,
+            ha="left",
+            va="top",
+            fontsize=min(self.m.f.bbox.width * 72 / self.m.f.dpi / 60, 12),
+            bbox=dict(
+                boxstyle="round", facecolor=".8", edgecolor="k", lw=0.5, alpha=0.9
+            ),
+            zorder=1e6,
+            fontfamily="monospace",
+        )
+        return a
+
+    def _update_info_text(self):
+        if getattr(self, "_info_text", None) is not None:
+            self._info_text.set_fontsize(
+                min(self.m.f.bbox.width * 72 / self.m.f.dpi / 60, 15)
+            )
+
+    def _on_resize(self, *args, **kwargs):
+        # update snap-grid on resize
+        if self.modifier_pressed:
+            self._add_snap_grid()
+            self._update_info_text()
+
+    @property
+    def modifier_pressed(self):
+        return self._modifier_pressed
+
+    @modifier_pressed.setter
+    def modifier_pressed(self, val):
+        self._modifier_pressed = val
+        self.m.cb.execute_callbacks(not val)
+
+        if self._modifier_pressed:
+            self.m.BM._disable_draw = True
+            self.m.BM._disable_update = True
+        else:
+            self.m.BM._disable_draw = False
+            self.m.BM._disable_update = False
+
+    @property
+    def ms(self):
+        return [self.m.parent, *self.m.parent._children]
+
+    @property
+    def maxes(self):
+        return [m.ax for m in self.ms]
+
+    @property
+    def axes(self):
+        return self.f.axes
+
+    @staticmethod
+    def roundto(x, base=10):
+        if base == 0:
+            return x
+        if x % base <= base / 2:
+            return x - x % base
+        else:
+            return x + (base - x % base)
+
+    def _get_move_with_key_bbox(self, ax, key):
+        snapx, snapy = self._snap
+        intervalx, intervaly = (
+            max(0.25, snapx),
+            max(0.25, snapy),
+        )
+
+        if key == "left":
+            bbox = Bbox.from_bounds(
+                self.roundto(ax.bbox.x0 - intervalx, snapx),
+                ax.bbox.y0,
+                ax.bbox.width,
+                ax.bbox.height,
+            )
+        elif key == "right":
+            bbox = Bbox.from_bounds(
+                self.roundto(ax.bbox.x0 + intervalx, snapx),
+                ax.bbox.y0,
+                ax.bbox.width,
+                ax.bbox.height,
+            )
+        elif key == "up":
+            bbox = Bbox.from_bounds(
+                ax.bbox.x0,
+                self.roundto(ax.bbox.y0 + intervaly, snapy),
+                ax.bbox.width,
+                ax.bbox.height,
+            )
+        elif key == "down":
+            bbox = Bbox.from_bounds(
+                ax.bbox.x0,
+                self.roundto(ax.bbox.y0 - intervaly, snapy),
+                ax.bbox.width,
+                ax.bbox.height,
+            )
+
+        bbox = bbox.transformed(self.f.transFigure.inverted())
+        return bbox
+
+    def _get_move_bbox(self, ax, x, y):
+        w, h = ax.bbox.width, ax.bbox.height
+        x0, y0 = (
+            self._start_ax_position[ax][0] + (x - self._start_position[0]),
+            self._start_ax_position[ax][1] + (y - self._start_position[1]),
+        )
+
+        if self._snap_id > 0:
+            sx, sy = self._snap
+            x0 = self.roundto(x0, sx)
+            y0 = self.roundto(y0, sy)
+
+        bbox = Bbox.from_bounds(x0, y0, w, h).transformed(self.f.transFigure.inverted())
+
+        return bbox
+
+    def _get_resize_bbox(self, ax, step):
+        origw, origh = ax.bbox.width, ax.bbox.height
+        x0, y0 = ax.bbox.x0, ax.bbox.y0
+
+        sx, sy = self._snap
+
+        h, w = origh, origw
+
+        if self._scale_direction == "horizontal":
+            w += max(0.25, sx) * step
+            w = self.roundto(w, sx)
+        elif self._scale_direction == "vertical":
+            h += max(0.25, sy) * step
+            h = self.roundto(h, sy)
+        else:
+            w += max(0.25, sx) * step
+            w = self.roundto(w, sx)
+
+            h += max(0.25, sy) * step
+            h = self.roundto(h, sy)
+
+        if h <= 0 or w <= 0:
+            return
+
+        # x0 = self.roundto(x0, sx)
+        # y0 = self.roundto(y0, sy)
+
+        # keep the center-position of the scaled axis
+        x0 = x0 + (origw - w) / 2
+        y0 = y0 + (origh - h) / 2
+
+        bbox = Bbox.from_bounds(x0, y0, w, h).transformed(self.f.transFigure.inverted())
+
+        if bbox.width <= 0 or bbox.height <= 0:
+            return
+
+        return bbox
+
+    def _color_unpicked(self, ax):
+        for spine in ax.spines.values():
+            spine.set_edgecolor("b")
+            spine._EOmaps_linestyle = spine.get_linestyle()
+            spine.set_linestyle("-")
+            spine.set_linewidth(1)
+
+    def _color_picked(self, ax):
+        for spine in ax.spines.values():
+            spine.set_edgecolor("r")
+            spine.set_linestyle("-")
+            spine.set_linewidth(2)
+
+    def _color_axes(self):
+        for ax in self.axes:
+            self._color_unpicked(ax)
+
+        for ax in self._ax_picked:
+            if ax is not None:
+                self._color_picked(ax)
+
+    def _set_startpos(self, event):
+        self._start_position = (event.x, event.y)
+        self._start_ax_position = {i: (i.bbox.x0, i.bbox.y0) for i in self._ax_picked}
+
+    def _add_to_history(self):
+        self._history_undone.clear()
+        self._history = self._history[: self._max_hist_steps]
+        self._history.append(self.get_layout())
+
+    def _undo(self):
+        if len(self._history) > 0:
+            l = self._history.pop(-1)
+            self._history_undone.append(l)
+            self.m.apply_layout(l)
+            self.m.redraw()
+
+    def _redo(self):
+        if len(self._history_undone) > 0:
+            l = self._history_undone.pop(-1)
+            self._history.append(l)
+            self.m.apply_layout(l)
+            self.m.redraw()
+
+    def cb_release(self, event):
+        self._set_startpos(event)
+
+    def cb_pick(self, event):
+        if not self.modifier_pressed:
+            return
+        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
+            return False
+
+        # toggle info-text visibility on left-click
+        if event.button == 3:
+            if getattr(self, "_info_text", None) is not None:
+                vis = not self._info_text.get_visible()
+                self._info_text.set_visible(vis)
+                self._info_text_hidden = not vis
+
+        eventax = event.inaxes
+
+        if eventax not in self.axes:
+            # if no axes is clicked "unpick" previously picked axes
+            if len(self._ax_picked) == 0:
+                # if there was nothing picked there's nothing to do
+                # except updating the info-text visibility
+
+                if getattr(self, "_info_text", None) is not None:
+                    self.blit_artists()
+
+                return
+
+            self._ax_picked = []
+            self._m_picked = []
+            self._color_axes()
+            self._remove_snap_grid()
+            self.fetch_current_background()
+            self.blit_artists()
+            return
+
+        if self._shift_pressed:
+            if eventax in self.maxes:
+                m = self.ms[self.maxes.index(eventax)]
+                if eventax in self._ax_picked:
+                    self._ax_picked.remove(eventax)
+                else:
+                    self._ax_picked.append(eventax)
+
+                if m in self._m_picked:
+                    self._m_picked.remove(m)
+                else:
+                    self._m_picked.append(m)
+            else:
+                if eventax in self._ax_picked:
+                    self._ax_picked.remove(eventax)
+                    # handle colorbar axes
+                    if eventax.get_label() == "cb":
+                        for cbax in getattr(eventax, "_eomaps_cb_axes", []):
+                            self._ax_picked.remove(cbax)
+                else:
+                    self._ax_picked.append(eventax)
+                    # handle colorbar axes
+                    if eventax.get_label() == "cb":
+                        for cbax in getattr(eventax, "_eomaps_cb_axes", []):
+                            self._ax_picked.append(cbax)
+        else:
+            if eventax not in self._ax_picked:
+                self._m_picked = []
+                self._ax_picked = []
+
+                if eventax in self.axes:
+                    if eventax in self.maxes:
+                        self._ax_picked.append(eventax)
+                        self._m_picked.append(self.ms[self.maxes.index(eventax)])
+                    else:
+                        self._m_picked = []
+                        self._ax_picked.append(eventax)
+                        # handle colorbar axes
+                        if eventax.get_label() == "cb":
+                            for cbax in getattr(eventax, "_eomaps_cb_axes", []):
+                                self._ax_picked.append(cbax)
+
+                    self._add_snap_grid()
+            else:
+                self._add_snap_grid()
+
+        self._set_startpos(event)
+        self._color_axes()
+        self.fetch_current_background()
+        self.blit_artists()
+
+    def fetch_current_background(self):
+        # clear the renderer to avoid drawing on existing backgrounds
+        renderer = self.m.BM.canvas.get_renderer()
+        renderer.clear()
+
+        with ExitStack() as stack:
+            for ax in self._ax_picked:
+                stack.enter_context(ax._cm_set(visible=False))
+
+            self.m.BM.blit_artists(self.axes, None, False)
+
+            grid = getattr(self, "_snap_grid_artist", None)
+            if grid is not None:
+                self.m.BM.blit_artists([grid], None, False)
+
+            self.m.BM.canvas.blit()
+            self._current_bg = self.m.BM.canvas.copy_from_bbox(self.m.f.bbox)
+
+    def cb_move_with_key(self, event):
+        if not self.modifier_pressed:
+            return
+        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
+            return False
+
+        if event.key not in ["left", "right", "up", "down"]:
+            return
+
+        for ax in self._ax_picked:
+            bbox = self._get_move_with_key_bbox(ax, event.key)
+            ax.set_position(bbox)
+
+        self._add_to_history()
+        self._color_axes()
+        self.blit_artists()
+
+    def cb_move(self, event):
+        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
+            return False
+        if self.modifier is not None:
+            if not self.modifier_pressed:
+                return False
+
+        if event.button != 1:
+            return
+
+        for ax in self._ax_picked:
+            if ax is None:
+                return
+
+            bbox = self._get_move_bbox(ax, event.x, event.y)
+            ax.set_position(bbox)
+
+        self._add_to_history()
+        self._color_axes()
+        self.blit_artists()
+
+    def blit_artists(self):
+        artists = [*self._ax_picked]
+
+        if getattr(self, "_info_text", None) is not None:
+            artists.append(self._info_text)
+
+        self.m.BM.blit_artists(artists, self._current_bg)
+
+    def cb_scroll(self, event):
+        if (self.f.canvas.toolbar is not None) and self.f.canvas.toolbar.mode != "":
+            return False
+        if self.modifier is not None:
+            if not self.modifier_pressed:
+                return False
+
+        if self._scale_direction == "set_hist_size":
+            # resize colorbar histogram
+            for ax in self._ax_picked:
+                if not ax.get_label() == "cb":
+                    continue
+
+                # identify all relevant colorbars
+                # (e.g. all colorbars that share the container-ax "cb._ax")
+                cbs = []
+                for m in self.ms:
+                    for cb in m._colorbars:
+                        if cb._ax is ax:
+                            cbs.append(cb)
+
+                if len(cbs) == 0:
+                    return False
+
+                # use the hist-size of the first colorbar as start (to avoid ambiguity)
+                start_size = cbs[0]._hist_size
+                for cb in cbs:
+                    new_size = np.clip(start_size + event.step * 0.02, 0.0, 1.0)
+                    cb._set_hist_size(new_size)
+
+            self._add_to_history()
+            self.blit_artists()
+        else:
+            # resize axes
+            for ax in self._ax_picked:
+                if ax is None:
+                    continue
+                resize_bbox = self._get_resize_bbox(ax, event.step)
+                if resize_bbox is not None:
+                    ax.set_position(resize_bbox)
+            self._add_to_history()
+            self.blit_artists()
+
+    def cb_key_press(self, event):
+        # release shift key on every keypress
+        self._shift_pressed = False
+
+        if (event.key == self.modifier) and (not self.modifier_pressed):
+            self._make_draggable()
+            return
+        elif (event.key == self.modifier or event.key == "escape") and (
+            self.modifier_pressed
+        ):
+            self._undo_draggable()
+            return
+        elif (event.key.lower() == "p") and (self.modifier_pressed):
+            s = "\nlayout = {\n    "
+            s += "\n    ".join(
+                f'"{key}": {val},' for key, val in self.get_layout().items()
+            )
+            s += "\n}\n"
+            print(s)
+        elif (event.key.lower() == "q") and (self.modifier_pressed):
+            print(
+                "\n##########################\n\n"
+                "EOmaps Layout Editor controls:\n\n"
+                "Click on axes to select them for editing.\n"
+                "(Hold 'shift' while clicking on axes to select multiple axes.)\n\n"
+                "Drag selected axes with the mouse or use the 'arrow-keys' to "
+                "change their position.\n\n"
+                "Use the 'scroll-wheel' or the '+' and '-' keys to change the size "
+                "of selected axes.\n"
+                "For normal matplotlib axes: Hold down 'h' or 'v' to adjust only "
+                "the horizontal or vertical size of the axes.\n"
+                "For EOmaps colorbars: Hold down 'control' to adjust the relative "
+                "size of the histogram.\n\n"
+                "Use the keys 1-9 to adjust the spacing of the 'snap grid' (Note that "
+                "the grid-spacing also determines the step-size for size- and "
+                "position-changes!) Press 0 to disable grid-snapping.\n\n"
+                f"To exit, press 'escape' or '{self.modifier}'\n"
+                "\n##########################\n\n"
+            )
+            return
+
+        else:
+            if not self.modifier_pressed:
+                # only continue if  modifier is pressed!
+                return
+
+        if event.key in ("ctrl+z", "control+z"):
+            self._undo()
+            return
+        elif event.key in ("ctrl+y", "control+y"):
+            self._redo()
+            return
+        elif event.key == "h":
+            self._scale_direction = "horizontal"
+        elif event.key == "v":
+            self._scale_direction = "vertical"
+        elif event.key in ("control", "ctrl", "ctrl++", "ctrl+-"):
+            self._scale_direction = "set_hist_size"
+
+        elif event.key == "shift":
+            self._shift_pressed = True
+
+        # assign snaps with keys 0-9
+        if event.key in map(str, range(10)):
+            self._snap_id = int(event.key)
+            self._add_snap_grid()
+            self.fetch_current_background()
+            self.blit_artists()
+
+        # assign snaps with keys 0-9
+        if event.key in ["+", "-", "ctrl++", "ctrl+-"]:
+
+            class dummyevent:
+                pass
+
+            d = dummyevent()
+            d.key = event.key
+            d.step = 1 * {"+": 1, "ctrl++": 1, "ctrl+-": -1, "-": -1}[event.key]
+
+            self.cb_scroll(d)
+
+    def cb_key_release(self, event):
+        # reset scale direction on every key release event
+        if event.key in ("h", "v", "control", "ctrl", "ctrl++", "ctrl+-"):
+            self._scale_direction = "both"
+        if event.key in ("shift"):
+            self._shift_pressed = False
+
+    @property
+    def _snap(self):
+        # grid-separation distance
+        if self._snap_id == 0:
+            snap = (0, 0)
+        else:
+            n = (self.f.bbox.width / 400) * (self._snap_id)
+
+            snap = (n, n)
+
+        return snap
+
+    def ax_on_layer(self, ax):
+        if ax in self.m.BM._get_unmanaged_axes():
+            return True
+        elif ax in self.maxes:
+            return True
+        else:
+            for layer in (self.m.BM.bg_layer, "__SPINES__", "all"):
+                # logos are put on the spines-layer to appear on top of spines!
+                if ax in self.m.BM.get_bg_artists(layer):
+                    return True
+                elif ax in self.m.BM.get_artists(self.m.BM.bg_layer):
+                    return True
+
+        return False
+
+    def _make_draggable(self, filepath=None):
+        # Uncheck active pan/zoom actions of the matplotlib toolbar.
+        # use a try-except block to avoid issues with ipympl in jupyter notebooks
+        # (see https://github.com/matplotlib/ipympl/issues/530#issue-1780919042)
+        try:
+            toolbar = getattr(self.m.BM.canvas, "toolbar", None)
+            if toolbar is not None:
+                for key in ["pan", "zoom"]:
+                    val = toolbar._actions.get(key, None)
+                    if val is not None and val.isCheckable() and val.isChecked():
+                        val.trigger()
+        except AttributeError:
+            pass
+
+        self._filepath = filepath
+        self.modifier_pressed = True
+        _log.info(
+            "EOmaps: Layout Editor activated! (press 'esc' to exit " "and 'q' for info)"
+        )
+
+        self._history.clear()
+        self._history_undone.clear()
+        self._add_to_history()
+
+        self._revert_props = []
+        for ax in self.f.axes:
+            # only handle axes that have a finite size (in pixels) to avoid
+            # singular matrix errors for initially hidden zero-size axes
+            # (can happen for colorbar/colorbar histogram axes)
+            singularax = False
+            if ax.bbox.width <= 1 or ax.bbox.height <= 1:
+                singularax = True
+
+            # check if the axis is the container-axes of a colorbar
+            cbaxQ = ax.get_label() == "cb"
+
+            if not ax.axison:
+                showXY = False
+                self._revert_props.append(ax.set_axis_off)
+                ax.set_axis_on()
+            else:
+                showXY = True
+
+            # keep singular axes hidden
+            self._revert_props.append((ax.set_visible, ax.get_visible()))
+            if not singularax:
+                if self.ax_on_layer(ax):
+                    ax.set_visible(True)
+                else:
+                    ax.set_visible(False)
+            else:
+                ax.set_visible(False)
+
+            self._revert_props.append((ax.set_animated, ax.get_animated()))
+            ax.set_animated(False)
+
+            self._revert_props.append((ax.set_frame_on, ax.get_frame_on()))
+            ax.set_frame_on(True)
+
+            for child in ax.get_children():
+                # make sure we don't treat axes again (in case they are child-axes)
+                if child in self.f.axes:
+                    continue
+                revert_props = [
+                    "edgecolor",
+                    "linewidth",
+                    "alpha",
+                    "animated",
+                    "visible",
+                ]
+                self._add_revert_props(child, *revert_props)
+
+                if isinstance(child, Spine) and not cbaxQ:
+                    # make sure spines are visible (and re-drawn on draw)
+                    child.set_animated(False)
+                    child.set_visible(True)
+                    if hasattr(child, "_EOmaps_linestyle"):
+                        child.set_linestyle(getattr(child, "_EOmaps_linestyle", "-"))
+                        del child._EOmaps_linestyle
+                elif (
+                    ax not in self.maxes
+                    and showXY
+                    and isinstance(child, (XAxis, YAxis))
+                ):
+                    # keep all tick labels etc. of normal axes and colorbars visible
+                    child.set_animated(False)
+                    child.set_visible(True)
+
+                elif child is ax.patch and not cbaxQ:
+                    # only reset facecolors for axes-patches to avoid issues with
+                    # black spines (TODO check why this happens!)
+                    self._add_revert_props(child, "facecolor")
+
+                    # make sure patches are visible (and re-drawn on draw)
+                    child.set_visible(True)
+                    child.set_facecolor("w")
+                    child.set_alpha(0.75)  # for overlapping axes
+
+                else:
+                    # make all other children invisible (to avoid drawing them)
+                    child.set_visible(False)
+                    child.set_animated(True)
+
+        # only re-draw if info-text is None
+        if getattr(self, "_info_text", None) is None:
+            self._info_text = self.add_info_text()
+
+        self._color_axes()
+        self._attach_callbacks()
+
+        self.m._emit_signal("layoutEditorActivated")
+
+        self.m.redraw()
+
+    def _add_revert_props(self, child, *args):
+        for prop in args:
+            if hasattr(child, f"set_{prop}") and hasattr(child, f"get_{prop}"):
+                self._revert_props.append(
+                    (
+                        getattr(child, f"set_{prop}"),
+                        getattr(child, f"get_{prop}")(),
+                    )
+                )
+
+    def _undo_draggable(self):
+        if getattr(self, "_info_text", None) not in (None, False):
+            self._info_text.remove()
+            # set to None to avoid crating the info-text again
+            self._info_text = None
+
+        self._history.clear()
+        self._history_undone.clear()
+
+        toolbar = getattr(self.m.f, "toolbar", None)
+        if toolbar is not None:
+            # Reset the axes stack to make sure the "home" "back" and "forward" buttons
+            # of the toolbar do not reset axis positions
+            # see "matplotlib.backend_bases.NavigationToolbar2.update"
+            if hasattr(toolbar, "update"):
+                try:
+                    toolbar.update()
+                except Exception:
+                    _log.exception(
+                        "EOmaps: Error while trying to reset the axes stack!"
+                    )
+
+        # clear all picks on exit
+        self._ax_picked = []
+        self._m_picked = []
+
+        _log.info("EOmaps: Exiting layout-editor mode...")
+
+        # in case a filepath was provided, save the new layout
+        if self._filepath:
+            try:
+                self.m.get_layout(filepath=self._filepath, override=True)
+            except Exception:
+                _log.exception(
+                    "EOmaps: Layout could not be saved to the provided "
+                    + f"filepath: '{self._filepath}'."
+                )
+
+        self._reset_callbacks()
+        # revert all changes to artists
+        for p in self._revert_props:
+            if isinstance(p, tuple):
+                p[0](p[1])
+            else:
+                p()
+
+        self.modifier_pressed = False
+
+        # reset the histogram-size of all colorbars to make sure previously hidden
+        # axes (e.g. size=0) become visible if the size is now > 0.
+        for m in self.ms:
+            for cb in m._colorbars:
+                cb._set_hist_size(update_all=True)
+
+        # remove snap-grid (if it's still visible)
+        self._remove_snap_grid()
+
+        self.m._emit_signal("layoutEditorDeactivated")
+
+        self.m.redraw()
+        # try to push the current view to the "home" toolbar button
+        try:
+            self.m.f.canvas.toolbar.push_current()
+        except Exception:
+            pass
+
+    def _reset_callbacks(self):
+        # disconnect all callbacks of the layout-editor
+        while len(self.cids) > 0:
+            cid = self.cids.pop(-1)
+            self.f.canvas.mpl_disconnect(cid)
+
+    def _attach_callbacks(self):
+        # make sure all previously set callbacks are reset
+        self._reset_callbacks()
+
+        events = (
+            ("scroll_event", self.cb_scroll),
+            ("button_press_event", self.cb_pick),
+            ("button_release_event", self.cb_release),
+            ("motion_notify_event", self.cb_move),
+            ("key_press_event", self.cb_move_with_key),
+            ("key_release_event", self.cb_key_release),
+        )
+
+        for event, cb in events:
+            self.cids.append(self.f.canvas.mpl_connect(event, cb))
+
+    def _add_snap_grid(self, snap=None):
+        # snap = (snapx, snapy)
+
+        if snap is None:
+            if self._snap_id == 0:
+                self._remove_snap_grid()
+                return
+            else:
+                snapx, snapy = self._snap
+        else:
+            snapx, snapy = snap
+
+        self._remove_snap_grid()
+
+        bbox = self.m.f.bbox
+        t = self.m.f.transFigure.inverted()
+
+        gx, gy = np.mgrid[
+            0 : int(bbox.width) + int(snapx) : snapx,
+            0 : int(bbox.height) + int(snapy) : snapy,
+        ]
+        g = t.transform(np.column_stack((gx.flat, gy.flat)))
+
+        l = Line2D(
+            *g.T,
+            lw=0,
+            marker=".",
+            markerfacecolor="steelblue",
+            markeredgecolor="none",
+            ms=(snapx + snapy) / 6,
+        )
+        self._snap_grid_artist = self.m.f.add_artist(l)
+
+    def _remove_snap_grid(self):
+        if hasattr(self, "_snap_grid_artist"):
+            self._snap_grid_artist.remove()
+            del self._snap_grid_artist
+
+    def get_layout(self, filepath=None, override=False, precision=5):
+        """
+        Get the positions of all axes within the current plot.
+
+        To re-apply a layout, use:
+
+            >>> l = m.get_layout()
+            >>> m.set_layout(l)
+
+        Note
+        ----
+        The returned list is only a snapshot of the current layout.
+        It can only be re-applied to a given figure if the order at which the axes are
+        created remains the same!
+
+        Parameters
+        ----------
+        filepath : str or pathlib.Path, optional
+            If provided, a json-file will be created at the specified destination that
+            can be used in conjunction with `m.set_layout(...)` to apply the layout:
+
+            >>> m.get_layout(filepath=<FILEPATH>, override=True)
+            >>> m.apply_layout_layout(<FILEPATH>)
+
+            You can also manually read-in the layout-dict via:
+            >>> import json
+            >>> layout = json.load(<FILEPATH>)
+        override: bool
+            Indicator if the file specified as 'filepath' should be overwritten if it
+            already exists.
+            The default is False.
+        precision : int or None
+            The precision of the returned floating-point numbers.
+            If None, all available digits are returned
+            The default is 5
+        Returns
+        -------
+        layout : dict or None
+            A dict of the positions of all axes, e.g.: {1:(x0, y0, width height), ...}
+        """
+        figsize = [*self.f.get_size_inches()]
+
+        axes = [
+            a for a in self.axes if a.get_label() not in ["EOmaps_cb", "EOmaps_cb_hist"]
+        ]
+
+        # identify relevant colorbars
+        colorbars = [getattr(m, "colorbar", None) for m in self.ms]
+        cbaxes = [getattr(cb, "_ax", None) for cb in colorbars]
+        cbs = [(colorbars[cbaxes.index(a)] if a in cbaxes else None) for a in axes]
+        # -----------
+
+        layout = dict()
+        layout["figsize"] = figsize
+
+        for i, ax in enumerate(axes):
+            if cbs[i] is not None:
+                if cbs[i]._ax.get_axes_locator() is not None:
+                    continue
+
+            label = ax.get_label()
+            name = f"{i}_{label}"
+            if precision is not None:
+                layout[name] = np.round(ax.get_position().bounds, precision).tolist()
+            else:
+                layout[name] = ax.get_position().bounds
+
+            if cbs[i] is not None:
+                layout[f"{name}_histogram_size"] = cbs[i]._hist_size
+
+        if filepath is not None:
+            filepath = Path(filepath)
+            assert (
+                not filepath.exists() or override
+            ), f"The file {filepath} already exists! Use override=True to relace it."
+            with open(filepath, "w") as file:
+                json.dump(layout, file)
+            _log.info(f"EOmaps: Layout saved to:\n       {filepath}")
+
+        return layout
+
+    def apply_layout(self, layout):
+        """
+        Set the positions of all axes within the current plot based on a previously
+        defined layout.
+
+        To apply a layout, use:
+
+            >>> l = m.get_layout()
+            >>> m.set_layout(l)
+
+        To save a layout to disc and apply it at a later stage, use
+            >>> m.get_layout(filepath=<FILEPATH>)
+            >>> m.set_layout(<FILEPATH>)
+
+        Note
+        ----
+        The returned list is only a snapshot of the current layout.
+        It can only be re-applied to a given figure if the order at which the axes are
+        created remains the same!
+
+        Parameters
+        ----------
+        layout : dict, str or pathlib.Path
+            If a dict is provided, it is directly used to define the layout.
+
+            If a string or a pathlib.Path object is provided, it will be used to
+            read a previously dumped layout (e.g. with `m.get_layout(filepath)`)
+
+        """
+        if isinstance(layout, (str, Path)):
+            with open(layout, "r") as file:
+                layout = json.load(file)
+
+        # check if all relevant axes are specified in the layout
+        valid_keys = set(self.get_layout())
+        if valid_keys != set(layout):
+            _log.warning(
+                "EOmaps: The the layout does not match the expected structure! "
+                "Layout might not be properly restored. "
+                "Invalid or missing keys:\n"
+                f"{sorted(valid_keys.symmetric_difference(set(layout)))}\n"
+            )
+
+        # set the figsize
+        figsize = layout.get("figsize", None)
+        if figsize is not None:
+            self.f.set_size_inches(*figsize)
+
+        axes = [
+            a for a in self.axes if a.get_label() not in ["EOmaps_cb", "EOmaps_cb_hist"]
+        ]
+
+        # identify relevant colorbars
+        colorbars = [getattr(m, "colorbar", None) for m in self.ms]
+        cbaxes = [getattr(cb, "_ax", None) for cb in colorbars]
+        cbs = [(colorbars[cbaxes.index(a)] if a in cbaxes else None) for a in axes]
+
+        for key in valid_keys.intersection(set(layout)):
+            if key == "figsize":
+                continue
+            val = layout[key]
+
+            i = int(key[: key.find("_")])
+            if key.endswith("_histogram_size"):
+                cbs[i]._set_hist_size(val)
+            else:
+                axes[i].set_position(val)
+
+        self.m.redraw()
+
+        # try to push the current view to the "home" toolbar button
+        try:
+            self.m.f.canvas.toolbar.push_current()
+        except Exception:
+            pass

From dbb4a958a8231e03691fe308bae8886a5c3462d9 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 12:24:58 +0100
Subject: [PATCH 138/240] minor

---
 eomaps/layout_editor.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/eomaps/layout_editor.py b/eomaps/layout_editor.py
index 5d720841e..742c86a1f 100644
--- a/eomaps/layout_editor.py
+++ b/eomaps/layout_editor.py
@@ -1,3 +1,10 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
+"""Definition of the LayoutEditor used to interactively re-position axes."""
+
 import logging
 from contextlib import ExitStack
 from pathlib import Path

From feee183dadae3e93b66ffe877edbb6ac7138bd5d Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 12:25:35 +0100
Subject: [PATCH 139/240] move BlitManager to dedicated module

---
 eomaps/_blit_manager.py | 1545 +++++++++++++++++++++++++++++++++++++++
 eomaps/eomaps.py        |    2 +-
 eomaps/helpers.py       | 1537 +-------------------------------------
 3 files changed, 1547 insertions(+), 1537 deletions(-)
 create mode 100644 eomaps/_blit_manager.py

diff --git a/eomaps/_blit_manager.py b/eomaps/_blit_manager.py
new file mode 100644
index 000000000..d8b04b842
--- /dev/null
+++ b/eomaps/_blit_manager.py
@@ -0,0 +1,1545 @@
+# Copyright EOmaps Contributors
+#
+# This file is part of EOmaps and is released under the BSD 3-clause license.
+# See LICENSE in the root of the repository for full licensing details.
+
+"""The BlitManager used to handle drawing and caching of backgrounds."""
+
+import logging
+from itertools import chain
+from contextlib import contextmanager, ExitStack
+from weakref import WeakSet
+from functools import lru_cache
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.transforms import Bbox
+from matplotlib.spines import Spine
+
+_log = logging.getLogger(__name__)
+
+
+class LayerParser:
+    @staticmethod
+    def _parse_single_layer_str(layer):
+        """
+        Parse a single layer-string (with optional transparency assignment).
+
+        Parameters
+        ----------
+        layer : str
+            A layer-string (with transparency provided in curly brackets).
+
+        Returns
+        -------
+        name: str
+            The name of the layer.
+        alpha:
+            The transparency of the layer.
+
+        """
+        # split transparency
+        t_split = layer.find("{")
+        if t_split > 0:
+            name = layer[:t_split]
+            alpha = layer[t_split + 1 :]
+            if not alpha.endswith("}"):
+                raise TypeError(
+                    f"EOmaps: unable to parse multilayer-transparency for '{layer}'"
+                )
+            return name, float(alpha[:-1])
+        else:
+            return layer, 1
+
+    @classmethod
+    def _parse_multi_layer_str(cls, layer=None):
+        layers, alphas = zip(*map(cls._parse_single_layer_str, layer.split("|")))
+        return list(layers), list(alphas)
+
+    @classmethod
+    def _layer_is_subset(cls, layer1, layer2):
+        """
+        Return True if combined layer-name 'layer2' is a subset of 'layer1'.
+
+        - Transparency assignments are stripped off before comparison
+
+        Parameters
+        ----------
+        layer1, layer2 : str
+            The combined layer-names to check.
+
+        Returns
+        -------
+        subset: bool
+            True if layer2 is a subset of layer1, False otherwise
+
+        """
+        # get a list of the currently visible layers
+        layers1, _ = cls._parse_multi_layer_str(layer1)
+        layers2, _ = cls._parse_multi_layer_str(layer2)
+
+        return set(layers1).issubset(layers2)
+
+    @staticmethod
+    def _get_combined_layer_name(*args):
+        """
+        Create a combine layer name from layer-names or tuples (name, transparency).
+
+        Parameters
+        ----------
+        *args : str or tuple
+            The layers to combine. (e.g. `"A"`, `"B"` or `("A", .5)`, `("B", .23)`, ...)
+
+        Returns
+        -------
+        str
+            The combined layer-name.
+
+        """
+        try:
+            combnames = []
+            for i in args:
+                if isinstance(i, str):
+                    combnames.append(i)
+                elif isinstance(i, (list, tuple)):
+                    assert (
+                        len(i) == 2
+                        and isinstance(i[0], str)
+                        and i[1] >= 0
+                        and i[1] <= 1
+                    ), (
+                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
+                        "You can provide either a single layer-name as string, a list "
+                        "of layer-names or a list of tuples of the form: "
+                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
+                    )
+
+                    if i[1] < 1:
+                        combnames.append(i[0] + "{" + str(i[1]) + "}")
+                    else:
+                        combnames.append(i[0])
+                else:
+                    raise TypeError(
+                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
+                        "You can provide either a single layer-name as string, a list "
+                        "of layer-names or a list of tuples of the form: "
+                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
+                    )
+            return "|".join(combnames)
+        except Exception:
+            raise TypeError(f"EOmaps: Unable to combine the layer-names {args}")
+
+    @staticmethod
+    def _check_layer_name(layer):
+        if not isinstance(layer, str):
+            _log.info("EOmaps: All layer-names are converted to strings!")
+            layer = str(layer)
+
+        if layer.startswith("__") and not layer.startswith("__inset_"):
+            raise TypeError(
+                "EOmaps: Layer-names starting with '__' are reserved "
+                "for internal use and cannot be used as Maps-layer-names!"
+            )
+
+        reserved_symbs = {
+            # "|": (
+            #     "It is used as a separation-character to combine multiple "
+            #     "layers (e.g. m.show_layer('A|B') will overlay the layer 'B' "
+            #     "on top of 'A'."
+            # ),
+            "{": (
+                "It is used to specify transparency when combining multiple "
+                "layers (e.g. m.show_layer('A|B{0.5}') will overlay the layer "
+                "'B' with 50% transparency on top of the layer 'A'."
+            ),
+        }
+
+        reserved_symbs["}"] = reserved_symbs["{"]
+
+        for symb, explanation in reserved_symbs.items():
+            if symb in layer:
+                raise TypeError(
+                    f"EOmaps: The symbol '{symb}' is not allowed in layer-names!\n"
+                    + explanation
+                )
+
+        return layer
+
+
+# taken from https://matplotlib.org/stable/tutorials/advanced/blitting.html#class-based-example
+class BlitManager(LayerParser):
+    """Manager used to schedule draw events, cache backgrounds, etc."""
+
+    _snapshot_on_update = False
+
+    def __init__(self, m):
+        """
+        Manager used to schedule draw events, cache backgrounds, etc.
+
+        Parameters
+        ----------
+        canvas : FigureCanvasAgg
+            The canvas to work with, this only works for sub-classes of the Agg
+            canvas which have the `~FigureCanvasAgg.copy_from_bbox` and
+            `~FigureCanvasAgg.restore_region` methods.
+
+        animated_artists : Iterable[Artist]
+            List of the artists to manage
+
+        """
+        self._disable_draw = False
+        self._disable_update = False
+
+        self._m = m
+
+        self._artists = dict()
+
+        self._bg_artists = dict()
+        self._bg_layers = dict()
+
+        self._pending_webmaps = dict()
+
+        # the name of the layer at which all "unmanaged" artists are drawn
+        self._unmanaged_artists_layer = "base"
+
+        # grab the background on every draw
+        self.cid = self.canvas.mpl_connect("draw_event", self.on_draw)
+
+        self._after_update_actions = []
+        self._after_restore_actions = []
+        self._bg_layer = "base"
+
+        self._artists_to_clear = dict()
+
+        self._hidden_artists = set()
+
+        self._refetch_bg = True
+        self._layers_to_refetch = set()
+
+        # TODO these activate some crude fixes for jupyter notebook and webagg
+        # backends... proper fixes would be nice
+        self._mpl_backend_blit_fix = any(
+            i in plt.get_backend().lower() for i in ["webagg", "nbagg"]
+        )
+
+        # self._mpl_backend_force_full = any(
+        #     i in plt.get_backend().lower() for i in ["nbagg"]
+        # )
+        # recent fixes seem to take care of this nbagg issue...
+        self._mpl_backend_force_full = False
+        self._mpl_backend_blit_fix = False
+
+        # True = persistent, False = execute only once
+        self._on_layer_change = {True: list(), False: list()}
+        self._on_layer_activation = {True: dict(), False: dict()}
+
+        self._on_add_bg_artist = list()
+        self._on_remove_bg_artist = list()
+
+        self._before_fetch_bg_actions = list()
+        self._before_update_actions = list()
+
+        self._refetch_blank = True
+        self._blank_bg = None
+
+        self._managed_axes = set()
+
+        self._clear_on_layer_change = False
+
+        self._on_layer_change_running = False
+
+        # a weak set containing artists that should NOT be identified as
+        # unmanaged artists
+        self._ignored_unmanaged_artists = WeakSet()
+
+    def _get_renderer(self):
+        # don't return the renderer if the figure is saved.
+        # in this case the normal draw-routines are used (see m.savefig) so there is
+        # no need to trigger updates (also `canvas.get_renderer` is undefined for
+        # pdf/svg exports since those canvas do not expose the renderer)
+        # ... this is required to support vector format outputs!
+        if self.canvas.is_saving():
+            return None
+
+        try:
+            return self.canvas.get_renderer()
+        except Exception:
+            return None
+
+    def _get_all_map_axes(self):
+        maxes = {
+            m.ax for m in (self._m.parent, *self._m.parent._children) if m._new_axis_map
+        }
+        return maxes
+
+    def _get_managed_axes(self):
+        return (*self._get_all_map_axes(), *self._managed_axes)
+
+    def _get_unmanaged_axes(self):
+        # return a list of all axes that are not managed by the blit-manager
+        # (to ensure that "unmanaged" axes are drawn as well)
+
+        # EOmaps axes
+        managed_axes = self._get_managed_axes()
+        allaxes = set(self._m.f.axes)
+
+        unmanaged_axes = allaxes.difference(managed_axes)
+        return unmanaged_axes
+
+    @property
+    def figure(self):
+        """The matplotlib figure instance."""
+        return self._m.f
+
+    @property
+    def canvas(self):
+        """The figure canvas instance."""
+        return self.figure.canvas
+
+    @contextmanager
+    def _cx_on_layer_change_running(self):
+        # a context-manager to avoid recursive on_layer_change calls
+        try:
+            self._on_layer_change_running = True
+            yield
+        finally:
+            self._on_layer_change_running = False
+
+    def _do_on_layer_change(self, layer, new=False):
+        # avoid recursive calls to "_do_on_layer_change"
+        # This is required in case the executed functions trigger actions that would
+        # trigger "_do_on_layer_change" again which can result in a mixed-up order of
+        # the scheduled functions.
+        if self._on_layer_change_running is True:
+            return
+
+        # do not execute layer-change callbacks on private layer activation!
+        if layer.startswith("__"):
+            return
+
+        with self._cx_on_layer_change_running():
+            # only execute persistent layer-change callbacks if the layer changed!
+            if new:
+                # general callbacks executed on any layer change
+                # persistent callbacks
+                for f in reversed(self._on_layer_change[True]):
+                    f(layer=layer)
+
+            # single-shot callbacks
+            # (execute also if the layer is already active)
+            while len(self._on_layer_change[False]) > 0:
+                try:
+                    f = self._on_layer_change[False].pop(0)
+                    f(layer=layer)
+                except Exception:
+                    _log.error(
+                        "EOmaps: Issue while executing a layer-change action",
+                        exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
+                    )
+
+            sublayers, _ = self._parse_multi_layer_str(layer)
+            if new:
+                for l in sublayers:
+                    # individual callables executed if a specific layer is activated
+                    # persistent callbacks
+                    for f in reversed(self._on_layer_activation[True].get(layer, [])):
+                        f(layer=l)
+
+            for l in sublayers:
+                # single-shot callbacks
+                single_shot_funcs = self._on_layer_activation[False].get(l, [])
+                while len(single_shot_funcs) > 0:
+                    try:
+                        f = single_shot_funcs.pop(0)
+                        f(layer=l)
+                    except Exception:
+                        _log.error(
+                            "EOmaps: Issue while executing a layer-change action",
+                            exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
+                        )
+
+            # clear the list of pending webmaps once the layer has been activated
+            if layer in self._pending_webmaps:
+                self._pending_webmaps.pop(layer)
+
+    @contextmanager
+    def _without_artists(self, artists=None, layer=None):
+        try:
+            removed_artists = {layer: set(), "all": set()}
+            if artists is None:
+                yield
+            else:
+                for a in artists:
+                    if a in self._artists.get(layer, []):
+                        self.remove_artist(a, layer=layer)
+                        removed_artists[layer].add(a)
+                    elif a in self._artists.get("all", []):
+                        self.remove_artist(a, layer="all")
+                        removed_artists["all"].add(a)
+
+                yield
+        finally:
+            for layer, artists in removed_artists.items():
+                for a in artists:
+                    self.add_artist(a, layer=layer)
+
+    def _get_active_bg(self, exclude_artists=None):
+        with self._without_artists(artists=exclude_artists, layer=self.bg_layer):
+            # fetch the current background (incl. dynamic artists)
+            self.update()
+
+            with ExitStack() as stack:
+                # get rid of the figure background patch
+                # (done by putting the patch on the __BG__ layer!)
+
+                # get rid of the axes background patch
+                for ax_i in self._get_all_map_axes():
+                    stack.enter_context(
+                        ax_i.patch._cm_set(facecolor="none", edgecolor="none")
+                    )
+                bg = self.canvas.copy_from_bbox(self.figure.bbox)
+
+        return bg
+
+    @property
+    def bg_layer(self):
+        """The currently visible layer-name."""
+        return self._bg_layer
+
+    @bg_layer.setter
+    def bg_layer(self, val):
+        if val == self._bg_layer:
+            # in case the layer did not change, do nothing
+            return
+
+        # check if a new layer is activated (or added to a multi-layer)
+        old_layers = set(self._parse_multi_layer_str(self._bg_layer)[0])
+        new_layers = set(self._parse_multi_layer_str(val)[0])
+
+        new = old_layers != new_layers
+
+        # make sure we use a "full" update for webagg and ipympl backends
+        # (e.g. force full redraw of canvas instead of a diff)
+        self.canvas._force_full = True
+        self._bg_layer = val
+
+        # a general callable to be called on every layer change
+        self._do_on_layer_change(layer=val, new=new)
+
+        # hide all colorbars that are not on the visible layer
+        for m in [self._m.parent, *self._m.parent._children]:
+            layer_visible = self._layer_is_subset(val, m.layer)
+
+            for cb in m._colorbars:
+                cb._hide_singular_axes()
+
+                if layer_visible:
+                    if cb in self._hidden_artists:
+                        self._hidden_artists.remove(cb)
+                else:
+                    if cb not in self._hidden_artists:
+                        self._hidden_artists.add(cb)
+
+        # hide all wms_legends that are not on the visible layer
+        if hasattr(self._m.parent, "_wms_legend"):
+            for layer, legends in self._m.parent._wms_legend.items():
+                layer_visible = self._layer_is_subset(val, layer)
+
+                if layer_visible:
+                    for i in legends:
+                        i.set_visible(True)
+                else:
+                    for i in legends:
+                        i.set_visible(False)
+
+        if self._clear_on_layer_change:
+            self._clear_temp_artists("on_layer_change")
+
+    @contextmanager
+    def _cx_dont_clear_on_layer_change(self):
+        # a context-manager to avoid clearing artists on layer-changes
+        # (used in savefig to avoid clearing artists when re-fetching
+        # layers with backgrounds)
+        init_val = self._clear_on_layer_change
+        try:
+            self._clear_on_layer_change = False
+            yield
+        finally:
+            self._clear_on_layer_change = init_val
+
+    def on_layer(self, func, layer=None, persistent=False, m=None):
+        """
+        Add callables that are executed whenever the visible layer changes.
+
+        NOTE: if m is None this function always falls back to the parent Maps-object!!
+
+        Parameters
+        ----------
+        func : callable
+            The callable to use.
+            The call-signature is:
+
+            >>> def func(m, layer):
+            >>>    # m     ... the Maps-object
+            >>>    # layer ... the name of the layer
+
+
+        layer : str or None, optional
+            - If str: The function will only be called if the specified layer is
+              activated.
+            - If None: The function will be called on any layer-change.
+
+            The default is None.
+        persistent : bool, optional
+            Indicator if the function should be called only once (False) or if it
+            should be called whenever a layer is activated.
+            The default is False.
+        m : eomaps.Maps
+            The Maps-object to pass as argument to the function execution.
+            If None, the parent Maps-object is used.
+
+        """
+        if m is None:
+            m = self._m
+
+        def cb(*args, **kwargs):
+            func(m=m, *args, **kwargs)
+
+        if _log.getEffectiveLevel() <= 10:
+            logmsg = (
+                f"Adding {'persistent' if persistent else 'single-shot'} "
+                f"layer change action for: '{layer}'"
+            )
+            _log.debug(logmsg)
+
+        if layer is None:
+            self._on_layer_change[persistent].append(cb)
+        else:
+            # treat inset-map layers like normal layers
+            if layer.startswith("__inset_"):
+                layer = layer[8:]
+            self._on_layer_activation[persistent].setdefault(layer, list()).append(cb)
+
+    def _refetch_layer(self, layer):
+        if layer == "all":
+            # if the all layer changed, all backgrounds need a refetch
+            self._refetch_bg = True
+        else:
+            # set any background that contains the layer for refetch
+            self._layers_to_refetch.add(layer)
+
+            for l in self._bg_layers:
+                sublayers, _ = self._parse_multi_layer_str(l)
+                if layer in sublayers:
+                    self._layers_to_refetch.add(l)
+
+    def _bg_artists_sort(self, art):
+        sortp = []
+
+        # ensure that inset-map artists are always drawn after all other artists
+        if art.axes is not None:
+            if art.axes.get_label() == "inset_map":
+                sortp.append(1)
+            else:
+                sortp.append(0)
+
+        sortp.append(getattr(art, "zorder", -1))
+        return sortp
+
+    def get_bg_artists(self, layer):
+        """
+        Get all (sorted) background artists assigned to a given layer-name.
+
+        Parameters
+        ----------
+        layer : str
+            The layer name for which artists should be fetched.
+
+        Returns
+        -------
+        artists : list
+            A list of artists on the specified layer, sorted with respect to the
+            vertical stacking (layer-order / zorder).
+
+        """
+        artists = list()
+        for l in np.atleast_1d(layer):
+            # get all relevant artists for combined background layers
+            l = str(l)  # w make sure we convert non-string layer names to string!
+
+            # get artists defined on the layer itself
+            # Note: it's possible to create explicit multi-layers and attach
+            # artists that are only visible if both layers are visible! (e.g. "l1|l2")
+            artists.extend(self._bg_artists.get(l, []))
+
+            if l == self._unmanaged_artists_layer:
+                artists.extend(self._get_unmanaged_artists())
+
+        # make the list unique but maintain order (dicts keep order for python>3.7)
+        artists = dict.fromkeys(artists)
+        # sort artists by zorder (respecting inset-map priority)
+        artists = sorted(artists, key=self._bg_artists_sort)
+
+        return artists
+
+    def get_artists(self, layer):
+        """
+        Get all (sorted) dynamically updated artists assigned to a given layer-name.
+
+        Parameters
+        ----------
+        layer : str
+            The layer name for which artists should be fetched.
+
+        Returns
+        -------
+        artists : list
+            A list of artists on the specified layer, sorted with respect to the
+            vertical stacking (layer-order / zorder).
+
+        """
+
+        artists = list()
+        for l in np.atleast_1d(layer):
+            # get all relevant artists for combined background layers
+            l = str(l)  # w make sure we convert non-string layer names to string!
+
+            # get artists defined on the layer itself
+            # Note: it's possible to create explicit multi-layers and attach
+            # artists that are only visible if both layers are visible! (e.g. "l1|l2")
+            artists.extend(self._artists.get(l, []))
+
+        # make the list unique but maintain order (dicts keep order for python>3.7)
+        artists = dict.fromkeys(artists)
+        # sort artists by zorder (respecting inset-map priority)
+        artists = sorted(artists, key=self._bg_artists_sort)
+
+        return artists
+
+    def _layer_visible(self, layer):
+        """
+        Return True if the layer is currently visible.
+
+        - layer is considered visible if all sub-layers of a combined layer are visible
+        - transparency assignments do not alter the layer visibility
+
+        Parameters
+        ----------
+        layer : str
+            The combined layer-name to check. (e.g. 'A|B{.4}|C{.3}')
+
+        Returns
+        -------
+        visible: bool
+            True if the layer is currently visible, False otherwise
+
+        """
+        return layer in "all" or self._layer_is_subset(layer, self.bg_layer)
+
+    @property
+    def _get_active_layers_alphas(self):
+        """
+        Return the currently visible layers (and their associated transparencies)
+
+        Returns
+        -------
+        layers, alphas: list of str, list of float
+            2 lists of layer-names and associated global transparencies.
+
+        """
+        return self._parse_multi_layer_str(self.bg_layer)
+
+    # cache the last 10 combined backgrounds to avoid re-combining backgrounds
+    # on updates of interactive artists
+    # cache is automatically cleared on draw if any layer is tagged for re-fetch!
+    @lru_cache(10)
+    def _combine_bgs(self, layer):
+        layers, alphas = self._parse_multi_layer_str(layer)
+
+        # make sure all layers are already fetched
+        for l in layers:
+            if l not in self._bg_layers:
+                # execute actions on layer-changes
+                # (to make sure all lazy WMS services are properly added)
+                self._do_on_layer_change(layer=l, new=False)
+                self.fetch_bg(l)
+
+        renderer = self._get_renderer()
+        # clear the renderer to avoid drawing on existing backgrounds
+        renderer.clear()
+        if renderer:
+            gc = renderer.new_gc()
+            gc.set_clip_rectangle(self.canvas.figure.bbox)
+
+            x0, y0, w, h = self.figure.bbox.bounds
+            for l, a in zip(layers, alphas):
+                rgba = self._get_array(l, a=a)
+                if rgba is None:
+                    # to handle completely empty layers
+                    continue
+                renderer.draw_image(
+                    gc,
+                    int(x0),
+                    int(y0),
+                    rgba[int(y0) : int(y0 + h), int(x0) : int(x0 + w), :],
+                )
+            bg = renderer.copy_from_bbox(self._m.f.bbox)
+            gc.restore()
+            return bg
+
+    def _get_array(self, l, a=1):
+        if l not in self._bg_layers:
+            return None
+        rgba = np.array(self._bg_layers[l])[::-1, :, :]
+        if a != 1:
+            rgba = rgba.copy()
+            rgba[..., -1] = (rgba[..., -1] * a).astype(rgba.dtype)
+        return rgba
+
+    def _get_background(self, layer, bbox=None, cache=False):
+        if layer not in self._bg_layers:
+            if "|" in layer:
+                bg = self._combine_bgs(layer)
+            else:
+                self.fetch_bg(layer, bbox=bbox)
+                bg = self._bg_layers[layer]
+        else:
+            bg = self._bg_layers[layer]
+
+        if cache is True:
+            # explicitly cache the layer
+            # (for peek-layer callbacks to avoid re-fetching the layers all the time)
+            self._bg_layers[layer] = bg
+
+        return bg
+
+    def _do_fetch_bg(self, layer, bbox=None):
+        renderer = self._get_renderer()
+        renderer.clear()
+
+        if bbox is None:
+            bbox = self.figure.bbox
+
+        if "|" in layer:
+            if layer not in self._bg_layers:
+                self._combine_bgs(layer)
+            return
+
+        # update axes spines and patches since they are used to clip artists!
+        for ax in self._get_all_map_axes():
+            ax.spines["geo"]._adjust_location()
+            ax.patch._adjust_location()
+
+        # use contextmanagers to make sure the background patches are not stored
+        # in the buffer regions!
+        with ExitStack() as stack:
+            if layer not in ["__BG__"]:
+                # get rid of the axes background patches for all layers except
+                # the __BG__ layer
+                # (the figure background patch is on the "__BG__" layer)
+                for ax_i in self._get_all_map_axes():
+                    stack.enter_context(
+                        ax_i.patch._cm_set(facecolor="none", edgecolor="none")
+                    )
+
+            # execute actions before fetching new artists
+            # (e.g. update data based on extent etc.)
+            for action in self._before_fetch_bg_actions:
+                action(layer=layer, bbox=bbox)
+
+            # get all relevant artists to plot and remember zorders
+            # self.get_bg_artists() already returns artists sorted by zorder!
+            if layer in ["__SPINES__", "__BG__", "__inset___SPINES__"]:
+                # avoid fetching artists from the "all" layer for private layers
+                allartists = self.get_bg_artists(layer)
+            else:
+                if layer.startswith("__inset"):
+                    allartists = self.get_bg_artists(["__inset_all", layer])
+                else:
+                    allartists = self.get_bg_artists(["all", layer])
+
+            # check if all artists are not stale
+            no_stale_artists = all(not art.stale for art in allartists)
+
+            # don't re-fetch the background if it is not necessary
+            if no_stale_artists and (self._bg_layers.get(layer, None) is not None):
+                return
+
+            if renderer:
+                for art in allartists:
+                    if art not in self._hidden_artists:
+                        try:
+                            art.draw(renderer)
+                            art.stale = False
+                        except Exception:
+                            if _log.getEffectiveLevel() <= logging.DEBUG:
+                                _log.error(
+                                    "Unable to draw artist:"
+                                    f"{art} ("
+                                    f"figure={getattr(art, 'figure', '??')}, "
+                                    f"axes={getattr(art, 'axes', '??')})"
+                                )
+
+                self._bg_layers[layer] = renderer.copy_from_bbox(bbox)
+
+    def fetch_bg(self, layer=None, bbox=None):
+        """
+        Trigger fetching (and caching) the background for a given layer-name.
+
+        Parameters
+        ----------
+        layer : str, optional
+            The layer for which the background should be fetched.
+            If None, the currently visible layer is fetched.
+            The default is None.
+        bbox : bbox, optional
+            The region-boundaries (in figure coordinates) for which the background
+            should be fetched (x0, y0, w, h). If None, the whole figure is fetched.
+            The default is None.
+
+        """
+
+        if layer is None:
+            layer = self.bg_layer
+
+        if layer in self._bg_layers:
+            # don't re-fetch existing layers
+            # (layers get cleared automatically if re-draw is necessary)
+            return
+
+        with self._disconnect_draw():
+            self._do_fetch_bg(layer, bbox)
+
+    @contextmanager
+    def _disconnect_draw(self):
+        try:
+            # temporarily disconnect draw-event callback to avoid recursion
+            if self.cid is not None:
+                self.canvas.mpl_disconnect(self.cid)
+                self.cid = None
+            yield
+        finally:
+            # reconnect draw event
+            if self.cid is None:
+                self.cid = self.canvas.mpl_connect("draw_event", self.on_draw)
+
+    def on_draw(self, event):
+        """Callback to register with 'draw_event'."""
+
+        if self._disable_draw:
+            return
+
+        cv = self.canvas
+        loglevel = _log.getEffectiveLevel()
+
+        if hasattr(cv, "get_renderer") and not cv.is_saving():
+
+            renderer = cv.get_renderer()
+            if renderer is None:
+                # don't run on_draw if no renderer is available
+                return
+        else:
+            # don't run on_draw if no renderer is available
+            # (this is true for svg export where mpl export routines
+            # are used to avoid issues)
+            if loglevel <= 5:
+                _log.log(5, " not drawing")
+
+            return
+
+        if loglevel <= 5:
+            _log.log(5, "draw")
+
+        if event is not None:
+            if event.canvas != cv:
+                raise RuntimeError
+        try:
+            # reset all background-layers and re-fetch the default one
+            if self._refetch_bg:
+                self._bg_layers.clear()
+                self._layers_to_refetch.clear()
+                self._refetch_bg = False
+                type(self)._combine_bgs.cache_clear()  # clear combined_bg cache
+
+            else:
+                # in case there is a stale (unmanaged) artists and the
+                # stale-artist layer is attempted to be drawn, re-draw the
+                # cached background for the unmanaged-artists layer
+                active_layers, _ = self._get_active_layers_alphas
+                if self._unmanaged_artists_layer in active_layers and any(
+                    a.stale for a in self._get_unmanaged_artists()
+                ):
+                    self._refetch_layer(self._unmanaged_artists_layer)
+                    type(self)._combine_bgs.cache_clear()  # clear combined_bg cache
+
+                # remove all cached backgrounds that were tagged for refetch
+                while len(self._layers_to_refetch) > 0:
+                    self._bg_layers.pop(self._layers_to_refetch.pop(), None)
+                    type(self)._combine_bgs.cache_clear()  # clear combined_bg cache
+
+            # workaround for nbagg backend to avoid glitches
+            # it's slow but at least it works...
+            # check progress of the following issuse
+            # https://github.com/matplotlib/matplotlib/issues/19116
+            if self._mpl_backend_blit_fix:
+                self.update()
+            else:
+                self.update(blit=False)
+
+            # re-draw indicator-shapes of active drawer
+            # (to show indicators during zoom-events)
+            active_drawer = getattr(self._m.parent, "_active_drawer", None)
+            if active_drawer is not None:
+                active_drawer.redraw(blit=False)
+
+        except Exception:
+            # we need to catch exceptions since QT does not like them...
+            if loglevel <= 5:
+                _log.log(5, "There was an error during draw!", exc_info=True)
+
+    def add_artist(self, art, layer=None):
+        """
+        Add a dynamic-artist to be managed.
+        (Dynamic artists are re-drawn on every update!)
+
+        Parameters
+        ----------
+        art : Artist
+
+            The artist to be added.  Will be set to 'animated' (just
+            to be safe).  *art* must be in the figure associated with
+            the canvas this class is managing.
+        layer : str or None, optional
+            The layer name at which the artist should be drawn.
+
+            - If "all": the corresponding feature will be added to ALL layers
+
+            The default is None in which case the layer of the base-Maps object is used.
+        """
+
+        if art.figure != self.figure:
+            raise RuntimeError(
+                "EOmaps: The artist does not belong to the figure"
+                "of this Maps-object!"
+            )
+
+        if layer is None:
+            layer = self._m.layer
+
+        # make sure all layers are converted to string
+        layer = str(layer)
+
+        self._artists.setdefault(layer, list())
+
+        if art in self._artists[layer]:
+            return
+        else:
+            art.set_animated(True)
+            self._artists[layer].append(art)
+
+            if isinstance(art, plt.Axes):
+                self._managed_axes.add(art)
+
+    def add_bg_artist(self, art, layer=None, draw=True):
+        """
+        Add a background-artist to be managed.
+        (Background artists are only updated on zoom-events... they are NOT animated!)
+
+        Parameters
+        ----------
+        art : Artist
+            The artist to be added.  Will be set to 'animated' (just
+            to be safe).  *art* must be in the figure associated with
+            the canvas this class is managing.
+        layer : str or None, optional
+            The layer name at which the artist should be drawn.
+
+            - If "all": the corresponding feature will be added to ALL layers
+
+            The default is None in which case the layer of the base-Maps object is used.
+        draw : bool, optional
+            If True, `figure.draw_idle()` is called after adding the artist.
+            The default is True.
+        """
+
+        if layer is None:
+            layer = self._m.layer
+
+        # make sure all layer names are converted to string
+        layer = str(layer)
+
+        if art.figure != self.figure:
+            raise RuntimeError
+
+        # put all artist of inset-maps on dedicated layers
+        if (
+            getattr(art, "axes", None) is not None
+            and art.axes.get_label() == "inset_map"
+            and not layer.startswith("__inset_")
+        ):
+            layer = "__inset_" + str(layer)
+        if layer in self._bg_artists and art in self._bg_artists[layer]:
+            _log.info(f"EOmaps: Background-artist '{art}' already added")
+            return
+
+        art.set_animated(True)
+        self._bg_artists.setdefault(layer, []).append(art)
+
+        if isinstance(art, plt.Axes):
+            self._managed_axes.add(art)
+
+        # tag all relevant layers for refetch
+        self._refetch_layer(layer)
+
+        for f in self._on_add_bg_artist:
+            f()
+
+        if draw:
+            self.canvas.draw_idle()
+
+    def remove_bg_artist(self, art, layer=None, draw=True):
+        """
+        Remove a (background) artist from the map.
+
+        Parameters
+        ----------
+        art : Artist
+            The artist that should be removed.
+        layer : str or None, optional
+            If provided, the artist is only searched on the provided layer, otherwise
+            all map layers are searched. The default is None.
+        draw : bool, optional
+            If True, `figure.draw_idle()` is called after removing the artist.
+            The default is True.
+
+        Note
+        ----
+        This only removes the artist from the blit-manager and does not call its
+        remove method!
+
+        """
+        # handle the "__inset_" prefix of inset-map artists
+        if (
+            layer is not None
+            and getattr(art, "axes", None) is not None
+            and art.axes.get_label() == "inset_map"
+            and not layer.startswith("__inset_")
+        ):
+            layer = "__inset_" + str(layer)
+
+        removed = False
+        if layer is None:
+            layers = []
+            for key, val in self._bg_artists.items():
+                if art in val:
+                    art.set_animated(False)
+                    val.remove(art)
+
+                    # remove axes from the managed_axes set as well!
+                    if art in self._managed_axes:
+                        self._managed_axes.remove(art)
+
+                    removed = True
+                    layers.append(key)
+                layer = self._get_combined_layer_name(*layers)
+        else:
+            if layer not in self._bg_artists:
+                return
+            if art in self._bg_artists[layer]:
+                art.set_animated(False)
+                self._bg_artists[layer].remove(art)
+
+                # remove axes from the managed_axes set as well!
+                if art in self._managed_axes:
+                    self._managed_axes.remove(art)
+
+                removed = True
+
+        if removed:
+            for f in self._on_remove_bg_artist:
+                f()
+
+            # tag all relevant layers for refetch
+            self._refetch_layer(layer)
+
+        if draw:
+            self.canvas.draw_idle()
+
+    def remove_artist(self, art, layer=None):
+        """
+        Remove a (dynamically updated) artist from the blit-manager.
+
+        Parameters
+        ----------
+        art : matpltolib.Artist
+            The artist to remove.
+        layer : str, optional
+            The layer to search for the artist. If None, all layers are searched.
+            The default is None.
+
+        Note
+        ----
+        This only removes the artist from the blit-manager and does not call its
+        remove method!
+
+        """
+        if layer is None:
+            for key, layerartists in self._artists.items():
+                if art in layerartists:
+                    art.set_animated(False)
+                    layerartists.remove(art)
+
+                    # remove axes from the managed_axes set as well!
+                    if art in self._managed_axes:
+                        self._managed_axes.remove(art)
+
+        else:
+            if art in self._artists.get(layer, []):
+                art.set_animated(False)
+                self._artists[layer].remove(art)
+
+                # remove axes from the managed_axes set as well!
+                if art in self._managed_axes:
+                    self._managed_axes.remove(art)
+            else:
+                _log.debug(f"The artist {art} is not on the layer '{layer}'")
+
+    def _get_artist_zorder(self, a):
+        try:
+            return a.get_zorder()
+        except Exception:
+            _log.error(f"EOmaps: unalble to identify zorder of {a}... using 99")
+            return 99
+
+    def _draw_animated(self, layers=None, artists=None):
+        """
+        Draw animated artists
+
+        - if layers is None and artists is None: active layer artists will be re-drawn
+        - if layers is not None: all artists from the selected layers will be re-drawn
+        - if artists is not None: all provided artists will be redrawn
+
+        """
+        fig = self.canvas.figure
+        renderer = self._get_renderer()
+        if renderer is None:
+            return
+
+        if layers is None:
+            active_layers, _ = self._get_active_layers_alphas
+            layers = [self.bg_layer, *active_layers]
+        else:
+            (layers,) = list(
+                chain(*(self._parse_multi_layer_str(l)[0] for l in layers))
+            )
+
+        if artists is None:
+            artists = []
+
+        # always redraw artists from the "all" layer
+        layers.append("all")
+
+        # make the list unique but maintain order (dicts keep order for python>3.7)
+        layers = list(dict.fromkeys(layers))
+
+        # draw all "unmanaged" axes (e.g. axes that are found in the figure but
+        # not in the blit-manager)
+        # TODO would be nice to find a better way to handle this!
+        # - NOTE: this must be done before drawing managed artists to properly support
+        #   temporary artists on unmanaged axes!
+        for ax in self._get_unmanaged_axes():
+            ax.draw(renderer)
+
+        # redraw artists from the selected layers and explicitly provided artists
+        # (sorted by zorder for each layer)
+        layer_artists = list(
+            sorted(self._artists.get(layer, []), key=self._get_artist_zorder)
+            for layer in layers
+        )
+
+        with ExitStack() as stack:
+            # avoid drawing the background-patches of managed (dynamic) axes
+            # since they might interfere with consecutive draws issued by callbacks
+            for ax_i in self._managed_axes:
+                stack.enter_context(
+                    ax_i.patch._cm_set(facecolor="none", edgecolor="none")
+                )
+
+            for a in chain(*layer_artists, artists):
+                fig.draw_artist(a)
+
+    def _get_unmanaged_artists(self):
+        # return all artists not explicitly managed by the blit-manager
+        # (e.g. any artist added via cartopy or matplotlib functions)
+        managed_artists = set(
+            chain(
+                *self._bg_artists.values(),
+                *self._artists.values(),
+                self._ignored_unmanaged_artists,
+            )
+        )
+
+        axes = {m.ax for m in (self._m, *self._m._children) if m.ax is not None}
+
+        allartists = set()
+        for ax in axes:
+            # only include axes titles if they are actually set
+            # (otherwise empty artists appear in the widget)
+            titles = [
+                i
+                for i in (ax.title, ax._left_title, ax._right_title)
+                if len(i.get_text()) > 0
+            ]
+
+            axartists = {
+                *ax._children,
+                *titles,
+                *([ax.legend_] if ax.legend_ is not None else []),
+            }
+
+            allartists.update(axartists)
+
+        return allartists.difference(managed_artists)
+
+    def _clear_all_temp_artists(self):
+        for method in self._m.cb._methods:
+            container = getattr(self._m.cb, method, None)
+            if container:
+                container._clear_temporary_artists()
+            self._clear_temp_artists(method)
+
+    def _clear_temp_artists(self, method, forward=True):
+        # clear artists from connected methods
+        if method == "_click_move" and forward:
+            self._clear_temp_artists("click", False)
+        elif method == "click" and forward:
+            self._clear_temp_artists("_click_move", False)
+        elif method == "pick" and forward:
+            self._clear_temp_artists("click", True)
+        elif method == "on_layer_change" and forward:
+            self._clear_temp_artists("pick", False)
+            self._clear_temp_artists("click", True)
+            self._clear_temp_artists("move", False)
+
+        if method == "on_layer_change":
+            # clear all artists from "on_layer_change" list irrespective of the method
+            artists = self._artists_to_clear.pop("on_layer_change", [])
+            for art in artists:
+                for met, met_artists in self._artists_to_clear.items():
+                    if art in met_artists:
+                        art.set_visible(False)
+                        self.remove_artist(art)
+                        try:
+                            art.remove()
+                        except ValueError:
+                            # ignore errors if the artist no longer exists
+                            pass
+                        met_artists.remove(art)
+        else:
+            artists = self._artists_to_clear.pop(method, [])
+            while len(artists) > 0:
+                art = artists.pop(-1)
+                art.set_visible(False)
+                self.remove_artist(art)
+                try:
+                    art.remove()
+                except ValueError:
+                    # ignore errors if the artist no longer exists
+                    pass
+
+                try:
+                    self._artists_to_clear.get("on_layer_change", []).remove(art)
+                except ValueError:
+                    # ignore errors if the artist is not present in the list
+                    pass
+
+    def _get_showlayer_name(self, layer=None, transparent=False):
+        # combine all layers that should be shown
+        # (e.g. to add spines, backgrounds and inset-maps)
+
+        if layer is None:
+            layer = self.bg_layer
+
+        # pass private layers through
+        if layer.startswith("__"):
+            return layer
+
+        if transparent is True:
+            show_layers = [layer, "__SPINES__"]
+        else:
+            show_layers = ["__BG__", layer, "__SPINES__"]
+
+        # show inset map layers and spines only if they contain at least 1 artist
+        inset_Q = False
+        for l in self._parse_multi_layer_str(layer)[0]:
+            narts = len(self._bg_artists.get("__inset_" + l, []))
+
+            if narts > 0:
+                show_layers.append(f"__inset_{l}")
+                inset_Q = True
+
+        if inset_Q:
+            show_layers.append("__inset___SPINES__")
+
+        return self._get_combined_layer_name(*show_layers)
+
+    def update(
+        self,
+        layers=None,
+        bbox_bounds=None,
+        bg_layer=None,
+        artists=None,
+        clear=False,
+        blit=True,
+        clear_snapshot=True,
+    ):
+        """
+        Update the screen with animated artists.
+
+        Parameters
+        ----------
+        layers : list, optional
+            The layers to redraw (if None and artists is None, all layers will be redrawn).
+            The default is None.
+        bbox_bounds : tuple, optional
+            the blit-region bounds to update. The default is None.
+        bg_layer : int, optional
+            the background-layer name to restore. The default is None.
+        artists : list, optional
+            A list of artists to update.
+            If provided NO layer will be automatically updated!
+            The default is None.
+        clear : bool, optional
+            If True, all temporary artists tagged for removal will be cleared.
+            The default is False.
+        blit : bool, optional
+            If True, figure.cavas.blit() will be called to update the figure.
+            If False, changes will only be visible on the next blit-event!
+            The default is True.
+        clear_snapshot : bool, optional
+            Only relevant if the `inline` backend is used in a jupyter-notebook
+            or an Ipython console.
+
+            If True, clear the active cell before plotting a snapshot of the figure.
+            The default is True.
+        """
+        if self._disable_update:
+            # don't update during layout-editing
+            return
+
+        cv = self.canvas
+
+        if bg_layer is None:
+            bg_layer = self.bg_layer
+
+        for action in self._before_update_actions:
+            action()
+
+        if clear:
+            self._clear_temp_artists(clear)
+
+        # restore the background
+        # add additional layers (background, spines etc.)
+        show_layer = self._get_showlayer_name()
+
+        if show_layer not in self._bg_layers:
+            # make sure the background is properly fetched
+            self.fetch_bg(show_layer)
+
+        cv.restore_region(self._get_background(show_layer))
+
+        # execute after restore actions (e.g. peek layer callbacks)
+        while len(self._after_restore_actions) > 0:
+            action = self._after_restore_actions.pop(0)
+            action()
+
+        # draw all of the animated artists
+        self._draw_animated(layers=layers, artists=artists)
+        if blit:
+            # workaround for nbagg backend to avoid glitches
+            # it's slow but at least it works...
+            # check progress of the following issuse
+            # https://github.com/matplotlib/matplotlib/issues/19116
+            if self._mpl_backend_force_full:
+                cv._force_full = True
+
+            if bbox_bounds is not None:
+
+                class bbox:
+                    bounds = bbox_bounds
+
+                cv.blit(bbox)
+            else:
+                # update the GUI state
+                cv.blit(self.figure.bbox)
+
+        # execute all actions registered to be called after blitting
+        while len(self._after_update_actions) > 0:
+            action = self._after_update_actions.pop(0)
+            action()
+
+        # let the GUI event loop process anything it has to do
+        # don't do this! it is causing infinite loops
+        # cv.flush_events()
+
+        if blit and BlitManager._snapshot_on_update is True:
+            self._m.snapshot(clear=clear_snapshot)
+
+    def blit_artists(self, artists, bg="active", blit=True):
+        """
+        Blit artists (optionally on top of a given background)
+
+        Parameters
+        ----------
+        artists : iterable
+            the artists to draw
+        bg : matpltolib.BufferRegion, None or "active", optional
+            A fetched background that is restored before drawing the artists.
+            The default is "active".
+        blit : bool
+            Indicator if canvas.blit() should be called or not.
+            The default is True
+        """
+        cv = self.canvas
+        renderer = self._get_renderer()
+        if renderer is None:
+            _log.error("EOmaps: encountered a problem while trying to blit artists...")
+            return
+
+        # restore the background
+        if bg is not None:
+            if bg == "active":
+                bg = self._get_active_bg()
+            cv.restore_region(bg)
+
+        for a in artists:
+            self.figure.draw_artist(a)
+
+        if blit:
+            cv.blit()
+
+    def _get_restore_bg_action(
+        self,
+        layer,
+        bbox_bounds=None,
+        alpha=1,
+        clip_path=None,
+        set_clip_path=False,
+    ):
+        """
+        Update a part of the screen with a different background
+        (intended as after-restore action)
+
+        bbox_bounds = (x, y, width, height)
+        """
+        if bbox_bounds is None:
+            bbox = self.figure.bbox
+        else:
+            bbox = Bbox.from_bounds(*bbox_bounds)
+
+        def action():
+            renderer = self._get_renderer()
+            if renderer is None:
+                return
+
+            if self.bg_layer == layer:
+                return
+
+            x0, y0, w, h = bbox.bounds
+
+            # make sure to restore the initial background
+            init_bg = renderer.copy_from_bbox(self._m.f.bbox)
+            # convert the buffer to rgba so that we can add transparency
+            buffer = self._get_background(layer, cache=True)
+            self.canvas.restore_region(init_bg)
+
+            x = buffer.get_extents()
+            ncols, nrows = x[2] - x[0], x[3] - x[1]
+
+            argb = (
+                np.frombuffer(buffer, dtype=np.uint8).reshape((nrows, ncols, 4)).copy()
+            )
+            argb = argb[::-1, :, :]
+
+            argb[:, :, -1] = (argb[:, :, -1] * alpha).astype(np.int8)
+
+            gc = renderer.new_gc()
+
+            gc.set_clip_rectangle(bbox)
+            if set_clip_path is True:
+                gc.set_clip_path(clip_path)
+
+            renderer.draw_image(
+                gc,
+                int(x0),
+                int(y0),
+                argb[int(y0) : int(y0 + h), int(x0) : int(x0 + w), :],
+            )
+            gc.restore()
+
+        return action
+
+    def cleanup_layer(self, layer):
+        """Trigger cleanup methods for a given layer."""
+        self._cleanup_bg_artists(layer)
+        self._cleanup_artists(layer)
+        self._cleanup_bg_layers(layer)
+        self._cleanup_on_layer_activation(layer)
+
+    def _cleanup_bg_artists(self, layer):
+        if layer not in self._bg_artists:
+            return
+
+        artists = self._bg_artists[layer]
+        while len(artists) > 0:
+            a = artists.pop()
+            try:
+                self.remove_bg_artist(a, layer, draw=False)
+                # no need to remove spines (to avoid NotImplementedErrors)!
+                if not isinstance(a, Spine):
+                    a.remove()
+            except Exception:
+                _log.debug(f"EOmaps-cleanup: Problem while clearing bg artist:\n {a}")
+
+        del self._bg_artists[layer]
+
+    def _cleanup_artists(self, layer):
+        if layer not in self._artists:
+            return
+
+        artists = self._artists[layer]
+        while len(artists) > 0:
+            a = artists.pop()
+            try:
+                self.remove_artist(a)
+                # no need to remove spines (to avoid NotImplementedErrors)!
+                if not isinstance(a, Spine):
+                    a.remove()
+            except Exception:
+                _log.debug(
+                    f"EOmaps-cleanup: Problem while clearing dynamic artist:\n {a}"
+                )
+
+        del self._artists[layer]
+
+    def _cleanup_bg_layers(self, layer):
+        try:
+            # remove cached background-layers
+            if layer in self._bg_layers:
+                del self._bg_layers[layer]
+        except Exception:
+            _log.debug(
+                "EOmaps-cleanup: Problem while clearing cached background layers"
+            )
+
+    def _cleanup_on_layer_activation(self, layer):
+
+        try:
+            # remove not yet executed lazy-activation methods
+            # (e.g. not yet fetched WMS services)
+            if layer in self._on_layer_activation:
+                del self._on_layer_activation[layer]
+        except Exception:
+            _log.debug(
+                "EOmaps-cleanup: Problem while clearing layer activation methods"
+            )
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index a465bd5cc..485183c4d 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -36,7 +36,6 @@
 from .helpers import (
     pairwise,
     cmap_alpha,
-    BlitManager,
     progressbar,
     SearchTree,
     _TransformedBoundsLocator,
@@ -45,6 +44,7 @@
     _key_release_event,
 )
 
+from ._blit_manager import BlitManager
 from .layout_editor import LayoutEditor
 from .shapes import Shapes
 from .colorbar import ColorBar
diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index f749ff084..bf14b395d 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -3,25 +3,21 @@
 # This file is part of EOmaps and is released under the BSD 3-clause license.
 # See LICENSE in the root of the repository for full licensing details.
 
-"""a collection of useful helper-functions."""
+"""A collection of useful helper-functions."""
 
 import logging
 from itertools import tee
 import re
 import sys
-from itertools import chain
-from contextlib import contextmanager, ExitStack
 from importlib import import_module
 from textwrap import indent, dedent
 from functools import wraps, lru_cache
 import warnings
-from weakref import WeakSet
 
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.colors import LinearSegmentedColormap, ListedColormap
 from matplotlib.transforms import Bbox, TransformedBbox
-from matplotlib.spines import Spine
 from matplotlib.backend_bases import KeyEvent
 
 _log = logging.getLogger(__name__)
@@ -280,153 +276,6 @@ def inner(*args, **kwargs):
     return decorator
 
 
-class LayerParser:
-    @staticmethod
-    def _parse_single_layer_str(layer):
-        """
-        Parse a single layer-string (with optional transparency assignment).
-
-        Parameters
-        ----------
-        layer : str
-            A layer-string (with transparency provided in curly brackets).
-
-        Returns
-        -------
-        name: str
-            The name of the layer.
-        alpha:
-            The transparency of the layer.
-
-        """
-        # split transparency
-        t_split = layer.find("{")
-        if t_split > 0:
-            name = layer[:t_split]
-            alpha = layer[t_split + 1 :]
-            if not alpha.endswith("}"):
-                raise TypeError(
-                    f"EOmaps: unable to parse multilayer-transparency for '{layer}'"
-                )
-            return name, float(alpha[:-1])
-        else:
-            return layer, 1
-
-    @classmethod
-    def _parse_multi_layer_str(cls, layer=None):
-        layers, alphas = zip(*map(cls._parse_single_layer_str, layer.split("|")))
-        return list(layers), list(alphas)
-
-    @classmethod
-    def _layer_is_subset(cls, layer1, layer2):
-        """
-        Return True if combined layer-name 'layer2' is a subset of 'layer1'.
-
-        - Transparency assignments are stripped off before comparison
-
-        Parameters
-        ----------
-        layer1, layer2 : str
-            The combined layer-names to check.
-
-        Returns
-        -------
-        subset: bool
-            True if layer2 is a subset of layer1, False otherwise
-
-        """
-        # get a list of the currently visible layers
-        layers1, _ = cls._parse_multi_layer_str(layer1)
-        layers2, _ = cls._parse_multi_layer_str(layer2)
-
-        return set(layers1).issubset(layers2)
-
-    @staticmethod
-    def _get_combined_layer_name(*args):
-        """
-        Create a combine layer name from layer-names or tuples (name, transparency).
-
-        Parameters
-        ----------
-        *args : str or tuple
-            The layers to combine. (e.g. `"A"`, `"B"` or `("A", .5)`, `("B", .23)`, ...)
-
-        Returns
-        -------
-        str
-            The combined layer-name.
-
-        """
-        try:
-            combnames = []
-            for i in args:
-                if isinstance(i, str):
-                    combnames.append(i)
-                elif isinstance(i, (list, tuple)):
-                    assert (
-                        len(i) == 2
-                        and isinstance(i[0], str)
-                        and i[1] >= 0
-                        and i[1] <= 1
-                    ), (
-                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
-                        "You can provide either a single layer-name as string, a list "
-                        "of layer-names or a list of tuples of the form: "
-                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
-                    )
-
-                    if i[1] < 1:
-                        combnames.append(i[0] + "{" + str(i[1]) + "}")
-                    else:
-                        combnames.append(i[0])
-                else:
-                    raise TypeError(
-                        f"EOmaps: unable to identify the layer-assignment: {i} .\n"
-                        "You can provide either a single layer-name as string, a list "
-                        "of layer-names or a list of tuples of the form: "
-                        "(< layer-name (str) >, < layer-transparency [0-1] > )"
-                    )
-            return "|".join(combnames)
-        except Exception:
-            raise TypeError(f"EOmaps: Unable to combine the layer-names {args}")
-
-    @staticmethod
-    def _check_layer_name(layer):
-        if not isinstance(layer, str):
-            _log.info("EOmaps: All layer-names are converted to strings!")
-            layer = str(layer)
-
-        if layer.startswith("__") and not layer.startswith("__inset_"):
-            raise TypeError(
-                "EOmaps: Layer-names starting with '__' are reserved "
-                "for internal use and cannot be used as Maps-layer-names!"
-            )
-
-        reserved_symbs = {
-            # "|": (
-            #     "It is used as a separation-character to combine multiple "
-            #     "layers (e.g. m.show_layer('A|B') will overlay the layer 'B' "
-            #     "on top of 'A'."
-            # ),
-            "{": (
-                "It is used to specify transparency when combining multiple "
-                "layers (e.g. m.show_layer('A|B{0.5}') will overlay the layer "
-                "'B' with 50% transparency on top of the layer 'A'."
-            ),
-        }
-
-        reserved_symbs["}"] = reserved_symbs["{"]
-
-        for symb, explanation in reserved_symbs.items():
-            if symb in layer:
-                raise TypeError(
-                    f"EOmaps: The symbol '{symb}' is not allowed in layer-names!\n"
-                    + explanation
-                )
-
-        return layer
-
-
 class SearchTree:
     """Class to perform fast nearest-neighbour queries."""
 
@@ -675,1387 +524,3 @@ def query(self, x, k=1, d=None, pick_relative_to_closest=True):
             i = None
 
         return i
-
-
-# taken from https://matplotlib.org/stable/tutorials/advanced/blitting.html#class-based-example
-class BlitManager(LayerParser):
-    """Manager used to schedule draw events, cache backgrounds, etc."""
-
-    _snapshot_on_update = False
-
-    def __init__(self, m):
-        """
-        Manager used to schedule draw events, cache backgrounds, etc.
-
-        Parameters
-        ----------
-        canvas : FigureCanvasAgg
-            The canvas to work with, this only works for sub-classes of the Agg
-            canvas which have the `~FigureCanvasAgg.copy_from_bbox` and
-            `~FigureCanvasAgg.restore_region` methods.
-
-        animated_artists : Iterable[Artist]
-            List of the artists to manage
-
-        """
-        self._disable_draw = False
-        self._disable_update = False
-
-        self._m = m
-
-        self._artists = dict()
-
-        self._bg_artists = dict()
-        self._bg_layers = dict()
-
-        self._pending_webmaps = dict()
-
-        # the name of the layer at which all "unmanaged" artists are drawn
-        self._unmanaged_artists_layer = "base"
-
-        # grab the background on every draw
-        self.cid = self.canvas.mpl_connect("draw_event", self.on_draw)
-
-        self._after_update_actions = []
-        self._after_restore_actions = []
-        self._bg_layer = "base"
-
-        self._artists_to_clear = dict()
-
-        self._hidden_artists = set()
-
-        self._refetch_bg = True
-        self._layers_to_refetch = set()
-
-        # TODO these activate some crude fixes for jupyter notebook and webagg
-        # backends... proper fixes would be nice
-        self._mpl_backend_blit_fix = any(
-            i in plt.get_backend().lower() for i in ["webagg", "nbagg"]
-        )
-
-        # self._mpl_backend_force_full = any(
-        #     i in plt.get_backend().lower() for i in ["nbagg"]
-        # )
-        # recent fixes seem to take care of this nbagg issue...
-        self._mpl_backend_force_full = False
-        self._mpl_backend_blit_fix = False
-
-        # True = persistent, False = execute only once
-        self._on_layer_change = {True: list(), False: list()}
-        self._on_layer_activation = {True: dict(), False: dict()}
-
-        self._on_add_bg_artist = list()
-        self._on_remove_bg_artist = list()
-
-        self._before_fetch_bg_actions = list()
-        self._before_update_actions = list()
-
-        self._refetch_blank = True
-        self._blank_bg = None
-
-        self._managed_axes = set()
-
-        self._clear_on_layer_change = False
-
-        self._on_layer_change_running = False
-
-        # a weak set containing artists that should NOT be identified as
-        # unmanaged artists
-        self._ignored_unmanaged_artists = WeakSet()
-
-    def _get_renderer(self):
-        # don't return the renderer if the figure is saved.
-        # in this case the normal draw-routines are used (see m.savefig) so there is
-        # no need to trigger updates (also `canvas.get_renderer` is undefined for
-        # pdf/svg exports since those canvas do not expose the renderer)
-        # ... this is required to support vector format outputs!
-        if self.canvas.is_saving():
-            return None
-
-        try:
-            return self.canvas.get_renderer()
-        except Exception:
-            return None
-
-    def _get_all_map_axes(self):
-        maxes = {
-            m.ax for m in (self._m.parent, *self._m.parent._children) if m._new_axis_map
-        }
-        return maxes
-
-    def _get_managed_axes(self):
-        return (*self._get_all_map_axes(), *self._managed_axes)
-
-    def _get_unmanaged_axes(self):
-        # return a list of all axes that are not managed by the blit-manager
-        # (to ensure that "unmanaged" axes are drawn as well)
-
-        # EOmaps axes
-        managed_axes = self._get_managed_axes()
-        allaxes = set(self._m.f.axes)
-
-        unmanaged_axes = allaxes.difference(managed_axes)
-        return unmanaged_axes
-
-    @property
-    def figure(self):
-        """The matplotlib figure instance."""
-        return self._m.f
-
-    @property
-    def canvas(self):
-        """The figure canvas instance."""
-        return self.figure.canvas
-
-    @contextmanager
-    def _cx_on_layer_change_running(self):
-        # a context-manager to avoid recursive on_layer_change calls
-        try:
-            self._on_layer_change_running = True
-            yield
-        finally:
-            self._on_layer_change_running = False
-
-    def _do_on_layer_change(self, layer, new=False):
-        # avoid recursive calls to "_do_on_layer_change"
-        # This is required in case the executed functions trigger actions that would
-        # trigger "_do_on_layer_change" again which can result in a mixed-up order of
-        # the scheduled functions.
-        if self._on_layer_change_running is True:
-            return
-
-        # do not execute layer-change callbacks on private layer activation!
-        if layer.startswith("__"):
-            return
-
-        with self._cx_on_layer_change_running():
-            # only execute persistent layer-change callbacks if the layer changed!
-            if new:
-                # general callbacks executed on any layer change
-                # persistent callbacks
-                for f in reversed(self._on_layer_change[True]):
-                    f(layer=layer)
-
-            # single-shot callbacks
-            # (execute also if the layer is already active)
-            while len(self._on_layer_change[False]) > 0:
-                try:
-                    f = self._on_layer_change[False].pop(0)
-                    f(layer=layer)
-                except Exception:
-                    _log.error(
-                        "EOmaps: Issue while executing a layer-change action",
-                        exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
-                    )
-
-            sublayers, _ = self._parse_multi_layer_str(layer)
-            if new:
-                for l in sublayers:
-                    # individual callables executed if a specific layer is activated
-                    # persistent callbacks
-                    for f in reversed(self._on_layer_activation[True].get(layer, [])):
-                        f(layer=l)
-
-            for l in sublayers:
-                # single-shot callbacks
-                single_shot_funcs = self._on_layer_activation[False].get(l, [])
-                while len(single_shot_funcs) > 0:
-                    try:
-                        f = single_shot_funcs.pop(0)
-                        f(layer=l)
-                    except Exception:
-                        _log.error(
-                            "EOmaps: Issue while executing a layer-change action",
-                            exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
-                        )
-
-            # clear the list of pending webmaps once the layer has been activated
-            if layer in self._pending_webmaps:
-                self._pending_webmaps.pop(layer)
-
-    @contextmanager
-    def _without_artists(self, artists=None, layer=None):
-        try:
-            removed_artists = {layer: set(), "all": set()}
-            if artists is None:
-                yield
-            else:
-                for a in artists:
-                    if a in self._artists.get(layer, []):
-                        self.remove_artist(a, layer=layer)
-                        removed_artists[layer].add(a)
-                    elif a in self._artists.get("all", []):
-                        self.remove_artist(a, layer="all")
-                        removed_artists["all"].add(a)
-
-                yield
-        finally:
-            for layer, artists in removed_artists.items():
-                for a in artists:
-                    self.add_artist(a, layer=layer)
-
-    def _get_active_bg(self, exclude_artists=None):
-        with self._without_artists(artists=exclude_artists, layer=self.bg_layer):
-            # fetch the current background (incl. dynamic artists)
-            self.update()
-
-            with ExitStack() as stack:
-                # get rid of the figure background patch
-                # (done by putting the patch on the __BG__ layer!)
-
-                # get rid of the axes background patch
-                for ax_i in self._get_all_map_axes():
-                    stack.enter_context(
-                        ax_i.patch._cm_set(facecolor="none", edgecolor="none")
-                    )
-                bg = self.canvas.copy_from_bbox(self.figure.bbox)
-
-        return bg
-
-    @property
-    def bg_layer(self):
-        """The currently visible layer-name."""
-        return self._bg_layer
-
-    @bg_layer.setter
-    def bg_layer(self, val):
-        if val == self._bg_layer:
-            # in case the layer did not change, do nothing
-            return
-
-        # check if a new layer is activated (or added to a multi-layer)
-        old_layers = set(self._parse_multi_layer_str(self._bg_layer)[0])
-        new_layers = set(self._parse_multi_layer_str(val)[0])
-
-        new = old_layers != new_layers
-
-        # make sure we use a "full" update for webagg and ipympl backends
-        # (e.g. force full redraw of canvas instead of a diff)
-        self.canvas._force_full = True
-        self._bg_layer = val
-
-        # a general callable to be called on every layer change
-        self._do_on_layer_change(layer=val, new=new)
-
-        # hide all colorbars that are not on the visible layer
-        for m in [self._m.parent, *self._m.parent._children]:
-            layer_visible = self._layer_is_subset(val, m.layer)
-
-            for cb in m._colorbars:
-                cb._hide_singular_axes()
-
-                if layer_visible:
-                    if cb in self._hidden_artists:
-                        self._hidden_artists.remove(cb)
-                else:
-                    if cb not in self._hidden_artists:
-                        self._hidden_artists.add(cb)
-
-        # hide all wms_legends that are not on the visible layer
-        if hasattr(self._m.parent, "_wms_legend"):
-            for layer, legends in self._m.parent._wms_legend.items():
-                layer_visible = self._layer_is_subset(val, layer)
-
-                if layer_visible:
-                    for i in legends:
-                        i.set_visible(True)
-                else:
-                    for i in legends:
-                        i.set_visible(False)
-
-        if self._clear_on_layer_change:
-            self._clear_temp_artists("on_layer_change")
-
-    @contextmanager
-    def _cx_dont_clear_on_layer_change(self):
-        # a context-manager to avoid clearing artists on layer-changes
-        # (used in savefig to avoid clearing artists when re-fetching
-        # layers with backgrounds)
-        init_val = self._clear_on_layer_change
-        try:
-            self._clear_on_layer_change = False
-            yield
-        finally:
-            self._clear_on_layer_change = init_val
-
-    def on_layer(self, func, layer=None, persistent=False, m=None):
-        """
-        Add callables that are executed whenever the visible layer changes.
-
-        NOTE: if m is None this function always falls back to the parent Maps-object!!
-
-        Parameters
-        ----------
-        func : callable
-            The callable to use.
-            The call-signature is:
-
-            >>> def func(m, layer):
-            >>>    # m     ... the Maps-object
-            >>>    # layer ... the name of the layer
-
-
-        layer : str or None, optional
-            - If str: The function will only be called if the specified layer is
-              activated.
-            - If None: The function will be called on any layer-change.
-
-            The default is None.
-        persistent : bool, optional
-            Indicator if the function should be called only once (False) or if it
-            should be called whenever a layer is activated.
-            The default is False.
-        m : eomaps.Maps
-            The Maps-object to pass as argument to the function execution.
-            If None, the parent Maps-object is used.
-
-        """
-        if m is None:
-            m = self._m
-
-        def cb(*args, **kwargs):
-            func(m=m, *args, **kwargs)
-
-        if _log.getEffectiveLevel() <= 10:
-            logmsg = (
-                f"Adding {'persistent' if persistent else 'single-shot'} "
-                f"layer change action for: '{layer}'"
-            )
-            _log.debug(logmsg)
-
-        if layer is None:
-            self._on_layer_change[persistent].append(cb)
-        else:
-            # treat inset-map layers like normal layers
-            if layer.startswith("__inset_"):
-                layer = layer[8:]
-            self._on_layer_activation[persistent].setdefault(layer, list()).append(cb)
-
-    def _refetch_layer(self, layer):
-        if layer == "all":
-            # if the all layer changed, all backgrounds need a refetch
-            self._refetch_bg = True
-        else:
-            # set any background that contains the layer for refetch
-            self._layers_to_refetch.add(layer)
-
-            for l in self._bg_layers:
-                sublayers, _ = self._parse_multi_layer_str(l)
-                if layer in sublayers:
-                    self._layers_to_refetch.add(l)
-
-    def _bg_artists_sort(self, art):
-        sortp = []
-
-        # ensure that inset-map artists are always drawn after all other artists
-        if art.axes is not None:
-            if art.axes.get_label() == "inset_map":
-                sortp.append(1)
-            else:
-                sortp.append(0)
-
-        sortp.append(getattr(art, "zorder", -1))
-        return sortp
-
-    def get_bg_artists(self, layer):
-        """
-        Get all (sorted) background artists assigned to a given layer-name.
-
-        Parameters
-        ----------
-        layer : str
-            The layer name for which artists should be fetched.
-
-        Returns
-        -------
-        artists : list
-            A list of artists on the specified layer, sorted with respect to the
-            vertical stacking (layer-order / zorder).
-
-        """
-        artists = list()
-        for l in np.atleast_1d(layer):
-            # get all relevant artists for combined background layers
-            l = str(l)  # w make sure we convert non-string layer names to string!
-
-            # get artists defined on the layer itself
-            # Note: it's possible to create explicit multi-layers and attach
-            # artists that are only visible if both layers are visible! (e.g. "l1|l2")
-            artists.extend(self._bg_artists.get(l, []))
-
-            if l == self._unmanaged_artists_layer:
-                artists.extend(self._get_unmanaged_artists())
-
-        # make the list unique but maintain order (dicts keep order for python>3.7)
-        artists = dict.fromkeys(artists)
-        # sort artists by zorder (respecting inset-map priority)
-        artists = sorted(artists, key=self._bg_artists_sort)
-
-        return artists
-
-    def get_artists(self, layer):
-        """
-        Get all (sorted) dynamically updated artists assigned to a given layer-name.
-
-        Parameters
-        ----------
-        layer : str
-            The layer name for which artists should be fetched.
-
-        Returns
-        -------
-        artists : list
-            A list of artists on the specified layer, sorted with respect to the
-            vertical stacking (layer-order / zorder).
-
-        """
-
-        artists = list()
-        for l in np.atleast_1d(layer):
-            # get all relevant artists for combined background layers
-            l = str(l)  # w make sure we convert non-string layer names to string!
-
-            # get artists defined on the layer itself
-            # Note: it's possible to create explicit multi-layers and attach
-            # artists that are only visible if both layers are visible! (e.g. "l1|l2")
-            artists.extend(self._artists.get(l, []))
-
-        # make the list unique but maintain order (dicts keep order for python>3.7)
-        artists = dict.fromkeys(artists)
-        # sort artists by zorder (respecting inset-map priority)
-        artists = sorted(artists, key=self._bg_artists_sort)
-
-        return artists
-
-    def _layer_visible(self, layer):
-        """
-        Return True if the layer is currently visible.
-
-        - layer is considered visible if all sub-layers of a combined layer are visible
-        - transparency assignments do not alter the layer visibility
-
-        Parameters
-        ----------
-        layer : str
-            The combined layer-name to check. (e.g. 'A|B{.4}|C{.3}')
-
-        Returns
-        -------
-        visible: bool
-            True if the layer is currently visible, False otherwise
-
-        """
-        return layer in "all" or self._layer_is_subset(layer, self.bg_layer)
-
-    @property
-    def _get_active_layers_alphas(self):
-        """
-        Return the currently visible layers (and their associated transparencies)
-
-        Returns
-        -------
-        layers, alphas: list of str, list of float
-            2 lists of layer-names and associated global transparencies.
-
-        """
-        return self._parse_multi_layer_str(self.bg_layer)
-
-    # cache the last 10 combined backgrounds to avoid re-combining backgrounds
-    # on updates of interactive artists
-    # cache is automatically cleared on draw if any layer is tagged for re-fetch!
-    @lru_cache(10)
-    def _combine_bgs(self, layer):
-        layers, alphas = self._parse_multi_layer_str(layer)
-
-        # make sure all layers are already fetched
-        for l in layers:
-            if l not in self._bg_layers:
-                # execute actions on layer-changes
-                # (to make sure all lazy WMS services are properly added)
-                self._do_on_layer_change(layer=l, new=False)
-                self.fetch_bg(l)
-
-        renderer = self._get_renderer()
-        # clear the renderer to avoid drawing on existing backgrounds
-        renderer.clear()
-        if renderer:
-            gc = renderer.new_gc()
-            gc.set_clip_rectangle(self.canvas.figure.bbox)
-
-            x0, y0, w, h = self.figure.bbox.bounds
-            for l, a in zip(layers, alphas):
-                rgba = self._get_array(l, a=a)
-                if rgba is None:
-                    # to handle completely empty layers
-                    continue
-                renderer.draw_image(
-                    gc,
-                    int(x0),
-                    int(y0),
-                    rgba[int(y0) : int(y0 + h), int(x0) : int(x0 + w), :],
-                )
-            bg = renderer.copy_from_bbox(self._m.f.bbox)
-            gc.restore()
-            return bg
-
-    def _get_array(self, l, a=1):
-        if l not in self._bg_layers:
-            return None
-        rgba = np.array(self._bg_layers[l])[::-1, :, :]
-        if a != 1:
-            rgba = rgba.copy()
-            rgba[..., -1] = (rgba[..., -1] * a).astype(rgba.dtype)
-        return rgba
-
-    def _get_background(self, layer, bbox=None, cache=False):
-        if layer not in self._bg_layers:
-            if "|" in layer:
-                bg = self._combine_bgs(layer)
-            else:
-                self.fetch_bg(layer, bbox=bbox)
-                bg = self._bg_layers[layer]
-        else:
-            bg = self._bg_layers[layer]
-
-        if cache is True:
-            # explicitly cache the layer
-            # (for peek-layer callbacks to avoid re-fetching the layers all the time)
-            self._bg_layers[layer] = bg
-
-        return bg
-
-    def _do_fetch_bg(self, layer, bbox=None):
-        cv = self.canvas
-        renderer = self._get_renderer()
-        renderer.clear()
-
-        if bbox is None:
-            bbox = self.figure.bbox
-
-        if "|" in layer:
-            if layer not in self._bg_layers:
-                self._combine_bgs(layer)
-            return
-
-        # update axes spines and patches since they are used to clip artists!
-        for ax in self._get_all_map_axes():
-            ax.spines["geo"]._adjust_location()
-            ax.patch._adjust_location()
-
-        # use contextmanagers to make sure the background patches are not stored
-        # in the buffer regions!
-        with ExitStack() as stack:
-            if layer not in ["__BG__"]:
-                # get rid of the axes background patches for all layers except
-                # the __BG__ layer
-                # (the figure background patch is on the "__BG__" layer)
-                for ax_i in self._get_all_map_axes():
-                    stack.enter_context(
-                        ax_i.patch._cm_set(facecolor="none", edgecolor="none")
-                    )
-
-            # execute actions before fetching new artists
-            # (e.g. update data based on extent etc.)
-            for action in self._before_fetch_bg_actions:
-                action(layer=layer, bbox=bbox)
-
-            # get all relevant artists to plot and remember zorders
-            # self.get_bg_artists() already returns artists sorted by zorder!
-            if layer in ["__SPINES__", "__BG__", "__inset___SPINES__"]:
-                # avoid fetching artists from the "all" layer for private layers
-                allartists = self.get_bg_artists(layer)
-            else:
-                if layer.startswith("__inset"):
-                    allartists = self.get_bg_artists(["__inset_all", layer])
-                else:
-                    allartists = self.get_bg_artists(["all", layer])
-
-            # check if all artists are not stale
-            no_stale_artists = all(not art.stale for art in allartists)
-
-            # don't re-fetch the background if it is not necessary
-            if no_stale_artists and (self._bg_layers.get(layer, None) is not None):
-                return
-
-            if renderer:
-                for art in allartists:
-                    if art not in self._hidden_artists:
-                        try:
-                            art.draw(renderer)
-                            art.stale = False
-                        except Exception:
-                            if _log.getEffectiveLevel() <= logging.DEBUG:
-                                _log.error(
-                                    "Unable to draw artist:"
-                                    f"{art} ("
-                                    f"figure={getattr(art, 'figure', '??')}, "
-                                    f"axes={getattr(art, 'axes', '??')})"
-                                )
-
-                self._bg_layers[layer] = renderer.copy_from_bbox(bbox)
-
-    def fetch_bg(self, layer=None, bbox=None):
-        """
-        Trigger fetching (and caching) the background for a given layer-name.
-
-        Parameters
-        ----------
-        layer : str, optional
-            The layer for which the background should be fetched.
-            If None, the currently visible layer is fetched.
-            The default is None.
-        bbox : bbox, optional
-            The region-boundaries (in figure coordinates) for which the background
-            should be fetched (x0, y0, w, h). If None, the whole figure is fetched.
-            The default is None.
-
-        """
-
-        if layer is None:
-            layer = self.bg_layer
-
-        if layer in self._bg_layers:
-            # don't re-fetch existing layers
-            # (layers get cleared automatically if re-draw is necessary)
-            return
-
-        with self._disconnect_draw():
-            self._do_fetch_bg(layer, bbox)
-
-    @contextmanager
-    def _disconnect_draw(self):
-        try:
-            # temporarily disconnect draw-event callback to avoid recursion
-            if self.cid is not None:
-                self.canvas.mpl_disconnect(self.cid)
-                self.cid = None
-            yield
-        finally:
-            # reconnect draw event
-            if self.cid is None:
-                self.cid = self.canvas.mpl_connect("draw_event", self.on_draw)
-
-    def on_draw(self, event):
-        """Callback to register with 'draw_event'."""
-
-        if self._disable_draw:
-            return
-
-        cv = self.canvas
-        loglevel = _log.getEffectiveLevel()
-
-        if hasattr(cv, "get_renderer") and not cv.is_saving():
-
-            renderer = cv.get_renderer()
-            if renderer is None:
-                # don't run on_draw if no renderer is available
-                return
-        else:
-            # don't run on_draw if no renderer is available
-            # (this is true for svg export where mpl export routines
-            # are used to avoid issues)
-            if loglevel <= 5:
-                _log.log(5, " not drawing")
-
-            return
-
-        if loglevel <= 5:
-            _log.log(5, "draw")
-
-        if event is not None:
-            if event.canvas != cv:
-                raise RuntimeError
-        try:
-            # reset all background-layers and re-fetch the default one
-            if self._refetch_bg:
-                self._bg_layers.clear()
-                self._layers_to_refetch.clear()
-                self._refetch_bg = False
-                type(self)._combine_bgs.cache_clear()  # clear combined_bg cache
-
-            else:
-                # in case there is a stale (unmanaged) artists and the
-                # stale-artist layer is attempted to be drawn, re-draw the
-                # cached background for the unmanaged-artists layer
-                active_layers, _ = self._get_active_layers_alphas
-                if self._unmanaged_artists_layer in active_layers and any(
-                    a.stale for a in self._get_unmanaged_artists()
-                ):
-                    self._refetch_layer(self._unmanaged_artists_layer)
-                    type(self)._combine_bgs.cache_clear()  # clear combined_bg cache
-
-                # remove all cached backgrounds that were tagged for refetch
-                while len(self._layers_to_refetch) > 0:
-                    self._bg_layers.pop(self._layers_to_refetch.pop(), None)
-                    type(self)._combine_bgs.cache_clear()  # clear combined_bg cache
-
-            # workaround for nbagg backend to avoid glitches
-            # it's slow but at least it works...
-            # check progress of the following issuse
-            # https://github.com/matplotlib/matplotlib/issues/19116
-            if self._mpl_backend_blit_fix:
-                self.update()
-            else:
-                self.update(blit=False)
-
-            # re-draw indicator-shapes of active drawer
-            # (to show indicators during zoom-events)
-            active_drawer = getattr(self._m.parent, "_active_drawer", None)
-            if active_drawer is not None:
-                active_drawer.redraw(blit=False)
-
-        except Exception:
-            # we need to catch exceptions since QT does not like them...
-            if loglevel <= 5:
-                _log.log(5, "There was an error during draw!", exc_info=True)
-
-    def add_artist(self, art, layer=None):
-        """
-        Add a dynamic-artist to be managed.
-        (Dynamic artists are re-drawn on every update!)
-
-        Parameters
-        ----------
-        art : Artist
-
-            The artist to be added.  Will be set to 'animated' (just
-            to be safe).  *art* must be in the figure associated with
-            the canvas this class is managing.
-        layer : str or None, optional
-            The layer name at which the artist should be drawn.
-
-            - If "all": the corresponding feature will be added to ALL layers
-
-            The default is None in which case the layer of the base-Maps object is used.
-        """
-
-        if art.figure != self.figure:
-            raise RuntimeError(
-                "EOmaps: The artist does not belong to the figure"
-                "of this Maps-object!"
-            )
-
-        if layer is None:
-            layer = self._m.layer
-
-        # make sure all layers are converted to string
-        layer = str(layer)
-
-        self._artists.setdefault(layer, list())
-
-        if art in self._artists[layer]:
-            return
-        else:
-            art.set_animated(True)
-            self._artists[layer].append(art)
-
-            if isinstance(art, plt.Axes):
-                self._managed_axes.add(art)
-
-    def add_bg_artist(self, art, layer=None, draw=True):
-        """
-        Add a background-artist to be managed.
-        (Background artists are only updated on zoom-events... they are NOT animated!)
-
-        Parameters
-        ----------
-        art : Artist
-            The artist to be added.  Will be set to 'animated' (just
-            to be safe).  *art* must be in the figure associated with
-            the canvas this class is managing.
-        layer : str or None, optional
-            The layer name at which the artist should be drawn.
-
-            - If "all": the corresponding feature will be added to ALL layers
-
-            The default is None in which case the layer of the base-Maps object is used.
-        draw : bool, optional
-            If True, `figure.draw_idle()` is called after adding the artist.
-            The default is True.
-        """
-
-        if layer is None:
-            layer = self._m.layer
-
-        # make sure all layer names are converted to string
-        layer = str(layer)
-
-        if art.figure != self.figure:
-            raise RuntimeError
-
-        # put all artist of inset-maps on dedicated layers
-        if (
-            getattr(art, "axes", None) is not None
-            and art.axes.get_label() == "inset_map"
-            and not layer.startswith("__inset_")
-        ):
-            layer = "__inset_" + str(layer)
-        if layer in self._bg_artists and art in self._bg_artists[layer]:
-            _log.info(f"EOmaps: Background-artist '{art}' already added")
-            return
-
-        art.set_animated(True)
-        self._bg_artists.setdefault(layer, []).append(art)
-
-        if isinstance(art, plt.Axes):
-            self._managed_axes.add(art)
-
-        # tag all relevant layers for refetch
-        self._refetch_layer(layer)
-
-        for f in self._on_add_bg_artist:
-            f()
-
-        if draw:
-            self.canvas.draw_idle()
-
-    def remove_bg_artist(self, art, layer=None, draw=True):
-        """
-        Remove a (background) artist from the map.
-
-        Parameters
-        ----------
-        art : Artist
-            The artist that should be removed.
-        layer : str or None, optional
-            If provided, the artist is only searched on the provided layer, otherwise
-            all map layers are searched. The default is None.
-        draw : bool, optional
-            If True, `figure.draw_idle()` is called after removing the artist.
-            The default is True.
-
-        Note
-        ----
-        This only removes the artist from the blit-manager and does not call its
-        remove method!
-
-        """
-        # handle the "__inset_" prefix of inset-map artists
-        if (
-            layer is not None
-            and getattr(art, "axes", None) is not None
-            and art.axes.get_label() == "inset_map"
-            and not layer.startswith("__inset_")
-        ):
-            layer = "__inset_" + str(layer)
-
-        removed = False
-        if layer is None:
-            layers = []
-            for key, val in self._bg_artists.items():
-                if art in val:
-                    art.set_animated(False)
-                    val.remove(art)
-
-                    # remove axes from the managed_axes set as well!
-                    if art in self._managed_axes:
-                        self._managed_axes.remove(art)
-
-                    removed = True
-                    layers.append(key)
-                layer = self._get_combined_layer_name(*layers)
-        else:
-            if layer not in self._bg_artists:
-                return
-            if art in self._bg_artists[layer]:
-                art.set_animated(False)
-                self._bg_artists[layer].remove(art)
-
-                # remove axes from the managed_axes set as well!
-                if art in self._managed_axes:
-                    self._managed_axes.remove(art)
-
-                removed = True
-
-        if removed:
-            for f in self._on_remove_bg_artist:
-                f()
-
-            # tag all relevant layers for refetch
-            self._refetch_layer(layer)
-
-        if draw:
-            self.canvas.draw_idle()
-
-    def remove_artist(self, art, layer=None):
-        """
-        Remove a (dynamically updated) artist from the blit-manager.
-
-        Parameters
-        ----------
-        art : matpltolib.Artist
-            The artist to remove.
-        layer : str, optional
-            The layer to search for the artist. If None, all layers are searched.
-            The default is None.
-
-        Note
-        ----
-        This only removes the artist from the blit-manager and does not call its
-        remove method!
-
-        """
-        if layer is None:
-            for key, layerartists in self._artists.items():
-                if art in layerartists:
-                    art.set_animated(False)
-                    layerartists.remove(art)
-
-                    # remove axes from the managed_axes set as well!
-                    if art in self._managed_axes:
-                        self._managed_axes.remove(art)
-
-        else:
-            if art in self._artists.get(layer, []):
-                art.set_animated(False)
-                self._artists[layer].remove(art)
-
-                # remove axes from the managed_axes set as well!
-                if art in self._managed_axes:
-                    self._managed_axes.remove(art)
-            else:
-                _log.debug(f"The artist {art} is not on the layer '{layer}'")
-
-    def _get_artist_zorder(self, a):
-        try:
-            return a.get_zorder()
-        except Exception:
-            _log.error(f"EOmaps: unalble to identify zorder of {a}... using 99")
-            return 99
-
-    def _draw_animated(self, layers=None, artists=None):
-        """
-        Draw animated artists
-
-        - if layers is None and artists is None: active layer artists will be re-drawn
-        - if layers is not None: all artists from the selected layers will be re-drawn
-        - if artists is not None: all provided artists will be redrawn
-
-        """
-        fig = self.canvas.figure
-        renderer = self._get_renderer()
-        if renderer is None:
-            return
-
-        if layers is None:
-            active_layers, _ = self._get_active_layers_alphas
-            layers = [self.bg_layer, *active_layers]
-        else:
-            (layers,) = list(
-                chain(*(self._parse_multi_layer_str(l)[0] for l in layers))
-            )
-
-        if artists is None:
-            artists = []
-
-        # always redraw artists from the "all" layer
-        layers.append("all")
-
-        # make the list unique but maintain order (dicts keep order for python>3.7)
-        layers = list(dict.fromkeys(layers))
-
-        # draw all "unmanaged" axes (e.g. axes that are found in the figure but
-        # not in the blit-manager)
-        # TODO would be nice to find a better way to handle this!
-        # - NOTE: this must be done before drawing managed artists to properly support
-        #   temporary artists on unmanaged axes!
-        for ax in self._get_unmanaged_axes():
-            ax.draw(renderer)
-
-        # redraw artists from the selected layers and explicitly provided artists
-        # (sorted by zorder for each layer)
-        layer_artists = list(
-            sorted(self._artists.get(layer, []), key=self._get_artist_zorder)
-            for layer in layers
-        )
-
-        with ExitStack() as stack:
-            # avoid drawing the background-patches of managed (dynamic) axes
-            # since they might interfere with consecutive draws issued by callbacks
-            for ax_i in self._managed_axes:
-                stack.enter_context(
-                    ax_i.patch._cm_set(facecolor="none", edgecolor="none")
-                )
-
-            for a in chain(*layer_artists, artists):
-                fig.draw_artist(a)
-
-    def _get_unmanaged_artists(self):
-        # return all artists not explicitly managed by the blit-manager
-        # (e.g. any artist added via cartopy or matplotlib functions)
-        managed_artists = set(
-            chain(
-                *self._bg_artists.values(),
-                *self._artists.values(),
-                self._ignored_unmanaged_artists,
-            )
-        )
-
-        axes = {m.ax for m in (self._m, *self._m._children) if m.ax is not None}
-
-        allartists = set()
-        for ax in axes:
-            # only include axes titles if they are actually set
-            # (otherwise empty artists appear in the widget)
-            titles = [
-                i
-                for i in (ax.title, ax._left_title, ax._right_title)
-                if len(i.get_text()) > 0
-            ]
-
-            axartists = {
-                *ax._children,
-                *titles,
-                *([ax.legend_] if ax.legend_ is not None else []),
-            }
-
-            allartists.update(axartists)
-
-        return allartists.difference(managed_artists)
-
-    def _clear_all_temp_artists(self):
-        for method in self._m.cb._methods:
-            container = getattr(self._m.cb, method, None)
-            if container:
-                container._clear_temporary_artists()
-            self._clear_temp_artists(method)
-
-    def _clear_temp_artists(self, method, forward=True):
-        # clear artists from connected methods
-        if method == "_click_move" and forward:
-            self._clear_temp_artists("click", False)
-        elif method == "click" and forward:
-            self._clear_temp_artists("_click_move", False)
-        elif method == "pick" and forward:
-            self._clear_temp_artists("click", True)
-        elif method == "on_layer_change" and forward:
-            self._clear_temp_artists("pick", False)
-            self._clear_temp_artists("click", True)
-            self._clear_temp_artists("move", False)
-
-        if method == "on_layer_change":
-            # clear all artists from "on_layer_change" list irrespective of the method
-            artists = self._artists_to_clear.pop("on_layer_change", [])
-            for art in artists:
-                for met, met_artists in self._artists_to_clear.items():
-                    if art in met_artists:
-                        art.set_visible(False)
-                        self.remove_artist(art)
-                        try:
-                            art.remove()
-                        except ValueError:
-                            # ignore errors if the artist no longer exists
-                            pass
-                        met_artists.remove(art)
-        else:
-            artists = self._artists_to_clear.pop(method, [])
-            while len(artists) > 0:
-                art = artists.pop(-1)
-                art.set_visible(False)
-                self.remove_artist(art)
-                try:
-                    art.remove()
-                except ValueError:
-                    # ignore errors if the artist no longer exists
-                    pass
-
-                try:
-                    self._artists_to_clear.get("on_layer_change", []).remove(art)
-                except ValueError:
-                    # ignore errors if the artist is not present in the list
-                    pass
-
-    def _get_showlayer_name(self, layer=None, transparent=False):
-        # combine all layers that should be shown
-        # (e.g. to add spines, backgrounds and inset-maps)
-
-        if layer is None:
-            layer = self.bg_layer
-
-        # pass private layers through
-        if layer.startswith("__"):
-            return layer
-
-        if transparent is True:
-            show_layers = [layer, "__SPINES__"]
-        else:
-            show_layers = ["__BG__", layer, "__SPINES__"]
-
-        # show inset map layers and spines only if they contain at least 1 artist
-        inset_Q = False
-        for l in self._parse_multi_layer_str(layer)[0]:
-            narts = len(self._bg_artists.get("__inset_" + l, []))
-
-            if narts > 0:
-                show_layers.append(f"__inset_{l}")
-                inset_Q = True
-
-        if inset_Q:
-            show_layers.append("__inset___SPINES__")
-
-        return self._get_combined_layer_name(*show_layers)
-
-    def update(
-        self,
-        layers=None,
-        bbox_bounds=None,
-        bg_layer=None,
-        artists=None,
-        clear=False,
-        blit=True,
-        clear_snapshot=True,
-    ):
-        """
-        Update the screen with animated artists.
-
-        Parameters
-        ----------
-        layers : list, optional
-            The layers to redraw (if None and artists is None, all layers will be redrawn).
-            The default is None.
-        bbox_bounds : tuple, optional
-            the blit-region bounds to update. The default is None.
-        bg_layer : int, optional
-            the background-layer name to restore. The default is None.
-        artists : list, optional
-            A list of artists to update.
-            If provided NO layer will be automatically updated!
-            The default is None.
-        clear : bool, optional
-            If True, all temporary artists tagged for removal will be cleared.
-            The default is False.
-        blit : bool, optional
-            If True, figure.cavas.blit() will be called to update the figure.
-            If False, changes will only be visible on the next blit-event!
-            The default is True.
-        clear_snapshot : bool, optional
-            Only relevant if the `inline` backend is used in a jupyter-notebook
-            or an Ipython console.
-
-            If True, clear the active cell before plotting a snapshot of the figure.
-            The default is True.
-        """
-        if self._disable_update:
-            # don't update during layout-editing
-            return
-
-        cv = self.canvas
-
-        if bg_layer is None:
-            bg_layer = self.bg_layer
-
-        for action in self._before_update_actions:
-            action()
-
-        if clear:
-            self._clear_temp_artists(clear)
-
-        # restore the background
-        # add additional layers (background, spines etc.)
-        show_layer = self._get_showlayer_name()
-
-        if show_layer not in self._bg_layers:
-            # make sure the background is properly fetched
-            self.fetch_bg(show_layer)
-
-        cv.restore_region(self._get_background(show_layer))
-
-        # execute after restore actions (e.g. peek layer callbacks)
-        while len(self._after_restore_actions) > 0:
-            action = self._after_restore_actions.pop(0)
-            action()
-
-        # draw all of the animated artists
-        self._draw_animated(layers=layers, artists=artists)
-        if blit:
-            # workaround for nbagg backend to avoid glitches
-            # it's slow but at least it works...
-            # check progress of the following issuse
-            # https://github.com/matplotlib/matplotlib/issues/19116
-            if self._mpl_backend_force_full:
-                cv._force_full = True
-
-            if bbox_bounds is not None:
-
-                class bbox:
-                    bounds = bbox_bounds
-
-                cv.blit(bbox)
-            else:
-                # update the GUI state
-                cv.blit(self.figure.bbox)
-
-        # execute all actions registered to be called after blitting
-        while len(self._after_update_actions) > 0:
-            action = self._after_update_actions.pop(0)
-            action()
-
-        # let the GUI event loop process anything it has to do
-        # don't do this! it is causing infinite loops
-        # cv.flush_events()
-
-        if blit and BlitManager._snapshot_on_update is True:
-            self._m.snapshot(clear=clear_snapshot)
-
-    def blit_artists(self, artists, bg="active", blit=True):
-        """
-        Blit artists (optionally on top of a given background)
-
-        Parameters
-        ----------
-        artists : iterable
-            the artists to draw
-        bg : matpltolib.BufferRegion, None or "active", optional
-            A fetched background that is restored before drawing the artists.
-            The default is "active".
-        blit : bool
-            Indicator if canvas.blit() should be called or not.
-            The default is True
-        """
-        cv = self.canvas
-        renderer = self._get_renderer()
-        if renderer is None:
-            _log.error("EOmaps: encountered a problem while trying to blit artists...")
-            return
-
-        # restore the background
-        if bg is not None:
-            if bg == "active":
-                bg = self._get_active_bg()
-            cv.restore_region(bg)
-
-        for a in artists:
-            self.figure.draw_artist(a)
-
-        if blit:
-            cv.blit()
-
-    def _get_restore_bg_action(
-        self,
-        layer,
-        bbox_bounds=None,
-        alpha=1,
-        clip_path=None,
-        set_clip_path=False,
-    ):
-        """
-        Update a part of the screen with a different background
-        (intended as after-restore action)
-
-        bbox_bounds = (x, y, width, height)
-        """
-        if bbox_bounds is None:
-            bbox = self.figure.bbox
-        else:
-            bbox = Bbox.from_bounds(*bbox_bounds)
-
-        def action():
-            renderer = self._get_renderer()
-            if renderer is None:
-                return
-
-            if self.bg_layer == layer:
-                return
-
-            # TODO properly check if this is necessary...
-            # update to make sure spines etc. are properly displayed
-            # self.update()
-
-            x0, y0, w, h = bbox.bounds
-
-            # make sure to restore the initial background
-            init_bg = renderer.copy_from_bbox(self._m.f.bbox)
-            # convert the buffer to rgba so that we can add transparency
-            buffer = self._get_background(layer, cache=True)
-            self.canvas.restore_region(init_bg)
-
-            x = buffer.get_extents()
-            ncols, nrows = x[2] - x[0], x[3] - x[1]
-
-            argb = (
-                np.frombuffer(buffer, dtype=np.uint8).reshape((nrows, ncols, 4)).copy()
-            )
-            argb = argb[::-1, :, :]
-
-            argb[:, :, -1] = (argb[:, :, -1] * alpha).astype(np.int8)
-
-            gc = renderer.new_gc()
-
-            gc.set_clip_rectangle(bbox)
-            if set_clip_path is True:
-                gc.set_clip_path(clip_path)
-
-            renderer.draw_image(
-                gc,
-                int(x0),
-                int(y0),
-                argb[int(y0) : int(y0 + h), int(x0) : int(x0 + w), :],
-            )
-            gc.restore()
-
-        return action
-
-    def cleanup_layer(self, layer):
-        """Trigger cleanup methods for a given layer."""
-        self._cleanup_bg_artists(layer)
-        self._cleanup_artists(layer)
-        self._cleanup_bg_layers(layer)
-        self._cleanup_on_layer_activation(layer)
-
-    def _cleanup_bg_artists(self, layer):
-        if layer not in self._bg_artists:
-            return
-
-        artists = self._bg_artists[layer]
-        while len(artists) > 0:
-            a = artists.pop()
-            try:
-                self.remove_bg_artist(a, layer, draw=False)
-                # no need to remove spines (to avoid NotImplementedErrors)!
-                if not isinstance(a, Spine):
-                    a.remove()
-            except Exception:
-                _log.debug(f"EOmaps-cleanup: Problem while clearing bg artist:\n {a}")
-
-        del self._bg_artists[layer]
-
-    def _cleanup_artists(self, layer):
-        if layer not in self._artists:
-            return
-
-        artists = self._artists[layer]
-        while len(artists) > 0:
-            a = artists.pop()
-            try:
-                self.remove_artist(a)
-                # no need to remove spines (to avoid NotImplementedErrors)!
-                if not isinstance(a, Spine):
-                    a.remove()
-            except Exception:
-                _log.debug(
-                    f"EOmaps-cleanup: Problem while clearing dynamic artist:\n {a}"
-                )
-
-        del self._artists[layer]
-
-    def _cleanup_bg_layers(self, layer):
-        try:
-            # remove cached background-layers
-            if layer in self._bg_layers:
-                del self._bg_layers[layer]
-        except Exception:
-            _log.debug(
-                "EOmaps-cleanup: Problem while clearing cached background layers"
-            )
-
-    def _cleanup_on_layer_activation(self, layer):
-
-        try:
-            # remove not yet executed lazy-activation methods
-            # (e.g. not yet fetched WMS services)
-            if layer in self._on_layer_activation:
-                del self._on_layer_activation[layer]
-        except Exception:
-            _log.debug(
-                "EOmaps-cleanup: Problem while clearing layer activation methods"
-            )

From b1e51a7ce4adb48dc9ef6a110cd066938b7ad16d Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 12:27:46 +0100
Subject: [PATCH 140/240] update unittests

---
 tests/test_config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_config.py b/tests/test_config.py
index 36613ca8f..ca2937cbe 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -1,7 +1,7 @@
 import unittest
 import matplotlib.pyplot as plt
 from eomaps import Maps, _log
-from eomaps.helpers import BlitManager
+from eomaps._blit_manager import BlitManager
 
 
 class TestConfig(unittest.TestCase):

From eb54c83812832e64cd7b123bc8d44647e681c700 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 12:29:17 +0100
Subject: [PATCH 141/240] minor

---
 docs/FAQ.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index fbee4684c..bf27599f7 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -380,7 +380,7 @@ Important changes between major versions
 
     - ❗ Starting with `v8.x` eomaps is licensed under a "BSD 3 clause" license!
 
-    - ⚠️ Some functions and classes have been re-named to better follow PEP8 naming conventions.
+    - ⚠️ Some functions and classes have been re-named/moved to better follow PEP8 naming conventions.
       While this should not interfere with the public API, more extensive customizations might need to be
       adjusted with respect to the new names.
 

From a3ba7cefbe02ecc6c20afac1a7561f17e6bd16dd Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 12:43:19 +0100
Subject: [PATCH 142/240] avoid setting limit to 0 on logarithmic axes

---
 eomaps/colorbar.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 9223f0525..155e0b18e 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -207,13 +207,19 @@ def _attach_lim_cbs(self):
         def ychanged(event):
             if self.orientation == "horizontal":
                 with self.ax_cb_plot.callbacks.blocked(signal="ylim_changed"):
-                    self.ax_cb_plot.set_ylim(0, None, emit=False)
+                    if self.ax_cb_plot.get_yscale() == "log":
+                        pass
+                    else:
+                        self.ax_cb_plot.set_ylim(0, None, emit=False)
 
         def xchanged(event):
             if self.orientation == "vertical":
                 with self.ax_cb_plot.callbacks.blocked(signal="xlim_changed"):
                     self.ax_cb_plot.xaxis.set_inverted(True)
-                    self.ax_cb_plot.set_xlim(left=None, right=0, emit=False)
+                    if self.ax_cb_plot.get_xscale() == "log":
+                        pass
+                    else:
+                        self.ax_cb_plot.set_xlim(left=None, right=0, emit=False)
 
         self.ax_cb_plot.callbacks.connect("xlim_changed", xchanged)
         self.ax_cb_plot.callbacks.connect("ylim_changed", ychanged)
@@ -446,7 +452,7 @@ def _plot_histogram(
                 transform=self.ax_cb_plot.transAxes,
             )
             # make sure lower y-limit is 0
-            if self.ax_cb_plot.get_yscale() == "log":
+            if self.ax_cb_plot.get_yscale() != "log":
                 self.ax_cb_plot.set_ylim(0)
         else:
             self.ax_cb_plot.grid(axis="x", dashes=[5, 5], c="k", alpha=0.5)

From 36876da0a5bde200f6cd378541368f6564dc1216 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 14:13:43 +0100
Subject: [PATCH 143/240] minor doc updates

---
 docs/api/eomaps.inset_maps.InsetMaps.rst |  4 ----
 eomaps/colorbar.py                       |  6 ++++--
 eomaps/compass.py                        | 10 +++++++++-
 eomaps/grid.py                           | 10 +++++++++-
 eomaps/inset_maps.py                     | 10 +++++++++-
 eomaps/scalebar.py                       | 10 +++++++++-
 6 files changed, 40 insertions(+), 10 deletions(-)

diff --git a/docs/api/eomaps.inset_maps.InsetMaps.rst b/docs/api/eomaps.inset_maps.InsetMaps.rst
index 80edee062..7f2ec19c8 100644
--- a/docs/api/eomaps.inset_maps.InsetMaps.rst
+++ b/docs/api/eomaps.inset_maps.InsetMaps.rst
@@ -22,10 +22,6 @@
 
     Checkout the docs for :py:class:`Maps` objects for all available methods!
 
-    +++
-
-    To create a new inset-map, have a look at :py:meth:`Maps.new_inset_map`.
-
 
 .. currentmodule:: eomaps.inset_maps
 
diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 155e0b18e..2c4d5e127 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -581,10 +581,12 @@ def _set_labels(self, cb_label=None, hist_label=None, **kwargs):
 
 class ColorBar(ColorBarBase):
     """
-    A colorbar with a histogram on top.
+    Base class for EOmaps colorbars with a histogram on top.
 
+    Note
+    ----
     To add a colorbar to a map, use
-    :py:meth:`Maps.add_colorbar <eomaps.eomaps.Maps.add_colorbar>`!
+    :py:meth:`Maps.add_colorbar <eomaps.eomaps.Maps.add_colorbar>`.
 
     """
 
diff --git a/eomaps/compass.py b/eomaps/compass.py
index 6343c3416..b9eb41f23 100644
--- a/eomaps/compass.py
+++ b/eomaps/compass.py
@@ -19,7 +19,15 @@
 
 
 class Compass:
-    """Base class for EOmaps compass objects."""
+    """
+    Base class for EOmaps compass (or North-arrow) objects.
+
+    Note
+    ----
+    To add a new compass (or north-arrow) to a map, see
+    :py:meth:`Maps.add_compass <eomaps.eomaps.Maps.add_compass>`.
+
+    """
 
     def __init__(self, m):
         self._m = m
diff --git a/eomaps/grid.py b/eomaps/grid.py
index f9589f625..5ced8cce6 100644
--- a/eomaps/grid.py
+++ b/eomaps/grid.py
@@ -50,7 +50,15 @@ def _get_intersect(a1, a2, b1, b2):
 
 
 class GridLines:
-    """Class to draw grid-lines."""
+    """
+    Base class for EOmaps grid-lines (and associated grid-labels).
+
+    Note
+    ----
+    To add new grid-lines to a map, see
+    :py:meth:`Maps.add_gridlines <eomaps.eomaps.Maps.add_gridlines>`.
+
+    """
 
     def __init__(
         self, m, d=None, auto_n=10, layer=None, bounds=None, n=100, dynamic=False
diff --git a/eomaps/inset_maps.py b/eomaps/inset_maps.py
index 2748364a2..4fb014c9a 100644
--- a/eomaps/inset_maps.py
+++ b/eomaps/inset_maps.py
@@ -14,7 +14,15 @@
 
 
 class InsetMaps(Maps):
-    """Base class to create inset maps."""
+    """
+    Base class to create inset maps.
+
+    Note
+    ----
+    To create a new inset-map, see
+    :py:meth:`Maps.new_inset_map <eomaps.eomaps.Maps.new_inset_map>`.
+
+    """
 
     # a subclass of Maps that includes some special functions for inset maps
 
diff --git a/eomaps/scalebar.py b/eomaps/scalebar.py
index dbbb9b9b4..76e6ddc5e 100644
--- a/eomaps/scalebar.py
+++ b/eomaps/scalebar.py
@@ -26,7 +26,15 @@
 
 
 class ScaleBar:
-    """Base class for EOmaps scalebars."""
+    """
+    Base class for EOmaps scalebars.
+
+    Note
+    ----
+    To add a new scalebar to a map, see
+    :py:meth:`Maps.add_scalebar <eomaps.eomaps.Maps.add_scalebar>`.
+
+    """
 
     def __init__(
         self,

From 9125c6a511ab5c86de1adf2af96d96b8ede41aff Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 30 Jan 2024 14:32:37 +0100
Subject: [PATCH 144/240] update docs - explicitly skip __init__ methods from
 class docstrings

---
 docs/conf.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/docs/conf.py b/docs/conf.py
index 5cfd14931..1e6626776 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -36,6 +36,9 @@ def setup(app):
 
     app.add_css_file("custom_css.css")
 
+    # add handling for skip-member event
+    app.connect("autodoc-skip-member", autodoc_skip_member)
+
     # By default, autodoc will print 'alias of ...' for aliases.
     # This can be avoided by explicitly setting the __name__ property.
     # see https://stackoverflow.com/a/58982001/9703451
@@ -108,6 +111,13 @@ def setup(app):
     "member-order": "alphabetical",
 }
 
+
+def autodoc_skip_member(app, what, name, obj, skip, options):
+    # explicitly skip __init__ methods (to ensure they don't show up as methods)
+    exclude = name in ("__init__",)
+    return True if exclude else None
+
+
 # Napoleon settings
 napoleon_numpy_docstring = True
 napoleon_google_docstring = False

From 9c5f4dbe3e5c1d72817238292adfe797cfe7b244 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 31 Jan 2024 15:55:24 +0100
Subject: [PATCH 145/240] fix call to "_get_clip_path()" for peek-layer
 callbacks

---
 eomaps/callbacks.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index 7f653a2c9..e9b0264c6 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -792,7 +792,7 @@ def peek_layer(
                 x1m, y1m = ax.transData.inverted().transform((x0 + blitw, y0 + blith))
                 w, h = abs(x1m - x0m), abs(y1m - y0m)
 
-                clip_path = self._get_clip_path(
+                clip_path = self.m.cb.click.attach._get_clip_path(
                     (x0m + x1m) / 2,
                     (y0m + y1m) / 2,
                     "out",
@@ -850,7 +850,7 @@ def peek_layer(
             )
             w, h = abs(x1m - x0m), abs(y1m - y0m)
 
-            clip_path = self._get_clip_path(
+            clip_path = self.m.cb.click.attach._get_clip_path(
                 x1m, y1m, "out", (w / 2, h / 2), "out", shape, 100
             )
         else:

From 5aa28f421c0e448165c82ca72fc8848606f3cdd0 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 1 Feb 2024 12:12:58 +0100
Subject: [PATCH 146/240] fix issues with figure-exports after
 `m.apply_layout(...)` (see #226)

---
 eomaps/layout_editor.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/eomaps/layout_editor.py b/eomaps/layout_editor.py
index 742c86a1f..6c7da7735 100644
--- a/eomaps/layout_editor.py
+++ b/eomaps/layout_editor.py
@@ -273,14 +273,12 @@ def _undo(self):
             l = self._history.pop(-1)
             self._history_undone.append(l)
             self.m.apply_layout(l)
-            self.m.redraw()
 
     def _redo(self):
         if len(self._history_undone) > 0:
             l = self._history_undone.pop(-1)
             self._history.append(l)
             self.m.apply_layout(l)
-            self.m.redraw()
 
     def cb_release(self, event):
         self._set_startpos(event)
@@ -991,7 +989,10 @@ def apply_layout(self, layout):
             else:
                 axes[i].set_position(val)
 
+        # force an immediate draw (rather than using draw_idle) to avoid issues with
+        # stacking order for tkagg backend
         self.m.redraw()
+        self.m.f.canvas.draw()
 
         # try to push the current view to the "home" toolbar button
         try:

From e5011ef659166e2c1255274d89f8bd04391347a0 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 5 Feb 2024 16:47:15 +0100
Subject: [PATCH 147/240] filter warnings that can savely be ignored for pytest
 (#227)

---
 pyproject.toml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index d90eaf0dc..2766869f3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -79,3 +79,11 @@ eomaps = "eomaps.scripts.open:cli"
 [project.urls]
 Documentation = "https://eomaps.readthedocs.io/"
 Repository = "https://github.com/raphaelquast/eomaps"
+
+[tool.pytest.ini_options]
+filterwarnings = [
+    "ignore:DownloadWarning",
+    "ignore:Geometry is in a geographic CRS. Results from *:UserWarning",
+    "ignore:Back azimuth is being returned by default *:UserWarning",
+    "ignore:Conversion of an array with ndim > 0:DeprecationWarning"
+]

From 2f1fb40752d9d63742fd08d6ef7db2647b0b14db Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 5 Feb 2024 17:02:09 +0100
Subject: [PATCH 148/240] ignore more warnings with pytest

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 2766869f3..0c8b27855 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,7 +82,8 @@ Repository = "https://github.com/raphaelquast/eomaps"
 
 [tool.pytest.ini_options]
 filterwarnings = [
-    "ignore:DownloadWarning",
+    "ignore::DownloadWarning",
+    "ignore:Pyarrow will become a required dependency of pandas:DeprecationWarning",
     "ignore:Geometry is in a geographic CRS. Results from *:UserWarning",
     "ignore:Back azimuth is being returned by default *:UserWarning",
     "ignore:Conversion of an array with ndim > 0:DeprecationWarning"

From cbc99aa1690061e7c0b62e80c529b79a59627b55 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 5 Feb 2024 17:13:01 +0100
Subject: [PATCH 149/240] fix filtering custom warning category

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0c8b27855..4f271932e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,7 +82,7 @@ Repository = "https://github.com/raphaelquast/eomaps"
 
 [tool.pytest.ini_options]
 filterwarnings = [
-    "ignore::DownloadWarning",
+    "ignore:cartopy.io.DownloadWarning",
     "ignore:Pyarrow will become a required dependency of pandas:DeprecationWarning",
     "ignore:Geometry is in a geographic CRS. Results from *:UserWarning",
     "ignore:Back azimuth is being returned by default *:UserWarning",

From 6b830578b14b45dcb8d21a9ba7d75869f3a3ee03 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 5 Feb 2024 17:25:49 +0100
Subject: [PATCH 150/240] ignore more warnings

---
 pyproject.toml | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 4f271932e..d9a3956df 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,8 +82,10 @@ Repository = "https://github.com/raphaelquast/eomaps"
 
 [tool.pytest.ini_options]
 filterwarnings = [
-    "ignore:cartopy.io.DownloadWarning",
-    "ignore:Pyarrow will become a required dependency of pandas:DeprecationWarning",
+    "ignore:Downloading*",
+    "ignore:Passing a SingleBlockManager to Series is deprecated *:DeprecationWarning",
+    "ignore:Passing a BlockManager to GeoDataFrame is deprecated *:DeprecationWarning",
+    "ignore:Pyarrow will become a required dependency of pandas *:DeprecationWarning",
     "ignore:Geometry is in a geographic CRS. Results from *:UserWarning",
     "ignore:Back azimuth is being returned by default *:UserWarning",
     "ignore:Conversion of an array with ndim > 0:DeprecationWarning"

From c5f791c4a482c01fde15b11e43a086c7522a54a6 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 5 Feb 2024 17:46:20 +0100
Subject: [PATCH 151/240] avoid \* escape sequence in docstrings

---
 eomaps/ne_features.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/ne_features.py b/eomaps/ne_features.py
index 365fdeb6c..19f34346b 100644
--- a/eomaps/ne_features.py
+++ b/eomaps/ne_features.py
@@ -493,7 +493,7 @@ def __call__(self, *args, scale=50, layer=None, **kwargs):
 
         Parameters
         ----------
-        \*args : str
+        args : str
             The names of the features to add.
         scale : int or str
             Set the scale of the feature preset (10, 50, 110 or "auto")
@@ -505,7 +505,7 @@ def __call__(self, *args, scale=50, layer=None, **kwargs):
             - If None, the layer of the parent object is used.
 
             The default is None.
-        \*\*kwargs:
+        kwargs:
             Additional style kwargs passed to all features
             (e.g. alpha, facecolor, edgecolor, linewidth, ...)
         """

From 6aac958c4a94a1aa55171de578ff410b297723f3 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 6 Feb 2024 11:36:57 +0100
Subject: [PATCH 152/240] fix using reprojected 1d datasets with shade_raster

---
 eomaps/eomaps.py | 64 +++++++++++++++++++++++++++++-------------------
 1 file changed, 39 insertions(+), 25 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 485183c4d..a1850f52e 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -4938,33 +4938,47 @@ def _shade_map(
                     )
                     df = xar.Dataset(dict(val=df))
             else:
-                # first convert 1D inputs to 2D, then reproject the grid and use
-                # a curvilinear QuadMesh to display the data
-
-                # use pandas to convert to 2D
-                df = (
-                    pd.DataFrame(
-                        dict(
-                            x=x0.ravel(),
-                            y=y0.ravel(),
-                            val=zdata.ravel(),
-                        ),
-                        copy=False,
+                try:
+                    # try if reprojected coordinates can be used as 2d grid and if yes,
+                    # directly use a curvilinear QuadMesh based on the reprojected
+                    # coordinates to display the data
+                    idx = pd.MultiIndex.from_arrays(
+                        [x0.ravel(), y0.ravel()], names=["x", "y"]
                     )
-                    .set_index(["x", "y"])
-                    .to_xarray()
-                )
-                xg, yg = np.meshgrid(df.x, df.y)
-
-                # transform the grid from input-coordinates to the plot-coordinates
-                crs1 = CRS.from_user_input(self.data_specs.crs)
-                crs2 = CRS.from_user_input(self._crs_plot)
-                if crs1 != crs2:
-                    transformer = self._get_transformer(
-                        crs1,
-                        crs2,
+
+                    df = pd.DataFrame(
+                        data=dict(val=zdata.ravel()), index=idx, copy=False
                     )
-                    xg, yg = transformer.transform(xg, yg)
+                    df = df.to_xarray()
+                    xg, yg = np.meshgrid(df.x, df.y)
+                except Exception:
+                    # first convert original coordinates of the 1D inputs to 2D,
+                    # then reproject the grid and use a curvilinear QuadMesh to display
+                    # the data
+                    _log.warning(
+                        "EOmaps: 1D data is converted to 2D prior to reprojection... "
+                        "Consider using 'shade_points' as plot-shape instead!"
+                    )
+                    xorig = self._data_manager.xorig.ravel()
+                    yorig = self._data_manager.yorig.ravel()
+
+                    idx = pd.MultiIndex.from_arrays([xorig, yorig], names=["x", "y"])
+
+                    df = pd.DataFrame(
+                        data=dict(val=zdata.ravel()), index=idx, copy=False
+                    )
+                    df = df.to_xarray()
+                    xg, yg = np.meshgrid(df.x, df.y)
+
+                    # transform the grid from input-coordinates to the plot-coordinates
+                    crs1 = CRS.from_user_input(self.data_specs.crs)
+                    crs2 = CRS.from_user_input(self._crs_plot)
+                    if crs1 != crs2:
+                        transformer = self._get_transformer(
+                            crs1,
+                            crs2,
+                        )
+                        xg, yg = transformer.transform(xg, yg)
 
                 # use a curvilinear QuadMesh
                 if self.shape.name == "shade_raster":

From 1dfcb2d34009d051b4efc1536d685796c827b815 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 6 Feb 2024 11:48:52 +0100
Subject: [PATCH 153/240] fix parsing maxsize argument in companion widget for
 raster-shape

---
 eomaps/qtcompanion/widgets/files.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/eomaps/qtcompanion/widgets/files.py b/eomaps/qtcompanion/widgets/files.py
index fdba463f7..c4dc3f20f 100644
--- a/eomaps/qtcompanion/widgets/files.py
+++ b/eomaps/qtcompanion/widgets/files.py
@@ -36,6 +36,11 @@ def _none_or_val(val):
         return val
 
 
+def _floatstr_to_int(val):
+    # pythons int() cannot convert float-strings to integer!
+    return int(float(val))
+
+
 def _identify_radius(r):
     r = r.replace(" ", "")
     try:
@@ -175,6 +180,7 @@ class ShapeSelector(QtWidgets.QFrame):
         mask_radius=_none_or_val,
         radius=_identify_radius,
         n=_none_or_val,
+        maxsize=_floatstr_to_int,
     )
 
     _argtypes = dict(
@@ -186,7 +192,6 @@ class ShapeSelector(QtWidgets.QFrame):
         mask_radius=(float,),
         flat=(str_to_bool,),
         aggregator=(str,),
-        maxsize=(int,),
     )
 
     def __init__(self, *args, m=None, default_shape="shade_raster", **kwargs):

From 91b85f646084e7b4308201f940c9c0d403adc1e0 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 7 Feb 2024 11:40:53 +0100
Subject: [PATCH 154/240] fix identification of data-selection margins

---
 eomaps/_data_manager.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index c1c248e42..d2b3a1ac6 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -130,7 +130,7 @@ def set_props(
 
         # estimate the radius (used as margin on data selection)
         try:
-            self._r = self.m._shapes._estimate_radius(
+            self._r = self.m.set_shape._estimate_radius(
                 self.m, radius_crs="out", method=np.nanmax
             )
             if self._r is not None and all(np.isfinite(i) for i in self._r):
@@ -138,6 +138,8 @@ def set_props(
             else:
                 self._radius_margin = None
         except Exception:
+            if _log.getEffectiveLevel() <= logging.DEBUG:
+                _log.debug("Estimation of data radius for data-margin failed!")
             self._r = None
             self._radius_margin = None
 

From 501989baf8954d5181ae56c2152d9e874630d825 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 7 Feb 2024 11:43:11 +0100
Subject: [PATCH 155/240] fix passing kwargs to colorbar creation

---
 eomaps/colorbar.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 2c4d5e127..b7508b03c 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -301,6 +301,8 @@ def _preprocess_data(self, out_of_range_vals="keep"):
 
     def _plot_colorbar(self, **kwargs):
 
+        self._cb_kwargs = kwargs
+
         kwargs.setdefault("extendfrac", self._extend_frac)
         kwargs.setdefault("spacing", "proportional")
         kwargs.setdefault("extend", "both")
@@ -526,7 +528,7 @@ def _redraw(self, *args, **kwargs):
 
         self._set_hist_size()
 
-        self._plot_colorbar()
+        self._plot_colorbar(**self._cb_kwargs)
 
         self._plot_histogram(
             bins=self._hist_bins,
@@ -645,9 +647,10 @@ def _identify_parent_cb(self):
         parent_cb = None
         # check if there is already an existing colorbar for a Maps-object that shares
         # the same plot-axis. If yes, inherit the position of this colorbar!
-
-        if self._m.colorbar is not None and not self._inherit_position:
+        if self._m.colorbar is not None and self._inherit_position is False:
             parent_cb = None  # self._m.colorbar
+        elif isinstance(self._inherit_position, ColorBar):
+            parent_cb = self._inherit_position
         else:
             # check if self is actually just another layer of an existing Maps object
             # that already has a colorbar assigned
@@ -688,10 +691,7 @@ def remove(self):
     def _set_map(self, m):
         self._m = m
 
-        if isinstance(self._inherit_position, ColorBarBase):
-            self._parent_cb = self._inherit_position
-        else:
-            self._parent_cb = self._identify_parent_cb()
+        self._parent_cb = self._identify_parent_cb()
 
         self._vmin = self._m.coll.norm.vmin
         self._vmax = self._m.coll.norm.vmax
@@ -1141,6 +1141,10 @@ def set_bin_labels(self, bins, names, tick_lines="center", show_values=False):
         self._m.BM._refetch_layer(self.layer)
 
     def _set_tick_formatter(self):
+        if "format" in self._cb_kwargs:
+            self.cb.set_ticks(self.cb.get_ticks())
+            return
+
         if self._m._classified:
             self.cb.set_ticks(
                 np.unique(np.clip(self._m.classify_specs._bins, self._vmin, self._vmax))
@@ -1484,7 +1488,7 @@ def _new_colorbar(
 
         cb._set_hist_size(hist_size)
         cb.set_scale(log)
-        cb._plot_colorbar(extend=extend)
+        cb._plot_colorbar(extend=extend, **kwargs)
 
         bins = (
             m.classify_specs._bins

From 78c12f77204556e49e7326fbf40a824b602ed9f6 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 7 Feb 2024 11:43:21 +0100
Subject: [PATCH 156/240] ignore more warnings

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index d9a3956df..6a0d696e3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -85,6 +85,7 @@ filterwarnings = [
     "ignore:Downloading*",
     "ignore:Passing a SingleBlockManager to Series is deprecated *:DeprecationWarning",
     "ignore:Passing a BlockManager to GeoDataFrame is deprecated *:DeprecationWarning",
+    "ignore:Passing a SingleBlockManager to GeoSeries is deprecated *:DeprecationWarning",
     "ignore:Pyarrow will become a required dependency of pandas *:DeprecationWarning",
     "ignore:Geometry is in a geographic CRS. Results from *:UserWarning",
     "ignore:Back azimuth is being returned by default *:UserWarning",

From 6df415429d4309f537b84e09a0112c84db7fb2bf Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 7 Feb 2024 21:05:56 +0100
Subject: [PATCH 157/240] trim very long parameter names in annotation text

---
 eomaps/callbacks.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/eomaps/callbacks.py b/eomaps/callbacks.py
index e9b0264c6..997ad5b44 100644
--- a/eomaps/callbacks.py
+++ b/eomaps/callbacks.py
@@ -171,6 +171,8 @@ def _get_annotation_text(
                             ID = self._fmt(np.asanyarray(ID))
 
                 equal_crs = self.m.data_specs.crs == self.m._crs_plot
+                if len(parameter) > 15:
+                    parameter = parameter[:15] + " ..."
                 printstr = (
                     (f"# Picked {n_ids} points\n" if multipick else "")
                     + f"{xlabel} = {x}\n"

From 05c491914cabc4188e6146ce4556ffa2a4374062 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 7 Feb 2024 21:07:06 +0100
Subject: [PATCH 158/240] fix treatment of infinite values in weighted colorbar
 histograms

---
 eomaps/colorbar.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index b7508b03c..3dc7c50b0 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -279,7 +279,12 @@ def _preprocess_data(self, out_of_range_vals="keep"):
             data = data.ravel()
 
         # make sure we only consider valid values in the histogram
-        data = data[np.isfinite(data)]
+        finitemask = np.isfinite(data)
+        data = data[finitemask]
+
+        # make sure that histogram weights are masked accordingly if provided
+        if "weights" in self._hist_kwargs:
+            self._hist_kwargs["weights"] = self._hist_kwargs["weights"][finitemask]
 
         if out_of_range_vals == "mask":
             data_range_mask = (data >= self._vmin) & (data <= self._vmax)

From 30b72496c65c90d95e2c9cb4feb203a59ec9ab2d Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 7 Feb 2024 21:07:27 +0100
Subject: [PATCH 159/240] fix histogram tick-styles are resetted on
 layout-change

---
 eomaps/colorbar.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 3dc7c50b0..33761aa6e 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -196,6 +196,7 @@ def _setup_axes(self, pos, parent_ax=None, f=None, zorder=9999):
         self.ax_cb_plot.spines["left"].set_visible(False)
 
         self._set_hist_size()
+        self._style_hist_ticks()
 
         self._attach_lim_cbs()
 
@@ -268,7 +269,6 @@ def _set_hist_size(self, size=None, update_all=False):
             l_hist_bounds = (l, b, w - s, h)
 
         self._set_axes_locators(l_cb_bounds, l_hist_bounds)
-        self._style_hist_ticks()
 
     def _preprocess_data(self, out_of_range_vals="keep"):
         data = self._get_data()

From 2a995beb8ef3d7a48136cb687d49fd2d15263fcc Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 7 Feb 2024 21:07:48 +0100
Subject: [PATCH 160/240] allow passing transform-kwarg to m.add_text

---
 eomaps/eomaps.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index a1850f52e..eb9ff2c0b 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -1643,8 +1643,9 @@ def add_title(self, title, x=0.5, y=1.01, **kwargs):
         kwargs.setdefault("fontsize", "large")
         kwargs.setdefault("horizontalalignment", "center")
         kwargs.setdefault("verticalalignment", "bottom")
+        kwargs.setdefault("transform", self.ax.transAxes)
 
-        self.text(x, y, title, transform=self.ax.transAxes, layer=self.layer, **kwargs)
+        self.text(x, y, title, layer=self.layer, **kwargs)
 
     @lru_cache()
     def get_crs(self, crs="plot"):

From d61e5e97b9b5db9334877a6d07a21be861ad4261 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 8 Feb 2024 11:03:50 +0100
Subject: [PATCH 161/240] fix all-layer check in BM._layer_visible

---
 eomaps/_blit_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/_blit_manager.py b/eomaps/_blit_manager.py
index d8b04b842..fb6af498c 100644
--- a/eomaps/_blit_manager.py
+++ b/eomaps/_blit_manager.py
@@ -634,7 +634,7 @@ def _layer_visible(self, layer):
             True if the layer is currently visible, False otherwise
 
         """
-        return layer in "all" or self._layer_is_subset(layer, self.bg_layer)
+        return layer == "all" or self._layer_is_subset(layer, self.bg_layer)
 
     @property
     def _get_active_layers_alphas(self):

From 31a6fad8a2d615be52e8b60269db982c1ba05479 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 8 Feb 2024 11:05:32 +0100
Subject: [PATCH 162/240] fix selecting multiple layers in layer-dropdown -
 make sure tab-bar is properly updated - make sure transparencies are kept
 as-is

---
 eomaps/qtcompanion/widgets/editor.py | 2 +-
 eomaps/qtcompanion/widgets/layer.py  | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/eomaps/qtcompanion/widgets/editor.py b/eomaps/qtcompanion/widgets/editor.py
index 396e1dc48..e20a2507d 100644
--- a/eomaps/qtcompanion/widgets/editor.py
+++ b/eomaps/qtcompanion/widgets/editor.py
@@ -975,7 +975,7 @@ def populate_on_layer(self, *args, **kwargs):
         currlayer = self.m.BM.bg_layer
         # only populate if the current layer is not part of the last set of layers
         # (e.g. to allow show/hide of selected layers without removing the tabs)
-        if not self.m.BM._layer_visible(lastlayer):
+        if not self.m.BM._layer_is_subset(currlayer, lastlayer):
             self.populate(*args, **kwargs)
             self._last_populated_layer = currlayer
         else:
diff --git a/eomaps/qtcompanion/widgets/layer.py b/eomaps/qtcompanion/widgets/layer.py
index 1cdc019ed..426fcb0a2 100644
--- a/eomaps/qtcompanion/widgets/layer.py
+++ b/eomaps/qtcompanion/widgets/layer.py
@@ -331,7 +331,8 @@ def actionClicked(self):
         modifiers = QtWidgets.QApplication.keyboardModifiers()
         actionwidget = action.defaultWidget()
 
-        active_layers = self.m.BM._get_active_layers_alphas
+        # just split here to keep transparency-assignments in tact!
+        active_layers = self.m.BM.bg_layer.split("|")
 
         checked_layers = [l for l in active_layers if l != "_"]
         selected_layer = action.data()

From e8a68e35bd4e64740de8bcb812da06451f72ecdf Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 8 Feb 2024 11:06:59 +0100
Subject: [PATCH 163/240] minor

---
 eomaps/qtcompanion/widgets/editor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/qtcompanion/widgets/editor.py b/eomaps/qtcompanion/widgets/editor.py
index e20a2507d..84393575f 100644
--- a/eomaps/qtcompanion/widgets/editor.py
+++ b/eomaps/qtcompanion/widgets/editor.py
@@ -1002,7 +1002,7 @@ def populate(self, *args, **kwargs):
                 )
                 LayerTabBar._n_layer_msg_shown = True
 
-            # if more than 200 layers are available, show only active tabs to
+            # if more than max_n_layers layers are available, show only active tabs to
             # avoid performance issues when too many tabs are created
             alllayers = [i for i in self.m.BM._bg_layer.split("|") if i in alllayers]
             for i in range(self.count(), -1, -1):

From 8116c28518337942206171f987ddb1e7f4c71bde Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 8 Feb 2024 11:36:39 +0100
Subject: [PATCH 164/240] make sure dataset-dropdown in companion-widget does
 not expand main window (for very long layer names)

---
 eomaps/qtcompanion/widgets/click_callbacks.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/eomaps/qtcompanion/widgets/click_callbacks.py b/eomaps/qtcompanion/widgets/click_callbacks.py
index 3df86e7f4..d1f6c7a44 100644
--- a/eomaps/qtcompanion/widgets/click_callbacks.py
+++ b/eomaps/qtcompanion/widgets/click_callbacks.py
@@ -115,8 +115,11 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         self.setStyleSheet("border-style:none;")
+
+        # set fixed width to avoid expanding window-width on startup for extremely
+        # long layer names... (full name is shown in dropdown)
         self.setMinimumWidth(150)
-        self.setMaximumWidth(400)
+        self.setMaximumWidth(150)
         self.setSizeAdjustPolicy(self.AdjustToContents)
 
     def enterEvent(self, e):

From 4577a61a833396d235a15bcbd18e67ad7d688ba9 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 8 Feb 2024 11:37:38 +0100
Subject: [PATCH 165/240] make sure window-title (e.g. layer-name) is properly
 truncated if too long

---
 eomaps/qtcompanion/widgets/layer.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/eomaps/qtcompanion/widgets/layer.py b/eomaps/qtcompanion/widgets/layer.py
index 426fcb0a2..6baf48441 100644
--- a/eomaps/qtcompanion/widgets/layer.py
+++ b/eomaps/qtcompanion/widgets/layer.py
@@ -107,7 +107,7 @@ def update_layers(self):
 
 
 class AutoUpdateLayerLabel(QtWidgets.QLabel):
-    def __init__(self, *args, m=None, max_length=100, **kwargs):
+    def __init__(self, *args, m=None, max_length=60, **kwargs):
         super().__init__(*args, **kwargs)
         self.m = m
 
@@ -141,6 +141,9 @@ def get_text(self):
 
             s += ls
 
+        if len(s) > self._max_length:
+            s = s[: self._max_length - 3] + "..."
+
         return prefix + s + suffix
 
     def update(self, *args, **kwargs):

From 0d308d46df1e2d52a3426dd0c40152623671d4e1 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 11 Feb 2024 18:51:38 +0100
Subject: [PATCH 166/240] add pyhton 3.12 to unittest matrix

---
 .github/workflows/testMaps.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/testMaps.yml b/.github/workflows/testMaps.yml
index 37139204c..aa0cb301f 100644
--- a/.github/workflows/testMaps.yml
+++ b/.github/workflows/testMaps.yml
@@ -13,7 +13,7 @@ jobs:
         # set operating systems to test
         os: [ubuntu-latest]
         # set python versions to test
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     name: test_Maps ${{ matrix.os }}  ${{ matrix.python-version }}
     steps:

From 0fc0dadefdd21031225f95fef56e31acdaa4aa8a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 12 Feb 2024 11:57:12 +0100
Subject: [PATCH 167/240] add new kwarg "ensure_same_id" to
 `m.cb.pick.share_events` (to ensure that all pick-events are triggered with
 the same ID)

---
 eomaps/_data_manager.py | 28 ++++++++++++---
 eomaps/cb_container.py  | 77 ++++++++++++++++++++++++++++++++++++-----
 2 files changed, 93 insertions(+), 12 deletions(-)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index d2b3a1ac6..ebfe7bcff 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -1070,9 +1070,9 @@ def _get_id_from_index(self, ind):
             ID = None
         return ID
 
-    def _get_xy_from_ID(self, ID, reprojected=False):
+    def _get_ind_from_ID(self, ID):
         """
-        Get x and y coordinates from a list of data IDs
+        Get numerical indexes from a list of data IDs
 
         Parameters
         ----------
@@ -1081,7 +1081,7 @@ def _get_xy_from_ID(self, ID, reprojected=False):
 
         Returns
         -------
-        (x, y) : a tuple of x- and y- coordinate arrays
+        inds : list of indexes or None
         """
         ids = self.ids
 
@@ -1091,7 +1091,7 @@ def _get_xy_from_ID(self, ID, reprojected=False):
             # if "ids" is range-like, so is "ind", therefore we can simply
             # select the values.
             inds = np.array([ids[i] for i in ID])
-        if isinstance(ids, list):
+        elif isinstance(ids, list):
             # for lists, using .index to identify the index
             inds = np.array([ids.index(i) for i in ID])
         elif isinstance(ids, np.ndarray):
@@ -1099,6 +1099,26 @@ def _get_xy_from_ID(self, ID, reprojected=False):
         else:
             inds = None
 
+        if len(inds) == 0:
+            inds = None
+
+        return inds
+
+    def _get_xy_from_ID(self, ID, reprojected=False):
+        """
+        Get x and y coordinates from a list of data IDs
+
+        Parameters
+        ----------
+        ID : single ID or list of IDs
+            The IDs to search for.
+
+        Returns
+        -------
+        (x, y) : a tuple of x- and y- coordinate arrays
+        """
+
+        inds = self._get_ind_from_ID(ID)
         return self._get_xy_from_index(inds, reprojected=reprojected)
 
     def cleanup(self):
diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 620f21f5f..77d0460c8 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -1357,6 +1357,9 @@ def __init__(self, picker_name="default", picker=None, *args, **kwargs):
         else:
             self._picker = picker
 
+        # indicator how shared pick-events identify the relevant datapoint
+        self._ensure_same_pick_id = False
+
     class _attach(_ClickContainer._attach, PickCallbacks):
         __doc__ = _ClickContainer._attach.__doc__
         pass
@@ -1383,6 +1386,35 @@ def __getitem__(self, name):
                 f"the picker {name} does not exist...", "use `m.cb.add_picker` first!"
             )
 
+    def share_events(self, *args, ensure_same_id=False):
+        """
+        Share callback-events between this Maps-object and all other Maps-objects.
+
+        (e.g. share events both ways)
+
+        Note
+        ----
+        For **pick-events**, you can use the additional keyword-argument
+        `ensure_same_id` to make sure that all events pick the exact same ID.
+
+        Parameters
+        ----------
+        args : eomaps.Maps
+            The Maps-objects that should execute the callback.
+
+        ensure_same_id : bool
+            If True, all pick-events are triggered by the same ID value.
+            (e.g. the ID of the datapoint that was actually picked)
+
+            If False, each map executes the pick-event with respect to the reprojected
+            mouse-position and identifies the closest datapoint to use.
+
+            The default is False
+        """
+        self._ensure_same_pick_id = ensure_same_id
+
+        super().share_events(*args)
+
     def set_props(
         self,
         n=None,
@@ -1710,15 +1742,44 @@ def _fwd_cb(self, event, picker_name):
                 # picker_name=picker_name,
             )
 
-            pick = obj._picker(obj._artist, dummymouseevent)
-            if pick[1] is not None:
-                dummyevent.ID = pick[1].get("ID", None)
-                dummyevent.ind = pick[1].get("ind", None)
-                dummyevent.val = pick[1].get("val", None)
-                dummyevent.pos = pick[1].get("pos", None)
-                dummyevent.val_color = pick[1].get("val_color", None)
+            if self._ensure_same_pick_id is False:
+                # execute a new pick-event and use the identified point
+
+                pick = obj._picker(obj._artist, dummymouseevent)
+                if pick[1] is not None:
+                    dummyevent.ID = pick[1].get("ID", None)
+                    dummyevent.ind = pick[1].get("ind", None)
+                    dummyevent.val = pick[1].get("val", None)
+                    dummyevent.pos = pick[1].get("pos", None)
+                    dummyevent.val_color = pick[1].get("val_color", None)
+                else:
+                    dummyevent.ind = None
             else:
-                dummyevent.ind = None
+                # use the ID identified by the parent pick-event to obtain values etc.
+                ID = getattr(event, "ID", None)
+                if ID is not None:
+                    ind = m._data_manager._get_ind_from_ID(ID)
+                    if ind is not None:
+                        # TODO check why arrays with 1 entry need to be converted here
+                        if len(ind) == 1:
+                            ind = ind[0]
+
+                        val = m._data_manager._get_val_from_index(ind)
+                        pos = m._data_manager._get_xy_from_index(ind, reprojected=True)
+
+                        try:
+                            val_color = obj._artist.cmap(obj._artist.norm(val))
+                        except Exception:
+                            val_color = None
+
+                        dummyevent.ID = ID
+                        dummyevent.ind = ind
+                        dummyevent.val = val
+                        dummyevent.pos = pos
+                        dummyevent.val_color = val_color
+                    else:
+                        dummyevent.ID = None
+                        dummyevent.ind = None
 
             obj._onpick(dummyevent)
 

From c3f21583d1ee4b41e7aaba3c14eccaef09244de9 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 12 Feb 2024 12:03:21 +0100
Subject: [PATCH 168/240] minor

---
 eomaps/cb_container.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 77d0460c8..4f4fc0c80 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -221,7 +221,7 @@ def forward_events(self, *args):
 
     def share_events(self, *args):
         """
-        Share callback-events between this Maps-object and all other Maps-objects.
+        Share callback-events between this Maps-object and other Maps-objects.
 
         (e.g. share events both ways)
 
@@ -1388,7 +1388,7 @@ def __getitem__(self, name):
 
     def share_events(self, *args, ensure_same_id=False):
         """
-        Share callback-events between this Maps-object and all other Maps-objects.
+        Share callback-events between this Maps-object and other Maps-objects.
 
         (e.g. share events both ways)
 

From c1dfe8dae6019ae8d12855b00c9304ffe8472c73 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 12 Feb 2024 12:04:42 +0100
Subject: [PATCH 169/240] add kwarg to adjust the style of the line between
 colorbar and histogram

---
 eomaps/colorbar.py | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 33761aa6e..8b2c9c6b4 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -63,6 +63,7 @@ def __init__(
         extend_frac=0.025,
         tick_precision=2,
         margin=None,
+        divider_linestyle=None,
     ):
 
         self._hist_size = 0.9
@@ -80,6 +81,11 @@ def __init__(
         self._cmap = None
         self._data = None
 
+        if divider_linestyle is None:
+            self._divider_linestyle = dict(color="k", linestyle="--", alpha=0.5)
+        else:
+            self._divider_linestyle = divider_linestyle
+
     @property
     def _scm(self):
         return plt.cm.ScalarMappable(cmap=self._cmap, norm=self._norm)
@@ -454,9 +460,8 @@ def _plot_histogram(
             self.ax_cb_plot.plot(
                 [0, 1],
                 [0, 0],
-                "k--",
-                alpha=0.5,
                 transform=self.ax_cb_plot.transAxes,
+                **self._divider_linestyle,
             )
             # make sure lower y-limit is 0
             if self.ax_cb_plot.get_yscale() != "log":
@@ -467,9 +472,8 @@ def _plot_histogram(
             self.ax_cb_plot.plot(
                 [1, 1],
                 [0, 1],
-                "k--",
-                alpha=0.5,
                 transform=self.ax_cb_plot.transAxes,
+                **self._divider_linestyle,
             )
             # make sure lower x-limit is 0
             if self.ax_cb_plot.get_xscale() is False:
@@ -1318,6 +1322,7 @@ def _new_colorbar(
         show_outline=False,
         hist_kwargs=None,
         margin=None,
+        divider_linestyle=None,
         **kwargs,
     ):
         """
@@ -1450,6 +1455,10 @@ def _new_colorbar(
             NOTE: In most cases you should NOT need to adjust the layer!
             The layer is automatically assigned to the layer at which the
             data was plotted and Colorbars are only visible on the assigned layer!
+        divider_linestyle : dict or None
+            A dictionary that specifies the style of the line between the histogram and
+            the colorbar. If None a black dashed line is drawn
+            (e.g. `{"color": "k", "linestyle":"--"}`). The default is None.
         kwargs :
             All additional kwargs are passed to the creation of the colorbar
             (e.g. `plt.colorbar()`)
@@ -1486,6 +1495,7 @@ def _new_colorbar(
             inherit_position=inherit_position,
             extend_frac=extend_frac,
             margin=margin,
+            divider_linestyle=divider_linestyle,
         )
         cb._set_map(m)
         cb._setup_axes(pos, m.ax)

From 8ecb2bb1e3f1eb7e0cfc9abb6fe886b94d79e58c Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 12 Feb 2024 12:05:13 +0100
Subject: [PATCH 170/240] warn if weights are used for dynamic-shade indicators
 (not yet supported)

---
 eomaps/colorbar.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 8b2c9c6b4..952ffeea6 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -764,6 +764,13 @@ def _check_data_updated(self, *args, **kwargs):
         self._last_ds_data = dsdata
 
     def _make_dynamic(self):
+        if "weights" in self._hist_kwargs:
+            _log.warn(
+                "EOmaps: Weighted histograms for 'dynamic-shade indicator' colorbars "
+                "are not supported! Histogram weights will be ignored!"
+            )
+            self._hist_kwargs.pop("weights")
+
         self._dynamic_shade_indicator = True
 
         if not hasattr(self._m.coll, "get_ds_data"):

From 6796b904d87a535dc57b322198126bbcd092c282 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 12 Feb 2024 13:24:56 +0100
Subject: [PATCH 171/240] deprecate colorbar kwargs - "show_outline" ->
 "outline" - "ylabel" -> "hist_label"

---
 eomaps/colorbar.py | 89 ++++++++++++++++++++++++++++++----------------
 1 file changed, 58 insertions(+), 31 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 952ffeea6..7efc4d9e9 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -332,13 +332,13 @@ def _plot_colorbar(self, **kwargs):
         self._set_hist_size()
 
     def _plot_histogram(
-        self, bins=None, out_of_range_vals="keep", show_outline=False, **kwargs
+        self, bins=None, out_of_range_vals="keep", outline=False, **kwargs
     ):
 
         self._hist_kwargs = kwargs
         self._hist_bins = bins
         self._out_of_range_vals = out_of_range_vals
-        self._show_outline = show_outline
+        self._outline = outline
 
         if "range" not in self._hist_kwargs:
             self._hist_kwargs["range"] = (
@@ -354,11 +354,11 @@ def _plot_histogram(
             **self._hist_kwargs,
         )
 
-        if self._show_outline:
-            if self._show_outline is True:
+        if self._outline:
+            if self._outline is True:
                 outline_props = dict(color="k", lw=1)
             else:
-                outline_props = self._show_outline
+                outline_props = self._outline
 
             if self.orientation == "horizontal":
                 self.ax_cb_plot.step(
@@ -542,7 +542,7 @@ def _redraw(self, *args, **kwargs):
         self._plot_histogram(
             bins=self._hist_bins,
             out_of_range_vals=self._out_of_range_vals,
-            show_outline=self._show_outline,
+            outline=self._outline,
             **self._hist_kwargs,
         )
 
@@ -1325,9 +1325,9 @@ def _new_colorbar(
         extend_frac=0.025,
         log=False,
         label=None,
-        ylabel=None,
-        show_outline=False,
+        outline=False,
         hist_kwargs=None,
+        hist_label=None,
         margin=None,
         divider_linestyle=None,
         **kwargs,
@@ -1376,22 +1376,6 @@ def _new_colorbar(
               colorbar, use "inherit_position=True", else use "inherit_position=False".
 
             The default is None
-        hist_size : float or None
-            The fraction of the colorbar occupied by the histogram.
-
-            - None: no histogram will be drawn
-            - 0:
-            - 0.9: 90% histogram, 10% colorbar
-            - 1: only histogram
-
-        hist_bins : int, list, tuple, array or "bins", optional
-
-            - If int: The number of histogram-bins to use for the colorbar.
-            - If list, tuple or numpy-array: the bins to use
-            - If "bins": use the bins obtained from the classification
-              (ONLY possible if a classification scheme is used!)
-
-            The default is 256.
         extend : str or None, optional
             Set how extension-arrows should be added.
 
@@ -1420,7 +1404,7 @@ def _new_colorbar(
               the shaded pixel values within the current field of view.
 
             The default is False.
-        show_outline : bool or dict
+        outline : bool or dict
             Indicator if an outline should be added to the histogram.
             (e.g. a line encompassing the histogram)
             If a dict is provided, it is passed to `plt.step()` to style the line.
@@ -1447,16 +1431,32 @@ def _new_colorbar(
               min/max bins of the histogram)
 
             The default is "clip"
-        hist_kwargs : dict
-            A dictionary with keyword-arguments passed to the creation of the histogram
-            (e.g. passed to `plt.hist()` )
         label : str, optional
             The label used for the colorbar.
             Use `ColorBar.set_labels()` to set the labels (and styling) for the
             colorbar and the histogram.
             The default is None.
-        ylabel : str, optional
+        hist_size : float or None
+            The fraction of the colorbar occupied by the histogram.
+
+            - None: no histogram will be drawn
+            - 0:
+            - 0.9: 90% histogram, 10% colorbar
+            - 1: only histogram
+
+        hist_bins : int, list, tuple, array or "bins", optional
+
+            - If int: The number of histogram-bins to use for the colorbar.
+            - If list, tuple or numpy-array: the bins to use
+            - If "bins": use the bins obtained from the classification
+              (ONLY possible if a classification scheme is used!)
+
+            The default is 256.
+        hist_label : str, optional
             The label used for the y-axis of the colorbar. The default is None
+        hist_kwargs : dict
+            A dictionary with keyword-arguments passed to the creation of the histogram
+            (e.g. passed to `plt.hist()` )
         layer : str
             The layer at which the colorbar will be drawn.
             NOTE: In most cases you should NOT need to adjust the layer!
@@ -1493,6 +1493,33 @@ def _new_colorbar(
         >>> m.add_colorbar(hist_bins="bins", label="some data")
 
         """
+        if "show_outline" in kwargs:
+            import warnings
+
+            warnings.simplefilter("default", DeprecationWarning)
+            warnings.warn(
+                "EOmaps: The colorbar argument 'show_outline' is deprecated and will "
+                "be removed in EOmaps v8.1. Use 'outline' instead!",
+                category=DeprecationWarning,
+                stacklevel=2,
+            )
+
+            outline = kwargs.pop("show_outline")
+
+        if "ylabel" in kwargs:
+            import warnings
+
+            warnings.simplefilter("default", DeprecationWarning)
+            warnings.warn(
+                "EOmaps: The colorbar argument 'ylabel' is deprecated and will "
+                "be removed in EOmaps v8.1. Use 'hist_label' instead!",
+                category=DeprecationWarning,
+                stacklevel=2,
+            )
+
+            if hist_label is None:
+                hist_label = kwargs.pop("ylabel")
+
         if hist_kwargs is None:
             hist_kwargs = dict()
 
@@ -1521,13 +1548,13 @@ def _new_colorbar(
         cb._plot_histogram(
             bins=bins,
             out_of_range_vals=out_of_range_vals,
-            show_outline=show_outline,
+            outline=outline,
             **hist_kwargs,
         )
 
         cb._set_tick_formatter()
 
-        cb.set_labels(cb_label=label, hist_label=ylabel)
+        cb.set_labels(cb_label=label, hist_label=hist_label)
 
         if dynamic_shade_indicator:
             cb._make_dynamic()

From bdea9a6993fd482a3c752fe9d1f4738e1f7d407a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 12 Feb 2024 13:28:50 +0100
Subject: [PATCH 172/240] minor

---
 docs/FAQ.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index bf27599f7..ec2274e8a 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -395,6 +395,8 @@ Important changes between major versions
 
     - scripts for the examples have been re-named and are now located in ``docs\examples``
 
+    - Colorbar kwargs ``show_outline`` and ``ylabel`` have been renamed to ``outline`` and ``hist_label``
+
     The following (previously deprecated) methods are now **removed**:
 
     - `m.set_data_specs` -> use ``m.set_data`` instead

From 8691c9664f18a61585f08532cec00412d34dc251 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 13 Feb 2024 10:49:23 +0100
Subject: [PATCH 173/240] avoid memory-leaks caused by references to artists in
 pick-containers

---
 eomaps/cb_container.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 4f4fc0c80..6bce492b7 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -7,8 +7,9 @@
 
 import logging
 from types import SimpleNamespace
-from functools import update_wrapper, partial, wraps
+from functools import partial, wraps
 from itertools import chain
+from weakref import proxy
 
 from .callbacks import (
     ClickCallbacks,
@@ -1476,7 +1477,9 @@ def set_props(
             self._search_radius = search_radius
 
     def _set_artist(self, artist):
-        self._artist = artist
+        # use a weakref-proxy to make sure the artist can be garbage-collected
+        # if it is deleted (or if the figure is closed)
+        self._artist = proxy(artist)
         self._artist.set_picker(self._picker)
 
     def _init_cbs(self):
@@ -1484,7 +1487,6 @@ def _init_cbs(self):
         self._add_pick_callback()
 
     def _default_picker(self, artist, event):
-
         # make sure that objects are only picked if we are on the right layer
         if not self._execute_cb(self._m.layer):
             return False, None
@@ -1659,7 +1661,8 @@ def _reset_cids(self):
         self._cid_pick_event.clear()
 
     def _artist_picked(self, event):
-        if self._artist is event.artist:
+        # use == instead of "is" here since self._artist is a weakref proxy!
+        if self._artist == event.artist:
             return True
         else:
             # handle contour-plot artists explicitly

From fce0011e2776aabd3d8ca78e5cb775b425aade82 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 13 Feb 2024 11:04:49 +0100
Subject: [PATCH 174/240] include cleanup exception tracebacks for debug logs

---
 eomaps/eomaps.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index eb9ff2c0b..2cc37a4c2 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -3857,7 +3857,10 @@ def cleanup(self):
                         self.ax.callbacks.disconnect(self._cid_xlim)
                         del self._cid_xlim
                 except Exception:
-                    _log.error("EOmaps-cleanup: Problem while clearing xlim-cid")
+                    _log.error(
+                        "EOmaps-cleanup: Problem while clearing xlim-cid",
+                        exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
+                    )
 
             # clear data-specs and all cached properties of the data
             try:
@@ -3868,7 +3871,10 @@ def cleanup(self):
                     del self.tree
                 self.data_specs.delete()
             except Exception:
-                _log.error("EOmaps-cleanup: Problem while clearing data specs")
+                _log.error(
+                    "EOmaps-cleanup: Problem while clearing data specs",
+                    exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
+                )
 
             # disconnect all click, pick and keypress callbacks
             try:
@@ -3876,7 +3882,10 @@ def cleanup(self):
                 # cleanup callback-containers
                 self.cb._clear_callbacks()
             except Exception:
-                _log.error("EOmaps-cleanup: Problem while clearing callbacks")
+                _log.error(
+                    "EOmaps-cleanup: Problem while clearing callbacks",
+                    exc_info=_log.getEffectiveLevel() <= logging.DEBUG,
+                )
 
             # cleanup all artists and cached background-layers from the blit-manager
             if not self._is_sublayer:

From 26aaef522e76abb21488a880edf72b3a3421161a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 13 Feb 2024 11:10:44 +0100
Subject: [PATCH 175/240] minor

---
 eomaps/_data_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index ebfe7bcff..fd01f91b0 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -530,7 +530,7 @@ def on_fetch_bg(self, layer=None, bbox=None, check_redraw=True):
 
             self.m.cb.pick._set_artist(coll)
 
-        except Exception as ex:
+        except Exception:
             _log.exception(
                 f"EOmaps: Unable to plot the data for the layer '{layer}'!",
                 exc_info=_log.getEffectiveLevel() <= logging.DEBUG,

From 08f82acca249565fffc7968c66e5f4af933ec03e Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 13 Feb 2024 11:29:33 +0100
Subject: [PATCH 176/240] move "_identify_data(..)" to _data_manager module

---
 eomaps/_data_manager.py | 137 +++++++++++++++++++++++++++++++++++++++-
 eomaps/eomaps.py        | 133 --------------------------------------
 2 files changed, 136 insertions(+), 134 deletions(-)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index fd01f91b0..372e30d90 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -8,6 +8,8 @@
 import numpy as np
 from pyproj import CRS, Transformer
 
+from .helpers import register_modules
+
 _log = logging.getLogger(__name__)
 
 
@@ -163,6 +165,139 @@ def cleanup_callbacks(self):
         if self.on_fetch_bg in self.m.BM._before_update_actions:
             self.m.BM._before_update_actions.remove(self.on_fetch_bg)
 
+    def _identify_data(self, data=None, x=None, y=None, parameter=None):
+        # identify the way how the data has been provided and convert to the internal
+        # structure
+
+        if data is None:
+            data = self.m.data_specs.data
+        if x is None:
+            x = self.m.data_specs.x
+        if y is None:
+            y = self.m.data_specs.y
+        if parameter is None:
+            parameter = self.m.data_specs.parameter
+
+        # check other types before pandas to avoid unnecessary import
+        if data is not None and not isinstance(data, (list, tuple, np.ndarray)):
+            (pd,) = register_modules("pandas", raise_exception=False)
+
+            if pd is None:
+                raise TypeError(
+                    f"EOmaps: Unable to handle the input-data type: {type(data)}"
+                )
+
+            if isinstance(data, pd.DataFrame):
+                if parameter is not None:
+                    # get the data-values
+                    z_data = data[parameter].values
+                else:
+                    z_data = np.repeat(np.nan, len(data))
+
+                # get the index-values
+                ids = data.index.values
+
+                if isinstance(x, str) and isinstance(y, str):
+                    # get the data-coordinates
+                    xorig = data[x].values
+                    yorig = data[y].values
+                else:
+                    assert isinstance(x, (list, np.ndarray, pd.Series)), (
+                        "'x' must be either a column-name, or explicit values "
+                        " specified as a list, a numpy-array or a pandas"
+                        + f" Series object if you provide the data as '{type(data)}'"
+                    )
+                    assert isinstance(y, (list, np.ndarray, pd.Series)), (
+                        "'y' must be either a column-name, or explicit values "
+                        " specified as a list, a numpy-array or a pandas"
+                        + f" Series object if you provide the data as '{type(data)}'"
+                    )
+
+                    xorig = np.asanyarray(x)
+                    yorig = np.asanyarray(y)
+
+                return z_data, xorig, yorig, ids, parameter
+
+        # identify all other types except for pandas.DataFrames
+        # lazily check if pandas was used
+        pandas_series_data = False
+        for iname, i in zip(("x", "y", "data"), (x, y, data)):
+            if iname == "data" and i is None:
+                # allow empty datasets
+                continue
+
+            if not isinstance(i, (list, tuple, np.ndarray)):
+                (pd,) = register_modules("pandas", raise_exception=False)
+
+                if pd and not isinstance(i, pd.Series):
+                    raise AssertionError(
+                        f"{iname} values must be a list, numpy-array or pandas.Series"
+                    )
+                else:
+                    if iname == "data":
+                        pandas_series_data = True
+
+        # set coordinates by extent
+        if isinstance(x, tuple) and isinstance(y, tuple):
+            assert data is not None, (
+                "EOmaps: If x- and y are provided as tuples, the data must be a 2D list"
+                " or numpy-array!"
+            )
+
+            shape = np.shape(data)
+            assert len(shape) == 2, (
+                "EOmaps: If x- and y are provided as tuples, the data must be a 2D list"
+                " or numpy-array!"
+            )
+
+            # get the data-coordinates
+            xorig = np.linspace(*x, shape[0])
+            yorig = np.linspace(*y, shape[1])
+
+        else:
+            # get the data-coordinates
+            xorig = np.asanyarray(x)
+            yorig = np.asanyarray(y)
+
+        if data is not None:
+            # get the data-values
+            z_data = np.asanyarray(data)
+        else:
+            if xorig.shape == yorig.shape:
+                z_data = np.full(xorig.shape, np.nan)
+            elif (
+                (xorig.shape != yorig.shape)
+                and (len(xorig.shape) == 1)
+                and (len(yorig.shape) == 1)
+            ):
+                z_data = np.full((xorig.shape[0], yorig.shape[0]), np.nan)
+
+        # get the index-values
+        if pandas_series_data is True:
+            # use actual index values if pd.Series was passed as "data"
+            ids = data.index.values
+        else:
+            # use numeric index values for all other types
+            ids = range(z_data.size)
+
+        if len(xorig.shape) == 1 and len(yorig.shape) == 1 and len(z_data.shape) == 2:
+            assert (
+                z_data.shape[0] == xorig.shape[0] and z_data.shape[0] == xorig.shape[0]
+            ), (
+                "The shape of the coordinate-arrays is not valid! "
+                f"data={z_data.shape} expects x={(z_data.shape[0],)}, "
+                f"y={(z_data.shape[1],)}, but the provided shapes are:"
+                f"x={xorig.shape}, y={yorig.shape}"
+            )
+
+        if len(xorig.shape) == len(z_data.shape):
+            assert xorig.shape == z_data.shape and yorig.shape == z_data.shape, (
+                f"EOmaps: The data-shape {z_data.shape} and coordinate-shape "
+                + f"x={xorig.shape}, y={yorig.shape} do not match!"
+            )
+
+        return z_data, np.asanyarray(xorig), np.asanyarray(yorig), ids, parameter
+
     def _prepare_data(self, assume_sorted=True):
         in_crs = self.m.data_specs.crs
         cpos = self.m.data_specs.cpos
@@ -176,7 +311,7 @@ def _prepare_data(self, assume_sorted=True):
         crs2 = CRS.from_user_input(self.m._crs_plot)
 
         # identify the provided data and get it in the internal format
-        z_data, xorig, yorig, ids, parameter = self.m._identify_data()
+        z_data, xorig, yorig, ids, parameter = self._identify_data()
 
         if cpos is not None and cpos != "c":
             # fix position of pixel-center in the input-crs
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 2cc37a4c2..89a4ce266 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -4217,139 +4217,6 @@ def _add_child(self, m):
             except Exception:
                 _log.exception("EOmaps: Problem executing 'on_add_child' action:")
 
-    def _identify_data(self, data=None, x=None, y=None, parameter=None):
-        # identify the way how the data has been provided and convert to the internal
-        # structure
-
-        if data is None:
-            data = self.data_specs.data
-        if x is None:
-            x = self.data_specs.x
-        if y is None:
-            y = self.data_specs.y
-        if parameter is None:
-            parameter = self.data_specs.parameter
-
-        # check other types before pandas to avoid unnecessary import
-        if data is not None and not isinstance(data, (list, tuple, np.ndarray)):
-            (pd,) = register_modules("pandas", raise_exception=False)
-
-            if pd is None:
-                raise TypeError(
-                    f"EOmaps: Unable to handle the input-data type: {type(data)}"
-                )
-
-            if isinstance(data, pd.DataFrame):
-                if parameter is not None:
-                    # get the data-values
-                    z_data = data[parameter].values
-                else:
-                    z_data = np.repeat(np.nan, len(data))
-
-                # get the index-values
-                ids = data.index.values
-
-                if isinstance(x, str) and isinstance(y, str):
-                    # get the data-coordinates
-                    xorig = data[x].values
-                    yorig = data[y].values
-                else:
-                    assert isinstance(x, (list, np.ndarray, pd.Series)), (
-                        "'x' must be either a column-name, or explicit values "
-                        " specified as a list, a numpy-array or a pandas"
-                        + f" Series object if you provide the data as '{type(data)}'"
-                    )
-                    assert isinstance(y, (list, np.ndarray, pd.Series)), (
-                        "'y' must be either a column-name, or explicit values "
-                        " specified as a list, a numpy-array or a pandas"
-                        + f" Series object if you provide the data as '{type(data)}'"
-                    )
-
-                    xorig = np.asanyarray(x)
-                    yorig = np.asanyarray(y)
-
-                return z_data, xorig, yorig, ids, parameter
-
-        # identify all other types except for pandas.DataFrames
-        # lazily check if pandas was used
-        pandas_series_data = False
-        for iname, i in zip(("x", "y", "data"), (x, y, data)):
-            if iname == "data" and i is None:
-                # allow empty datasets
-                continue
-
-            if not isinstance(i, (list, tuple, np.ndarray)):
-                (pd,) = register_modules("pandas", raise_exception=False)
-
-                if pd and not isinstance(i, pd.Series):
-                    raise AssertionError(
-                        f"{iname} values must be a list, numpy-array or pandas.Series"
-                    )
-                else:
-                    if iname == "data":
-                        pandas_series_data = True
-
-        # set coordinates by extent
-        if isinstance(x, tuple) and isinstance(y, tuple):
-            assert data is not None, (
-                "EOmaps: If x- and y are provided as tuples, the data must be a 2D list"
-                " or numpy-array!"
-            )
-
-            shape = np.shape(data)
-            assert len(shape) == 2, (
-                "EOmaps: If x- and y are provided as tuples, the data must be a 2D list"
-                " or numpy-array!"
-            )
-
-            # get the data-coordinates
-            xorig = np.linspace(*x, shape[0])
-            yorig = np.linspace(*y, shape[1])
-
-        else:
-            # get the data-coordinates
-            xorig = np.asanyarray(x)
-            yorig = np.asanyarray(y)
-
-        if data is not None:
-            # get the data-values
-            z_data = np.asanyarray(data)
-        else:
-            if xorig.shape == yorig.shape:
-                z_data = np.full(xorig.shape, np.nan)
-            elif (
-                (xorig.shape != yorig.shape)
-                and (len(xorig.shape) == 1)
-                and (len(yorig.shape) == 1)
-            ):
-                z_data = np.full((xorig.shape[0], yorig.shape[0]), np.nan)
-
-        # get the index-values
-        if pandas_series_data is True:
-            # use actual index values if pd.Series was passed as "data"
-            ids = data.index.values
-        else:
-            # use numeric index values for all other types
-            ids = range(z_data.size)
-
-        if len(xorig.shape) == 1 and len(yorig.shape) == 1 and len(z_data.shape) == 2:
-            assert (
-                z_data.shape[0] == xorig.shape[0] and z_data.shape[0] == xorig.shape[0]
-            ), (
-                "The shape of the coordinate-arrays is not valid! "
-                f"data={z_data.shape} expects x={(z_data.shape[0],)}, "
-                f"y={(z_data.shape[1],)}, but the provided shapes are:"
-                f"x={xorig.shape}, y={yorig.shape}"
-            )
-
-        if len(xorig.shape) == len(z_data.shape):
-            assert xorig.shape == z_data.shape and yorig.shape == z_data.shape, (
-                f"EOmaps: The data-shape {z_data.shape} and coordinate-shape "
-                + f"x={xorig.shape}, y={yorig.shape} do not match!"
-            )
-
-        return z_data, np.asanyarray(xorig), np.asanyarray(yorig), ids, parameter
-
     def inherit_classification(self, m):
         """
         Use the classification of another Maps-object when plotting the data.

From bb3283909a637cd8e5c62ceb777cea52ee8dd3a4 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 13 Feb 2024 11:54:47 +0100
Subject: [PATCH 177/240] make sure "_ensure_same_pick_id" is set on all
 involved pick-containers

---
 eomaps/cb_container.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 6bce492b7..9b7007145 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -1414,6 +1414,9 @@ def share_events(self, *args, ensure_same_id=False):
         """
         self._ensure_same_pick_id = ensure_same_id
 
+        for m in args:
+            self._getobj(m)._ensure_same_pick_id = ensure_same_id
+
         super().share_events(*args)
 
     def set_props(

From 667e76b591e2bfef2f2f1f44f7c269de362465f7 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 13 Feb 2024 12:07:35 +0100
Subject: [PATCH 178/240] fix inheriting hist_size from parent colorbars

---
 eomaps/colorbar.py | 25 +++++++++++++++++++------
 1 file changed, 19 insertions(+), 6 deletions(-)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 7efc4d9e9..4386f1d66 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -64,9 +64,10 @@ def __init__(
         tick_precision=2,
         margin=None,
         divider_linestyle=None,
+        hist_size=0.9,
     ):
-
-        self._hist_size = 0.9
+        self._parent_cb = None
+        self._hist_size_ = hist_size
 
         self._extend_frac = extend_frac
 
@@ -94,6 +95,21 @@ def _scm(self):
     def _hist_orientation(self):
         return "vertical" if self.orientation == "horizontal" else "horizontal"
 
+    @property
+    def _hist_size(self):
+        if self._parent_cb is None:
+            return self._hist_size_
+        else:
+            return self._parent_cb._hist_size_
+
+    @_hist_size.setter
+    def _hist_size(self, size):
+        if self._parent_cb is None:
+            self._hist_size_ = size
+        else:
+            self._hist_size_ = size
+            self._parent_cb._hist_size_ = size
+
     def _get_data(self):
         # TODO
         return self._data
@@ -535,8 +551,6 @@ def _redraw(self, *args, **kwargs):
 
         self._attach_lim_cbs()  # re-attach ylim callbacks
 
-        self._set_hist_size()
-
         self._plot_colorbar(**self._cb_kwargs)
 
         self._plot_histogram(
@@ -725,7 +739,6 @@ def _add_axes_to_layer(self, dynamic):
         # we need to re-draw since the background axis size has changed!
         BM._refetch_layer(self._layer)
         BM._refetch_layer("__SPINES__")
-        self._m.redraw("__SPINES__")
 
     def _set_hist_size(self, *args, **kwargs):
         super()._set_hist_size(*args, **kwargs)
@@ -1530,12 +1543,12 @@ def _new_colorbar(
             extend_frac=extend_frac,
             margin=margin,
             divider_linestyle=divider_linestyle,
+            hist_size=hist_size,
         )
         cb._set_map(m)
         cb._setup_axes(pos, m.ax)
         cb._add_axes_to_layer(dynamic=dynamic_shade_indicator)
 
-        cb._set_hist_size(hist_size)
         cb.set_scale(log)
         cb._plot_colorbar(extend=extend, **kwargs)
 

From 69f1e0cbb5037a65d0f5b519b40beb2f2f2b1cbd Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 13 Feb 2024 23:25:25 +0100
Subject: [PATCH 179/240] Update README.md

---
 README.md | 114 +++++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 78 insertions(+), 36 deletions(-)

diff --git a/README.md b/README.md
index a03fbbdaf..54b820f6e 100644
--- a/README.md
+++ b/README.md
@@ -13,64 +13,110 @@
 <a href="https://app.gitter.im/#/room/#EOmaps:gitter.im" target="_blank"><img src="https://img.shields.io/gitter/room/raphaelquast/EOmaps?style=social" alt="chat on gitter" align="left" style="height: 20px !important;" ></a>
 
 ----
+  
 
-# EOmaps - Interactive maps in python!
+<h3 align="center">A python package to visualize and analyze geographical datasets.</h3>
 
-**EOmaps** is a <tt>python</tt> package to visualize and analyze geographical datasets.
+> **EOmaps** aims to provide a comprehensive, flexible, well-documented and easy-to-use API to create publication-ready maps that can directly be used for interactive data analysis.
 
-It is built on top of [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/) and provides an intuitive and easy-to-use interface to speed up and simplify the creation and comparison of maps.
+## How does it work?
 
+**EOmaps** is built on top of [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/) and integrates well with the scientific python infrastructure (e.g., `numpy`, `pandas`, `geopandas`, `xarray` etc.), allowing you to visualize point-, raster- or vector-datasets provided in almost any format you can imagine, no matter if you're dealing with just a few unsorted datapoints or multi-dimensional stacks of global high-resolution datasets.  
+
+Figures created with EOmaps are multi-layered, so you can (transparently) overlay and interactively compare your datasets with ease. With the accompanying GUI widget, you can quickly switch layers, change the layout, examine the large collection of features and web-map services, and explore the capabilities of EOmaps. 
+Once you're map is ready, you can export it as high-resolution image or vector-graphic for further editing.
+Leveraging the powers of matplotlib, you can also embed interactive maps in Jupyter Notebooks, GUI frameworks like `Qt`, `tkinter`, `wx` or on a webpage (html).
+
+
+> [!TIP]
 > For a quick hands-on introduction, checkout this article on dev.to:  
 > [Geographic data visualization and analysis with EOmaps: Interactive maps in python!](https://dev.to/raphaelquast/geographic-data-visualization-and-analysis-with-eomaps-interactive-maps-in-python-48e1)
->
-> ---
+
+---
 
 ### What can EOmaps do for you?
 
-- Create [▤ multi-layered maps](https://eomaps.readthedocs.io/en/latest/api_basics.html#layer-management) and interactively compare different layers with each other
-- [🔴 Visualize datasets](https://eomaps.readthedocs.io/en/latest/api_data_visualization.html) with  millions of datapoints and handle reprojections
+<!-- 
+<img src=https://eomaps.readthedocs.io/en/latest/_images/intro.png width=40%>
+<img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/minigifs/companion_widget.gif?raw=true" alt="EOmaps GUI" width=40%>
+-->
+
+Checkout the [🌱 Basics](https://eomaps.readthedocs.io/en/latest/api_basics.html) in the documentation to get started!
+
+<table><tr><td valign="top">
+
+- Create [▤ multi-layered maps](https://eomaps.readthedocs.io/en/latest/api_basics.html#layer-management) (and interactively compare layers with each other)
+- [🔴 Visualize datasets](https://eomaps.readthedocs.io/en/latest/api_data_visualization.html) with millions of datapoints (and handle reprojections)
+- Get a useful [🧰 CompanionWidget GUI](https://eomaps.readthedocs.io/en/latest/api_companion_widget.html)
+- Use [🛸 Callbacks](https://eomaps.readthedocs.io/en/latest/api_callbacks.html) to make your figure interactive
+- Adjust the layout with the [🏗️ LayoutEditor](https://eomaps.readthedocs.io/en/latest/api_layout_editor.html)
+- [🗺 Export](https://eomaps.readthedocs.io/en/latest/api_basics.html#image-export-jpeg-png-svg-etc) high resolution images or vektor graphics
+    
+</td><td valign="top">
+
 - Provide a comprehensive set of tools to customize the map
   - [🌵NaturalEarth features](https://eomaps.readthedocs.io/en/latest/api_naturalearth_features.html)
   - [📏Scalebars](https://eomaps.readthedocs.io/en/latest/api_scalebar.html)
   - [▦ Gridlines](https://eomaps.readthedocs.io/en/latest/api_gridlines.html)
   - [🛰 WebMap layers](https://eomaps.readthedocs.io/en/latest/api_webmaps.html)
   - [🏕 Annotations, Markers, Lines, Logos...](https://eomaps.readthedocs.io/en/latest/api_annotations_markers_etc.html)
-  - . . .
-- Get a useful [🧰 CompanionWidget](https://eomaps.readthedocs.io/en/latest/api_companion_widget.html) GUI
-- Use [🛸 Callbacks](https://eomaps.readthedocs.io/en/latest/api_callbacks.html) to interact with the figure
-- Interactively re-arrange multiple maps in a figure with the [🏗️ LayoutEditor](https://eomaps.readthedocs.io/en/latest/api_layout_editor.html)
-- [🗺 Export](https://eomaps.readthedocs.io/en/latest/api_basics.html#image-export-jpeg-png-svg-etc) publication ready high resolution images (png, jpeg, tiff, ...)  
-or export figures as vector graphics (svg, eps, pdf ...)
 - . . . and much more!  
+    
+</td></tr></table>
 
-Checkout the [🌱 Basics](https://eomaps.readthedocs.io/en/latest/api_basics.html) in the documentation to get started!
-
-<img src=https://eomaps.readthedocs.io/en/latest/_images/intro.png width=50%>
 
 ## 🔨 Installation
 
-To install EOmaps (and all its dependencies) via the [conda](https://docs.conda.io/projects/conda/en/stable/) package-manager, simply use:
+To install **EOmaps** (and all its dependencies) with the [conda](https://docs.conda.io/projects/conda/en/stable/) package-manager, simply use:
 ```python
 conda install -c conda-forge eomaps
 ```
-> ... to get a <u>**huge speedup**</u>, use [mamba](https://mamba.readthedocs.io/en/latest/) to solve the dependencies!
+
+> [!TIP]
+> To get a <u>**huge speedup**</u>, use [mamba](https://mamba.readthedocs.io/en/latest/) to solve the dependencies!
 > ```python
-> conda install -c conda-forge mamba
 > mamba install -c conda-forge eomaps
 > ```
+ 
+
+> Advanced users can also use `pip` to install **EOmaps** (and selectively install optional dependency groups)
+> ```pyhton
+> pip install eomaps       # install only minimal required dependencies
+> pip install eomaps[all]  # install all optional dependencies
+> ...
+> ```
+
 Need more information?
 - Have a look at the [🐛Installation](https://eomaps.readthedocs.io/en/latest/installation.html) instructions in the docs.
 - Checkout the quickstart guide [🚀 From 0 to EOmaps](https://eomaps.readthedocs.io/en/latest/FAQ.html#from-0-to-eomaps-a-quickstart-guide).
 
 ## 📖 Documentation
 
-Make sure to have a look at the <a href=https://eomaps.readthedocs.io/en/latest><b>📖 Documentation </b></a> which provides a lot of <a href=https://eomaps.readthedocs.io/en/latest/EOmaps_examples.html><b>🌐Examples</b></a> on how to create awesome interactive maps (incl. 🐍 source code)!
+Make sure to have a look at the <a href=https://eomaps.readthedocs.io/en/latest><b>📖 Documentation </b></a>!  
+It provides a lot of <a href=https://eomaps.readthedocs.io/en/latest/EOmaps_examples.html><b>🌐Examples</b></a> on how to create awesome interactive maps (incl. 🐍 source code)!
 
 ## ✔️ Citation
 Did EOmaps help in your research?  
 Support the development and add a citation to your publication!
 
-[![https://doi.org/10.5281/zenodo.6459598](https://zenodo.org/badge/410829039.svg)](https://zenodo.org/badge/latestdoi/410829039)
+<a href="https://doi.org/10.5281/zenodo.6459598"><img src="https://zenodo.org/badge/410829039.svg" alt="https://zenodo.org/badge/latestdoi/410829039" align="left"></a>
+
+<details>
+<summary>BibTeX</summary>
+<br>
+The following BibTeX entry uses the DOI that always points to the latest release of EOmaps!<br>
+(You can get the DOI for a specific version form the <a href="https://doi.org/10.5281/zenodo.6459598">zenodo-page</a>)
+
+```bibtex
+@software{eomaps,
+  author       = {Raphael Quast},
+  title        = {EOmaps: A python package to visualize and analyze geographical datasets.},
+  doi          = {10.5281/zenodo.6459598},
+  url          = {https://doi.org/10.5281/zenodo.6459598}
+}
+```
+    
+</details>
 
 
 ## 🌟 Contribute
@@ -85,21 +131,12 @@ Interested in contributing to EOmaps? Awesome!
 
 ---------------
 
-
-<center><img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/minigifs/companion_widget.gif?raw=true" alt="EOmaps example 6" width=50%></center>
-
-
 <table>
   <tr>
-    <td valign="center" style="width:50%">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig6.gif?raw=true" alt="EOmaps example 6">
-      </td>
-    <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/minigifs/advanced_wms.gif?raw=true" alt="EOmaps example 2">
-      </td>
-  </tr>
-  <tr>
-    <td valign="center">
+    <td colspan=2 valign="center">
+        <img src="https://github.com/raphaelquast/EOmaps/assets/22773387/fe27e290-019e-4179-929d-d33bc590758e" alt="EOmaps GUI">
+    </td>
+      <td valign="center">
         <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig9.gif?raw=true" alt="EOmaps example 9">
     </td>
     <td valign="center">
@@ -107,6 +144,12 @@ Interested in contributing to EOmaps? Awesome!
     </td>
   </tr>
   <tr>
+    <td valign="center" style="width:50%">
+        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig6.gif?raw=true" alt="EOmaps example 6">
+      </td>
+    <td valign="center">
+        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/minigifs/advanced_wms.gif?raw=true" alt="EOmaps example 2">
+      </td>
     <td valign="center">
         <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig7.gif?raw=true" alt="EOmaps example 7">
       </td>
@@ -121,14 +164,13 @@ Interested in contributing to EOmaps? Awesome!
     <td valign="center">
         <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig8.gif?raw=true" alt="EOmaps example 3">
     </td>
-  </tr>
-  <tr>
     <td valign="center">
         <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/example_lines.png?raw=true" alt="EOmaps example 9">
     </td>
     <td valign="center">
         <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/inset_maps.png?raw=true" alt="EOmaps example 4">
     </td>
+
   </tr>
 </table>
 

From 4fa5bcbab84686e4a2ee616845ac69bbfaa469ac Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 29 Jan 2024 21:39:19 +0100
Subject: [PATCH 180/240] add spherical voronoi

---
 eomaps/shapes.py | 181 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 181 insertions(+)

diff --git a/eomaps/shapes.py b/eomaps/shapes.py
index 56866d6d5..b3f3da9b0 100644
--- a/eomaps/shapes.py
+++ b/eomaps/shapes.py
@@ -1351,6 +1351,178 @@ def radius(self):
         def radius_crs(self):
             return "in"
 
+    class _SphericalVoronoiDiagram(object):
+        name = "spherical_voronoi_diagram"
+
+        def __init__(self, m):
+            self._m = m
+            self._mask_radius = None
+
+        def __call__(self, masked=True, mask_radius=None):
+            """
+            Draw a Spherical Voronoi-Diagram of the data.
+
+            Parameters
+            ----------
+            masked : bool
+                Indicator if the voronoi-diagram should be masked or not
+
+            mask_radius : float, optional
+                The radius used for masking the voronoi-diagram
+                (in units of the plot-crs)
+                The default is 4 times the estimated data-radius.
+            """
+            from . import MapsGrid  # do this here to avoid circular imports!
+
+            for m in self._m if isinstance(self._m, MapsGrid) else [self._m]:
+                shape = self.__class__(m)
+
+                shape.mask_radius = mask_radius
+                shape.masked = masked
+
+                m._shape = shape
+
+        @property
+        def _initargs(self):
+            return dict(mask_radius=self.mask_radius, masked=self.masked)
+
+        def __repr__(self):
+            try:
+                s = f"voronoi_diagram(mask_radius={self.mask_radius}, masked={self.masked})"
+            except AttributeError:
+                s = "voronoi_diagram(mask_radius, masked)"
+
+            return s
+
+        @property
+        def mask_radius(self):
+            r = Shapes._get_radius(self._m, self._mask_radius, "out")
+            if self._mask_radius is None:
+                return [i * 4 for i in r]
+            else:
+                return r
+
+        @mask_radius.setter
+        def mask_radius(self, val):
+            self._mask_radius = val
+
+        def _get_voronoi_verts_and_mask(self, x, y, crs, radius, masked=True):
+            try:
+                from scipy.spatial import SphericalVoronoi
+                from itertools import zip_longest
+            except ImportError:
+                raise ImportError("'scipy' is required for 'voronoi'!")
+
+            # transform from crs to the lon/lat
+
+            t_in_lonlat = self._m._get_transformer(
+                self._m.get_crs(crs), self._m.get_crs(crs).geodetic_crs
+            )
+
+            t_lonlat_plot = self._m._get_transformer(
+                self._m.get_crs(crs).geodetic_crs, self._m.crs_plot
+            )
+
+            xy_orig = np.column_stack((x, y))  # remember orig input coords
+
+            x0, y0 = t_in_lonlat.transform(x, y)
+
+            x0 = (x0 + 360) % 360
+            y0 = (y0 + 180) % 180
+
+            datamask = np.isfinite(x0) & np.isfinite(y0)
+            [radiusx, radiusy] = radius
+
+            maxdist = 2 * np.mean(np.sqrt(radiusx**2 + radiusy**2))
+
+            xy = np.column_stack((x0[datamask], y0[datamask]))
+
+            # plot the unit sphere for reference (optional)
+            r = 1
+
+            theta, phi = np.deg2rad(xy.T)
+            x = r * np.cos(theta) * np.sin(phi)
+            y = r * np.sin(theta) * np.sin(phi)
+            z = r * np.cos(phi)
+
+            vor = SphericalVoronoi(np.column_stack((x, y, z)), r, [0, 0, 0])
+            vor.sort_vertices_of_regions()
+
+            rect_regions = np.array(list(zip_longest(*vor.regions, fillvalue=-2))).T
+            # (use -2 instead of None to make np.take work as expected)
+
+            # rect_regions = rect_regions[vor.point_region]
+            # exclude all points at infinity
+            mask = np.all(np.not_equal(rect_regions, -1), axis=1)
+
+            # get the mask for the artificially added vertices
+            rect_mask = rect_regions == -2
+
+            x_s = np.ma.masked_array(
+                np.take(vor.vertices[:, 0], rect_regions), mask=rect_mask
+            )
+            y_s = np.ma.masked_array(
+                np.take(vor.vertices[:, 1], rect_regions), mask=rect_mask
+            )
+            z_s = np.ma.masked_array(
+                np.take(vor.vertices[:, 2], rect_regions), mask=rect_mask
+            )
+
+            # convert back to lon/lat
+            x = np.rad2deg(np.arctan2(y_s, x_s))
+            y = np.rad2deg(np.arccos(z_s / r))
+
+            # convert back to the -180, 180 and -90, 90 range
+            x = (x + 180) % 360 - 180
+            y = (y + 90) % 180 - 90
+
+            x, y = t_lonlat_plot.transform(x, y)
+
+            rect_verts = np.ma.stack((x, y)).swapaxes(0, 1).swapaxes(1, 2)
+
+            if masked:
+                # exclude any polygon whose defining point is farther away than maxdist
+                cdist = np.sqrt(np.sum((rect_verts - xy_orig[:, None]) ** 2, axis=2))
+                polymask = np.all(cdist < maxdist, axis=1)
+                mask = np.logical_and(mask, polymask)
+
+            verts = list(i.compressed().reshape(-1, 2) for i in rect_verts[mask])
+            return verts, mask, datamask
+
+        def get_coll(self, x, y, crs, **kwargs):
+
+            verts, mask, datamask = self._get_voronoi_verts_and_mask(
+                x, y, crs, self.mask_radius, masked=self.masked
+            )
+
+            # find the masked points that are not masked by the datamask
+            mask2 = ~datamask.copy()
+            mask2[np.where(datamask)[0][mask]] = True
+            # remember the mask
+            self._m._data_mask = mask2
+
+            color_and_array = Shapes._get_colors_and_array(
+                kwargs, np.logical_and(datamask, mask)
+            )
+
+            coll = PolyCollection(
+                verts=verts,
+                **color_and_array,
+                # transOffset=self._m.ax.transData,
+                **kwargs,
+            )
+
+            return coll
+
+        @property
+        def radius(self):
+            radius = Shapes._get_radius(self._m, "estimate", "in")
+            return radius
+
+        @property
+        def radius_crs(self):
+            return "in"
+
     class _DelaunayTriangulation(object):
         name = "delaunay_triangulation"
 
@@ -2117,6 +2289,15 @@ def voronoi_diagram(self, *args, **kwargs):
 
         return shp.__call__(*args, **kwargs)
 
+    @wraps(_SphericalVoronoiDiagram.__call__)
+    def spherical_voronoi_diagram(self, *args, **kwargs):
+        shp = self._SphericalVoronoiDiagram(m=self._m)
+        # increase radius margins for voronoi diagrams since
+        # outer points are otherwise always masked!
+        self._m._data_manager.set_margin_factors(20, 0.1)
+
+        return shp.__call__(*args, **kwargs)
+
     @wraps(_DelaunayTriangulation.__call__)
     def delaunay_triangulation(self, *args, **kwargs):
         shp = self._DelaunayTriangulation(m=self._m)

From 7b54a43bacfcd053338a4071212206e27693ecb0 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 14 Feb 2024 13:15:40 +0100
Subject: [PATCH 181/240] make blit-manager methods private

---
 eomaps/_blit_manager.py | 18 +++++++++---------
 eomaps/draw.py          | 10 +++++-----
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/eomaps/_blit_manager.py b/eomaps/_blit_manager.py
index fb6af498c..bd221af29 100644
--- a/eomaps/_blit_manager.py
+++ b/eomaps/_blit_manager.py
@@ -203,7 +203,7 @@ def __init__(self, m):
         self._unmanaged_artists_layer = "base"
 
         # grab the background on every draw
-        self.cid = self.canvas.mpl_connect("draw_event", self.on_draw)
+        self._cid_draw = self.canvas.mpl_connect("draw_event", self._on_draw_cb)
 
         self._after_update_actions = []
         self._after_restore_actions = []
@@ -814,16 +814,16 @@ def fetch_bg(self, layer=None, bbox=None):
     def _disconnect_draw(self):
         try:
             # temporarily disconnect draw-event callback to avoid recursion
-            if self.cid is not None:
-                self.canvas.mpl_disconnect(self.cid)
-                self.cid = None
+            if self._cid_draw is not None:
+                self.canvas.mpl_disconnect(self._cid_draw)
+                self._cid_draw = None
             yield
         finally:
             # reconnect draw event
-            if self.cid is None:
-                self.cid = self.canvas.mpl_connect("draw_event", self.on_draw)
+            if self._cid_draw is None:
+                self._cid_draw = self.canvas.mpl_connect("draw_event", self._on_draw_cb)
 
-    def on_draw(self, event):
+    def _on_draw_cb(self, event):
         """Callback to register with 'draw_event'."""
 
         if self._disable_draw:
@@ -836,10 +836,10 @@ def on_draw(self, event):
 
             renderer = cv.get_renderer()
             if renderer is None:
-                # don't run on_draw if no renderer is available
+                # don't run if no renderer is available
                 return
         else:
-            # don't run on_draw if no renderer is available
+            # don't run if no renderer is available
             # (this is true for svg export where mpl export routines
             # are used to avoid issues)
             if loglevel <= 5:
diff --git a/eomaps/draw.py b/eomaps/draw.py
index 76b87c6ba..911a95b22 100644
--- a/eomaps/draw.py
+++ b/eomaps/draw.py
@@ -253,7 +253,7 @@ def remove_last_shape(self):
         for cb in self._on_poly_remove:
             cb()
 
-        self._m.BM.on_draw(None)
+        self._m.BM._on_draw_cb(None)
 
     def _init_draw_line(self):
         if self._line is None:
@@ -291,7 +291,7 @@ def redraw(self, blit=True, *args):
         """Trigger re-drawing shapes."""
         # NOTE: If a drawer is active, this function is also called on any ordinary
         # draw-event (e.g. zoom/pan/resize) to keep the indicators visible.
-        # see "m.BM.on_draw()"
+        # see "m.BM._on_draw_cb()"
 
         artists = self._indicator_artists
 
@@ -656,7 +656,7 @@ def _polygon(self, **kwargs):
                     self._m.BM.add_artist(ph, layer=self.layer)
                 else:
                     self._m.BM.add_bg_artist(ph, layer=self.layer)
-                    self._m.BM.on_draw(None)
+                    self._m.BM._on_draw_cb(None)
 
                 ID = max(self._artists) + 1 if self._artists else 0
                 self._artists[ID] = ph
@@ -748,7 +748,7 @@ def _circle(self, **kwargs):
                     self._m.BM.add_artist(ph, layer=self.layer)
                 else:
                     self._m.BM.add_bg_artist(ph, layer=self.layer)
-                    self._m.BM.on_draw(None)
+                    self._m.BM._on_draw_cb(None)
 
                 ID = max(self._artists) + 1 if self._artists else 0
                 self._artists[ID] = ph
@@ -833,7 +833,7 @@ def _rectangle(self, **kwargs):
                     self._m.BM.add_artist(ph, layer=self.layer)
                 else:
                     self._m.BM.add_bg_artist(ph, layer=self.layer)
-                    self._m.BM.on_draw(None)
+                    self._m.BM._on_draw_cb(None)
 
                 ID = max(self._artists) + 1 if self._artists else 0
                 self._artists[ID] = ph

From 0b30c7ad12779076fbe0ca506233ada4426a8ed8 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 14 Feb 2024 13:18:10 +0100
Subject: [PATCH 182/240] make "BM.cleanup_layer" method private

---
 eomaps/_blit_manager.py | 2 +-
 eomaps/eomaps.py        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/_blit_manager.py b/eomaps/_blit_manager.py
index bd221af29..d49f5b0d8 100644
--- a/eomaps/_blit_manager.py
+++ b/eomaps/_blit_manager.py
@@ -1479,7 +1479,7 @@ def action():
 
         return action
 
-    def cleanup_layer(self, layer):
+    def _cleanup_layer(self, layer):
         """Trigger cleanup methods for a given layer."""
         self._cleanup_bg_artists(layer)
         self._cleanup_artists(layer)
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 89a4ce266..0171fe444 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -3889,7 +3889,7 @@ def cleanup(self):
 
             # cleanup all artists and cached background-layers from the blit-manager
             if not self._is_sublayer:
-                self.BM.cleanup_layer(self.layer)
+                self.BM._cleanup_layer(self.layer)
 
             # remove the child from the parent Maps object
             if self in self.parent._children:

From ccf4937d1579da4a6960058e34201d63836d347e Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 14 Feb 2024 23:30:28 +0100
Subject: [PATCH 183/240] init blit-manager with layer of parent maps-object

---
 eomaps/_blit_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/_blit_manager.py b/eomaps/_blit_manager.py
index d49f5b0d8..f07e7cbaa 100644
--- a/eomaps/_blit_manager.py
+++ b/eomaps/_blit_manager.py
@@ -191,6 +191,7 @@ def __init__(self, m):
         self._disable_update = False
 
         self._m = m
+        self._bg_layer = self._m.layer
 
         self._artists = dict()
 
@@ -207,7 +208,6 @@ def __init__(self, m):
 
         self._after_update_actions = []
         self._after_restore_actions = []
-        self._bg_layer = "base"
 
         self._artists_to_clear = dict()
 

From 01515635641aedb421d7b65fffb6754183632a86 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 14 Feb 2024 23:34:42 +0100
Subject: [PATCH 184/240] make blit-manager, data_specs and classify_specs
 accessible for sphinx

---
 eomaps/eomaps.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 0171fe444..8c43a1d81 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -355,6 +355,10 @@ class Maps(metaclass=_MapsMeta):
     add_feature = NaturalEarthFeatures
     util = Utilities
     cb = CallbackContainer
+    BM = BlitManager
+
+    classify_specs = ClassifySpecs
+    data_specs = DataSpecs
 
     if WebMapContainer is not None:
         add_wms = WebMapContainer
@@ -470,6 +474,12 @@ def __init__(
 
         self._init_figure(ax=ax, plot_crs=crs, **kwargs)
 
+        # Initialize the  Blit-Manager used to dynamically update the plots.
+        if self.parent == self:  # use == instead of "is" since the parent is a proxy!:
+            self.BM = BlitManager(weakref.proxy(self))
+        else:
+            self.BM = self.parent.BM
+
         if WebMapContainer is not None:
             self.add_wms = self.add_wms(weakref.proxy(self))
 
@@ -684,15 +694,6 @@ def shape(self):
 
         return self._shape
 
-    @property
-    def BM(self):
-        """The Blit-Manager used to dynamically update the plots."""
-        m = weakref.proxy(self)
-        if self.parent._BM is None:
-            self.parent._BM = BlitManager(m)
-            self.parent._BM._bg_layer = m.parent.layer
-        return self.parent._BM
-
     @property
     def parent(self):
         """

From 0572b823c2b6792e4bf4d8075a88df173faf9861 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 14 Feb 2024 23:44:43 +0100
Subject: [PATCH 185/240] update docs

---
 docs/api/eomaps.eomaps.Maps.rst | 49 +++++++++++++++++++++------------
 docs/conf.py                    |  5 ++++
 docs/gen_autodoc_file.py        |  3 ++
 3 files changed, 40 insertions(+), 17 deletions(-)

diff --git a/docs/api/eomaps.eomaps.Maps.rst b/docs/api/eomaps.eomaps.Maps.rst
index 21f05e27c..8211ab6c4 100644
--- a/docs/api/eomaps.eomaps.Maps.rst
+++ b/docs/api/eomaps.eomaps.Maps.rst
@@ -11,23 +11,6 @@
 
 .. autoclass:: Maps
 
-Class Methods
--------------
-
-.. autosummary::
-    :toctree: ../generated
-    :template: obj_with_attributes_no_toc.rst
-    :nosignatures:
-
-    Maps.config
-
-.. autosummary::
-    :toctree: ../generated
-    :template: obj_with_attributes_no_toc.rst
-    :nosignatures:
-
-    Maps.from_file
-
 Properties
 ----------
 
@@ -47,6 +30,8 @@ Properties
     :nosignatures:
 
     Maps.data
+    Maps.data_specs
+    Maps.classify_specs
     Maps.colorbar
 
 
@@ -62,13 +47,23 @@ Layers and Maps
     Maps.new_map
     Maps.new_inset_map
 
+
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
     :nosignatures:
 
     Maps.new_layer
+
+
+.. autosummary::
+    :toctree: ../generated
+    :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
+
     Maps.new_layer_from_file
+    Maps.from_file
+
 
 .. autosummary::
     :toctree: ../generated
@@ -91,6 +86,7 @@ Map Features
     Maps.add_feature
     Maps.add_wms
 
+
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
@@ -125,6 +121,17 @@ Callbacks
     Maps.cb
 
 
+.. autosummary::
+    :toctree: ../generated
+    :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
+
+    Maps.cb.click
+    Maps.cb.pick
+    Maps.cb.keypress
+    Maps.cb.move
+
+
 Data visualization
 ------------------
 
@@ -215,6 +222,14 @@ Utilities
 Miscellaneous
 -------------
 
+.. autosummary::
+    :toctree: ../generated
+    :template: obj_with_attributes_no_toc.rst
+    :nosignatures:
+
+    Maps.config
+    Maps.BM
+
 .. autosummary::
     :toctree: ../generated
     :template: obj_with_attributes_no_toc.rst
diff --git a/docs/conf.py b/docs/conf.py
index 1e6626776..99d53f9e2 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -68,6 +68,11 @@ def setup(app):
     Maps.cb.move.attach.__name__ = "attach"
     Maps.cb.move.get.__name__ = "get"
 
+    Maps.BM.__name__ = "BM"
+
+    Maps.data_specs.__name__ = "data_specs"
+    Maps.classify_specs.__name__ = "classify_specs"
+
 
 # -- Project information
 
diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index ad82f7094..3001a905e 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -66,6 +66,9 @@ def make_feature_toctree_file():
         "read_file",
         "util",
         "add_wms",
+        "BM",
+        "data_specs",
+        "classify_specs",
     ):
         members.extend(get_members(Maps, key, False))
     for key in ("add_feature", "cb"):

From 8420944e8b36561d565b544b6f2fe083a5e37547 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 15 Feb 2024 00:03:57 +0100
Subject: [PATCH 186/240] add basic docstrings for data container properties

---
 eomaps/_containers.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/eomaps/_containers.py b/eomaps/_containers.py
index 0b965fa77..5f114d3c6 100644
--- a/eomaps/_containers.py
+++ b/eomaps/_containers.py
@@ -132,6 +132,7 @@ def keys(self):
 
     @property
     def data(self):
+        """The data assigned to this Maps-object."""
         return self._data
 
     @data.setter
@@ -140,6 +141,7 @@ def data(self, data):
 
     @property
     def crs(self):
+        """The projection of the data-coordinates."""
         return self._crs
 
     @crs.setter
@@ -148,6 +150,7 @@ def crs(self, crs):
 
     @property
     def x(self):
+        """The x-coordinate values of the data (or the name of the column to use)."""
         return self._x
 
     @x.setter
@@ -156,6 +159,7 @@ def x(self, x):
 
     @property
     def y(self):
+        """The y-coordinate values of the data (or the name of the column to use)."""
         return self._y
 
     @y.setter
@@ -164,6 +168,7 @@ def y(self, y):
 
     @property
     def parameter(self):
+        """The name of the parameter (or the name of the column to use as parameter)."""
         return self._parameter
 
     @parameter.setter
@@ -196,6 +201,7 @@ def parameter(self):
 
     @property
     def encoding(self):
+        """The encoding of the data-values."""
         return self._encoding
 
     @encoding.setter
@@ -206,6 +212,7 @@ def encoding(self, encoding):
 
     @property
     def cpos(self):
+        """Indicator if the coordinates represent center- or corner-positions."""
         return self._cpos
 
     @cpos.setter
@@ -214,6 +221,7 @@ def cpos(self, cpos):
 
     @property
     def cpos_radius(self):
+        """The pixel-extent used for determining corner-positions."""
         return self._cpos_radius
 
     @cpos_radius.setter
@@ -280,6 +288,7 @@ def keys(self):
 
     @property
     def scheme(self):
+        """The name of the used classification scheme"""
         return self._scheme
 
     @scheme.setter

From 0184b7d51f6eb969ea6a09ceffd562680cad5151 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 15 Feb 2024 10:28:45 +0100
Subject: [PATCH 187/240] fix masked-array issues with datashader/numba when
 using "shade_points"

---
 eomaps/eomaps.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 8c43a1d81..fdb5189be 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -4752,6 +4752,12 @@ def _shade_map(
 
         # the shape is always set after _prepare data!
         if self.shape.name == "shade_points" and self._data_manager.x0_1D is None:
+            # fill masked-values with None to avoid issues with numba not being
+            # able to deal with numpy-arrays
+            # TODO report this to datashader to get it fixed properly?
+            if isinstance(zdata, np.ma.masked_array):
+                zdata = zdata.filled(None)
+
             df = pd.DataFrame(
                 dict(
                     x=x0.ravel(),

From b455a956998f588f93e06d17ccab3ec74607ee78 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 15 Feb 2024 10:29:38 +0100
Subject: [PATCH 188/240] properly handle pick-events that do not have an "idx"
 property

---
 eomaps/cb_container.py | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 9b7007145..05bbbe978 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -1552,7 +1552,12 @@ def _default_picker(self, artist, event):
         return False, None
 
     def _get_pickdict(self, event):
-        event_ind = event.ind
+        event_ind = getattr(event, "ind", None)
+        if event_ind is None:
+            if _log.getEffectiveLevel() <= logging.DEBUG:
+                _log.debug(f"Pick-event without index encountered: {event}")
+            return
+
         n_inds = len(np.atleast_1d(event_ind))
         # mouseevent = event.mouseevent
         noval = [None] * n_inds if n_inds > 1 else None
@@ -1619,6 +1624,10 @@ def _onpick(self, event):
 
         clickdict = self._get_pickdict(event)
 
+        # if no data was found, don't execute the callbacks
+        if clickdict is None:
+            return
+
         if event.mouseevent.dblclick:
             cbs = self.get.cbs.get("double", dict())
         else:
@@ -1647,12 +1656,11 @@ def _onpick(self, event):
                     return
 
                 cb = bcbs[key]
-                if clickdict is not None:
-                    if self._consecutive_multipick is False:
-                        cb(**clickdict)
-                    else:
-                        for c in clickdict:
-                            cb(**c)
+                if self._consecutive_multipick is False:
+                    cb(**clickdict)
+                else:
+                    for c in clickdict:
+                        cb(**c)
 
     def _reset_cids(self):
         # clear all temporary artists

From 527d149481d5bc08798e37c7c2543db923a37a8a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 16 Feb 2024 10:19:34 +0100
Subject: [PATCH 189/240] avoid issues if collection has no _coll_dynamic
 attribute (e.g. shade shapes)

---
 eomaps/_data_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index 372e30d90..349d9410f 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -578,7 +578,7 @@ def redraw_required(self, layer):
     def _remove_existing_coll(self):
         if self.m.coll is not None:
             try:
-                if self.m._coll_dynamic:
+                if getattr(self.m, "_coll_dynamic", False):
                     self.m.BM.remove_artist(self.m._coll)
                 else:
                     self.m.BM.remove_bg_artist(self.m._coll)

From 8e1f80b3aebe89e596999cfa151ccc5047859f83 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 16 Feb 2024 10:20:26 +0100
Subject: [PATCH 190/240] trigger keypress callbacks irrespective of the
 toolbar mode

---
 eomaps/cb_container.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/eomaps/cb_container.py b/eomaps/cb_container.py
index 05bbbe978..5cb938948 100644
--- a/eomaps/cb_container.py
+++ b/eomaps/cb_container.py
@@ -164,10 +164,11 @@ def _objs(self):
                     obj = self._getobj(m)
 
                     # only include objects that are on the same layer
+                    # (irrespective if a toolbar mode is active or not)
                     if (
                         obj is not None
                         and obj._execute_cb(obj._m.layer)
-                        and not obj._check_toolbar_mode()
+                        # and not obj._check_toolbar_mode()
                     ):
                         objs.append(obj)
             else:
@@ -177,6 +178,7 @@ def _objs(self):
                     if event.inaxes == m.ax:
                         obj = self._getobj(m)
                         # only include objects that are on the same layer
+                        # (and only if no toolbar mode is active!)
                         if (
                             obj is not None
                             and obj._execute_cb(obj._m.layer)

From 1c40f50b320b74f299940fcd9ac82c22c4efc260 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 16 Feb 2024 10:21:18 +0100
Subject: [PATCH 191/240] handle raster-aggregation if block-size cannot be
 evaluated (e.g. None)

---
 eomaps/_data_manager.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index 349d9410f..79f1d2ecb 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -779,6 +779,8 @@ def _estimate_slice_blocksize(self, qx, qy):
 
                 x1 = x1 - x1 % bs + bs
                 y1 = y1 - y1 % bs + bs
+            else:
+                bs = None
         else:
             bs = None
 
@@ -923,6 +925,8 @@ def _zoom(self, blocksize):
         # only zoom if the shape provides a _maxsize attribute
         if self._current_data["z_data"] is None or maxsize is None:
             return
+        elif blocksize is None:
+            return
         elif self._current_data["z_data"].size < maxsize:
             return
 

From 72f07b0e168a137afd17e3002faed9f6d2437974 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 16 Feb 2024 11:23:03 +0100
Subject: [PATCH 192/240] update docs

---
 docs/_static/minigifs/basics_first_map.png | Bin 0 -> 75508 bytes
 docs/api_basics.rst                        |  57 +++++++++++++++------
 2 files changed, 41 insertions(+), 16 deletions(-)
 create mode 100644 docs/_static/minigifs/basics_first_map.png

diff --git a/docs/_static/minigifs/basics_first_map.png b/docs/_static/minigifs/basics_first_map.png
new file mode 100644
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HcmV?d00001

diff --git a/docs/api_basics.rst b/docs/api_basics.rst
index dd0e593fc..1ea14bfcb 100644
--- a/docs/api_basics.rst
+++ b/docs/api_basics.rst
@@ -19,16 +19,34 @@ The first :py:class:`Maps` object that is created will initialize a
 `cartopy.GeoAxes <https://scitools.org.uk/cartopy/docs/latest/reference/generated/cartopy.mpl.geoaxes.GeoAxes.html?highlight=geoaxes#cartopy.mpl.geoaxes.GeoAxes>`_
 for a map.
 
-.. code-block:: python
-    :name: test_init_map_objects
 
-    from eomaps import Maps
-    m = Maps(crs=4326,               # create a Maps-object with a map in lon/lat (epsg=4326) projection
-        layer="first layer",         # assign the layer "first_layer"
-        figsize=(7, 5))              # set the figure-size to 7x5
-    m.set_extent((-25, 35, 25, 70))  # set the extent of the map
-    m.add_feature.preset.coastline() # add coastlines to the map
-    m.add_title("my map")            # add a title above the map
+.. grid:: 1 1 1 2
+
+    .. grid-item::
+        :columns: 12 12 12 8
+
+        .. code-block:: python
+            :name: test_init_map_objects
+
+            from eomaps import Maps
+            m = Maps(                        # Create a Maps-object
+                crs=4326,                    #     Use lon/lat (epsg=4326) projection
+                layer="base",                #     Assign the layer-name "base"
+                figsize=(6, 5))              #     Set the figure-size to 6x5
+
+            m.set_extent((-25, 35, 30, 70))  # Set the extent of the map
+            m.set_frame(linewidth=3)         # Set the linewidth of the frame
+
+            m.add_feature.preset.coastline() # Add coastlines
+            m.add_feature.preset.ocean()     # Add ocean-coloring
+            m.add_title("My first map",      # Add a title
+                        fontsize=16)         #     With a fontsize of 16pt
+
+    .. grid-item::
+        :columns: 8 8 8 4
+
+        .. image:: _static/minigifs/basics_first_map.png
+
 
 - ``crs`` represents the projection used for plotting
 - ``layer`` represents the name of the layer associated with the Maps-object (see :ref:`layers`).
@@ -379,6 +397,7 @@ To position the map in a (virtual) grid, one of the following options are possib
 .. grid:: 1 1 1 2
 
     .. grid-item::
+        :columns: 12 12 12 8
 
          .. code-block:: python
              :name: test_gridpos_1
@@ -392,13 +411,14 @@ To position the map in a (virtual) grid, one of the following options are possib
              ax = m.f.add_subplot(2, 1, 2)
 
     .. grid-item-card::
-        :width: 50%
+        :columns: 6 6 6 4
         :img-background: _static/grids/grid1.png
 
 
 .. grid:: 1 1 1 2
 
     .. grid-item::
+        :columns: 12 12 12 8
 
         .. code-block:: python
             :name: test_gridpos_2
@@ -415,13 +435,14 @@ To position the map in a (virtual) grid, one of the following options are possib
             ax = m.f.add_subplot(1, 2, 2)
 
     .. grid-item-card::
-        :width: 50%
+        :columns: 6 6 6 4
         :img-background: _static/grids/grid2.png
 
 
 .. grid:: 1 1 1 2
 
     .. grid-item::
+        :columns: 12 12 12 8
 
         .. code-block:: python
             :name: test_gridpos_3
@@ -438,7 +459,7 @@ To position the map in a (virtual) grid, one of the following options are possib
             ax = m.f.add_subplot(1, 2, 2)
 
     .. grid-item-card::
-        :width: 50%
+        :columns: 6 6 6 4
         :img-background: _static/grids/grid3.png
 
 
@@ -453,6 +474,7 @@ To position the map in a (virtual) grid, one of the following options are possib
 .. grid:: 1 1 1 2
 
     .. grid-item::
+        :columns: 12 12 12 8
 
         .. code-block:: python
             :name: test_gridpos_4
@@ -464,13 +486,14 @@ To position the map in a (virtual) grid, one of the following options are possib
             ax = m.f.add_subplot(212)
 
     .. grid-item-card::
-        :width: 50%
+        :columns: 6 6 6 4
         :img-background: _static/grids/grid4.png
 
 
 .. grid:: 1 1 1 2
 
     .. grid-item::
+        :columns: 12 12 12 8
 
         .. code-block:: python
             :name: test_gridpos_5
@@ -485,7 +508,7 @@ To position the map in a (virtual) grid, one of the following options are possib
             ax = m.f.add_subplot(224)
 
     .. grid-item-card::
-        :width: 50%
+        :columns: 6 6 6 4
         :img-background: _static/grids/grid5.png
 
 
@@ -495,6 +518,7 @@ To position the map in a (virtual) grid, one of the following options are possib
 .. grid:: 1 1 1 2
 
     .. grid-item::
+        :columns: 12 12 12 8
 
         .. code-block:: python
             :name: test_gridpos_6
@@ -508,7 +532,7 @@ To position the map in a (virtual) grid, one of the following options are possib
             ax = m.f.add_subplot(gs[1,:])
 
     .. grid-item-card::
-        :width: 50%
+        :columns: 6 6 6 4
         :img-background: _static/grids/grid6.png
 
 
@@ -530,6 +554,7 @@ To set the absolute position of the map, provide a list of 4 floats representing
 .. grid:: 1 1 1 2
 
     .. grid-item::
+        :columns: 12 12 12 8
 
         .. code-block:: python
             :name: test_gridpos_abspos
@@ -541,7 +566,7 @@ To set the absolute position of the map, provide a list of 4 floats representing
             ax = m.f.add_axes((.35, .15, .6, .2))
 
     .. grid-item-card::
-        :width: 50%
+        :columns: 6 6 6 4
         :img-background: _static/grids/grid7.png
 
 

From ad578e24c1a88e71d430fdbc7fe40dbae8535113 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 16 Feb 2024 16:24:34 +0100
Subject: [PATCH 193/240] Update README.md

---
 README.md | 136 +++++++++++++++++++++---------------------------------
 1 file changed, 53 insertions(+), 83 deletions(-)

diff --git a/README.md b/README.md
index 54b820f6e..df2f47e78 100644
--- a/README.md
+++ b/README.md
@@ -13,57 +13,58 @@
 <a href="https://app.gitter.im/#/room/#EOmaps:gitter.im" target="_blank"><img src="https://img.shields.io/gitter/room/raphaelquast/EOmaps?style=social" alt="chat on gitter" align="left" style="height: 20px !important;" ></a>
 
 ----
-  
 
 <h3 align="center">A python package to visualize and analyze geographical datasets.</h3>
 
-> **EOmaps** aims to provide a comprehensive, flexible, well-documented and easy-to-use API to create publication-ready maps that can directly be used for interactive data analysis.
+<table>
+<tr><td>  
+<i><b>EOmaps</b> aims to provide a comprehensive, flexible, well-documented and easy-to-use API to create publication-ready maps that can directly be used for interactive data analysis.</i>
+</td></tr>
+</table>
 
-## How does it work?
+## What can I do with EOmaps?
 
 **EOmaps** is built on top of [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/) and integrates well with the scientific python infrastructure (e.g., `numpy`, `pandas`, `geopandas`, `xarray` etc.), allowing you to visualize point-, raster- or vector-datasets provided in almost any format you can imagine, no matter if you're dealing with just a few unsorted datapoints or multi-dimensional stacks of global high-resolution datasets.  
 
-Figures created with EOmaps are multi-layered, so you can (transparently) overlay and interactively compare your datasets with ease. With the accompanying GUI widget, you can quickly switch layers, change the layout, examine the large collection of features and web-map services, and explore the capabilities of EOmaps. 
+Figures created with EOmaps are multi-layered, so you can (transparently) overlay and interactively compare your datasets with ease. With the accompanying GUI widget, you can quickly switch layers, change the layout, examine the large collection of features and web-map services, and explore the capabilities of EOmaps.
 Once you're map is ready, you can export it as high-resolution image or vector-graphic for further editing.
-Leveraging the powers of matplotlib, you can also embed interactive maps in Jupyter Notebooks, GUI frameworks like `Qt`, `tkinter`, `wx` or on a webpage (html).
+Leveraging the powers of matplotlib, you can also embed interactive maps in Jupyter Notebooks, on a webpage or in GUI frameworks like Qt, tkinter etc..
+
+> [!IMPORTANT]
+>
+> EOmaps is 100% free and open-source.  
+> As such, acknowledgement is extremely important to allow continued support and development of the package.
+>  
+> Did EOmaps help in your research? $\Rightarrow$ **Add a ✔️ Citation to your publication!**
+>
+> <a href="https://doi.org/10.5281/zenodo.6459598"><img src="https://zenodo.org/badge/410829039.svg" alt="https://zenodo.org/badge/latestdoi/410829039" align="left"></a>
+>
+> <details>
+> <summary>BibTeX</summary>
+> <br>
+> The following BibTeX entry uses a DOI that always points to the latest release of EOmaps!<br>
+> (You can get the DOI for a specific version form the <a href="https://doi.org/10.5281/zenodo.6459598">zenodo-page</a>)
+>
+> ```bibtex
+> @software{eomaps,
+>   author       = {Raphael Quast},
+>   title        = {EOmaps: A python package to visualize and analyze geographical datasets.},
+>   doi          = {10.5281/zenodo.6459598},
+>   url          = {https://doi.org/10.5281/zenodo.6459598}
+> }
+> ```
+>  
+> </details>
+>
 
+## 🚀 Getting started
+
+Head over to the start-page of the <a href=https://eomaps.readthedocs.io/en/latest><b>📖 Documentation </b></a> to get an overview of all available features and functionalities!
 
 > [!TIP]
 > For a quick hands-on introduction, checkout this article on dev.to:  
 > [Geographic data visualization and analysis with EOmaps: Interactive maps in python!](https://dev.to/raphaelquast/geographic-data-visualization-and-analysis-with-eomaps-interactive-maps-in-python-48e1)
 
----
-
-### What can EOmaps do for you?
-
-<!-- 
-<img src=https://eomaps.readthedocs.io/en/latest/_images/intro.png width=40%>
-<img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/minigifs/companion_widget.gif?raw=true" alt="EOmaps GUI" width=40%>
--->
-
-Checkout the [🌱 Basics](https://eomaps.readthedocs.io/en/latest/api_basics.html) in the documentation to get started!
-
-<table><tr><td valign="top">
-
-- Create [▤ multi-layered maps](https://eomaps.readthedocs.io/en/latest/api_basics.html#layer-management) (and interactively compare layers with each other)
-- [🔴 Visualize datasets](https://eomaps.readthedocs.io/en/latest/api_data_visualization.html) with millions of datapoints (and handle reprojections)
-- Get a useful [🧰 CompanionWidget GUI](https://eomaps.readthedocs.io/en/latest/api_companion_widget.html)
-- Use [🛸 Callbacks](https://eomaps.readthedocs.io/en/latest/api_callbacks.html) to make your figure interactive
-- Adjust the layout with the [🏗️ LayoutEditor](https://eomaps.readthedocs.io/en/latest/api_layout_editor.html)
-- [🗺 Export](https://eomaps.readthedocs.io/en/latest/api_basics.html#image-export-jpeg-png-svg-etc) high resolution images or vektor graphics
-    
-</td><td valign="top">
-
-- Provide a comprehensive set of tools to customize the map
-  - [🌵NaturalEarth features](https://eomaps.readthedocs.io/en/latest/api_naturalearth_features.html)
-  - [📏Scalebars](https://eomaps.readthedocs.io/en/latest/api_scalebar.html)
-  - [▦ Gridlines](https://eomaps.readthedocs.io/en/latest/api_gridlines.html)
-  - [🛰 WebMap layers](https://eomaps.readthedocs.io/en/latest/api_webmaps.html)
-  - [🏕 Annotations, Markers, Lines, Logos...](https://eomaps.readthedocs.io/en/latest/api_annotations_markers_etc.html)
-- . . . and much more!  
-    
-</td></tr></table>
-
 
 ## 🔨 Installation
 
@@ -77,7 +78,7 @@ conda install -c conda-forge eomaps
 > ```python
 > mamba install -c conda-forge eomaps
 > ```
- 
+
 
 > Advanced users can also use `pip` to install **EOmaps** (and selectively install optional dependency groups)
 > ```pyhton
@@ -86,44 +87,13 @@ conda install -c conda-forge eomaps
 > ...
 > ```
 
-Need more information?
-- Have a look at the [🐛Installation](https://eomaps.readthedocs.io/en/latest/installation.html) instructions in the docs.
-- Checkout the quickstart guide [🚀 From 0 to EOmaps](https://eomaps.readthedocs.io/en/latest/FAQ.html#from-0-to-eomaps-a-quickstart-guide).
-
-## 📖 Documentation
-
-Make sure to have a look at the <a href=https://eomaps.readthedocs.io/en/latest><b>📖 Documentation </b></a>!  
-It provides a lot of <a href=https://eomaps.readthedocs.io/en/latest/EOmaps_examples.html><b>🌐Examples</b></a> on how to create awesome interactive maps (incl. 🐍 source code)!
-
-## ✔️ Citation
-Did EOmaps help in your research?  
-Support the development and add a citation to your publication!
-
-<a href="https://doi.org/10.5281/zenodo.6459598"><img src="https://zenodo.org/badge/410829039.svg" alt="https://zenodo.org/badge/latestdoi/410829039" align="left"></a>
-
-<details>
-<summary>BibTeX</summary>
-<br>
-The following BibTeX entry uses the DOI that always points to the latest release of EOmaps!<br>
-(You can get the DOI for a specific version form the <a href="https://doi.org/10.5281/zenodo.6459598">zenodo-page</a>)
-
-```bibtex
-@software{eomaps,
-  author       = {Raphael Quast},
-  title        = {EOmaps: A python package to visualize and analyze geographical datasets.},
-  doi          = {10.5281/zenodo.6459598},
-  url          = {https://doi.org/10.5281/zenodo.6459598}
-}
-```
-    
-</details>
+For more detailes, have a look at the [Installation Instructions](https://eomaps.readthedocs.io/en/latest/installation.html) or the quickstart guide ["From 0 to EOmaps"](https://eomaps.readthedocs.io/en/latest/FAQ.html#from-0-to-eomaps-a-quickstart-guide)!
 
 
 ## 🌟 Contribute
 
-Interested in contributing to EOmaps? Awesome!
-
-- Checkout the [🚀 Contribution Guide](https://eomaps.readthedocs.io/en/latest/contribute.html) on how to get started!
+Interested in contributing to EOmaps? **Awesome!**  
+You can find detailed instructions on how to setup EOmaps for development in the [Contribution Guide](https://eomaps.readthedocs.io/en/latest/contribute.html)!
 
 > Found a bug or got an idea for an interesting feature?  
 > Open an [issue](https://github.com/raphaelquast/EOmaps/issues) or start a [discussion](https://github.com/raphaelquast/EOmaps/discussions), and I'll see what I can do!  
@@ -134,41 +104,41 @@ Interested in contributing to EOmaps? Awesome!
 <table>
   <tr>
     <td colspan=2 valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/assets/22773387/fe27e290-019e-4179-929d-d33bc590758e" alt="EOmaps GUI">
+        <a href="https://eomaps.readthedocs.io/en/dev/api_companion_widget.html"><img src="https://github.com/raphaelquast/EOmaps/assets/22773387/fe27e290-019e-4179-929d-d33bc590758e" alt="EOmaps GUI Example"></a>
     </td>
       <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig9.gif?raw=true" alt="EOmaps example 9">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-timeseries"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_timeseries.gif?raw=true" alt="EOmaps Timeseries Example"></a>
     </td>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig4.gif?raw=true" alt="EOmaps example 4">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-callbacks"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_callbacks.gif?raw=true" alt="EOmaps Callbacks Example"></a>
     </td>
   </tr>
   <tr>
     <td valign="center" style="width:50%">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig6.gif?raw=true" alt="EOmaps example 6">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-webmaps"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_webmaps.gif?raw=true" alt="EOmaps Webmaps Example"></a>
       </td>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/minigifs/advanced_wms.gif?raw=true" alt="EOmaps example 2">
+        <a href="[https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html](https://eomaps.readthedocs.io/en/dev/api_webmaps.html#setting-date-style-and-other-webmap-properties)"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/minigifs/advanced_wms.gif?raw=true" alt="EOmaps Webmaps Example 2"></a>
       </td>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig7.gif?raw=true" alt="EOmaps example 7">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-scalebars"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_scalebars.gif?raw=true" alt="EOmaps Scalebars Example"></a>
       </td>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig3.gif?raw=true" alt="EOmaps example 8">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-multiple-maps"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_multiple_maps.gif?raw=true" alt="EOmaps Multiple Maps Example"></a>
       </td>
   </tr>
   <tr>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/minigifs/layout_editor.gif?raw=true" alt="EOmaps inset-maps">
+        <a href="https://eomaps.readthedocs.io/en/dev/api_layout_editor.html"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/minigifs/layout_editor.gif?raw=true" alt="EOmaps LayoutEditor Example"></a>
     </td>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/fig8.gif?raw=true" alt="EOmaps example 3">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-vector-data"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_vector_data.gif?raw=true" alt="EOmaps Vector Data Example"></a>
     </td>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/example_lines.png?raw=true" alt="EOmaps example 9">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-lines"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_lines.png?raw=true" alt="EOmaps Lines Example"></a>
     </td>
     <td valign="center">
-        <img src="https://github.com/raphaelquast/EOmaps/blob/master/docs/_static/inset_maps.png?raw=true" alt="EOmaps example 4">
+        <a href="https://eomaps.readthedocs.io/en/dev/EOmaps_examples.html#example-inset-maps"><img src="https://github.com/raphaelquast/EOmaps/blob/dev/docs/_static/example_images/example_inset_maps.png?raw=true" alt="EOmaps InsetMaps Example"></a>
     </td>
 
   </tr>

From 159b7c93d5c6beb05cc5224b4dd6efc4a56445d0 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 16 Feb 2024 16:26:07 +0100
Subject: [PATCH 194/240] use parameter-name as colorbar label by default

---
 eomaps/colorbar.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/eomaps/colorbar.py b/eomaps/colorbar.py
index 4386f1d66..c5c008eb0 100644
--- a/eomaps/colorbar.py
+++ b/eomaps/colorbar.py
@@ -1567,6 +1567,9 @@ def _new_colorbar(
 
         cb._set_tick_formatter()
 
+        if label is None:
+            label = m.data_specs.parameter
+
         cb.set_labels(cb_label=label, hist_label=hist_label)
 
         if dynamic_shade_indicator:

From 962c68938c2f2c1986a7ff06c4282c0d4c3cb84b Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 16 Feb 2024 17:07:06 +0100
Subject: [PATCH 195/240] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index df2f47e78..aacf7966e 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@
 
 ## What can I do with EOmaps?
 
-**EOmaps** is built on top of [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/) and integrates well with the scientific python infrastructure (e.g., `numpy`, `pandas`, `geopandas`, `xarray` etc.), allowing you to visualize point-, raster- or vector-datasets provided in almost any format you can imagine, no matter if you're dealing with just a few unsorted datapoints or multi-dimensional stacks of global high-resolution datasets.  
+**EOmaps** is built on top of [matplotlib](https://matplotlib.org/) and [cartopy](https://scitools.org.uk/cartopy/docs/latest/) and integrates well with the scientific python infrastructure (e.g., [numpy](https://numpy.org/), [pandas](https://pandas.pydata.org/), [geopandas](https://geopandas.org/), [xarray](https://xarray.dev/) etc.), allowing you to visualize point-, raster- or vector-datasets provided in almost any format you can imagine, no matter if you're dealing with just a few unsorted datapoints or multi-dimensional stacks of global high-resolution datasets.  
 
 Figures created with EOmaps are multi-layered, so you can (transparently) overlay and interactively compare your datasets with ease. With the accompanying GUI widget, you can quickly switch layers, change the layout, examine the large collection of features and web-map services, and explore the capabilities of EOmaps.
 Once you're map is ready, you can export it as high-resolution image or vector-graphic for further editing.

From a85152619a0c27f8184d0d101632f7adb025c75b Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 17 Feb 2024 21:35:41 +0100
Subject: [PATCH 196/240] fix jupyter notebook issues if blit-manager is not
 defined as property

---
 docs/gen_autodoc_file.py |  8 ++++++++
 eomaps/eomaps.py         | 17 +++++++++--------
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index 3001a905e..549e91325 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -4,6 +4,14 @@
 
 from eomaps import Maps
 
+# TODO there must be a better way than this...
+# BM needs to be a property otherwise there are problems with jupyter notebooks
+# In order to make BM still accessible to sphinx, override it prior to generating
+# the autodoc-files
+from eomaps._blit_manager import BlitManager
+
+Maps.BM = BlitManager
+
 
 def get_autosummary(
     currentmodule="eomaps.eomaps",
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index fdb5189be..b01ff7228 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -355,7 +355,6 @@ class Maps(metaclass=_MapsMeta):
     add_feature = NaturalEarthFeatures
     util = Utilities
     cb = CallbackContainer
-    BM = BlitManager
 
     classify_specs = ClassifySpecs
     data_specs = DataSpecs
@@ -471,15 +470,8 @@ def __init__(
         self._layout_editor = None
 
         self.cb = self.cb(weakref.proxy(self))  # accessor for the callbacks
-
         self._init_figure(ax=ax, plot_crs=crs, **kwargs)
 
-        # Initialize the  Blit-Manager used to dynamically update the plots.
-        if self.parent == self:  # use == instead of "is" since the parent is a proxy!:
-            self.BM = BlitManager(weakref.proxy(self))
-        else:
-            self.BM = self.parent.BM
-
         if WebMapContainer is not None:
             self.add_wms = self.add_wms(weakref.proxy(self))
 
@@ -644,6 +636,15 @@ def log_message(*args, **kwargs):
                 event, log_message
             )
 
+    @property
+    def BM(self):
+        """The Blit-Manager used to dynamically update the plots."""
+        m = weakref.proxy(self)
+        if self.parent._BM is None:
+            self.parent._BM = BlitManager(m)
+            self.parent._BM._bg_layer = m.parent.layer
+        return self.parent._BM
+
     @property
     def layer(self):
         """The layer-name associated with this Maps-object."""

From 90322273babb1d345aaf76095c8fd39150f609ea Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 18 Feb 2024 00:27:31 +0100
Subject: [PATCH 197/240] update docs

---
 docs/_static/custom_css.css |   7 ++
 docs/api_basics.rst         | 190 ++++++++++++++++++++++--------------
 docs/conf.py                |   3 +-
 3 files changed, 125 insertions(+), 75 deletions(-)

diff --git a/docs/_static/custom_css.css b/docs/_static/custom_css.css
index 8a0f9f3db..2b5e019a3 100644
--- a/docs/_static/custom_css.css
+++ b/docs/_static/custom_css.css
@@ -1,3 +1,10 @@
 .wy-nav-content {
    max-width: 1200px !important;
 }
+
+
+:root {
+    --sd-color-info: #b8c5c6;
+    --sd-color-info-highlight: #148a9c;
+    --sd-color-info-text: #000;
+}
diff --git a/docs/api_basics.rst b/docs/api_basics.rst
index 1ea14bfcb..5005cf936 100644
--- a/docs/api_basics.rst
+++ b/docs/api_basics.rst
@@ -54,16 +54,19 @@ for a map.
   (e.g.: ``figsize``, ``frameon``, ``edgecolor`` etc).
 
 
-Possible ways for specifying the ``crs`` for plotting are:
+.. dropdown:: Possible ways for specifying the ``crs`` for plotting
+    :open:
+    :icon: info
+    :color: info
 
-- If you provide an integer, it is identified as an epsg-code (e.g. ``4326``, ``3035``, etc.)
+    - If you provide an integer, it is identified as an epsg-code (e.g. ``4326``, ``3035``, etc.)
 
-  - 4326 defaults to `PlateCarree` projection
+      - 4326 defaults to `PlateCarree` projection
 
-- All other CRS usable for plotting are accessible via ``Maps.CRS``, e.g.: ``crs=Maps.CRS.Orthographic()``, ``crs=Maps.CRS.Equi7_EU``...
+    - All other CRS usable for plotting are accessible via ``Maps.CRS``, e.g.: ``crs=Maps.CRS.Orthographic()``, ``crs=Maps.CRS.Equi7_EU``...
 
-  - ``Maps.CRS`` is just an accessor for ``cartopy.crs``
-  - For a full list of available projections see: `Cartopy projections <https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html>`_
+      - ``Maps.CRS`` is just an accessor for ``cartopy.crs``
+      - For a full list of available projections see: `Cartopy projections <https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html>`_
 
 .. autosummary::
     :nosignatures:
@@ -79,48 +82,70 @@ Possible ways for specifying the ``crs`` for plotting are:
 Layer management
 ~~~~~~~~~~~~~~~~
 
+A :py:class:`Maps` object represents one (or more) of the following things **on the assigned layer**:
+
+- a collection of features, callbacks,..
+- a single dataset  (and associated callbacks)
+
+
+You can create as many layers as you need! The following image explains how it works in general:
+
 .. image:: _static/intro.png
    :width: 70%
 
+.. dropdown:: Creating new layers
+    :icon: info
+    :color: info
 
-A :py:class:`Maps` object represents a collection of features, callbacks,.. **on the assigned layer**.
+    To create **a NEW layer**, use :py:meth:`m.new_layer("layer-name") <Maps.new_layer>`.
 
-Once you have created a map, you can create **additional** :py:class:`Maps` **objects for the same map** by using :py:meth:`Maps.new_layer`.
+    - Features, Colorbars etc. added to a :py:class:`Maps` object are only visible if the associated layer is visible.
+    - Callbacks are only executed if the associated layer is visible.
+    - See :ref:`combine_layers` on how to select the currently visible layer(s).
 
-🌱  If no explicit layer-name is provided, the returned :py:class:`Maps` object will use the same layer as the parent :py:class:`Maps` object.
 
-  - This is especially useful if you want to plot **multiple datasets on the same map and layer**.
+    .. code-block:: python
+        :name: test_layers_create_new_layer
 
-🌱  To create **a NEW layer** named ``"my_layer"``, use ``m2 = m.new_layer("my_layer")``
+        from eomaps import Maps
+        m = Maps()                           # same as `m = Maps(crs=4326, layer="base")`
+        m.add_feature.preset.coastline()     # add coastlines to the "base" layer
 
-  - Features, Colorbars etc. added to a :py:class:`Maps` object are only visible if the associated layer is visible.
-  - Callbacks are only executed if the associated layer is visible.
-  - See :ref:`combine_layers` on how to select the currently visible layer(s).
+        m_ocean = m.new_layer(layer="ocean") # create a new layer named "ocean"
+        m_ocean.add_feature.preset.ocean()   # features on this layer will only be visible if the "ocean" layer is visible!
 
+        m.show_layer("ocean")                # show the "ocean" layer
+        m.util.layer_selector()              # get a utility widget to quickly switch between existing layers
 
-.. code-block:: python
-    :name: test_layers_01
 
-    from eomaps import Maps
-    m = Maps()                           # same as `m = Maps(crs=4326, layer="base")`
-    m.add_feature.preset.coastline()     # add coastlines to the "base" layer
+.. dropdown:: Multiple ``Maps`` objects on the same layer
+    :icon: info
+    :color: info
+
+    If no explicit layer-name is provided, (e.g. :py:meth:`m.new_layer() <Maps.new_layer>`) the returned :py:class:`Maps` object will use the same layer as the parent :py:class:`Maps` object.
 
-    m_ocean = m.new_layer(layer="ocean") # create a new layer named "ocean"
-    m_ocean.add_feature.preset.ocean()   # features on this layer will only be visible if the "ocean" layer is visible!
+    - This is especially useful if you want to plot **multiple datasets on the same map and layer**.
 
-    m_ocean2 = m_ocean.new_layer()       # "m_ocean2" is just another Maps-object on the same layer as "m_ocean"!
-    m_ocean2.set_data(                   # assign a dataset to this Maps-object
-        data=[.14,.25,.38],
-        x=[1,2,3], y=[3,5,7],
-        crs=4326)
-    m_ocean2.set_shape.ellipses()        # set the shape that is used to represent the datapoints
-    m_ocean2.plot_map()                  # plot the data
 
-    m.show_layer("ocean")                # show the "ocean" layer
-    m.util.layer_selector()              # get a utility widget to quickly switch between existing layers
+    .. code-block:: python
+        :name: test_layers_on_same_layer
 
+        from eomaps import Maps
+        m = Maps()                           # same as `m = Maps(layer="base")`
+        m.add_feature.preset.coastline()     # add coastlines to the "base" layer
 
-.. admonition:: The "all" layer
+        m2 = m.new_layer()                   # "m2" is just another Maps-object on the same layer as "m"!
+        m2.set_data(                         # assign a dataset to this Maps-object
+            data=[.14,.25,.38],
+            x=[1,2,3], y=[3,5,7],
+            crs=4326)
+        m2.plot_map()                        # plot the data
+        m2.cb.pick.attach.annotate()         # attach a callback that picks datapoints from the data assigned to "m2"
+
+
+.. dropdown:: The "all" layer
+    :icon: info
+    :color: info
 
     | There is one layer-name that has a special meaning... the ``"all"`` layer.
     | Any callbacks and features added to this layer will be **executed on ALL other layers** as well!
@@ -142,8 +167,10 @@ Once you have created a map, you can create **additional** :py:class:`Maps` **ob
         m_ocean.add_feature.preset.ocean()   # add ocean-coloring to the "ocean" layer
         m.show_layer("ocean")                # show the "ocean" layer (note that it has coastlines as well!)
 
+.. dropdown:: Artists added with methods **outside of EOmaps**
+    :icon: info
+    :color: info
 
-.. admonition:: Artists added with methods **outside of EOmaps**
 
     If you use methods that are **NOT provided by EOmaps**, the corresponding artists will always appear on the ``"base"`` layer by default!
     (e.g. ``cartopy`` or ``matplotlib`` methods accessible via ``m.ax.`` or ``m.f.`` like ``m.ax.plot(...)``)
@@ -177,7 +204,13 @@ Once you have created a map, you can create **additional** :py:class:`Maps` **ob
 🗗 Combine & compare multiple layers
 ************************************
 
-.. admonition:: Using the :ref:`companion_widget`
+All maps of a figure always show **the same visible layer**.
+
+The visible layer can be a **single layer-name**, or a **combination of multiple layer-names** in order to to transparently combine/overlay multiple layers.
+
+.. dropdown:: Using the :ref:`companion_widget` to switch/overlay layers
+    :icon: info
+    :color: info
 
     Usually it is most convenient to combine and compare layers via the :ref:`companion_widget`.
 
@@ -198,69 +231,76 @@ Once you have created a map, you can create **additional** :py:class:`Maps` **ob
 
     .. image:: _static/minigifs/rearrange_layers.gif
 
+.. dropdown:: Programmatically switch/overlay layers
+    :icon: info
+    :color: info
 
-To programmatically switch between layers or view a layer that represents a **combination of multiple existing layers**, use :py:meth:`Maps.show_layer`.
-
-🌱 If you provide a single layer-name, the map will show the corresponding layer, e.g. ``m.show_layer("my_layer")``
 
-🌱 To **(transparently) overlay multiple existing layers**, use one of the following options:
+    To programmatically switch between layers or view a layer that represents a **combination of multiple existing layers**, use :py:meth:`Maps.show_layer`.
 
-- Provide **multiple layer names or tuples** of the form ``(< layer-name >, < transparency [0-1] >)``
+    If you provide a single layer-name, the map will show the corresponding layer, e.g. ``m.show_layer("my_layer")``
 
-  - ``m.show_layer("A", "B")`` will overlay all features of the layer ``B`` on top of the layer ``A``.
-  - ``m.show_layer("A", ("B", 0.5))`` will overlay the layer ``B`` with 50% transparency on top of the layer ``A``.
+    To **(transparently) overlay multiple existing layers**, use one of the following options:
 
-- Provide a **combined layer name** by separating the individual layer names you want to show with a ``"|"`` character.
+    - Provide **multiple layer names or tuples** of the form ``(< layer-name >, < transparency [0-1] >)``
 
-  - ``m.show_layer("A|B")`` will overlay all features of the layer ``B`` on top of the layer ``A``.
-  - To transparently overlay a layer, add the transparency to the layer-name in curly-brackets, e.g.: ``"<layer-name>{<transparency>}"``.
+      - ``m.show_layer("A", "B")`` will overlay all features of the layer ``B`` on top of the layer ``A``.
+      - ``m.show_layer("A", ("B", 0.5))`` will overlay the layer ``B`` with 50% transparency on top of the layer ``A``.
 
-    - ``m.show_layer("A|B{0.5}")`` will overlay the layer ``B`` with 50% transparency on top of the layer ``A``.
+    - Provide a **combined layer name** by separating the individual layer names you want to show with a ``"|"`` character.
 
+      - ``m.show_layer("A|B")`` will overlay all features of the layer ``B`` on top of the layer ``A``.
+      - To transparently overlay a layer, add the transparency to the layer-name in curly-brackets, e.g.: ``"<layer-name>{<transparency>}"``.
 
-.. code-block:: python
-    :name: test_transparent_layer_overlay
+        - ``m.show_layer("A|B{0.5}")`` will overlay the layer ``B`` with 50% transparency on top of the layer ``A``.
 
-    from eomaps import Maps
-    m = Maps(layer="first")
-    m.add_feature.physical.land(fc="k")
 
-    m2 = m.new_layer("second")                # create a new layer and plot some data
-    m2.add_feature.preset.ocean(zorder=2)
-    m2.set_data(data=[.14,.25,.38],
-                x=[10,20,30], y=[30,50,70],
-                crs=4326)
-    m2.plot_map(zorder=1)                     # plot the data "below" the ocean
+    .. code-block:: python
+        :name: test_transparent_layer_overlay
 
-    m.show_layer("first", ("second", .75))   # overlay the second layer with 25% transparency
+        from eomaps import Maps
+        m = Maps(layer="first")
+        m.add_feature.physical.land(fc="k")
 
+        m2 = m.new_layer("second")                # create a new layer and plot some data
+        m2.add_feature.preset.ocean(zorder=2)
+        m2.set_data(data=[.14,.25,.38],
+                    x=[10,20,30], y=[30,50,70],
+                    crs=4326)
+        m2.plot_map(zorder=1)                     # plot the data "below" the ocean
 
-.. currentmodule:: eomaps.callbacks.ClickCallbacks
+        m.show_layer("first", ("second", .75))   # overlay the second layer with 25% transparency
 
-🌱 If you want to overlay a part of the screen with a different layer, have a look at :py:meth:`peek_layer` callbacks**!
 
+.. dropdown:: Interactively overlay layers
+    :icon: info
+    :color: info
 
-.. autosummary::
-    :nosignatures:
+    .. currentmodule:: eomaps.callbacks.ClickCallbacks
 
-    peek_layer
+    If you want to interactively overlay a part of the screen with a different layer, have a look at :py:meth:`peek_layer` callbacks!
 
+    .. autosummary::
+        :nosignatures:
 
+        peek_layer
 
-.. code-block:: python
-    :name: test_peek_layer_cb
+    .. code-block:: python
+        :name: test_peek_layer_cb
 
-    from eomaps import Maps
-    m = Maps()
-    m.all.add_feature.preset.coastline()
-    m.add_feature.preset.urban_areas()
+        from eomaps import Maps
+        m = Maps()
+        m.all.add_feature.preset.coastline()
+        m.add_feature.preset.urban_areas()
 
-    m.add_feature.preset.ocean(layer="ocean")
-    m.add_feature.physical.land(layer="land", fc="g")
-    m.cb.click.attach.peek_layer(layer=["ocean", ("land", 0.5)], shape="round", how=0.4)
+        m.add_feature.preset.ocean(layer="ocean")
+        m.add_feature.physical.land(layer="land", fc="g")
+        m.cb.click.attach.peek_layer(layer=["ocean", ("land", 0.5)], shape="round", how=0.4)
 
 
-.. admonition:: The "stacking order" of features and layers
+.. dropdown:: The "stacking order" of features and layers
+    :icon: info
+    :color: info
 
     The stacking order of features at the **same layer** is controlled by the ``zorder`` argument.
 
@@ -306,14 +346,18 @@ To adjust the margins of the subplots, use :py:meth:`m.subplots_adjust`, or have
     m = Maps()
     m.subplots_adjust(left=0.1, right=0.9, bottom=0.05, top=0.95)
 
-.. admonition:: Export to clipboard (``ctrl + c``)
+.. dropdown:: Export to clipboard (``ctrl + c``)
+    :icon: info
+    :color: info
 
     If you use ``PyQt5`` as matplotlib-backend, you can also press (``control`` + ``c``) to export the figure to the clipboard.
 
     The export will be performed using the **currently set export-parameters** in the :ref:`companion_widget` .
     Alternatively, you can also programmatically set the export-parameters via :py:meth:`Maps.set_clipboard_kwargs` .
 
-.. admonition:: Notes on exporting high-dpi figures
+.. dropdown:: Notes on exporting high-dpi figures
+    :icon: info
+    :color: info
 
     EOmaps tries its best to follow the WYSIWYG concept (e.g. *"What You See Is What You Get"*).
     However, if you export the map with a dpi-value other than ``100``, there are certain circumstances
diff --git a/docs/conf.py b/docs/conf.py
index 99d53f9e2..712aab14d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -34,8 +34,6 @@ def setup(app):
     # add rc role to avoid undefined role warnings from inherited docstrings.
     app.add_role("rc", mpl_rc_role_subst)
 
-    app.add_css_file("custom_css.css")
-
     # add handling for skip-member event
     app.connect("autodoc-skip-member", autodoc_skip_member)
 
@@ -106,6 +104,7 @@ def setup(app):
 
 templates_path = ["_templates"]
 html_static_path = ["_static"]
+html_css_files = ["custom_css.css"]
 
 html_theme = "sphinx_rtd_theme"
 

From 6a64e77e21d450f547ae2cfb890d41ab14e32f29 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 27 Feb 2024 12:00:17 +0100
Subject: [PATCH 198/240] minor

---
 eomaps/scalebar.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/eomaps/scalebar.py b/eomaps/scalebar.py
index 76e6ddc5e..1561d7520 100644
--- a/eomaps/scalebar.py
+++ b/eomaps/scalebar.py
@@ -84,10 +84,10 @@ def __init__(
 
         Parameters
         ----------
-        lon, lat : float
+        pos : (float, float)
             The longitude and latitude of the starting point for the scalebar
             (If None, the center of the axis is used )
-        azim : float
+        rotation : float
             The azimuth-direction (in degrees) in which the scalebar points.
             The default is 90.
         preset : str

From 70058365f66bfc4bdb872c48eddc4843dd2747ec Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 27 Feb 2024 12:03:53 +0100
Subject: [PATCH 199/240] fix identification of coordinate-names on read_file
 (and in widget)

---
 eomaps/qtcompanion/widgets/files.py | 34 ++++++++++++++++-------------
 eomaps/reader.py                    | 19 ++++++++++++++--
 2 files changed, 36 insertions(+), 17 deletions(-)

diff --git a/eomaps/qtcompanion/widgets/files.py b/eomaps/qtcompanion/widgets/files.py
index c4dc3f20f..8e889618b 100644
--- a/eomaps/qtcompanion/widgets/files.py
+++ b/eomaps/qtcompanion/widgets/files.py
@@ -194,7 +194,7 @@ class ShapeSelector(QtWidgets.QFrame):
         aggregator=(str,),
     )
 
-    def __init__(self, *args, m=None, default_shape="shade_raster", **kwargs):
+    def __init__(self, *args, m=None, default_shape="shade_points", **kwargs):
         super().__init__(*args, **kwargs)
 
         self.m = m
@@ -319,7 +319,7 @@ def clear_item(self, item):
 class PlotFileWidget(QtWidgets.QWidget):
 
     file_endings = None
-    default_shape = "shade_raster"
+    default_shape = "shade_points"
 
     def __init__(
         self,
@@ -884,19 +884,23 @@ def do_open_file(self, file_path):
         self.x.set_complete_vals(cols)
         self.y.set_complete_vals(cols)
 
-        if "lon" in cols:
-            self.x.setText("lon")
-        elif "x" in cols:
-            self.x.setText("x")
-        else:
-            self.x.setText(cols[0])
-
-        if "lat" in cols:
-            self.y.setText("lat")
-        elif "y" in cols:
-            self.y.setText("y")
-        else:
-            self.x.setText(cols[1])
+        self.x.setText("?")
+        self.y.setText("?")
+
+        # check if coordinate variable-names can be identified
+        cols_lower = [i.casefold() for i in cols]
+        for c0, c1 in [
+            ("x", "y"),
+            ("lon", "lat"),
+            ("longitude", "latitude"),
+        ]:
+            if (c0.casefold() in cols_lower) and (c1.casefold() in cols_lower):
+                col0 = cols[cols_lower.index(c0)]
+                col1 = cols[cols_lower.index(c1)]
+
+                self.x.setText(col0)
+                self.y.setText(col1)
+                break
 
         self.parameter.set_complete_vals(cols)
         self.parameter.setText(
diff --git a/eomaps/reader.py b/eomaps/reader.py
index daf350b55..09c9a484a 100644
--- a/eomaps/reader.py
+++ b/eomaps/reader.py
@@ -464,8 +464,23 @@ def NetCDF(
 
             data = usencfile[parameter]
             if coords is None:
-                coords = list(data.dims)
-                if len(coords) != 2:
+                dims = list(data.dims)
+
+                # check if coordinate variable-names can be identified
+                dims_lower = [i.casefold() for i in dims]
+                for c0, c1 in [
+                    ("x", "y"),
+                    ("lon", "lat"),
+                    ("longitude", "latitude"),
+                ]:
+                    if (c0.casefold() in dims_lower) and (c1.casefold() in dims_lower):
+                        coords = (
+                            dims[dims_lower.index(c0)],
+                            dims[dims_lower.index(c1)],
+                        )
+                        break
+
+                if coords is None:
                     raise AssertionError(
                         "EOmaps: could not identify the coordinate-dimensions! "
                         + "Please provide coordinate-names explicitly via the "

From 79c0034774f2ba6ee63a5c7b3b5f4ce1713a36d4 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 27 Feb 2024 22:56:16 +0100
Subject: [PATCH 200/240] minor

---
 eomaps/compass.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/eomaps/compass.py b/eomaps/compass.py
index b9eb41f23..033dac0f6 100644
--- a/eomaps/compass.py
+++ b/eomaps/compass.py
@@ -81,7 +81,8 @@ def __call__(
 
             The default is "axes".
         scale : float, optional
-            A scale-factor for the size of the compass. The default is 10.
+            A scale-factor for the size of the compass in relation to the size of the
+            whole figure. The default is 10.
         style : str, optional
 
             - "north arrow" : draw only a north-arrow

From 286b8a328eb01d12bd00c68f3afe9a72df2b47e8 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 28 Feb 2024 11:13:06 +0100
Subject: [PATCH 201/240] minor

---
 docs/_static/custom_css.css | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/_static/custom_css.css b/docs/_static/custom_css.css
index 2b5e019a3..b4ec9fb22 100644
--- a/docs/_static/custom_css.css
+++ b/docs/_static/custom_css.css
@@ -2,7 +2,10 @@
    max-width: 1200px !important;
 }
 
-
+ /*
+ Set colors used by sphinx-design info-boxes
+ For details, see: https://sphinx-design.readthedocs.io/en/latest/css_variables.html
+ */
 :root {
     --sd-color-info: #b8c5c6;
     --sd-color-info-highlight: #148a9c;

From 9c35aa0357c971ea3171a4626a1e24badfc5a0be Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 28 Feb 2024 18:10:19 +0100
Subject: [PATCH 202/240] make sure to update after adding a text to a figure

---
 eomaps/eomaps.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index b01ff7228..34441a69e 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -3414,6 +3414,7 @@ def text(self, *args, layer=None, **kwargs):
         if layer is None:
             layer = self.layer
         self.BM.add_artist(a, layer=layer)
+        self.BM.update()
 
         return a
 

From a23eedace0dd5718da0af59331d28bb14ef9a358 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 28 Feb 2024 18:11:33 +0100
Subject: [PATCH 203/240] fix issues with string-type dpi values passed to
 savefig (encountered with datashader in JupyterLab if show is called after
 plot)

---
 eomaps/eomaps.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 34441a69e..aed277677 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -4141,12 +4141,14 @@ def _update_shade_axis_size(self, dpi=None):
 
         for m in (self.parent, *self.parent._children):
             if m.coll is not None and m.shape.name.startswith("shade_"):
-                if dpi is None:
-                    m.coll.plot_width = int(w)
-                    m.coll.plot_height = int(h)
-                else:
+                # TODO for now, only handle numeric dpi-values to avoid issues.
+                # (savefig also seems to support strings like "figure" etc.)
+                if isinstance(dpi, (int, float, np.number)):
                     m.coll.plot_width = int(w / fig_dpi * dpi)
                     m.coll.plot_height = int(h / fig_dpi * dpi)
+                else:
+                    m.coll.plot_width = int(w)
+                    m.coll.plot_height = int(h)
 
     def _on_close(self, event):
         # reset attributes that might use up a lot of memory when the figure is closed

From 11f3486db679e10512059ec5c714488082959434 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 28 Feb 2024 19:44:19 +0100
Subject: [PATCH 204/240] set version to 8.0rc0

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6a0d696e3..bc40ad12f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ eomaps = ["logo.png", "NE_features.json", "qtcompanion/icons/*"]
 
 [project]
 name = "eomaps"
-version = "8.0"
+version = "8.0rc0"
 description = "A library to create interactive maps of geographical datasets."
 readme = "README.md"
 license = {file = "LICENSE"}

From 4e90a41ac829e477532f5d3f82514f32b9b7d491 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 28 Feb 2024 21:11:09 +0100
Subject: [PATCH 205/240] explicitly include subpackes in pyproject.toml

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index bc40ad12f..24354cc40 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ requires = ["setuptools>=61.0"]
 build-backend = "setuptools.build_meta"
 
 [tool.setuptools.packages.find]
-include = ["eomaps"]
+include = ["eomaps", "eomaps.scripts", "eomaps.qtcompanion"]
 
 [tool.setuptools.package-data]
 eomaps = ["logo.png", "NE_features.json", "qtcompanion/icons/*"]

From c038a7c28b15489cd126ced25968a035673a0dff Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 4 Mar 2024 22:39:27 +0100
Subject: [PATCH 206/240] add widgets module

---
 eomaps/widgets.py | 379 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 379 insertions(+)
 create mode 100644 eomaps/widgets.py

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
new file mode 100644
index 000000000..57871f904
--- /dev/null
+++ b/eomaps/widgets.py
@@ -0,0 +1,379 @@
+from abc import abstractmethod
+from functools import wraps
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from . import _log
+from ._blit_manager import LayerParser
+
+try:
+    import ipywidgets
+except ImportError:
+    _log.exception("EOmaps-widgets are missing the required dependency 'ipywidgets'!")
+
+
+def _check_backend():
+    backend = plt.get_backend()
+    if "ipympl" not in backend.lower():
+        raise AssertionError(
+            "EOmaps-widgets only work with the 'ipympl (widget)' backend! "
+            "Make sure you have 'ipympl' installed and use the magic-command "
+            "'%matplotlib widget' to switch to the interactive jupyter backend!"
+        )
+
+
+# %% Layer Selector Widgets
+
+
+class _LayerSelectionWidget:
+    def __init__(self, m, layers=None):
+        """
+        A widget to switch layers of a given Maps-object.
+
+        Parameters
+        ----------
+        m : eomaps.Maps
+            The Maps-object to use.
+        layers : list, optional
+            A list of layer-names to use.
+            If None, all available layers of the provided Maps-object are used.
+
+            The following options are possible:
+
+            A list of layer-names
+
+            >>> ["layer1", "layer2", ...]
+
+            A list of layer-names and/or layer-names with transparency-assignments
+
+            >>> ["layer1", "layer2", ("layer", 0.4)]
+
+            To provide explict display-names for a layer, pass a list of the form
+            `[display-name, <layer-assignment>]`
+
+            >>> [["My Layer", "layer1"],
+            >>>  ["My secondl lyer", "layer2"],
+            >>>  ["Multiple layers", ("layer1",
+            >>>                       ("layer", 0.4))]
+            >>> ]
+
+        """
+        _check_backend()
+
+        self._m = m
+        self._set_layers(layers)
+
+    def _set_layers(self, layers):
+        if layers is None:
+            self._layers = self._m._get_layers()
+        else:
+
+            self._layers = []
+            for l in layers:
+                if isinstance(l, str):
+                    self._layers.append((l, l))
+                elif isinstance(l, tuple):
+                    l = self._parse_layer(l)
+                    self._layers.append((l, l))
+                elif isinstance(l, list):
+                    self._layers.append((l[0], self._parse_layer(l[1])))
+
+    @staticmethod
+    def _parse_layer(l):
+        # check if a single transparent layer is provided
+        if isinstance(l, tuple):
+            if (
+                len(l) == 2
+                and isinstance(l[0], str)
+                and isinstance(l[1], (int, float, np.number))
+            ):
+                return LayerParser._get_combined_layer_name(l)
+            else:
+                return LayerParser._get_combined_layer_name(*l)
+        else:
+            return l
+
+    def handler(self, change):
+        try:
+            if self.value is not None:
+                self._m.show_layer(self.value)
+                self._m.BM.update()
+        except Exception:
+            _log.error("Problem in LayerSelectionWidget handler...", exc_info=True)
+
+
+class LayerDropdown(ipywidgets.Dropdown, _LayerSelectionWidget):
+    _description = "Layers"
+
+    @wraps(_LayerSelectionWidget.__init__)
+    def __init__(self, *args, description=None, **kwargs):
+
+        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+
+        super().__init__(
+            options=self._layers,
+            description=self._description if description is None else description,
+            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
+        )
+
+        self.observe(self.handler)
+
+
+class LayerSelect(ipywidgets.Select, _LayerSelectionWidget):
+    _description = "Layers"
+
+    @wraps(_LayerSelectionWidget.__init__)
+    def __init__(self, *args, description=None, **kwargs):
+
+        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+
+        super().__init__(
+            options=self._layers,
+            description=self._description if description is None else description,
+            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
+        )
+
+        self.observe(self.handler)
+
+
+class LayerSelectionSlider(ipywidgets.SelectionSlider, _LayerSelectionWidget):
+    _description = "Layers"
+
+    @wraps(_LayerSelectionWidget.__init__)
+    def __init__(self, *args, description=None, **kwargs):
+
+        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+
+        super().__init__(
+            options=self._layers,
+            description=self._description if description is None else description,
+            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
+        )
+
+        self.observe(self.handler)
+
+
+class LayerToggleButtons(ipywidgets.ToggleButtons, _LayerSelectionWidget):
+    _description = "Layers"
+
+    @wraps(_LayerSelectionWidget.__init__)
+    def __init__(self, *args, description=None, **kwargs):
+
+        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+
+        super().__init__(
+            options=self._layers,
+            description=self._description if description is None else description,
+            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
+        )
+
+        self.observe(self.handler)
+
+
+class LayerRadioButtons(ipywidgets.RadioButtons, _LayerSelectionWidget):
+    _description = "Layers"
+
+    @wraps(_LayerSelectionWidget.__init__)
+    def __init__(self, *args, description=None, **kwargs):
+
+        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+
+        super().__init__(
+            options=self._layers,
+            description=self._description if description is None else description,
+            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
+        )
+
+        self.observe(self.handler)
+
+
+class LayerSelectionRangeSlider(ipywidgets.SelectionRangeSlider, _LayerSelectionWidget):
+    _description = "Layers"
+
+    @wraps(_LayerSelectionWidget.__init__)
+    def __init__(self, *args, description=None, **kwargs):
+
+        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+
+        super().__init__(
+            options=self._layers,
+            description=self._description if description is None else description,
+            value=(self._m.BM.bg_layer, self._m.BM.bg_layer)
+            if self._m.BM.bg_layer in self._layers
+            else (self._layers[0][1],),
+        )
+
+        self.observe(self.handler)
+
+    def handler(self, change):
+        try:
+            if self.value is not None:
+                i0 = self._layers.index(self.value[0])
+                i1 = self._layers.index(self.value[1])
+
+                if i0 == i1:
+                    self._m.show_layer(self.value[0])
+                else:
+                    self._m.show_layer(*self._layers[i0 : i1 + 1])
+        except Exception:
+            _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
+
+
+class LayerSelectMultiple(ipywidgets.SelectMultiple, _LayerSelectionWidget):
+    _description = "Layers"
+
+    @wraps(_LayerSelectionWidget.__init__)
+    def __init__(self, *args, description=None, **kwargs):
+
+        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+        super().__init__(
+            options=self._layers,
+            description=self._description if description is None else description,
+            value=(self._m.BM.bg_layer,)
+            if self._m.BM.bg_layer in self._layers
+            else (self._layers[0][1],),
+        )
+
+        self.observe(self.handler)
+
+    def handler(self, change):
+        try:
+            if self.value is not None:
+                self._m.show_layer(*self.value)
+        except Exception:
+            _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
+
+
+# %% Overlay Widgets
+
+
+class OverlaySlider(ipywidgets.FloatSlider):
+    def __init__(self, m, layer):
+        """
+        A Slider to overlay a selected layer on top of other layers
+
+        Parameters
+        ----------
+        m : eomaps.Maps
+            The Maps-object to use.
+        layer : str
+            The layer to overlay.
+
+        """
+        self._m = m
+        _check_backend()
+
+        self._layer = layer
+
+        super().__init__(
+            value=0, min=0, max=1, step=0.01, description=f"Overlay\n'{layer}':"
+        )
+
+        self._last_value = self.value
+
+        self.observe(self.handler)
+
+    def handler(self, change):
+        try:
+            layers, alphas = LayerParser._parse_multi_layer_str(self._m.BM.bg_layer)
+
+            # in case the active layer has the overlay on top, strip off the overlay
+            # from the active layer!
+            if layers[-1] == self._layer and alphas[-1] == self._last_value:
+                base = LayerParser._get_combined_layer_name(
+                    *zip(layers[:-1], alphas[:-1])
+                )
+            else:
+                base = self._m.BM.bg_layer
+
+            self._m.show_layer(base, (self._layer, self.value))
+
+            self._last_value = self.value
+            plt.pause(0.01)  # spin event-loop to avoid flickering for very fast updates
+        except Exception:
+            _log.error("Problem in OverlaySlider handler...", exc_info=True)
+
+
+# %% CallbackWidgets
+
+
+class _CallbackWidget:
+    def __init__(self, m, **kwargs):
+        self._m = m
+        _check_backend()
+
+        self._kwargs = kwargs
+        self._cid = None
+
+    @abstractmethod
+    def attach_callback(self, **kwargs):
+        """Attach the callback to the map and return the cid."""
+        return "cid"
+
+    def handler(self, change):
+        try:
+            if self.value is True and self._cid is None:
+                self._cid = self.attach_callback(**self._kwargs)
+
+            if self.value is False and self._cid is not None:
+                self._cid = self._m.all.cb.click.remove(self._cid)
+        except Exception:
+            _log.error("Problem in Checkbox handler...", exc_info=True)
+
+
+class _CallbackCheckbox(ipywidgets.Checkbox, _CallbackWidget):
+    _description = "Callback Checkbox"
+
+    @wraps(_CallbackWidget.__init__)
+    def __init__(self, *args, value=False, description=None, **kwargs):
+        _CallbackWidget.__init__(self, *args, **kwargs)
+        super().__init__(
+            value=value,
+            description=description if description is not None else self._description,
+        )
+        self.observe(self.handler)
+
+
+class ClickAnnotateCheckbox(_CallbackCheckbox):
+    _description = "Annotate (Click)"
+
+    def attach_callback(self, **kwargs):
+        return self._m.all.cb.click.attach.annotate(**kwargs)
+
+
+class ClickMarkCheckbox(_CallbackCheckbox):
+    _description = "Mark (Click)"
+
+    def attach_callback(self, **kwargs):
+        return self._m.all.cb.click.attach.mark(**kwargs)
+
+
+class ClickPrintToConsoleCheckbox(_CallbackCheckbox):
+    _description = "Print (Click)"
+
+    def attach_callback(self, **kwargs):
+        return self._m.all.cb.click.attach.print_to_console(**kwargs)
+
+
+# NOTE: pick callbacks are attached to the provided Maps objects,
+#       click callback are attached to m.all!
+class PickAnnotateCheckbox(_CallbackCheckbox):
+    _description = "Annotate (Pick)"
+
+    def attach_callback(self, **kwargs):
+        return self._m.cb.pick.attach.annotate(**kwargs)
+
+
+class PickMarkCheckbox(_CallbackCheckbox):
+    _description = "Mark (Pick)"
+
+    def attach_callback(self, **kwargs):
+        return self._m.cb.pick.attach.mark(**kwargs)
+
+
+class PickPrintToConsoleCheckbox(_CallbackCheckbox):
+    _description = "Print (Pick)"
+
+    def attach_callback(self, **kwargs):
+        return self._m.cb.pick.attach.print_to_console(**kwargs)

From 6dc34e4c6fe05f73e0888ada9b5069828738af03 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 4 Mar 2024 22:40:02 +0100
Subject: [PATCH 207/240] don't call .show() automatically with ipympl backend

---
 eomaps/eomaps.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index aed277677..eb4b2cd46 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -4102,11 +4102,6 @@ def _init_figure(self, ax=None, plot_crs=None, **kwargs):
             self.parent._layout_editor = LayoutEditor(self.parent, modifier="alt+l")
 
         active_backend = plt.get_backend()
-        # we only need to call show if a new figure has been created!
-        if newfig and active_backend == "module://ipympl.backend_nbagg":
-            # make sure to call show only if we use an interactive backend...
-            # or within the ipympl backend (otherwise it will block subsequent code!)
-            plt.show()
 
         if active_backend == "module://matplotlib_inline.backend_inline":
             # close the figure to avoid duplicated (empty) plots created

From d52b078a76ccb6c9eff4d4ba9c045fc307ec66b0 Mon Sep 17 00:00:00 2001
From: Raphael Quast <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 4 Mar 2024 23:43:27 +0100
Subject: [PATCH 208/240] add PeekLayerCheckbox widget

---
 eomaps/widgets.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index 57871f904..27a6ab64f 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -356,6 +356,22 @@ def attach_callback(self, **kwargs):
         return self._m.all.cb.click.attach.print_to_console(**kwargs)
 
 
+class ClickPeekLayerCheckbox(_CallbackCheckbox):
+    _description = "Peek Layer (Click)"
+
+    def __init__(self, *args, layer=None, **kwargs):
+        assert (
+            layer is not None
+        ), "EOmaps: You must specify the layer for the PeekLayerCheckbox!"
+
+        self._description = f"Peek Layer: '{layer}'"
+
+        super().__init__(*args, layer=layer, **kwargs)
+
+    def attach_callback(self, **kwargs):
+        return self._m.all.cb.click.attach.peek_layer(**kwargs)
+
+
 # NOTE: pick callbacks are attached to the provided Maps objects,
 #       click callback are attached to m.all!
 class PickAnnotateCheckbox(_CallbackCheckbox):

From 9c433c5bef1a65f78807eb5f161f89f4852e1a0c Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Tue, 5 Mar 2024 18:01:37 +0100
Subject: [PATCH 209/240] update widgets - improve inheritance concept - pass
 additional kwargs to ipython widgets - force full updates on layer-changes

---
 eomaps/widgets.py | 208 ++++++++++++++++++++--------------------------
 1 file changed, 88 insertions(+), 120 deletions(-)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index 27a6ab64f..5fadf909d 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -1,5 +1,6 @@
 from abc import abstractmethod
 from functools import wraps
+from contextlib import contextmanager
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -23,10 +24,24 @@ def _check_backend():
         )
 
 
+@contextmanager
+def _force_full(m):
+    """A contextmanager to force a full update of the figure (to avoid glitches)"""
+    force_full = getattr(m.BM, "_mpl_backend_force_full", False)
+
+    try:
+        m.BM._mpl_backend_force_full = True
+        yield
+    finally:
+        m.BM._mpl_backend_force_full = force_full
+
+
 # %% Layer Selector Widgets
 
 
 class _LayerSelectionWidget:
+    _description = "LayerSelectionWidget"
+
     def __init__(self, m, layers=None):
         """
         A widget to switch layers of a given Maps-object.
@@ -62,22 +77,28 @@ def __init__(self, m, layers=None):
         _check_backend()
 
         self._m = m
-        self._set_layers(layers)
+        self._set_layers_options(layers)
+
+    def _set_layers_options(self, layers):
+        # _layers is a list of the actual layer-names
+        # _options is a list of tuples (name, value) passed to the widget-init
 
-    def _set_layers(self, layers):
         if layers is None:
             self._layers = self._m._get_layers()
+            self._options = [*self._layers]
         else:
-
-            self._layers = []
+            self._layers, self._options = [], []
             for l in layers:
                 if isinstance(l, str):
-                    self._layers.append((l, l))
+                    self._layers.append(l)
+                    self._options.append((l, l))
                 elif isinstance(l, tuple):
                     l = self._parse_layer(l)
-                    self._layers.append((l, l))
+                    self._layers.append(l)
+                    self._options.append((l, l))
                 elif isinstance(l, list):
-                    self._layers.append((l[0], self._parse_layer(l[1])))
+                    self._options.append((l[0], self._parse_layer(l[1])))
+                    self._layers.append(self._parse_layer(l[1]))
 
     @staticmethod
     def _parse_layer(l):
@@ -94,153 +115,94 @@ def _parse_layer(l):
         else:
             return l
 
-    def handler(self, change):
-        try:
-            if self.value is not None:
-                self._m.show_layer(self.value)
-                self._m.BM.update()
-        except Exception:
-            _log.error("Problem in LayerSelectionWidget handler...", exc_info=True)
-
 
-class LayerDropdown(ipywidgets.Dropdown, _LayerSelectionWidget):
+class _SingleLayerSelectionWidget(_LayerSelectionWidget):
     _description = "Layers"
+    _widget_cls = None
 
     @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, *args, description=None, **kwargs):
+    def __init__(self, m, layers=None, **kwargs):
 
-        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+        _LayerSelectionWidget.__init__(self, m=m, layers=layers)
 
-        super().__init__(
-            options=self._layers,
-            description=self._description if description is None else description,
-            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
-        )
+        self._set_default_kwargs(kwargs)
+        self._widget_cls.__init__(self, options=self._options, **kwargs)
 
         self.observe(self.handler)
 
+    def _set_default_kwargs(self, kwargs):
+        kwargs.setdefault("description", self._description)
+        if self._m.BM.bg_layer in self._layers:
+            kwargs.setdefault("value", self._m.BM.bg_layer)
 
-class LayerSelect(ipywidgets.Select, _LayerSelectionWidget):
-    _description = "Layers"
-
-    @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, *args, description=None, **kwargs):
-
-        _LayerSelectionWidget.__init__(self, *args, **kwargs)
-
-        super().__init__(
-            options=self._layers,
-            description=self._description if description is None else description,
-            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
-        )
-
-        self.observe(self.handler)
-
-
-class LayerSelectionSlider(ipywidgets.SelectionSlider, _LayerSelectionWidget):
-    _description = "Layers"
-
-    @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, *args, description=None, **kwargs):
-
-        _LayerSelectionWidget.__init__(self, *args, **kwargs)
-
-        super().__init__(
-            options=self._layers,
-            description=self._description if description is None else description,
-            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
-        )
-
-        self.observe(self.handler)
-
+    def handler(self, change):
+        try:
+            if self.value is not None:
+                with _force_full(self._m):
+                    self._m.show_layer(self.value)
+                    self._m.BM.update()
 
-class LayerToggleButtons(ipywidgets.ToggleButtons, _LayerSelectionWidget):
-    _description = "Layers"
+        except Exception:
+            _log.error("Problem in LayerSelectionWidget handler...", exc_info=True)
 
-    @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, *args, description=None, **kwargs):
 
-        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+class _MultiLayerSelectionWidget(_SingleLayerSelectionWidget):
+    def _set_default_kwargs(self, kwargs):
+        kwargs.setdefault("description", self._description)
 
-        super().__init__(
-            options=self._layers,
-            description=self._description if description is None else description,
-            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
-        )
-
-        self.observe(self.handler)
+        if self._m.BM.bg_layer in self._layers:
+            kwargs.setdefault("value", (self._m.BM.bg_layer, self._m.BM.bg_layer))
+        else:
+            kwargs.setdefault("value", (self._layers[0][1],))
 
 
-class LayerRadioButtons(ipywidgets.RadioButtons, _LayerSelectionWidget):
-    _description = "Layers"
+class LayerDropdown(_SingleLayerSelectionWidget, ipywidgets.Dropdown):
+    _widget_cls = ipywidgets.Dropdown
 
-    @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, *args, description=None, **kwargs):
 
-        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+class LayerSelect(_SingleLayerSelectionWidget, ipywidgets.Select):
+    _widget_cls = ipywidgets.Select
 
-        super().__init__(
-            options=self._layers,
-            description=self._description if description is None else description,
-            value=self._m.BM.bg_layer if self._m.BM.bg_layer in self._layers else None,
-        )
 
-        self.observe(self.handler)
+class LayerSelectionSlider(_SingleLayerSelectionWidget, ipywidgets.SelectionSlider):
+    _widget_cls = ipywidgets.SelectionSlider
 
 
-class LayerSelectionRangeSlider(ipywidgets.SelectionRangeSlider, _LayerSelectionWidget):
-    _description = "Layers"
+class LayerToggleButtons(_SingleLayerSelectionWidget, ipywidgets.ToggleButtons):
+    _widget_cls = ipywidgets.ToggleButtons
 
-    @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, *args, description=None, **kwargs):
 
-        _LayerSelectionWidget.__init__(self, *args, **kwargs)
+class LayerRadioButtons(_SingleLayerSelectionWidget, ipywidgets.RadioButtons):
+    _widget_cls = ipywidgets.RadioButtons
 
-        super().__init__(
-            options=self._layers,
-            description=self._description if description is None else description,
-            value=(self._m.BM.bg_layer, self._m.BM.bg_layer)
-            if self._m.BM.bg_layer in self._layers
-            else (self._layers[0][1],),
-        )
 
-        self.observe(self.handler)
+class LayerSelectionRangeSlider(
+    _MultiLayerSelectionWidget, ipywidgets.SelectionRangeSlider
+):
+    _widget_cls = ipywidgets.SelectionRangeSlider
 
     def handler(self, change):
         try:
             if self.value is not None:
                 i0 = self._layers.index(self.value[0])
                 i1 = self._layers.index(self.value[1])
-
-                if i0 == i1:
-                    self._m.show_layer(self.value[0])
-                else:
-                    self._m.show_layer(*self._layers[i0 : i1 + 1])
+                with _force_full(self._m):
+                    if i0 == i1:
+                        self._m.show_layer(self.value[0])
+                    else:
+                        self._m.show_layer(*self._layers[i0 : i1 + 1])
         except Exception:
             _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
 
 
-class LayerSelectMultiple(ipywidgets.SelectMultiple, _LayerSelectionWidget):
-    _description = "Layers"
-
-    @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, *args, description=None, **kwargs):
-
-        _LayerSelectionWidget.__init__(self, *args, **kwargs)
-        super().__init__(
-            options=self._layers,
-            description=self._description if description is None else description,
-            value=(self._m.BM.bg_layer,)
-            if self._m.BM.bg_layer in self._layers
-            else (self._layers[0][1],),
-        )
-
-        self.observe(self.handler)
+class LayerSelectMultiple(_MultiLayerSelectionWidget, ipywidgets.SelectMultiple):
+    _widget_cls = ipywidgets.SelectMultiple
 
     def handler(self, change):
         try:
             if self.value is not None:
-                self._m.show_layer(*self.value)
+                with _force_full(self._m):
+                    self._m.show_layer(*self.value)
         except Exception:
             _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
 
@@ -249,7 +211,7 @@ def handler(self, change):
 
 
 class OverlaySlider(ipywidgets.FloatSlider):
-    def __init__(self, m, layer):
+    def __init__(self, m, layer, **kwargs):
         """
         A Slider to overlay a selected layer on top of other layers
 
@@ -259,6 +221,8 @@ def __init__(self, m, layer):
             The Maps-object to use.
         layer : str
             The layer to overlay.
+        kwargs:
+            Additional kwargs passed to the used `ipywidgets.FloatSlider`.
 
         """
         self._m = m
@@ -266,9 +230,13 @@ def __init__(self, m, layer):
 
         self._layer = layer
 
-        super().__init__(
-            value=0, min=0, max=1, step=0.01, description=f"Overlay\n'{layer}':"
-        )
+        kwargs.setdefault("value", 0)
+        kwargs.setdefault("min", 0)
+        kwargs.setdefault("max", 1)
+        kwargs.setdefault("step", 0.01)
+        kwargs.setdefault("description", f"Overlay\n'{layer}':")
+
+        super().__init__(**kwargs)
 
         self._last_value = self.value
 
@@ -287,10 +255,10 @@ def handler(self, change):
             else:
                 base = self._m.BM.bg_layer
 
-            self._m.show_layer(base, (self._layer, self.value))
+            with _force_full(self._m):
+                self._m.show_layer(base, (self._layer, self.value))
 
             self._last_value = self.value
-            plt.pause(0.01)  # spin event-loop to avoid flickering for very fast updates
         except Exception:
             _log.error("Problem in OverlaySlider handler...", exc_info=True)
 

From 600aae88a0c0241c6809b131140e21af0812c80a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 6 Mar 2024 19:51:03 +0100
Subject: [PATCH 210/240] add basic docstrings for widgets

---
 eomaps/widgets.py | 187 +++++++++++++++++++++++++++++++++-------------
 1 file changed, 137 insertions(+), 50 deletions(-)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index 5fadf909d..fe8acd6e2 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -36,44 +36,71 @@ def _force_full(m):
         m.BM._mpl_backend_force_full = force_full
 
 
+from textwrap import dedent, indent
+
+
+def _add_docstring(prefix="", suffix="", replace_with=None):
+    def _add_docstring(cls):
+
+        if replace_with is None and cls.__doc__ is not None:
+            doc = f"{prefix}\n{dedent(cls.__doc__)}\n{suffix}"
+        elif replace_with is not None:
+            doc = f"{prefix}\n{dedent(replace_with.__doc__)}\n{suffix}"
+        else:
+            doc = f"{prefix}\n{dedent(suffix)}"
+
+        doc = indent(doc, "    ")
+        cls.__doc__ = doc
+        cls.__init__.__doc__ = doc
+
+        return cls
+
+    return _add_docstring
+
+
 # %% Layer Selector Widgets
 
 
 class _LayerSelectionWidget:
-    _description = "LayerSelectionWidget"
+    # A widget to switch layers of a given Maps-object.
 
-    def __init__(self, m, layers=None):
-        """
-        A widget to switch layers of a given Maps-object.
+    """
+
+    For more information on how to customize the widgets, have a look at the
+    documentation for Jupyter Widgets (https://ipywidgets.readthedocs.io).
 
-        Parameters
-        ----------
-        m : eomaps.Maps
-            The Maps-object to use.
-        layers : list, optional
-            A list of layer-names to use.
-            If None, all available layers of the provided Maps-object are used.
+    Parameters
+    ----------
+    m : eomaps.Maps
+        The Maps-object to use.
+    layers : list, optional
+        A list of layer-names to use.
+        If None, all available layers of the provided Maps-object are used.
 
-            The following options are possible:
+        The following options are possible:
 
-            A list of layer-names
+        A list of layer-names
 
-            >>> ["layer1", "layer2", ...]
+        >>> ["layer1", "layer2", ...]
 
-            A list of layer-names and/or layer-names with transparency-assignments
+        A list of layer-names and/or layer-names with transparency-assignments
 
-            >>> ["layer1", "layer2", ("layer", 0.4)]
+        >>> ["layer1", "layer2", ("layer", 0.4)]
 
-            To provide explict display-names for a layer, pass a list of the form
-            `[display-name, <layer-assignment>]`
+        To provide explict display-names for a layer, pass a list of the form
+        `[display-name, <layer-assignment>]`
 
-            >>> [["My Layer", "layer1"],
-            >>>  ["My secondl lyer", "layer2"],
-            >>>  ["Multiple layers", ("layer1",
-            >>>                       ("layer", 0.4))]
-            >>> ]
+        >>> [["My Layer", "layer1"],
+        >>>  ["My secondl lyer", "layer2"],
+        >>>  ["Multiple layers", ("layer1",
+        >>>                       ("layer", 0.4))]
+        >>> ]
 
-        """
+    """
+
+    _description = "LayerSelectionWidget"
+
+    def __init__(self, m, layers=None):
         _check_backend()
 
         self._m = m
@@ -156,26 +183,62 @@ def _set_default_kwargs(self, kwargs):
             kwargs.setdefault("value", (self._layers[0][1],))
 
 
+@_add_docstring(
+    "A Dropdown list to select the visible layer.", replace_with=_LayerSelectionWidget
+)
 class LayerDropdown(_SingleLayerSelectionWidget, ipywidgets.Dropdown):
     _widget_cls = ipywidgets.Dropdown
 
 
+@_add_docstring(
+    "A list-box to select a single visible layer.", replace_with=_LayerSelectionWidget
+)
 class LayerSelect(_SingleLayerSelectionWidget, ipywidgets.Select):
     _widget_cls = ipywidgets.Select
 
 
-class LayerSelectionSlider(_SingleLayerSelectionWidget, ipywidgets.SelectionSlider):
-    _widget_cls = ipywidgets.SelectionSlider
+@_add_docstring(
+    "A list-box to select multiple visible layers.", replace_with=_LayerSelectionWidget
+)
+class LayerSelectMultiple(_MultiLayerSelectionWidget, ipywidgets.SelectMultiple):
+    _widget_cls = ipywidgets.SelectMultiple
+
+    def handler(self, change):
+        try:
+            if self.value is not None:
+                with _force_full(self._m):
+                    self._m.show_layer(*self.value)
+        except Exception:
+            _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
 
 
+@_add_docstring(
+    "Toggle buttons to select a single visible layer.",
+    replace_with=_LayerSelectionWidget,
+)
 class LayerToggleButtons(_SingleLayerSelectionWidget, ipywidgets.ToggleButtons):
     _widget_cls = ipywidgets.ToggleButtons
 
 
+@_add_docstring(
+    "Radio buttons to select a single visible layer.",
+    replace_with=_LayerSelectionWidget,
+)
 class LayerRadioButtons(_SingleLayerSelectionWidget, ipywidgets.RadioButtons):
     _widget_cls = ipywidgets.RadioButtons
 
 
+@_add_docstring(
+    "A slider to select a single visible layer.", replace_with=_LayerSelectionWidget
+)
+class LayerSelectionSlider(_SingleLayerSelectionWidget, ipywidgets.SelectionSlider):
+    _widget_cls = ipywidgets.SelectionSlider
+
+
+@_add_docstring(
+    "A range-slider to view a combination of a range of layers.",
+    replace_with=_LayerSelectionWidget,
+)
 class LayerSelectionRangeSlider(
     _MultiLayerSelectionWidget, ipywidgets.SelectionRangeSlider
 ):
@@ -195,36 +258,25 @@ def handler(self, change):
             _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
 
 
-class LayerSelectMultiple(_MultiLayerSelectionWidget, ipywidgets.SelectMultiple):
-    _widget_cls = ipywidgets.SelectMultiple
+# %% Layer Overlay Widgets
 
-    def handler(self, change):
-        try:
-            if self.value is not None:
-                with _force_full(self._m):
-                    self._m.show_layer(*self.value)
-        except Exception:
-            _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
 
+class LayerOverlaySlider(ipywidgets.FloatSlider):
+    """
+    A Slider to overlay a selected layer on top of other layers.
 
-# %% Overlay Widgets
+    Parameters
+    ----------
+    m : eomaps.Maps
+        The Maps-object to use.
+    layer : str
+        The layer to overlay.
+    kwargs:
+        Additional kwargs passed to the used `ipywidgets.FloatSlider`.
 
+    """
 
-class OverlaySlider(ipywidgets.FloatSlider):
     def __init__(self, m, layer, **kwargs):
-        """
-        A Slider to overlay a selected layer on top of other layers
-
-        Parameters
-        ----------
-        m : eomaps.Maps
-            The Maps-object to use.
-        layer : str
-            The layer to overlay.
-        kwargs:
-            Additional kwargs passed to the used `ipywidgets.FloatSlider`.
-
-        """
         self._m = m
         _check_backend()
 
@@ -267,6 +319,18 @@ def handler(self, change):
 
 
 class _CallbackWidget:
+    """
+
+    For more information on how to customize the widgets, have a look at the
+    documentation for Jupyter Widgets (https://ipywidgets.readthedocs.io).
+
+    Parameters
+    ----------
+    m : eomaps.Maps
+        The Maps-object to use.
+
+    """
+
     def __init__(self, m, **kwargs):
         self._m = m
         _check_backend()
@@ -303,6 +367,9 @@ def __init__(self, *args, value=False, description=None, **kwargs):
         self.observe(self.handler)
 
 
+@_add_docstring(
+    "Checkbox to toggle the 'click.annotate' callback.", replace_with=_CallbackWidget
+)
 class ClickAnnotateCheckbox(_CallbackCheckbox):
     _description = "Annotate (Click)"
 
@@ -310,6 +377,9 @@ def attach_callback(self, **kwargs):
         return self._m.all.cb.click.attach.annotate(**kwargs)
 
 
+@_add_docstring(
+    "Checkbox to toggle the 'click.mark' callback.", replace_with=_CallbackWidget
+)
 class ClickMarkCheckbox(_CallbackCheckbox):
     _description = "Mark (Click)"
 
@@ -317,6 +387,10 @@ def attach_callback(self, **kwargs):
         return self._m.all.cb.click.attach.mark(**kwargs)
 
 
+@_add_docstring(
+    "Checkbox to toggle the 'click.print_to_console' callback.",
+    replace_with=_CallbackWidget,
+)
 class ClickPrintToConsoleCheckbox(_CallbackCheckbox):
     _description = "Print (Click)"
 
@@ -324,6 +398,9 @@ def attach_callback(self, **kwargs):
         return self._m.all.cb.click.attach.print_to_console(**kwargs)
 
 
+@_add_docstring(
+    "Checkbox to toggle the 'click.peek_layer' callback.", replace_with=_CallbackWidget
+)
 class ClickPeekLayerCheckbox(_CallbackCheckbox):
     _description = "Peek Layer (Click)"
 
@@ -342,6 +419,9 @@ def attach_callback(self, **kwargs):
 
 # NOTE: pick callbacks are attached to the provided Maps objects,
 #       click callback are attached to m.all!
+@_add_docstring(
+    "Checkbox to toggle the 'pick.annotate' callback.", replace_with=_CallbackWidget
+)
 class PickAnnotateCheckbox(_CallbackCheckbox):
     _description = "Annotate (Pick)"
 
@@ -349,6 +429,9 @@ def attach_callback(self, **kwargs):
         return self._m.cb.pick.attach.annotate(**kwargs)
 
 
+@_add_docstring(
+    "Checkbox to toggle the 'pick.mark' callback.", replace_with=_CallbackWidget
+)
 class PickMarkCheckbox(_CallbackCheckbox):
     _description = "Mark (Pick)"
 
@@ -356,6 +439,10 @@ def attach_callback(self, **kwargs):
         return self._m.cb.pick.attach.mark(**kwargs)
 
 
+@_add_docstring(
+    "Checkbox to toggle the 'pick.print_to_console' callback.",
+    replace_with=_CallbackWidget,
+)
 class PickPrintToConsoleCheckbox(_CallbackCheckbox):
     _description = "Print (Pick)"
 

From 2f9996407eafb40eb19defbb40d7eaf9dc8de1b4 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 6 Mar 2024 19:56:47 +0100
Subject: [PATCH 211/240] add section on Jupyter Widgets to the docs

---
 docs/gen_autodoc_file.py     |   7 +-
 docs/index.rst               |  11 +
 docs/notebooks/widgets.ipynb | 394 +++++++++++++++++++++++++++++++++++
 eomaps/widgets.py            |   2 +-
 4 files changed, 412 insertions(+), 2 deletions(-)
 create mode 100644 docs/notebooks/widgets.ipynb

diff --git a/docs/gen_autodoc_file.py b/docs/gen_autodoc_file.py
index 549e91325..dd680900c 100644
--- a/docs/gen_autodoc_file.py
+++ b/docs/gen_autodoc_file.py
@@ -2,7 +2,7 @@
 from operator import attrgetter
 from itertools import chain
 
-from eomaps import Maps
+from eomaps import Maps, widgets
 
 # TODO there must be a better way than this...
 # BM needs to be a property otherwise there are problems with jupyter notebooks
@@ -120,6 +120,11 @@ def make_feature_toctree_file():
         "obj_with_attributes_no_toc",
     )
 
+    s += get_autosummary(
+        "eomaps.widgets",
+        [i for i in get_members(widgets) if not i.rsplit(".", 1)[-1][0].islower()],
+    )
+
     basepath = Path(__file__).parent
 
     with open(basepath / "api" / "autodoc_additional_props.rst", "w") as file:
diff --git a/docs/index.rst b/docs/index.rst
index 7d810a0b5..4bd0221f6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -212,6 +212,16 @@ With a few lines of code, you can turn your maps into interactive data-analysis
 
         A collection of utility widgets (layer-sliders, layer-selectors)
 
+    .. grid-item-card:: :doc:`notebooks/widgets`
+        :link: notebooks/widgets
+        :link-type: doc
+        :shadow: none
+
+        A collection of Jupyter Widgets (for Jupyter Notebooks)
+
+
+
+
 
 Miscellaneous
 ~~~~~~~~~~~~~
@@ -316,6 +326,7 @@ Make sure to check out the :doc:`Examples <EOmaps_examples>` for an overview of
     api_companion_widget
     api_layout_editor
     api_draw
+    notebooks/widgets.ipynb
     api_utils
 
 
diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
new file mode 100644
index 000000000..bfa1e7b33
--- /dev/null
+++ b/docs/notebooks/widgets.ipynb
@@ -0,0 +1,394 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0b4e5c01-36f1-4426-8f43-b877a2bbea8f",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "385f7c06-a5c6-4035-a134-55b4808f875b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# 🕹 Jupyter Widgets\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "EOmaps provides a set of pre-configured [Jupyter Widgets](https://ipywidgets.readthedocs.io) that can be used to create interactive browser controls for a map. \n",
+    "\n",
+    "These widgets work just like any other [Jupyter Widgets](https://ipywidgets.readthedocs.io), but they have a pre-configured handler to perform commonly used tasks (switching layers, overlay layers, add/remove callbacks etc.)\n",
+    "\n",
+    "\n",
+    ":::{note}\n",
+    "\n",
+    "Widgets are **only for Jupyter Notebooks** and require the additional dependencies: `ipywidgets` and `ipympl`\n",
+    "\n",
+    "You can install it with `pip`:\n",
+    "```\n",
+    "pip install ipywidgets, ipympl\n",
+    "```\n",
+    "\n",
+    "or with `conda`:\n",
+    "```\n",
+    "conda install -c conda-forge ipywidgets, ipympl\n",
+    "```\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d648dada-2208-475b-bedd-c977a6badfc4",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "**The following pre-defined widgets exist:**\n",
+    "\n",
+    ":::{dropdown} Widgets to change the visible layer\n",
+    "\n",
+    "```{eval-rst}\n",
+    "\n",
+    ".. currentmodule:: eomaps.widgets\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   LayerDropdown\n",
+    "   LayerSelect\n",
+    "   LayerSelectMultiple\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "\n",
+    "   LayerSelectionSlider\n",
+    "   LayerSelectionRangeSlider\n",
+    "   \n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "\n",
+    "   LayerRadioButtons\n",
+    "   LayerToggleButtons\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   LayerOverlaySlider\n",
+    "\n",
+    "```\n",
+    ":::\n",
+    "\n",
+    "\n",
+    "\n",
+    ":::{dropdown} Widgets to attach callbacks\n",
+    "\n",
+    "```{eval-rst}\n",
+    "\n",
+    ".. currentmodule:: eomaps.widgets\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   ClickAnnotateCheckbox\n",
+    "   ClickMarkCheckbox\n",
+    "   ClickPrintToConsoleCheckbox\n",
+    "   ClickPeekLayerCheckbox\n",
+    "\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   ClickMarkCheckbox\n",
+    "   ClickPrintToConsoleCheckbox\n",
+    "\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   PickAnnotateCheckbox\n",
+    "   PickMarkCheckbox\n",
+    "   PickPrintToConsoleCheckbox\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00624ef5-0190-44ec-b85e-6b8b2e90b280",
+   "metadata": {},
+   "source": [
+    "## How to use the widgets\n",
+    "\n",
+    "All widgets expect a [`Maps`](eomaps.Maps) object as the first argument.  (e.g. use ``Widget(m)`` to connect the widget to the [`Maps`](eomaps.Maps) object `m`)\n",
+    "\n",
+    "The basic call-signature for **layer-selector widgets** (buttons, sliders, dropdowns etc.) is:\n",
+    "\n",
+    "```\n",
+    "layer_widget = Layer< WidgetName >(m, layers=[list of layer-names], **<kwargs passed to the widget>)\n",
+    "```\n",
+    "\n",
+    "The basic call-signature for **layer-overlay widgets** is:\n",
+    "\n",
+    "```\n",
+    "layer_overlay_widget = LayerOverlay< WidgetName >(m, layer=< layer to overlay >, **<kwargs passed to the widget>)\n",
+    "```\n",
+    "\n",
+    "\n",
+    "The basic call-signature for **callback widgets** is:\n",
+    "\n",
+    "```\n",
+    "click_callback_widget = Click< WidgetName >(m, **<kwargs passed to the callback>)\n",
+    "pick_callback_widget = Pick< WidgetName >(m, **<kwargs passed to the callback>)\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6f985bbf-2312-4c30-a879-a01f223788e2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib widget\n",
+    "from eomaps import Maps, widgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2f7e965d-d79a-4a71-bb00-60927b8fa4ab",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e2d04a390084f7eab12c600ab71ae30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerToggleButtons(description='Layers', index=1, options=('Countries', 'Ocean', 'base'), value='Ocean')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "978c8cf2f45a452987f32024e174568c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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vLOaSSaqi8P6ZHOpco8r5wLA7Y3K0N85UxSVmqDyZbjMy095Qoa0Q0VX805lojM6R3jjdGYOktTUfcyFE9JxeyFjJpidjUkibTJYdsgm9k01ZK1NXdsW2/vmiaF1V8LdwCbE7azKQjzFatDncY1FIm8TNpUtZyzF1hZMDSV45mu40jQ1CwdPZNpdOZHjrZJarj+p8+3yeeEyjO2OSsjQEMF1xuf20SSFtcKR387Od1gsCt+eDq3mKoqBrAArGMg+haQq5hE531qTU8NA1hZ9c6may4tB2QgppAz8QVJoeXWkTXVM6PermHe2NslbZhI5pqAghKNY9bs9lk0dnn2UODS3qgReEm99uwtQVjgzt/HK+tLVkDdY+tlk1WKau0J0x6cmaDHdZJGIa6YSOoUVtB7wgSs3XWj6P59oVaEq0DFZpePihoGVHM/XmT17rOW8lYlHLBEVRONob7+z4WmnO3XzrhJ6MSSIWnVT8QJCO6/TnzSVdyOf5geDBZJNizWOsaFNt+Yt2VJ0/nOI75/OdXVE3nzb49HYZ1xdYhsprxzPkkjqWqZGb+wJX50aczM8T9IKQ6YpLX85cciLyg5CWEy25Ndo+t8eazFbddRffnhpMcKg7ju0GxAyV4wOJzpiczSJENBexVI9q3ipNj/GizfH+BK4vuDveXPfOQU3d3pqoRCxqwmnoKqcHE2iawon+BBMlh4Sl0ZMxO81gH0+3t6yWzDJVutNRE1oFmKw4UYF5TGO0uPxSYcxQ+Zt3eunKmIt+fnesyYc3S/ynbw88G79E1G5i4WimqYrDbM3lwuG9WZvZdqJM8MtOJFgoFIJ6y0dRFBKxaEboPM8PeTpr82/fzK7pu2v+viv1MtM1Bd9u8P/42XlZg7WPyQyWtKr+fIx/92bPirUZlqlhzf3/pKUzULCWvR1EJ2VFiYKyqYrTWS57Mt1momx3dukNdllUmx6ZhE5X2oy2ievRNvGUpXO8P8FUxWGoy2J01ubEQKIzTqRY8yg3PR5OtskljeikpUSZouWWRDw/ZLRo050xScd1jvYmiJsOLSeg1oqahJ4ZSnKkN86DiRZ/uF4imzQ4O5ykP2fy5sksd8eaOH7I1/ernYDvULfFe2dznc7gd8eaNNpRHYmhq/i+4Hh/nLPDKWKmOjdfUCWTiH7PhZRBKq7zeKrN1ce1dV1Bz498absh10caZBKrvy4boShzW/UT0Tb8Pjeaw3h3vMXk3Gu5XlsdXFmGiuuHnB5K0p+PddofRLvktM77s5B+FrSk4jptNyCb1LlwOEUQCm4+beD70YDrYt1bV0PQwz0W6bjOnbFm5362Gy4JpFoEqAq8czrLcJeF6wvabsBM1eXpTJumE3BrtMEbJ7IkYs/e144XUkibCKIgyjJUMgl9yee3Lxfbc73VForHNDY796YqCtm5EVPPM3SV4W6LH7zWxVjRZiBvdS4WXT+c+70beL7gy/tVerMmP3q9G2/u7+bf2vW2P7dM6XHnye7b/CFtLpnB2sfmM1gfXXlK0TaYLLtrOon1ZE2+e6FAOq69MN2/WcJQ0HIDNEUhHtM6u4w2WnTr+SHOXNboq/tVSnWPwa5Ypy+V64VomkI6HtVZuX7IyEwb2w1puwG2G2LM7Uo61p/gzRMZSnWPz+5WyKcM3jkdNa6EKGswWIgRhIK//3gS01B580SWM8NJVCXK7vzpRomxokNsroFpEAouHE6ha9Gy2HCXxfH+eCfDEI3jCXgw2eL2aLPTUXutErGoSL9h+7x7OrcpI3ZaToDtRnMZa3OZnVrLp9r0GJ8bL7QbJS2NlBUt3dpeyNHeBIX08ifS9QrCaGn43niTsaJNfcE4GstQOXc4halFmyQadhSoASRMje6sSToeZcq+flBjuNvC1FVMXWWoK8aNkQbD3daygZDjhXPjfHzePpXtvL62G/DLr2bIJHTeO5MjFHD5QY3vXixsyr/3oJvvTr/a91Kj7UeB+wrB2rxqtUoul5MZrH1MBlj72HyANf8B9oOQx1NtPrxVBsB5Qe3UUFeMD84VSCeiXk57cWyL44WU6m4nYLNMjXRcp972eTTVoj8f41hfonN7IQQz1ej2iZiGFwgsU6XthsR0hdGizatHl/8ybLsBH98qc/5QioGC1cmIiLkC96uPnzWEzCR0vnexQLnhcfVRnVAILh5J8/rxpY8dBFG2ZKricu1xnYmy88JA+QevdnG4J+oGHze1dfcret6jqRaf3q6su65qp80Pdz7SY/Gtc3nSCX3D72MhBLYbdur5llOsu/zzFzM0nQBTV/hP3xng2uM6D+ZmDH77fJ7BgoW5ye0QIFri+uZhjW8e1vjuhUJn00W54RE31W27WJLW5vnvZ2n/kQHWPjb/AZ6cLtHXk+/8/NbTaGeRqkS9oq49qfNkut1Z1knEVPpzMfrzMW48baACrx7L0JczFy2f7HVBEC27LKxVWY0fRFvIX7Q7r1hzeTzdJp8yqLd9psoOD6faK94+EdN47Viai0fSi3ZFOl5IsR5tUT/aF0eIqKFqre3z4Y3yqi0Qzh1K8dbJLIaurFhUvJJQCKpNn1xSZ6ri8ngq6ha+li+KuBl1Hp+pulHdmaoQ01UE0bifMBSMFl8cIG4WXVP43sUCJwcSnezgy2jaUf+zYs0lFddJWVE9YsLUaDoBT2dsHk21GOyyKNZcLh5JM9xtUW36JC1t0/tMAZ3f9cRc+4LTQ8lNrU2StoYMsPY/+Sk8AD66VWawFF3Kv3Ei29lZM12JTvzVpt8Jrk70J3jzZKYTSL16bP9+8DVNWXNwBSxbHO/PDbmdrjhoqtLp8v7WqRyPplo8nbWXLfYeyMdIxTWadkAhbXCiP7Gkn5eqQrHm8fHtMmlL54evd9F2Aj6/W11xm/q8R5MtBvIxjvXFCUPxwiXCUAjaTsjNkTqTFRdNjTIfC5e8XsTUFY71J8gmdF45GgWLhZSBPrcbq1NgXXb4083Slvcg0jWF75zPvzC4mq44fHK7Eo1V0VXeO5ujO7P8hUTS0jk1GL1n/nCtRCiiDGfLCbEMlb9+p5dLJ5Z+ZjZrSXI5PdnoWOfbGUi7lx+IZ01eZW5j35MB1gHwZKbNdDMaTXF/okVP1uRIb5yrj+pLTtQPJluUmx5He+MEoSAMo2WW6arLX7zVs+5syH4132pB11Q0VdCbixEKwR+vl1BUhZ5stOvy4pEgqsG6XqLa8jud5aNsFczWPBq2z73xFq8cTS/auWRoKq8cTXNiIMGDiRb/8MnUmo/P9kI+vlUmaWmYuto5Ca/47wkElx9Wuf5kaUPWtXJ9wVjRpmUbUfH+c9maIIxGlzyZblOqb21wdWowwdunci/M5IRzy7cTZYe4qfLGxUKnTmo1h7otjvXHKdU9LhxO0Z01qTT8RQXnkjQvFIIgiLraVxoekxWH6w/W/nmW9iYZYB0wDTugYbd5NLdk1ZONeu8EYTSnLwgFpbpHqe6RtDQUop2Eg4UY8oLrGT+I6nGCUHBjpN4ZuKupCkd649HOQi/kq/tVTEPlcI+FWXWZqriYusrXD6rU2/6i3YE3Rup8cD7P0d6o2H101qYyF+yeO5Ti6Wx7XR2+bS/k6wdVfvpGT6cebDmhiArqx2bX1z18OdWmj+8L/o+PJuhKm/Rmo35E80HVVo4jiRkqZ4eT9GRNerPRCKOVNGyfBxMt7k+0aDsB75zO8sqR9Jqbs1qmxg9f68afX2a2NHqze3dXnrS1VEVB1RXyKZV8yiBpaXxzR36h7ncywDrgVupVNFCI8aPXurasSeVep6sKVx7VeDjZWjTuJggFDydbnZ5Jl45nUBT4+kENU1cY6ooxVXE5dyiF44WUGx6KEgUHj6ba/OrrWYa6Lay59hA9GZOrj+uMzLRx/fUHJ2NFh//6yRQ/faObzFxLhedNVVx++eX0pjVTnG/e2mi3eTK9cu3ZcuaXE2137f/WwUK0UeFYfzwq6H/BcujITJvfXCl2Nj789Tu9G15e0+d2okoHz1f3q3RnTIa6rEWZ55VUml7UBLjmUkgZ5LZw2VjaHeQ3g7SEqsDrxzIbWu7w53a7daUNDC3qQRUKsWJzz70oCAT//NU0Y8UXz9m7/LC26M9tJyQIBId74kuW7eptn49ulhkr2niB4P7czjNTj5YiN5pBLDc8fv7FNP/uzV7yKb3Tk6czFLvq7ooO6rByY8aVaCp861wey1AxNfWFwRVEbQsWDhueLDuyfkla1shMO3q/+CGqAj95o4e2G3DjSYPpqsvrxzOrvudmay43R6Kh3bX24h247dbunNMpbR4ZYB1gy40mMXWF4S6LT26XUZQ8hZRBEIoVsx/zhBBzY15s3j2TA6JGhx/eKmNo0bJZfy5Gd9YkDAWmrqKqyqpLV7vVp3crawqunuf6glLDQ1OjYOr5ACsd1/npmz34geA3V2aZKDm4fojrC1w/oCdr0nbX1x19Xr0d8F8+miCfMghDwfnDaU4NJrC9cEkQuNOWC7LyKYNK01sSZIYhnSL+1ZYEF3rtWBoBnSkDszWXqbJDT87EcUNm6y7Fmofjh2iKwvH+OPmUsefep9LGNdo+X96vcnu0uejnv/xyBtsLeOtkjjdPZoHoe05TohYu9bZPNqnTnTH56n6V8aKz7WPEpN1DBlgH2HJZC9cXzNRc2k7IL7+aIZc0qLU8/k/fH8IyVRRFIQgEmvYsOPKDkA9vlrk71uTNk1muPa5zY6SO44VoajTOZqriokDny8bUo0LwdFznjRNZ0vEoW+b5Ykt6BG2mydLGrjznezKBsupVr6JEzTEX1itdOJzivbM5/vtn00yvcwTNvFBAca64/KNbZT67W0FVln8f7Ba6Gu22WqnRqiDaxHHpeHZRk9aJskNfbvmxSEd649RaPh/Ptbl4NNWO2mokDYa7rc6Sn+8LjgzEScejrN9LthKTdtBs1aVYdwnmXkdNU1CIRjPdn2iSTRj05kyO9yWotX3ujze5N744uNLU6L3z+vEMpq7wy69mqDR8am1/28c8SXuDDLCkJRZuzZ8/sf3qm1mqTY9c0kBVokHPb5zIIISg3g5o2AGDXRZf3KsueqxwQTZi4ddPtOPMARxujzY5dyhFPqVjGVHX7f5CbNc2Nv3exQJNJ+BXl2fWNRJmPvvSnTFWXX5VFHj1aIbhLotbo02O9cU5MTdP8Kdv9vCLL6Y7gdLLWO9y3HZQgN6cSaMd8OqxNI12wI2R+qr3GZmxGSxY9GRNns7YfHa3Qq3l85M3uhc1kYXoRPvrb2YXNUxVFXjzZJZi3Zt7H8ramP2i3vb54m6Fu+MvGktjE59Q+f210rKBUiFt8MG5PENdUQPhf/l6dlGbERlcScuRAZa0ILOysvkmhi3HoZAyONIbZ7rq8vndCjFD4+LhFNdHNr7F/9bTBilL43sXC8zWXCYrDq8fz+zKIKs7a9JN1MxzbNYGRVnXKJupisvffzy5YnG1qiik4xqpeJwjvfFFS1OJmMbP3uvjHz+ZemEvrL0oZkZ1VN86l+dYX5zH02368iZ/uFZacRTPTNXlv38+DUQDwPvyMfIpg49ulunPxRZ1Xp+qOlRbPhePpLgx0kAIGO62SFoa15/UqTQSMsDaB8JQcGesyVf3q2selt5eZmNFOq7xs3f7oh3VCz6Hl45n+MUX07t2PJS0O8gAS4o6hK8hyMolo+W8mKHyye0KY3MDak1dYWS6jf+SV3ENO+DfrhQ5PZgkn9K5P97iSG98S7pfb4YPzuU7X7qOF/Jkus1ndyqdXXTLOdxjMTE3t69Yc1csrlYUhZVCS1VRONRj7csAy3ajnZWqCihR88yPbpXXfCL7wWtdDBUsvnpQjV6PuxW+/0oXrhfSsP1O7dzCfl8/fK27M9Rb1lntfQ3b51+/mmWmtrGl9HlJS+M/fjCw5PsnKosQpOP6vvwMSptHBlgSsHJwZeoKx/oSpOIabSfk2uP6oi8uhc2t4XG8kGtP6hztjXNmOMmDiSbD3XHiprrmHkXbZeHJOGaonB5KcnIgQbHu8XCyxUzV7dQPzV8dT5ajotfXj6W5cCS9oefVNYV3TuUopAyuPq5vynLhbmBoCvmUQcxUufKoTjYRDeL+zvl8J4B9kbYTUm56BKHguxcKlBout542uD3amHs9Ft9eVaIh1jFDlcHVHtdygk7rlPVMIFjJoW5r2Yu7Yt3l519Md/6sKmDq6pb2eJP2JhlgSUsoRCfxoW4L1wt5MNFaMTu1VQny+b5P01WXU4Mu3z5f2KJn2lzqgi7u8x5OtvjV5VkAhrssfvBa10u3rdA0hTPDKU4NJhkvOVx5VOPpJjQK3SlDXTFODiQ5O5xEAL/8aoZPblfIJnXeP5PnO+fzGJrSaV2xkjtjDf5qsBeIfu+puMborM101SWfNDjcG+dwj0WjHVBueFw6kUFfQ2sHafcr1l00NepLNp9hmqm5CAHZhL7uQeWN54I0IQS3njY7vevmL0pDMdeKRlVeOosv7S8ywJI6utIGXiBIxjQmyg6PVxlQvB0uHknz+d0KpbrHyHSbbFLfk9vlM3GdrrRBqe7xZKbNyot/69N2AmZrLgOFGNlkgZmqy++vFXf1rsDlqApcOJzmWF9Ub1auuzydsckm9aj1ggKpuM53LhSYqjiLshOqAt2ZKKDty8Uw9Wjm4Qfn8gii5dQgFLzv5kjJprn72qHuOL3ZGIe6XXQtGg81Omvzye0yrx5N88W9tddjAYyX7M6O6VAIJksOf7xRWva2e+0zJ20P+Y1zgMyf1tNxnbYbRLMGRXSSAig1oj5DtXVe6W2FUMDvrhbpyphMlh1+d63IK0fSnD+cwlpDt+7dJJcy6M/HqLd9/EDwcKrFqcHkSz9uPKZxqCcOQDqukoxppKy9VxeiqQqqEmVDbTfotKHIxHV+/Hp3J6COGSr/4f1+Hky2+PBmGYiC8PfP5pYNupUFjy+Dq4MhZqgMFJ7VNQ51xebKDVLcHmuuK8AKBYwWbY70xvniXpXLD9bfLy5mqCiwaPlwYbsaaX+T3zoHgAokYioKCk0nWNJReLdmtb1AMDnXq8j1BVce1+nNxTjUs7cG6uqawrfP50nFdZq2z2Bhc2fWTVUcJssOyZi2J5coVDVq0nhjpMFY0SYUUT1WIqYxXnLoyZid3mihENx4UicZ0zg5mOCtk9k9l9GUtk+15fNous3FI2lySb2zG3qtns62qbd9rj5avVXISpxl6rIUZa4MYw9dJEobIwOsAyAEWs7eL8A0NIWG7TNedDB1hWzSwNAU1D3wRaUoCpeOZwiFWHfriUrD69SPzH85J2Iajh9yf6LFzZEGZ4eT5JIGTXvns4/r5Xjhol19AHFTI5PQmao43HraoO2GuF6IG4Qc6rb2TE2etLMeTrYo1T28QHBqIMmtp80X32lOX87k/nhr04vX56+B9uLFkLQ+MsCS9oy2G/LRzTLnD6e4dDzD3bEmqhL1o9oLQRawruCq5QTcn2hy/XGDpKXRdgMGCxaqCndGm53WBXFT5d0zOcIQLh3P4vrRjrv1FvXuJrW232laO/8a217I+UMpBjY5AyjtX/O7dzUVAiHIJfVFw9lXYmgKU5WXa/MgSTLAkvaUUEQ9jG6ONPj+K11Umh53x5ucHU7t9KFtCiEEfiAYLzmUGh7H+uKcHUoRhIJrT+p8/VwdiKZGvZ+suTYDb53Kdh6j2tp449fd4PRQknOHUtSaHj1Zk0LafPGdJGlO2w14MtVGUxVmax6//GqGtV6H7cYpB9LeIwMsaU8KRdTK4c9e7WJmg7P5diNFUSjWXXJJnSO9UQG7EILZsrto8KyhRTMeEzGNO6NN7o23QAj6Cxa1ls/JwQRjJZvqGq7Wd6vHUy0abZ8TA7uju/pKy7ueH9K0A+Ixbc1NcYUQeEE09FzafC0n4B8/nerUm86/bC9alZsvQJfhlbQZZIAl7VkPJ1t861ye/vzeXzISQuD6AlWBnqy5aJekoigMFixePZrm6WwbRVFotH0uHE7zye1ypzeUosCTGRvPDxkr2tT2cHAF0caG8ZLDRNlBURTOH9q5LOV0xeHWaIMLh9OkLA1dU7gx0uDpjM1szeV7rxTIJl/8deoFUbPe20+bNGwfQ1f5T98eIGntrY0bu1kYCv7tm9nObuhETKNU9+jNmisOSje0qIfVi6ZZSNJ6yABL2rNCQdQnaY+bL95erpVAEApqLZ/pikMmofPemTy2F1Br+tyfaC06IQjxbNfSfsrqCQHfPKxxciCx7ozP7dGoQP7kQIJ0fGNfd2NFm19+NYMfCJ5Mt/nR69305WKU6h4CwYUjKY4+NzNyOTdHGnz94FkvpuEui0LawDJlFmuzOF7I764WGV+wWzARU7kxUl/S3d0yVVRFIQwFrh/K4EradDLAkvasmKFivGRH9N3ANNROG4J5wdyw2vGizWjR5i/f6sULQm6M1BkvOktabex3KUtbU3A1VrTRNYXerImiKEyUHO5PNLnxpM7fvt+37n5YU2WH31wpdmpyWk7IV/erxHSVkRkbP4zq3fIpg+P9iRU3MbScgC/uVTpF14d7LL7/SheJmMxcbZZG2+eXX80sGR0VNzV++mYPf7xeYnTWxjI1hBBoqrKuvliStF4ywJL2rO7MztflbIW7Y00+ulXuZKM0NcrgPJ5uEez9bhvL0jWFM0NJBgsWthsQCEG9FaCq0QDo+Xq0hR5Ptai2fHqzJgL48l6ViVI06/HPXi1wZijFdy8WSMU1vrpf458+neb142mO9yewzBcHNvMbC1rPDe+eHxg9b6riMvVNkcFCg2+fzy9bjJ+IaZw9lOo0qxyZsSnWXRKxpf8uaW3aToCqKsQMlamKw++vlSg/12Q3m9A5NZgkZen8xVu9+IHgV5dnGJ21d23/P2n/kAGWtGflkvsvwBJCcP1JfVGDwiCEB5Orz+Db64QQvHYsQyax9q+kj29Xlkwd6M2adGdMPr1dwdRUjvUneONElpmqy8iMzYc3y3z9oMbFI2lePZpetb3HTNV94ezDhcZLDv/7h5O8czrLGyeyS/7+7FCSWyONTl+lX345w1unspwdTslM1jo9mmrx8a0y/txGj+UGnsdNlTdPZqk0PUZm2pQbHp/frezbixRp95EBlrRnPZ3Zu8ONn9dyAop1l9ma1xmvcZAusIMw+h28KMASQlBueNwdb9FcZnmn5QY0bJ+2G/Kvl2c5O5zk7HCKv3irl7YT8NtrRUZnbD69U0EAl45nVnyuvpzJuUMppisOXiDIJ3VeP56hLxcjFDBZdrg73mR01qblBOiagqYqKy5lZpMGf/NuL//wyRReEI2pejTV5ou7Vf76nV4Gu6xl7yctNll2+NevZzt/nl92fV7bDSnVXR5Mtpa0N5Gk7SADLGnP2i87r1w/5A/XSxTrLo32wa0JWct2hcsPa3x+t7roPgsD0UvHMyRjGiNzwfft0Sa3R5sc7Y3zZ6928d0LBW6PNngy3WZhVfNM1cV2Az6/W8XxQ94/k6Pa8lGAn77Zs6RAXgWGuy2Gu62o71go0FUFIVg1K1ZIm3znQoEPb5ZwfUEYCn72Xt+a2zvsZ0II7k+0+PJelb95t4+kFdVKzW8eEEJQqnt8cru8psdTlWhWZSah88cba7uPJG0mGWBJe9ZE2WGm6tKT3ZsNKGstnyAUTJTs6IS/BygKW7LbqidjvvB1nKo4fH63iqpAV9okFIJK01u05OMHYtlh14+n2/zdn8b5yRs9XH1URxAFa725GIOFGHFT5ZdfzdByAmKGSiAEr6+S3VpIURQMTZn7/6vfVgjBw8kWrh/9Ekt1j8mys+bn2o88P6RhB3x4s8RY0eG1Y2keTbW4N94kFPCd83l6czFujTb54/XSmh9XUxXiMY0TA0kZYEk7Ql42SXvaZ3cquF5IsMcqVl0/ZLRo43jhnvryXxhcRbs4N6dNhh+EtN3Vs3e2G6Kp0bihmZpLse4tqaf57E5lUYZLVxUsU8UyVHJJgwcTLbwg2vnn+oI/3igxUXKotny+d7FANqHjByEPJ1qEWxBJNuyAxwuCaQHcGt3bHfdf1mTZ4RdfTHc2D9wda/LhzTJTFZdKw+tsSDg7nOTcoeSaHtPQFM4Op1AU+NONtQdlkrSZZIAl7WmjRZuPb5cXNebcC3Qt6r+zl7/8HS/szEN82SWuctOn3g6otfxFBf7zbo40+MP1IkG4/JBchWhJKBQwUIh1Ar982uD7Fwv85z8bIhRw9XF90f2a7YDJikMhZfD1g2h5UNdURos2Y7M2YpODrJSl8c7pLOn4s+Vtz99bFweb7VBPnP/pe4P0ZEx+8kY3rx7LMDDXPNgLBP/98yk+vlVGVRQ+OJenK73y5hbLVIkZKucPp3j/XA4E69qoIEmbSS4RSnve7dEmFw+n6d5DS4WqonC4J87ndys7fSibYrmgaL3+8dMpAPrzMT44l+fakzqNto8fiE4H7r6cyZHeOClLpztjYOgqtZYfLS+KKCMUM1TuTzT57ZUiM1WXf/l6lmxSX7Yo3g8FXz+oMVZ05jJiohOo/elmmXzK4MLhFGNFm2Ld493TOTQVvrpfQ1Ph2+cLS3qYrUZRFN44keXikTRPZ2xUFfIpY1Gt0UGkqgr/wwf9jJdsHk22Fi3z1ttBpxmrrqkMFGKLdg3Ov14Q1eCdHEiQnOt39nBKBlfSzpEBlrQv/PrKLH/zTt+eKXwXQjBbczE0Fdc/uIXtyyk3PGwvoNr0UBUF2w052hvn0okMPVlzSTPP5Tq09+cWj0+qt/wV+x75gWCsaJOJ6xTSBkEomCw7tOwoo7awPq7cmGEgH+PJTJu4ubRB7FqZusqJgcSG7rtfzQfFy71OQSA646QWBvP9+RjDXRaJuYL4s8MpNFXBD0Imyg7F2v6ZaCDtPTLAkvaFatPno1tlzgwlGe62dv2S4dXHdW6MNGg6Mrh63uvH0hzqjnOoe+NNOK8+ri86Ua+lRK/W9qm3fVilkH9+rp0vBzVvOgWFuKkt+5lo2AE//2KGYt0lDAWaCm+dzDFQiNF2AyoNn2N9ce6NN3ky3abe9qk0/U4HfknaCTLAkvaNR1MtHk62SMc1vn2+sGz3751Wa/n885fTVPb4IOat0Jczee1YhmN9L/+6HemNM1l2VhzuuxLR+a/lLZzxuFwzUWnjTgwkcPxw2Z2C01W306U9l9T51rk8E2WH0VmblKUxVXGYrjqMzNh7bsOLtH/JAEvaN+azDvV2wJ9ulBjuGkTbpF1um+H20waf3ql0OnkfJKoS1R+tdPLrzhj8zTt9m/Z6DXVZ/Idv9VNpeggBIzPtuZquxdmRS8cznBlK4geCD2+VmSw7KzziYsmYxrH+3RfA73XHeuPcyZlMVRYHxvPBlTY3OsnzBQP5GL+9UjyQnydpb5ABlrQvNeyAq4/rXDqR4eNbZbozJqeH1rbFeyuMl2x+v44ePvtNKFi1gdY7p3NbEgzPj1PKpwwyCZ1vHtYo1T28ICpmv3Qi01nq+9v3+hBCcOtpg5YTMlG2mSq7KAqcHIzmFz6Y25H21snsioOdpY1TVYVzh1JMVZb/rAQhBGFIreUxWrRlcCXtajLAkvatrx5UOXcoScP2uTFSZ7jb2pKZb422T8MOKDU8VCUaEnysL4Gpq2hqlLkpLTMrTXpmO4KVY30JjvUl8APBjZE6tZaP/lytnqIonD+cnvtTliCIurTPt6F493Ruw88/X6Rdb/nk08aurxPcCbYbMDpro6msOjPw8sPagW9vIe1+MsCS9p35buN+IPj99RJvnMjQdkOuPa7z7pncSz22F4R8cqtCse6STRrETZWjfQnujDa4Pdrs3O7ygxpCQG/O5IevdXG4J46ilLekC/pepGtKpwA5nzLo3cYWG7qm8NqxtXVO1zRlUWZtdNZmsCuG44bE1xms+4Hg7z+apNb2uXQ889Lvxf0omzR4/XjmhcPNXRlcSXuADLCkfWdhEPN0pk3TDvjx6938/Itp8iljQ0uFQSi4/qTO7dFmpx5kvk7kyqP6kts35nouPZpqMzprc7QvwdHeOI+m9sZInM1mmSrJmEa15aOpCpoaZXRODSb51tn8htsdbLdcUufpjI2mKgwa6qpzB59n6CrH+uNceVTnyqMaZ4aTnSVMKSKE4PZoY9ULkZihbkrfNUnaanvjW02SNigIYbjLIpPQef1Ymt9eLfLFvcq6O3TfH2/yye1KJ7haj/lg6+TAztWA7SQFcNyQYt1D1xQcLyRl6fzff3yI77/StWeCK4BUXCcIBb+6PMPHaxw6vNCl4xm6MwahgH/4ZIrP71YI5jJ5QghqrYO9u/TGSIPrT1YeHaQom9PUVpK2g8xgSfve5Yc1RmbafHA+zytH0nx1v0YYsq4lmpfpsv353QrH+hIMdUXNLxOxKPPx/I62/WphKGu7IfmUwXcuFPZsDdKRnji6pnL9SYND3fF1tQOxTI2/fa+P//WPE9huwNcPatwbbzJYsKi2fBptn3//fl+nE/lBMlGy+fROZcW/N3WFcG7pX5L2gr1z6ShJL6FY9/jtlSKvHkujawoPp1qLOnS/yMvM2nN9wa+/meXhXF2J7Ya07GDPBhgv6/2zuWi0zR6laQrvn80B0SDh9jqbxeqayoXDKd46FT1GvR1wZ6xJd8agPx/jn7+c4cObJa4/qfNwskWl4TFVcXgy3ebJdJs7Y43Obsb9IgwFd8aa+IEgs0xnfog+RzK4kvaSg3eZJB1YthcyOmtz8UiaK49q/OvXM/zwte41jSx5MNF84W1WM1V2Oj2WMgkdQ1Mp1l00deXeUPtRT9bsDPLdy04OJLgx0mCy7HB9pM6b62zb8NrRDFcf14ibKrqmcGYoxZsno+Lu+xOtRbP2lqNrCklLo38f/C79IOQ3V4o8mmpzvD/Oq0cz/OOnU8xffwixau9XSdq1ZIAlHRh+IPjD9RJnhpKcO5Ti5kiDX38zixcUODucWvW+jWUGBa/V/JKg7YT4oaDS9DvLHQdp2oquKfzlWz0Y++AfrSgKx/qibvFf3a/heCHfPl9Y8/01TeH14xleP55ZtPx8ciBJd9rk6azNx7fKKwYWfiD45VczvHUyyytH04RCbGqrC8cLXypru5owFJ3NAbNVl99fLzJbiwLKt0/luDseXcwcoOsOaZ+SAZa0LpapYrt7u8j0zliThatzf7heIpc0Vs0GbKTnTk/W5MKhFE9nbS4cTtGVMXkw0eLJdIvRog1EJ0pdVRBEJ579fE559Wgay9wbw7jX4uxwipmqy/2JFjdHGrx7JoehrT0oWamuL5cyyKUMFAU+vLlyIb3jhXx0q0wQCk4MJEjHdVpOsOFeb0IIwhBCBNMVh5mau+njgIQQ3HzaoN72GS85lOreogzuw8kWN54s3ZUrSXuRDLCkNVMUcL0QU1fQNRXbDfbsVebzx31jpL5qgHWox2KmtvxcO11VGO6x6M2aKChcfVyj7YbU2z5/vFEim9D5p8+nMTSFo31xLh5N8/rxDF8/qDFRcvBDQdLSaNlBp4fXfqMQ7aDbT2KGyncvFhiZaeP6AscNMeKbk/UJQsHorL2m246XbF45kiYIBN88rPH+2dy6N2W4fsiHN8s0bZ9ay6flhChKFBTr6wganz+uGyMNBvIx+nIxbj1tMFF2Vt2J+8W96oaeS5J2IxlgSWs2Xwvh+gLXDyikDVpOsOczWkJEs+VWc/FImjujTZrLFDRfOJLi/bN5AKYqDpcfRhHS/O+lPDfY2QsE98aj4vpXjqb5y7d7mam6XHlUiwrg92lwBdH75ot7Vb51Lr/Th7KpTF0lHdcp1j0+ulXmexcLL52lc/2QT25XeLzGTRgjMzZ/9+EEnh8SN7UN7XidrbncHVtaZ/inG2W+c6GAvs4xRtce1/n4dtRYd7rikE8ZjMysLWBUkDVX0v4gAyxpw0p1j795p5dffDmz5wu1H023OX84TSax/EciEdP4zoU8//L17KKfK8Dhnmfb9B9MtJbtMr0wM+X6ghtPGlx5WEdVoz8nYhrfu1ggHddwvJDP71aZWOPg4b2itc7ddntFIqZRrHs8mmpTbkzxP34w8FJzFWdrLreertwLajnz/bOySQMhxJqDLNcL+c3V4orZsjtjTbxA8KPXu9Zc41WsuXx0qzx3PDp/+VYvbTdYe4C1jy80pINl71ebSjvqnz6f3vPBFUC16fO//mGcyw9quP7yGbmjfQn+8u0ejvRYnB1O8v2LBf78UjdDXdbcY3iMl5Y/iSw8YcQMlbPDSTJJHdcX5JI6Q10xai2fpBX97Hh/guP9cRIxlUsnMqTjGooSBXR71XhpfwWM8949k+sE5pWmz73xje84fTTV4pdfzmz4/sUVlrGX43ghv74yy5Pp9qqf4ZGZ9pqXKyHazBA3o1OLpijM1lz+uMZB54OFGK8cTW/JzFBJ2m4ygyVJcwTw2d0Kn92tcHowwbtn8iStxV/0h7rjHOpevrFk0w4o1l7c6d3xQu5PtjqF85WmP3dibvHNoxrN53YsXn1UJwhFdGUPqEpUIL2bAtuFswVXsl/bfnVnTP7izR7+tz9NAPDRrTKFjEFvdu0tFMJQ8JurRR5Otl4qe+MF0S7VfGr1ETwzVZffXpntLF+vxg8E4yWHwz1xPD+qzVqtLiubjBrJ/vryLKWGx68uz6542+ed6E9w4UiaYs3btxlP6eCQAZYkLePueIt74y0O9VicHEhyciCBqiq4Xkip4dGbM5csmUxWHARrqyFZqYv788EV0Amk5k+8oYBUTH2p1hGbLQhFp6eXqSvLLpOupd/YXpVLGeRTBuWGRyjgi7tV/vLt3hfe7954k+tP6p25lpthpuquGmCNzLT5+FaZyhqCq3mFlIHjhXx2p0IuqfPqC4ZlH+9P8MPXu/jDtRLeGpuDmrpCIqbhB4JQrhFK+4AMsCRpBYKogHhkxubDmyX6cjEmyw5eIDjcY/Gj17sxF/R0OtIb51hfgiAUpOM6Tdvn99dKTFc37+Q5b60nre0iBARCYBkq9gqz4lbK/O0Xb57I8OGtMrYbrrjMPM/zo6a3H90sr/j72qhrT+orDjT/l69meDLdXncR+R+vlzANlZYTMFCIvTDAgrnZmwL+7Upxzc9ztC/O1w9qW7qcLIvope0ia7AkaQ1cX/B01u4ENiMzNr/4YnpRxqkrbZJPGXRnTGKGSiFt8vbpze0jNC8Mxbp3dm01VQFnhcAiZWl7ejzOWpwcTPIfv9XPUFeMmaqLHyz+XQghaLsB//r1DP/f347xr5dnNz24giiDNT+W6XnFuruh4MIPRWfJrlhz17w8fWIgsWSZfSWuL/jkdoXerLml7xUZXEnbRWawJGmDpiou/8vvx0haGqcGkxzqiWNoCt2ZZyeHiS26Eg9FdMLWVAh2SZeM1c65DTtgZKbN8f7Evp7BmIrr/PSNHn71zSyTZZfhbouZqst4yebRVLszLmmr3R1rcrx/6ZJsytKpr2PIuKkrc0t2z37m+oIHEy1ODSa49ri+ajZLURQKaWPZpe/lVJoeoRAUUgYnB6LH38ylcF1V8HdR7aK0v8kAS5JeQiiiYb1fP6jx9YMa/fkYP32ju9MLaa0nFgBNheHuOKOzdmcZY6VMgRBzJ709dK7wA7Gvg6t5hq7y55e6+fhWmW8e1hgr2asWrnelDdpuQMvZvEh5rGhHTYEXjLsJQrGu9yNEAWNpmbmIE2WHEwMJPr9XZabm8trRDN0vmXVSFebGSKmMztp4fshgIUa15W9ajZoMrqTtJJcIJWkTTZYd/uXrWZy5pZ/VulbPS8RUjvXF+dm7fagKnBpM8M6ZLIX04kJlRXm2Ey8U0Z/3ko9ulfnk9sqjX/aTMIyydqPFZ8FV0tIYLMQ4O5zkZ+/1kY5HQXix7m1qcAVRjd58L6p5pbpHrb32wnaA9go7+Tw/5PFUG3+uee5/+2yKtrv0trWWz/QagyPTUKm3fB5MtHj3TI7JikMooDe39wdaSweTzGBJ0iabLDvMVF2GumKcHEisWuR+4XCK98/m0DUVIQSnBpOdpZ3DPXEeTrawDJUbIw2yCZ3jAwnuT7SImyq3nm6839JO8APBlUd1TF3ljROZDXUc3ytihsqPXuvmm4c1+nImCUujJ2N2/s1eEL6wrcXLujfe5L0zOeJzPaXmm5Gux0rHmI7r3Bl71gzVCwS/+GKaN05kOdYXp9ryuTnS4MZIY031WrqmMFiweDjZQlFguuryzukcj6baFNfRg0uSdhMZYEnSFvjjjRL9+Rjvn83RsAOuPq6TiGmcP5Si2vJ4MNFC1xRODyU7PYVCAbeeNjoBVi5pdIbtnj+c7jz2kZ44k2Vnxa7xu90X96qoClza5EHCu03MUHn3TG7ZvyvWPNpbPGIqFFFLhjPDKapNjz9cL3L+cIqbI2vvEr/SblXLVEla2qIdebM1j09ul+nOmPzhWmnNkwgMTaEvH+sU5gsRZcjGSw5tJyCX1Gm7e38kl3TwyABLkrZArRUNzfWDkPOH0pweSqIo8KvLs5waSPKTN3pQFOhbsPyhqcqaeicZukouZfDumRz3x1t7cqTOzacNXj2WORA1Wc8TQvD53cq2PNflhzXyaYNffjlDzFD5zvk81abHWHHt75nlRtfEDJVvny8wOmsvKpp3vJD/9unUsjM7V5JLGUs6xT+dtbl4JEVX2uDikTS/uTLLw8m1zWaUpN1C1mBJ0hZ6ONnm519M8/trRcaLDtWmz72JJgP5GMPd1oYfNx3XuXA4zd+827vsbrHdrt4O+Op+Fe8F/aL2owcTrW0bG1Rp+vzXj6douyGWEQ2CfvPk+jKHxjLtQP54vcSjqVZnTNQ81xfrCq5gaZ2iqsD5wymqTZ/TQ0n8QCzambucAxinS3uADLAkaRvM1rxO0XG16fP1g+qah+euRlEU3j+b4+Qe7JL+9YMaf//xJNOV9QUbxZrLvfEm1x7XGSvurfocIQSf36vuyHNXmh6uF65pnNNCy9VQhQI+uV3hjRNZ/vKtHixzY6eSN09muHgkvehnmqpwc6TB/YkWrhdy62mDG09WX9bc6s2BMoCTNkIuEUrSDljvVf5q0nGd779S4Nvn86hq1LfoyXSbyw9q6941tt0qTZ9/+HSK710scHY41fl50/ZptANUVaErY+DN9V66N95csiT61+/0Lsmk7FZ3x5obKjbfDF4g+P/82+i6O3vM91m7eCRFueFRbwfUWj5NO2C8ZHN2OMXP3u3jnz6bWldd2ZHeOG+ezCJC6M4YfHSzjOtH7Ue8uV24f/enCRKWtqmfl42Q3R2kjZABliTtA7qmos81zDZ1OHcoxbG+OL/+ZnZd9TY7QQj4/bUS9+dmP96faDGzYOdlzFAJQrHijrYv71Vp2gGlRpSh+eB8flfWdtXb/pLWCdvtZeKEcsPjp2/0oCgK/8dHE1SbPuMlh7PDKfIpgxMDCa4/l2labgi4oSn89M0e+ubmebb9gFtPG8sGZ6GI5nauNN9SknYzuUQoSTtgO7qvW6bGj1/v3von2iSjRZtPblcWBVcQFU6v1tJgouzw26vFTlPPn38xvdWHum5tJ+AXX0zv6SBhrOjwXz+ZwvECfvZuH8f64jyealFtRkuOyzVTfX6ck64q/PhSN0NdVmf37LUn9VUvAlQFvD38e5MOLhlgSdI2O9Jj8e3z+W15LsvUGMgfnEaN1aa/67bzVxoe//TZFJXm7l6ufRFDU2g7Ab+/VkLXokBpuDvOZ3crNGyfU4PRTtmFIdXC1+Jb53L83340zOGexUO/VxpMPS8UUeZNVVjSfFeSdjMZYEkbosl3zoYYmsK3zuVJxNY2AHcz/Oj1rmV3gu1XuyWgFELwxb0Kf/fhBOU9HlxBVMNleyG2G/LPX86gKgo/fr2Ld07l+OP1ElMVh0LKWLIM+bfv9fH9VwqcHkyiLfM+TMd1zg5HQZahKeRTRidIG+qKEZsb93N6KLnmwdGStBvIGixpQ3bLgOGdpiprL4DVNYX/yw+HOksj20EIwWjRRtOUFZtG7gcLa3RiG9zRtplCIfj91SJ3x1s7fSibru0FNGoBs1WX7qxJLmXwvYtdfH63QvW5An5DU+hKG/SvEvQ+nmozWrSJGSoXDqcwtOi1jBkqxbqL40XLh44X7qnZm5K0899EkrSHHe2Nk08Z6Gsoqj5/KLWtwRVEJy9TU/n37/Xx2rE0R3qsTkZgP5kPrkxd4dQOt6wQQvDRzfK+DK4gKjq3DJWffzFNfW6XatLS6M6YS9o1eIFYdVQUwEzVodEO6M2a3B5t8s2jqP3GzacN7i34HQoBUxUHBRgsxDjUbfGd83mO9cVRFUjEtD03n1Pa3/bfN60kbaOHU23KDQ9/DWmsF9WabLamHZBPG6QTOg074EhPnH/3Vi//+c8G+e6FApn4/ktgu77Y0UydENHw4xvrGEezFyUtDVWFr+4/6+n1ytE0J/oTHO6xOsXtCvCbK0XCFT4ft0cbXHlUB6LPx1+/08tfvNVDLmWgKNCVNkhZGqoC1ZaPHwg0VcHxQgppg/OHU/zkjR7yKYMwFBRSBsf746tmzOaPa54sd5C2yv77hpWkXerxVJuYoZLepsAmqlfREELgeCGWGdWv6JrK+cNRz6m2G1CsuczWPDRNodr09nzPn49vlfmrt3sx9O0/c157UufjW5Vtf97tVqx76JrC6KxNpemRS0bF5xePpJkoOWQSOmNFB9sNaDkBMzWXrrSJrikIITpDr6crLoIo8zjcZVG3fUxd5fHUszmbmhotw5cbHul49B4uNTyKdQ9VURjutujNxbg92uCD43lO9CdQVYXpisNY0ebxdJupyuIsmmlEw9VdXxCEkLI0HC/c18vo0vaTAZYkbRMvCEluY3H7PEVROsHVQmeGlhYdV5reugb17kZTFZd//mqGv3q7d1v7YTVtn8/v7kyX9p3gB4K2CPmXud91Kq6TjuvoPQq/v1ZE15ROreY/fDJF3FQZLFi0nICzh5LkkwbFehT4qIpCse7xYLLJ26dyHO1LcHesCUT1nt8+n+fqozr9hRgIgWVqXH1cZ6riMNRl8ebJDKcHEwwUnjWc7c3F6M3FuHQiS7Hm8miqzc2nDVpOENVzLdCwAxIxFctUF81WlKSXIQMsSdoGfTmT989uT2uGtVpuR1cuafCXb/fyxxulzgluL5ooOfz2SpEfvd7VyZZstcsPa6v263oRhb1Xwx2EgkrT5xdfzvDj17sopE28uW7sz/f8arshDyajmqqZqrtoWd32Qh5PtwhC+PROhf5cjNFZm9ZcB/cPb0YNWmtji4vox0sOk+VpDF3l7HCSUES7SNXnAuuujElXxuS142k+uVXh5tOlS7g92RhdaYOrj+sv9TpK0jwZYEnSNig3PDw/3JFlq/XSNYUfvNrFB+fyPJ1p829Xijt9SBvyYLLFK5U0fTlzy4Msxwt5Mt1+qcfYy6f0Wsvj7z+e4rsX8viB6BSbP998dD6IXK5m0fVCyk2fSsPD8ULcNQwC1zWFMBSkLI0rj+pceVQnk9D52bu9JK2lpzdDU3njZIa2GzBVcckldXpzMeKmSsrS+c2VWUIBhZRByw12XU81aW/Z/d/2krQPeIHgi3vVZQfn7lYxQ+VoX2JNOyR3q8dTrW3LYKWWOaEfFEEYZbN+d63Eo6k2/9cfDvM/fW+Q14+lycR1BgsxjvRYmIZKaq6X1fPF5XfHW5TqLt7cLM21ZJH8IMqW9WTNzs9qLZ9vHtZXvE/K0vnzS928dizNeMnh6Uybe+NNfv3NbKf+sNz0ODecIhGTp0hp4w7uN4IkbSMh4OrjOnFT49KJzE4fzprpmkIqru3ZLuQ3Rhq8eTK75ZnDmKFSSBt7unZts4wWbf75y2mO9ycY6rY43BunLxdDUxVqLZ8bI3UeTbaXHUS+XH89TVXIJHTKDQ9l7s9+KDjUbfF01gbg9mhz0e3zKZ0gFKgKnQDb9UJGizYD+RjxmEaxHo34mf/fhYSAbx7VsAyVpKUhBNhusOc3gEjbSwZY0o5ZT5PO/eKLexWO9cc7u652O88P93TRrxcIxksOR3rjL77xS5J1O89MVdxFO/cSMZWudNQnS9dUhrstbC8kE9eIxzRsL+TeeJNzwymqLZ9q08P2QkxdZSAfIwhFpzjdDwUKMDoXXD3PMlRGZm2+fljj3dM5Tg0m8fyQP90sdfpq/ei1Lv7slQK2GzAyEz3O899HQkR1YwP5GNNVh2zSQAhBteUvO3dRkp4nAyxpx4Ti4AVZ4Vwm67sXCjt9KGvyye3KnlrWXM781v6tdrgnzp09vDFgK7WckJazfEA0/x1w8UiKiZLDaDG6XSFl8K2zOb66X+v8bN5q78imE9CciurhfnslGgKua8qiIeK/u1YiaWn8+aVuLj+o8WiqTanhYSwz8WBhVrIrbRKEYk9fdEjbRy4wSztqj5+7N+TuWJOGvbuX3IQQfHZ3+d1We83VxyvX42ymY31xzmxzM9n9YP474PqTBk0nQFWioEtTFX51eXZJcLUegmgJsNFevLwXhIL//vk0f//xFOWGR9LS0NTVx0mVGx4PJlvomoq1YBqC+tyAa0maJwMsSdpmfiD48t7u7pd0a7TJ5Qe1nT6MTVGsLa2x2QqqqhBf0OfM2ocjibZauRE1ug0FzNRc2ivs4js7nKQ/H+PUYIKBfAxFAUVhxQ0ZP77UvSQImm9e+nCqzdNZu5OpNXVlxZE7hqZQbkTLlwsf5wBeJ0prIJcIJWkH3BlrcmYouagx4m5Rqru7PgBcK4VohMt2MRb0FnO8cK7RqegUb8cMlVeOpPny/v74/e4Uxwt593SWREwjmzTw/JCWE9VTfXSrvOi2/fkY/fkYwwuK4lfzfP+uhWSnd2k95CXWAZaJ6/ty8O9eIAR8/XD3ZYiCQPAPn0x1GjzudWcPJbd1BqS5YLeiIFqKmg+uVAXePpXl0onMokBMejFDUzrZqURM47sXCgwULLJzm0UMXSUR07g3vrQGricTtXAY7t7ai5k93M1E2iIyg3WA1dq+nD6/g2aqLvW2v22zCddE2V9X6Ud7E9v6fLnkyq/lQCHGxSNRNi2fMpiuuiveVnrm7HCSd8/ksN2QX38zy+Ge+KKl2HkPJls07aUXBmJuAe94fwJVUfjsbmXTd3yauiJ3kUpLyPTFASe3G+8c2w35b59OrbjdfCeoStT7aj8wdYXBQmxbn3O8tLQPVsxQee1Ymj+/1ANEdT+rBWLSM6cHE3z7fJ64qZFPGfyH9/t4+1R22dse64v6bj2fSbKMKBhLx3VeOZpmuGvzM1npuE4qrmPq++OzI20O+SmXNtVenKe2kxp2wM+/mGa42+LS8QxDW/Dlvx6uLwj2yZV4Oq5v22giIQR3xpp889yy75snMrx5MrtoNp7jhdyd68ckLS+b1MkkdF4/nkFf0PJdf779+wIxQ+UHr3bx6tE0H94sU216tN1wSZA9UIjx+CXHGj2vWI+CZlVZ2zdgJqEzmNne7Kq0/WSAdQBs5zXV/FdL3FRX3AEkLTU6azM6a/PKkTTfOpfbtvEuzys3vH0TIG9HfaEQgltPG3zzsL5sZ/LXj2eWDB5+2ZmFB8EPXu0iEdM2tHzenTH52/f6uPW0QaXpMfjcRcvFI2m+ul9dtZh9IypNf03vuZMDCYSA6w/LL7yttLfJAOsA2IkTpgyuNubakzqKAt86l9+R5//m0e4rvN+oqYpLueGRT21N13whBN88qvPZncqKt2m7Swd8F+uy9mo1+ZRBJq4vW2e1HucOpZb9eTRKx1jUaX6zOF5IzFBxvJW//6YqjizNOCBkDZYEyEZ5u8nkDs2zC0JBbY/OHFxOEAr+cL2E2IKzmeOF/Ns3xVWDK4Cx55pktp2ApzO7p+ZuN1GA7ozBD17teung6kV+fKmbV46mt2STz4smH9Tbwb7ZpSutTmawJEDWTe0mO7WL79bTBqXG9jTl3C6TZYenszaHezZ3FmG97fNgcvU6KlWBvpy56GePplorftZ0LdqJlkvqe3a49nooSrScl03onBxIcLgnvmQ5daukLJ0PzuU51hvnnz6f3vDjnBlK8mS6jeOFGLqC64s17SY8iBMsDiIZYB0wB232317k+du/vBoKwSe392dNyGd3Kgx1WXNNPzdHd8akK21QrK8ckL52LEMh/VyAtUr9VTquU254ByK4SsQ03jiR4cLh1I7VGwIMdlmcHEhwf2Jjmw4eTbV45Uiap7M2laYnv1+lReQS4QEjP/y733zzxK0mhMDxQlwv5OefT3caYu43xbrHrS2YqXjoBVmx3uzi4KrlBIyusjxY3mfZw5X0ZE1+9m4vF4+kdzS4mvfGiQyXjmc2dF/XF3z1oEYoBK4v5PertIjMYEnSLjOQ39reTV4QMl50+Ph2mXrLPxAnBXcLsoIvqu16MNniWP+zrfhjRVsuxRNl6rbrImItCmmTC0dU7k00abQ3Vhs1u03zLqW9RQZYkrTLaFvY6HNkps3vrhYP3C7PyiZnh8oNb0kB+3K3EUJ0sjR3RpeOcTmI8qndd9pJWTr/4wcD/OOnU1Sb3oG46JC23u57p0vSAadvUaHv3bEmv7taPJBZlMlN2pJfa/n8+ptZZtYw5qZY9/hvn00zVIjxcLJF+QDUVr2IosDjqTavHs3sujmoMUPlJ5e6+cWX09Q3mMmSpIVkgCVJu0wmsTUfy9HZ9oEMriDa9ddyAhIvuf0/FGJNwdW8ybKzY203diMhoi7t4S5tBJVLGbJHlbRpdtclhCRtk50vrV2eqkD/FtVgmbssY7CdhID74y+/RFeVWaiX9niqPTdSZnfqzpgvvpEkrcHB/caVDrTdepE61GVtydKJ7QbLDiI+SD69U+HRC3pXrSYIBbdHN3834kETCri5Bbs6N8vANg8Il/YvGWBJ0i6yFR2sg1Dw8y+mKa3Ss+kgCAX87loRfwP9KIQQ/MtXMzyaknMEN8OVh7t3JNP0FozQkQ4mGWBJ0i6y2S0axos2n96uyG3kc1xfcG98/VmsW6NNns7KETebxfbCXTsuZrwkX2dpc8gAS5J2kRfNMVsP1wv56kGNW3JZa5HP7lRo2ms/uT+YaPHRzf3Z5X4nff2guu771Ns+d8YaXH9S5/Zog2CTx0rVWv6Ba2EibR25i1CSdpH2Bq7q/SBE15ZeK43MtF/Yq+kg8oKQetsnaa2+HCuE4NrjOh/frmzPgR0wa2nq2XYDbo40mCw72G7ITG3x8t3vr5XozZr85I2ezuvpeOGG6hj9IOTTfTouStoZMsCSpF1kIzsIHW9pgFWsu3xyp7JJR7W/BOHymcL5zuwtJ2S66nD9SZ2x4sHeGLCVjvYtHjUkhMB2wyhAMlWadsA/fjKF/4Ks7nTV5VeXZ/ird3rxA8HX92t8cD6/7uP59E6Fh7LGTtpEMsCSpF3C1BWGu61136/c8Ln5tMHh7jiGruD4gt9emV3XMthB87trRd48kaXtBpQbHk+m2wRh1Gxyt9YG7TelutfJNo0Vbb68V2Vigz3DpiouN0eipcOTA0lGZtocfsGsyIUabZ+bI5u/lG5oCooS1f5JB48MsCRpl3j1aGZDw2+Huy28IOQfPp3agqPanxrtgD9cLy35uQyuts/Vx3WuPq5jaAreJtRSfXK7Qjqu4Xgh9ydaZBNrn3l4+WFtS8bjeIHg/bM5ZmvuhjZXSHubLHKXpF1A1xQuHE5t+P4D+Rjdmd0zQFeS1mozgqt59XbArdEGd8ea/PHG2uqpQiG4twlNaFfy2Z0KiZhGLinzGQeNDLAkaRd440TmpXpg1dsB5YbsMi5J86NuijWXSnNxe5IgFJ1auzAUFGsuj6faW7qEFwqYKDn87L0++nIm2YROV1peDB0EMqSWpF3A8da+Ndz1QjRNwfVDKg2PibLD9SeNTW3xIEl7ne2F/O9/mmCgEKMrbTLcbXH5QY3DPRZ9uRgf3SpT3Kbmu5mETtzU+Pfv9xOEgn+Uy/kHggywJGkXaKyjIL3W8vkvH09u4dFI0v4QChgrOowVnc54nomyg6qAoihoarSrdCtZhsqZ4SRCCEIBf/enCWotmW0+COQSoSTtAq8fy6z690EgqLejL+XZuhzlIUnrFQQCU1dIWRqhAENXyMS3NscwWIjhBSG/+GKGWsvn6uO6DK4OEJnBkqRdoGn7GLpCboVdT14Q8r/8fpxMXKdhyy9oSVovAZi6yn/8oJ+2G5KO6/zzl9OUm1vzecqnDP7dWz207IA/3Shz7UmdG0/kVIWDRGawJGmH5ZM6E2WHSmPlepCYoXLxSIpUXNuS7eSSdBBUmj6Pptqk4zptJ2CitDWNZA1N4YevdWFoKmMlh9GizfUnDeRH92CRGawDxNQV2fBuF/JDwVjR5p1TuRVvU2n63J9oYcs5aZL0Uj68Wabe9rk71tzUFhEACnBiIMG3zuWJmypPptt8eGNpvzXpYJAB1gEid5ntTvmUQTqu4wYhcW1pqwYhBE+m2zK4kqRN4Hghn99d/6DpF+nLRTMRE3PtVn751QxPpuXonYNMBlgHyFbvlpE2JhXXGeqysJYZUDtVcfj15dl17TKUJGl7daUN/urtXgw9+gzP1lwZXEkywJKkndSTNTl/KEXK0pYdk1Nt+jTl+JZdTVXYUF2cArImZ5+ot31+c7VIfy4a1v7V/c3PkEl7jwywJGkHleoeVx/V+Na5/LJ/f3ooyZHeOP/140mqcnv3rrTRlXcZXO0fri+YKDmMTLflJhSpQwZYkrSDTg0mODGQQF1hyHPD9vn8TkUGV9KydFVBVZGbV3aB9UxjkA4G2aZBknaQ44U4bjT65nl+EPLH6yXujrd24MikvcCKqZwZSlFIydl2krTbyAyWJO2gStMnFFGXaU19FmTZbsA/fT5NaZtmpUl7U6MdcO1JfacP48DTNQV/k1s+SHufzGBJ0g5q2j4jM21sL6S5oEO7EFBfZVlQAYa7Lc4MJXn7VBZ9mQyYJEnbQwZX0nJkBkuSdpDrC8oNj8myQzah4/oC2wvx/JAjvXHuTyy/PCiAUt2l0lC4Nx7IwlpJkqRdRmawJGmHlRseo0Wbetun2vS4/qROOq7z5sks6fjSxqPzWk5Iw5bBlbSYIbOZkrQryABLknZYKODuWJM/3igxUXboShs02j6aqvA37/bxwbk8PVkTU9++E2c6rnGsL44qz9V7zmaPf5GiJXlJWi+5RChJu4TrC648qhMzVLrSBvGYFg2kLTuIbT5n1tsB9bbsRC1JIHuWSRsjAyxJ2mUcL2S85Oz0YUiSJEkvQS4RSpIkSZIkbTIZYEmS9FJkfYokSdJSMsCSJOmlyPoUSZKkpWSAJUkH0HbuSJQkSTqIZIAlSXucptJpp6BryppaK8jhwJIkSVtLBliStMcocwFUKq6hqwrJmN5p4+AHQjYelSRJ2gVkmwZJ2mPmgynPF+RSOn25GK4fUmn6BIGg1JADoiVJknaaDLAkaY/QVIXj/XFMXSVuaqQTGrM1j9FZm5SlMVN1d/oQJUmSpDkywJKkXU5VwNRVskmd987kEQj+68dT2O6zOYRlmbXakzQVLENDwKLXc7fQVIVglYPqShs07ABdUwiCaFD5ao+VtDT8QNBygq04XEnaVWSAJUm7VNLSEEKQjuu8czpHNqHzxb0Kri9IxFRcLyTc7hk60ksxNAU/FJwdTvH6sTSZhI4yV1TneiGf36twc6SxrYFWytJouwHxmIZlqAgBcVMjl4pq+/xAoKkKAkHK0rHdEF1TaLsB3RmTU4NJYkZUzttyAsoNj0rDo9rymao4tJyA84fSvH48jaIoCCG4M9bk1tMGU5Xls675lEEmrqFrKt1ZE9cLufq4ThAKhrss3jyZQVEUDF3B1FXGiza5lEG16ROGgnLTo+2GNNp+Z/lcV5VVA8D10NTo37HbAmJpd5EBliTtQqoCrxxJU2l6vH4sw2zN5ZdfzuDLb/Q9xTJUerImR3rjzNZcVEXhndNZ/DAKVhbSdYVXjqR593SOctNjouSgqQr1to/jhcTNKBBqOwGuL/CCENsLUYhmR65FzFDJJXXScZ2utMnRuYHeiZiGob/8nqdETCMR0xjqsla8jSC6eHjvTI50QgcBQSg6/5ZMQscytSX3e+tUFscLiRkq2nNbZc8MpwDoy8WWf04hEAI+u1vB8UKmyg7lpr/hf+d8Vk/XFHw5XFtagQywJGkXScQ0DE2h2vL59E6FREzjO+cLCOBQj4UXCEZnbSAKwmS8tb0sQ6U/H+PpbJtgmWTIQD7GYFeM/lyM/nxsXUGLqihkkwYAvdkYvdnlg4Xnjcy0+ecvZ1a9zaXjGV45miZuqp2M2U5RFYVD3fElP8++4H6aqpCILQ281kJRFBQF3j+bB8DzQ+5PtHD9EFWJWps8mWnjBwJTV0lYGndGm6sujwIyuJJWJQMsSdpBmYTeqUnpyZj8+FI3SUvj41tlmnbAiYEEqhotmfzkjR7aTsCNkQbXntRxNmm546CwTBVVUV6q/idhafz4Ujf1ls/XD2v4QUjbCZmtubxyNM07p3Obd8AvEApBox3w9YPaop9nEzqKEmVXzg6nGO6yyKWMbTuuvcDQVc4dSi362YUj6UV//uBsnlrbx3ajjOFM1eWbRzUZVElrJgMsSdohpq5QSBvEdJUn021mai7/+vUMrxyJTtSf3a2QTxmLMg7xmMaFwykeTbXQVYXmPigWXpiJyyZ1kjGNStOn5QRkEjrpuMZY0encPhHT0DWFmK5SSBuEIso6ZBI6mqqQSeiMzLQpNzzODqdIxDRcPwpGTV1lsuyQSeh8/aCG64VcPJqmWHNRFXB8wdOZNgOFGC0npJA2GMjHyCZ1LEMjl4yeI5cy+MGrXZ1j8vxwU5bY1qNU95iuOFSb0QaHfFLnzHCK146ldzxLtR9omkI+ZQBRcHqkN86Fwyl+dXmW4/0JinUXQ1fx/BDXF5TqLpWXWHaU9h8ZYEnSMkxdwTRU2k5IJqFTb/lYpkrD3ryARghotH0KPXEuHEkRNzUKaYO+bAwUONYX5/LDGj98rQt1wQkzZqq8dSrLry/Pbtqx7ATLUBnutjjen6A7Y/L1gyrnD6foSps8nmojEGiKgqoqnBoMmKm6HOq26MvFiL9gqehwz9IlqHnz9UGnBpPL/v18vY66lpb4c7Y7uIJoyezWaBPbC7FMlW+dy3NolX+39PLiMY2fvdeHEGJJEBsKwXTFZaLkMFV1eDzV3qGjlHYLGWBJ0gKWEWVCQiGIGSrDXTpJSyMMwQ8FozNtWm6I64UIoC9nUm/7mLqKoaur9qJSWDwY2QsELSfk0VSbRttnsMuiMlfcrKoKt542yKcMwhDU5+KJrrTJcLfFyIy9Fb+GLWHqCq4v0FWFrkyUGerLxzjWlwDg+688ywidGEgsuf/Z4e05zvl6nd0sCAX/9k2USXntWJqjvQl0bZcf9D6yXHClKlHGSwjB5YfVHToyaTeRAZa0p2TiOicGEtTbPpWmR6Xh01+IIYToZJdqTR9BtCXeC8SadvoogKEreEHIiYEEMUMlCAQDhRjZpEGj7VNr+RztjROGAtNQ0eczHEoU8DTaPrW2j+NGy1HjZYd6y6fpBKTjGm+fyqGpCndGGwhgouzwypE0fiCoND0sUyUR07AMDctUuXA4hblMZkQBqk2P3Xw6VRRIx3VqrWdLJum4zvdf6cLUnxVzSxvjeCHfvVCgL7+2QnhpazRtn3LDZ7RoM1l2qLV8DE2Rsz4lQAZY0i5i6gp9uRheILDdgERMIx3XmShH9TeJmEZfzmS66hI3VQ73xIkZLod7LAxNZaxoR/17juidbdxtN+Ti0RS/vVKkVPd440SGqYrLzaeNRc8teDYA+ZPbFfpyJicHkmQSBpoaBQQvCgpScZ1U/NlH6lj/0iwMwNsvUQjdaPvYXsiDyRajxd2bvbIMFVOPXgNVgbdPZUkndOptnyO9chnrZc23Q5B2RqPtc3+iBcA3D2ub1l9L2l9kgCVtOXUuw6Mo0JM1MQ2VWtOnN2cykI/qaRSimpIX1das5vldQQv95I0eIOqWXUibnD+UwgtC2m7UeydpacQMtVN3U2l6NO2AsaJNX85cti/PdhFCUGv5zNRcbjxpdALO3SQR00haGqb+LLDKJHQO9cQ53pegJ2vu8BFK0sub/yxee1Ln+pPGi+8gHWgywJI2XcqKejn1FyyCUGDqChcPp3fFVnHL1NYULKXjW/vRcL2Q+5MthBAYmoquKWiqgq4peH7IyKxNqe5Rb/m0d9EIFU2F3lyMwXyM7oxJMq6RSxrLLmVK0n6jzPUq68/HZIAlvZAMsKR1G+qyeP9sDlVhbpuyYLbm4syNbqk2fYJQMJCPETNUGra/64uGt8r8biMvCCnVPWK62hnvMZiPMTLbxnZ9GnbAndGtHZGiawrpuI6mQssJ19QPSlcVjvTFOTWQIJs0SMd1WUwtHXgn+hP4rwg+vlXG9QXH++OEIbh+yFTFfWGDUulgkAGWtKpCysA0VEp1t1OjNFa0+dXXsyTjGqcHkxzuiXN6aPkt7wfd/YkWlaZHJq6TSehMV13KDY/H023qLZ9ETENRoN72O8FVMha1a0gndJptn6ez9ksFXpmEzncu5Bnusjq7nzw/ZLrq8ofrpUWF6AtZpspfvd1LV9qQfZUkaU616eEHgmxC5+RAsjOyqFSPygokaZ4MsKQVWYbK4d442YROysrQdAKeTLd5NNWmNrdjbrriYOpRh2xVhf58jELKIB6Llo76D/gup1ODSYQQzNRcZmseCVOlN2fSkzWZqbpcfri4C/eZoShgHZlpMzLdpjtjcrw/wWTZWdSDS9cUujMmxZqLt8oOybip8s7p7JLRJIauMtRl8Zdv9fC//XGC5R4hDAW/u1oEok0AcVPD0BVSltYZ46JrSmd5U1MVcsnl58hJ0l4XCsEntyrcGKnvmiV7aXeTAZa0ItsL+ea5AOB5QQi2G3ZO0PV2i660wcnBJF1pmfWAqG5jfrZcEAiaTkAQCgopA8cPGSvaBGFUi+V4IbeeNhgvRVmrht0mNVeA350xsb2Q/nyMw90W2aTBry7PMFVZ3HsraWm8fTJLwtJImBohgsdTLRp2gO2G2F70v203oFT3FgVXqgKmoWK7IaGIsljpuD5XvK4SM1SyST0KtrSoCaiqRE05NVXpFLhL0n7ieNE4JNcPZXAlrZkMsKRVZZN61E/KjwKDaBhq1OelKx21LhgqxDg5mERVo52AqlxOWpGmRaNc5n33QmHZ2wkh8EPRKXQfLdoU6x5tJ+B2w1s18G3aAb+/Xur8WZ2bS6epCs5cg1Sx4CQxUIhx4VCK7qw5V6OlEIQCVVnaUFGSDqKYEWV8U3M7ZSfKNrM1b6cPS9rlZIB1ACk8G3wL0HKDRSfchVwvxPOjouh58007i3WPasvH8UJScV32N9pEiqJgaFFfsPnRMAO2T0xXqTR9bo82qLd9grmXJWVp0dJjxiSfMsgkdHRNJW6qi8a4CCGotwPuTzRJxjR6siaF9NIWCto6xsRI0kGRTRpcOpHhjJOk3PAIwrkWKlWXibIjB0FLi8gA6wASRA04LUMlndDpyUY9qixToysdtVLwA4HjhXRlDDRFodTwEAICIYjpKl0Zg75cDENTZJZjG8zPz5t3tDeq01JVhYFCVPe2ltdBUaIM2hsnslt1qJK0r803ee3OLL4w8fyQyYpDvR3geiEN26dlB53RWg3blx3eDxgZYB0A379YwEpEI1lcP0RVotqpQAiCQOAFAs8PadlR0XoookxHNhntZDO1aKlQVaKCZkNTMOYK22VwtTNyKWNX9BWTJCli6OqSzSTPC4XA9aJdh5MzFv/zNh2btDNkgHUAnBhIksms3OV8rUIh8PwoGHP9ED8I0TW5Y0ySJGktVEXpNDtWA+vFd5D2NBlgSWumKgoxQyFmyK7dkiRJkrQaeaaUJEmSJEnaZDLAkiRJkiRJ2mRyifAAqNVWbxYqSZIkbS/5vbz/yQBrHzNNk/7+fg4dOrTThyJJkiQ9p7+/H9Nc2odO2h8UIVZqMSntB7Zt47rui28oSZIkbSvTNLEsuZtwv5IBliRJkiRJ0iaTRe6SJEmSJEmbTAZYkiRJkiRJm0wGWJIkSZIkSZtMBliSJEmSJEmbTAZYkiRJkiRJm0wGWJIkSZIkSZtMBliSJEmSJEmb7P8PVE+t2OmaVTwAAAAASUVORK5CYII=",
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+       "\n",
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+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m = Maps(figsize=(6, 2))\n",
+    "m.add_feature.preset.coastline()\n",
+    "\n",
+    "m.add_feature.preset.countries(layer=\"Countries\")\n",
+    "m.add_feature.preset.ocean(layer=\"Ocean\")\n",
+    "m.show_layer(\"Ocean\")\n",
+    "\n",
+    "display(widgets.LayerToggleButtons(m), m.f.canvas)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "15f06cddfd5941dba2da7800413fcf85": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
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",
+       "_figure_label": "Figure 1",
+       "_image_mode": "diff",
+       "_model_module_version": "^0.11",
+       "_size": [
+        600,
+        200
+       ],
+       "_view_module_version": "^0.11",
+       "header_visible": false,
+       "layout": "IPY_MODEL_7a5b01fb49bd4d8595ce74b8bbfe0ea1",
+       "toolbar": "IPY_MODEL_37a91e5ba4424e88af1ee8a77e57f337",
+       "toolbar_position": "left"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index fe8acd6e2..2a7094138 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -11,7 +11,7 @@
 try:
     import ipywidgets
 except ImportError:
-    _log.exception("EOmaps-widgets are missing the required dependency 'ipywidgets'!")
+    _log.warning("EOmaps-widgets are missing the required dependency 'ipywidgets'!")
 
 
 def _check_backend():

From d7bedf1e9c3aa28d02dd8689915168f6ee9533a9 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Wed, 6 Mar 2024 20:02:54 +0100
Subject: [PATCH 212/240] add ipympl to the docs env

---
 docs/docs_env.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/docs_env.yml b/docs/docs_env.yml
index 503958ea3..42bc80cc3 100644
--- a/docs/docs_env.yml
+++ b/docs/docs_env.yml
@@ -18,3 +18,4 @@ dependencies:
   - pip
   - pip:
     - ../.
+    - ipywidgets  # for widgets docs

From 046d28a65b3d7cf5e8e6cbda5f59699ccd0c1cc3 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 10:09:45 +0100
Subject: [PATCH 213/240] update widget implementations

---
 eomaps/widgets.py | 37 +++++++++++++++++++++++++------------
 1 file changed, 25 insertions(+), 12 deletions(-)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index 2a7094138..8c9a3bc19 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -17,7 +17,7 @@
 def _check_backend():
     backend = plt.get_backend()
     if "ipympl" not in backend.lower():
-        raise AssertionError(
+        _log.warning(
             "EOmaps-widgets only work with the 'ipympl (widget)' backend! "
             "Make sure you have 'ipympl' installed and use the magic-command "
             "'%matplotlib widget' to switch to the interactive jupyter backend!"
@@ -331,12 +331,13 @@ class _CallbackWidget:
 
     """
 
+    _cid = None
+
     def __init__(self, m, **kwargs):
         self._m = m
         _check_backend()
 
         self._kwargs = kwargs
-        self._cid = None
 
     @abstractmethod
     def attach_callback(self, **kwargs):
@@ -349,28 +350,40 @@ def handler(self, change):
                 self._cid = self.attach_callback(**self._kwargs)
 
             if self.value is False and self._cid is not None:
-                self._cid = self._m.all.cb.click.remove(self._cid)
+                self.remove_callback()
         except Exception:
             _log.error("Problem in Checkbox handler...", exc_info=True)
 
 
-class _CallbackCheckbox(ipywidgets.Checkbox, _CallbackWidget):
+class _CallbackCheckbox(_CallbackWidget, ipywidgets.Checkbox):
     _description = "Callback Checkbox"
 
     @wraps(_CallbackWidget.__init__)
     def __init__(self, *args, value=False, description=None, **kwargs):
         _CallbackWidget.__init__(self, *args, **kwargs)
-        super().__init__(
+
+        ipywidgets.Checkbox.__init__(
+            self,
             value=value,
             description=description if description is not None else self._description,
         )
         self.observe(self.handler)
 
 
+class _ClickCallbackCheckbox(_CallbackCheckbox):
+    def remove_callback(self, **kwargs):
+        self._m.all.cb.click.remove(self._cid)
+
+
+class _PickCallbackCheckbox(_CallbackCheckbox):
+    def remove_callback(self, **kwargs):
+        self._m.cb.pick.remove(self._cid)
+
+
 @_add_docstring(
     "Checkbox to toggle the 'click.annotate' callback.", replace_with=_CallbackWidget
 )
-class ClickAnnotateCheckbox(_CallbackCheckbox):
+class ClickAnnotateCheckbox(_ClickCallbackCheckbox):
     _description = "Annotate (Click)"
 
     def attach_callback(self, **kwargs):
@@ -380,7 +393,7 @@ def attach_callback(self, **kwargs):
 @_add_docstring(
     "Checkbox to toggle the 'click.mark' callback.", replace_with=_CallbackWidget
 )
-class ClickMarkCheckbox(_CallbackCheckbox):
+class ClickMarkCheckbox(_ClickCallbackCheckbox):
     _description = "Mark (Click)"
 
     def attach_callback(self, **kwargs):
@@ -391,7 +404,7 @@ def attach_callback(self, **kwargs):
     "Checkbox to toggle the 'click.print_to_console' callback.",
     replace_with=_CallbackWidget,
 )
-class ClickPrintToConsoleCheckbox(_CallbackCheckbox):
+class ClickPrintToConsoleCheckbox(_ClickCallbackCheckbox):
     _description = "Print (Click)"
 
     def attach_callback(self, **kwargs):
@@ -401,7 +414,7 @@ def attach_callback(self, **kwargs):
 @_add_docstring(
     "Checkbox to toggle the 'click.peek_layer' callback.", replace_with=_CallbackWidget
 )
-class ClickPeekLayerCheckbox(_CallbackCheckbox):
+class ClickPeekLayerCheckbox(_ClickCallbackCheckbox):
     _description = "Peek Layer (Click)"
 
     def __init__(self, *args, layer=None, **kwargs):
@@ -422,7 +435,7 @@ def attach_callback(self, **kwargs):
 @_add_docstring(
     "Checkbox to toggle the 'pick.annotate' callback.", replace_with=_CallbackWidget
 )
-class PickAnnotateCheckbox(_CallbackCheckbox):
+class PickAnnotateCheckbox(_PickCallbackCheckbox):
     _description = "Annotate (Pick)"
 
     def attach_callback(self, **kwargs):
@@ -432,7 +445,7 @@ def attach_callback(self, **kwargs):
 @_add_docstring(
     "Checkbox to toggle the 'pick.mark' callback.", replace_with=_CallbackWidget
 )
-class PickMarkCheckbox(_CallbackCheckbox):
+class PickMarkCheckbox(_PickCallbackCheckbox):
     _description = "Mark (Pick)"
 
     def attach_callback(self, **kwargs):
@@ -443,7 +456,7 @@ def attach_callback(self, **kwargs):
     "Checkbox to toggle the 'pick.print_to_console' callback.",
     replace_with=_CallbackWidget,
 )
-class PickPrintToConsoleCheckbox(_CallbackCheckbox):
+class PickPrintToConsoleCheckbox(_PickCallbackCheckbox):
     _description = "Print (Pick)"
 
     def attach_callback(self, **kwargs):

From bcb6df33850a5e06425b481ff64f5b328e6ea4ad Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 10:09:57 +0100
Subject: [PATCH 214/240] add basic unittests for widgets

---
 tests/test_env.yml    |   2 +
 tests/test_widgets.py | 142 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 144 insertions(+)
 create mode 100644 tests/test_widgets.py

diff --git a/tests/test_env.yml b/tests/test_env.yml
index a3d643485..583e9de4d 100644
--- a/tests/test_env.yml
+++ b/tests/test_env.yml
@@ -27,6 +27,8 @@ dependencies:
   - requests
   # --------------to support multiple Qt versions
   - qtpy
+  # --------------for jupyter widgets
+  - ipywidgets
 
   ### To run the tests
   - coveralls
diff --git a/tests/test_widgets.py b/tests/test_widgets.py
new file mode 100644
index 000000000..af466953a
--- /dev/null
+++ b/tests/test_widgets.py
@@ -0,0 +1,142 @@
+import pytest
+from eomaps import Maps, widgets
+import matplotlib.pyplot as plt
+
+import warnings
+
+warnings.filterwarnings("ignore", "EOmaps-widgets only work with the")
+
+
+@pytest.mark.parametrize(
+    "widget",
+    [
+        widgets.LayerDropdown,
+        widgets.LayerSelect,
+        widgets.LayerSelectMultiple,
+        widgets.LayerSelectionSlider,
+        widgets.LayerSelectionRangeSlider,
+        widgets.LayerToggleButtons,
+        widgets.LayerRadioButtons,
+    ],
+)
+@pytest.mark.parametrize(
+    "use_layers", [None, [["layer1", ("coast",)], ["layer2", ("ocean", "coast")]]]
+)
+def test_selector_widgets(widget, use_layers):
+    m = Maps(layer="all")
+    m.add_feature.preset.coastline(layer="coast")
+    m.add_feature.preset.ocean(layer="ocean")
+    m.show_layer("coast")
+
+    w = widget(m, layers=use_layers)
+    layers = w._layers
+
+    # check if layers are correctly identified
+    if use_layers is None:
+        assert layers == m._get_layers(), "layers not correctly identified"
+    else:
+        assert layers == [m.BM._get_combined_layer_name(*i[1]) for i in use_layers]
+
+    state = w.get_state()
+
+    # check if labels are correctly identified
+    if use_layers is None:
+        if use_layers is None:
+            assert state["_options_labels"] == tuple(
+                layers
+            ), "layers not correctly identified"
+        else:
+            assert state["_options_labels"] == [
+                i[0] for i in use_layers
+            ], "layers not correctly identified"
+
+    for i in range(len(layers)):
+        if widget in (widgets.LayerSelectMultiple, widgets.LayerSelectionRangeSlider):
+            state["index"] = (0, i)
+        else:
+            state["index"] = i
+
+        w.set_state(state)
+        m.redraw()
+
+        found_layer = m.BM.bg_layer
+
+        if widget in (widgets.LayerSelectMultiple,):
+            expected_layer = m.BM._get_combined_layer_name(layers[0], layers[i])
+        elif widget in (widgets.LayerSelectionRangeSlider,):
+            expected_layer = m.BM._get_combined_layer_name(*layers[0 : i + 1])
+        else:
+            expected_layer = layers[i]
+
+        assert (
+            found_layer == expected_layer
+        ), f"layer not properly changed... found: '{found_layer}', expected: '{expected_layer}'"
+
+    plt.close("all")
+
+
+@pytest.mark.parametrize(
+    "widget",
+    [
+        widgets.ClickAnnotateCheckbox,
+        widgets.ClickMarkCheckbox,
+        widgets.ClickPrintToConsoleCheckbox,
+        widgets.ClickPeekLayerCheckbox,
+        widgets.PickAnnotateCheckbox,
+        widgets.PickMarkCheckbox,
+        widgets.PickPrintToConsoleCheckbox,
+    ],
+)
+def test_callback_widgets(widget):
+    m = Maps(layer="all")
+    m.set_data(*[[1, 2, 3]] * 3)
+    m.plot_map()
+
+    m.add_feature.preset.coastline(layer="coast")
+    m.add_feature.preset.ocean(layer="ocean")
+    m.show_layer("coast")
+
+    if widget in (widgets.ClickPeekLayerCheckbox,):
+        w = widget(m, layer="coast")
+    else:
+        w = widget(m)
+
+    state = w.get_state()
+    state["value"] = True
+    w.set_state(state)
+
+    if widget.__name__.startswith("Pick"):
+        cbs = m.cb.pick
+    elif widget.__name__.startswith("Click"):
+        cbs = m.all.cb.click
+
+    assert cbs.get.attached_callbacks == [w._cid], "callback not attached"
+
+    state["value"] = False
+    w.set_state(state)
+
+    assert cbs.get.attached_callbacks == [], "callback not removed"
+
+
+@pytest.mark.parametrize(
+    "widget",
+    [
+        widgets.LayerOverlaySlider,
+    ],
+)
+def test_overlay_widgets(widget):
+    m = Maps(layer="all")
+    m.add_feature.preset.coastline(layer="coast")
+    m.add_feature.preset.ocean(layer="ocean")
+    m.show_layer("coast")
+
+    w = widget(m, layer="ocean")
+    state = w.get_state()
+
+    for val in [0.0, 0.25, 0.5, 0.75, 1.0]:
+        state["value"] = val
+        w.set_state(state)
+
+        assert m.BM.bg_layer == m.BM._get_combined_layer_name(
+            "coast", ("ocean", val)
+        ), "Overlay not properly assigned"

From b18073170548e588aa52ffa878de0d26a00056a9 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 10:25:40 +0100
Subject: [PATCH 215/240] fix init of multiselector if unavailable layer is
 visible

---
 eomaps/widgets.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index 8c9a3bc19..d22898157 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -179,8 +179,6 @@ def _set_default_kwargs(self, kwargs):
 
         if self._m.BM.bg_layer in self._layers:
             kwargs.setdefault("value", (self._m.BM.bg_layer, self._m.BM.bg_layer))
-        else:
-            kwargs.setdefault("value", (self._layers[0][1],))
 
 
 @_add_docstring(

From b742ace78256785754896378b3c28816c21fa5d2 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 10:26:39 +0100
Subject: [PATCH 216/240] fix doc unittests (add option to ignore specific
 code-cells) - use the tag "ignore_in_unittest" to ignore a code-cell

---
 tests/test_doc_notebooks.py | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/tests/test_doc_notebooks.py b/tests/test_doc_notebooks.py
index a9a7a2c9f..876846096 100644
--- a/tests/test_doc_notebooks.py
+++ b/tests/test_doc_notebooks.py
@@ -7,6 +7,9 @@
 
 This is done to avoid issues with cells that are not "standalone"
 (e.g. that require previous cells to be executed)
+
+> Cells with a cell-tag: "ignore_in_unittest"  will be ignored!
+
 """
 
 from pathlib import Path
@@ -21,6 +24,18 @@
 
 
 class TestDocNotebooks:
+    def _use_cell(self, cell):
+        # select cells that should be used for testing
+        # - cell must be a code-cell
+        # - cell tags must not contain a tag called "ignore_in_unittest"
+
+        checks = (
+            cell.get("cell_type", "") == "code",
+            "ignore_in_unittest" not in cell.get("metadata", {}).get("tags", []),
+        )
+
+        return all(checks)
+
     @pytest.mark.parametrize(
         "notebook",
         filter(lambda x: x.suffix == ".ipynb", basepath.iterdir()),
@@ -30,7 +45,7 @@ def test_doc_notebook(self, notebook):
         with open(notebook, encoding="utf-8") as f:
             nb = nbformat.read(f, as_version=4)
             # parse all code-cells from notebook
-            code_cells = [i["source"] for i in nb["cells"] if i["cell_type"] == "code"]
+            code_cells = [i["source"] for i in nb["cells"] if self._use_cell(i)]
             # make sure plt.ion() is called before each test!
             code = "import matplotlib.pyplot as plt\n" "plt.ion()\n" "\n"
 

From 28f2f9727829acfcc9b7510085839bfe2d2e7c3f Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 11:29:58 +0100
Subject: [PATCH 217/240] add LayerButton widget

---
 eomaps/widgets.py     | 55 +++++++++++++++++++++++++++++++++++++++++++
 tests/test_widgets.py | 16 +++++++++++++
 2 files changed, 71 insertions(+)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index d22898157..077b3fee9 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -259,6 +259,61 @@ def handler(self, change):
 # %% Layer Overlay Widgets
 
 
+class LayerButton(ipywidgets.Button):
+    """
+    A Button to show a selected layer.
+
+    Parameters
+    ----------
+    m : eomaps.Maps
+        The Maps-object to use.
+    layer : str, list or tuple
+        The layer to overlay.
+        Can be eiter a string, a tuple of a layer and a transparency or a list
+        of the aforementioned types.
+
+        See :py:meth:`Maps.show_layer` for more details.
+
+    kwargs:
+        Additional kwargs passed to the used `ipywidgets.FloatSlider`.
+
+    """
+
+    def __init__(self, m, layer, **kwargs):
+        self._m = m
+        _check_backend()
+
+        self._layer = self._parse_layer(layer)
+
+        kwargs.setdefault("description", self._layer)
+
+        super().__init__(**kwargs)
+        self.on_click(self.click_handler)
+
+    @staticmethod
+    def _parse_layer(l):
+        # check if a single transparent layer is provided
+        if isinstance(l, tuple):
+            if (
+                len(l) == 2
+                and isinstance(l[0], str)
+                and isinstance(l[1], (int, float, np.number))
+            ):
+                return LayerParser._get_combined_layer_name(l)
+            else:
+                return LayerParser._get_combined_layer_name(*l)
+        elif isinstance(l, list):
+            return LayerParser._get_combined_layer_name(*l)
+        else:
+            return l
+
+    def click_handler(self, b):
+        try:
+            self._m.show_layer(self._layer)
+        except Exception:
+            _log.error("Problem in LayerButton handler...", exc_info=True)
+
+
 class LayerOverlaySlider(ipywidgets.FloatSlider):
     """
     A Slider to overlay a selected layer on top of other layers.
diff --git a/tests/test_widgets.py b/tests/test_widgets.py
index af466953a..751488eac 100644
--- a/tests/test_widgets.py
+++ b/tests/test_widgets.py
@@ -140,3 +140,19 @@ def test_overlay_widgets(widget):
         assert m.BM.bg_layer == m.BM._get_combined_layer_name(
             "coast", ("ocean", val)
         ), "Overlay not properly assigned"
+
+
+@pytest.mark.parametrize(
+    "layer",
+    ["ocean", "coast", ["ocean", ("coast", 0.5)], ("coast", 0.5)],
+)
+def test_layer_button(layer):
+    m = Maps(layer="all")
+    m.add_feature.preset.coastline(layer="coast")
+    m.add_feature.preset.ocean(layer="ocean")
+    m.show_layer("coast")
+
+    b = widgets.LayerButton(m, layer=layer)
+    layername = b._parse_layer(layer)
+    b.click()
+    assert m.BM.bg_layer == layername, "layer not correctly switched"

From 14d174d56e5e6b8e1e0d233a7e05243cc28f2676 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 11:30:07 +0100
Subject: [PATCH 218/240] update docs

---
 docs/notebooks/widgets.ipynb | 1149 +++++++++++++++++++++++++++++++---
 1 file changed, 1058 insertions(+), 91 deletions(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index bfa1e7b33..2922e7716 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -138,37 +138,47 @@
   {
    "cell_type": "markdown",
    "id": "00624ef5-0190-44ec-b85e-6b8b2e90b280",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## How to use the widgets\n",
     "\n",
-    "All widgets expect a [`Maps`](eomaps.Maps) object as the first argument.  (e.g. use ``Widget(m)`` to connect the widget to the [`Maps`](eomaps.Maps) object `m`)\n",
-    "\n",
-    "The basic call-signature for **layer-selector widgets** (buttons, sliders, dropdowns etc.) is:\n",
-    "\n",
-    "```\n",
-    "layer_widget = Layer< WidgetName >(m, layers=[list of layer-names], **<kwargs passed to the widget>)\n",
-    "```\n",
-    "\n",
-    "The basic call-signature for **layer-overlay widgets** is:\n",
+    "To attach a widget to a map, simply initialize the widget and pass the associated [`Maps`](eomaps.Maps) object as first argument.\n",
+    "(e.g. use ``Widget(m)`` to connect the widget to the [`Maps`](eomaps.Maps) object `m`)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "847539f3-7ee3-4092-bdfc-37552edba379",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "For example, here's how you can connect the available layer-selection widgets to the following map:\n",
     "\n",
-    "```\n",
-    "layer_overlay_widget = LayerOverlay< WidgetName >(m, layer=< layer to overlay >, **<kwargs passed to the widget>)\n",
-    "```\n",
+    ":::{note}\n",
     "\n",
+    "At the moment, widgets are **not** connected by default!\n",
+    "(e.g. changing the value in one widget will not change the values in others accordingly)\n",
     "\n",
-    "The basic call-signature for **callback widgets** is:\n",
+    "Any contributions to enhance the widget functionalities are highly appreciated!\n",
     "\n",
-    "```\n",
-    "click_callback_widget = Click< WidgetName >(m, **<kwargs passed to the callback>)\n",
-    "pick_callback_widget = Pick< WidgetName >(m, **<kwargs passed to the callback>)\n",
-    "```\n"
+    ":::"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "6f985bbf-2312-4c30-a879-a01f223788e2",
+   "id": "12a15112-ed5a-498a-b0a0-0688e429b729",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -176,16 +186,50 @@
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "836c14443b724a6aa4eaf6dc128ba33f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib widget\n",
-    "from eomaps import Maps, widgets"
+    "from eomaps import Maps, widgets\n",
+    "\n",
+    "m = Maps(figsize=(6, 2), layer=\"coastline\")\n",
+    "m.add_feature.preset.coastline()\n",
+    "\n",
+    "m.add_feature.preset.countries(layer=\"countries\")\n",
+    "m.add_feature.preset.ocean(layer=\"ocean\")\n",
+    "m.show_layer(\"ocean\", \"countries\", \"coastline\")\n",
+    "m.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "2f7e965d-d79a-4a71-bb00-60927b8fa4ab",
+   "execution_count": 9,
+   "id": "2c26797b-7c8e-4847-b711-4db1f3e3084f",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -193,16 +237,41 @@
     },
     "tags": []
    },
+   "outputs": [],
+   "source": [
+    "button = widgets.LayerButton(m, layer=\"ocean\")\n",
+    "toggle_buttons = widgets.LayerToggleButtons(m)\n",
+    "radio_buttons = widgets.LayerRadioButtons(m)\n",
+    "select_single = widgets.LayerSelect(m)\n",
+    "select_multi = widgets.LayerSelectMultiple(m)\n",
+    "dropdown = widgets.LayerDropdown(m)\n",
+    "slider = widgets.LayerSelectionSlider(m)\n",
+    "range_slider = widgets.LayerSelectionRangeSlider(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e850d223-3326-4afb-a500-d0c08b8ee624",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "ignore_in_unittest"
+    ]
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e2d04a390084f7eab12c600ab71ae30",
+       "model_id": "164f46af43da47e585b67b0441c5f581",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerToggleButtons(description='Layers', index=1, options=('Countries', 'Ocean', 'base'), value='Ocean')"
+       "LayerButton(description='ocean', style=ButtonStyle())"
       ]
      },
      "metadata": {},
@@ -211,23 +280,96 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "978c8cf2f45a452987f32024e174568c",
+       "model_id": "e4fa62881a124369af70305b20d4f0b8",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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vLOaSSaqi8P6ZHOpco8r5wLA7Y3K0N85UxSVmqDyZbjMy095Qoa0Q0VX805lojM6R3jjdGYOktTUfcyFE9JxeyFjJpidjUkibTJYdsgm9k01ZK1NXdsW2/vmiaF1V8LdwCbE7azKQjzFatDncY1FIm8TNpUtZyzF1hZMDSV45mu40jQ1CwdPZNpdOZHjrZJarj+p8+3yeeEyjO2OSsjQEMF1xuf20SSFtcKR387Od1gsCt+eDq3mKoqBrAArGMg+haQq5hE531qTU8NA1hZ9c6may4tB2QgppAz8QVJoeXWkTXVM6PermHe2NslbZhI5pqAghKNY9bs9lk0dnn2UODS3qgReEm99uwtQVjgzt/HK+tLVkDdY+tlk1WKau0J0x6cmaDHdZJGIa6YSOoUVtB7wgSs3XWj6P59oVaEq0DFZpePihoGVHM/XmT17rOW8lYlHLBEVRONob7+z4WmnO3XzrhJ6MSSIWnVT8QJCO6/TnzSVdyOf5geDBZJNizWOsaFNt+Yt2VJ0/nOI75/OdXVE3nzb49HYZ1xdYhsprxzPkkjqWqZGb+wJX50aczM8T9IKQ6YpLX85cciLyg5CWEy25Ndo+t8eazFbddRffnhpMcKg7ju0GxAyV4wOJzpiczSJENBexVI9q3ipNj/GizfH+BK4vuDveXPfOQU3d3pqoRCxqwmnoKqcHE2iawon+BBMlh4Sl0ZMxO81gH0+3t6yWzDJVutNRE1oFmKw4UYF5TGO0uPxSYcxQ+Zt3eunKmIt+fnesyYc3S/ynbw88G79E1G5i4WimqYrDbM3lwuG9WZvZdqJM8MtOJFgoFIJ6y0dRFBKxaEboPM8PeTpr82/fzK7pu2v+viv1MtM1Bd9u8P/42XlZg7WPyQyWtKr+fIx/92bPirUZlqlhzf3/pKUzULCWvR1EJ2VFiYKyqYrTWS57Mt1momx3dukNdllUmx6ZhE5X2oy2ievRNvGUpXO8P8FUxWGoy2J01ubEQKIzTqRY8yg3PR5OtskljeikpUSZouWWRDw/ZLRo050xScd1jvYmiJsOLSeg1oqahJ4ZSnKkN86DiRZ/uF4imzQ4O5ykP2fy5sksd8eaOH7I1/ernYDvULfFe2dznc7gd8eaNNpRHYmhq/i+4Hh/nLPDKWKmOjdfUCWTiH7PhZRBKq7zeKrN1ce1dV1Bz498absh10caZBKrvy4boShzW/UT0Tb8Pjeaw3h3vMXk3Gu5XlsdXFmGiuuHnB5K0p+PddofRLvktM77s5B+FrSk4jptNyCb1LlwOEUQCm4+beD70YDrYt1bV0PQwz0W6bjOnbFm5362Gy4JpFoEqAq8czrLcJeF6wvabsBM1eXpTJumE3BrtMEbJ7IkYs/e144XUkibCKIgyjJUMgl9yee3Lxfbc73VForHNDY796YqCtm5EVPPM3SV4W6LH7zWxVjRZiBvdS4WXT+c+70beL7gy/tVerMmP3q9G2/u7+bf2vW2P7dM6XHnye7b/CFtLpnB2sfmM1gfXXlK0TaYLLtrOon1ZE2+e6FAOq69MN2/WcJQ0HIDNEUhHtM6u4w2WnTr+SHOXNboq/tVSnWPwa5Ypy+V64VomkI6HtVZuX7IyEwb2w1puwG2G2LM7Uo61p/gzRMZSnWPz+5WyKcM3jkdNa6EKGswWIgRhIK//3gS01B580SWM8NJVCXK7vzpRomxokNsroFpEAouHE6ha9Gy2HCXxfH+eCfDEI3jCXgw2eL2aLPTUXutErGoSL9h+7x7OrcpI3ZaToDtRnMZa3OZnVrLp9r0GJ8bL7QbJS2NlBUt3dpeyNHeBIX08ifS9QrCaGn43niTsaJNfcE4GstQOXc4halFmyQadhSoASRMje6sSToeZcq+flBjuNvC1FVMXWWoK8aNkQbD3daygZDjhXPjfHzePpXtvL62G/DLr2bIJHTeO5MjFHD5QY3vXixsyr/3oJvvTr/a91Kj7UeB+wrB2rxqtUoul5MZrH1MBlj72HyANf8B9oOQx1NtPrxVBsB5Qe3UUFeMD84VSCeiXk57cWyL44WU6m4nYLNMjXRcp972eTTVoj8f41hfonN7IQQz1ej2iZiGFwgsU6XthsR0hdGizatHl/8ybLsBH98qc/5QioGC1cmIiLkC96uPnzWEzCR0vnexQLnhcfVRnVAILh5J8/rxpY8dBFG2ZKricu1xnYmy88JA+QevdnG4J+oGHze1dfcret6jqRaf3q6su65qp80Pdz7SY/Gtc3nSCX3D72MhBLYbdur5llOsu/zzFzM0nQBTV/hP3xng2uM6D+ZmDH77fJ7BgoW5ye0QIFri+uZhjW8e1vjuhUJn00W54RE31W27WJLW5vnvZ2n/kQHWPjb/AZ6cLtHXk+/8/NbTaGeRqkS9oq49qfNkut1Z1knEVPpzMfrzMW48baACrx7L0JczFy2f7HVBEC27LKxVWY0fRFvIX7Q7r1hzeTzdJp8yqLd9psoOD6faK94+EdN47Viai0fSi3ZFOl5IsR5tUT/aF0eIqKFqre3z4Y3yqi0Qzh1K8dbJLIaurFhUvJJQCKpNn1xSZ6ri8ngq6ha+li+KuBl1Hp+pulHdmaoQ01UE0bifMBSMFl8cIG4WXVP43sUCJwcSnezgy2jaUf+zYs0lFddJWVE9YsLUaDoBT2dsHk21GOyyKNZcLh5JM9xtUW36JC1t0/tMAZ3f9cRc+4LTQ8lNrU2StoYMsPY/+Sk8AD66VWawFF3Kv3Ei29lZM12JTvzVpt8Jrk70J3jzZKYTSL16bP9+8DVNWXNwBSxbHO/PDbmdrjhoqtLp8v7WqRyPplo8nbWXLfYeyMdIxTWadkAhbXCiP7Gkn5eqQrHm8fHtMmlL54evd9F2Aj6/W11xm/q8R5MtBvIxjvXFCUPxwiXCUAjaTsjNkTqTFRdNjTIfC5e8XsTUFY71J8gmdF45GgWLhZSBPrcbq1NgXXb4083Slvcg0jWF75zPvzC4mq44fHK7Eo1V0VXeO5ujO7P8hUTS0jk1GL1n/nCtRCiiDGfLCbEMlb9+p5dLJ5Z+ZjZrSXI5PdnoWOfbGUi7lx+IZ01eZW5j35MB1gHwZKbNdDMaTXF/okVP1uRIb5yrj+pLTtQPJluUmx5He+MEoSAMo2WW6arLX7zVs+5syH4132pB11Q0VdCbixEKwR+vl1BUhZ5stOvy4pEgqsG6XqLa8jud5aNsFczWPBq2z73xFq8cTS/auWRoKq8cTXNiIMGDiRb/8MnUmo/P9kI+vlUmaWmYuto5Ca/47wkElx9Wuf5kaUPWtXJ9wVjRpmUbUfH+c9maIIxGlzyZblOqb21wdWowwdunci/M5IRzy7cTZYe4qfLGxUKnTmo1h7otjvXHKdU9LhxO0Z01qTT8RQXnkjQvFIIgiLraVxoekxWH6w/W/nmW9iYZYB0wDTugYbd5NLdk1ZONeu8EYTSnLwgFpbpHqe6RtDQUop2Eg4UY8oLrGT+I6nGCUHBjpN4ZuKupCkd649HOQi/kq/tVTEPlcI+FWXWZqriYusrXD6rU2/6i3YE3Rup8cD7P0d6o2H101qYyF+yeO5Ti6Wx7XR2+bS/k6wdVfvpGT6cebDmhiArqx2bX1z18OdWmj+8L/o+PJuhKm/Rmo35E80HVVo4jiRkqZ4eT9GRNerPRCKOVNGyfBxMt7k+0aDsB75zO8sqR9Jqbs1qmxg9f68afX2a2NHqze3dXnrS1VEVB1RXyKZV8yiBpaXxzR36h7ncywDrgVupVNFCI8aPXurasSeVep6sKVx7VeDjZWjTuJggFDydbnZ5Jl45nUBT4+kENU1cY6ooxVXE5dyiF44WUGx6KEgUHj6ba/OrrWYa6Lay59hA9GZOrj+uMzLRx/fUHJ2NFh//6yRQ/faObzFxLhedNVVx++eX0pjVTnG/e2mi3eTK9cu3ZcuaXE2137f/WwUK0UeFYfzwq6H/BcujITJvfXCl2Nj789Tu9G15e0+d2okoHz1f3q3RnTIa6rEWZ55VUml7UBLjmUkgZ5LZw2VjaHeQ3g7SEqsDrxzIbWu7w53a7daUNDC3qQRUKsWJzz70oCAT//NU0Y8UXz9m7/LC26M9tJyQIBId74kuW7eptn49ulhkr2niB4P7czjNTj5YiN5pBLDc8fv7FNP/uzV7yKb3Tk6czFLvq7ooO6rByY8aVaCp861wey1AxNfWFwRVEbQsWDhueLDuyfkla1shMO3q/+CGqAj95o4e2G3DjSYPpqsvrxzOrvudmay43R6Kh3bX24h247dbunNMpbR4ZYB1gy40mMXWF4S6LT26XUZQ8hZRBEIoVsx/zhBBzY15s3j2TA6JGhx/eKmNo0bJZfy5Gd9YkDAWmrqKqyqpLV7vVp3crawqunuf6glLDQ1OjYOr5ACsd1/npmz34geA3V2aZKDm4fojrC1w/oCdr0nbX1x19Xr0d8F8+miCfMghDwfnDaU4NJrC9cEkQuNOWC7LyKYNK01sSZIYhnSL+1ZYEF3rtWBoBnSkDszWXqbJDT87EcUNm6y7Fmofjh2iKwvH+OPmUsefep9LGNdo+X96vcnu0uejnv/xyBtsLeOtkjjdPZoHoe05TohYu9bZPNqnTnTH56n6V8aKz7WPEpN1DBlgH2HJZC9cXzNRc2k7IL7+aIZc0qLU8/k/fH8IyVRRFIQgEmvYsOPKDkA9vlrk71uTNk1muPa5zY6SO44VoajTOZqriokDny8bUo0LwdFznjRNZ0vEoW+b5Ykt6BG2mydLGrjznezKBsupVr6JEzTEX1itdOJzivbM5/vtn00yvcwTNvFBAca64/KNbZT67W0FVln8f7Ba6Gu22WqnRqiDaxHHpeHZRk9aJskNfbvmxSEd649RaPh/Ptbl4NNWO2mokDYa7rc6Sn+8LjgzEScejrN9LthKTdtBs1aVYdwnmXkdNU1CIRjPdn2iSTRj05kyO9yWotX3ujze5N744uNLU6L3z+vEMpq7wy69mqDR8am1/28c8SXuDDLCkJRZuzZ8/sf3qm1mqTY9c0kBVokHPb5zIIISg3g5o2AGDXRZf3KsueqxwQTZi4ddPtOPMARxujzY5dyhFPqVjGVHX7f5CbNc2Nv3exQJNJ+BXl2fWNRJmPvvSnTFWXX5VFHj1aIbhLotbo02O9cU5MTdP8Kdv9vCLL6Y7gdLLWO9y3HZQgN6cSaMd8OqxNI12wI2R+qr3GZmxGSxY9GRNns7YfHa3Qq3l85M3uhc1kYXoRPvrb2YXNUxVFXjzZJZi3Zt7H8ramP2i3vb54m6Fu+MvGktjE59Q+f210rKBUiFt8MG5PENdUQPhf/l6dlGbERlcScuRAZa0ILOysvkmhi3HoZAyONIbZ7rq8vndCjFD4+LhFNdHNr7F/9bTBilL43sXC8zWXCYrDq8fz+zKIKs7a9JN1MxzbNYGRVnXKJupisvffzy5YnG1qiik4xqpeJwjvfFFS1OJmMbP3uvjHz+ZemEvrL0oZkZ1VN86l+dYX5zH02368iZ/uFZacRTPTNXlv38+DUQDwPvyMfIpg49ulunPxRZ1Xp+qOlRbPhePpLgx0kAIGO62SFoa15/UqTQSMsDaB8JQcGesyVf3q2selt5eZmNFOq7xs3f7oh3VCz6Hl45n+MUX07t2PJS0O8gAS4o6hK8hyMolo+W8mKHyye0KY3MDak1dYWS6jf+SV3ENO+DfrhQ5PZgkn9K5P97iSG98S7pfb4YPzuU7X7qOF/Jkus1ndyqdXXTLOdxjMTE3t69Yc1csrlYUhZVCS1VRONRj7csAy3ajnZWqCihR88yPbpXXfCL7wWtdDBUsvnpQjV6PuxW+/0oXrhfSsP1O7dzCfl8/fK27M9Rb1lntfQ3b51+/mmWmtrGl9HlJS+M/fjCw5PsnKosQpOP6vvwMSptHBlgSsHJwZeoKx/oSpOIabSfk2uP6oi8uhc2t4XG8kGtP6hztjXNmOMmDiSbD3XHiprrmHkXbZeHJOGaonB5KcnIgQbHu8XCyxUzV7dQPzV8dT5ajotfXj6W5cCS9oefVNYV3TuUopAyuPq5vynLhbmBoCvmUQcxUufKoTjYRDeL+zvl8J4B9kbYTUm56BKHguxcKlBout542uD3amHs9Ft9eVaIh1jFDlcHVHtdygk7rlPVMIFjJoW5r2Yu7Yt3l519Md/6sKmDq6pb2eJP2JhlgSUsoRCfxoW4L1wt5MNFaMTu1VQny+b5P01WXU4Mu3z5f2KJn2lzqgi7u8x5OtvjV5VkAhrssfvBa10u3rdA0hTPDKU4NJhkvOVx5VOPpJjQK3SlDXTFODiQ5O5xEAL/8aoZPblfIJnXeP5PnO+fzGJrSaV2xkjtjDf5qsBeIfu+puMborM101SWfNDjcG+dwj0WjHVBueFw6kUFfQ2sHafcr1l00NepLNp9hmqm5CAHZhL7uQeWN54I0IQS3njY7vevmL0pDMdeKRlVeOosv7S8ywJI6utIGXiBIxjQmyg6PVxlQvB0uHknz+d0KpbrHyHSbbFLfk9vlM3GdrrRBqe7xZKbNyot/69N2AmZrLgOFGNlkgZmqy++vFXf1rsDlqApcOJzmWF9Ub1auuzydsckm9aj1ggKpuM53LhSYqjiLshOqAt2ZKKDty8Uw9Wjm4Qfn8gii5dQgFLzv5kjJprn72qHuOL3ZGIe6XXQtGg81Omvzye0yrx5N88W9tddjAYyX7M6O6VAIJksOf7xRWva2e+0zJ20P+Y1zgMyf1tNxnbYbRLMGRXSSAig1oj5DtXVe6W2FUMDvrhbpyphMlh1+d63IK0fSnD+cwlpDt+7dJJcy6M/HqLd9/EDwcKrFqcHkSz9uPKZxqCcOQDqukoxppKy9VxeiqQqqEmVDbTfotKHIxHV+/Hp3J6COGSr/4f1+Hky2+PBmGYiC8PfP5pYNupUFjy+Dq4MhZqgMFJ7VNQ51xebKDVLcHmuuK8AKBYwWbY70xvniXpXLD9bfLy5mqCiwaPlwYbsaaX+T3zoHgAokYioKCk0nWNJReLdmtb1AMDnXq8j1BVce1+nNxTjUs7cG6uqawrfP50nFdZq2z2Bhc2fWTVUcJssOyZi2J5coVDVq0nhjpMFY0SYUUT1WIqYxXnLoyZid3mihENx4UicZ0zg5mOCtk9k9l9GUtk+15fNous3FI2lySb2zG3qtns62qbd9rj5avVXISpxl6rIUZa4MYw9dJEobIwOsAyAEWs7eL8A0NIWG7TNedDB1hWzSwNAU1D3wRaUoCpeOZwiFWHfriUrD69SPzH85J2Iajh9yf6LFzZEGZ4eT5JIGTXvns4/r5Xjhol19AHFTI5PQmao43HraoO2GuF6IG4Qc6rb2TE2etLMeTrYo1T28QHBqIMmtp80X32lOX87k/nhr04vX56+B9uLFkLQ+MsCS9oy2G/LRzTLnD6e4dDzD3bEmqhL1o9oLQRawruCq5QTcn2hy/XGDpKXRdgMGCxaqCndGm53WBXFT5d0zOcIQLh3P4vrRjrv1FvXuJrW232laO/8a217I+UMpBjY5AyjtX/O7dzUVAiHIJfVFw9lXYmgKU5WXa/MgSTLAkvaUUEQ9jG6ONPj+K11Umh53x5ucHU7t9KFtCiEEfiAYLzmUGh7H+uKcHUoRhIJrT+p8/VwdiKZGvZ+suTYDb53Kdh6j2tp449fd4PRQknOHUtSaHj1Zk0LafPGdJGlO2w14MtVGUxVmax6//GqGtV6H7cYpB9LeIwMsaU8KRdTK4c9e7WJmg7P5diNFUSjWXXJJnSO9UQG7EILZsrto8KyhRTMeEzGNO6NN7o23QAj6Cxa1ls/JwQRjJZvqGq7Wd6vHUy0abZ8TA7uju/pKy7ueH9K0A+Ixbc1NcYUQeEE09FzafC0n4B8/nerUm86/bC9alZsvQJfhlbQZZIAl7VkPJ1t861ye/vzeXzISQuD6AlWBnqy5aJekoigMFixePZrm6WwbRVFotH0uHE7zye1ypzeUosCTGRvPDxkr2tT2cHAF0caG8ZLDRNlBURTOH9q5LOV0xeHWaIMLh9OkLA1dU7gx0uDpjM1szeV7rxTIJl/8deoFUbPe20+bNGwfQ1f5T98eIGntrY0bu1kYCv7tm9nObuhETKNU9+jNmisOSje0qIfVi6ZZSNJ6yABL2rNCQdQnaY+bL95erpVAEApqLZ/pikMmofPemTy2F1Br+tyfaC06IQjxbNfSfsrqCQHfPKxxciCx7ozP7dGoQP7kQIJ0fGNfd2NFm19+NYMfCJ5Mt/nR69305WKU6h4CwYUjKY4+NzNyOTdHGnz94FkvpuEui0LawDJlFmuzOF7I764WGV+wWzARU7kxUl/S3d0yVVRFIQwFrh/K4EradDLAkvasmKFivGRH9N3ANNROG4J5wdyw2vGizWjR5i/f6sULQm6M1BkvOktabex3KUtbU3A1VrTRNYXerImiKEyUHO5PNLnxpM7fvt+37n5YU2WH31wpdmpyWk7IV/erxHSVkRkbP4zq3fIpg+P9iRU3MbScgC/uVTpF14d7LL7/SheJmMxcbZZG2+eXX80sGR0VNzV++mYPf7xeYnTWxjI1hBBoqrKuvliStF4ywJL2rO7MztflbIW7Y00+ulXuZKM0NcrgPJ5uEez9bhvL0jWFM0NJBgsWthsQCEG9FaCq0QDo+Xq0hR5Ptai2fHqzJgL48l6ViVI06/HPXi1wZijFdy8WSMU1vrpf458+neb142mO9yewzBcHNvMbC1rPDe+eHxg9b6riMvVNkcFCg2+fzy9bjJ+IaZw9lOo0qxyZsSnWXRKxpf8uaW3aToCqKsQMlamKw++vlSg/12Q3m9A5NZgkZen8xVu9+IHgV5dnGJ21d23/P2n/kAGWtGflkvsvwBJCcP1JfVGDwiCEB5Orz+Db64QQvHYsQyax9q+kj29Xlkwd6M2adGdMPr1dwdRUjvUneONElpmqy8iMzYc3y3z9oMbFI2lePZpetb3HTNV94ezDhcZLDv/7h5O8czrLGyeyS/7+7FCSWyONTl+lX345w1unspwdTslM1jo9mmrx8a0y/txGj+UGnsdNlTdPZqk0PUZm2pQbHp/frezbixRp95EBlrRnPZ3Zu8ONn9dyAop1l9ma1xmvcZAusIMw+h28KMASQlBueNwdb9FcZnmn5QY0bJ+2G/Kvl2c5O5zk7HCKv3irl7YT8NtrRUZnbD69U0EAl45nVnyuvpzJuUMppisOXiDIJ3VeP56hLxcjFDBZdrg73mR01qblBOiagqYqKy5lZpMGf/NuL//wyRReEI2pejTV5ou7Vf76nV4Gu6xl7yctNll2+NevZzt/nl92fV7bDSnVXR5Mtpa0N5Gk7SADLGnP2i87r1w/5A/XSxTrLo32wa0JWct2hcsPa3x+t7roPgsD0UvHMyRjGiNzwfft0Sa3R5sc7Y3zZ6928d0LBW6PNngy3WZhVfNM1cV2Az6/W8XxQ94/k6Pa8lGAn77Zs6RAXgWGuy2Gu62o71go0FUFIVg1K1ZIm3znQoEPb5ZwfUEYCn72Xt+a2zvsZ0II7k+0+PJelb95t4+kFdVKzW8eEEJQqnt8cru8psdTlWhWZSah88cba7uPJG0mGWBJe9ZE2WGm6tKT3ZsNKGstnyAUTJTs6IS/BygKW7LbqidjvvB1nKo4fH63iqpAV9okFIJK01u05OMHYtlh14+n2/zdn8b5yRs9XH1URxAFa725GIOFGHFT5ZdfzdByAmKGSiAEr6+S3VpIURQMTZn7/6vfVgjBw8kWrh/9Ekt1j8mys+bn2o88P6RhB3x4s8RY0eG1Y2keTbW4N94kFPCd83l6czFujTb54/XSmh9XUxXiMY0TA0kZYEk7Ql42SXvaZ3cquF5IsMcqVl0/ZLRo43jhnvryXxhcRbs4N6dNhh+EtN3Vs3e2G6Kp0bihmZpLse4tqaf57E5lUYZLVxUsU8UyVHJJgwcTLbwg2vnn+oI/3igxUXKotny+d7FANqHjByEPJ1qEWxBJNuyAxwuCaQHcGt3bHfdf1mTZ4RdfTHc2D9wda/LhzTJTFZdKw+tsSDg7nOTcoeSaHtPQFM4Op1AU+NONtQdlkrSZZIAl7WmjRZuPb5cXNebcC3Qt6r+zl7/8HS/szEN82SWuctOn3g6otfxFBf7zbo40+MP1IkG4/JBchWhJKBQwUIh1Ar982uD7Fwv85z8bIhRw9XF90f2a7YDJikMhZfD1g2h5UNdURos2Y7M2YpODrJSl8c7pLOn4s+Vtz99bFweb7VBPnP/pe4P0ZEx+8kY3rx7LMDDXPNgLBP/98yk+vlVGVRQ+OJenK73y5hbLVIkZKucPp3j/XA4E69qoIEmbSS4RSnve7dEmFw+n6d5DS4WqonC4J87ndys7fSibYrmgaL3+8dMpAPrzMT44l+fakzqNto8fiE4H7r6cyZHeOClLpztjYOgqtZYfLS+KKCMUM1TuTzT57ZUiM1WXf/l6lmxSX7Yo3g8FXz+oMVZ05jJiohOo/elmmXzK4MLhFGNFm2Ld493TOTQVvrpfQ1Ph2+cLS3qYrUZRFN44keXikTRPZ2xUFfIpY1Gt0UGkqgr/wwf9jJdsHk22Fi3z1ttBpxmrrqkMFGKLdg3Ov14Q1eCdHEiQnOt39nBKBlfSzpEBlrQv/PrKLH/zTt+eKXwXQjBbczE0Fdc/uIXtyyk3PGwvoNr0UBUF2w052hvn0okMPVlzSTPP5Tq09+cWj0+qt/wV+x75gWCsaJOJ6xTSBkEomCw7tOwoo7awPq7cmGEgH+PJTJu4ubRB7FqZusqJgcSG7rtfzQfFy71OQSA646QWBvP9+RjDXRaJuYL4s8MpNFXBD0Imyg7F2v6ZaCDtPTLAkvaFatPno1tlzgwlGe62dv2S4dXHdW6MNGg6Mrh63uvH0hzqjnOoe+NNOK8+ri86Ua+lRK/W9qm3fVilkH9+rp0vBzVvOgWFuKkt+5lo2AE//2KGYt0lDAWaCm+dzDFQiNF2AyoNn2N9ce6NN3ky3abe9qk0/U4HfknaCTLAkvaNR1MtHk62SMc1vn2+sGz3751Wa/n885fTVPb4IOat0Jczee1YhmN9L/+6HemNM1l2VhzuuxLR+a/lLZzxuFwzUWnjTgwkcPxw2Z2C01W306U9l9T51rk8E2WH0VmblKUxVXGYrjqMzNh7bsOLtH/JAEvaN+azDvV2wJ9ulBjuGkTbpF1um+H20waf3ql0OnkfJKoS1R+tdPLrzhj8zTt9m/Z6DXVZ/Idv9VNpeggBIzPtuZquxdmRS8cznBlK4geCD2+VmSw7KzziYsmYxrH+3RfA73XHeuPcyZlMVRYHxvPBlTY3OsnzBQP5GL+9UjyQnydpb5ABlrQvNeyAq4/rXDqR4eNbZbozJqeH1rbFeyuMl2x+v44ePvtNKFi1gdY7p3NbEgzPj1PKpwwyCZ1vHtYo1T28ICpmv3Qi01nq+9v3+hBCcOtpg5YTMlG2mSq7KAqcHIzmFz6Y25H21snsioOdpY1TVYVzh1JMVZb/rAQhBGFIreUxWrRlcCXtajLAkvatrx5UOXcoScP2uTFSZ7jb2pKZb422T8MOKDU8VCUaEnysL4Gpq2hqlLkpLTMrTXpmO4KVY30JjvUl8APBjZE6tZaP/lytnqIonD+cnvtTliCIurTPt6F493Ruw88/X6Rdb/nk08aurxPcCbYbMDpro6msOjPw8sPagW9vIe1+MsCS9p35buN+IPj99RJvnMjQdkOuPa7z7pncSz22F4R8cqtCse6STRrETZWjfQnujDa4Pdrs3O7ygxpCQG/O5IevdXG4J46ilLekC/pepGtKpwA5nzLo3cYWG7qm8NqxtXVO1zRlUWZtdNZmsCuG44bE1xms+4Hg7z+apNb2uXQ889Lvxf0omzR4/XjmhcPNXRlcSXuADLCkfWdhEPN0pk3TDvjx6938/Itp8iljQ0uFQSi4/qTO7dFmpx5kvk7kyqP6kts35nouPZpqMzprc7QvwdHeOI+m9sZInM1mmSrJmEa15aOpCpoaZXRODSb51tn8htsdbLdcUufpjI2mKgwa6qpzB59n6CrH+uNceVTnyqMaZ4aTnSVMKSKE4PZoY9ULkZihbkrfNUnaanvjW02SNigIYbjLIpPQef1Ymt9eLfLFvcq6O3TfH2/yye1KJ7haj/lg6+TAztWA7SQFcNyQYt1D1xQcLyRl6fzff3yI77/StWeCK4BUXCcIBb+6PMPHaxw6vNCl4xm6MwahgH/4ZIrP71YI5jJ5QghqrYO9u/TGSIPrT1YeHaQom9PUVpK2g8xgSfve5Yc1RmbafHA+zytH0nx1v0YYsq4lmpfpsv353QrH+hIMdUXNLxOxKPPx/I62/WphKGu7IfmUwXcuFPZsDdKRnji6pnL9SYND3fF1tQOxTI2/fa+P//WPE9huwNcPatwbbzJYsKi2fBptn3//fl+nE/lBMlGy+fROZcW/N3WFcG7pX5L2gr1z6ShJL6FY9/jtlSKvHkujawoPp1qLOnS/yMvM2nN9wa+/meXhXF2J7Ya07GDPBhgv6/2zuWi0zR6laQrvn80B0SDh9jqbxeqayoXDKd46FT1GvR1wZ6xJd8agPx/jn7+c4cObJa4/qfNwskWl4TFVcXgy3ebJdJs7Y43Obsb9IgwFd8aa+IEgs0xnfog+RzK4kvaSg3eZJB1YthcyOmtz8UiaK49q/OvXM/zwte41jSx5MNF84W1WM1V2Oj2WMgkdQ1Mp1l00deXeUPtRT9bsDPLdy04OJLgx0mCy7HB9pM6b62zb8NrRDFcf14ibKrqmcGYoxZsno+Lu+xOtRbP2lqNrCklLo38f/C79IOQ3V4o8mmpzvD/Oq0cz/OOnU8xffwixau9XSdq1ZIAlHRh+IPjD9RJnhpKcO5Ti5kiDX38zixcUODucWvW+jWUGBa/V/JKg7YT4oaDS9DvLHQdp2oquKfzlWz0Y++AfrSgKx/qibvFf3a/heCHfPl9Y8/01TeH14xleP55ZtPx8ciBJd9rk6azNx7fKKwYWfiD45VczvHUyyytH04RCbGqrC8cLXypru5owFJ3NAbNVl99fLzJbiwLKt0/luDseXcwcoOsOaZ+SAZa0LpapYrt7u8j0zliThatzf7heIpc0Vs0GbKTnTk/W5MKhFE9nbS4cTtGVMXkw0eLJdIvRog1EJ0pdVRBEJ579fE559Wgay9wbw7jX4uxwipmqy/2JFjdHGrx7JoehrT0oWamuL5cyyKUMFAU+vLlyIb3jhXx0q0wQCk4MJEjHdVpOsOFeb0IIwhBCBNMVh5mau+njgIQQ3HzaoN72GS85lOreogzuw8kWN54s3ZUrSXuRDLCkNVMUcL0QU1fQNRXbDfbsVebzx31jpL5qgHWox2KmtvxcO11VGO6x6M2aKChcfVyj7YbU2z5/vFEim9D5p8+nMTSFo31xLh5N8/rxDF8/qDFRcvBDQdLSaNlBp4fXfqMQ7aDbT2KGyncvFhiZaeP6AscNMeKbk/UJQsHorL2m246XbF45kiYIBN88rPH+2dy6N2W4fsiHN8s0bZ9ay6flhChKFBTr6wganz+uGyMNBvIx+nIxbj1tMFF2Vt2J+8W96oaeS5J2IxlgSWs2Xwvh+gLXDyikDVpOsOczWkJEs+VWc/FImjujTZrLFDRfOJLi/bN5AKYqDpcfRhHS/O+lPDfY2QsE98aj4vpXjqb5y7d7mam6XHlUiwrg92lwBdH75ot7Vb51Lr/Th7KpTF0lHdcp1j0+ulXmexcLL52lc/2QT25XeLzGTRgjMzZ/9+EEnh8SN7UN7XidrbncHVtaZ/inG2W+c6GAvs4xRtce1/n4dtRYd7rikE8ZjMysLWBUkDVX0v4gAyxpw0p1j795p5dffDmz5wu1H023OX84TSax/EciEdP4zoU8//L17KKfK8Dhnmfb9B9MtJbtMr0wM+X6ghtPGlx5WEdVoz8nYhrfu1ggHddwvJDP71aZWOPg4b2itc7ddntFIqZRrHs8mmpTbkzxP34w8FJzFWdrLreertwLajnz/bOySQMhxJqDLNcL+c3V4orZsjtjTbxA8KPXu9Zc41WsuXx0qzx3PDp/+VYvbTdYe4C1jy80pINl71ebSjvqnz6f3vPBFUC16fO//mGcyw9quP7yGbmjfQn+8u0ejvRYnB1O8v2LBf78UjdDXdbcY3iMl5Y/iSw8YcQMlbPDSTJJHdcX5JI6Q10xai2fpBX97Hh/guP9cRIxlUsnMqTjGooSBXR71XhpfwWM8949k+sE5pWmz73xje84fTTV4pdfzmz4/sUVlrGX43ghv74yy5Pp9qqf4ZGZ9pqXKyHazBA3o1OLpijM1lz+uMZB54OFGK8cTW/JzFBJ2m4ygyVJcwTw2d0Kn92tcHowwbtn8iStxV/0h7rjHOpevrFk0w4o1l7c6d3xQu5PtjqF85WmP3dibvHNoxrN53YsXn1UJwhFdGUPqEpUIL2bAtuFswVXsl/bfnVnTP7izR7+tz9NAPDRrTKFjEFvdu0tFMJQ8JurRR5Otl4qe+MF0S7VfGr1ETwzVZffXpntLF+vxg8E4yWHwz1xPD+qzVqtLiubjBrJ/vryLKWGx68uz6542+ed6E9w4UiaYs3btxlP6eCQAZYkLePueIt74y0O9VicHEhyciCBqiq4Xkip4dGbM5csmUxWHARrqyFZqYv788EV0Amk5k+8oYBUTH2p1hGbLQhFp6eXqSvLLpOupd/YXpVLGeRTBuWGRyjgi7tV/vLt3hfe7954k+tP6p25lpthpuquGmCNzLT5+FaZyhqCq3mFlIHjhXx2p0IuqfPqC4ZlH+9P8MPXu/jDtRLeGpuDmrpCIqbhB4JQrhFK+4AMsCRpBYKogHhkxubDmyX6cjEmyw5eIDjcY/Gj17sxF/R0OtIb51hfgiAUpOM6Tdvn99dKTFc37+Q5b60nre0iBARCYBkq9gqz4lbK/O0Xb57I8OGtMrYbrrjMPM/zo6a3H90sr/j72qhrT+orDjT/l69meDLdXncR+R+vlzANlZYTMFCIvTDAgrnZmwL+7Upxzc9ztC/O1w9qW7qcLIvope0ia7AkaQ1cX/B01u4ENiMzNr/4YnpRxqkrbZJPGXRnTGKGSiFt8vbpze0jNC8Mxbp3dm01VQFnhcAiZWl7ejzOWpwcTPIfv9XPUFeMmaqLHyz+XQghaLsB//r1DP/f347xr5dnNz24giiDNT+W6XnFuruh4MIPRWfJrlhz17w8fWIgsWSZfSWuL/jkdoXerLml7xUZXEnbRWawJGmDpiou/8vvx0haGqcGkxzqiWNoCt2ZZyeHiS26Eg9FdMLWVAh2SZeM1c65DTtgZKbN8f7Evp7BmIrr/PSNHn71zSyTZZfhbouZqst4yebRVLszLmmr3R1rcrx/6ZJsytKpr2PIuKkrc0t2z37m+oIHEy1ODSa49ri+ajZLURQKaWPZpe/lVJoeoRAUUgYnB6LH38ylcF1V8HdR7aK0v8kAS5JeQiiiYb1fP6jx9YMa/fkYP32ju9MLaa0nFgBNheHuOKOzdmcZY6VMgRBzJ709dK7wA7Gvg6t5hq7y55e6+fhWmW8e1hgr2asWrnelDdpuQMvZvEh5rGhHTYEXjLsJQrGu9yNEAWNpmbmIE2WHEwMJPr9XZabm8trRDN0vmXVSFebGSKmMztp4fshgIUa15W9ajZoMrqTtJJcIJWkTTZYd/uXrWZy5pZ/VulbPS8RUjvXF+dm7fagKnBpM8M6ZLIX04kJlRXm2Ey8U0Z/3ko9ulfnk9sqjX/aTMIyydqPFZ8FV0tIYLMQ4O5zkZ+/1kY5HQXix7m1qcAVRjd58L6p5pbpHrb32wnaA9go7+Tw/5PFUG3+uee5/+2yKtrv0trWWz/QagyPTUKm3fB5MtHj3TI7JikMooDe39wdaSweTzGBJ0iabLDvMVF2GumKcHEisWuR+4XCK98/m0DUVIQSnBpOdpZ3DPXEeTrawDJUbIw2yCZ3jAwnuT7SImyq3nm6839JO8APBlUd1TF3ljROZDXUc3ytihsqPXuvmm4c1+nImCUujJ2N2/s1eEL6wrcXLujfe5L0zOeJzPaXmm5Gux0rHmI7r3Bl71gzVCwS/+GKaN05kOdYXp9ryuTnS4MZIY031WrqmMFiweDjZQlFguuryzukcj6baFNfRg0uSdhMZYEnSFvjjjRL9+Rjvn83RsAOuPq6TiGmcP5Si2vJ4MNFC1xRODyU7PYVCAbeeNjoBVi5pdIbtnj+c7jz2kZ44k2Vnxa7xu90X96qoClza5EHCu03MUHn3TG7ZvyvWPNpbPGIqFFFLhjPDKapNjz9cL3L+cIqbI2vvEr/SblXLVEla2qIdebM1j09ul+nOmPzhWmnNkwgMTaEvH+sU5gsRZcjGSw5tJyCX1Gm7e38kl3TwyABLkrZArRUNzfWDkPOH0pweSqIo8KvLs5waSPKTN3pQFOhbsPyhqcqaeicZukouZfDumRz3x1t7cqTOzacNXj2WORA1Wc8TQvD53cq2PNflhzXyaYNffjlDzFD5zvk81abHWHHt75nlRtfEDJVvny8wOmsvKpp3vJD/9unUsjM7V5JLGUs6xT+dtbl4JEVX2uDikTS/uTLLw8m1zWaUpN1C1mBJ0hZ6ONnm519M8/trRcaLDtWmz72JJgP5GMPd1oYfNx3XuXA4zd+827vsbrHdrt4O+Op+Fe8F/aL2owcTrW0bG1Rp+vzXj6douyGWEQ2CfvPk+jKHxjLtQP54vcSjqVZnTNQ81xfrCq5gaZ2iqsD5wymqTZ/TQ0n8QCzambucAxinS3uADLAkaRvM1rxO0XG16fP1g+qah+euRlEU3j+b4+Qe7JL+9YMaf//xJNOV9QUbxZrLvfEm1x7XGSvurfocIQSf36vuyHNXmh6uF65pnNNCy9VQhQI+uV3hjRNZ/vKtHixzY6eSN09muHgkvehnmqpwc6TB/YkWrhdy62mDG09WX9bc6s2BMoCTNkIuEUrSDljvVf5q0nGd779S4Nvn86hq1LfoyXSbyw9q6941tt0qTZ9/+HSK710scHY41fl50/ZptANUVaErY+DN9V66N95csiT61+/0Lsmk7FZ3x5obKjbfDF4g+P/82+i6O3vM91m7eCRFueFRbwfUWj5NO2C8ZHN2OMXP3u3jnz6bWldd2ZHeOG+ezCJC6M4YfHSzjOtH7Ue8uV24f/enCRKWtqmfl42Q3R2kjZABliTtA7qmos81zDZ1OHcoxbG+OL/+ZnZd9TY7QQj4/bUS9+dmP96faDGzYOdlzFAJQrHijrYv71Vp2gGlRpSh+eB8flfWdtXb/pLWCdvtZeKEcsPjp2/0oCgK/8dHE1SbPuMlh7PDKfIpgxMDCa4/l2labgi4oSn89M0e+ubmebb9gFtPG8sGZ6GI5nauNN9SknYzuUQoSTtgO7qvW6bGj1/v3von2iSjRZtPblcWBVcQFU6v1tJgouzw26vFTlPPn38xvdWHum5tJ+AXX0zv6SBhrOjwXz+ZwvECfvZuH8f64jyealFtRkuOyzVTfX6ck64q/PhSN0NdVmf37LUn9VUvAlQFvD38e5MOLhlgSdI2O9Jj8e3z+W15LsvUGMgfnEaN1aa/67bzVxoe//TZFJXm7l6ufRFDU2g7Ab+/VkLXokBpuDvOZ3crNGyfU4PRTtmFIdXC1+Jb53L83340zOGexUO/VxpMPS8UUeZNVVjSfFeSdjMZYEkbosl3zoYYmsK3zuVJxNY2AHcz/Oj1rmV3gu1XuyWgFELwxb0Kf/fhBOU9HlxBVMNleyG2G/LPX86gKgo/fr2Ld07l+OP1ElMVh0LKWLIM+bfv9fH9VwqcHkyiLfM+TMd1zg5HQZahKeRTRidIG+qKEZsb93N6KLnmwdGStBvIGixpQ3bLgOGdpiprL4DVNYX/yw+HOksj20EIwWjRRtOUFZtG7gcLa3RiG9zRtplCIfj91SJ3x1s7fSibru0FNGoBs1WX7qxJLmXwvYtdfH63QvW5An5DU+hKG/SvEvQ+nmozWrSJGSoXDqcwtOi1jBkqxbqL40XLh44X7qnZm5K0899EkrSHHe2Nk08Z6Gsoqj5/KLWtwRVEJy9TU/n37/Xx2rE0R3qsTkZgP5kPrkxd4dQOt6wQQvDRzfK+DK4gKjq3DJWffzFNfW6XatLS6M6YS9o1eIFYdVQUwEzVodEO6M2a3B5t8s2jqP3GzacN7i34HQoBUxUHBRgsxDjUbfGd83mO9cVRFUjEtD03n1Pa3/bfN60kbaOHU23KDQ9/DWmsF9WabLamHZBPG6QTOg074EhPnH/3Vi//+c8G+e6FApn4/ktgu77Y0UydENHw4xvrGEezFyUtDVWFr+4/6+n1ytE0J/oTHO6xOsXtCvCbK0XCFT4ft0cbXHlUB6LPx1+/08tfvNVDLmWgKNCVNkhZGqoC1ZaPHwg0VcHxQgppg/OHU/zkjR7yKYMwFBRSBsf746tmzOaPa54sd5C2yv77hpWkXerxVJuYoZLepsAmqlfREELgeCGWGdWv6JrK+cNRz6m2G1CsuczWPDRNodr09nzPn49vlfmrt3sx9O0/c157UufjW5Vtf97tVqx76JrC6KxNpemRS0bF5xePpJkoOWQSOmNFB9sNaDkBMzWXrrSJrikIITpDr6crLoIo8zjcZVG3fUxd5fHUszmbmhotw5cbHul49B4uNTyKdQ9VURjutujNxbg92uCD43lO9CdQVYXpisNY0ebxdJupyuIsmmlEw9VdXxCEkLI0HC/c18vo0vaTAZYkbRMvCEluY3H7PEVROsHVQmeGlhYdV5reugb17kZTFZd//mqGv3q7d1v7YTVtn8/v7kyX9p3gB4K2CPmXud91Kq6TjuvoPQq/v1ZE15ROreY/fDJF3FQZLFi0nICzh5LkkwbFehT4qIpCse7xYLLJ26dyHO1LcHesCUT1nt8+n+fqozr9hRgIgWVqXH1cZ6riMNRl8ebJDKcHEwwUnjWc7c3F6M3FuHQiS7Hm8miqzc2nDVpOENVzLdCwAxIxFctUF81WlKSXIQMsSdoGfTmT989uT2uGtVpuR1cuafCXb/fyxxulzgluL5ooOfz2SpEfvd7VyZZstcsPa6v263oRhb1Xwx2EgkrT5xdfzvDj17sopE28uW7sz/f8arshDyajmqqZqrtoWd32Qh5PtwhC+PROhf5cjNFZm9ZcB/cPb0YNWmtji4vox0sOk+VpDF3l7HCSUES7SNXnAuuujElXxuS142k+uVXh5tOlS7g92RhdaYOrj+sv9TpK0jwZYEnSNig3PDw/3JFlq/XSNYUfvNrFB+fyPJ1p829Xijt9SBvyYLLFK5U0fTlzy4Msxwt5Mt1+qcfYy6f0Wsvj7z+e4rsX8viB6BSbP998dD6IXK5m0fVCyk2fSsPD8ULcNQwC1zWFMBSkLI0rj+pceVQnk9D52bu9JK2lpzdDU3njZIa2GzBVcckldXpzMeKmSsrS+c2VWUIBhZRByw12XU81aW/Z/d/2krQPeIHgi3vVZQfn7lYxQ+VoX2JNOyR3q8dTrW3LYKWWOaEfFEEYZbN+d63Eo6k2/9cfDvM/fW+Q14+lycR1BgsxjvRYmIZKaq6X1fPF5XfHW5TqLt7cLM21ZJH8IMqW9WTNzs9qLZ9vHtZXvE/K0vnzS928dizNeMnh6Uybe+NNfv3NbKf+sNz0ODecIhGTp0hp4w7uN4IkbSMh4OrjOnFT49KJzE4fzprpmkIqru3ZLuQ3Rhq8eTK75ZnDmKFSSBt7unZts4wWbf75y2mO9ycY6rY43BunLxdDUxVqLZ8bI3UeTbaXHUS+XH89TVXIJHTKDQ9l7s9+KDjUbfF01gbg9mhz0e3zKZ0gFKgKnQDb9UJGizYD+RjxmEaxHo34mf/fhYSAbx7VsAyVpKUhBNhusOc3gEjbSwZY0o5ZT5PO/eKLexWO9cc7u652O88P93TRrxcIxksOR3rjL77xS5J1O89MVdxFO/cSMZWudNQnS9dUhrstbC8kE9eIxzRsL+TeeJNzwymqLZ9q08P2QkxdZSAfIwhFpzjdDwUKMDoXXD3PMlRGZm2+fljj3dM5Tg0m8fyQP90sdfpq/ei1Lv7slQK2GzAyEz3O899HQkR1YwP5GNNVh2zSQAhBteUvO3dRkp4nAyxpx4Ti4AVZ4Vwm67sXCjt9KGvyye3KnlrWXM781v6tdrgnzp09vDFgK7WckJazfEA0/x1w8UiKiZLDaDG6XSFl8K2zOb66X+v8bN5q78imE9CciurhfnslGgKua8qiIeK/u1YiaWn8+aVuLj+o8WiqTanhYSwz8WBhVrIrbRKEYk9fdEjbRy4wSztqj5+7N+TuWJOGvbuX3IQQfHZ3+d1We83VxyvX42ymY31xzmxzM9n9YP474PqTBk0nQFWioEtTFX51eXZJcLUegmgJsNFevLwXhIL//vk0f//xFOWGR9LS0NTVx0mVGx4PJlvomoq1YBqC+tyAa0maJwMsSdpmfiD48t7u7pd0a7TJ5Qe1nT6MTVGsLa2x2QqqqhBf0OfM2ocjibZauRE1ug0FzNRc2ivs4js7nKQ/H+PUYIKBfAxFAUVhxQ0ZP77UvSQImm9e+nCqzdNZu5OpNXVlxZE7hqZQbkTLlwsf5wBeJ0prIJcIJWkH3BlrcmYouagx4m5Rqru7PgBcK4VohMt2MRb0FnO8cK7RqegUb8cMlVeOpPny/v74/e4Uxwt593SWREwjmzTw/JCWE9VTfXSrvOi2/fkY/fkYwwuK4lfzfP+uhWSnd2k95CXWAZaJ6/ty8O9eIAR8/XD3ZYiCQPAPn0x1GjzudWcPJbd1BqS5YLeiIFqKmg+uVAXePpXl0onMokBMejFDUzrZqURM47sXCgwULLJzm0UMXSUR07g3vrQGricTtXAY7t7ai5k93M1E2iIyg3WA1dq+nD6/g2aqLvW2v22zCddE2V9X6Ud7E9v6fLnkyq/lQCHGxSNRNi2fMpiuuiveVnrm7HCSd8/ksN2QX38zy+Ge+KKl2HkPJls07aUXBmJuAe94fwJVUfjsbmXTd3yauiJ3kUpLyPTFASe3G+8c2w35b59OrbjdfCeoStT7aj8wdYXBQmxbn3O8tLQPVsxQee1Ymj+/1ANEdT+rBWLSM6cHE3z7fJ64qZFPGfyH9/t4+1R22dse64v6bj2fSbKMKBhLx3VeOZpmuGvzM1npuE4qrmPq++OzI20O+SmXNtVenKe2kxp2wM+/mGa42+LS8QxDW/Dlvx6uLwj2yZV4Oq5v22giIQR3xpp889yy75snMrx5MrtoNp7jhdyd68ckLS+b1MkkdF4/nkFf0PJdf779+wIxQ+UHr3bx6tE0H94sU216tN1wSZA9UIjx+CXHGj2vWI+CZlVZ2zdgJqEzmNne7Kq0/WSAdQBs5zXV/FdL3FRX3AEkLTU6azM6a/PKkTTfOpfbtvEuzys3vH0TIG9HfaEQgltPG3zzsL5sZ/LXj2eWDB5+2ZmFB8EPXu0iEdM2tHzenTH52/f6uPW0QaXpMfjcRcvFI2m+ul9dtZh9IypNf03vuZMDCYSA6w/LL7yttLfJAOsA2IkTpgyuNubakzqKAt86l9+R5//m0e4rvN+oqYpLueGRT21N13whBN88qvPZncqKt2m7Swd8F+uy9mo1+ZRBJq4vW2e1HucOpZb9eTRKx1jUaX6zOF5IzFBxvJW//6YqjizNOCBkDZYEyEZ5u8nkDs2zC0JBbY/OHFxOEAr+cL2E2IKzmeOF/Ns3xVWDK4Cx55pktp2ApzO7p+ZuN1GA7ozBD17teung6kV+fKmbV46mt2STz4smH9Tbwb7ZpSutTmawJEDWTe0mO7WL79bTBqXG9jTl3C6TZYenszaHezZ3FmG97fNgcvU6KlWBvpy56GePplorftZ0LdqJlkvqe3a49nooSrScl03onBxIcLgnvmQ5daukLJ0PzuU51hvnnz6f3vDjnBlK8mS6jeOFGLqC64s17SY8iBMsDiIZYB0wB232317k+du/vBoKwSe392dNyGd3Kgx1WXNNPzdHd8akK21QrK8ckL52LEMh/VyAtUr9VTquU254ByK4SsQ03jiR4cLh1I7VGwIMdlmcHEhwf2Jjmw4eTbV45Uiap7M2laYnv1+lReQS4QEjP/y733zzxK0mhMDxQlwv5OefT3caYu43xbrHrS2YqXjoBVmx3uzi4KrlBIyusjxY3mfZw5X0ZE1+9m4vF4+kdzS4mvfGiQyXjmc2dF/XF3z1oEYoBK4v5PertIjMYEnSLjOQ39reTV4QMl50+Ph2mXrLPxAnBXcLsoIvqu16MNniWP+zrfhjRVsuxRNl6rbrImItCmmTC0dU7k00abQ3Vhs1u03zLqW9RQZYkrTLaFvY6HNkps3vrhYP3C7PyiZnh8oNb0kB+3K3EUJ0sjR3RpeOcTmI8qndd9pJWTr/4wcD/OOnU1Sb3oG46JC23u57p0vSAadvUaHv3bEmv7taPJBZlMlN2pJfa/n8+ptZZtYw5qZY9/hvn00zVIjxcLJF+QDUVr2IosDjqTavHs3sujmoMUPlJ5e6+cWX09Q3mMmSpIVkgCVJu0wmsTUfy9HZ9oEMriDa9ddyAhIvuf0/FGJNwdW8ybKzY203diMhoi7t4S5tBJVLGbJHlbRpdtclhCRtk50vrV2eqkD/FtVgmbssY7CdhID74y+/RFeVWaiX9niqPTdSZnfqzpgvvpEkrcHB/caVDrTdepE61GVtydKJ7QbLDiI+SD69U+HRC3pXrSYIBbdHN3834kETCri5Bbs6N8vANg8Il/YvGWBJ0i6yFR2sg1Dw8y+mKa3Ss+kgCAX87loRfwP9KIQQ/MtXMzyaknMEN8OVh7t3JNP0FozQkQ4mGWBJ0i6y2S0axos2n96uyG3kc1xfcG98/VmsW6NNns7KETebxfbCXTsuZrwkX2dpc8gAS5J2kRfNMVsP1wv56kGNW3JZa5HP7lRo2ms/uT+YaPHRzf3Z5X4nff2guu771Ns+d8YaXH9S5/Zog2CTx0rVWv6Ba2EibR25i1CSdpH2Bq7q/SBE15ZeK43MtF/Yq+kg8oKQetsnaa2+HCuE4NrjOh/frmzPgR0wa2nq2XYDbo40mCw72G7ITG3x8t3vr5XozZr85I2ezuvpeOGG6hj9IOTTfTouStoZMsCSpF1kIzsIHW9pgFWsu3xyp7JJR7W/BOHymcL5zuwtJ2S66nD9SZ2x4sHeGLCVjvYtHjUkhMB2wyhAMlWadsA/fjKF/4Ks7nTV5VeXZ/ird3rxA8HX92t8cD6/7uP59E6Fh7LGTtpEMsCSpF3C1BWGu61136/c8Ln5tMHh7jiGruD4gt9emV3XMthB87trRd48kaXtBpQbHk+m2wRh1Gxyt9YG7TelutfJNo0Vbb68V2Vigz3DpiouN0eipcOTA0lGZtocfsGsyIUabZ+bI5u/lG5oCooS1f5JB48MsCRpl3j1aGZDw2+Huy28IOQfPp3agqPanxrtgD9cLy35uQyuts/Vx3WuPq5jaAreJtRSfXK7Qjqu4Xgh9ydaZBNrn3l4+WFtS8bjeIHg/bM5ZmvuhjZXSHubLHKXpF1A1xQuHE5t+P4D+Rjdmd0zQFeS1mozgqt59XbArdEGd8ea/PHG2uqpQiG4twlNaFfy2Z0KiZhGLinzGQeNDLAkaRd440TmpXpg1dsB5YbsMi5J86NuijWXSnNxe5IgFJ1auzAUFGsuj6faW7qEFwqYKDn87L0++nIm2YROV1peDB0EMqSWpF3A8da+Ndz1QjRNwfVDKg2PibLD9SeNTW3xIEl7ne2F/O9/mmCgEKMrbTLcbXH5QY3DPRZ9uRgf3SpT3Kbmu5mETtzU+Pfv9xOEgn+Uy/kHggywJGkXaKyjIL3W8vkvH09u4dFI0v4QChgrOowVnc54nomyg6qAoihoarSrdCtZhsqZ4SRCCEIBf/enCWotmW0+COQSoSTtAq8fy6z690EgqLejL+XZuhzlIUnrFQQCU1dIWRqhAENXyMS3NscwWIjhBSG/+GKGWsvn6uO6DK4OEJnBkqRdoGn7GLpCboVdT14Q8r/8fpxMXKdhyy9oSVovAZi6yn/8oJ+2G5KO6/zzl9OUm1vzecqnDP7dWz207IA/3Shz7UmdG0/kVIWDRGawJGmH5ZM6E2WHSmPlepCYoXLxSIpUXNuS7eSSdBBUmj6Pptqk4zptJ2CitDWNZA1N4YevdWFoKmMlh9GizfUnDeRH92CRGawDxNQV2fBuF/JDwVjR5p1TuRVvU2n63J9oYcs5aZL0Uj68Wabe9rk71tzUFhEACnBiIMG3zuWJmypPptt8eGNpvzXpYJAB1gEid5ntTvmUQTqu4wYhcW1pqwYhBE+m2zK4kqRN4Hghn99d/6DpF+nLRTMRE3PtVn751QxPpuXonYNMBlgHyFbvlpE2JhXXGeqysJYZUDtVcfj15dl17TKUJGl7daUN/urtXgw9+gzP1lwZXEkywJKkndSTNTl/KEXK0pYdk1Nt+jTl+JZdTVXYUF2cArImZ5+ot31+c7VIfy4a1v7V/c3PkEl7jwywJGkHleoeVx/V+Na5/LJ/f3ooyZHeOP/140mqcnv3rrTRlXcZXO0fri+YKDmMTLflJhSpQwZYkrSDTg0mODGQQF1hyHPD9vn8TkUGV9KydFVBVZGbV3aB9UxjkA4G2aZBknaQ44U4bjT65nl+EPLH6yXujrd24MikvcCKqZwZSlFIydl2krTbyAyWJO2gStMnFFGXaU19FmTZbsA/fT5NaZtmpUl7U6MdcO1JfacP48DTNQV/k1s+SHufzGBJ0g5q2j4jM21sL6S5oEO7EFBfZVlQAYa7Lc4MJXn7VBZ9mQyYJEnbQwZX0nJkBkuSdpDrC8oNj8myQzah4/oC2wvx/JAjvXHuTyy/PCiAUt2l0lC4Nx7IwlpJkqRdRmawJGmHlRseo0Wbetun2vS4/qROOq7z5sks6fjSxqPzWk5Iw5bBlbSYIbOZkrQryABLknZYKODuWJM/3igxUXboShs02j6aqvA37/bxwbk8PVkTU9++E2c6rnGsL44qz9V7zmaPf5GiJXlJWi+5RChJu4TrC648qhMzVLrSBvGYFg2kLTuIbT5n1tsB9bbsRC1JIHuWSRsjAyxJ2mUcL2S85Oz0YUiSJEkvQS4RSpIkSZIkbTIZYEmS9FJkfYokSdJSMsCSJOmlyPoUSZKkpWSAJUkH0HbuSJQkSTqIZIAlSXucptJpp6BryppaK8jhwJIkSVtLBliStMcocwFUKq6hqwrJmN5p4+AHQjYelSRJ2gVkmwZJ2mPmgynPF+RSOn25GK4fUmn6BIGg1JADoiVJknaaDLAkaY/QVIXj/XFMXSVuaqQTGrM1j9FZm5SlMVN1d/oQJUmSpDkywJKkXU5VwNRVskmd987kEQj+68dT2O6zOYRlmbXakzQVLENDwKLXc7fQVIVglYPqShs07ABdUwiCaFD5ao+VtDT8QNBygq04XEnaVWSAJUm7VNLSEEKQjuu8czpHNqHzxb0Kri9IxFRcLyTc7hk60ksxNAU/FJwdTvH6sTSZhI4yV1TneiGf36twc6SxrYFWytJouwHxmIZlqAgBcVMjl4pq+/xAoKkKAkHK0rHdEF1TaLsB3RmTU4NJYkZUzttyAsoNj0rDo9rymao4tJyA84fSvH48jaIoCCG4M9bk1tMGU5Xls675lEEmrqFrKt1ZE9cLufq4ThAKhrss3jyZQVEUDF3B1FXGiza5lEG16ROGgnLTo+2GNNp+Z/lcV5VVA8D10NTo37HbAmJpd5EBliTtQqoCrxxJU2l6vH4sw2zN5ZdfzuDLb/Q9xTJUerImR3rjzNZcVEXhndNZ/DAKVhbSdYVXjqR593SOctNjouSgqQr1to/jhcTNKBBqOwGuL/CCENsLUYhmR65FzFDJJXXScZ2utMnRuYHeiZiGob/8nqdETCMR0xjqsla8jSC6eHjvTI50QgcBQSg6/5ZMQscytSX3e+tUFscLiRkq2nNbZc8MpwDoy8WWf04hEAI+u1vB8UKmyg7lpr/hf+d8Vk/XFHw5XFtagQywJGkXScQ0DE2h2vL59E6FREzjO+cLCOBQj4UXCEZnbSAKwmS8tb0sQ6U/H+PpbJtgmWTIQD7GYFeM/lyM/nxsXUGLqihkkwYAvdkYvdnlg4Xnjcy0+ecvZ1a9zaXjGV45miZuqp2M2U5RFYVD3fElP8++4H6aqpCILQ281kJRFBQF3j+bB8DzQ+5PtHD9EFWJWps8mWnjBwJTV0lYGndGm6sujwIyuJJWJQMsSdpBmYTeqUnpyZj8+FI3SUvj41tlmnbAiYEEqhotmfzkjR7aTsCNkQbXntRxNmm546CwTBVVUV6q/idhafz4Ujf1ls/XD2v4QUjbCZmtubxyNM07p3Obd8AvEApBox3w9YPaop9nEzqKEmVXzg6nGO6yyKWMbTuuvcDQVc4dSi362YUj6UV//uBsnlrbx3ajjOFM1eWbRzUZVElrJgMsSdohpq5QSBvEdJUn021mai7/+vUMrxyJTtSf3a2QTxmLMg7xmMaFwykeTbXQVYXmPigWXpiJyyZ1kjGNStOn5QRkEjrpuMZY0encPhHT0DWFmK5SSBuEIso6ZBI6mqqQSeiMzLQpNzzODqdIxDRcPwpGTV1lsuyQSeh8/aCG64VcPJqmWHNRFXB8wdOZNgOFGC0npJA2GMjHyCZ1LEMjl4yeI5cy+MGrXZ1j8vxwU5bY1qNU95iuOFSb0QaHfFLnzHCK146ldzxLtR9omkI+ZQBRcHqkN86Fwyl+dXmW4/0JinUXQ1fx/BDXF5TqLpWXWHaU9h8ZYEnSMkxdwTRU2k5IJqFTb/lYpkrD3ryARghotH0KPXEuHEkRNzUKaYO+bAwUONYX5/LDGj98rQt1wQkzZqq8dSrLry/Pbtqx7ATLUBnutjjen6A7Y/L1gyrnD6foSps8nmojEGiKgqoqnBoMmKm6HOq26MvFiL9gqehwz9IlqHnz9UGnBpPL/v18vY66lpb4c7Y7uIJoyezWaBPbC7FMlW+dy3NolX+39PLiMY2fvdeHEGJJEBsKwXTFZaLkMFV1eDzV3qGjlHYLGWBJ0gKWEWVCQiGIGSrDXTpJSyMMwQ8FozNtWm6I64UIoC9nUm/7mLqKoaur9qJSWDwY2QsELSfk0VSbRttnsMuiMlfcrKoKt542yKcMwhDU5+KJrrTJcLfFyIy9Fb+GLWHqCq4v0FWFrkyUGerLxzjWlwDg+688ywidGEgsuf/Z4e05zvl6nd0sCAX/9k2USXntWJqjvQl0bZcf9D6yXHClKlHGSwjB5YfVHToyaTeRAZa0p2TiOicGEtTbPpWmR6Xh01+IIYToZJdqTR9BtCXeC8SadvoogKEreEHIiYEEMUMlCAQDhRjZpEGj7VNr+RztjROGAtNQ0eczHEoU8DTaPrW2j+NGy1HjZYd6y6fpBKTjGm+fyqGpCndGGwhgouzwypE0fiCoND0sUyUR07AMDctUuXA4hblMZkQBqk2P3Xw6VRRIx3VqrWdLJum4zvdf6cLUnxVzSxvjeCHfvVCgL7+2QnhpazRtn3LDZ7RoM1l2qLV8DE2Rsz4lQAZY0i5i6gp9uRheILDdgERMIx3XmShH9TeJmEZfzmS66hI3VQ73xIkZLod7LAxNZaxoR/17juidbdxtN+Ti0RS/vVKkVPd440SGqYrLzaeNRc8teDYA+ZPbFfpyJicHkmQSBpoaBQQvCgpScZ1U/NlH6lj/0iwMwNsvUQjdaPvYXsiDyRajxd2bvbIMFVOPXgNVgbdPZUkndOptnyO9chnrZc23Q5B2RqPtc3+iBcA3D2ub1l9L2l9kgCVtOXUuw6Mo0JM1MQ2VWtOnN2cykI/qaRSimpIX1das5vldQQv95I0eIOqWXUibnD+UwgtC2m7UeydpacQMtVN3U2l6NO2AsaJNX85cti/PdhFCUGv5zNRcbjxpdALO3SQR00haGqb+LLDKJHQO9cQ53pegJ2vu8BFK0sub/yxee1Ln+pPGi+8gHWgywJI2XcqKejn1FyyCUGDqChcPp3fFVnHL1NYULKXjW/vRcL2Q+5MthBAYmoquKWiqgq4peH7IyKxNqe5Rb/m0d9EIFU2F3lyMwXyM7oxJMq6RSxrLLmVK0n6jzPUq68/HZIAlvZAMsKR1G+qyeP9sDlVhbpuyYLbm4syNbqk2fYJQMJCPETNUGra/64uGt8r8biMvCCnVPWK62hnvMZiPMTLbxnZ9GnbAndGtHZGiawrpuI6mQssJ19QPSlcVjvTFOTWQIJs0SMd1WUwtHXgn+hP4rwg+vlXG9QXH++OEIbh+yFTFfWGDUulgkAGWtKpCysA0VEp1t1OjNFa0+dXXsyTjGqcHkxzuiXN6aPkt7wfd/YkWlaZHJq6TSehMV13KDY/H023qLZ9ETENRoN72O8FVMha1a0gndJptn6ez9ksFXpmEzncu5Bnusjq7nzw/ZLrq8ofrpUWF6AtZpspfvd1LV9qQfZUkaU616eEHgmxC5+RAsjOyqFSPygokaZ4MsKQVWYbK4d442YROysrQdAKeTLd5NNWmNrdjbrriYOpRh2xVhf58jELKIB6Llo76D/gup1ODSYQQzNRcZmseCVOlN2fSkzWZqbpcfri4C/eZoShgHZlpMzLdpjtjcrw/wWTZWdSDS9cUujMmxZqLt8oOybip8s7p7JLRJIauMtRl8Zdv9fC//XGC5R4hDAW/u1oEok0AcVPD0BVSltYZ46JrSmd5U1MVcsnl58hJ0l4XCsEntyrcGKnvmiV7aXeTAZa0ItsL+ea5AOB5QQi2G3ZO0PV2i660wcnBJF1pmfWAqG5jfrZcEAiaTkAQCgopA8cPGSvaBGFUi+V4IbeeNhgvRVmrht0mNVeA350xsb2Q/nyMw90W2aTBry7PMFVZ3HsraWm8fTJLwtJImBohgsdTLRp2gO2G2F70v203oFT3FgVXqgKmoWK7IaGIsljpuD5XvK4SM1SyST0KtrSoCaiqRE05NVXpFLhL0n7ieNE4JNcPZXAlrZkMsKRVZZN61E/KjwKDaBhq1OelKx21LhgqxDg5mERVo52AqlxOWpGmRaNc5n33QmHZ2wkh8EPRKXQfLdoU6x5tJ+B2w1s18G3aAb+/Xur8WZ2bS6epCs5cg1Sx4CQxUIhx4VCK7qw5V6OlEIQCVVnaUFGSDqKYEWV8U3M7ZSfKNrM1b6cPS9rlZIB1ACk8G3wL0HKDRSfchVwvxPOjouh58007i3WPasvH8UJScV32N9pEiqJgaFFfsPnRMAO2T0xXqTR9bo82qLd9grmXJWVp0dJjxiSfMsgkdHRNJW6qi8a4CCGotwPuTzRJxjR6siaF9NIWCto6xsRI0kGRTRpcOpHhjJOk3PAIwrkWKlWXibIjB0FLi8gA6wASRA04LUMlndDpyUY9qixToysdtVLwA4HjhXRlDDRFodTwEAICIYjpKl0Zg75cDENTZJZjG8zPz5t3tDeq01JVhYFCVPe2ltdBUaIM2hsnslt1qJK0r803ee3OLL4w8fyQyYpDvR3geiEN26dlB53RWg3blx3eDxgZYB0A379YwEpEI1lcP0RVotqpQAiCQOAFAs8PadlR0XoookxHNhntZDO1aKlQVaKCZkNTMOYK22VwtTNyKWNX9BWTJCli6OqSzSTPC4XA9aJdh5MzFv/zNh2btDNkgHUAnBhIksms3OV8rUIh8PwoGHP9ED8I0TW5Y0ySJGktVEXpNDtWA+vFd5D2NBlgSWumKgoxQyFmyK7dkiRJkrQaeaaUJEmSJEnaZDLAkiRJkiRJ2mRyifAAqNVWbxYqSZIkbS/5vbz/yQBrHzNNk/7+fg4dOrTThyJJkiQ9p7+/H9Nc2odO2h8UIVZqMSntB7Zt47r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' 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-       "            </div>\n",
-       "        "
-      ],
       "text/plain": [
-       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
+       "LayerToggleButtons(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "606e15353d3d445e9028c33102c77134",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerDropdown(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1e9d00db02f488ca6623762b53ec557",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerRadioButtons(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ada39bb960b484988116bebd163af5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerSelect(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "baf9ae7c23fd425b89484f957216f938",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerSelectMultiple(description='Layers', index=(2, 2), options=('coastline', 'countries', 'ocean'), value=('o…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa503ca772e741b6892ff1a7ed14d7e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerSelectionSlider(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean'…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19af3d8fe4f2421795bc99922f729996",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerSelectionRangeSlider(description='Layers', index=(2, 2), options=('coastline', 'countries', 'ocean'), val…"
       ]
      },
      "metadata": {},
@@ -235,14 +377,110 @@
     }
    ],
    "source": [
-    "m = Maps(figsize=(6, 2))\n",
-    "m.add_feature.preset.coastline()\n",
-    "\n",
-    "m.add_feature.preset.countries(layer=\"Countries\")\n",
-    "m.add_feature.preset.ocean(layer=\"Ocean\")\n",
-    "m.show_layer(\"Ocean\")\n",
+    "display(\n",
+    "    button,\n",
+    "    toggle_buttons,\n",
+    "    dropdown, \n",
+    "    radio_buttons,\n",
+    "    select_single, \n",
+    "    select_multi,\n",
+    "    slider, \n",
+    "    range_slider\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6501121d-06a2-4f8b-b279-8d684ddb9b95",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    ":::{tip}\n",
+    "You can also specify custom labels or use combined-layer assignments.\n",
     "\n",
-    "display(widgets.LayerToggleButtons(m), m.f.canvas)"
+    "In addition, you can style the widgets as any other Jupyter Widget (more info in the [Widget Styling](https://ipywidgets.readthedocs.io/en/7.6.2/examples/Widget%20Styling.html#) section of the ipywidgets docs.)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ad083853-4209-4a23-bf43-02fd0560f269",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd341ec83aa44d72ae512dcbe2f49ca5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerSelectMultiple(description='Layers', layout=Layout(height='80px', width='50%'), options=(('A nice coastli…"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "custom_selector = widgets.LayerSelectMultiple(\n",
+    "    m,\n",
+    "    layers=[[\"A nice coastline\", \"coastline\"], \n",
+    "            [\"Overlay 'coastline' + 'ocean'\", (\"coast\", \"ocean\")],\n",
+    "            [\"Transparent overlay: 'coastline' + 'ocean'\", (\"coast\", (\"ocean\", 0.4))]\n",
+    "           ],\n",
+    "    layout=dict(width='50%', height='80px', )\n",
+    ")\n",
+    "custom_selector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "db03b116-c43b-4802-90d1-dcde8ca901cd",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a05311ef3ad84756b6dd4002888c40d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "LayerButton(description='Custom Ocean Button', layout=Layout(height='5ex', width='30ex'), style=ButtonStyle(bu…"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "custom_button = widgets.LayerButton(m, layer=\"ocean\", description=\"Custom Ocean Button\", layout=dict(width=\"30ex\", height=\"5ex\"))\n",
+    "custom_button.style.button_color = 'lightblue'\n",
+    "custom_button.style.font_weight = 'bold'\n",
+    "custom_button.style.font_size = '24px'\n",
+    "custom_button"
    ]
   }
  ],
@@ -267,40 +505,191 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "15f06cddfd5941dba2da7800413fcf85": {
+     "00300632abc44177af6a49ff4a2a98f0": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "0ce486faa7784b48b8f139365dec99ca": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "height": "80px",
+       "width": "50%"
+      }
+     },
+     "0dade8feedc34cc6a91b0d868070b2df": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
       "state": {}
      },
-     "37a91e5ba4424e88af1ee8a77e57f337": {
-      "model_module": "jupyter-matplotlib",
-      "model_module_version": "^0.11",
-      "model_name": "ToolbarModel",
+     "115be2e46de0423fb2ac5b8acf1fbb7e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "VBoxModel",
       "state": {
-       "_model_module_version": "^0.11",
-       "_view_module_version": "^0.11",
-       "collapsed": true,
-       "layout": "IPY_MODEL_15f06cddfd5941dba2da7800413fcf85",
-       "orientation": "vertical",
-       "toolitems": [
-        [
-         "Home",
-         "Reset original view",
-         "home",
-         "home"
-        ],
-        [
-         "Back",
-         "Back to previous view",
-         "arrow-left",
-         "back"
-        ],
-        [
-         "Forward",
-         "Forward to next view",
-         "arrow-right",
-         "forward"
+       "children": [
+        "IPY_MODEL_164f46af43da47e585b67b0441c5f581",
+        "IPY_MODEL_e4fa62881a124369af70305b20d4f0b8",
+        "IPY_MODEL_606e15353d3d445e9028c33102c77134",
+        "IPY_MODEL_f1e9d00db02f488ca6623762b53ec557",
+        "IPY_MODEL_2ada39bb960b484988116bebd163af5d",
+        "IPY_MODEL_baf9ae7c23fd425b89484f957216f938",
+        "IPY_MODEL_fa503ca772e741b6892ff1a7ed14d7e6",
+        "IPY_MODEL_19af3d8fe4f2421795bc99922f729996"
+       ],
+       "layout": "IPY_MODEL_d275741ab86c4716b54d3db5261c72ed"
+      }
+     },
+     "14c688d4b72f4e52a1134ae7de11cb54": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "164f46af43da47e585b67b0441c5f581": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ButtonModel",
+      "state": {
+       "description": "ocean",
+       "layout": "IPY_MODEL_1a033d565947464999611c0471e301fa",
+       "style": "IPY_MODEL_2a04abc6fbc444dbaecb11f8f2f6e1e1",
+       "tooltip": null
+      }
+     },
+     "17fc2557567e45b3883019a036e0b00d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectionSliderModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "behavior": "drag-tap",
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_ed88c97c871c400eb67c5800165d41eb",
+       "style": "IPY_MODEL_f3453e1b3e484e5497ea80dfe30aa6e4"
+      }
+     },
+     "19af3d8fe4f2421795bc99922f729996": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectionRangeSliderModel",
+      "state": {
+       "_model_name": "SelectionRangeSliderModel",
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "_view_name": "SelectionRangeSliderView",
+       "behavior": "drag-tap",
+       "description": "Layers",
+       "index": [
+        2,
+        2
+       ],
+       "layout": "IPY_MODEL_7e6bb04688dc4226956b31b27690167d",
+       "style": "IPY_MODEL_243adf05996e4d6ea4dfce0dd476aa14"
+      }
+     },
+     "1a033d565947464999611c0471e301fa": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "24057ca6512a44468774840c821514ea": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectionRangeSliderModel",
+      "state": {
+       "_model_name": "SelectionRangeSliderModel",
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "_view_name": "SelectionRangeSliderView",
+       "behavior": "drag-tap",
+       "description": "Layers",
+       "index": [
+        0,
+        0
+       ],
+       "layout": "IPY_MODEL_cef9f3584eea492ea4291952b85c5b6f",
+       "style": "IPY_MODEL_3d0641653f554e6c8fba757fdf214bc8"
+      }
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "295aeaa6f44449c39950ec8eb7fe9ce4": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_14c688d4b72f4e52a1134ae7de11cb54",
+       "style": "IPY_MODEL_888f64490ec24f118cf88b9ba47cc272"
+      }
+     },
+     "29a656a721bd48539e239cf5b3ae39a9": {
+      "model_module": "jupyter-matplotlib",
+      "model_module_version": "^0.11",
+      "model_name": "ToolbarModel",
+      "state": {
+       "_model_module_version": "^0.11",
+       "_view_module_version": "^0.11",
+       "collapsed": true,
+       "layout": "IPY_MODEL_fd6cc8ab10e140748466899f69597333",
+       "orientation": "vertical",
+       "toolitems": [
+        [
+         "Home",
+         "Reset original view",
+         "home",
+         "home"
+        ],
+        [
+         "Back",
+         "Back to previous view",
+         "arrow-left",
+         "back"
+        ],
+        [
+         "Forward",
+         "Forward to next view",
+         "arrow-right",
+         "forward"
         ],
         [
          "Pan",
@@ -323,54 +712,218 @@
        ]
       }
      },
-     "43296dfeb4cf4d9db89ad3062d95468a": {
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+       "font_family": null,
+       "font_size": null,
+       "font_style": null,
+       "font_variant": null,
+       "font_weight": null,
+       "text_color": null,
+       "text_decoration": null
+      }
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": 2,
+       "layout": "IPY_MODEL_e6581233a5ec448b857e361d1b7dd45f",
+       "style": "IPY_MODEL_75b02b3ca94b458c8216fa9f6bde4dfc"
+      }
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       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
-      "state": {}
+      "state": {
+       "height": "5ex",
+       "width": "30ex"
+      }
      },
-     "49da046497434d0eba54c09cab729573": {
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+       "width": "30ex"
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "ToggleButtonsStyleModel",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
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       "state": {
        "_options_labels": [
-        "Countries",
-        "Ocean",
-        "base"
+        "coastline",
+        "countries",
+        "ocean"
        ],
        "button_style": "",
        "description": "Layers",
        "icons": [],
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-       "layout": "IPY_MODEL_43296dfeb4cf4d9db89ad3062d95468a",
-       "style": "IPY_MODEL_49da046497434d0eba54c09cab729573",
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+        "A nice coastline",
+        "Overlay 'coastline' + 'ocean'",
+        "Transparent overlay: 'coastline' + 'ocean'"
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+       "index": [],
+       "layout": "IPY_MODEL_0ce486faa7784b48b8f139365dec99ca",
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+       "style": "IPY_MODEL_ff8be3ec300444f784616984a94d3b3b",
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+       "layout": "IPY_MODEL_30fbe7128e8a4153a26fa442b67d4a44",
+       "style": "IPY_MODEL_992fb18b8d764107bae5571b22b06607",
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       "model_module": "jupyter-matplotlib",
       "model_module_version": "^0.11",
       "model_name": "MPLCanvasModel",
       "state": {
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+        2
+       ],
+       "layout": "IPY_MODEL_95a0f5a02c9142d79113ab5c46ff6df4",
+       "rows": 5,
+       "style": "IPY_MODEL_00300632abc44177af6a49ff4a2a98f0"
+      }
+     },
+     "bedb8bdd542c4d36b160a7e8ac747553": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DropdownModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_dadac6e8be4b495e9e2d4bbe8efe1717",
+       "style": "IPY_MODEL_8a88833604ac44d1aff8edc6e0692541"
+      }
+     },
+     "cd341ec83aa44d72ae512dcbe2f49ca5": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectMultipleModel",
+      "state": {
+       "_options_labels": [
+        "A nice coastline",
+        "Overlay 'coastline' + 'ocean'",
+        "Transparent overlay: 'coastline' + 'ocean'"
+       ],
+       "description": "Layers",
+       "index": [],
+       "layout": "IPY_MODEL_615cda7c550840d794459343fecf2dd3",
+       "rows": 5,
+       "style": "IPY_MODEL_599293c026224217adb706a576bcb147"
+      }
+     },
+     "cef9f3584eea492ea4291952b85c5b6f": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "d275741ab86c4716b54d3db5261c72ed": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "d9a61c6dc2a842e987167dcb44008d89": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
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+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "dc94da2387094ae59728897c307f02fc": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ToggleButtonsStyleModel",
+      "state": {
+       "button_width": "",
+       "description_width": ""
+      }
+     },
+     "debb82c087ae4960a6437aa14664b817": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "height": "5ex",
+       "width": "30ex"
+      }
+     },
+     "e4fa62881a124369af70305b20d4f0b8": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ToggleButtonsModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "button_style": "",
+       "description": "Layers",
+       "icons": [],
+       "index": 2,
+       "layout": "IPY_MODEL_8c21d03d7a70457d8baf173298bd1411",
+       "style": "IPY_MODEL_85f26793c1fd4effbd94698845090a2d",
+       "tooltips": []
+      }
+     },
+     "e5279598198f43849dacbcba7e750918": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectMultipleModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": [],
+       "layout": "IPY_MODEL_70da4a6e5d7d438eabbdac8d7b6b4a89",
+       "rows": 5,
+       "style": "IPY_MODEL_37b20b04e65d442bbca89ee2f404f4c7"
+      }
+     },
+     "e6581233a5ec448b857e361d1b7dd45f": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "e83045abddcd48caace63182a908a53e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "e906ce4f97794dada32f2a8d02839795": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ButtonStyleModel",
+      "state": {
+       "button_color": "lightblue",
+       "font_family": null,
+       "font_size": "24px",
+       "font_style": null,
+       "font_variant": null,
+       "font_weight": "bold",
+       "text_color": null,
+       "text_decoration": null
+      }
+     },
+     "ed88c97c871c400eb67c5800165d41eb": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "ee70c8487d9b45219b27e449a69c35dd": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "eea7a30d36d84710951b1698169dfa3e": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "f1e9d00db02f488ca6623762b53ec557": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "RadioButtonsModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": 2,
+       "layout": "IPY_MODEL_9968216f831d4e17afa1b5b6f869ea27",
+       "style": "IPY_MODEL_38f9281338b44eadb3435cdaadfd0412"
+      }
+     },
+     "f3453e1b3e484e5497ea80dfe30aa6e4": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "f650197f759f42948ecbe7a82098fe34": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ButtonStyleModel",
+      "state": {
+       "button_color": "lightblue",
+       "font_family": null,
+       "font_size": "24px",
+       "font_style": null,
+       "font_variant": null,
+       "font_weight": "bold",
+       "text_color": null,
+       "text_decoration": null
+      }
+     },
+     "fa503ca772e741b6892ff1a7ed14d7e6": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectionSliderModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "behavior": "drag-tap",
+       "description": "Layers",
+       "index": 2,
+       "layout": "IPY_MODEL_9359bad23e7644069c18f02976cb62bb",
+       "style": "IPY_MODEL_5e31ffd632114f29ad8f9796b1759a49"
+      }
+     },
+     "fd6cc8ab10e140748466899f69597333": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "ff8be3ec300444f784616984a94d3b3b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ButtonStyleModel",
+      "state": {
+       "font_family": null,
+       "font_size": null,
+       "font_style": null,
+       "font_variant": null,
+       "font_weight": null,
+       "text_color": null,
+       "text_decoration": null
+      }
      }
     },
     "version_major": 2,

From b8bb6563a745c0ae666b1e940caff3468abb68f4 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 12:14:55 +0100
Subject: [PATCH 219/240] update widget docs

---
 docs/notebooks/widgets.ipynb | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index 2922e7716..0ca6461f5 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "e850d223-3326-4afb-a500-d0c08b8ee624",
    "metadata": {
     "editable": true,
@@ -377,16 +377,7 @@
     }
    ],
    "source": [
-    "display(\n",
-    "    button,\n",
-    "    toggle_buttons,\n",
-    "    dropdown, \n",
-    "    radio_buttons,\n",
-    "    select_single, \n",
-    "    select_multi,\n",
-    "    slider, \n",
-    "    range_slider\n",
-    ")\n"
+    "display(button, toggle_buttons, dropdown, radio_buttons, select_single, select_multi, slider, range_slider)"
    ]
   },
   {
@@ -924,6 +915,7 @@
       "state": {
        "_data_url": 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",
        "_figure_label": "Figure 1",
+       "_message": "81, 41.62 (41.616699°N, 81.000000°E)",
        "_model_module_version": "^0.11",
        "_size": [
         600,

From d592dd1031abca6039ac014b9308ee82d59ed2a4 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 12:32:33 +0100
Subject: [PATCH 220/240] add LayerButton to widget docs

---
 docs/notebooks/widgets.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index 0ca6461f5..e683036e4 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -90,6 +90,7 @@
     ".. autosummary:: \n",
     "   :nosignatures:\n",
     "\n",
+    "   LayerButton\n",
     "   LayerRadioButtons\n",
     "   LayerToggleButtons\n",
     "\n",
@@ -915,7 +916,7 @@
       "state": {
        "_data_url": 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",
        "_figure_label": "Figure 1",
-       "_message": "81, 41.62 (41.616699°N, 81.000000°E)",
+       "_message": "-126, -29.38 (29.383301°S, 126.000000°W)",
        "_model_module_version": "^0.11",
        "_size": [
         600,

From f653e4d9595a132b6c65034979d2687ea15c9023 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 12:46:08 +0100
Subject: [PATCH 221/240] update version to 8.0rc1

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 24354cc40..00c0fae5b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ eomaps = ["logo.png", "NE_features.json", "qtcompanion/icons/*"]
 
 [project]
 name = "eomaps"
-version = "8.0rc0"
+version = "8.0rc1"
 description = "A library to create interactive maps of geographical datasets."
 readme = "README.md"
 license = {file = "LICENSE"}

From e14d9937abe6b07dfcbbdfafa4d3867c208fd245 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Thu, 7 Mar 2024 12:48:03 +0100
Subject: [PATCH 222/240] update layout editor - allow "-1" as width/height -
 make sure x0 and y0 are fixed to the provided values - update docstrings
 accordingly

---
 eomaps/layout_editor.py | 79 ++++++++++++++++++++++++++++++++++++-----
 1 file changed, 71 insertions(+), 8 deletions(-)

diff --git a/eomaps/layout_editor.py b/eomaps/layout_editor.py
index 6c7da7735..a77cfc413 100644
--- a/eomaps/layout_editor.py
+++ b/eomaps/layout_editor.py
@@ -844,6 +844,18 @@ def get_layout(self, filepath=None, override=False, precision=5):
         """
         Get the positions of all axes within the current plot.
 
+        The returned layout has the following structure:
+
+            >>> {"figsize":     [width, height],          # figure size
+            >>>  "0_map":       [x0, y0, width, height],  # map position
+            >>>  "1_inset_map": [x0, y0, width, height],  # inset-map position
+            >>>  "2_logo":      [x0, y0, width, height],  # logo position
+            >>>  "3_cb":        [x0, y0, width, height],  # colorbar position
+            >>>  "3_cb_histogram_size": 0.5,  # histogram size of colorbar
+            >>>  ...
+            >>>  }
+
+
         To re-apply a layout, use:
 
             >>> l = m.get_layout()
@@ -851,10 +863,16 @@ def get_layout(self, filepath=None, override=False, precision=5):
 
         Note
         ----
-        The returned list is only a snapshot of the current layout.
+        The layout is dependent on the order at which the axes ahve been created!
         It can only be re-applied to a given figure if the order at which the axes are
         created remains the same!
 
+        Maps aways preserve the aspect ratio.
+        If you provide values for width/height that do not match the aspect-ratio
+        of the map, the values will be adjusted accordingly. By default, smaller values
+        take precedence. To fix one value and adjust the other accordingly, use `-1`
+        for width or height! (e.g. `{"0_map": [0.1, 0.1, 0.8, -1]}`)
+
         Parameters
         ----------
         filepath : str or pathlib.Path, optional
@@ -923,24 +941,48 @@ def get_layout(self, filepath=None, override=False, precision=5):
 
     def apply_layout(self, layout):
         """
-        Set the positions of all axes within the current plot based on a previously
-        defined layout.
+        Set the positions of all axes of the current figure based on a given layout.
+
+        The layout has the following structure:
+
+            >>> {"figsize":     [width, height],          # figure size
+            >>>  "0_map":       [x0, y0, width, height],  # map position
+            >>>  "1_inset_map": [x0, y0, width, height],  # inset-map position
+            >>>  "2_logo":      [x0, y0, width, height],  # logo position
+            >>>  "3_cb":        [x0, y0, width, height],  # colorbar position
+            >>>  "3_cb_histogram_size": 0.5,  # histogram size of colorbar
+            >>>  ...
+            >>>  }
+
+        - The positions are hereby specified in relative figure-units (0-1)
+            - If `width` or `height` is set to -1, its value will be determined such
+              that the current aspect-ratio of the axes remains the same.
+
+        To get the current layout, use:
+
+            >>> layout = m.get_layout()
 
         To apply a layout, use:
 
-            >>> l = m.get_layout()
-            >>> m.set_layout(l)
+            >>> m.apply_layout(layout)
 
         To save a layout to disc and apply it at a later stage, use
             >>> m.get_layout(filepath=<FILEPATH>)
-            >>> m.set_layout(<FILEPATH>)
+            >>> m.apply_layout(<FILEPATH>)
 
         Note
         ----
-        The returned list is only a snapshot of the current layout.
+        The layout is dependent on the order at which the axes ahve been created!
         It can only be re-applied to a given figure if the order at which the axes are
         created remains the same!
 
+        Maps aways preserve the aspect ratio.
+        If you provide values for width/height that do not match the aspect-ratio
+        of the map, the values will be adjusted accordingly. By default, smaller values
+        take precedence. To fix one value and adjust the other accordingly, use `-1`
+        for width or height! (e.g. `{"0_map": [0.1, 0.1, 0.8, -1]}`)
+
+
         Parameters
         ----------
         layout : dict, str or pathlib.Path
@@ -987,7 +1029,28 @@ def apply_layout(self, layout):
             if key.endswith("_histogram_size"):
                 cbs[i]._set_hist_size(val)
             else:
-                axes[i].set_position(val)
+                ax = axes[i]
+                bbox = ax.get_position()
+                aspect = bbox.width / bbox.height
+
+                # if any value is passed as -1, set it to the corresponding aspect
+                if val[2] == -1 and val[3] == -1:
+                    raise TypeError(
+                        "EOmaps: You can only set width or height to -1, not both... "
+                        f"Check the values for '{key}' in your layout!"
+                    )
+
+                if val[2] == -1:
+                    val[2] = val[3] * aspect
+                elif val[3] == -1:
+                    val[3] = val[2] / aspect
+
+                # To ensure x0 and y0 are fixed to the provided values,
+                # we set the position and then set it again using the actual
+                # width and height from the new position.
+                ax.set_position(val)
+                bbox = ax.get_position()
+                ax.set_position((*val[:2], bbox.width, bbox.height))
 
         # force an immediate draw (rather than using draw_idle) to avoid issues with
         # stacking order for tkagg backend

From 69e6527eb64880803c22980f7597cc51060f0663 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Mar 2024 15:19:38 +0100
Subject: [PATCH 223/240] synchronize layer-change widgets and avoid multiple
 handler calls

---
 eomaps/widgets.py | 141 ++++++++++++++++++++++++++++++++--------------
 1 file changed, 98 insertions(+), 43 deletions(-)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index 077b3fee9..0f9a34317 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -1,5 +1,3 @@
-from abc import abstractmethod
-from functools import wraps
 from contextlib import contextmanager
 
 import numpy as np
@@ -98,14 +96,25 @@ class _LayerSelectionWidget:
 
     """
 
-    _description = "LayerSelectionWidget"
+    _description = "Layers"
+    _widget_cls = None
 
-    def __init__(self, m, layers=None):
+    def __init__(self, m, layers=None, **kwargs):
         _check_backend()
 
         self._m = m
         self._set_layers_options(layers)
 
+        self._set_default_kwargs(kwargs)
+        self._widget_cls.__init__(self, options=self._options, **kwargs)
+
+        if hasattr(self, "change_handler"):
+            self.observe(self.change_handler, names="value", type="change")
+
+        # add a callback to update the widget values if the map-layer changes
+        if hasattr(self, "_cb_on_layer_change"):
+            self._m.BM.on_layer(self._cb_on_layer_change, persistent=True)
+
     def _set_layers_options(self, layers):
         # _layers is a list of the actual layer-names
         # _options is a list of tuples (name, value) passed to the widget-init
@@ -142,43 +151,41 @@ def _parse_layer(l):
         else:
             return l
 
+    @contextmanager
+    def _unobserve_change_handler(self):
+        try:
+            self.unobserve(self.change_handler, names="value", type="change")
 
-class _SingleLayerSelectionWidget(_LayerSelectionWidget):
-    _description = "Layers"
-    _widget_cls = None
-
-    @wraps(_LayerSelectionWidget.__init__)
-    def __init__(self, m, layers=None, **kwargs):
-
-        _LayerSelectionWidget.__init__(self, m=m, layers=layers)
-
-        self._set_default_kwargs(kwargs)
-        self._widget_cls.__init__(self, options=self._options, **kwargs)
+            yield
+        finally:
+            self.observe(self.change_handler, names="value", type="change")
 
-        self.observe(self.handler)
 
+class _SingleLayerSelectionWidget(_LayerSelectionWidget):
     def _set_default_kwargs(self, kwargs):
         kwargs.setdefault("description", self._description)
         if self._m.BM.bg_layer in self._layers:
             kwargs.setdefault("value", self._m.BM.bg_layer)
 
-    def handler(self, change):
+    def change_handler(self, change):
         try:
             if self.value is not None:
                 with _force_full(self._m):
                     self._m.show_layer(self.value)
-                    self._m.BM.update()
 
         except Exception:
             _log.error("Problem in LayerSelectionWidget handler...", exc_info=True)
 
+    def _cb_on_layer_change(self, **kwargs):
+        """A callback that is executed on all layer changes to update the widget-value."""
+        try:
+            layer = self._m.BM.bg_layer
+            if layer in self._layers:
+                with self._unobserve_change_handler():
+                    self.value = layer
 
-class _MultiLayerSelectionWidget(_SingleLayerSelectionWidget):
-    def _set_default_kwargs(self, kwargs):
-        kwargs.setdefault("description", self._description)
-
-        if self._m.BM.bg_layer in self._layers:
-            kwargs.setdefault("value", (self._m.BM.bg_layer, self._m.BM.bg_layer))
+        except Exception:
+            _log.exception(f"Unable to update widget value to {self._m.BM.bg_layer}")
 
 
 @_add_docstring(
@@ -195,21 +202,6 @@ class LayerSelect(_SingleLayerSelectionWidget, ipywidgets.Select):
     _widget_cls = ipywidgets.Select
 
 
-@_add_docstring(
-    "A list-box to select multiple visible layers.", replace_with=_LayerSelectionWidget
-)
-class LayerSelectMultiple(_MultiLayerSelectionWidget, ipywidgets.SelectMultiple):
-    _widget_cls = ipywidgets.SelectMultiple
-
-    def handler(self, change):
-        try:
-            if self.value is not None:
-                with _force_full(self._m):
-                    self._m.show_layer(*self.value)
-        except Exception:
-            _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
-
-
 @_add_docstring(
     "Toggle buttons to select a single visible layer.",
     replace_with=_LayerSelectionWidget,
@@ -233,6 +225,47 @@ class LayerSelectionSlider(_SingleLayerSelectionWidget, ipywidgets.SelectionSlid
     _widget_cls = ipywidgets.SelectionSlider
 
 
+# %% Multi Selector Widgets
+
+
+class _MultiLayerSelectionWidget(_LayerSelectionWidget):
+    def _set_default_kwargs(self, kwargs):
+        kwargs.setdefault("description", self._description)
+
+        if self._m.BM.bg_layer in self._layers:
+            kwargs.setdefault("value", (self._m.BM.bg_layer, self._m.BM.bg_layer))
+
+
+@_add_docstring(
+    "A list-box to select multiple visible layers.", replace_with=_LayerSelectionWidget
+)
+class LayerSelectMultiple(_MultiLayerSelectionWidget, ipywidgets.SelectMultiple):
+    _widget_cls = ipywidgets.SelectMultiple
+
+    def change_handler(self, change):
+        try:
+            if len(self.value) > 0 and None not in self.value:
+                with _force_full(self._m):
+                    self._m.show_layer(*self.value)
+        except Exception:
+            _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
+
+    def _cb_on_layer_change(self, **kwargs):
+        """A callback that is executed on all layer changes to update the widget-value."""
+        try:
+            # Identify all layers that are part of the currently visible layer
+            # TODO transparencies are currently ignored (e.g. treated as selected)
+            active_layers = self._m.BM._get_active_layers_alphas[0]
+            found = [l for l in self._layers if l in active_layers]
+
+            if len(found) > 0:
+                with self._unobserve_change_handler():
+                    self.value = found
+
+        except Exception:
+            _log.exception(f"Unable to update widget value to {self._m.BM.bg_layer}")
+
+
 @_add_docstring(
     "A range-slider to view a combination of a range of layers.",
     replace_with=_LayerSelectionWidget,
@@ -242,9 +275,9 @@ class LayerSelectionRangeSlider(
 ):
     _widget_cls = ipywidgets.SelectionRangeSlider
 
-    def handler(self, change):
+    def change_handler(self, change):
         try:
-            if self.value is not None:
+            if len(self.value) > 0 and None not in self.value:
                 i0 = self._layers.index(self.value[0])
                 i1 = self._layers.index(self.value[1])
                 with _force_full(self._m):
@@ -255,6 +288,28 @@ def handler(self, change):
         except Exception:
             _log.error("Problem in MultiLayerSelectionWidget handler...", exc_info=True)
 
+    def _cb_on_layer_change(self, **kwargs):
+        """A callback that is executed on all layer changes to update the widget-value."""
+        try:
+            # identify all layers that are part of the currently visible layer
+            # TODO transparencies are currently ignored (e.g. treated as selected)
+            # TODO properly handle case where intermediate layers are not selected
+            #      (right now only start- and stop determines the range independent
+            #       of the selected layers in between)
+            active_layers = self._m.BM._get_active_layers_alphas[0]
+            found_idx = [
+                self._layers.index(l) for l in self._layers if l in active_layers
+            ]
+
+            if len(found_idx) > 0:
+                mi, ma = min(found_idx), max(found_idx)
+
+                with self._unobserve_change_handler():
+                    self.value = (self._layers[mi], self._layers[ma])
+
+        except Exception:
+            _log.exception(f"Unable to update widget value to {self._m.BM.bg_layer}")
+
 
 # %% Layer Overlay Widgets
 
@@ -345,9 +400,9 @@ def __init__(self, m, layer, **kwargs):
 
         self._last_value = self.value
 
-        self.observe(self.handler)
+        self.observe(self.change_handler, names="value", type="change")
 
-    def handler(self, change):
+    def change_handler(self, change):
         try:
             layers, alphas = LayerParser._parse_multi_layer_str(self._m.BM.bg_layer)
 

From 1a354c3d50a264be928a51a0574ba64aa767c8f7 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Mar 2024 15:19:54 +0100
Subject: [PATCH 224/240] fix deactivating callback checkbox widgets

---
 eomaps/widgets.py | 37 ++++++++++++++++++++++---------------
 1 file changed, 22 insertions(+), 15 deletions(-)

diff --git a/eomaps/widgets.py b/eomaps/widgets.py
index 0f9a34317..c8fd6aa84 100644
--- a/eomaps/widgets.py
+++ b/eomaps/widgets.py
@@ -436,23 +436,38 @@ class _CallbackWidget:
     ----------
     m : eomaps.Maps
         The Maps-object to use.
+    widget_kwargs : dict
+        A dict of kwargs passed to the creation of the Jupyter widget.
+    kwargs:
+        All remaining kwargs are passed to the callback.
+        (e.g. m.cb.<method>.<name>(...kwargs...))
 
     """
 
     _cid = None
+    _widget_cls = None
 
-    def __init__(self, m, **kwargs):
+    def __init__(self, m, widget_kwargs=None, **kwargs):
         self._m = m
         _check_backend()
 
         self._kwargs = kwargs
 
-    @abstractmethod
+        if widget_kwargs is None:
+            widget_kwargs = dict()
+
+        widget_kwargs.setdefault("value", False)
+        widget_kwargs.setdefault("description", self._description)
+
+        self._widget_cls.__init__(self, **widget_kwargs)
+
+        self.observe(self.change_handler, names="value", type="change")
+
     def attach_callback(self, **kwargs):
         """Attach the callback to the map and return the cid."""
-        return "cid"
+        raise NotImplementedError()
 
-    def handler(self, change):
+    def change_handler(self, change):
         try:
             if self.value is True and self._cid is None:
                 self._cid = self.attach_callback(**self._kwargs)
@@ -465,27 +480,19 @@ def handler(self, change):
 
 class _CallbackCheckbox(_CallbackWidget, ipywidgets.Checkbox):
     _description = "Callback Checkbox"
-
-    @wraps(_CallbackWidget.__init__)
-    def __init__(self, *args, value=False, description=None, **kwargs):
-        _CallbackWidget.__init__(self, *args, **kwargs)
-
-        ipywidgets.Checkbox.__init__(
-            self,
-            value=value,
-            description=description if description is not None else self._description,
-        )
-        self.observe(self.handler)
+    _widget_cls = ipywidgets.Checkbox
 
 
 class _ClickCallbackCheckbox(_CallbackCheckbox):
     def remove_callback(self, **kwargs):
         self._m.all.cb.click.remove(self._cid)
+        self._cid = None
 
 
 class _PickCallbackCheckbox(_CallbackCheckbox):
     def remove_callback(self, **kwargs):
         self._m.cb.pick.remove(self._cid)
+        self._cid = None
 
 
 @_add_docstring(

From 135b29675f236c993c1d35e44411d1e7e887e6a9 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 8 Mar 2024 15:25:35 +0100
Subject: [PATCH 225/240] update docs

---
 docs/notebooks/widgets.ipynb | 932 +++++++++--------------------------
 1 file changed, 243 insertions(+), 689 deletions(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index e683036e4..f81933a58 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -164,16 +164,7 @@
     "tags": []
    },
    "source": [
-    "For example, here's how you can connect the available layer-selection widgets to the following map:\n",
-    "\n",
-    ":::{note}\n",
-    "\n",
-    "At the moment, widgets are **not** connected by default!\n",
-    "(e.g. changing the value in one widget will not change the values in others accordingly)\n",
-    "\n",
-    "Any contributions to enhance the widget functionalities are highly appreciated!\n",
-    "\n",
-    ":::"
+    "For example, here's how you can connect the available layer-selection widgets to the following map:"
    ]
   },
   {
@@ -191,18 +182,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "836c14443b724a6aa4eaf6dc128ba33f",
+       "model_id": "6dd2393cdf8548d481798d16f77300e4",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": "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",
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",
       "text/html": [
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
        "                    Figure\n",
        "                </div>\n",
-       "                <img 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' 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+       "                <img 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' width=600.0/>\n",
        "            </div>\n",
        "        "
       ],
@@ -223,13 +214,12 @@
     "\n",
     "m.add_feature.preset.countries(layer=\"countries\")\n",
     "m.add_feature.preset.ocean(layer=\"ocean\")\n",
-    "m.show_layer(\"ocean\", \"countries\", \"coastline\")\n",
     "m.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "2c26797b-7c8e-4847-b711-4db1f3e3084f",
    "metadata": {
     "editable": true,
@@ -252,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "id": "e850d223-3326-4afb-a500-d0c08b8ee624",
    "metadata": {
     "editable": true,
@@ -267,7 +257,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "164f46af43da47e585b67b0441c5f581",
+       "model_id": "ee1aacc5566b405ab48ac49da4add14c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -281,12 +271,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e4fa62881a124369af70305b20d4f0b8",
+       "model_id": "431139b4f88641a3a80cee3235db7c1f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerToggleButtons(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+       "LayerToggleButtons(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -295,12 +285,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "606e15353d3d445e9028c33102c77134",
+       "model_id": "163f8312d4b546fb9eb3b73b3c012661",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerDropdown(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+       "LayerDropdown(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -309,12 +299,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f1e9d00db02f488ca6623762b53ec557",
+       "model_id": "4308ea9e29e04146aeba2c68ea73fd9f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerRadioButtons(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+       "LayerRadioButtons(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -323,12 +313,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ada39bb960b484988116bebd163af5d",
+       "model_id": "789270683f1a452c9b5543cfe1195b67",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelect(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean')"
+       "LayerSelect(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -337,12 +327,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "baf9ae7c23fd425b89484f957216f938",
+       "model_id": "84bdcc50b5ed4df0b94ebef0cb76a411",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelectMultiple(description='Layers', index=(2, 2), options=('coastline', 'countries', 'ocean'), value=('o…"
+       "LayerSelectMultiple(description='Layers', index=(0, 0), options=('coastline', 'countries', 'ocean'), value=('c…"
       ]
      },
      "metadata": {},
@@ -351,12 +341,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fa503ca772e741b6892ff1a7ed14d7e6",
+       "model_id": "345aa2deda5f48528d1d41d37c7493d0",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelectionSlider(description='Layers', index=2, options=('coastline', 'countries', 'ocean'), value='ocean'…"
+       "LayerSelectionSlider(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -365,12 +355,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "19af3d8fe4f2421795bc99922f729996",
+       "model_id": "daa83da3dd06453686e2907a45d9ff9a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelectionRangeSlider(description='Layers', index=(2, 2), options=('coastline', 'countries', 'ocean'), val…"
+       "LayerSelectionRangeSlider(description='Layers', index=(0, 0), options=('coastline', 'countries', 'ocean'), val…"
       ]
      },
      "metadata": {},
@@ -401,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "id": "ad083853-4209-4a23-bf43-02fd0560f269",
    "metadata": {
     "editable": true,
@@ -414,15 +404,15 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd341ec83aa44d72ae512dcbe2f49ca5",
+       "model_id": "3c36bdd52c754e6ab403a9db7c1c0abc",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelectMultiple(description='Layers', layout=Layout(height='80px', width='50%'), options=(('A nice coastli…"
+       "LayerSelectMultiple(description='Layers', index=(0, 0), layout=Layout(height='80px', width='50%'), options=(('…"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,8 +421,8 @@
     "custom_selector = widgets.LayerSelectMultiple(\n",
     "    m,\n",
     "    layers=[[\"A nice coastline\", \"coastline\"], \n",
-    "            [\"Overlay 'coastline' + 'ocean'\", (\"coast\", \"ocean\")],\n",
-    "            [\"Transparent overlay: 'coastline' + 'ocean'\", (\"coast\", (\"ocean\", 0.4))]\n",
+    "            [\"Overlay 'coastline' + 'ocean'\", (\"coastline\", \"ocean\")],\n",
+    "            [\"Transparent overlay: 'coastline' + 'ocean'\", (\"coastline\", (\"ocean\", 0.4))]\n",
     "           ],\n",
     "    layout=dict(width='50%', height='80px', )\n",
     ")\n",
@@ -441,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "id": "db03b116-c43b-4802-90d1-dcde8ca901cd",
    "metadata": {
     "editable": true,
@@ -454,7 +444,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a05311ef3ad84756b6dd4002888c40d9",
+       "model_id": "3264fb21090f4896a2785c3aa9045aeb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -462,7 +452,7 @@
        "LayerButton(description='Custom Ocean Button', layout=Layout(height='5ex', width='30ex'), style=ButtonStyle(bu…"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -497,164 +487,233 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "00300632abc44177af6a49ff4a2a98f0": {
+     "163f8312d4b546fb9eb3b73b3c012661": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "DropdownModel",
       "state": {
-       "description_width": ""
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_63820bfbf41c48959bc19c98e17acd9e",
+       "style": "IPY_MODEL_7f51856d7a1f45618ee30ab33f372fc1"
       }
      },
-     "0ce486faa7784b48b8f139365dec99ca": {
-      "model_module": "@jupyter-widgets/base",
+     "1a6cf787eee04158b8d55254e055e8c7": {
+      "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
-       "height": "80px",
-       "width": "50%"
+       "description_width": ""
       }
      },
-     "0dade8feedc34cc6a91b0d868070b2df": {
-      "model_module": "@jupyter-widgets/base",
+     "3264fb21090f4896a2785c3aa9045aeb": {
+      "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
+      "model_name": "ButtonModel",
+      "state": {
+       "description": "Custom Ocean Button",
+       "layout": "IPY_MODEL_5eca8b0f320847018f9b4f451e2f45e9",
+       "style": "IPY_MODEL_9e3bb8f55bc844b49453d8a5a20947a7",
+       "tooltip": null
+      }
      },
-     "115be2e46de0423fb2ac5b8acf1fbb7e": {
+     "345aa2deda5f48528d1d41d37c7493d0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "VBoxModel",
+      "model_name": "SelectionSliderModel",
       "state": {
-       "children": [
-        "IPY_MODEL_164f46af43da47e585b67b0441c5f581",
-        "IPY_MODEL_e4fa62881a124369af70305b20d4f0b8",
-        "IPY_MODEL_606e15353d3d445e9028c33102c77134",
-        "IPY_MODEL_f1e9d00db02f488ca6623762b53ec557",
-        "IPY_MODEL_2ada39bb960b484988116bebd163af5d",
-        "IPY_MODEL_baf9ae7c23fd425b89484f957216f938",
-        "IPY_MODEL_fa503ca772e741b6892ff1a7ed14d7e6",
-        "IPY_MODEL_19af3d8fe4f2421795bc99922f729996"
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
        ],
-       "layout": "IPY_MODEL_d275741ab86c4716b54d3db5261c72ed"
+       "behavior": "drag-tap",
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_ca533057a7124d4db80fe7a0306d911b",
+       "style": "IPY_MODEL_44f195cde31f4c0a8be117dc6dd84470"
       }
      },
-     "14c688d4b72f4e52a1134ae7de11cb54": {
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       "model_name": "LayoutModel",
       "state": {}
      },
-     "164f46af43da47e585b67b0441c5f581": {
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "ButtonModel",
+      "model_name": "SelectMultipleModel",
       "state": {
-       "description": "ocean",
-       "layout": "IPY_MODEL_1a033d565947464999611c0471e301fa",
-       "style": "IPY_MODEL_2a04abc6fbc444dbaecb11f8f2f6e1e1",
-       "tooltip": null
+       "_options_labels": [
+        "A nice coastline",
+        "Overlay 'coastline' + 'ocean'",
+        "Transparent overlay: 'coastline' + 'ocean'"
+       ],
+       "description": "Layers",
+       "index": [
+        0,
+        0
+       ],
+       "layout": "IPY_MODEL_9d6941c03ada408993a6c43f052e41dc",
+       "rows": 5,
+       "style": "IPY_MODEL_420aac90d2174a10a5d56b7ffec50065"
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      },
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SelectionSliderModel",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "4308ea9e29e04146aeba2c68ea73fd9f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
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       "state": {
        "_options_labels": [
         "coastline",
         "countries",
         "ocean"
        ],
-       "behavior": "drag-tap",
        "description": "Layers",
        "index": 0,
-       "layout": "IPY_MODEL_ed88c97c871c400eb67c5800165d41eb",
-       "style": "IPY_MODEL_f3453e1b3e484e5497ea80dfe30aa6e4"
+       "layout": "IPY_MODEL_6707c540258141829f0cc181f9f150e7",
+       "style": "IPY_MODEL_1a6cf787eee04158b8d55254e055e8c7"
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SelectionRangeSliderModel",
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       "state": {
-       "_model_name": "SelectionRangeSliderModel",
        "_options_labels": [
         "coastline",
         "countries",
         "ocean"
        ],
-       "_view_name": "SelectionRangeSliderView",
-       "behavior": "drag-tap",
+       "button_style": "",
        "description": "Layers",
-       "index": [
-        2,
-        2
-       ],
-       "layout": "IPY_MODEL_7e6bb04688dc4226956b31b27690167d",
-       "style": "IPY_MODEL_243adf05996e4d6ea4dfce0dd476aa14"
+       "icons": [],
+       "index": 0,
+       "layout": "IPY_MODEL_57559d47e8a947da8eceac480cb896db",
+       "style": "IPY_MODEL_d640dfafc6b749fd9916c2d2a6c1ca26",
+       "tooltips": []
+      }
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+      "model_module_version": "2.0.0",
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+       "height": "5ex",
+       "width": "30ex"
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+      "model_module_version": "^0.11",
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",
+       "_figure_label": "Figure 1",
+       "_image_mode": "diff",
+       "_model_module_version": "^0.11",
+       "_size": [
+        600,
+        200
+       ],
+       "_view_module_version": "^0.11",
+       "header_visible": false,
+       "layout": "IPY_MODEL_694500996a854fe58a1f1515952f1601",
+       "toolbar": "IPY_MODEL_845db07aa7554ae98e6804c0724d19a0",
+       "toolbar_position": "left"
       }
      },
-     "24057ca6512a44468774840c821514ea": {
+     "789270683f1a452c9b5543cfe1195b67": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SelectionRangeSliderModel",
+      "model_name": "SelectModel",
       "state": {
-       "_model_name": "SelectionRangeSliderModel",
        "_options_labels": [
         "coastline",
         "countries",
         "ocean"
        ],
-       "_view_name": "SelectionRangeSliderView",
-       "behavior": "drag-tap",
        "description": "Layers",
-       "index": [
-        0,
-        0
-       ],
-       "layout": "IPY_MODEL_cef9f3584eea492ea4291952b85c5b6f",
-       "style": "IPY_MODEL_3d0641653f554e6c8fba757fdf214bc8"
+       "index": 0,
+       "layout": "IPY_MODEL_50eb528db6b945d485598abc11bb53ba",
+       "style": "IPY_MODEL_eae7166fee0f42cdbbcc09cd040700a6"
       }
      },
-     "243adf05996e4d6ea4dfce0dd476aa14": {
+     "7f51856d7a1f45618ee30ab33f372fc1": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SliderStyleModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
        "description_width": ""
       }
      },
-     "295aeaa6f44449c39950ec8eb7fe9ce4": {
+     "801655d3601a4adda911af4caf0c5445": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SelectModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "description": "Layers",
-       "index": 0,
-       "layout": "IPY_MODEL_14c688d4b72f4e52a1134ae7de11cb54",
-       "style": "IPY_MODEL_888f64490ec24f118cf88b9ba47cc272"
+       "description_width": ""
       }
      },
-     "29a656a721bd48539e239cf5b3ae39a9": {
+     "845db07aa7554ae98e6804c0724d19a0": {
       "model_module": "jupyter-matplotlib",
       "model_module_version": "^0.11",
       "model_name": "ToolbarModel",
@@ -662,7 +721,7 @@
        "_model_module_version": "^0.11",
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-       "layout": "IPY_MODEL_95a0f5a02c9142d79113ab5c46ff6df4",
-       "rows": 5,
-       "style": "IPY_MODEL_00300632abc44177af6a49ff4a2a98f0"
-      }
-     },
-     "bedb8bdd542c4d36b160a7e8ac747553": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "DropdownModel",
-      "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "description": "Layers",
-       "index": 0,
-       "layout": "IPY_MODEL_dadac6e8be4b495e9e2d4bbe8efe1717",
-       "style": "IPY_MODEL_8a88833604ac44d1aff8edc6e0692541"
-      }
-     },
-     "cd341ec83aa44d72ae512dcbe2f49ca5": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SelectMultipleModel",
-      "state": {
-       "_options_labels": [
-        "A nice coastline",
-        "Overlay 'coastline' + 'ocean'",
-        "Transparent overlay: 'coastline' + 'ocean'"
-       ],
-       "description": "Layers",
-       "index": [],
-       "layout": "IPY_MODEL_615cda7c550840d794459343fecf2dd3",
-       "rows": 5,
-       "style": "IPY_MODEL_599293c026224217adb706a576bcb147"
-      }
-     },
-     "cef9f3584eea492ea4291952b85c5b6f": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "d275741ab86c4716b54d3db5261c72ed": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "d9a61c6dc2a842e987167dcb44008d89": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "dadac6e8be4b495e9e2d4bbe8efe1717": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "dc94da2387094ae59728897c307f02fc": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ToggleButtonsStyleModel",
-      "state": {
-       "button_width": "",
-       "description_width": ""
-      }
-     },
-     "debb82c087ae4960a6437aa14664b817": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {
-       "height": "5ex",
-       "width": "30ex"
-      }
-     },
-     "e4fa62881a124369af70305b20d4f0b8": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ToggleButtonsModel",
-      "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "button_style": "",
-       "description": "Layers",
-       "icons": [],
-       "index": 2,
-       "layout": "IPY_MODEL_8c21d03d7a70457d8baf173298bd1411",
-       "style": "IPY_MODEL_85f26793c1fd4effbd94698845090a2d",
-       "tooltips": []
-      }
-     },
-     "e5279598198f43849dacbcba7e750918": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SelectMultipleModel",
-      "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "description": "Layers",
-       "index": [],
-       "layout": "IPY_MODEL_70da4a6e5d7d438eabbdac8d7b6b4a89",
-       "rows": 5,
-       "style": "IPY_MODEL_37b20b04e65d442bbca89ee2f404f4c7"
-      }
-     },
-     "e6581233a5ec448b857e361d1b7dd45f": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "e83045abddcd48caace63182a908a53e": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "description_width": ""
-      }
-     },
-     "e906ce4f97794dada32f2a8d02839795": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ButtonStyleModel",
-      "state": {
-       "button_color": "lightblue",
-       "font_family": null,
-       "font_size": "24px",
-       "font_style": null,
-       "font_variant": null,
-       "font_weight": "bold",
-       "text_color": null,
-       "text_decoration": null
-      }
-     },
-     "ed88c97c871c400eb67c5800165d41eb": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "ee70c8487d9b45219b27e449a69c35dd": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "eea7a30d36d84710951b1698169dfa3e": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "f1e9d00db02f488ca6623762b53ec557": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "RadioButtonsModel",
-      "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "description": "Layers",
-       "index": 2,
-       "layout": "IPY_MODEL_9968216f831d4e17afa1b5b6f869ea27",
-       "style": "IPY_MODEL_38f9281338b44eadb3435cdaadfd0412"
-      }
-     },
-     "f3453e1b3e484e5497ea80dfe30aa6e4": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SliderStyleModel",
-      "state": {
-       "description_width": ""
-      }
-     },
-     "f650197f759f42948ecbe7a82098fe34": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ButtonStyleModel",
-      "state": {
-       "button_color": "lightblue",
-       "font_family": null,
-       "font_size": "24px",
-       "font_style": null,
-       "font_variant": null,
-       "font_weight": "bold",
-       "text_color": null,
-       "text_decoration": null
-      }
-     },
-     "fa503ca772e741b6892ff1a7ed14d7e6": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SelectionSliderModel",
-      "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "behavior": "drag-tap",
-       "description": "Layers",
-       "index": 2,
-       "layout": "IPY_MODEL_9359bad23e7644069c18f02976cb62bb",
-       "style": "IPY_MODEL_5e31ffd632114f29ad8f9796b1759a49"
-      }
-     },
-     "fd6cc8ab10e140748466899f69597333": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "ff8be3ec300444f784616984a94d3b3b": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ButtonStyleModel",
-      "state": {
-       "font_family": null,
-       "font_size": null,
-       "font_style": null,
-       "font_variant": null,
-       "font_weight": null,
-       "text_color": null,
-       "text_decoration": null
-      }
      }
     },
     "version_major": 2,

From f0ab726ef927ab2d3a2386dfaca9bdc0f00d5a90 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Mar 2024 14:05:48 +0100
Subject: [PATCH 226/240] fix widget unittests

---
 tests/test_widgets.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/tests/test_widgets.py b/tests/test_widgets.py
index 751488eac..85f111a56 100644
--- a/tests/test_widgets.py
+++ b/tests/test_widgets.py
@@ -26,8 +26,8 @@ def test_selector_widgets(widget, use_layers):
     m = Maps(layer="all")
     m.add_feature.preset.coastline(layer="coast")
     m.add_feature.preset.ocean(layer="ocean")
+    # show a layer that is part of the available visible layers
     m.show_layer("coast")
-
     w = widget(m, layers=use_layers)
     layers = w._layers
 
@@ -62,7 +62,12 @@ def test_selector_widgets(widget, use_layers):
         found_layer = m.BM.bg_layer
 
         if widget in (widgets.LayerSelectMultiple,):
-            expected_layer = m.BM._get_combined_layer_name(layers[0], layers[i])
+            if layers[i] == found_layer:
+                # TODO if the layer is already active, it will not be changed and
+                # so the expected layer is NOT an overlay!
+                expected_layer = layers[i]
+            else:
+                expected_layer = m.BM._get_combined_layer_name(layers[0], layers[i])
         elif widget in (widgets.LayerSelectionRangeSlider,):
             expected_layer = m.BM._get_combined_layer_name(*layers[0 : i + 1])
         else:
@@ -136,10 +141,14 @@ def test_overlay_widgets(widget):
     for val in [0.0, 0.25, 0.5, 0.75, 1.0]:
         state["value"] = val
         w.set_state(state)
-
-        assert m.BM.bg_layer == m.BM._get_combined_layer_name(
-            "coast", ("ocean", val)
-        ), "Overlay not properly assigned"
+        if val > 0:
+            expected = m.BM._get_combined_layer_name("coast", ("ocean", val))
+        else:
+            expected = "coast"
+        found = m.BM.bg_layer
+        assert (
+            found == expected
+        ), f"Overlay not properly assigned, expected {expected}, found {found}"
 
 
 @pytest.mark.parametrize(

From 64b7b86d6431dbb37a4a9577b8d1a99f91348d7f Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Mar 2024 15:35:36 +0100
Subject: [PATCH 227/240] update docs

---
 docs/notebooks/widgets.ipynb | 907 ++++++++++++++++++++++++++---------
 1 file changed, 678 insertions(+), 229 deletions(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index f81933a58..1c4d7180a 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -34,25 +34,36 @@
     "\n",
     "<br>\n",
     "\n",
-    "EOmaps provides a set of pre-configured [Jupyter Widgets](https://ipywidgets.readthedocs.io) that can be used to create interactive browser controls for a map. \n",
+    "EOmaps provides a set of pre-configured [Jupyter Widgets][jupyter_widgets] that can be used to create interactive browser controls for a map. \n",
     "\n",
-    "These widgets work just like any other [Jupyter Widgets](https://ipywidgets.readthedocs.io), but they have a pre-configured handler to perform commonly used tasks (switching layers, overlay layers, add/remove callbacks etc.)\n",
+    "These widgets work just like any other [Jupyter Widgets][jupyter_widgets], but they have a pre-configured handler to perform commonly used tasks (switching layers, overlay layers, add/remove callbacks etc.)\n",
     "\n",
     "\n",
-    ":::{note}\n",
+    ":::::{note}\n",
     "\n",
-    "Widgets are **only for Jupyter Notebooks** and require the additional dependencies: `ipywidgets` and `ipympl`\n",
+    "Widgets are intended for **Jupyter Notebooks** and require the additional dependencies: `ipywidgets` and `ipympl`.  \n",
+    "They will only work if you use the interactive [ipympl (widget) backend][ipympl]. To activate, use the ``%matplotlib widget`` magic command.\n",
+    "\n",
+    "You can install the required additional dependencies with one of the following commands:\n",
+    "::::{grid} 1 1 2 2\n",
+    "\n",
+    "::: {grid-item-card}\n",
     "\n",
-    "You can install it with `pip`:\n",
     "```\n",
     "pip install ipywidgets, ipympl\n",
     "```\n",
+    ":::\n",
+    "::: {grid-item-card}\n",
     "\n",
-    "or with `conda`:\n",
     "```\n",
-    "conda install -c conda-forge ipywidgets, ipympl\n",
+    "mamba install -c conda-forge ipywidgets, ipympl\n",
     "```\n",
-    ":::"
+    ":::\n",
+    "::::\n",
+    ":::::\n",
+    "\n",
+    "[jupyter_widgets]: https://ipywidgets.readthedocs.io\n",
+    "[ipympl]: https://matplotlib.org/ipympl/"
    ]
   },
   {
@@ -66,9 +77,12 @@
     "tags": []
    },
    "source": [
-    "**The following pre-defined widgets exist:**\n",
+    "## Layer selector widgets\n",
     "\n",
-    ":::{dropdown} Widgets to change the visible layer\n",
+    "Layer selector widgets can be used to change the visible layer of a map.\n",
+    "\n",
+    ":::{dropdown} Available widgets\n",
+    ":open:\n",
     "\n",
     "```{eval-rst}\n",
     "\n",
@@ -100,39 +114,6 @@
     "   LayerOverlaySlider\n",
     "\n",
     "```\n",
-    ":::\n",
-    "\n",
-    "\n",
-    "\n",
-    ":::{dropdown} Widgets to attach callbacks\n",
-    "\n",
-    "```{eval-rst}\n",
-    "\n",
-    ".. currentmodule:: eomaps.widgets\n",
-    "\n",
-    ".. autosummary:: \n",
-    "   :nosignatures:\n",
-    "   \n",
-    "   ClickAnnotateCheckbox\n",
-    "   ClickMarkCheckbox\n",
-    "   ClickPrintToConsoleCheckbox\n",
-    "   ClickPeekLayerCheckbox\n",
-    "\n",
-    "\n",
-    ".. autosummary:: \n",
-    "   :nosignatures:\n",
-    "   \n",
-    "   ClickMarkCheckbox\n",
-    "   ClickPrintToConsoleCheckbox\n",
-    "\n",
-    "\n",
-    ".. autosummary:: \n",
-    "   :nosignatures:\n",
-    "   \n",
-    "   PickAnnotateCheckbox\n",
-    "   PickMarkCheckbox\n",
-    "   PickPrintToConsoleCheckbox\n",
-    "\n",
     ":::"
    ]
   },
@@ -147,10 +128,18 @@
     "tags": []
    },
    "source": [
-    "## How to use the widgets\n",
+    "### How to use the widgets\n",
+    "\n",
+    "To attach a widget to a map, all you need to do is to pass the associated [`Maps`](eomaps.Maps) object as first argument on initialization.\n",
+    "(e.g. use ``Widget(m)`` to connect the widget to the [`Maps`](eomaps.Maps) object `m`)\n",
     "\n",
-    "To attach a widget to a map, simply initialize the widget and pass the associated [`Maps`](eomaps.Maps) object as first argument.\n",
-    "(e.g. use ``Widget(m)`` to connect the widget to the [`Maps`](eomaps.Maps) object `m`)"
+    "In addition to the ipywidget-arguments, they support the following (optional) arguments:\n",
+    "\n",
+    "- `layers`: A list of layer specs `[<specs 1>, <specs 2>, ...]` to use in the widget.  \n",
+    "  `<specs>`can be one of:\n",
+    "    - A single layer-name `\"layer_name\"` or a tuple `(\"layer_name\", transparency)`\n",
+    "    - A tuple of multiple layer-names (and/or transparencies): `(\"layer_name_1\", (\"layer_name_2\", 0.5), ...)`\n",
+    "    - A list of a custom labels and a layer-assignments: `[\"Custom label\", <layer specs>]`\n"
    ]
   },
   {
@@ -182,7 +171,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6dd2393cdf8548d481798d16f77300e4",
+       "model_id": "99d887fccb6f4b28a3d2fe017be66bc7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -257,7 +246,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee1aacc5566b405ab48ac49da4add14c",
+       "model_id": "5963a322be874b7285b134e363a42dd0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -271,7 +260,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "431139b4f88641a3a80cee3235db7c1f",
+       "model_id": "49aaf254026b42bc86aec1bf5529148c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -285,7 +274,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "163f8312d4b546fb9eb3b73b3c012661",
+       "model_id": "3829d1590c57486fa56eecad03bd2a16",
        "version_major": 2,
        "version_minor": 0
       },
@@ -299,7 +288,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4308ea9e29e04146aeba2c68ea73fd9f",
+       "model_id": "bf43a1843d6e4f4cadf382206fbf0b67",
        "version_major": 2,
        "version_minor": 0
       },
@@ -313,7 +302,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "789270683f1a452c9b5543cfe1195b67",
+       "model_id": "91f88e3c4bb3462f90fb519638dd4a87",
        "version_major": 2,
        "version_minor": 0
       },
@@ -327,7 +316,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84bdcc50b5ed4df0b94ebef0cb76a411",
+       "model_id": "093f0f04d98d4754b7d373a630e7117d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -341,7 +330,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "345aa2deda5f48528d1d41d37c7493d0",
+       "model_id": "c1c2ea2326854314a933c8f0e25531ee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -355,7 +344,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "daa83da3dd06453686e2907a45d9ff9a",
+       "model_id": "7a1d940e65414230a4b1c301400125e3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -404,7 +393,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3c36bdd52c754e6ab403a9db7c1c0abc",
+       "model_id": "8b09d257580f4326b116e126849d1ead",
        "version_major": 2,
        "version_minor": 0
       },
@@ -444,7 +433,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3264fb21090f4896a2785c3aa9045aeb",
+       "model_id": "c57b0de32084445e8425589b7810334c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -464,6 +453,232 @@
     "custom_button.style.font_size = '24px'\n",
     "custom_button"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95d96081-6fea-4c50-9755-089c53517eed",
+   "metadata": {},
+   "source": [
+    "## Callback Widgets\n",
+    "\n",
+    "Callback widgets are used to toggle callbacks on a map.\n",
+    "\n",
+    ":::{dropdown} Available Widgets:\n",
+    ":open:\n",
+    "\n",
+    "```{eval-rst}\n",
+    "\n",
+    ".. currentmodule:: eomaps.widgets\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   ClickAnnotateCheckbox\n",
+    "   ClickMarkCheckbox\n",
+    "   ClickPrintToConsoleCheckbox\n",
+    "   ClickPeekLayerCheckbox\n",
+    "\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   ClickMarkCheckbox\n",
+    "   ClickPrintToConsoleCheckbox\n",
+    "\n",
+    "\n",
+    ".. autosummary:: \n",
+    "   :nosignatures:\n",
+    "   \n",
+    "   PickAnnotateCheckbox\n",
+    "   PickMarkCheckbox\n",
+    "   PickPrintToConsoleCheckbox\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d310fcf8-96f1-461c-81d1-cbde4ed4fd5b",
+   "metadata": {},
+   "source": [
+    "### How to use the widgets\n",
+    "\n",
+    "To attach a widget to a map, all you need to do is to pass the associated [`Maps`](eomaps.Maps) object as first argument on initialization.\n",
+    "(e.g. use ``Widget(m)`` to connect the widget to the [`Maps`](eomaps.Maps) object `m`)\n",
+    "\n",
+    "All additional arguments will be passed to the corresponding callback (e.g. `m.cb.< METHOD >.attach.< NAME >(**kwargs)`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bd38b152-7397-4b23-853b-43bbd78862ec",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "checkboxes = [\n",
+    "    widgets.ClickAnnotateCheckbox(m),\n",
+    "    widgets.ClickMarkCheckbox(m),\n",
+    "    widgets.ClickPrintToConsoleCheckbox(m),\n",
+    "    widgets.ClickPeekLayerCheckbox(m, layer=\"ocean\"),\n",
+    "    widgets.ClickMarkCheckbox(m),\n",
+    "    widgets.ClickPrintToConsoleCheckbox(m),\n",
+    "    widgets.PickAnnotateCheckbox(m),\n",
+    "    widgets.PickMarkCheckbox(m),\n",
+    "    widgets.PickPrintToConsoleCheckbox(m)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "86b55c35-9aba-49ee-ad77-ab896eecb4d5",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "ignore_in_unittest"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0938d8245dad433b8a5e03ae6fb19ff6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "ClickAnnotateCheckbox(value=False, description='Annotate (Click)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec612033fef64cfa8d8baefc22baf174",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "ClickMarkCheckbox(value=False, description='Mark (Click)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69702c1f082541eb93fcc53cd15822b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "ClickPrintToConsoleCheckbox(value=False, description='Print (Click)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc5d22ca64854b5bbfa46493e1c59e51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "ClickPeekLayerCheckbox(value=False, description=\"Peek Layer: 'ocean'\")"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ea35b6ea0f84c35870b7389dfaf51f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "ClickMarkCheckbox(value=False, description='Mark (Click)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cd9d5f586a847cca584d82295927469",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "ClickPrintToConsoleCheckbox(value=False, description='Print (Click)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bedf102d4504168a8c63897b4ebb849",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PickAnnotateCheckbox(value=False, description='Annotate (Pick)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8703f5936a0f4baebd83e6b13f7d902d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PickMarkCheckbox(value=False, description='Mark (Pick)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb03d7521b124917882e8ccb98f63a99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PickPrintToConsoleCheckbox(value=False, description='Print (Pick)')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(*checkboxes)"
+   ]
   }
  ],
  "metadata": {
@@ -487,23 +702,21 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "163f8312d4b546fb9eb3b73b3c012661": {
+     "0028a24996e14a838008e71290b9bcb2": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "DropdownModel",
+      "model_name": "CheckboxStyleModel",
       "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "description": "Layers",
-       "index": 0,
-       "layout": "IPY_MODEL_63820bfbf41c48959bc19c98e17acd9e",
-       "style": "IPY_MODEL_7f51856d7a1f45618ee30ab33f372fc1"
+       "description_width": ""
       }
      },
-     "1a6cf787eee04158b8d55254e055e8c7": {
+     "00bc30ab507948ccad518593e9a6a940": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
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+      "state": {}
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+     "037feca29b864171b2328e280aac315b": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "DescriptionStyleModel",
@@ -511,193 +724,187 @@
        "description_width": ""
       }
      },
-     "3264fb21090f4896a2785c3aa9045aeb": {
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+      "model_module_version": "2.0.0",
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
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+      "model_name": "CheckboxModel",
       "state": {
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-       "layout": "IPY_MODEL_5eca8b0f320847018f9b4f451e2f45e9",
-       "style": "IPY_MODEL_9e3bb8f55bc844b49453d8a5a20947a7",
-       "tooltip": null
+       "description": "Annotate (Click)",
+       "disabled": false,
+       "layout": "IPY_MODEL_077c1cfa068648b9b6d99d3aedf6cd90",
+       "style": "IPY_MODEL_3d323f6ca0ca40fcbc91a7607b3417d2",
+       "value": false
       }
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       "state": {
        "_options_labels": [
         "coastline",
         "countries",
         "ocean"
        ],
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-       "layout": "IPY_MODEL_ca533057a7124d4db80fe7a0306d911b",
-       "style": "IPY_MODEL_44f195cde31f4c0a8be117dc6dd84470"
+       "index": [
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+        0
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-       "index": [
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",
-       "_figure_label": "Figure 1",
-       "_image_mode": "diff",
-       "_model_module_version": "^0.11",
-       "_size": [
-        600,
-        200
-       ],
-       "_view_module_version": "^0.11",
-       "header_visible": false,
-       "layout": "IPY_MODEL_694500996a854fe58a1f1515952f1601",
-       "toolbar": "IPY_MODEL_845db07aa7554ae98e6804c0724d19a0",
-       "toolbar_position": "left"
+       "description_width": ""
       }
      },
-     "789270683f1a452c9b5543cfe1195b67": {
+     "49aaf254026b42bc86aec1bf5529148c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SelectModel",
+      "model_name": "ToggleButtonsModel",
       "state": {
        "_options_labels": [
         "coastline",
         "countries",
         "ocean"
        ],
+       "button_style": "",
        "description": "Layers",
+       "icons": [],
        "index": 0,
-       "layout": "IPY_MODEL_50eb528db6b945d485598abc11bb53ba",
-       "style": "IPY_MODEL_eae7166fee0f42cdbbcc09cd040700a6"
+       "layout": "IPY_MODEL_312b2235cef844948d6bb11b96428112",
+       "style": "IPY_MODEL_c08be7d55b764c97a5b4a927f0aab480",
+       "tooltips": []
       }
      },
-     "7f51856d7a1f45618ee30ab33f372fc1": {
+     "4d5c4e9c87044ab8b2e6b261570b3ceb": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "4ed61cc51bba4b6f9c25ee7da5ab5830": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "DescriptionStyleModel",
@@ -705,15 +912,36 @@
        "description_width": ""
       }
      },
-     "801655d3601a4adda911af4caf0c5445": {
+     "54746a1718eb453a898df569f0174aab": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "5963a322be874b7285b134e363a42dd0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "ButtonModel",
       "state": {
-       "description_width": ""
+       "description": "ocean",
+       "layout": "IPY_MODEL_0e52d68648484afaba0ab62e3d9a8296",
+       "style": "IPY_MODEL_a57320657cbb466686a21547bcfcb7f4",
+       "tooltip": null
+      }
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+     "ca0a3971664c41a58eac2c5bb59d8d1e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "ccf5b9d01dad4756bc8caddecf767324": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "cefd66b415754aed8fd1eee24c774a93": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "d18b449dbc844894ab76175722fde37d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "d301558997a8427ca93dfa5cf4f57081": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "height": "5ex",
+       "width": "30ex"
+      }
+     },
+     "da32bcabdc4548dca2c6d368789359e4": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "e7d976321c7a47d187070f2f0c3577b6": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
       "state": {}
      },
-     "f9e401921a284d90b7377b49943d1029": {
+     "ec612033fef64cfa8d8baefc22baf174": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxModel",
+      "state": {
+       "description": "Mark (Click)",
+       "disabled": false,
+       "layout": "IPY_MODEL_54746a1718eb453a898df569f0174aab",
+       "style": "IPY_MODEL_ca0a3971664c41a58eac2c5bb59d8d1e",
+       "value": false
+      }
+     },
+     "f94f557bb2b14a46948341b3a17a93b0": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "fa7f86784e494ab0b9adc0647e3af421": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "height": "80px",
+       "width": "50%"
+      }
+     },
+     "fe6eab90964041c0944b6ab5c432bb7b": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",

From 594c03d22c68b63dcd760fa8058339997e650904 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Mar 2024 15:36:17 +0100
Subject: [PATCH 228/240] update docs

---
 docs/notebooks/widgets.ipynb | 751 ++++++++++++++++++++++++++++++++---
 1 file changed, 701 insertions(+), 50 deletions(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index 1c4d7180a..3fad87373 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "12a15112-ed5a-498a-b0a0-0688e429b729",
    "metadata": {
     "editable": true,
@@ -171,7 +171,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99d887fccb6f4b28a3d2fe017be66bc7",
+       "model_id": "c4ea1efd784f4917ad38dcf5b8666b5e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "2c26797b-7c8e-4847-b711-4db1f3e3084f",
    "metadata": {
     "editable": true,
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "e850d223-3326-4afb-a500-d0c08b8ee624",
    "metadata": {
     "editable": true,
@@ -246,7 +246,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5963a322be874b7285b134e363a42dd0",
+       "model_id": "99690fd4cac14964b1ffc3de417de154",
        "version_major": 2,
        "version_minor": 0
       },
@@ -260,7 +260,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "49aaf254026b42bc86aec1bf5529148c",
+       "model_id": "dad7ab315d5f45fe80ed65f2dbe2caab",
        "version_major": 2,
        "version_minor": 0
       },
@@ -274,7 +274,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3829d1590c57486fa56eecad03bd2a16",
+       "model_id": "3fa0b71b14ac441493e68741804ef257",
        "version_major": 2,
        "version_minor": 0
       },
@@ -288,7 +288,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bf43a1843d6e4f4cadf382206fbf0b67",
+       "model_id": "7bce94e43a164cd5b966333f528b441f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -302,7 +302,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "91f88e3c4bb3462f90fb519638dd4a87",
+       "model_id": "4671d84897044fa8805d345a8377dd1d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -316,7 +316,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "093f0f04d98d4754b7d373a630e7117d",
+       "model_id": "84adf788b67a48bca2613a7dd3a29429",
        "version_major": 2,
        "version_minor": 0
       },
@@ -330,7 +330,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c1c2ea2326854314a933c8f0e25531ee",
+       "model_id": "1d463c61cb274a29a5774f926501023c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -344,7 +344,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7a1d940e65414230a4b1c301400125e3",
+       "model_id": "d6589e7605f64ba2a257ffca90a001e3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -380,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "ad083853-4209-4a23-bf43-02fd0560f269",
    "metadata": {
     "editable": true,
@@ -393,7 +393,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8b09d257580f4326b116e126849d1ead",
+       "model_id": "9c45e56c3add427aa56f5e7a81732885",
        "version_major": 2,
        "version_minor": 0
       },
@@ -401,7 +401,7 @@
        "LayerSelectMultiple(description='Layers', index=(0, 0), layout=Layout(height='80px', width='50%'), options=(('…"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "db03b116-c43b-4802-90d1-dcde8ca901cd",
    "metadata": {
     "editable": true,
@@ -433,7 +433,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c57b0de32084445e8425589b7810334c",
+       "model_id": "e4b903589ec944958589efe30d401d1f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -441,7 +441,7 @@
        "LayerButton(description='Custom Ocean Button', layout=Layout(height='5ex', width='30ex'), style=ButtonStyle(bu…"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -511,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "bd38b152-7397-4b23-853b-43bbd78862ec",
    "metadata": {
     "editable": true,
@@ -537,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "86b55c35-9aba-49ee-ad77-ab896eecb4d5",
    "metadata": {
     "editable": true,
@@ -552,7 +552,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0938d8245dad433b8a5e03ae6fb19ff6",
+       "model_id": "1fbf9a3bc7064dc1b1248640fe412976",
        "version_major": 2,
        "version_minor": 0
       },
@@ -566,7 +566,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec612033fef64cfa8d8baefc22baf174",
+       "model_id": "2f9b853cde7f4e1eacc1fbd283e58a2a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -580,7 +580,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "69702c1f082541eb93fcc53cd15822b3",
+       "model_id": "5bc2462a0a404a288be147aa8d934266",
        "version_major": 2,
        "version_minor": 0
       },
@@ -594,7 +594,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc5d22ca64854b5bbfa46493e1c59e51",
+       "model_id": "61c6b639d3ba4c8ab29b4c7a97afdbcd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -608,7 +608,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ea35b6ea0f84c35870b7389dfaf51f9",
+       "model_id": "418c87c3500748168129ce2f310a4983",
        "version_major": 2,
        "version_minor": 0
       },
@@ -622,7 +622,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8cd9d5f586a847cca584d82295927469",
+       "model_id": "3a7b6ed3c97443249db3bfefa0174ced",
        "version_major": 2,
        "version_minor": 0
       },
@@ -636,7 +636,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bedf102d4504168a8c63897b4ebb849",
+       "model_id": "45a8f7ca11104f74a71a5f7d014609d4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -650,7 +650,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8703f5936a0f4baebd83e6b13f7d902d",
+       "model_id": "b1b8e53055e3497f8d7b04b85e9e7d02",
        "version_major": 2,
        "version_minor": 0
       },
@@ -664,7 +664,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb03d7521b124917882e8ccb98f63a99",
+       "model_id": "3c874cabd6234092b7275ab9b86c3b06",
        "version_major": 2,
        "version_minor": 0
       },
@@ -679,6 +679,14 @@
    "source": [
     "display(*checkboxes)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64a681f1-a34a-46bd-b60a-7e6670d9da69",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -724,6 +732,14 @@
        "description_width": ""
       }
      },
+     "05c0efc27b1c472ca28aa16b114c85eb": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
      "077c1cfa068648b9b6d99d3aedf6cd90": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
@@ -762,12 +778,49 @@
        "style": "IPY_MODEL_037feca29b864171b2328e280aac315b"
       }
      },
+     "0dfc84b2aaee4307b36c07b3b853021f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
      "0e52d68648484afaba0ab62e3d9a8296": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
       "state": {}
      },
+     "1d463c61cb274a29a5774f926501023c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectionSliderModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "behavior": "drag-tap",
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_bd40d4a6f22d4c2ea12a44fb6cefe2e7",
+       "style": "IPY_MODEL_9eaaa295455c40ceb90e280d5e1a54ef"
+      }
+     },
+     "1fbf9a3bc7064dc1b1248640fe412976": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxModel",
+      "state": {
+       "description": "Annotate (Click)",
+       "disabled": false,
+       "layout": "IPY_MODEL_4db5e0b87eaa4e1d82f8efb38e87d240",
+       "style": "IPY_MODEL_7aee53e79f3b4d648b6d5bc8460ea690",
+       "value": false
+      }
+     },
      "216bce4b4b354b22a32d5be56b552272": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
@@ -818,12 +871,30 @@
        "value": false
       }
      },
+     "2f9b853cde7f4e1eacc1fbd283e58a2a": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxModel",
+      "state": {
+       "description": "Mark (Click)",
+       "disabled": false,
+       "layout": "IPY_MODEL_bbb6ef328218468a9a9acd6303ad648e",
+       "style": "IPY_MODEL_637874f12c9a4af18a2e613103d0db8e",
+       "value": false
+      }
+     },
      "312b2235cef844948d6bb11b96428112": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
       "state": {}
      },
+     "3679eb0df2dd4838ba1dc49302647159": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "3829d1590c57486fa56eecad03bd2a16": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -848,6 +919,18 @@
        "description_width": ""
       }
      },
+     "3a7b6ed3c97443249db3bfefa0174ced": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxModel",
+      "state": {
+       "description": "Print (Click)",
+       "disabled": false,
+       "layout": "IPY_MODEL_a1651e34548445e18376b1aa092c97a3",
+       "style": "IPY_MODEL_df2331170acb4f0f8945e29e1fdebcdb",
+       "value": false
+      }
+     },
      "3c45ad832a46489e8793aa176adfb25d": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -863,6 +946,18 @@
        "text_decoration": null
       }
      },
+     "3c874cabd6234092b7275ab9b86c3b06": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxModel",
+      "state": {
+       "description": "Print (Pick)",
+       "disabled": false,
+       "layout": "IPY_MODEL_a0118cc4fe1041d2a30d171a116b8ab5",
+       "style": "IPY_MODEL_a20e53b007e145fe8f8e4754a786da8e",
+       "value": false
+      }
+     },
      "3c9b6867ea884204a99a59b55ea3cf00": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -879,6 +974,74 @@
        "description_width": ""
       }
      },
+     "3fa0b71b14ac441493e68741804ef257": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DropdownModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_3679eb0df2dd4838ba1dc49302647159",
+       "style": "IPY_MODEL_87691d296c264e9d86b32881f5eb5991"
+      }
+     },
+     "418c87c3500748168129ce2f310a4983": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxModel",
+      "state": {
+       "description": "Mark (Click)",
+       "disabled": false,
+       "layout": "IPY_MODEL_f0dac2f88e1243af883a02b8feb003bd",
+       "style": "IPY_MODEL_bffee432bd7c4f93be9adf7fae09634a",
+       "value": false
+      }
+     },
+     "43bd291eac8c4a1bb21e08e33fa33d9a": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "45a8f7ca11104f74a71a5f7d014609d4": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxModel",
+      "state": {
+       "description": "Annotate (Pick)",
+       "disabled": false,
+       "layout": "IPY_MODEL_dea385cc88524e23ba2b42c68a051bfa",
+       "style": "IPY_MODEL_5dad09f987f54a11902987d189224d6b",
+       "value": false
+      }
+     },
+     "45ad8a1caa5e4e469756a2238e5831f5": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "4671d84897044fa8805d345a8377dd1d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "description": "Layers",
+       "index": 0,
+       "layout": "IPY_MODEL_fb4e701ee7904397aae25906b6debf7e",
+       "style": "IPY_MODEL_0dfc84b2aaee4307b36c07b3b853021f"
+      }
+     },
      "49aaf254026b42bc86aec1bf5529148c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -898,12 +1061,27 @@
        "tooltips": []
       }
      },
+     "4a5cf2fca93248cabde64057c6d03008": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
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",
+       "_figure_label": "Figure 1",
+       "_model_module_version": "^0.11",
+       "_size": [
+        600,
+        200
+       ],
+       "_view_module_version": "^0.11",
+       "header_visible": false,
+       "layout": "IPY_MODEL_9ec62a10d8d44160869a87820194ff47",
+       "toolbar": "IPY_MODEL_ca5c1b472abe4145bead7b3b382310aa",
+       "toolbar_position": "left"
+      }
+     },
+     "c533b9d6da654af99b10074891568859": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
      "c57b0de32084445e8425589b7810334c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -1270,6 +1758,56 @@
        "description_width": ""
       }
      },
+     "ca5c1b472abe4145bead7b3b382310aa": {
+      "model_module": "jupyter-matplotlib",
+      "model_module_version": "^0.11",
+      "model_name": "ToolbarModel",
+      "state": {
+       "_model_module_version": "^0.11",
+       "_view_module_version": "^0.11",
+       "collapsed": true,
+       "layout": "IPY_MODEL_43bd291eac8c4a1bb21e08e33fa33d9a",
+       "orientation": "vertical",
+       "toolitems": [
+        [
+         "Home",
+         "Reset original view",
+         "home",
+         "home"
+        ],
+        [
+         "Back",
+         "Back to previous view",
+         "arrow-left",
+         "back"
+        ],
+        [
+         "Forward",
+         "Forward to next view",
+         "arrow-right",
+         "forward"
+        ],
+        [
+         "Pan",
+         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
+         "arrows",
+         "pan"
+        ],
+        [
+         "Zoom",
+         "Zoom to rectangle\nx/y fixes axis",
+         "square-o",
+         "zoom"
+        ],
+        [
+         "Download",
+         "Download plot",
+         "floppy-o",
+         "save_figure"
+        ]
+       ]
+      }
+     },
      "ccf5b9d01dad4756bc8caddecf767324": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
@@ -1282,6 +1820,15 @@
       "model_name": "LayoutModel",
       "state": {}
      },
+     "d09de324ab1b451a932ae0c52e4be052": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ToggleButtonsStyleModel",
+      "state": {
+       "button_width": "",
+       "description_width": ""
+      }
+     },
      "d18b449dbc844894ab76175722fde37d": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -1299,6 +1846,28 @@
        "width": "30ex"
       }
      },
+     "d6589e7605f64ba2a257ffca90a001e3": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectionRangeSliderModel",
+      "state": {
+       "_model_name": "SelectionRangeSliderModel",
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "_view_name": "SelectionRangeSliderView",
+       "behavior": "drag-tap",
+       "description": "Layers",
+       "index": [
+        0,
+        0
+       ],
+       "layout": "IPY_MODEL_f11b7d82be3d4368a392c6e0ff39568b",
+       "style": "IPY_MODEL_c533b9d6da654af99b10074891568859"
+      }
+     },
      "da32bcabdc4548dca2c6d368789359e4": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -1307,12 +1876,64 @@
        "description_width": ""
       }
      },
+     "dad7ab315d5f45fe80ed65f2dbe2caab": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ToggleButtonsModel",
+      "state": {
+       "_options_labels": [
+        "coastline",
+        "countries",
+        "ocean"
+       ],
+       "button_style": "",
+       "description": "Layers",
+       "icons": [],
+       "index": 0,
+       "layout": "IPY_MODEL_45ad8a1caa5e4e469756a2238e5831f5",
+       "style": "IPY_MODEL_d09de324ab1b451a932ae0c52e4be052",
+       "tooltips": []
+      }
+     },
+     "dea385cc88524e23ba2b42c68a051bfa": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "df2331170acb4f0f8945e29e1fdebcdb": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "CheckboxStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "e4b903589ec944958589efe30d401d1f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ButtonModel",
+      "state": {
+       "description": "Custom Ocean Button",
+       "layout": "IPY_MODEL_aad5c79b1fef4180a8ad4721dc1751de",
+       "style": "IPY_MODEL_7fe22a6ef0fd4593b0bd5024d3821979",
+       "tooltip": null
+      }
+     },
      "e7d976321c7a47d187070f2f0c3577b6": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
       "state": {}
      },
+     "eba39c1852d24d1c94a3ed34c3d3ab99": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
      "ec612033fef64cfa8d8baefc22baf174": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
@@ -1325,6 +1946,24 @@
        "value": false
       }
      },
+     "f0dac2f88e1243af883a02b8feb003bd": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "f11b7d82be3d4368a392c6e0ff39568b": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "f8f2dd47badc415ebc71143c25fc9cb4": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "f94f557bb2b14a46948341b3a17a93b0": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
@@ -1340,6 +1979,18 @@
        "width": "50%"
       }
      },
+     "fb4e701ee7904397aae25906b6debf7e": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "fd58e54e422c475495de816c686639c2": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "fe6eab90964041c0944b6ab5c432bb7b": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",

From c4cf0995354663044095ef4cbcd677a8ca0fb746 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Mar 2024 17:11:38 +0100
Subject: [PATCH 229/240] update docs

---
 docs/notebooks/widgets.ipynb | 1610 +++++++++++++---------------------
 1 file changed, 618 insertions(+), 992 deletions(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index 3fad87373..7a412dbf2 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -45,17 +45,20 @@
     "They will only work if you use the interactive [ipympl (widget) backend][ipympl]. To activate, use the ``%matplotlib widget`` magic command.\n",
     "\n",
     "You can install the required additional dependencies with one of the following commands:\n",
-    "::::{grid} 1 1 2 2\n",
-    "\n",
-    "::: {grid-item-card}\n",
+    "::::{tab-set}\n",
     "\n",
+    ":::{tab-item} pip\n",
     "```\n",
     "pip install ipywidgets, ipympl\n",
     "```\n",
     ":::\n",
-    "::: {grid-item-card}\n",
+    ":::{tab-item} conda / mamba\n",
     "\n",
     "```\n",
+    "conda install -c conda-forge ipywidgets, ipympl\n",
+    "```\n",
+    "or\n",
+    "```\n",
     "mamba install -c conda-forge ipywidgets, ipympl\n",
     "```\n",
     ":::\n",
@@ -158,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "12a15112-ed5a-498a-b0a0-0688e429b729",
    "metadata": {
     "editable": true,
@@ -171,7 +174,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c4ea1efd784f4917ad38dcf5b8666b5e",
+       "model_id": "475c80e54fa34e92aee41f2cf1e6d833",
        "version_major": 2,
        "version_minor": 0
       },
@@ -206,10 +209,24 @@
     "m.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "067e41f5-cdd8-44bb-8a74-f117704c953c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Widgets to display a single specific layer"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "2c26797b-7c8e-4847-b711-4db1f3e3084f",
+   "execution_count": 3,
+   "id": "0ccdcea8-d463-43b0-8b5a-ebe99de46c18",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -219,19 +236,12 @@
    },
    "outputs": [],
    "source": [
-    "button = widgets.LayerButton(m, layer=\"ocean\")\n",
-    "toggle_buttons = widgets.LayerToggleButtons(m)\n",
-    "radio_buttons = widgets.LayerRadioButtons(m)\n",
-    "select_single = widgets.LayerSelect(m)\n",
-    "select_multi = widgets.LayerSelectMultiple(m)\n",
-    "dropdown = widgets.LayerDropdown(m)\n",
-    "slider = widgets.LayerSelectionSlider(m)\n",
-    "range_slider = widgets.LayerSelectionRangeSlider(m)"
+    "layer_button = widgets.LayerButton(m, layer=\"ocean\", description=\"Show ocean layer\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "id": "e850d223-3326-4afb-a500-d0c08b8ee624",
    "metadata": {
     "editable": true,
@@ -246,21 +256,76 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99690fd4cac14964b1ffc3de417de154",
+       "model_id": "75c788c63e034fec8ad9e180cc862956",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerButton(description='ocean', style=ButtonStyle())"
+       "LayerButton(description='Show ocean layer', style=ButtonStyle())"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(layer_button)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1914e079-bb49-48db-a919-f9f03a442cd0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Widgets to select one layer of a list of layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c3077287-240d-425a-a32b-648e8a83acc8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "selector_widgets = [\n",
+    "    widgets.LayerToggleButtons(m),\n",
+    "    widgets.LayerRadioButtons(m),\n",
+    "    widgets.LayerSelect(m),\n",
+    "    widgets.LayerDropdown(m),\n",
+    "    widgets.LayerSelectionSlider(m)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3611b713-bccf-4b90-b9f2-f8e72baf4b70",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
     },
+    "tags": [
+     "ignore_in_unittest"
+    ]
+   },
+   "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dad7ab315d5f45fe80ed65f2dbe2caab",
+       "model_id": "fd40abc79e2e4bb0ab58e675e4ac5963",
        "version_major": 2,
        "version_minor": 0
       },
@@ -274,12 +339,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fa0b71b14ac441493e68741804ef257",
+       "model_id": "751436e713794777bce3fd926ca370dd",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerDropdown(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
+       "LayerRadioButtons(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -288,12 +353,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7bce94e43a164cd5b966333f528b441f",
+       "model_id": "16a30ed51e534000a1a8bc982ccaedd6",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerRadioButtons(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
+       "LayerSelect(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -302,12 +367,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4671d84897044fa8805d345a8377dd1d",
+       "model_id": "e31373d1890d4066ae3003da25be3b8e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelect(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
+       "LayerDropdown(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
@@ -316,26 +381,78 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84adf788b67a48bca2613a7dd3a29429",
+       "model_id": "89c134827f65440b97e39e15229e5aa5",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelectMultiple(description='Layers', index=(0, 0), options=('coastline', 'countries', 'ocean'), value=('c…"
+       "LayerSelectionSlider(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(*selector_widgets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceb1bb57-f9a9-4ec8-98e4-4f8d7f6996ac",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Widgets to overlay multiple layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6b825194-c1a7-4268-8cc0-4093dd472540",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "multi_selector_widgets = [\n",
+    "    widgets.LayerSelectMultiple(m),\n",
+    "    widgets.LayerSelectionRangeSlider(m)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e0bf5aa7-a39d-4314-9bff-570f318bb0c4",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
     },
+    "tags": [
+     "ignore_in_unittest"
+    ]
+   },
+   "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d463c61cb274a29a5774f926501023c",
+       "model_id": "984f8ab2138849388d91ce3286ea318c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelectionSlider(description='Layers', options=('coastline', 'countries', 'ocean'), value='coastline')"
+       "LayerSelectMultiple(description='Layers', index=(0, 0), options=('coastline', 'countries', 'ocean'), value=('c…"
       ]
      },
      "metadata": {},
@@ -344,7 +461,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d6589e7605f64ba2a257ffca90a001e3",
+       "model_id": "ba4ef7862198434f87523c0527fde69d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -357,7 +474,7 @@
     }
    ],
    "source": [
-    "display(button, toggle_buttons, dropdown, radio_buttons, select_single, select_multi, slider, range_slider)"
+    "display(*multi_selector_widgets)"
    ]
   },
   {
@@ -372,15 +489,41 @@
    },
    "source": [
     ":::{tip}\n",
-    "You can also specify custom labels or use combined-layer assignments.\n",
+    "You can style the widgets as any other Jupyter Widget (more info in the [Widget Styling][widget_styling] section of the ipywidgets docs.)\n",
     "\n",
-    "In addition, you can style the widgets as any other Jupyter Widget (more info in the [Widget Styling](https://ipywidgets.readthedocs.io/en/7.6.2/examples/Widget%20Styling.html#) section of the ipywidgets docs.)\n",
-    "\n"
+    "To display widgets next to a figure, use the `canvas` of the figure:\n",
+    "\n",
+    "```python\n",
+    "display(m.f.canvas, ... other widgets...)\n",
+    "```\n",
+    "\n",
+    "To arrange widgets, checkout the [container and Layout widgets][container_widgets] like `HBox([list of widgets])`, `VBox([list of widgets])`!\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "[container_widgets]: https://ipywidgets.readthedocs.io/en/7.6.3/examples/Widget%20List.html#Container/Layout-widgets\n",
+    "[widget_styling]: https://ipywidgets.readthedocs.io/en/7.6.2/examples/Widget%20Styling.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
+   "id": "cca25482-eff6-43b0-8aba-776328d180ca",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import HBox, VBox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "id": "ad083853-4209-4a23-bf43-02fd0560f269",
    "metadata": {
     "editable": true,
@@ -389,38 +532,52 @@
     },
     "tags": []
    },
+   "outputs": [],
+   "source": [
+    "custom_selector = widgets.LayerSelectMultiple(\n",
+    "    m,\n",
+    "    layers=[[\"A nice coastline\", \"coastline\"], \n",
+    "            [\"Overlay 'coastline' + 'ocean'\", (\"coastline\", \"ocean\")],\n",
+    "            [\"Transparent overlay: 'coastline' + 'ocean'\", (\"coastline\", (\"ocean\", 0.4))]\n",
+    "           ],\n",
+    "    layout=dict(width='35%', height='80px', )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8da97f89-ed5e-4a51-8dca-d18fbbdefb4f",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c45e56c3add427aa56f5e7a81732885",
+       "model_id": "e1e306c4309440f39ff0ec3c448c83c5",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerSelectMultiple(description='Layers', index=(0, 0), layout=Layout(height='80px', width='50%'), options=(('…"
+       "HBox(children=(LayerSelectMultiple(description='Layers', index=(0, 0), layout=Layout(height='80px', width='35%…"
       ]
      },
-     "execution_count": 12,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "custom_selector = widgets.LayerSelectMultiple(\n",
-    "    m,\n",
-    "    layers=[[\"A nice coastline\", \"coastline\"], \n",
-    "            [\"Overlay 'coastline' + 'ocean'\", (\"coastline\", \"ocean\")],\n",
-    "            [\"Transparent overlay: 'coastline' + 'ocean'\", (\"coastline\", (\"ocean\", 0.4))]\n",
-    "           ],\n",
-    "    layout=dict(width='50%', height='80px', )\n",
-    ")\n",
-    "custom_selector"
+    "display(HBox([custom_selector, m.f.canvas]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "db03b116-c43b-4802-90d1-dcde8ca901cd",
    "metadata": {
     "editable": true,
@@ -429,35 +586,60 @@
     },
     "tags": []
    },
+   "outputs": [],
+   "source": [
+    "buttons = [\n",
+    "    widgets.LayerButton(m, layer=\"ocean\", description=\"Show ocean layer\", layout=dict(width=\"25ex\", height=\"5ex\")),\n",
+    "    widgets.LayerButton(m, layer=\"coastline\", description=\"Show coastline layer\", layout=dict(width=\"25ex\", height=\"5ex\"))\n",
+    "]\n",
+    "for b in buttons:\n",
+    "    b.style.font_weight = 'bold'\n",
+    "    b.style.font_size = '20px'\n",
+    "buttons[0].style.button_color = 'lightblue'\n",
+    "buttons[1].style.button_color = '#d0a2b9'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b52be482-9180-4104-b6cd-49abc12c2a41",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e4b903589ec944958589efe30d401d1f",
+       "model_id": "918eb8a3aa9f4e8781a46ac6f28d4dd3",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "LayerButton(description='Custom Ocean Button', layout=Layout(height='5ex', width='30ex'), style=ButtonStyle(bu…"
+       "HBox(children=(VBox(children=(LayerButton(description='Show ocean layer', layout=Layout(height='5ex', width='2…"
       ]
      },
-     "execution_count": 13,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "custom_button = widgets.LayerButton(m, layer=\"ocean\", description=\"Custom Ocean Button\", layout=dict(width=\"30ex\", height=\"5ex\"))\n",
-    "custom_button.style.button_color = 'lightblue'\n",
-    "custom_button.style.font_weight = 'bold'\n",
-    "custom_button.style.font_size = '24px'\n",
-    "custom_button"
+    "display(HBox([VBox(buttons), m.f.canvas]))"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "95d96081-6fea-4c50-9755-089c53517eed",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## Callback Widgets\n",
     "\n",
@@ -499,7 +681,13 @@
   {
    "cell_type": "markdown",
    "id": "d310fcf8-96f1-461c-81d1-cbde4ed4fd5b",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "### How to use the widgets\n",
     "\n",
@@ -552,7 +740,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1fbf9a3bc7064dc1b1248640fe412976",
+       "model_id": "4b35f68c7651410f9f844a68f1078083",
        "version_major": 2,
        "version_minor": 0
       },
@@ -566,7 +754,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f9b853cde7f4e1eacc1fbd283e58a2a",
+       "model_id": "79649e63c1674abbbaf890f0839d2763",
        "version_major": 2,
        "version_minor": 0
       },
@@ -580,7 +768,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5bc2462a0a404a288be147aa8d934266",
+       "model_id": "4e8ce3ee63484e93acab9b4d6b7c215c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -594,7 +782,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "61c6b639d3ba4c8ab29b4c7a97afdbcd",
+       "model_id": "c2840efbe2004e55bf47aae629c3d254",
        "version_major": 2,
        "version_minor": 0
       },
@@ -608,7 +796,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "418c87c3500748168129ce2f310a4983",
+       "model_id": "5e94e06eb5fe4f64b340dea12941e8f7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -622,7 +810,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3a7b6ed3c97443249db3bfefa0174ced",
+       "model_id": "bdba49ff00e643af893447db4211356c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -636,7 +824,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45a8f7ca11104f74a71a5f7d014609d4",
+       "model_id": "862853edce5d46e688f7b9bf399ccc6a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -650,7 +838,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1b8e53055e3497f8d7b04b85e9e7d02",
+       "model_id": "484d42e225f84ac2afeb2d63d2c5b37e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -664,7 +852,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3c874cabd6234092b7275ab9b86c3b06",
+       "model_id": "573855a2ccf14e9e9f24b7a900c274e7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -679,14 +867,6 @@
    "source": [
     "display(*checkboxes)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64a681f1-a34a-46bd-b60a-7e6670d9da69",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -710,255 +890,270 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "0028a24996e14a838008e71290b9bcb2": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "CheckboxStyleModel",
+     "0088869a26b24f0ab09dffb544b56ce9": {
+      "model_module": "jupyter-matplotlib",
+      "model_module_version": "^0.11",
+      "model_name": "ToolbarModel",
       "state": {
-       "description_width": ""
+       "_model_module_version": "^0.11",
+       "_view_module_version": "^0.11",
+       "collapsed": true,
+       "layout": "IPY_MODEL_17dc4fb8908547f7b343ca7847b48e7d",
+       "orientation": "vertical",
+       "toolitems": [
+        [
+         "Home",
+         "Reset original view",
+         "home",
+         "home"
+        ],
+        [
+         "Back",
+         "Back to previous view",
+         "arrow-left",
+         "back"
+        ],
+        [
+         "Forward",
+         "Forward to next view",
+         "arrow-right",
+         "forward"
+        ],
+        [
+         "Pan",
+         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
+         "arrows",
+         "pan"
+        ],
+        [
+         "Zoom",
+         "Zoom to rectangle\nx/y fixes axis",
+         "square-o",
+         "zoom"
+        ],
+        [
+         "Download",
+         "Download plot",
+         "floppy-o",
+         "save_figure"
+        ]
+       ]
       }
      },
-     "00bc30ab507948ccad518593e9a6a940": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "037feca29b864171b2328e280aac315b": {
+     "092d2b10293245ef976d5fd2e7618344": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
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-     "05c0efc27b1c472ca28aa16b114c85eb": {
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-      "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
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-      }
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-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "0938d8245dad433b8a5e03ae6fb19ff6": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "CheckboxModel",
-      "state": {
-       "description": "Annotate (Click)",
-       "disabled": false,
-       "layout": "IPY_MODEL_077c1cfa068648b9b6d99d3aedf6cd90",
-       "style": "IPY_MODEL_3d323f6ca0ca40fcbc91a7607b3417d2",
-       "value": false
-      }
-     },
-     "093f0f04d98d4754b7d373a630e7117d": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SelectMultipleModel",
+      "model_name": "SelectMultipleModel",
       "state": {
        "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
+        "A nice coastline",
+        "Overlay 'coastline' + 'ocean'",
+        "Transparent overlay: 'coastline' + 'ocean'"
        ],
        "description": "Layers",
        "index": [
         0,
         0
        ],
-       "layout": "IPY_MODEL_216bce4b4b354b22a32d5be56b552272",
+       "layout": "IPY_MODEL_7ff01dc7a0c64d9aa6f6adc9dc7d6230",
        "rows": 5,
-       "style": "IPY_MODEL_037feca29b864171b2328e280aac315b"
+       "style": "IPY_MODEL_772a0e3c61a14fe2bde319db0ca9effb"
       }
      },
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "ToggleButtonsStyleModel",
       "state": {
+       "button_width": "",
        "description_width": ""
       }
      },
-     "0e52d68648484afaba0ab62e3d9a8296": {
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SelectionSliderModel",
+      "model_name": "CheckboxStyleModel",
+      "state": {
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+      }
+     },
+     "16a30ed51e534000a1a8bc982ccaedd6": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SelectModel",
       "state": {
        "_options_labels": [
         "coastline",
         "countries",
         "ocean"
        ],
-       "behavior": "drag-tap",
        "description": "Layers",
        "index": 0,
-       "layout": "IPY_MODEL_bd40d4a6f22d4c2ea12a44fb6cefe2e7",
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-       "style": "IPY_MODEL_7aee53e79f3b4d648b6d5bc8460ea690",
-       "value": false
+       "layout": "IPY_MODEL_f22fcd446a3e4b73b5fb0e872a89d041",
+       "style": "IPY_MODEL_5642595995c74a9b8533a9fb31b1bc3a"
       }
      },
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-       "layout": "IPY_MODEL_233a9266bd134c219fce583977a7fd87",
-       "style": "IPY_MODEL_0028a24996e14a838008e71290b9bcb2",
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       "model_module_version": "2.0.0",
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-      "state": {
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-       "layout": "IPY_MODEL_88cb24f890f04d1bb78598e2c5587a7a",
-       "style": "IPY_MODEL_8fb596386ea04073b560bf6e05ef2671",
-       "value": false
-      }
+      "model_name": "LayoutModel",
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       "model_module_version": "2.0.0",
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       "state": {
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-       "style": "IPY_MODEL_637874f12c9a4af18a2e613103d0db8e",
-       "value": false
+       "description": "Show ocean layer",
+       "layout": "IPY_MODEL_ff04a7884e804cc18a8c08ae07f55223",
+       "style": "IPY_MODEL_426c94dc86534ff0af7bcafe61c9e03a",
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+      "state": {
+       "button_color": "lightblue",
+       "font_family": null,
+       "font_size": "20px",
+       "font_style": null,
+       "font_variant": null,
+       "font_weight": "bold",
+       "text_color": null,
+       "text_decoration": null
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       "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
+       "font_family": null,
+       "font_size": null,
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+       "font_variant": null,
+       "font_weight": null,
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+      "model_module": "jupyter-matplotlib",
+      "model_module_version": "^0.11",
+      "model_name": "MPLCanvasModel",
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",
+       "_figure_label": "Figure 1",
+       "_image_mode": "diff",
+       "_model_module_version": "^0.11",
+       "_size": [
+        600,
+        200
        ],
-       "description": "Layers",
-       "index": 0,
-       "layout": "IPY_MODEL_e7d976321c7a47d187070f2f0c3577b6",
-       "style": "IPY_MODEL_4ed61cc51bba4b6f9c25ee7da5ab5830"
+       "_view_module_version": "^0.11",
+       "header_visible": false,
+       "layout": "IPY_MODEL_6ae8216398f94b4fac6c11f9ac2e2b94",
+       "toolbar": "IPY_MODEL_0088869a26b24f0ab09dffb544b56ce9",
+       "toolbar_position": "left"
       }
      },
-     "3a69d5b4adbb40e093ebea8b8c11ef05": {
+     "484d42e225f84ac2afeb2d63d2c5b37e": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "CheckboxStyleModel",
+      "model_name": "CheckboxModel",
       "state": {
-       "description_width": ""
+       "description": "Mark (Pick)",
+       "disabled": false,
+       "layout": "IPY_MODEL_3d02468e6bf04a8e95e1add0f22482ee",
+       "style": "IPY_MODEL_b98437c319da4531a4e0a9a9928e0f1e",
+       "value": false
       }
      },
-     "3a7b6ed3c97443249db3bfefa0174ced": {
+     "4b35f68c7651410f9f844a68f1078083": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "CheckboxModel",
       "state": {
-       "description": "Print (Click)",
+       "description": "Annotate (Click)",
        "disabled": false,
-       "layout": "IPY_MODEL_a1651e34548445e18376b1aa092c97a3",
-       "style": "IPY_MODEL_df2331170acb4f0f8945e29e1fdebcdb",
+       "layout": "IPY_MODEL_fd5c8faa19a545dbac91e9d6b8194a6d",
+       "style": "IPY_MODEL_1414f25b94814de8ab78487bf61941eb",
        "value": false
       }
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",
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-      "model_name": "ButtonModel",
-      "state": {
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-       "style": "IPY_MODEL_3c45ad832a46489e8793aa176adfb25d",
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-         "home"
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-         "Back to previous view",
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-        [
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-         "arrow-right",
-         "forward"
-        ],
-        [
-         "Pan",
-         "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
-         "arrows",
-         "pan"
-        ],
-        [
-         "Zoom",
-         "Zoom to rectangle\nx/y fixes axis",
-         "square-o",
-         "zoom"
-        ],
-        [
-         "Download",
-         "Download plot",
-         "floppy-o",
-         "save_figure"
-        ]
-       ]
-      }
-     },
-     "ccf5b9d01dad4756bc8caddecf767324": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "cefd66b415754aed8fd1eee24c774a93": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "d09de324ab1b451a932ae0c52e4be052": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ToggleButtonsStyleModel",
-      "state": {
-       "button_width": "",
-       "description_width": ""
-      }
-     },
-     "d18b449dbc844894ab76175722fde37d": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SliderStyleModel",
-      "state": {
-       "description_width": ""
-      }
-     },
-     "d301558997a8427ca93dfa5cf4f57081": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {
-       "height": "5ex",
-       "width": "30ex"
-      }
-     },
-     "d6589e7605f64ba2a257ffca90a001e3": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SelectionRangeSliderModel",
-      "state": {
-       "_model_name": "SelectionRangeSliderModel",
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "_view_name": "SelectionRangeSliderView",
-       "behavior": "drag-tap",
-       "description": "Layers",
-       "index": [
-        0,
-        0
-       ],
-       "layout": "IPY_MODEL_f11b7d82be3d4368a392c6e0ff39568b",
-       "style": "IPY_MODEL_c533b9d6da654af99b10074891568859"
-      }
-     },
-     "da32bcabdc4548dca2c6d368789359e4": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "CheckboxStyleModel",
-      "state": {
-       "description_width": ""
-      }
-     },
-     "dad7ab315d5f45fe80ed65f2dbe2caab": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ToggleButtonsModel",
-      "state": {
-       "_options_labels": [
-        "coastline",
-        "countries",
-        "ocean"
-       ],
-       "button_style": "",
-       "description": "Layers",
-       "icons": [],
-       "index": 0,
-       "layout": "IPY_MODEL_45ad8a1caa5e4e469756a2238e5831f5",
-       "style": "IPY_MODEL_d09de324ab1b451a932ae0c52e4be052",
-       "tooltips": []
-      }
-     },
-     "dea385cc88524e23ba2b42c68a051bfa": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "df2331170acb4f0f8945e29e1fdebcdb": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "CheckboxStyleModel",
-      "state": {
-       "description_width": ""
-      }
-     },
-     "e4b903589ec944958589efe30d401d1f": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ButtonModel",
-      "state": {
-       "description": "Custom Ocean Button",
-       "layout": "IPY_MODEL_aad5c79b1fef4180a8ad4721dc1751de",
-       "style": "IPY_MODEL_7fe22a6ef0fd4593b0bd5024d3821979",
-       "tooltip": null
-      }
-     },
-     "e7d976321c7a47d187070f2f0c3577b6": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "eba39c1852d24d1c94a3ed34c3d3ab99": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "description_width": ""
-      }
-     },
-     "ec612033fef64cfa8d8baefc22baf174": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "CheckboxModel",
-      "state": {
-       "description": "Mark (Click)",
-       "disabled": false,
-       "layout": "IPY_MODEL_54746a1718eb453a898df569f0174aab",
-       "style": "IPY_MODEL_ca0a3971664c41a58eac2c5bb59d8d1e",
-       "value": false
-      }
-     },
-     "f0dac2f88e1243af883a02b8feb003bd": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "f11b7d82be3d4368a392c6e0ff39568b": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "f8f2dd47badc415ebc71143c25fc9cb4": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "f94f557bb2b14a46948341b3a17a93b0": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "fa7f86784e494ab0b9adc0647e3af421": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {
-       "height": "80px",
-       "width": "50%"
-      }
-     },
-     "fb4e701ee7904397aae25906b6debf7e": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "fd58e54e422c475495de816c686639c2": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
-     },
-     "fe6eab90964041c0944b6ab5c432bb7b": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
      }
     },
     "version_major": 2,

From 1d2ea0775df3c92ad3d129cd1a6e104b38df266e Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sat, 9 Mar 2024 17:30:10 +0100
Subject: [PATCH 230/240] update unittests

---
 docs/notebooks/widgets.ipynb | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/docs/notebooks/widgets.ipynb b/docs/notebooks/widgets.ipynb
index 7a412dbf2..2f506f3a4 100644
--- a/docs/notebooks/widgets.ipynb
+++ b/docs/notebooks/widgets.ipynb
@@ -553,7 +553,9 @@
     "slideshow": {
      "slide_type": ""
     },
-    "tags": []
+    "tags": [
+     "ignore_in_unittest"
+    ]
    },
    "outputs": [
     {
@@ -608,7 +610,9 @@
     "slideshow": {
      "slide_type": ""
     },
-    "tags": []
+    "tags": [
+     "ignore_in_unittest"
+    ]
    },
    "outputs": [
     {

From 2a98e4c86799f163823f6cb2edf8164b126f11c2 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 10 Mar 2024 23:21:17 +0100
Subject: [PATCH 231/240] improvements for data_identification + allow passing
 xarray.Dataset objects to m.set_data

---
 eomaps/_containers.py         |   4 +-
 eomaps/_data_manager.py       | 220 ++++++++++++++++++++++++----------
 eomaps/eomaps.py              |  10 +-
 tests/test_basic_functions.py |   2 +-
 tests/test_layout_editor.py   |   3 +-
 5 files changed, 171 insertions(+), 68 deletions(-)

diff --git a/eomaps/_containers.py b/eomaps/_containers.py
index 5f114d3c6..6939fa576 100644
--- a/eomaps/_containers.py
+++ b/eomaps/_containers.py
@@ -21,8 +21,8 @@ def __init__(
         self,
         m,
         data=None,
-        x="lon",
-        y="lat",
+        x=None,
+        y=None,
         crs=4326,
         parameter=None,
         encoding=None,
diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index 79f1d2ecb..8c3c2f6c5 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -165,79 +165,150 @@ def cleanup_callbacks(self):
         if self.on_fetch_bg in self.m.BM._before_update_actions:
             self.m.BM._before_update_actions.remove(self.on_fetch_bg)
 
-    def _identify_data(self, data=None, x=None, y=None, parameter=None):
-        # identify the way how the data has been provided and convert to the internal
-        # structure
+    def _identify_pandas(self, data=None, x=None, y=None, parameter=None):
+        (pd,) = register_modules("pandas", raise_exception=False)
 
-        if data is None:
-            data = self.m.data_specs.data
-        if x is None:
-            x = self.m.data_specs.x
-        if y is None:
-            y = self.m.data_specs.y
-        if parameter is None:
-            parameter = self.m.data_specs.parameter
+        if pd is None or not isinstance(data, pd.DataFrame):
+            return None
 
-        # check other types before pandas to avoid unnecessary import
-        if data is not None and not isinstance(data, (list, tuple, np.ndarray)):
-            (pd,) = register_modules("pandas", raise_exception=False)
+        if parameter is not None:
+            # get the data-values
+            z_data = data[parameter].values
+        else:
+            # use the first found numeric column of the DataFrame if possible.
+            numeric_columns = data.select_dtypes("number").columns
+            if len(numeric_columns) > 0:
+                z_data = data[numeric_columns[0]].values
+            else:
+                # TODO remove this and raise an error?
+                z_data = np.repeat(np.nan, len(data))
 
-            if pd is None:
+        # get the index-values
+        ids = data.index.values
+
+        if isinstance(x, str) and isinstance(y, str):
+            if (x in data) and (y in data):
+                # get the coordinates from columns
+                xorig = data[x].values
+                yorig = data[y].values
+            elif (x in data.index.names) and (y in data.index.names):
+                # get the coordinates from index-values
+                xorig = data.index.get_level_values(x)
+                yorig = data.index.get_level_values(y)
+            else:
                 raise TypeError(
-                    f"EOmaps: Unable to handle the input-data type: {type(data)}"
+                    "EOmaps: unable to identify coordinate values for " f"x={x}, y={y}"
                 )
+        elif (x is None) and (y is None):
+            if data.index.nlevels == 2:
+                # get the coordinates from index-values
+                xorig = data.index.get_level_values(0)
+                yorig = data.index.get_level_values(1)
+            else:
+                raise TypeError(
+                    "EOmaps: Either specify explicit column-names to use "
+                    "for `x` and `y` or pass a multi-index DataFrame "
+                    "with exactly 2 index-levels!"
+                )
+        else:
+            assert isinstance(x, (list, np.ndarray, pd.Series)), (
+                "'x' must be either a column-name, or explicit values "
+                " specified as a list, a numpy-array or a pandas.Series "
+                + f"if you provide the data as '{type(data)}'"
+            )
+            assert isinstance(y, (list, np.ndarray, pd.Series)), (
+                "'y' must be either a column-name, or explicit values "
+                " specified as a list, a numpy-array or a pandas.Series "
+                + f"object if you provide the data as '{type(data)}'"
+            )
 
-            if isinstance(data, pd.DataFrame):
-                if parameter is not None:
-                    # get the data-values
-                    z_data = data[parameter].values
-                else:
-                    z_data = np.repeat(np.nan, len(data))
+            xorig = np.asanyarray(x)
+            yorig = np.asanyarray(y)
 
-                # get the index-values
-                ids = data.index.values
+        return z_data, xorig, yorig, ids, parameter
 
-                if isinstance(x, str) and isinstance(y, str):
-                    # get the data-coordinates
-                    xorig = data[x].values
-                    yorig = data[y].values
-                else:
-                    assert isinstance(x, (list, np.ndarray, pd.Series)), (
-                        "'x' must be either a column-name, or explicit values "
-                        " specified as a list, a numpy-array or a pandas"
-                        + f" Series object if you provide the data as '{type(data)}'"
-                    )
-                    assert isinstance(y, (list, np.ndarray, pd.Series)), (
-                        "'y' must be either a column-name, or explicit values "
-                        " specified as a list, a numpy-array or a pandas"
-                        + f" Series object if you provide the data as '{type(data)}'"
-                    )
+    def _identify_xarray(self, data=None, x=None, y=None, parameter=None):
+        (xar,) = register_modules("xarray", raise_exception=False)
 
-                    xorig = np.asanyarray(x)
-                    yorig = np.asanyarray(y)
+        if xar is None or not isinstance(data, (xar.Dataset, xar.DataArray)):
+            return None
 
-                return z_data, xorig, yorig, ids, parameter
+        if isinstance(data, xar.Dataset):
+            if parameter is None:
+                # use the first variable of the Dataset
+                cols = list(data)
+                parameter = cols[0]
 
-        # identify all other types except for pandas.DataFrames
-        # lazily check if pandas was used
-        pandas_series_data = False
-        for iname, i in zip(("x", "y", "data"), (x, y, data)):
-            if iname == "data" and i is None:
-                # allow empty datasets
-                continue
+            assert len(data[parameter].dims) <= 2, (
+                "EOmaps: provided dataset has more than 2 dimensions..."
+                f"({data[parameter].dims})."
+            )
+            z_data = data[parameter].values
+        else:
+            assert len(data.dims) <= 2, (
+                "EOmaps: provided dataset has more than 2 dimensions..."
+                f"({data.dims})."
+            )
 
-            if not isinstance(i, (list, tuple, np.ndarray)):
-                (pd,) = register_modules("pandas", raise_exception=False)
+            z_data = data.values
+            parameter = data.name
+
+        # use numeric index values
+        ids = range(z_data.size)
+
+        if isinstance(x, str) and isinstance(y, str):
+            coords = list(data.coords)
+            if (x in coords) and (y in coords):
+                # get the coordinates from coordinates
+                xorig = data.coords[x].values
+                yorig = data.coords[y].values
+                # transpose dat in case x is before y
+                # (to account for matrix indexing order)
+                if coords.index(x) > coords.index(y):
+                    z_data = z_data.T
 
-                if pd and not isinstance(i, pd.Series):
-                    raise AssertionError(
-                        f"{iname} values must be a list, numpy-array or pandas.Series"
-                    )
-                else:
-                    if iname == "data":
-                        pandas_series_data = True
+            elif (x in data) and (y in data):
+                xorig = data[x].values
+                yorig = data[y].values
+
+        elif (x is None) and (y is None):
+            coords = list(data.coords)
+
+            if len(coords) == 2:
+                # get the coordinates from index-values
+                xorig = data[coords[0]].values
+                yorig = data[coords[1]].values
+            else:
+                raise TypeError(
+                    "EOmaps: Either specify explicit coordinate-names to use "
+                    "for `x` and `y` or pass a Dataset with exactly 2 "
+                    "coordinates!"
+                )
+        return z_data, xorig, yorig, ids, parameter
+
+    def _identify_array_like(self, data=None, x=None, y=None, parameter=None):
+        def check_dtype(val):
+            if val is None:
+                return True
+
+            if isinstance(val, (list, tuple, np.ndarray)):
+                return True
 
-        # set coordinates by extent
+            # lazily check if pandas was used
+            (pd,) = register_modules("pandas", raise_exception=False)
+            if pd and isinstance(val, pd.Series):
+                return "pandas"
+
+            return False
+
+        data_q = check_dtype(data)
+        if not data_q:
+            return None
+
+        if not (check_dtype(x) and check_dtype(y)):
+            return None
+
+        # set coordinates by extent-tuples (x0, x1) and (y0, y1)
         if isinstance(x, tuple) and isinstance(y, tuple):
             assert data is not None, (
                 "EOmaps: If x- and y are provided as tuples, the data must be a 2D list"
@@ -273,7 +344,7 @@ def _identify_data(self, data=None, x=None, y=None, parameter=None):
                 z_data = np.full((xorig.shape[0], yorig.shape[0]), np.nan)
 
         # get the index-values
-        if pandas_series_data is True:
+        if data_q == "pandas":
             # use actual index values if pd.Series was passed as "data"
             ids = data.index.values
         else:
@@ -298,6 +369,35 @@ def _identify_data(self, data=None, x=None, y=None, parameter=None):
 
         return z_data, np.asanyarray(xorig), np.asanyarray(yorig), ids, parameter
 
+    def _identify_data(self, data=None, x=None, y=None, parameter=None):
+        # identify the way how the data has been provided and convert to the internal
+        # structure
+
+        if data is None:
+            data = self.m.data_specs.data
+        if x is None:
+            x = self.m.data_specs.x
+        if y is None:
+            y = self.m.data_specs.y
+        if parameter is None:
+            parameter = self.m.data_specs.parameter
+
+        # check supported data-types
+        for identify_func in (
+            self._identify_array_like,
+            self._identify_pandas,
+            self._identify_xarray,
+        ):
+            ret = identify_func(data=data, x=x, y=y, parameter=parameter)
+
+            if ret is not None:
+                return ret
+
+        raise TypeError(
+            "EOmaps: Unable to handle the input-data types: \n"
+            f"data={type(data)}, x={type(x)}, y={type(y)}"
+        )
+
     def _prepare_data(self, assume_sorted=True):
         in_crs = self.m.data_specs.crs
         cpos = self.m.data_specs.cpos
diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index eb4b2cd46..8d8ecae23 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -457,8 +457,8 @@ def __init__(
 
         self.data_specs = DataSpecs(
             weakref.proxy(self),
-            x="lon",
-            y="lat",
+            x=None,
+            y=None,
             crs=4326,
         )
 
@@ -4359,9 +4359,11 @@ def _set_parent(self):
 
     def _set_default_shape(self):
         if self.data is not None:
-            size = np.size(self.data)
+            # size = np.size(self.data)
+            size = np.size(self._data_manager.z_data)
+            shape = np.shape(self._data_manager.z_data)
 
-            if len(np.shape(self.data)) == 2 and size > 200_000:
+            if len(shape) == 2 and size > 200_000:
                 self.set_shape.raster()
             else:
                 if size > 500_000:
diff --git a/tests/test_basic_functions.py b/tests/test_basic_functions.py
index 9272d8e23..b152f2bc2 100644
--- a/tests/test_basic_functions.py
+++ b/tests/test_basic_functions.py
@@ -454,7 +454,7 @@ def test_copy(self):
 
         self.assertTrue(
             m2.data_specs[["x", "y", "parameter", "crs"]]
-            == {"x": "lon", "y": "lat", "parameter": None, "crs": 4326}
+            == {"x": None, "y": None, "parameter": None, "crs": 4326}
         )
         self.assertTrue([*m.classify_specs] == [*m2.classify_specs])
         self.assertTrue(m2.data == None)
diff --git a/tests/test_layout_editor.py b/tests/test_layout_editor.py
index ac532dde0..49de71f69 100644
--- a/tests/test_layout_editor.py
+++ b/tests/test_layout_editor.py
@@ -59,7 +59,8 @@ def setUp(self):
     def test_layout_editor(self):
         # %%
         lon, lat = np.meshgrid(np.linspace(20, 50, 50), np.linspace(20, 50, 50))
-        data = pd.DataFrame(dict(lon=lon.flat, lat=lat.flat, value=1))
+        data = pd.DataFrame(dict(lon=lon.flat, lat=lat.flat, value=lon.flat))
+        data.set_index(["lon", "lat"], inplace=True)
 
         mg = MapsGrid()
         mg.set_data(data)

From 47d6687f0794427e56865cef725c76f1d4f2c2bf Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 10 Mar 2024 23:23:42 +0100
Subject: [PATCH 232/240] update docs

---
 docs/api_data_visualization.rst | 387 +++++++++++++++++++++-----------
 1 file changed, 258 insertions(+), 129 deletions(-)

diff --git a/docs/api_data_visualization.rst b/docs/api_data_visualization.rst
index 99c135d0e..61735058a 100644
--- a/docs/api_data_visualization.rst
+++ b/docs/api_data_visualization.rst
@@ -82,6 +82,197 @@ A dataset is fully specified by setting the following properties:
 - ``cpos``, ``cpos_radius`` (optional): the pixel offset
 
 
+The following data-types are currently accepted as input:
+
+.. dropdown:: **pandas.DataFrames**
+    :icon: square-fill
+    :color: info
+
+    .. tab-set::
+
+        .. tab-item:: Assignments
+
+            - ``data``: pandas.DataFrame
+            - ``x``, ``y``: The column-names to use as coordinates (``string``)
+            - ``parameter``: The column-name to use as data-values (``string``)
+
+        .. tab-item:: basic DataFrame
+
+            .. code-block:: python
+                :name: test_assign_pandas_01
+
+                from eomaps import Maps
+                import pandas as pd
+
+                df = pd.DataFrame(dict(lon=[1,2,3], lat=[2,5,4], data=[12, 43, 2]))
+                m = Maps()
+                m.set_data(df, x="lon", y="lat", crs=4326, parameter="data")
+                m.plot_map()
+
+        .. tab-item:: multiindex DataFrame
+
+            .. code-block:: python
+                :name: test_assign_pandas_02
+
+                from eomaps import Maps
+                import pandas as pd
+
+                data = dict(param=[10, 29, 39])
+                index = pd.MultiIndex.from_arrays([[10,20,30], [10,20,30]], names=("lon", "lat"))
+                df = pd.DataFrame(data=data, index=index)
+
+                m = Maps()
+                m.set_data(df, x="lon", y="lat", crs=4326, parameter="param")
+                m.plot_map()
+
+
+
+.. dropdown:: **numpy.Array** | **pandas.Series** | **list**
+    :icon: square-fill
+    :color: info
+
+    .. tab-set::
+
+        .. tab-item:: Assignments
+
+            - ``data``, ``x``, ``y``: ``numpy.array``, ``pandas.Series`` or ``list``
+
+              - either data and coordinates have the same 1D/2D shape or
+              - 2D ``data=(m, n)`` and 1D coordinates ``x=(m,)``, ``y=(n,)``
+
+            - ``parameter``: (optional) parameter name (``string``)
+
+        .. tab-item:: 1D list
+
+            .. code-block:: python
+                :name: test_assign_list
+
+                from eomaps import Maps
+
+                x, y, data = [1,2,3], [2, 5, 4], [12, 43, 2]
+                m = Maps()
+                m.set_data(data, x=x, y=y, crs=4326, parameter="param_name")
+                m.plot_map()
+
+        .. tab-item:: 1D numpy
+
+            .. code-block:: python
+                :name: test_assign_numpy_1d
+
+                from eomaps import Maps
+                import numpy as np
+
+                x, y, data = np.array([1,2,3]), np.array([5, 7, 9]), np.array([1, 2, 3])
+                m = Maps()
+                m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
+                m.plot_map()
+
+
+        .. tab-item:: 2D numpy
+
+            .. code-block:: python
+                :name: test_assign_numpy_2d
+
+                from eomaps import Maps
+                import numpy as np
+
+                x, y = np.meshgrid(np.array([1,2,3]), np.array([5, 7, 9]))
+                data = np.random.randint(0, 10, x.shape)
+                m = Maps()
+                m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
+                m.plot_map()
+
+
+        .. tab-item:: 2D + 1D numpy
+
+            .. code-block:: python
+                :name: test_assign_numpy_1d2d
+
+                from eomaps import Maps
+                import numpy as np
+
+                x, y = np.linspace(-20, 20, 100), np.linspace(15, 34, 50)
+                data = np.random.randint(0, 100, size=(100, 50)
+                m = Maps()
+                m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
+                m.plot_map()
+
+
+        .. tab-item:: pandas Series
+
+            .. code-block:: python
+                :name: test_assign_series
+
+                from eomaps import Maps
+                import pandas as pd
+
+                x, y, data = pd.Series([1,2,3]), pd.Series([2, 5, 4]), pd.Series([12, 43, 2])
+                m = Maps()
+                m.set_data(data, x=x, y=y, crs=4326, parameter="param_name")
+                m.plot_map()
+
+
+.. dropdown:: **xarray.Dataset**
+    :icon: square-fill
+    :color: info
+
+    .. tab-set::
+
+        .. tab-item:: Assignments
+
+            - ``data``: xarray.Dataset
+            - ``x``, ``y``: The variables to use as coordinates (``string``)
+            - ``parameter``: The variable to use as data-values (``string``)
+
+        .. tab-item:: 2D data and coords
+
+            .. code-block:: python
+                :name: test_assign_xarray_2d
+
+                from eomaps import Maps
+                import xarray as xar
+                import numpy as np
+
+                param = np.random.randint(0, 10, (2,2,3))
+                lon = [[-20, 20], [23, 54]]
+                lat = [[-10, 20], [-10, 20]]
+                time = [1,2,3]
+
+                ds = xar.Dataset(
+                    data_vars=dict(my_param=(["x", "y", "time"], param)),
+                    coords=dict(lon=(["x", "y"], lon), lat=(["x", "y"], lat), time=time),
+                )
+
+                m = Maps()
+                m.set_data(data=ds.sel(time=1), x="lon", y="lat", parameter="my_param", crs=4326)
+                m.plot_map()
+
+        .. tab-item:: 1D coords 2D data
+
+            .. code-block:: python
+                :name: test_assign_xarray_1d2d
+
+                from eomaps import Maps
+                import xarray as xar
+                import numpy as np
+
+                param = np.random.randint(0, 10, (2,2,3))
+                lon = [-20, 20]
+                lat = [30, 60]
+                time = [1,2,3]
+
+                ds = xar.Dataset(
+                    data_vars=dict(my_param=(["lon", "lat", "time"], param)),
+                    coords=dict(lon=lon, lat=lat, time=time),
+                )
+
+                m = Maps()
+                m.set_data(data=ds.sel(time=1), x="lon", y="lat", parameter="my_param", crs=4326)
+                m.plot_map()
+
+
+
+
 .. note::
 
     Make sure to use a individual :py:class:`Maps` object (e.g. with ``m2 = m.new_layer()``) for each dataset!
@@ -100,59 +291,6 @@ A dataset is fully specified by setting the following properties:
        - Note that reprojecting 1D coordinate vectors to a different crs will result in (possibly very large) 2D coordinate arrays!
 
 
-The following data-types are accepted as input:
-
-+---------------------------------------------------------------------+------------------------------------------------------------------------------------+
-| **pandas DataFrames**                                               | .. code-block:: python                                                             |
-|                                                                     |     :name: test_pandas_data_01                                                     |
-|                                                                     |                                                                                    |
-| - ``data``: ``pandas.DataFrame``                                    |     from eomaps import Maps                                                        |
-| - ``x``, ``y``: The column-names to use as coordinates (``string``) |     import pandas as pd                                                            |
-| - ``parameter``: The column-name to use as data-values (``string``) |                                                                                    |
-|                                                                     |     df = pd.DataFrame(dict(lon=[1,2,3], lat=[2,5,4], data=[12, 43, 2]))            |
-|                                                                     |     m = Maps()                                                                     |
-|                                                                     |     m.set_data(df, x="lon", y="lat", crs=4326, parameter="data")                   |
-|                                                                     |     m.plot_map()                                                                   |
-+---------------------------------------------------------------------+------------------------------------------------------------------------------------+
-| **pandas Series**                                                   | .. code-block:: python                                                             |
-|                                                                     |     :name: test_pandas_data_02                                                     |
-|                                                                     |                                                                                    |
-| - ``data``, ``x``, ``y``: ``pandas.Series``                         |     from eomaps import Maps                                                        |
-| - ``parameter``: (optional) parameter name (``string``)             |     import pandas as pd                                                            |
-|                                                                     |                                                                                    |
-|                                                                     |     x, y, data = pd.Series([1,2,3]), pd.Series([2, 5, 4]), pd.Series([12, 43, 2])  |
-|                                                                     |     m = Maps()                                                                     |
-|                                                                     |     m.set_data(data, x=x, y=y, crs=4326, parameter="param_name")                   |
-|                                                                     |     m.plot_map()                                                                   |
-+---------------------------------------------------------------------+------------------------------------------------------------------------------------+
-| **1D** or **2D** data **and** coordinates                           | .. code-block:: python                                                             |
-|                                                                     |     :name: test_numpy_data_01                                                      |
-|                                                                     |                                                                                    |
-| - ``data``, ``x``, ``y``: equal-size ``numpy.array`` (or ``list``)  |     from eomaps import Maps                                                        |
-| - ``parameter``: (optional) parameter name (``string``)             |     import numpy as np                                                             |
-|                                                                     |                                                                                    |
-|                                                                     |     x, y = np.mgrid[-20:20, -40:40]                                                |
-|                                                                     |     data = x + y                                                                   |
-|                                                                     |     m = Maps()                                                                     |
-|                                                                     |     m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")              |
-|                                                                     |     m.plot_map()                                                                   |
-+---------------------------------------------------------------------+------------------------------------------------------------------------------------+
-| **1D** coordinates and **2D** data                                  | .. code-block:: python                                                             |
-|                                                                     |     :name: test_numpy_data_02                                                      |
-|                                                                     |                                                                                    |
-| - ``data``: ``numpy.array`` (or ``list``) with shape ``(n, m)``     |     from eomaps import Maps                                                        |
-| - ``x``: ``numpy.array`` (or ``list``) with shape ``(n,)``          |     import numpy as np                                                             |
-| - ``y``: ``numpy.array`` (or ``list``) with shape ``(m,)``          |                                                                                    |
-| - ``parameter``: (optional) parameter name (``string``)             |     x = np.linspace(10, 50, 100)                                                   |
-|                                                                     |     y = np.linspace(10, 50, 50)                                                    |
-|                                                                     |     data = np.random.normal(size=(100, 50))                                        |
-|                                                                     |                                                                                    |
-|                                                                     |     m = Maps()                                                                     |
-|                                                                     |     m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")              |
-|                                                                     |     m.plot_map()                                                                   |
-+---------------------------------------------------------------------+------------------------------------------------------------------------------------+
-
-
 .. _set_the_shape:
 
 2) Plot shapes
@@ -202,15 +340,15 @@ Available shapes (see bleow for details on each plot-shape!):
     The suggested "suitable datasizes" mentioned below always refer to the number of datapoints that are
     visible in the desired plot-extent.
 
-    .. currentmodule:: eomaps.shapes.Shapes
+    .. currentmodule:: eomaps.eomaps
 
-    For very large datasets, make sure to have a look at the :py:class:`raster`, :py:class:`shade_raster`, and :py:class:`shade_points` shapes
+    For very large datasets, make sure to have a look at the :py:class:`raster <Maps.set_shape.raster>`, :py:class:`shade_raster <Maps.set_shape.shade_raster>`, and :py:class:`shade_points <Maps.set_shape.shade_points>` shapes
     which use fast aggregation techniques to resample the data prior to plotting. This way datasets with billions of datapoints can be
     visualized fast.
 
 .. admonition:: Optional dependencies
 
-    :py:class:`shade_raster`, and :py:class:`shade_points` require the `datashader <https://datashader.org/>`_ package!
+    :py:class:`shade_raster <Maps.set_shape.shade_raster>`, and :py:class:`shade_points <Maps.set_shape.shade_points>` require the `datashader <https://datashader.org/>`_ package!
     You can install it via:
 
     .. code-block:: python
@@ -221,9 +359,9 @@ Available shapes (see bleow for details on each plot-shape!):
 
     By default, the plot-shape is assigned based on the associated dataset.
 
-    - For datasets with less than 500 000 pixels, ``m.set_shape.ellipses()`` is used.
-    - | For larger 2D datasets ``m.set_shape.raster()`` is used
-      | ... and ``m.set_shape.shade_points()`` is attempted to be used for the rest.
+    - For datasets with less than 500 000 pixels, :py:class:`ellipses <Maps.set_shape.ellipses>` is used.
+    - | For larger 2D datasets :py:class:`raster <m.set_shape.raster>` is used
+      | ... and :py:class:`shade_points <Maps.set_shape.shade_points` is attempted to be used for the rest.
 
 
 .. _shp_ellipses:
@@ -231,8 +369,10 @@ Available shapes (see bleow for details on each plot-shape!):
 Ellipses
 ********
 
-.. image:: _static/shape_imgs/ellipses.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
+
+    Maps.set_shape.ellipses
 
 .. list-table::
    :header-rows: 1
@@ -242,10 +382,8 @@ Ellipses
    * - up to ~500k datapoints
      - 1D, 2D or mixed
 
-.. autosummary::
-    :nosignatures:
-
-    ellipses
+.. image:: _static/shape_imgs/ellipses.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -259,8 +397,10 @@ Ellipses
 Rectangles
 **********
 
-.. image:: _static/shape_imgs/rectangles.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
+
+    Maps.set_shape.rectangles
 
 .. list-table::
    :header-rows: 1
@@ -270,11 +410,8 @@ Rectangles
    * - up to ~500k datapoints
      - 1D, 2D or mixed
 
-
-.. autosummary::
-    :nosignatures:
-
-    rectangles
+.. image:: _static/shape_imgs/rectangles.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -289,8 +426,10 @@ Rectangles
 Geodesic Circles
 ****************
 
-.. image:: _static/shape_imgs/geod_circles.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
+
+    Maps.set_shape.geod_circles
 
 .. list-table::
    :header-rows: 1
@@ -300,11 +439,8 @@ Geodesic Circles
    * - up to ~500k
      - 1D, 2D or mixed
 
-
-.. autosummary::
-    :nosignatures:
-
-    geod_circles
+.. image:: _static/shape_imgs/geod_circles.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -317,9 +453,10 @@ Geodesic Circles
 Voronoi Diagram
 ***************
 
-.. image:: _static/shape_imgs/voronoi_diagram.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
 
+    Maps.set_shape.voronoi_diagram
 
 .. list-table::
    :header-rows: 1
@@ -329,11 +466,8 @@ Voronoi Diagram
    * - up to ~500k datapoints
      - 1D, 2D or mixed
 
-
-.. autosummary::
-    :nosignatures:
-
-    voronoi_diagram
+.. image:: _static/shape_imgs/voronoi_diagram.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -347,11 +481,10 @@ Voronoi Diagram
 Delaunay Triangulation
 **********************
 
-.. image:: _static/shape_imgs/delaunay_triangulation.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
 
-.. image:: _static/shape_imgs/delaunay_triangulation_flat.png
-    :width: 50%
+    Maps.set_shape.delaunay_triangulation
 
 .. list-table::
    :header-rows: 1
@@ -361,10 +494,11 @@ Delaunay Triangulation
    * - up to ~500k datapoints
      - 1D, 2D or mixed
 
-.. autosummary::
-    :nosignatures:
+.. image:: _static/shape_imgs/delaunay_triangulation.png
+    :width: 50%
 
-    voronoi_diagram
+.. image:: _static/shape_imgs/delaunay_triangulation_flat.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -379,11 +513,10 @@ Delaunay Triangulation
 Contour plots
 *************
 
-.. image:: _static/shape_imgs/contour_filled.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
 
-.. image:: _static/shape_imgs/contour.png
-    :width: 50%
+    Maps.set_shape.contour
 
 .. list-table::
    :header-rows: 1
@@ -393,11 +526,11 @@ Contour plots
    * - up to a few million datapoints
      - 1D, 2D or mixed
 
+.. image:: _static/shape_imgs/contour_filled.png
+    :width: 50%
 
-.. autosummary::
-    :nosignatures:
-
-    contour
+.. image:: _static/shape_imgs/contour.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -408,8 +541,10 @@ Contour plots
 Scatter Points
 **************
 
-.. image:: _static/shape_imgs/scatter_points.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
+
+    Maps.set_shape.scatter_points
 
 .. list-table::
    :header-rows: 1
@@ -419,11 +554,8 @@ Scatter Points
    * - ~500k datapoints
      - 1D, 2D or mixed
 
-
-.. autosummary::
-    :nosignatures:
-
-    scatter_points
+.. image:: _static/shape_imgs/scatter_points.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -436,8 +568,10 @@ Scatter Points
 Raster
 ******
 
-.. image:: _static/shape_imgs/raster.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
+
+    Maps.set_shape.raster
 
 .. list-table::
    :header-rows: 1
@@ -447,11 +581,8 @@ Raster
    * - billions of datapoints (large datasets are pre-aggregated)
      - 2D or 1D coordinates + 2D data
 
-
-.. autosummary::
-    :nosignatures:
-
-    raster
+.. image:: _static/shape_imgs/raster.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -465,8 +596,10 @@ Raster
 Shade Raster
 ************
 
-.. image:: _static/shape_imgs/shade_raster.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
+
+    Maps.set_shape.shade_raster
 
 .. list-table::
    :header-rows: 1
@@ -478,11 +611,8 @@ Shade Raster
      - 2D or 1D coordinates + 2D data
      - `datashader <https://datashader.org/>`_
 
-
-.. autosummary::
-    :nosignatures:
-
-    shade_raster
+.. image:: _static/shape_imgs/shade_raster.png
+    :width: 50%
 
 .. code-block:: python
 
@@ -496,8 +626,10 @@ Shade Raster
 Shade Points
 ************
 
-.. image:: _static/shape_imgs/shade_points.png
-    :width: 50%
+.. autosummary::
+    :nosignatures:
+
+    Maps.set_shape.shade_raster
 
 .. list-table::
    :header-rows: 1
@@ -509,11 +641,8 @@ Shade Points
      - 1D, 2D or mixed
      - `datashader <https://datashader.org/>`_
 
-
-.. autosummary::
-    :nosignatures:
-
-    shade_raster
+.. image:: _static/shape_imgs/shade_points.png
+    :width: 50%
 
 .. code-block:: python
 

From 171fe4fbdb7a65016454f9924d49bcfe9e9f23df Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Sun, 10 Mar 2024 23:51:52 +0100
Subject: [PATCH 233/240] address warnings

---
 eomaps/helpers.py             | 5 +++++
 eomaps/utilities.py           | 9 ++++++++-
 tests/test_basic_functions.py | 2 +-
 3 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/eomaps/helpers.py b/eomaps/helpers.py
index bf14b395d..3be8f79df 100644
--- a/eomaps/helpers.py
+++ b/eomaps/helpers.py
@@ -20,6 +20,11 @@
 from matplotlib.transforms import Bbox, TransformedBbox
 from matplotlib.backend_bases import KeyEvent
 
+import importlib.metadata
+from packaging import version
+
+mpl_version = version.parse(importlib.metadata.version("matplotlib"))
+
 _log = logging.getLogger(__name__)
 
 
diff --git a/eomaps/utilities.py b/eomaps/utilities.py
index 36cbc65ce..cfb95098e 100644
--- a/eomaps/utilities.py
+++ b/eomaps/utilities.py
@@ -11,6 +11,9 @@
 from functools import wraps
 from matplotlib.pyplot import Artist, rcParams
 
+from packaging import version
+from .helpers import mpl_version
+
 
 class SelectorButtons(Artist):
     # A custom button implementation that uses a legend as container-artist
@@ -72,7 +75,11 @@ def __init__(
 
         self.leg = f.legend(circles, self.labels, **kwargs)
 
-        self.circles = self.leg.legendHandles
+        # TODO remove once support for matplotlib <3.7 is dropped
+        if mpl_version >= version.Version("3.7"):
+            self.circles = self.leg.legend_handles
+        else:
+            self.circles = self.leg.legendHandles
 
         for c in self.circles:
             c.set_picker(10)
diff --git a/tests/test_basic_functions.py b/tests/test_basic_functions.py
index b152f2bc2..c6419d3d9 100644
--- a/tests/test_basic_functions.py
+++ b/tests/test_basic_functions.py
@@ -595,7 +595,7 @@ def test_add_colorbar(self):
             out_of_range_vals="mask",
             hist_bins=5,
             extend_frac=0.4,
-            show_outline=dict(color="r", lw=4),
+            outline=dict(color="r", lw=4),
         )
         self.assertTrue(len(m._colorbars) == 5)
         self.assertTrue(m.colorbar is cb4)

From e359513eaf0154d3055c698ec9ab958d39b0d0f8 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Mar 2024 09:48:52 +0100
Subject: [PATCH 234/240] add ipywidgets and ipympl to top-level
 "environment.yml" (as they are used for development and required in the
 tests)

---
 environment.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/environment.yml b/environment.yml
index 34e993fdb..41e9fdcb6 100644
--- a/environment.yml
+++ b/environment.yml
@@ -28,6 +28,9 @@ dependencies:
   - requests
   # --------------to support multiple Qt versions
   - qtpy
+  # --------------for jupyter widgets
+  - ipywidgets
+  - ipympl
 
   ### To run the tests
   - coveralls

From ba323b9002f09864202ea11798d45d89ef44ca26 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Mar 2024 09:49:03 +0100
Subject: [PATCH 235/240] update version to 8.0rc2

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 00c0fae5b..24d7d4834 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ eomaps = ["logo.png", "NE_features.json", "qtcompanion/icons/*"]
 
 [project]
 name = "eomaps"
-version = "8.0rc1"
+version = "8.0rc2"
 description = "A library to create interactive maps of geographical datasets."
 readme = "README.md"
 license = {file = "LICENSE"}

From 2793b9879889f5ea311086e27898b345d1da5b2a Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Mar 2024 14:53:35 +0100
Subject: [PATCH 236/240] make sure encoding fill-value mask is always applied
 if provided

---
 eomaps/_data_manager.py | 12 ++++++++++++
 eomaps/reader.py        | 21 ---------------------
 2 files changed, 12 insertions(+), 21 deletions(-)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index 8c3c2f6c5..a0aafe5ba 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -413,6 +413,18 @@ def _prepare_data(self, assume_sorted=True):
         # identify the provided data and get it in the internal format
         z_data, xorig, yorig, ids, parameter = self._identify_data()
 
+        # check if Fill-value is provided, and mask the data accordingly
+        if self.m.data_specs.encoding:
+            fill_value = self.m.data_specs.encoding.get("_FillValue", None)
+            if fill_value:
+                z_data = np.ma.MaskedArray(
+                    data=z_data,
+                    mask=z_data == fill_value,
+                    copy=False,
+                    fill_value=fill_value,
+                    hard_mask=True,
+                )
+
         if cpos is not None and cpos != "c":
             # fix position of pixel-center in the input-crs
             assert (
diff --git a/eomaps/reader.py b/eomaps/reader.py
index 09c9a484a..8fc15a666 100644
--- a/eomaps/reader.py
+++ b/eomaps/reader.py
@@ -273,17 +273,6 @@ def GeoTIFF(
             # Using masked-arrays ensures that we can deal with integers as well!
             if mask_and_scale is False:
                 encoding = usencfile.attrs
-                fill_value = encoding.get("_FillValue", None)
-
-                if fill_value and fill_values == "mask":
-                    data = np.ma.MaskedArray(
-                        data=data,
-                        mask=data == fill_value,
-                        copy=False,
-                        fill_value=fill_value,
-                        hard_mask=True,
-                    )
-
             else:
                 encoding = None
 
@@ -555,16 +544,6 @@ def NetCDF(
                     add_offset=getattr(usencfile[parameter], "add_offset", 0),
                     _FillValue=getattr(usencfile[parameter], "_FillValue", None),
                 )
-                fill_value = encoding.get("_FillValue", None)
-                if fill_value and fill_values == "mask":
-                    data = np.ma.MaskedArray(
-                        data=data,
-                        mask=data == fill_value,
-                        copy=False,
-                        fill_value=fill_value,
-                        hard_mask=True,
-                    )
-
             else:
                 encoding = None
 

From 0bb838f05ec6647900b010bce856ad86f2a73fba Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Mon, 11 Mar 2024 14:53:50 +0100
Subject: [PATCH 237/240] fix transposing xarray data if required

---
 eomaps/_data_manager.py | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/eomaps/_data_manager.py b/eomaps/_data_manager.py
index a0aafe5ba..dbdf9afb9 100644
--- a/eomaps/_data_manager.py
+++ b/eomaps/_data_manager.py
@@ -244,6 +244,7 @@ def _identify_xarray(self, data=None, x=None, y=None, parameter=None):
                 f"({data[parameter].dims})."
             )
             z_data = data[parameter].values
+            data_dims = data[parameter].dims
         else:
             assert len(data.dims) <= 2, (
                 "EOmaps: provided dataset has more than 2 dimensions..."
@@ -251,25 +252,26 @@ def _identify_xarray(self, data=None, x=None, y=None, parameter=None):
             )
 
             z_data = data.values
+            data_dims = data.dims
             parameter = data.name
 
         # use numeric index values
         ids = range(z_data.size)
 
-        if isinstance(x, str) and isinstance(y, str):
+        if isinstance(x, str) and isinstance(y, str):  #
             coords = list(data.coords)
             if (x in coords) and (y in coords):
                 # get the coordinates from coordinates
                 xorig = data.coords[x].values
                 yorig = data.coords[y].values
-                # transpose dat in case x is before y
-                # (to account for matrix indexing order)
-                if coords.index(x) > coords.index(y):
-                    z_data = z_data.T
+                x_dims = data.coords[x].dims
+                y_dims = data.coords[y].dims
 
             elif (x in data) and (y in data):
                 xorig = data[x].values
                 yorig = data[y].values
+                x_dims = data[x].dims
+                y_dims = data[y].dims
 
         elif (x is None) and (y is None):
             coords = list(data.coords)
@@ -278,12 +280,26 @@ def _identify_xarray(self, data=None, x=None, y=None, parameter=None):
                 # get the coordinates from index-values
                 xorig = data[coords[0]].values
                 yorig = data[coords[1]].values
+                x_dims = data[coords[0]].dims
+                y_dims = data[coords[1]].dims
             else:
                 raise TypeError(
                     "EOmaps: Either specify explicit coordinate-names to use "
                     "for `x` and `y` or pass a Dataset with exactly 2 "
                     "coordinates!"
                 )
+
+        # transpose dat in case data is transposed
+        # (to account for matrix indexing order)
+        # TODO make this prperly
+        if not (data_dims == x_dims and data_dims == y_dims):
+            if len(x_dims) == 2 and len(y_dims) == 2:
+                if data_dims == x_dims[::-1]:
+                    z_data = z_data.T
+            elif len(x_dims) == 1 and len(y_dims) == 1:
+                if data_dims.index(x_dims[0]) > data_dims.index(y_dims[0]):
+                    z_data = z_data.T
+
         return z_data, xorig, yorig, ids, parameter
 
     def _identify_array_like(self, data=None, x=None, y=None, parameter=None):

From a16412555173175e093ff2df0a14a219b107333b Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 15 Mar 2024 14:24:59 +0100
Subject: [PATCH 238/240] use numpy implementation for 1D/2D conversion with
 raster shape

---
 eomaps/eomaps.py | 80 +++++++++++++++++++++++++++++++++---------------
 1 file changed, 55 insertions(+), 25 deletions(-)

diff --git a/eomaps/eomaps.py b/eomaps/eomaps.py
index 8d8ecae23..a8170ac52 100644
--- a/eomaps/eomaps.py
+++ b/eomaps/eomaps.py
@@ -4609,6 +4609,53 @@ def _handle_explicit_colors(self, color):
 
         return color
 
+    @staticmethod
+    def _convert_1d_to_2d(data, x, y, fill_value=np.nan):
+        """A function to convert 1D vectors + data into 2D."""
+
+        if _log.getEffectiveLevel() <= logging.DEBUG:
+            _log.debug(
+                "EOmaps: Required conversion of 1D arrays to 2D for 'raster'"
+                "shape might be slow and consume a lot of memory!"
+            )
+
+        x, y, data = map(np.asanyarray, (x, y, data))
+        assert (
+            x.size == y.size == data.size
+        ), "EOmaps: You cannot use 1D arrays with different sizes for x, y and data"
+
+        x_vals, x_idx = np.unique(x, return_inverse=True)
+        y_vals, y_idx = np.unique(y, return_inverse=True)
+        # Get output array shape
+        m, n = (x_vals.size, y_vals.size)
+
+        # Get linear indices to be used as IDs with bincount
+        lidx = np.ravel_multi_index(np.vstack((x_idx, y_idx)), (m, n))
+        idx2d = np.unravel_index(lidx, (m, n))
+
+        # Distribute data to 2D
+
+        if not np.issubdtype(data.dtype, np.integer):
+            # Integer-dtypes do not support None!
+            data2d = np.full((m, n), fill_value=fill_value, dtype=data.dtype)
+            data2d[idx2d] = data
+
+        else:
+            # use smallest possible value as fill-value
+            fill_value = np.iinfo(data.dtype).min
+            data2d = np.full((m, n), fill_value=fill_value, dtype=data.dtype)
+            data2d[idx2d] = data
+
+            mask2d = np.full((m, n), fill_value=True, dtype=bool)
+            mask2d[idx2d] = False
+
+            data2d = np.ma.masked_array(data2d, mask2d)
+
+        # Distribute coordinates to 2D
+        x_vals, y_vals = np.meshgrid(x_vals, y_vals, indexing="ij")
+
+        return data2d, x_vals, y_vals
+
     def _get_coll(self, props, **kwargs):
         # handle selection of explicitly provided facecolors
         # (e.g. for rgb composites)
@@ -4651,34 +4698,17 @@ def _get_coll(self, props, **kwargs):
             if len(self._xshape) == 2 and len(self._yshape) == 2:
                 coll = self.shape.get_coll(props["xorig"], props["yorig"], "in", **args)
             else:
-                (pd,) = register_modules("pandas")
-                # TODO avoid having pandas as a dependency here
-                if pd:
-                    if (
-                        (len(self._xshape) == 1)
-                        and (len(self._yshape) == 1)
-                        and (len(self._zshape) == 1)
-                        and (props["x0"].size == props["y0"].size)
-                        and (props["x0"].size == props["z_data"].size)
-                    ):
-
-                        df = (
-                            pd.DataFrame(
-                                dict(
-                                    x=props["x0"].ravel(),
-                                    y=props["y0"].ravel(),
-                                    val=props["z_data"].ravel(),
-                                ),
-                                copy=False,
-                            ).set_index(["x", "y"])
-                        )["val"].unstack("y")
+                data2d, x2d, y2d = self._convert_1d_to_2d(
+                    data=props["z_data"].ravel(),
+                    x=props["x0"].ravel(),
+                    y=props["y0"].ravel(),
+                )
 
-                        xg, yg = np.meshgrid(df.index.values, df.columns.values)
+                if args["array"] is not None:
+                    args["array"] = data2d
 
-                        if args["array"] is not None:
-                            args["array"] = df.values.T
+                coll = self.shape.get_coll(x2d, y2d, "out", **args)
 
-                        coll = self.shape.get_coll(xg, yg, "out", **args)
         else:
             # convert to 1D for further processing
             if args["array"] is not None:

From 173eddb4a7a1efbdb418b97912fdafe959b179f0 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 15 Mar 2024 15:25:26 +0100
Subject: [PATCH 239/240] update docs

---
 docs/FAQ.rst              |  2 +
 docs/installation.md      | 84 +++++++++++++++++++++++++++++++++
 docs/installation.rst     | 98 ---------------------------------------
 docs/quickstart_quide.rst |  6 +--
 4 files changed, 89 insertions(+), 101 deletions(-)
 create mode 100644 docs/installation.md
 delete mode 100644 docs/installation.rst

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index ec2274e8a..7ef44974b 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -1,3 +1,5 @@
+.. _faq:
+
 👀 FAQ
 =======
 
diff --git a/docs/installation.md b/docs/installation.md
new file mode 100644
index 000000000..510e4494f
--- /dev/null
+++ b/docs/installation.md
@@ -0,0 +1,84 @@
+(installation)=
+# 🐛 Installation
+
+```{contents} Contents:
+:local:
+:depth: 1
+```
+
+The following sections provide information how to install **EOmaps**.
+
+- A quick tutorial on how to **get started from scratch** is available here: {ref}`quickstart_guide`
+- More details on how to **configure your favorite IDE** to get the most out of **EOmaps** can be found in the {ref}`faq` section {ref}`configuring_the_editor`.
+- If you want to know how to setup **EOmaps** for development, have a look at the {ref}`contribute`
+
+
+## Installation with ``conda`` or ``mamba``
+
+EOmaps is available on [conda-forge](https://anaconda.org/conda-forge/eomaps) and can be installed via:
+
+```
+conda install -c conda-forge eomaps
+```
+
+This will install all required and optional dependencies.
+
+
+:::{dropdown} Greatly speed up the installation!
+:color: info
+:icon: info
+:open:
+:margin: 3
+
+Since the dependencies of EOmaps can be demanding to solve for ``conda``, it is **highly recommended**
+that you use [mamba](https://github.com/mamba-org/mamba) to install EOmaps in a conda-environment!
+
+[mamba](https://github.com/mamba-org/mamba) is a reimplementation of the conda package manager in C++, capable of solving environments a lot faster.
+
+The recommended way to get started is to use [miniforge](https://github.com/conda-forge/miniforge), a minimalistic installer
+that provides both ``conda`` and ``mamba``, pre-configured to use the ``conda-forge`` channel by default.
+For other options, checkout the [mamba-docs](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)
+
+Once ``mamba`` is installed, you just need to replace the term ``conda`` with ``mamba`` and you're good to go!
+
+```
+mamba install -c conda-forge eomaps
+```
+
+:::
+
+
+## Installation with ``pip``
+
+
+EOmaps is also available on [pypi](https://pypi.org/project/eomaps/).
+
+To install EOmaps with a **minimal set of dependencies**, use:
+
+```
+pip install eomaps
+```
+
+### Optional dependencies
+
+
+Some features ({ref}`webmap_layers`, {ref}`companion_widget`, etc.) require additional dependencies.
+To use them you have to install the required dependency-groups:
+
+To get **all features of EOmaps**, you can use one of:
+
+- ``pip install eomaps[all]`` Install **ALL** requuired and optional dependencies
+- ``pip install eomaps[all_nogui]`` Same as ``all`` but without installing the ``Qt`` GUI framework
+
+
+In addition, you can use the following dependency-groups to activate only selected features:
+
+- ``pip install eomaps[wms]`` Add dependencies required to use {ref}`WebMap services <webmap_layers>`
+- ``pip install eomaps[gui]`` Add dependencies for ``Qt`` GUI framework (and the {ref}`CompanionWidget <companion_widget>`)
+- ``pip install eomaps[io]`` Add support for ``pandas``, ``xarray``, ``geopandas`` and ``rioxarray``
+- ``pip install eomaps[shade]`` Add capabilities to visualize extremely large datasets (via ``datashader``)
+- ``pip install eomaps[classify]`` Add support for ``mapclassify`` to classify datasets
+
+It is also possible to combine dependency-groups, e.g.: ``pip install eomaps[wms, gui]``.
+
+A full list of all associated packages can be found in {ref}`setup_a_dev_env` or in the ``pyproject.toml`` file.
diff --git a/docs/installation.rst b/docs/installation.rst
deleted file mode 100644
index f04040b94..000000000
--- a/docs/installation.rst
+++ /dev/null
@@ -1,98 +0,0 @@
-.. _installation:
-
-
-🐛 Installation
-================
-
-.. contents:: Contents:
-    :local:
-    :depth: 1
-
-
-Via ``conda`` and ``mamba``
----------------------------
-
-EOmaps is available via the ``conda-forge`` channel and can be installed via:
-
-  .. code-block:: console
-
-    conda install -c conda-forge eomaps
-
-
-This will install all required and optional dependencies.
-
-
-.. admonition:: Greatly speed up the installation!
-
-    Since the dependencies of EOmaps can be demanding to solve for ``conda``, it is **highly recommended**
-    that you use `mamba <https://github.com/mamba-org/mamba>`_ to install EOmaps!
-
-    ``mamba`` is a reimplementation of the conda package manager in C++, capable of solving environments a lot faster.
-
-    The best way to get started is to use `miniforge <https://github.com/conda-forge/miniforge>`_.
-
-    However, you can also install ``mamba`` into an existing ``conda`` environment with:
-
-    .. code-block:: console
-
-        conda install -c conda-forge mamba
-
-
-    Once ``mamba`` is installed, you just need to replace the term ``conda`` with ``mamba`` and you're good to go!
-
-    .. code-block:: console
-
-        mamba install -c conda-forge eomaps
-
-
-    Alternatively you can also configure ``conda`` to use the ``libmamba`` solver by default.
-    (More info here: `A Faster Solver for Conda: Libmamba <https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community>`_  )
-
-
-A quick tutorial on how to **get started from scratch** is available here: :ref:`quickstart_guide`
-
-More details on how to **configure your favorite IDE** to work with EOmaps can be found in the FAQ section
-:ref:`configuring_the_editor`.
-
-
-
-Via ``pip``
------------
-
-EOmaps is also available on ``pip``.
-
-To install EOmaps with a **minimal set of dependencies**, use:
-
-  .. code-block:: console
-
-    pip install eomaps
-
-
-Optional dependencies
-~~~~~~~~~~~~~~~~~~~~~
-
-Some features (:ref:`webmap_layers`, :ref:`companion_widget`, etc.) require additional dependencies.
-To use them you have to install the required dependency-groups:
-
-To get all features of EOmaps, you can use one of:
-
-.. code-block:: console
-
-    pip install eomaps[all]       # ALL optional dependencies
-    pip install eomaps[all_nogui] # All optional dependencies (except ``Qt`` GUI framework)
-
-
-In addition, you can use the following dependency-groups to activate only selected features:
-
-.. code-block:: console
-
-    pip install eomaps[wms]       # dependencies required for WebMap services
-    pip install eomaps[gui]       # dependencies for ``Qt`` GUI framework and the CompanionWidget
-    pip install eomaps[io]        # add support for ``pandas``, ``xarray``, ``geopandas`` and ``rioxarray``
-    pip install eomaps[shade]     # add capabilities to visualize extremely large datasets (via ``datashader``)
-    pip install eomaps[classify]  # add support for ``mapclassify`` to classify datasets
-
-
-(It is also possible to combine dependency-groups, e.g.: ``pip install eomaps[wms, gui]``)
-
-A list of all associated packages can be found in :ref:`setup_a_dev_env` or in the ``pyproject.toml`` file.
diff --git a/docs/quickstart_quide.rst b/docs/quickstart_quide.rst
index 0d0c89166..2cebbea9d 100644
--- a/docs/quickstart_quide.rst
+++ b/docs/quickstart_quide.rst
@@ -1,7 +1,7 @@
 
 .. _quickstart_guide:
 
-🚀 From 0 to EOmaps - a quickstart guide
+🚲 From 0 to EOmaps - a quickstart guide
 *****************************************
 
 The following section is intended to provide a quick overview on how to get started using ``EOmaps``.
@@ -27,8 +27,8 @@ Links to websites that provide additional information are provided throughout th
 - basic python knowledge
 
 
-🐍 Getting started - set up a python environment
-------------------------------------------------
+Getting started - set up a python environment
+---------------------------------------------
 
 There are of course many ways to set up a python environment... in the following, we will use the (free and open-source) ``conda``
 package manager which greatly simplifies the creation of environments that depend on both python and c++ libraries

From b68c7df17df29cdca1d84a961722ad367520f8ba Mon Sep 17 00:00:00 2001
From: Raphael <raphael.quast@geo.tuwien.ac.at>
Date: Fri, 15 Mar 2024 15:32:42 +0100
Subject: [PATCH 240/240] update version to v8.0.1

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 24d7d4834..5e8e5397d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ eomaps = ["logo.png", "NE_features.json", "qtcompanion/icons/*"]
 
 [project]
 name = "eomaps"
-version = "8.0rc2"
+version = "8.0.1"
 description = "A library to create interactive maps of geographical datasets."
 readme = "README.md"
 license = {file = "LICENSE"}