diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml
new file mode 100644
index 0000000..5f3be16
--- /dev/null
+++ b/.github/workflows/documentation.yml
@@ -0,0 +1,28 @@
+name: documentation
+
+on: [push, pull_request, workflow_dispatch]
+
+permissions:
+  contents: write
+
+jobs:
+  build_docs:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          lfs: false
+      - uses: prefix-dev/setup-pixi@v0.8.1
+        with:
+          pixi-version: latest
+          cache: true
+          locked: false
+          frozen: false
+          environments: doc
+      - run: pixi run -e doc build_docs
+      - uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./doc/build/html
+          publish_branch: 'gh-pages'
+          force_orphan: 'false'
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 340e55b..169b965 100644
--- a/.gitignore
+++ b/.gitignore
@@ -40,3 +40,4 @@ dist
 .pixi
 *.egg-info
 *.lcov
+/doc/build
diff --git a/README.md b/README.md
index f1bfda0..326c2f7 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ PyRASA is a Python library designed to separate and parametrize aperiodic (fract
 
 
 ## Documentation
-Documentation for PyRASA, including detailed descriptions of functions, parameters, and examples, will soon be available [here].
+Documentation for PyRASA, including detailed descriptions of functions, parameters, and tutorials is available [here](https://schmidtfa.github.io/pyrasa/index.html).
 
 
 ### Installation
diff --git a/doc/Makefile b/doc/Makefile
new file mode 100644
index 0000000..d0c3cbf
--- /dev/null
+++ b/doc/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/doc/make.bat b/doc/make.bat
new file mode 100644
index 0000000..dc1312a
--- /dev/null
+++ b/doc/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%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.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/doc/source/_autoexamples/pyrasa.irasa.irasa.rst b/doc/source/_autoexamples/pyrasa.irasa.irasa.rst
new file mode 100644
index 0000000..158ee6a
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.irasa.irasa.rst
@@ -0,0 +1,11 @@
+pyrasa.irasa.irasa
+==================
+
+.. currentmodule:: pyrasa.irasa
+
+.. autofunction:: irasa
+
+.. _sphx_glr_backreferences_pyrasa.irasa.irasa:
+
+.. minigallery:: pyrasa.irasa.irasa
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.irasa.irasa_sprint.rst b/doc/source/_autoexamples/pyrasa.irasa.irasa_sprint.rst
new file mode 100644
index 0000000..baececa
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.irasa.irasa_sprint.rst
@@ -0,0 +1,11 @@
+pyrasa.irasa.irasa\_sprint
+==========================
+
+.. currentmodule:: pyrasa.irasa
+
+.. autofunction:: irasa_sprint
+
+.. _sphx_glr_backreferences_pyrasa.irasa.irasa_sprint:
+
+.. minigallery:: pyrasa.irasa.irasa_sprint
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.irasa_mne.irasa_epochs.rst b/doc/source/_autoexamples/pyrasa.irasa_mne.irasa_epochs.rst
new file mode 100644
index 0000000..89afae7
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.irasa_mne.irasa_epochs.rst
@@ -0,0 +1,11 @@
+pyrasa.irasa\_mne.irasa\_epochs
+===============================
+
+.. currentmodule:: pyrasa.irasa_mne
+
+.. autofunction:: irasa_epochs
+
+.. _sphx_glr_backreferences_pyrasa.irasa_mne.irasa_epochs:
+
+.. minigallery:: pyrasa.irasa_mne.irasa_epochs
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.irasa_mne.irasa_raw.rst b/doc/source/_autoexamples/pyrasa.irasa_mne.irasa_raw.rst
new file mode 100644
index 0000000..ac93487
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.irasa_mne.irasa_raw.rst
@@ -0,0 +1,11 @@
+pyrasa.irasa\_mne.irasa\_raw
+============================
+
+.. currentmodule:: pyrasa.irasa_mne
+
+.. autofunction:: irasa_raw
+
+.. _sphx_glr_backreferences_pyrasa.irasa_mne.irasa_raw:
+
+.. minigallery:: pyrasa.irasa_mne.irasa_raw
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.aperiodic_utils.compute_aperiodic_model.rst b/doc/source/_autoexamples/pyrasa.utils.aperiodic_utils.compute_aperiodic_model.rst
new file mode 100644
index 0000000..49d3564
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.aperiodic_utils.compute_aperiodic_model.rst
@@ -0,0 +1,11 @@
+pyrasa.utils.aperiodic\_utils.compute\_aperiodic\_model
+=======================================================
+
+.. currentmodule:: pyrasa.utils.aperiodic_utils
+
+.. autofunction:: compute_aperiodic_model
+
+.. _sphx_glr_backreferences_pyrasa.utils.aperiodic_utils.compute_aperiodic_model:
+
+.. minigallery:: pyrasa.utils.aperiodic_utils.compute_aperiodic_model
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.aperiodic_utils.compute_aperiodic_model_sprint.rst b/doc/source/_autoexamples/pyrasa.utils.aperiodic_utils.compute_aperiodic_model_sprint.rst
new file mode 100644
index 0000000..a405e41
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.aperiodic_utils.compute_aperiodic_model_sprint.rst
@@ -0,0 +1,11 @@
+pyrasa.utils.aperiodic\_utils.compute\_aperiodic\_model\_sprint
+===============================================================
+
+.. currentmodule:: pyrasa.utils.aperiodic_utils
+
+.. autofunction:: compute_aperiodic_model_sprint
+
+.. _sphx_glr_backreferences_pyrasa.utils.aperiodic_utils.compute_aperiodic_model_sprint:
+
+.. minigallery:: pyrasa.utils.aperiodic_utils.compute_aperiodic_model_sprint
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.fit_funcs.AbstractFitFun.rst b/doc/source/_autoexamples/pyrasa.utils.fit_funcs.AbstractFitFun.rst
new file mode 100644
index 0000000..b390318
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.fit_funcs.AbstractFitFun.rst
@@ -0,0 +1,13 @@
+pyrasa.utils.fit\_funcs.AbstractFitFun
+======================================
+
+.. currentmodule:: pyrasa.utils.fit_funcs
+
+.. autoclass:: AbstractFitFun
+   :special-members: __contains__,__getitem__,__iter__,__len__,__add__,__sub__,__mul__,__div__,__neg__,__hash__
+   :members:
+
+.. _sphx_glr_backreferences_pyrasa.utils.fit_funcs.AbstractFitFun:
+
+.. minigallery:: pyrasa.utils.fit_funcs.AbstractFitFun
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.fit_funcs.FixedFitFun.rst b/doc/source/_autoexamples/pyrasa.utils.fit_funcs.FixedFitFun.rst
new file mode 100644
index 0000000..1034172
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.fit_funcs.FixedFitFun.rst
@@ -0,0 +1,13 @@
+pyrasa.utils.fit\_funcs.FixedFitFun
+===================================
+
+.. currentmodule:: pyrasa.utils.fit_funcs
+
+.. autoclass:: FixedFitFun
+   :special-members: __contains__,__getitem__,__iter__,__len__,__add__,__sub__,__mul__,__div__,__neg__,__hash__
+   :members:
+
+.. _sphx_glr_backreferences_pyrasa.utils.fit_funcs.FixedFitFun:
+
+.. minigallery:: pyrasa.utils.fit_funcs.FixedFitFun
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.fit_funcs.KneeFitFun.rst b/doc/source/_autoexamples/pyrasa.utils.fit_funcs.KneeFitFun.rst
new file mode 100644
index 0000000..2a4e61c
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.fit_funcs.KneeFitFun.rst
@@ -0,0 +1,13 @@
+pyrasa.utils.fit\_funcs.KneeFitFun
+==================================
+
+.. currentmodule:: pyrasa.utils.fit_funcs
+
+.. autoclass:: KneeFitFun
+   :special-members: __contains__,__getitem__,__iter__,__len__,__add__,__sub__,__mul__,__div__,__neg__,__hash__
+   :members:
+
+.. _sphx_glr_backreferences_pyrasa.utils.fit_funcs.KneeFitFun:
+
+.. minigallery:: pyrasa.utils.fit_funcs.KneeFitFun
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_band_info.rst b/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_band_info.rst
new file mode 100644
index 0000000..99949cb
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_band_info.rst
@@ -0,0 +1,11 @@
+pyrasa.utils.peak\_utils.get\_band\_info
+========================================
+
+.. currentmodule:: pyrasa.utils.peak_utils
+
+.. autofunction:: get_band_info
+
+.. _sphx_glr_backreferences_pyrasa.utils.peak_utils.get_band_info:
+
+.. minigallery:: pyrasa.utils.peak_utils.get_band_info
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_peak_params.rst b/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_peak_params.rst
new file mode 100644
index 0000000..059b508
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_peak_params.rst
@@ -0,0 +1,11 @@
+pyrasa.utils.peak\_utils.get\_peak\_params
+==========================================
+
+.. currentmodule:: pyrasa.utils.peak_utils
+
+.. autofunction:: get_peak_params
+
+.. _sphx_glr_backreferences_pyrasa.utils.peak_utils.get_peak_params:
+
+.. minigallery:: pyrasa.utils.peak_utils.get_peak_params
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_peak_params_sprint.rst b/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_peak_params_sprint.rst
new file mode 100644
index 0000000..55e7fde
--- /dev/null
+++ b/doc/source/_autoexamples/pyrasa.utils.peak_utils.get_peak_params_sprint.rst
@@ -0,0 +1,11 @@
+pyrasa.utils.peak\_utils.get\_peak\_params\_sprint
+==================================================
+
+.. currentmodule:: pyrasa.utils.peak_utils
+
+.. autofunction:: get_peak_params_sprint
+
+.. _sphx_glr_backreferences_pyrasa.utils.peak_utils.get_peak_params_sprint:
+
+.. minigallery:: pyrasa.utils.peak_utils.get_peak_params_sprint
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_static/style.css b/doc/source/_static/style.css
new file mode 100644
index 0000000..9bfeee2
--- /dev/null
+++ b/doc/source/_static/style.css
@@ -0,0 +1,33 @@
+a[class^="sphx-glr-backref-module-mne_bids"] {
+    /* make all MNE-BIDS backrefs bold */
+    font-weight: 800;
+  }
+  
+  span.option {
+    /* avoid breaking lines in our command-line parameters */
+    white-space: nowrap;
+  }
+  
+  /* ************************************************* Previous / Next buttons */
+  .prev-next-bottom a.left-prev:before {
+    content:"❮\00A0"
+  }
+  .prev-next-bottom a.right-next:after {
+    content:"\00A0❯"
+  }
+  .prev-next-bottom a.right-next {
+    text-align: right;
+  }
+  
+  /* ************************************************* truncate version string */
+  div.navbar-item:first-child {
+    overflow-x: hidden;
+  }
+  a.navbar-brand.logo {
+    flex-shrink: 1;
+    overflow-x: hidden;
+  }
+  p.logo__title {
+    overflow-x: hidden;
+    text-overflow: ellipsis;
+  }
\ No newline at end of file
diff --git a/doc/source/_templates/autosummary/class.rst b/doc/source/_templates/autosummary/class.rst
new file mode 100644
index 0000000..da36f79
--- /dev/null
+++ b/doc/source/_templates/autosummary/class.rst
@@ -0,0 +1,12 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :special-members: __contains__,__getitem__,__iter__,__len__,__add__,__sub__,__mul__,__div__,__neg__,__hash__
+   :members:
+
+.. _sphx_glr_backreferences_{{ fullname }}:
+
+.. minigallery:: {{ fullname }}
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_templates/autosummary/function.rst b/doc/source/_templates/autosummary/function.rst
new file mode 100644
index 0000000..5b2a51c
--- /dev/null
+++ b/doc/source/_templates/autosummary/function.rst
@@ -0,0 +1,10 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autofunction:: {{ objname }}
+
+.. _sphx_glr_backreferences_{{ fullname }}:
+
+.. minigallery:: {{ fullname }}
+    :add-heading:
\ No newline at end of file
diff --git a/doc/source/_templates/layout.html b/doc/source/_templates/layout.html
new file mode 100644
index 0000000..2d629e1
--- /dev/null
+++ b/doc/source/_templates/layout.html
@@ -0,0 +1,18 @@
+{%- extends "pydata_sphinx_theme/layout.html" %}
+
+{% block fonts %}
+    <!-- add `style` or `link` tags with your CSS `@font-face` declarations here -->
+    <!-- ... and a `style` tag with setting `font-family` in `body` and `.header-style` -->
+    <!-- ... and optionally preload the `woff2` for snappier page loads -->
+    <!-- or add a `style` tag with a font fallback chain with good cross-platform coverage -->
+    <style>
+        body,.header-style {font-family: 'Source Sans Pro', sans-serif;}
+        code,kbd,pre,samp {font-family: 'Source Code Pro', monospace;}
+    </style>
+{% endblock %}
+
+{% block extrahead %}
+    <script type="text/javascript" src="{{ pathto('_static/copybutton.js', 1) }}"></script>
+    <script type="text/javascript" src="{{ pathto('_static/scrollfix.js', 1) }}"></script>
+{{ super() }}
+{% endblock %}
\ No newline at end of file
diff --git a/doc/source/api.rst b/doc/source/api.rst
new file mode 100644
index 0000000..246012e
--- /dev/null
+++ b/doc/source/api.rst
@@ -0,0 +1,92 @@
+:orphan:
+
+.. _api_documentation:
+
+=================
+API Reference
+=================
+
+IRASA
+-----
+:py:mod:`pyrasa.irasa`
+
+.. automodule:: pyrasa.irasa
+   :no-members:
+   :no-inherited-members:
+
+.. currentmodule:: pyrasa.irasa
+
+.. autosummary::
+   :toctree: _autoexamples/
+
+   irasa
+   irasa_sprint
+
+IRASA MNE
+---------
+:py:mod:`pyrasa.irasa_mne`
+
+.. automodule:: pyrasa.irasa_mne
+   :no-members:
+   :no-inherited-members:
+
+.. currentmodule:: pyrasa.irasa_mne
+
+.. autosummary::
+   :toctree: _autoexamples/
+
+   irasa_raw
+   irasa_epochs
+
+
+Aperiodic Utilities
+-------------------
+:py:mod:`pyrasa.utils.aperiodic_utils`
+
+.. automodule:: pyrasa.utils.aperiodic_utils
+   :no-members:
+   :no-inherited-members:
+
+.. currentmodule:: pyrasa.utils.aperiodic_utils
+
+.. autosummary::
+   :toctree: _autoexamples/
+
+   compute_aperiodic_model
+   compute_aperiodic_model_sprint
+
+
+Aperiodic Model Fitting
+-----------------------
+:py:mod:`pyrasa.utils.fit_funcs`
+
+.. automodule:: pyrasa.utils.fit_funcs
+   :no-members:
+   :no-inherited-members:
+
+.. currentmodule:: pyrasa.utils.fit_funcs
+
+.. autosummary::
+   :toctree: _autoexamples/
+
+   AbstractFitFun
+   FixedFitFun
+   KneeFitFun
+
+   
+Periodic Feature Extraction
+---------------------------
+:py:mod:`pyrasa.utils.peak_utils`
+
+.. automodule:: pyrasa.utils.peak_utils
+   :no-members:
+   :no-inherited-members:
+
+.. currentmodule:: pyrasa.utils.peak_utils
+
+.. autosummary::
+   :toctree: _autoexamples/
+
+   get_peak_params
+   get_peak_params_sprint
+   get_band_info
\ No newline at end of file
diff --git a/doc/source/auto_examples/auto_examples_python.zip b/doc/source/auto_examples/auto_examples_python.zip
new file mode 100644
index 0000000..15cb0ec
Binary files /dev/null and b/doc/source/auto_examples/auto_examples_python.zip differ
diff --git a/doc/source/auto_examples/index.rst b/doc/source/auto_examples/index.rst
new file mode 100644
index 0000000..d97b805
--- /dev/null
+++ b/doc/source/auto_examples/index.rst
@@ -0,0 +1,32 @@
+:orphan:
+
+## Hello PyRasa Gallery
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-gallery
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/doc/source/auto_examples/searchindex.db b/doc/source/auto_examples/searchindex.db
new file mode 100644
index 0000000..0bae6c5
Binary files /dev/null and b/doc/source/auto_examples/searchindex.db differ
diff --git a/doc/source/auto_examples/sg_execution_times.rst b/doc/source/auto_examples/sg_execution_times.rst
new file mode 100644
index 0000000..ccb2da1
--- /dev/null
+++ b/doc/source/auto_examples/sg_execution_times.rst
@@ -0,0 +1,37 @@
+
+:orphan:
+
+.. _sphx_glr_auto_examples_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.000** total execution time for 0 files **from auto_examples**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - N/A
+     - N/A
+     - N/A
diff --git a/doc/source/conf.py b/doc/source/conf.py
new file mode 100644
index 0000000..ed08a5b
--- /dev/null
+++ b/doc/source/conf.py
@@ -0,0 +1,131 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+
+from sphinx.config import is_serializable
+
+import pyrasa
+
+# import pyrasa.irasa_mne
+
+project = 'PyRASA'
+copyright = '2024, Fabian Schmidt, Thomas Hartmann'
+author = 'Fabian Schmidt, Thomas Hartmann'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+
+extensions = [
+    'sphinx.ext.githubpages',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.napoleon',
+    'numpydoc',
+    'sphinx_gallery.gen_gallery',
+    'sphinx.ext.mathjax',  # optional, if you need to render math
+    'sphinx.ext.viewcode',
+    'nbsphinx',
+]
+
+
+numpydoc_xref_param_type = True
+numpydoc_class_members_toctree = False
+numpydoc_attributes_as_param_list = True
+numpydoc_xref_aliases = {
+    'array-like': ':term:`array_like <numpy:array_like>`',
+    'int': ':class:`int <python:int>`',
+    'bool': ':class:`bool <python:bool>`',
+    'float': ':class:`float <python:float>`',
+    'list': ':class:`list <python:list>`',
+    'tuple': ':class:`tuple <python:tuple>`',
+}
+numpydoc_xref_ignore = {
+    # words
+    'instance',
+    'instances',
+    'of',
+}
+
+
+# generate autosummary even if no references
+autosummary_generate = True
+autodoc_default_options = {'inherited-members': None}
+default_role = 'autolink'  # XXX silently allows bad syntax, someone should fix
+
+exclude_patterns = ['auto_examples/index.rst', '_build', 'Thumbs.db', '.DS_Store', 'generated']
+
+html_show_sourcelink = False
+html_copy_source = False
+
+html_theme = 'pydata_sphinx_theme'
+
+templates_path = ['_templates']
+html_static_path = ['_static']
+# html_css_files = ["style.css"]
+
+source_suffix = ['.rst', '.md']
+# master_doc = "index"
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = 'dev' if 'dev' in pyrasa.__version__ else pyrasa.__version__
+# The full version, including alpha/beta/rc tags.
+release = version
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+
+html_theme_options = {
+    #'navbar_sidebarrel': False,
+    'icon_links': [
+        dict(
+            name='GitHub',
+            url='https://github.com/schmidtfa/pyrasa',
+            icon='fab fa-github-square',
+        ),
+    ],
+    'icon_links_label': 'Quick Links',  # for screen reader
+    'use_edit_page_button': False,
+    'navigation_with_keys': False,
+    'show_toc_level': 1,
+    'header_links_before_dropdown': 6,
+    'navbar_end': ['theme-switcher', 'navbar-icon-links'],
+}
+
+html_context = {
+    'default_mode': 'auto',
+    'doc_path': 'doc',
+}
+
+html_sidebars = {}
+
+
+html_short_title = 'PyRASA'
+
+
+sphinx_gallery_conf = {
+    'doc_module': 'pyrasa',
+    'reference_url': {
+        'pyrasa': None,
+    },
+    'backreferences_dir': 'generated',
+    'examples_dirs': 'examples',
+    'within_subsection_order': 'ExampleTitleSortKey',
+    'gallery_dirs': 'auto_examples',
+    'filename_pattern': '^((?!sgskip).)*$',
+}
+
+assert is_serializable(sphinx_gallery_conf)
diff --git a/doc/source/examples/README.md b/doc/source/examples/README.md
new file mode 100644
index 0000000..4742abc
--- /dev/null
+++ b/doc/source/examples/README.md
@@ -0,0 +1 @@
+## Hello PyRasa Gallery
\ No newline at end of file
diff --git a/doc/source/examples/basic_functionality.ipynb b/doc/source/examples/basic_functionality.ipynb
new file mode 100644
index 0000000..632055b
--- /dev/null
+++ b/doc/source/examples/basic_functionality.ipynb
@@ -0,0 +1,950 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting started\n",
+    "\n",
+    "This notebook shows the basic usage of the `PyRASA` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neurodsp.sim import sim_combined\n",
+    "from neurodsp.utils import create_times\n",
+    "import numpy as np\n",
+    "import scipy.signal as dsp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first simulate a signal with a single oscillation and a spectral slope of -1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fs = 1000\n",
+    "n_seconds = 60\n",
+    "duration=4\n",
+    "overlap=0.5\n",
+    "\n",
+    "sim_components = {'sim_powerlaw': {'exponent' : -1}, \n",
+    "                  'sim_oscillation': {'freq' : 10}}\n",
+    "\n",
+    "\n",
+    "sig = sim_combined(n_seconds=n_seconds, fs=fs, components=sim_components)\n",
+    "times = create_times(n_seconds=n_seconds, fs=fs)\n",
+    "\n",
+    "max_times = times < 1\n",
+    "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
+    "axes[0].plot(times[max_times], sig[max_times])\n",
+    "axes[0].set_ylabel('Amplitude (a.u.)')\n",
+    "axes[0].set_xlabel('Time (s)')\n",
+    "freq, psd = dsp.welch(sig, fs=fs, nperseg=duration*fs, noverlap=duration*fs*overlap)\n",
+    "axes[1].loglog(freq, psd)\n",
+    "axes[1].set_ylabel('Power (a.u.)')\n",
+    "axes[1].set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use IRASA to seperate the signal in its periodic and aperiodic components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyrasa.irasa import irasa\n",
+    "\n",
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(1, 100), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
+    "axes[0].set_title('Periodic')\n",
+    "axes[0].plot(irasa_out.freqs, irasa_out.periodic[0,:])\n",
+    "axes[0].set_ylabel('Power (a.u.)')\n",
+    "axes[0].set_xlabel('Frequency (Hz)')\n",
+    "axes[1].set_title('Aperiodic')\n",
+    "axes[1].loglog(irasa_out.freqs, irasa_out.aperiodic[0,:])\n",
+    "axes[1].set_ylabel('Power (a.u.)')\n",
+    "axes[1].set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "f.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can further analyse the periodic and aperiodic components using the `get_peak_params` and `compute_slope` functions, which will return pandas dataframes containing specific information about the slope or the oscillatory parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ch_name</th>\n",
+       "      <th>cf</th>\n",
+       "      <th>bw</th>\n",
+       "      <th>pw</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.18842</td>\n",
+       "      <td>0.490839</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ch_name    cf       bw        pw\n",
+       "0       0  10.0  1.18842  0.490839"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# %% get periodic stuff\n",
+    "irasa_out.get_peaks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.328678</td>\n",
+       "      <td>0.984115</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Offset  Exponent fit_type ch_name\n",
+       "0 -1.328678  0.984115    fixed       0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# %% get aperiodic stuff\n",
+    "aperiodic_fit = irasa_out.fit_aperiodic_model()\n",
+    "aperiodic_fit.aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mse</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>R2_adj.</th>\n",
+       "      <th>BIC</th>\n",
+       "      <th>BIC_adj.</th>\n",
+       "      <th>AIC</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000363</td>\n",
+       "      <td>0.997522</td>\n",
+       "      <td>0.997509</td>\n",
+       "      <td>-35.430795</td>\n",
+       "      <td>-41.776853</td>\n",
+       "      <td>-43.398668</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mse        R2   R2_adj.        BIC   BIC_adj.        AIC fit_type  \\\n",
+       "0  0.000363  0.997522  0.997509 -35.430795 -41.776853 -43.398668    fixed   \n",
+       "\n",
+       "  ch_name  \n",
+       "0       0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aperiodic_fit.gof"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But how does all of this work in practice? The beauty of IRASA lies in its simplicity. Essentially, its just up/downsampling and averaging. I will deconstruct the algorithm below to show you its inner workings and highlight potential pitfalls.\n",
+    "\n",
+    "We start by simply computing a psd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Frequency (Hz)')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kwargs_psd = {'nperseg': duration*fs, \n",
+    "              'noverlap': duration*fs*overlap}\n",
+    "\n",
+    "freq, psd = dsp.welch(sig, fs=fs, **kwargs_psd)\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(4,4))\n",
+    "ax.loglog(freq, psd)\n",
+    "ax.set_ylabel('Power')\n",
+    "ax.set_xlabel('Frequency (Hz)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to create two other psds from an up-/downsampled version of the data.\n",
+    "Note that the data is up-/downsampled by the same factor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x111a35400>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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98fb2NjIzeHp6pjPHmJpXWpOZlpCQEC5dupQuy2pcXByXL18mMjKSO3fuGI1pZ2dHw4YNMzUzPX36FEdHR6O2jBbKnTx5kilTphAaGkpERISu3/Xr1/Hx8SE0NJTmzZtjb2dwW8lCJTx37hz16tUzKgvbtGlTUlJSOH/+vM58XLduXRwcHDIdq0iRIlSrVi3TPrnB6NGjdfu+vr4UK1aMnj176rQKw/nFxsY+8/nlhHwvIKKjo7l06ZLu/ZUrVwgNDdVVEBszZgxvvfUWDRs2pHHjxixatIjr168zZMiQPJx1wcbZGQxMwM/82tnFxsYm3c1Pm5E0K7QmyAYNGnDlyhX++OMPdu3aRa9evWjTpg2//PIL165do2PHjgwZMoSpU6dSvHhxDh48yODBg42uY6o2dWb1qrMzr7SkpKTg7+/PqlWr0h0rVapUluNmRMmSJYmNjSUuLk7XZkqgxMTE0LZtW9q2bcuPP/5IqVKluH79Ou3atdOZU4oUKaIdINvXF2nMwYaYW1f8wIEDdOjQIdM+//d//8f//d//ZXt+lvDSSy8BcOnSJSMBERERYfQgURDI9wLixIkTtGrVSvd+zJgxAAwYMIDly5fTu3dvHj58qIsiqFOnDlu3bqVSpUo5um5QUBBBQUEFLurAGmg0kI3/xzynVKlS3LlzR/c+KiqKK1eupOt35MgR+vfvb/S+fv36uvfu7u707t2b3r1707NnT9q3b09ERAQnTpwgKSmJGTNmYGMj3XVr16612vyPHDmS7n2tWrVM9m3QoAE///wzpUuXThc4ERUFTk7g5eXFkSNHaNGiBSDTzISEhBj5F9Li5+cHyCd5rVAzJSD++ecfwsPDmTZtmi70+4S2KFAqvr6+rFixgsSEBHTi0WAsBweHdP9PPj4+rFixwkgjCw4OxsbGxmxndMOGDXWaYUY8ixQ72nrmacPpz549S8+ePXP9+tYk3wuIwMDALCMxhg4dytChQ6163WHDhjFs2DBd5SVF/qN169YsX76cLl26UKxYMSZNmmSyTOi6deto2LAhzZo1Y9WqVRw7dky3KGzWrFl4eXnh5+eHjY0N69ato0yZMnh4eFC1alWSkpKYN28eXbp0ITg4mO+++85q8w8ODubrr7/m1VdfZefOnaxbt44tW7aY7NuvXz+++eYbunXrxmeffUb58uVTa15voHPnsZQtW56RI0cybdo0qlevTu3atZk5c2aWC9xKlSpFgwYNCA4O1kUrmdJ0KlasiIODA/PmzWPIkCGcPXs2XbW94cOHM2/ePN4YMIAJPXpQ1NWVI3//TUCRItSsWRNvb2+2b9/O+fPnKVGiBEWLFqVfv35MnjyZAQMGMGXKFB48eMCIESN466230kUnZoU1TExhYWEkJCQQERHBkydPdAJHK0iPHTtG//792b17N+XKlePw4cMcOXKEVq1aUbRoUY4fP87o0aPp2rUrFStW1I179epVbt26ZXb4bJ5jfddI4eJ5dFIXFCIjI0WvXr2Eu7u7qFChgli+fLlJJ3VQUJB45ZVXhKOjo6hUqZL46aefdMcXLVok/Pz8hIuLi3B3dxcvv/yy+Ouvv3THZ86cKby8vESRIkVEu3btxMqVKwUgHj16JISQTuqiRYsazWvy5MmiXr16Rm1pnZ6VKlUSn376qejVq5dwdnYWnp6eYvbs2UbnkCYo486dO6J///6iZMmSwtHRUVSpUkW88ca7Ys+eSHH8uHRKjxw5Uri7uwsPDw8xZswY0b9//yydrd99951o1KiRzkl99uxZk07q1atXC29vb+Ho6CgaN24sfvvtNwGIkydP6vqcOnVKtH35ZeHs5CTcXFxE8/r1xeXLl4UQQty/f1+88sorwtXVVQBiz549QgghTp8+LVq1aiWcnJxE8eLFxbvvvmvk4DfHYZxTKlWqJIB0m5Y9e/YIQFy5ckUIIURISIh48cUXRdGiRYWTk5OoWbOmmDx5soiJiTEa98svvxTt2rV7Jp9BizWc1KomdRaomtQFG41Gw8aNG/PdCl5vb29GjRrFqFGjcjTOhQvSxATQsKFlY8TFxVG9enU+/fRTfH19cXR0pG7dupZP6skTOH9e7ms04O9v+ViFgPj4eKpXr85PP/1E06ZNn9l1rVGTulCsg8gNnud1EIqCgzUWFTs5ObFw4UKdOSrHz4yG52sjmJ9jrl27xsSJE5+pcLAW+d4HkVcoH4SiIJDNoK0sadKkiW71sKoHYV1q1KiRa6u/cxslIBSFmvxqQTVMkZETrBVkZygUrKpBaN+bk8tJkW9QJiaFooBizQhsQ6FgdQ0inwppRdYoAaFQFFDi4603VloNIkdahBIQhQYlIDJAOakV+Z20Duqc3dNFpu/NHCzz94oCgxIQGTBs2DDCwsI4fvx4Xk9FoTBJWg0iJ/fhtGYlJSAUoASEQlFgsaaASCsQcuSHUAKi0KAEhEJRQFEahCK3UQJCUagIDAzM8erkgoI5AuLq1atoNJoMk9ll1weRre9XCFq89x6rt23Tvb979y6vvPIKLi4ueHh4ZH6+IlM2b95M/fr1cx5tlg2UgMgA5aRW5GeESC8gcmYVsp6JafOOHdx9+JA3tHU1hGDWrFncuXOH0NBQLly4YPlEDcjvDwN37tyhb9++1KxZExsbm2zPVVuS1XAzTBLZuXNnNBoNq1evzqWZ61ECIgOUk1qRnzGVwSK/mJjmLl7M250761KkA1y+fBl/f3+qV69O6dKlLR47N0hbHtRaxMfHU6pUKSZOnEi9evXMOnfZsmW60qx37txhwIABRsfffvvtLGuYWwMlIBQFlpiYGPr374+rqyteXl7MmDEjXZ9Hjx7Rv39/ihUrhrOzMx06dODixYuAvAmWKlWK9evX6/r7+fkZ3cAOHz6Mvb090akVlDQaDYsXL6Z79+44OztTvXp1fvvtN6Pr9evXj1KlSlGkSBGqV6/OsmXLdMfHjx9PjRo1cHZ2pkqVKkyaNMmo+NCUKVPw8/Nj6dKlVKxYEVdXV95//32Sk5P5+uuvKVOmDKVLl+aLL77QnWNjA40aafjf/xbSoUMHihQpQuXKlVm3bl2m319YWBgdO3bE1dWVRo0a8cknn+jyMaWkpGTr+01LeHg4uw4coGtqTQoA79q1Wb9+PStXrkSj0ejqWM+cOZO6devi4uJChQoVGDp0qO571hIcHEzLli1xdnamWLFitGvXjkePHjFw4ED27dvHnDlzdE/Z2tXp+/btIyAgAEdHR7y8vPjoo49ISkrSjRkYGMjw4cMZM2YMJUuW5JVXXsnyc1mCt7c3c+bMoX///man6/Hw8KBMmTK6TVeMKZWuXbty7Ngx/v33X2tOOR1KQCjSIwTExOTNZsaT69ixY9mzZw8bN25kx44d7N27l5CQEKM+AwcO5MSJE/z2228cPnwYIQQdO3YkMTERjUZDixYt2Lt3LyBv7mFhYSQmJhIWFgbA3r178ff3Nyof+umnn9KrVy9Onz5Nx44d6devHxEREQBMmjSJsLAw/vjjD86dO8fChQspWbKk7lw3NzeWL19OWFgYc+bM4fvvv2fWrFlGc758+TJ//PEH27Zt46effmLp0qV06tSJmzdvsm/fPqZPn84nn3zMmTOy4JD2QX3q1En06NGDU6dO8eabb9KnTx/OnTtn8ru7c+cOLVu2xM/PjxMnTrBkyRIiIiKYMGFC6p+AyNb3m5aDBw/iXKQItStX1rUd37eP9u3b06tXL+7cucOcOXNS523D3LlzOXv2LCtWrODPP/9k3LhxuvNCQ0N5+eWXeeGFFzh8+DAHDx6kS5cuJCcnM2fOHBo3bsy7776re8quUKECt27domPHjjRq1IhTp06xcOFClixZwueff240zxUrVmBnZ0dwcDD/+9//TH6WVatW4erqmulmqsKfNRg+fDglS5akUaNGfPfdd+k0vEqVKlG6dGkOHDiQK9fXYY2844WZ57IeRHS01oLx7DeD4vWZ8eTJE+Hg4CDWrFmja3v48KEoUqSIGDlypBBCiAsXLghABAcH6/qEh4eLIkWKiLVr1wohhJg7d66oU6eOEEKITZs2iYYNG4rXXntNBAUFCSGEaNu2rRg/frzufEB8/PHHuvfR0dFCo9GIP/74QwghRJcuXcTbb7+d7e/+66+/Fv7+/rr3kydPFs7OziIqKkrX1q5dO+Ht7S2Sk5N1bTVq1BTDhn0ljh8X4tQpOa/Bg4cYjf3iiy+K999/XwghxJUrV4xqN0yaNEm0bdtW1/fChQti8+bNAhC//PKLuHXrVpbfrylmzZolqlSqJMTx4/otOlp069ZNDBgwINPvYu3ataJEiRK693369BFNmzbNsH/Lli3TzeX//u//RM2aNUVKSoquLSgoSLi6uuq+v5YtWwo/P79M5yKEEFFRUeLixYuZboa/U2aYmmtGTJ06VRw6dEicPHlSfPvtt8LZ2VlMnTo1Xb/69euLKVOmZDiONepBqGR9igLJ5cuXSUhIoHHjxrq24sWLU7NmTd37c+fOYWdnx4svvqhrK1GiBDVr1tQ9WQcGBjJy5EjCw8PZt28fgYGBVKxYkX379vHee+9x6NChdM5FX19f3b6Liwtubm7cv38fgPfff58ePXrw119/0bZtW1599VWaNGmi6//LL78we/ZsLl26RHR0NElJSeny8Xt7e+Pm5qZ77+npia2trZFN39PTk0eP5DW1efACAhobjdO4ceMMo5ZCQkLYs2ePTjMSBuk1bt68ma3v1xRPnz7FydEx0z5a9uzZw5dffklYWBhRUVEkJSURFxenKz8aGhrK66+/nq2xtJw7d47GjRsb1bNu2rQp0dHR3Lx5U1flrWE2ime4ubkZ/Q7Pio8//li3r61k99lnnxm1g6ygFxsbm6tzUSYmRXqcnSE6Om82Z+dsTVF7M7OkjxBCdwOpU6cOJUqUYN++fToB0bJlS/bt28fx48d5+vQpzZo1MzpfW7tZi0aj0ZkAOnTowLVr1xg1ahS3b9/m5Zdf5sMPPwRkzek33niDDh06sHnzZk6ePMnEiRPTOUlNjZ+2DeQ1NRq9gDD1cQ1vlIakpKTQpUsXQkNDCQ0NZcOGDaxatYoNGzbQoEEDi6OYSpYsyaPISONGExO7du0aHTt2pE6dOqxfv56QkBCCgoIAdD6ZtHb37GD42xq2gfF34ZKNout5aWIy5KWXXiIqKop79+4ZtUdERFCqVKlcvbbSIDIgKCiIoKCgdEXWnws0GsjGP1BeUq1aNezt7Tly5IjuqfDRo0dcuHCBli1bAuDj40NSUhJHjx7VPcU/fPiQCxcuULt2bQCdH+LXX3/l7NmzNG/eHDc3NxITE/nuu+9o0KCB2U+RpUqVYuDAgQwcOJDmzZszduxYvv32W4KDg6lUqRITJ07U9b127VqOvgdDAXH8+BH+85/+umNHjhyhfv36Js9r0KAB69evx9vbGzs7OxISEoyeRkuVKpXl92uK+vXrc/f+fR5FRVEsk0plJ06cICkpiRkzZug0o7Vr1xr18fX1Zffu3Xz66acmx3BwcEj3/+nj48P69euNBMWhQ4dwc3OjXLlyGc7HFF27djXSPk1hbt1sSzh58iROTk5G60fi4uK4fPlyhr+vtVAaRAaoMNf8jaurK4MHD2bs2LHs3r2bs2fPMnDgQCMzTPXq1enWrRvvvvsuBw8e1Dlvy5UrR7du3XT9AgMDWb16Nb6+vri7u+uExqpVqwgMDDRrXp988gm//vorly5d4u+//2bz5s06YVStWjWuX7/OmjVruHz5MnPnzmXjxo05+h40Gr2TetOmdSxdupQLFy4wefJkjh07xvDhw02eN2zYMCIiIujTpw/Hjh3j+vXrHDlyhM8++4zk5GRcXFyy/H5NUb9+fUqVKEHwqVOZ9qtatSpJSUnMmzePf//9lx9++MEo1h9gwoQJHD9+nKFDh3L69Gn++ecfFi5cSHh4OCBNcUePHuXq1auEh4eTkpLC0KFDuXHjBiNGjOCff/7h119/ZfLkyYwZMybLuafFzc2NatWqZbpl9fCg1dCio6N58OABoaGhugAIgI0bN1KrVi3d+99//53vv/+es2fPcvnyZRYvXszEiRN57733cDQw3R05cgRHR0cjE2BuoASEosDyzTff0KJFC7p27UqbNm1o1qwZ/mnqHy9btgx/f386d+5M48aNEUKwdetWI5NNq1atSE5ONhIGLVu2JDk5OdOnZVM4ODgwYcIEfH19adGiBba2tqxZswaAbt26MXr0aIYPH46fnx+HDh1i0qRJln8BGGsQH330KWvWrMHX15cVK1awatUqfHx8TJ5XtmxZgoODSU5Opl27dnTv3p0ZM2bg7u6OjY0NKSkp2fp+02Jra8ug3r1ZpV1FnQF+fn7MnDmT6dOnU6dOHVatWsVXX31l1KdGjRrs2LGDU6dOERAQQOPGjfn111+xs5OGjw8//BBbW1t8fHwoVaoU169fp1y5cmzdupVjx45Rr149hgwZwuDBg9PZ758V9evXp379+oSEhLB69Wrq169Px44ddccjIyM5r63fjTQvLliwgMaNG+Pr68ucOXP47LPP0oUY//TTT/Tr1w/nbJpkLUUjsmPMfY7JbnHvgkxmxc0V+ZPYWAgLA3t7cHKCWrU0rFy5kbfeetWi8U6fPk1CQgKOjo7Ex8dToUIFi80n90JDeSEwkJAffqCSlxfUrAl54OwtrDx48IBatWpx4sQJKhuEE6cls//r7N7XlAahUBRAtI91hhpETtA6pW1tbY3eW4JnyZIsmTSJ63fv5nxiinRcuXKFBQsWZCocrIVyUisUBRBTAsIauZi0AiKnhoVuZprmFNknICCAgICAZ3ItJSAUigKI4f1bo4HjxwWpwUYWjpc+FNRqKCt2gUWZmBSKAkxW6yDMH0+TOpYV60EoCixKQCh0qHiFgoOhiUkbvWmNinK5okEo8gRr/D8rAZEBz1M9CG3IZ24v21dYD1M+iHyjQSjyBdr/5/Sr8LOP8kFkwLBhwxg2bJguHKwwY2tri4eHhy6fkLOzs3qSzOdoiwUJAdrFxImJEBdn2XhagaCNXtLmRbIIg9TagJyspWMpzEYIQWxsLPfv38fDw0MXeGAJSkAoAChTpgyATkgo8jexsRAeDo6O4OAAT55IARETY9l42tXJT58+JSYmhoSEBJ4+fWrZYBERckJabGzAgrxKipyhrSmRE5SAUADStODl5UXp0qWNCtgo8ifbtsGoURAQAC+8AMuWweDBMHas+WOlpKTQoUMHAHr06MH69et5++23GT9+vGWTW7UKfvxR/37RIjAoIKTIfezt7XOkOWhRAkJhhK2trVX+sBS5y9OncO0aVKsmLTjXrsHDh3JVtbkkJSXpkgZGR0dz7do1IiMjLV9V//ixnJD+ApZNTJHnKAGRXb75Jv0feWaOPI0G6taFVq1UmgGF1dEqeXZ20sQEYGlpZcOMqNo8RzlZSZ3u/yInYynyFCUgskuakoXZxt4emjWDdu2gfXvw9bVObgTFc43WD2wNAWEoDLQRLzkSEGnPVRFRBRYlILLLgAH6/8TsEB8PBw7AlSuwZ4/cPvoIvLyksGjXDl55BUqUyL05KwotWgFhby830GsV5mIoDHJFg1ACosCiBER2mTsXzM3mKgRcuiQ9itu3SyFx5w4sXy43jUZ6GbXaRaNG8pFQocgCa2oQhiYmrQaRo0JZysRUaFB3o9xEo4Hq1eU2YoSMBT94UAqLbdvg7Fk4elRun30GHh5Sq2jfXgoNMytgKZ4frOmDUCYmRUaoldTPEicnaNNGOrzPnIEbN2DJEnj9dSkcHj+GdetkvGL58tLJPXYs7N6tXxmlUGCsQSgTkyK3UBpEXlK+PAwaJLekJDh+XGoW27bJ/bNn5fbtt+DsLCOi2reXW7VqeT17RR5i6IPId1FMac9VJqYCixIQ+QU7O2jcWG6ffiqXye7apfdf3L0LW7bIDaBKFb0pSoXSPnfkRhSTRqOxSsEgpUEUHpSJKQPyPFlfyZLwxhvSmX37NoSGwrRpUhjY28O//8KCBdCtm4yEat0apk+HU6fUP+RzgKEPIqcmJq0GYWNjg01qaljlg1CAEhAZMmzYMMLCwjh+/HheT0U6u+vVg/Hj4c8/5ZLZX3+FoUOhcmV5Z9CG0fr5Sef2wIHSn5E2cZqiUJAbGoTVBISKYio0KBNTNmncuLFZKSg0Gg1169alXbt2tG3b1uIC8CZxc4OuXeWWUSjtihVyq1MHZs2SznFFoSE3BIStra3SIBRGKAGRTcLCwsw+5/Tp06xatQqAevXq0a5dO9q1a0fTpk1xdHS0zsTShtLGx8tQ2j/+kBnczp6VobNdukhnd40a1rmuIk8xtVAup07qXNMglIAosCgBkU02bdqEi4tLuvaM6ibEx8cTHBzM9u3bCQkJ4dSpU5w6dYqvv/4aZ2dnAgMDddpFzZo1rVd/wdERXn5Zbv/3f3J9RVAQ/P67FBojRsCkSVCsmHWup8gTTK2DyGmYa675IJSJqcCiBEQ2adWqFe5mrqTu2LEjX3zxBQ8ePGDnzp3s2LGD7du3c/fuXbZu3crWrVsBqFixok67ePnll/Hw8LDOpIsXh9mzYcgQ+O9/YetWaW5auVIKjvfeUyu3Cyj52sSkNIhCg3JSPwNKlSpF3759Wb58Obdv39ZpEm3atMHBwYHr16/z/fff07NnT0qUKEGTJk349NNPOXLkSM5SHmipVUuGx/7xB9SuLZ3cw4ZJh/aOHTkfX/HMyY1UGyqKSZEWJSCeMRqNBl9fX8aOHcvOnTt59OgRW7duZeTIkdSuXZuUlBQOHz7MlClTaNy4MSVLluT1119n8eLF3LhxI2cXb98eTp+G+fOldvH333IdRZcucP68dT6g4pmQG8n6VBSTIi1KQOQxzs7OdOjQgdmzZxMWFsa1a9d02oSHhwePHz/ml19+4d1336VixYrUrl2bUaNG8ccff+iKkpuFnZ3UHi5dkiXJ7Oxg82YZ7TR6NDx6ZPXPqLA+ysSkeBYoAZHPqFixIu+88w7r1q0jPDzcSJuwsbHhn3/+Yc6cOXTs2JFixYrRpk0bvvnmG06fPq0rPJ8tihWT/oizZ6FzZ3nHmT1bpvAIClLrJ/I5uVEwSDmpFWlRAiIfY2try0svvcTkyZM5dOgQDx8+NNImEhIS2L17N+PGjaNevXqULVuWAQMGsHr1ah48eJC9i9SsKSOctm+XxY0jImD4cLkwb/v23P2ACosxlazPGgvltGt9rJruW2kQBRYlIAoQHh4e9OjRg0WLFnH16lUjbcLZ2Zm7d++ycuVK+vXrR+nSpXnppZc4dOhQ9gZv21am81iwQKbuCAuTPotOneCff3L1cynMx1SyvpQUsOS+rhbKKTJCCYgCikajoWbNmnzwwQds2bKFiIgIdu3apdMmAI4ePUrz5s2ZOHEiCdl5vLSzg/ffh4sXYcwY+X7rVpl2fORI5Z/IR5jyQYBljupcXyinTEwFFiUgCgmOjo68/PLLTJ8+ndDQUG7fvk3//v1JSUnhyy+/5KWXXsr+avBixWDGDBnl1LWrvBvNnSvNUStWqCfCfICpZH1gmZkp1xfKqb+XAosSEIUULy8vVqxYwbp16yhevDgnT57E39+fuXPnZv+fv0YNmRRwxw65luLBA5kEsEULGS6ryDOsqUGoKCZFRigBkU18fWUJBsOtcuWMtypVoGdP+PHHvLXM9OzZk7Nnz9K+fXvi4uIYOXIk7dq14+bNm9kf5JVXZBrx6dNl4aKDB6FBAxkWGxWVe5NXZIihD8LWFlLv6xZpECqKSZERz4WA6N69O8WKFaNnz54Wj3HtGly5YrxdvZrxduUKrF8Pb70FpUrJZKrz58sqo88aLy8vtm7dSlBQEEWKFGHXrl3UrVuXn3/+OfuDODjAuHHSYd2jh/SGzp4tNYufflJPic8YQw0CchbJpDQIRUY8FwLigw8+YOXKlTkaY/duOHIk/Xb0qOlt716ZE69uXXkv3b1b5smrWBEaNoTPP5dLEJ7V/45Go2Ho0KGcPHmSRo0a8fjxY9544w369evHI3NUnAoV4JdfZHrxatVkavG+fWVyQAsy3iosw9AHATlL2Kd8EIqMeC4ERKtWrXDLYUnOhg3hxRfTbwEBpreWLWU+vNOn5aLlGTOgeXOZnTskRC88qleHDz+UVhtrpF3Kipo1axIcHMzkyZOxtbVl9erV+Pr6snv3bvMGatcOzpyBqVPByUnWodAWNYqOzp3JK3Sk1SByslhORTEpMiLPBcT+/fvp0qULZcuWRaPRsGnTpnR9FixYQOXKlXFycsLf358DBw48+4nmgKpVZdTo/v2ytPSSJTL9kaMjXL6sFx5eXvDOOzLzxdOnuTcfe3t7pkyZQnBwMNWrV+fmzZu0adOG0aNH89ScCzs5wccfS82hSxd51/r6a5kQcP169eSYi1hTQKh1EIqMyHMBERMTQ7169Zg/f77J4z///DOjRo1i4sSJnDx5kubNm9OhQweuX7+u6+Pv70+dOnXSbbdv3zZ7PvHx8URFRRlt1qR0aRg0CH77DcLD9X4KDw8ZJKQVHqVK5b6T+8UXX+TkyZMMGTIEgNmzZ9OwYUNOnjxp3kCVK8sP9Ntv4O0NN2/KybdvL9UnhdUxdFIbvuaLMFetQNB6zpWAKLiIfAQgNm7caNQWEBAghgwZYtRWq1Yt8dFHH5k19p49e0SPHj2y7Dd58mQBpNsiIyPNup65JCQIsXu3ECNGCFGhghDyv0putrZCvPyyEPPmCXH9eu5cf8uWLcLT01MAwt7eXnz55ZciKSnJ/IFiY4WYNEkIBwc5+eLFhbh61foTfs6pUUN+vfv3y/fVqsn3Bw+aP9bmzZsFIBo2bCi2bNkiAOHv72/55AID5WTs7eXrt99aPpYiV4iMjMzWfS3PNYjMSEhIICQkhLZt2xq1t23bNvspJMxkwoQJREZG6rYcp9jOJvb20Lq1XI927Zqxn+JZOLk7duzI2bNn6d69O4mJifzf//0fLVu25N9//zVvoCJFpPPl7FlZbyIiAnr1sjxRkMIkBcLEpK3hrjSIAku+FhDh4eEkJyfj6elp1O7p6cndu3ezPU67du14/fXX2bp1K+XLl+f48eMZ9nV0dMTd3d1oe9ZoNHKZgdbJffGiLCfdrFnuOrlLlizJ+vXrWb58OW5ubgQHB1OvXj2WLl1qXqZYkBPbuFGuyj52TE5SYTVyI8zVMFmfVUxMSkAUePK1gNCStl6zEMKsGs7bt2/nwYMHxMbGcvPmTRo1apTlOUFBQfj4+GSrb25TrZqsGHrggHRyL14sM3Rn5uSOi7PsWhqNhgEDBnDq1CmaN29OdHQ0gwcPpnv37jx58sS8wby94Ycf5P68eWDOugtFpqT1QeQkzDXXFsppBYSKYiqw5GsBUbJkSWxtbdNpC/fv30+nVVibYcOGERYWlqm2kReULg2DB8sM3eHhcknCm2+md3KXLAn9+snqopZQuXJl9uzZw/Tp07G3t+fXX39lwoQJ5g/UqRN89JHcf+cdVbnOSuS2ickq6b6Vk7rAk68FhIODA/7+/uzcudOofefOnTRp0iSPZpV/cHWVi5p/+AHu34ddu2Qph/LlISYGVq+WQsLSBzhbW1vGjRvH77//DsD//vc/Ll68aP5AU6fKhSHR0TK6yZJKeAoj0i6Uy1dRTMoHUWjIcwERHR1NaGgooaGhAFy5coXQ0FBdGOuYMWNYvHgxS5cu5dy5c4wePZrr16/rQjMVEnt7uZh53jy4fl2uWytSRNb8+eqrnI3drl07OnbsSFJSEhMnTjR/ADs7mY7D01M6r99/X900ckhGGkS+MDGl9UEoE1OBJc8FxIkTJ6hfvz7169cHpECoX78+n3zyCQC9e/dm9uzZfPbZZ/j5+bF//362bt1KpUqVcnVe+ckHYS4aDQQGysqhAJ98IlN/5IRp06ah0WhYt24dR48eNX8ALy9Ys0aaHVaulLYwhcVk5INQUUwKa5LnAiIwMBAhRLpt+fLluj5Dhw7l6tWrxMfHExISQosWLXJ9XvnVB2EOb78NAwbI/9c+feDePcvHqlu3LgMHDgRg3Lhx5kc1gZRan38u94cPlxXsFBaRW1FMuaJBKAFRYMlzAaHIXYKCwMdHRj/165ezUNjPPvsMJycn9u/fz+bNmy0bZPx46NgR4uOlPyIy0vIJPacIkXGyvnyRi0lFMRUalIAo5Li4wLp1sozD7t36B3hLKF++PKNGjQLgo48+Ikn7GGsONjbSq16pkozRfftt9YRpJob3W2tmc7V6um8VxVTgUQIiAwqyDyItPj6wcKHc//RTKSgsZfz48RQvXpywsDAjM6BZFC8Oa9dKu8jGjbKuhCLbGMrlfJmLSWkQhQYlIDKgMPggDOnfXyYJFEKWb7hzx7JxPDw8mDRpEgCTJ08mJibGsoECAmDmTLk/bhzkUuqUwoihgMiXJiblgyg0KAHxHDFvHtSpI9dM9O1rfKMxh/fffx9vb29u377N7Jw8/Q8bBr17y4n06iVX+imyxNCMlK9NTEpAFHiUgHiOcHaW/ggXFxn2+tlnlo3j6OjIl19+CcD06dN5YOmNXaOB77+HmjXh1i1ZW0KRJaY0CK2JKT7e/PGUk1qREUpAPGfUqgWLFsn9zz+HHTssG6d37974+/vz5MkTpk6davmE3NxkcimApUvB3OyxzyFaAaHR6P3A1io5atVkfcpJXeBRAiIDCpOTOi19+8J778n/2zffBAvqKmFjY8PXX38NwMKFC7mUk8JAzZpB27byzpcTYfOckHaRnOF+vjAxqYVyhQYlIDKgsDmp0zJ7tiwh/eABvPGGZf6I1q1b0759e8tTcBiiFQwrV8KFCzkbq5CTdg0E5ExAqFQbioxQAuI5pUgR6Y9wc5NpxFMzm5jN9OnT0Wg0rF27lmPHjlk+oYAAmcM8JUXG4ioyJO0qarCeiUlpEApDlIB4jqleXW/+/+or+OMP88fw9fWlf//+QA5ScGjRes1/+gn+/tvycQo5pgSENdZBWD3dtxIQBR4lIJ5zevWCoUPl/ltvgSUVVqdOnYqjoyP79u1j69atlk+mfn2Zv1wImDzZ8nEKOdb2QagoJkVGKAGhYOZMWeL04UPpjzD3JlOhQgVGjhwJyJXWOXr6/PRTGZ6zfr1K5pcBmWkQ+cLEpKKYCg1KQGRAYY5iSoujo8x84e4uFzRb4m+eMGECxYsX5++//2bFihWWT+aFF6SUAssdI4UcU05qle5bkRsoAZEBhT2KKS1Vq8plCADffCPrWpuDh4eHLpJp0qRJxOakatyUKfLp8/ffISeO70KKtTUIFcWkyAglIBQ6evSAESPkfv/+cO2aeecPGzZMl4Jjzpw5lk+kRg05AYDUvE8KPbm1DsLqPghlYirwKAGhMOKbb6BRI3j0SKZJMsdk4ejoyOep+cSnTZtGeHi45RP55BP5iLxjBxw8aPk4hZDcCnM1NDFpC3dZhIpiKjQoAaEwwtERfv4ZPDzg6FH46CPzzu/Tpw/169cnKipKJywsonJlGDxY7istwojMFspZK5srYD0BoUxMBRYlIBTpqFwZli2T+7NmwaZN2T/XMAXHggUL+DcnuZUmTpSPxnv3wp9/Wj5OIeNZRDEZtlswoHxVGkSBRwkIhUlefRVGj5b7AwfClSvZP7dNmza0a9eOxMRERo0aRVxcnGWTqFAB/vMfuT9pkrrRpPIscjEZtpuNMjEVGpSAyIDnKcw1I6ZNgxdflGWje/Uyr3z09OnTsbGx4ffff8fX15e9e/daNokJE8DJScbfbttm3rk//STT19arB926wahRMGcO/PqrTC9eQLG2D8LQxKTN5gpW1CCUianAYraAEEJw7do1nj59mhvzyTc8b2GupnBwkP6IYsXgxAnw8pLZX3fvzvp/vl69emzatAkvLy8uXrxIq1ateOedd4iIiDBvEl5eMHy43B8/Pns3diHg229l2trz5+H0afjtNykcRo2S6lGNGpalsc0HWNsHYXUTk1ooV2iwSEBUr16dmzdv5sZ8FPmMSpWkD8LHB54+hVWroE0bqFJFLlfIzPTUpUsXzp07x/vvvw/AkiVLqF27Nj///LN5DtBx46BoUThzRmoEc+ZknH42JQXGjIGxY+X7Dz6ArVshKEi29ewJpUtDbKzMHFsAyS0fhNVMTMoHUWgwW0DY2NhQvXp1Hj58mBvzUeRDWrSAs2dlVNOQIfJefe2azIpRpQq0bg0//CDvuWkpWrQoCxYs4ODBg9SuXZv79+/zxhtv0KVLF65fv57ltZ88ecLPf/7J+KZNuVq2LERHSy2gUaP0i+ji46XWoC2D+s03cr9DB5lw6uuvZQrb1Gp4LFtWIG9ezyIXE1jRB6FMTAUWi3wQX3/9NWPHjuXs2bPWno8in6LRyIzcCxfCnTuwejW88ops37NHrmsrU0YWIjp8OP19t2nTppw8eZJPP/0UBwcHtmzZgo+PD3PmzEmXu+nhw4csX76crl27UqpUKd544w2+3rqVKrdvc3r4cGnzCg2Fl16SN/7Hj6WDpEMHaROzt4cff4QPP5QTTEuvXrL+6oULcrIFjMx8EPnCxKQ0iMKDsAAPDw/h4OAgbGxshJOTkyhWrJjRVpiIjIwUgIiMjMzrqeRLrl0T4rPPhKhSRQh5J5BbrVpCTJsmxL176c8JCwsTzZo1E4AARKNGjcTevXvFggULRJs2bYStra3uGCBq1KghmjdvLgDh7+8vUu7eFaJ/f/3FPD2FqF1b7ru6CrFzZ9YTHzBA9h882OrfSW6zYIGc+muv6dsuX5Ztzs7mj/fuu+8KQHz++eciKSlJ972Hh4dbNkF3dzmZd9+Vr/37WzaOItfI7n3NLgO5kSmztSq84rmnYkUZgTpxoiw8tGyZtOL8849cZDd7trQEVaigP6d27drs27ePxYsXM27cOI4fP05gYKDRuL6+vvTo0YMePXrg4+NDeHg4lStXJiQkhC3Hj9N5xQoZf/v++9IRfe8eeHrKohb162c98bffhhUrpMYxZw64uFjza8lVnkUuJlAahALLNIjnCaVBmE9UlBBLlghRvbp8gGzYUIinT033vX37tujZs6ewt7cXL774opg+fbq4ePGiyb7jx4/XaxEpKbIxLk6Ir74SokcPIf79N/uTTEkRompVOcEVK8z8hHnLzJly2n376tvu3tUrVNqvJrsMHDhQAGLatGlCCKHTIO7evWvZBF1c5ESGDpWvb75p2TiKXCO79zWL10FcvnyZjz/+mD59+nD//n0Atm3bxt+qEthzj5sbDBok0yiVKCFDZIcMMf0g6eXlxbp164iPj+fIkSOMGzeOatWqmRz3v//9Ly4uLlKL2LJFNjo6SlXll1/kEvDsotFIDQT0y8YLCJk5qQ2PZxfDKCYg5wn71DqIQoNFAmLfvn3UrVuXo0ePsmHDBqKjowE4ffo0kwtJJTC1UC7neHtLC46NjbTmzJuXcV+NKWdyGkqVKsXw1DURU6ZMyVl5U4ABA6Sg2LsXcpIS5BmTmYkJzDczGZqYDF/VSmqFRQLio48+4vPPP2fnzp04aMMngFatWnG4AEaFmEItlLMOL78s16yBXJ5g6YJqLSa1CEupUEEu6gBYvjxnYz1DMlsoZ3g8u1hdg1ACotBgkYA4c+YM3bt3T9deqlQptT5CkY5Ro+QK7ORkeP118+tMGGJ1LWLQIPm6YkWBMYVYW4MwDHM1fFUmJoVFAsLDw4M7d+6kaz958iTlypXL8aQUhQuNBhYtknWvw8Ohe3fTi+qyi1W1iFdflbnNr18vMBljTfkgbG31mS3MXQuRayYmlWqjwGORgOjbty/jx4/n7t27aDQaUlJSCA4O5sMPP6S/thKYQmFAkSKwcSOUKgUnT8oFdZbeN6yqRTg5QZ8+cl9bczWfY0qDAMtDXdOamLSvKsxVYZGA+OKLL6hYsSLlypUjOjoaHx8fWrRoQZMmTfj444+tPUdFIaFiRblGwtZW5nSaNcvysT788EPraRFaM9OGDealrM0jTPkgwPKMrlY3MalUG4UGiwSEvb09q1at4sKFC6xdu5Yff/yRf/75hx9++MEoXbBCkZaWLfWpksaOhV27LBunZMmSjEgtoJ1jLcLfXyYBjI+H33/PuF9MjOXXsCJZaRD5xsSkNIgCj0UC4uLFiwBUrVqVnj170qtXL6pXr27ViSkKL8OGySUIKSmy7rU5xYgMsZovQqOR3nOQKo4p/vxTLvDIiW3MSpjyQRi+z9MoJsPvRgmIAo9FqTZq1qyJl5cXLVu2pGXLlgQGBlKzZk1rz01RSNFoZNK/v/+G48eln/jQIfOzXWi1iGnTpjFp0iQSExOJi4sjLi6O+Ph44uLisLOzo1evXpQuXTrzwV5/HaZOhe3bISoK3N2Nj8+bJ290338PtWvry+3lAbnlg7CKBmF4jjIxFXgs0iDu3LnDt99+i7u7O7NmzaJ27dp4eXnxxhtv8N1331l7jopCiJOTNPl7esp6PoMHW/agqdUiQkNDee211+jbty+DBg3i/fffZ/To0YwYMQJ/f39OnTqV+UB16kDNmtLMtHmz8bFHj2RNCS0ffmi5bcwKWNsHYVUTk+GPqKKYCjwWCQhPT0/69OnDd999xz///MOFCxdo164d69evZ9iwYdaeo6KQUr68zJBhZydXXA8caL6Zv2TJksyfP5+AgACaNm1K69at6dixI927d6dPnz5Uq1aNmzdv0qxZM7Ya3uTTotHIYkKQ3sz0yy/SsF+3rrFtLI9WX1vbB2FVE5MpDUIJiAKLRQIiOjqabdu28dFHH9G4cWPq1q3L6dOnGTFiBBs2bLD2HBWFmGbN4H//kw+bK1dCw4aycJw5DBw4kKNHj3Lw4EF2797Nli1b2LBhA6tXr+bYsWO0atWK6OhounTpQlBQUMYDaf0Qf/wBT57o21etkq/9+knbWEAARETIOtepaWaeJdb2QWSkQaSt05EtTPkglImpwGKRgChWrBgDBgwgKSmJjz/+mLt37/LXX38xc+ZMunXrZu055gkqF9OzY9AgWXSobFmZJjwgABYvts6DZ7Fixdi2bRtvv/02KSkpDB8+nFGjRpm++fn6QvXq0sykdXrfuAH79sn9Pn2kbWzjRlkd6exZKd2sSVIStG0rix9lsJowIw0iX4S5Gp6jTEwFHosERKdOnUhOTuaHH35g5cqVrF69mnPnzll7bnmKysX0bGnRQhaJ69AB4uLg3XflA3tUVM7HdnBwYMmSJXyZWmp0zpw59OjRg6S0aU9NmZl++kk/wYoV5X7ZsjBhgtzfti3nEzTkzBnYuVOOm0HEVEY+CBXFpLA2FgmITZs2ER4ezs6dO2nWrBm7d+8mMDCQMmXK8MYbb1h7jornhFKlpH/466/lveWnn+QShZMncz62RqNhwoQJrFmzBkdHR3799Ve+1WYRNERrZtq6VZqPfvxRvu/Xz7hf27by9cCBnOUNSYvhA8mqVbKYURry9ToIZWIqVFhcDwJk1a9mzZrRpEkTAgICePjwofJBKHKEjY1cQHfggHxgv3RJlp4OCrLOg2jv3r11kXaTJ09OX7/Ezw+qVpVqzPTp8onewUEvOLTUrCknGB8P+/fnfGJatAJCu67oww/TZTcscGGuSoMosFgkIGbNmkW3bt0oXrw4AQEB/PTTT9SsWZONGzcSHh5u7TkqnkMaN5aaQ7du8ol4+HB5jzb0HVvKgAED6NSpEwkJCQwcONDY1GS4aO6rr+Rrx45QrJjxIBqNXovYsYPk5GTLVx4bcuyYfJ02DRo1kilw0wgg7XTLn9sJa9fqbso59UHkmolJaRAFFosExKpVq6hevTorV67k4cOHHD9+nG+//ZbOnTvjnnaBkUJhIcWLS3/w7Nny6Xj9eqlNXLqUs3E1Gg2LFi3Cw8ODEydO8PXXXxt30PohtI7stOYlLakCImnrVipWrEirVq0si/zREhsrVw+C9NQ3bSr30/jBkpLAk7u0/LqjDLdt1w5u384fJialQRQqLBIQJ06cUAJB8UzQaGDkSGlyKlsWwsLkg3VOfcNly5Zl7ty5gMzldMYwtrZBA335Und36NzZ9CAvvww2NtidPw+3b7N//36WLFli+aROnpRCqUwZKFdOflBIJyASE6EH67FJTlUldu2Czp2xtxO64+aQ1sSUo2yuaqFcocJiH8Tjx4+ZMWMG77zzDu+++y4zZ84ksgBkwlQUTF58Uda2btwYHj+GTp2kMzsn954333yTrl27kpiYyIABA0jU3lk1GujbV+737g1OTpw/L9dsrF1rMEDx4iT6+QGQamzi448/zvr/4O5daN06fXpxrSBo1EjOQSsgTp40UguSkuB1UqOshg4FV1c4eZJGD6XUzFMTk0q1UaiwWIOoWrUqs2bNIiIigvDwcGbNmkXVqlX566+/rD1HhQIALy+5XuKdd+Q9Z/x4eR+3NIhIo9Hw3XffUaxYMU6ePMm0adP0Bz/+WJYhnTEDIeA//4HgYPlqWDRxn6MjAG8UL06tWrV48OABU6dOzfS6yd99B3v2kDJihEzjoUXrfwgIkK/VqsliRvHxcs1FKu5P79GCVL/EuHFyUsCr56cD+SjVhjIxFXyEBTRr1kwMHDhQJCYm6toSExPFgAEDRPPmzS0ZMt8SGRkpABEZGZnXU1GkkpIixIIFQtjZCQFC+PkJceWK5eOtWrVKQGlha1tRhIaGpju+fr28jnb74APZfv/+ffGyo6MQIOLd3cXWzZsFIOzt7cWFCxdMXis6Olr86+amG2xXYKB4+vSpPFitmmzftk1/wiuvyLbvvtM1TS27QAgQj2sFyIYbN4SwtxcCxB5aijN+bwqRkJDtz+/j4yMA8eeffwohhPDz8xOA2L59e7bH0HHvnv6LWrtWvhaye0JhILv3NYs1iPHjx2NnEGdnZ2fHuHHjOHHihDXklkKRIRoNvP++zMBdurRcYFe/vlxc9/vv8PRp9seSC6X7oNHcJDn5H3r0WEiCgTknLk5GmkqkCWf+/GTOnElgxowZ7IuPJ9rGBoeoKDp4etKhQwcSExP573//m+5aDx48YGDjxlQ2CMXy3buXJn5+PPr3X733vWFD/UlaM5NWu0hJoXPESgDCA1Od6eXLy6LfQCD7qBP6o371dzbIlSgmjUbvg1AmpgKLRQLC3d2d69evp2u/ceMGbm5uOZ6UQpEdmjeXfomGDaVfYvFi6NoVSpSQKcSXLYP7902fe+uWDJ2tVg0WLdIghD1QhMuXZ/Huuz/p+s2era1XcRPoAfxOSootTZsGM3/+fJKAJ1qT0I4dzJw5Ezs7O37//Xc2btyoGyc6OpqWLVtSPdUZ/rhhQ6I9PSkFND9/nr9Tix9RpYr8AFq0Y2v9E59+il/cEZ7iRMQrBotSZ87kl+ZzOMxL8n1ISLa/x1yJYtJo5AbKxFSQsUQ9GTFihChfvrxYs2aNuH79urhx44b46aefRPny5cXIkSMtGTLfokxM+Z/ERCF27BBi+HAhKlQwNgeBEE5OQpQsKUTlykLUrStE48ZCpFqGBAjRqpUQu3cL0aDBzdS2GPH99xfEnTtCuLgkp7b1ExMnThRz5mwTkJDa1lo0aNBApAQFyYFatBBCCPHRRx8JQJQqVUrcv39fCCHEe++9JwBxOtUUJL7/XoiFC4UAkWw42Q8/NP5wt27JdhsbIT75RNfvLVaII0eMu44eLcRYpss+r7+e7e+vatWqAhDBwcFCCCECAgIEIH7//XfzfwztfG1thdi4Ue43bmz+OIpcJbv3NYsERHx8vBg5cqRwcHAQNjY2wsbGRjg6OopRo0aJuLg4iyacX1EComCRkiLEyZNCTJkiRIMG6YWF4dasmRCpZnchhBBPn6aIMmVCBAih0cSKli21wuGIqF/fX8THxwshhHjnndjUe+AZsXv3XiH+/Vc/6IkTIi4uTtSpU0cA4vXXXxe///67AERlbR9bWyEePBDi6VORVKqUECAiQUR9/rmUdmkpW9Zo4oucRggQ4swZ427jxgnRml2yX5Uq2f7OKleuLABxJFXivPTSSwIQv/76q7lfv/SHgPSJbNok91980fxxFLlKrgiImJgYMXToUFG2bFlRqlQp0atXL7F//35x6tQpERMTk6MJ51eUgCjYPH4sxNWr8mZ6+LDUNDZsEOLQISlM0nLt2j1hb/+nkSCxt28u/v77b12f8HAhPDzksTVrUhvffFM2tG4tREqKCAkJEXZ2dgIQLi4uAhC/NW8u+7z8sv6Chw+L/3l5iTIgvv/+e9MfYt48IV54QYju3YWYPVu4OkoN5upV424TJwrhQYR+4hERWX9BSUlivbOzGA3i2LFjQgghmjRpIgCxYcOGrM9Py/Xr8toODkL8+qvcDwgwfxxFrpIrTurJkyezfPlyOnXqRJ8+ffjzzz+ZO3cuvr6+ODs7W8nopVBYj6JFoVIlWTDupZfglVege3e5nkJrIjekYsXSLFsWAWgrxv3AN9/0wMfHR9enRAmZohxg797UxqlTZa6LP/+E7dtp0KABEydOBCAmJoYXfHzopI3H1a7UBnjpJR4MG8ZdMPJZGDF8uAxz3bCBpGEjiY6XS6ZdXY27OTjAY4rxwL2KbMhOyPmpU7wWG8skrBzmamOjFsoVAswSEBs2bGDJkiUsWrSIOXPmsGXLFjZt2pSz9AK5zI0bNwgMDMTHxwdfX1/WZVSUXqFIpV+/HvTsuRxoTatWPzJC60A24MUX5asuaM/bG7T9xo2D5GQmTpxI48aNcXV1ZVunTtiEhMgMe6++ajTWq6nvd+3aRVQW+c0NK+6lFRDaVBtXi/vLnew4qu/cAaAYkLpqwfpOahXFVGAxS0DcuHGD5s2b694HBARgZ2fH7du3rT4xa2FnZ8fs2bMJCwtj165djB49mhhz61oqnjtWrFjEsmX92bhxre6GaYg2EvX0aYNFzv/3f3Jh25kz8OOP2Nvbs3fvXu6uXEn5GTNkn2nTZCoNA3x8fKhevToJCQn88ccfmc5LGyFrbw+pa/R0aAXE5WJmCIh79/Tnp8YHW02DUFFMBR6zBERycjIO2pSRqdjZ2aUvvJKP8PLywi81HULp0qUpXrw4EREReTspRb7H2dmZgQMHUrRoUZPHK1eWCV4TEgwWORcvLoUEyJzlo0bhsHQpLm+/LZ+iBw6EMWPSjaXRaOjevTsAv/76a6bz0lY4Tas9gD6b62X3BnInNDTTsQAjAWGXKn2spkEoE1OBxywBIYRg4MCBvPbaa7otLi6OIUOGGLWZw/79++nSpQtly5ZFo9GwadOmdH0WLFhA5cqVcXJywt/fnwMHDph1DS0nTpwgJSWFChUqWHS+QqFFo9FrEUZrQ0eMkLUcHjyQxX7efx8iI6FJE/juO9OOD6BtambYw4cPZ3pdrYCwsUmvBWs1iCvOL8idy5dlmo7MMBAQDqmatVWS9RlqEMrEVGCxy7qLngEDBqRrezN1BaelxMTEUK9ePd5++2169OiR7vjPP//MqFGjWLBgAU2bNuV///sfHTp0ICwsjIqpJSD9/f2JN/GPsGPHDsqWLQvAw4cP6d+/P4sXL87RfBUKLQ0byuqgJ07I6qCArFl97Jhc0h0SIjcHB1i9Or1NyGgsKW2uXr3KgwcPKFWqlMl+kZEpgA0PH14lOPgxTbUpwdELiHs2XtI7HxkJFy9KD31G3L2r27W6BqFMTAUeswTEsmXLrD6BDh060KFDhwyPz5w5k8GDB/POO+8AMHv2bLZv387ChQv5KrWgS0gWttb4+Hi6d+/OhAkTaNKkSZZ9DYVNVk5DxfOLSQ0CpB/irbfklk2KFi1KzZo1OX/+PMePH6djx44m+4WGXgJqANHs3LnTpIBITNKAjw8cPizzo2cmIKxtYlJRTIWKHJUczW0SEhIICQnRqd9a2rZty6FDh7I1htYs1rp1a97Kxj/sV199RdGiRXWbMkcpMkIrIM6ckTmbckqj1LxLx9PUfzDk0KHTqXvR7NXF2EqMKsppw3LDwjK/aG76IJSJqcCTrwVEeHg4ycnJeHp6GrV7enpy10A1zozg4GB+/vlnNm3ahJ+fH35+fsbFYdIwYcIEIiMjdduNGzdy9BkUhZcKFaBkSVmf4fTprPtnRUBq3qVj2sR8JggJuZC6F83hw4d5apCZ0KgmdXYFhMH/ka21NQhlYirwmGViyis0aRx7Qoh0bRnRrFkzs/7QHR0dcczEVqxQaNE6qrdtk2YmbV49Q5KT4cIFuH1bbjVq6NdQpMVQgzD1N37v3j2uXZM13x0c4klISODw4cO0bt0a0AuI+PgUtvz7L50gcwGRkGBUj8I21ZyaTkAkJcHo0eDrK1PmZoaKYipU5GsNomTJktja2qbTFu7fv59Oq7A2QUFB+Pj46P5pFQpTZOiHSKVrV/kw36YN9O8PLVrAzZum+/r5+WFnZ8eDBw9MZkuWayRkfGvFijLjq6GZSWti+vffmwwNCgIg5fz5jCsIpUl1a5eRgPjpJ5g/X3ris7rZG6b7ViamAk++FhAODg74+/uzc+dOo/adO3dm6WzOKcOGDSMsLCxTe7BCoRUQpuIk7t+HrVvlvo8PeHrKh/bvvzc9VkSEE3Xr1gNMm5m2bNmCVkBUqVIagD179uiOazWI8PDH3ACiAZukJCLS/g0LAV98IXOdG2CTRkDYxMVJoTB8uL7TgwemJ69FKwyUialQkOcCIjo6mtDQUEJTF/VcuXKF0NBQ3RPUmDFjWLx4MUuXLuXcuXOMHj2a69evM2TIkDyctUIh0QqIv/9OX/pU+1xTv748PneufL9oUfqH+h07oFw5uHdvFeCS7sEkMTGRHTt2ALLeSq1a5QA4evQosakXtrHRprxxYOy4cfybaipN7NtXhrtqGTpUllRNExqeVkDUOHRISjPDSL7LlzP/QlTBoEJFnguIEydOUL9+ferXrw9IgVC/fn0++eQTAHr37s3s2bP57LPP8PPzY//+/WzdupVKlSrl6ryUiUmRHcqWlZkzkpPh1CnjY9qsGe3by9dXX5V9796FtOtBv/1Wvt6+XRPYxuHDfxsdDw4OJioqCkdHaVqqUKEYFSpUIDExkeDgYAAuXToHgI2NI1988QWnXn+dSMDz2jVZyBukz+G770x+FpvIyNTz5W2huim7WXYFhNIgCgV5LiACAwMRMu240bZ8+XJdn6FDh3L16lXi4+MJCQmhRYsWuT4vZWJSZIeMVlSnpMD27XJfKyAcHPQ+3gUL9H0vXJDahkYDbm7JQDMOH/6QpCR9Esytqbaq0qVlplY3Nw2tWrUC9H6Io0dlhgEnJzfs7OzwGjCANtoBtGFWWgdIiRKweTOiaFFWp3YxFBAVgbL//isPlCunn+zKlcbaSFrUQrlCRZ4LCIWioGNKQPz1F4SHg5ubTC2u5b33wNZWpgnXBhgtXChfO3WCXbsEkEJyciuOHLmkO0/6H6Bo0fKAHFcrILR+iEOHZB1qe3uZev/FF1/kgvYm/fixzPR365Z8X64cdOpE4t27fJF6Dc3jx4AUEK21F27aVAqVL7+U73fuhM6dM/4yTC2UUyamAosSEApFDtEKiEOHpKkJZOgryOglrfMYoHx56NJF7o8dC9evy9rZIF0DAQF2ODrKxWtbt8on+KtXrxIWFoatrS2OjiUBmawvMDAQkGGxFy9e5J9/pJZgY+MEgJubG1X9/HisvfiNG8YCAkgBtIGumshISEnBxsYGb+05L6TmdapaVf8hLlzI+KavNIhChRIQCkUOadIEXFzg0iWYOVO2aQWE1rxkyH//Kx+ut26VgUSRkVClCrRrJ497eT0G4PBheevWmpeaNGlCXJxcuuTqCt7e3nh7e5OUlMSYMWMA6flOStL/Wzdt2hRdwOyNG3oTU3mpiSQnJ+sFREoKPHmCjY0NOg+f1tdXo4bxh0jVNtKhfBCFCiUgMkA5qRXZpVgxmD1b7k+cCPv2yTRIoL/pG9Ksmezj66uPZnr/fb1FpnZtuXPunLyxas1LHTt21NWD0Kb71pqZNm/eDMjCFIYRUk2bNkWXC8CUBpGSQhygyxTy8CG2trbpBUS9erKWhZY0ayh0mFoop0xMBRYlIDJAOakV5jB4sFwUl5gofQkpKVC7tv7+mpZmzeTaiQULYNQoaV7S0qRJcQDu3y9OeHg4u3bJ8qddunTRpft2k9GuOjMTwOuvy5oShgKiTp06egFx/Xo6AaGtBqlzO589i4MQehOTd+qeRgPjx+vXTmS0HsIcDeLKFWlv27/f9HFFnlMgUm0oFPkdjUYuGThyRP9wbcq8ZIidndQc0tKsmfQzCFGDL7/8koSEBGrVqoWPj0+6gkGvvvoqr7zyCgEBAYwY8Rnr1kk/SEqKvEd7e3vropQSLl/GIY2JSbtaOgSoC/Deeyw0SOCXTsKVLi1taeZoEBkJiL595Re2ebMyQ+VTlAahUFiJ0qVh6VL9e1PmpexQu7Y2B1Nl5s+X9Ut69uxJYqJGV95UKyDc3d3ZsWMHn3/+OU5O+n9nrRbh6upKZKq6EX/pkkkTE8Bf2hMNhEOyrS14eRlPTlunwhwNIiMT05Ej+v1580z3UeQpSkBkgPJBKCyhUydZSG7YMHj5ZcvGKF0anJxiARsSE+UTfI8ePXTaA5guOWoYLWVoZkpOFQa2//4LDx/KxjQmJlMVVRLt7GRMbtrJQfY0CHOc1B98kHUfxTNHCYgMUD4IhaV88IHMbWdnoQFXowFvb63buBZVq1alXr16OgHh6GgsDLQYtmk1jZgYOP1IFtZyDpeZYClSRHrW0WsQZ0xkR3YyVa40uxqEJdlck5Oz7qN4pigBoVDkQ/z8nFL3atOzZ080Gk06/0NaDAWSVoPYuhVC7qWp2FiunO7pXisg4kxIsxN+fkbv4+PjWfzbbwD8sWIFv739dvrUtKaS9ZkyMWnDsQxJXcmtyD8oAaFQ5EPq13dO3avNG2+8AejvqdoIprRoNGmKBiFXdCfgyB3K6DsapM7QmphsbGxk3eyOHZn07rt0BbakhtBq2bdvH3+ePQtAhydP6Lp8ucxEaIipdN+mNAitqSurNkWeogSEQpEPqVVLvtao0QW/1Cf5rDQIMC0gAEIwSO1tcOPXahA2NjbQpw9s2UKsmxu/A0+1BSZSOXz4MOk8D1qzlZbs1qSOiUnfFhGRvi0pSUZNKfIEJSAUinyIVkDcvOmqs9CYIyASEuR9WSsg+uJNC/bh63KJ5I8n6/prBYStgTM6o5Kjhw4d4h5ZoPVb2NllbmLKroAYMQKqV4cNG7K6siIXUAIiA1QUkyIvqVxZ3uxjY+UCaCDdKmpTaB/6ExNlRGt4ONjaCp6wkQM04ExMVaMqpEYmplRMCYiUlBSOHj2qT9uREXfuyNcyZTI3MZkSEKZMTNrU5Knp/xXPFiUgMkBFMSnyEnt7fZ68Q4fka9pV1KbQCoi4OL328MILGsqXLwbIKnWGyw+MTEypaPeTDaKKzp8/T2RkJEnOzgh394wnoHValyuXcxOTYaxuyZIZX1ORaygBoVDkU7TrKLSV6bJjYkpdIM2VK3oB0aABNG7cGJAJog4d0t+ws2tiOpVaDalevXpoKlY0vqihANAuxCtfPmMT0+nT+pS2hqTVIAx9D2n8IYpngxIQCkU+5ZVX5OvOnfIenB0TU+3a8vWff/QCon59mDZtGvb2cjncjh36EqLacqWGAkK7byggTqcWHPL19YUKFYwvGhen308VECF37xL59Klsi4/XC5GUFJn4T0vHjjLvOci1FYZrLwwX492+nfGHVuQaSkAoFPmU5s3lg/PNm3D+fPZMTFrntqGAaNAAqlSpwujRsnLR7dtFeZSa4/vYMWl2qlGjKall4E1qEEYCQqumaDFY4i1SBcTEhQuZEhQkGxMT9Wsc0kYkubjo8z0tXCgX8P3xh6w5YZhSPO16C8UzQQkIhSKf4uwss74C7NqVPROTVoM4cEA+zGs0+gf2/v07os3beuiQ9C/s27cP0PDPP4uoU0emYspMQNSrV09OzJDoaHlzb9IETWq/W8C8xYtJKFJE9rl7V75eu2Z8rosLeHrq3z99KrWKmjWlkNASGSkFx59/SoFz6pReK4mPh/Xr0Uk9hdVQAkKhyMcYmpmyIyC0GoQ28ql6db3GUbt2beztZV3U3367jxAiVUD4ER5ekidPYM+e9AIiIiKCG6kD1qlTJ50/IO7BA5mvXFsEA7iJdHJfSTUzJWt9E1evGk/YxUVGPJli927j9x07SsfM0KHg56fPjDh1KvTsCd27Z/zFKCxCCYgMUGGuivyAVkDs2aN/QM7MxFSlinFOpgYN9Ps2NjZUrSpzKB04kMiZM5e5e/d1bGz0xSjeew+++OJDoJU+FXhISOrYVShatKjMSGjAw9Sa2FqiQFfmNFVv4NrRozIE9t9/jSfs7GysQRhiqEEYslhmuOWzz+Tr99/L1337TPdXWIwSEBmgwlwV+YH69aFECemg3rFDtmWmQdjZSa1Bi6GAAGjRQkqPS5dKMmgQwFxSUt7RHX/yBOLinIB5LF68mO3btxMcHAzIkqcAtGwJu3fzONWZrUktiaolDKiVqspoF9ZVmTgRypY1rkoH/H3mTMYC4sqVjD8o6G1naTPOKqyGEhAKRT7GxgY+/FDe+LXLEjw8Mj9Ha2aC9AKia9cqwGUSE50JCalGxngDNnTu3JlPP/0UkOVLdbRuzZ2iRQEofuKE0Zn/2Nqya9cuVqxYgfdLL2U615NHjmSuEmWGjY1cT6FdnKewOkpAKBT5nI8+kkE833wDo0fLB/jM0DqqIX0uvSZNArCx6Qq6rEobcHdPoXnztKO4ULToKyQlvQJUAOz49dc3jBY026SqMk6pobJabhcvTrly5ejfvz/uhuqMCRKiohCgrzNhDhERsmar0YAJ6fv98gscPGj++AolIBSKgoCnp9QkZs6U5RwyQ6tBVKoExYsbHytWrBijR3cAAoA3CQz8jps3bfjzT/jtN3jzTX0uv8jIbcBWYBXQjG3bPJg6VQYSAdin1pRIS4q2ZgTgUqNGpnO1S0mRazH27s24U0YmqIgIoxBbIP1iu3/+gddfx4QEVGQDJSAUikJG167QoQNMmmT6+BdffIGfXzFgFe++OxA3N2nC6tIFfvgBhgxJe0ZzevX6RvduwgT5Wq5mTaNeYUifw826dXVt7r17m5yDNiB1GxAZGSnVnh9+SN/x1VflTX7NGhmpZMjDh3pHtZbHj+HMGTh3Tr6/fFl/TNW9NhslIBSKQoa7uywUNHiw6eOOjo7s2bOH7du306dPn3THe/WSywwWLdK3Xb/eULd/4oR8cA+9XELXlgjUAyoCxatU0bUXrV6d303MwQfoAqwBrly5wty5c4kxUdWOxo05duEC50wt0NOurTDk9m3w9QUfH+mfSErSHzPM7aTIFkpAKBTPIR4eHrRt2xaNqZsy8h777rv6hIHGCf5kFu59IfpwqhtAEpAAlE9zI+8FvAwYFjC9C2wGBDB27FhGjhzJmMmTSUvko0e8+OKL+Pj4kJIdZ7ZhaOyRI8ZlTGNipNQz/DCKTFECQqFQZIihwxv0D/HLl0M0egFhuD66TJqFb3HAn0DqagVSXRjYpy7YOJy6wO6koTkolTsGq6PvaXM7mUIrPAwX4k2aBCtX6t9v3Qr/+Q80bpzxOAojlIDIALVQTqEwFhC1aoFh1GpGAsIjTRzukiVLKFGiBOOBgUiNAqB+mhCrNO5mqF+fA9X0obgX76erZyfp1g3atZP7hmsnDh+GX3/Vv//7b4OLRUPTplJNUmSIEhAZoBbKKRQyJZKW8ePBMCjpX6SvIQX4BdBoNAwcOJDAwECSk+HiRekXHjRoEA8ePKD9a6+xAngIVK5cGc800UkXDfbvjRgBf/3FWW3OEOBGlD4LLY6O+v3XX9cvDslscZ1hpthly2ShDe2qbIVJlIBQKBQZ0rSpTL30wgsyBNZwWcOvdMOfE5QgAofu+4iKimLZsmUIYUPHjlKYbNsm+2o0GsqVK6c794UXXpBpOwxIAjyBoUDtefOYO3cut7Q5nDA2MSX95z9yx8NDhm1pQ27T5noyxEDYGO0vXaoinDJACQiFQpEh3t4y1XhwsAyF9fExPKrhL/x5TDE2bmxBkSLS5PTDD/q0IPv363tXN5AuPj4+6QQEyOV7C5FhsCNHjuSJtggGcMegCl3PTZtI2bBBprl1c9NrEOHhGX8YQ6FgmPl18GD94g6FEUpAKBSKTPH2Bu29vFEjaNvWdL/y5aF3bzh5Ut9mGInar18/3X7lypVNCoi0RBsshDM0MZ25fp1bDRuCv79syGDRnhEZCQhIn0Tw3j0lNFACQqFQmIFGA1u2yASq2jo/Wu7ehbVrYft2fZu2/MOECfDWW8VZtmw1PXv25M0338yWgLhrIGFuGhQQigIuXjTwWmSVoAqMczY9eGB8LK3AaNRIphbftCnrcQsxSkAoFAqzsLODd96RJRm0aDNug3ROa7l2TS5unjZNRpl6evZh3bp1uLq6Ujob+Zf+NXiy/8cgiikSuGRYnS47GoRhbey0JUzTJvzTahsrVmQ9biFGCQiFQmERU6dKLWLRIikwXnxRthveh69fh2HD9O8Nfchp10tkxcO4OHyBOsiV29cMq9NlR4MwJG3pU61/Qwjj/E7PeaZYJSAUCoVF1K0rb/japQSmHuKTkmD1av17bYokME9AdOzYEYAzgHY1wyNDs5BBgsC0Fe+yxfffS7NT377GH+ToUbkq8DmNclICQqFQWAXDh3g7O6hcOX2fsDD9vpeXl8lxnJyc0rX5a53RBmgFxMmTJ/nlr7/0BwzzL5nDhAkyKWDa899+Wx+vq6VfPxkDbJjKQ0tMDPzvf+nNWAUQJSAUCoVVMBQQ5cpBs2b691oz0/nz+raSJUsC5YH2RuOEhYXRo0cPo7aqVaumu15ERAQAPXv25PVevTg1aBARxYuTOHKkZR8gtbSqSTp21Cf7i42VatGhQ8YfSMt//ytT4rZpY9k88hFKQCgUCqtgKCDKlzdOedStm3y9c0f/0G1rawtcBv4A9LGzpUuXppiBmcfe3p4KFSqkvpuBq+tCAHbs2MGFCxd0jmy/pUspERHBxKVLM56knV3Gx0JDM/t4+vAsw3BZU8U5tDY1Q3taAUUJiAxQuZgUCvMwFBAVKkgLjJbmzWWIbHJy2rVs0l/g5tZN1+Li4oK7u7vuvZubW2pajtLAGKKjhwAyOd+AAQPSzeNxZGTGk0xTQen/Mv9Ixhw/LkOyUjUXQC/t/vgDhg6F+/dlYe9CghIQGaByMSkU5mHo2y1fXqYMX7cO9uwBJyd9VdHbt2XePMP7bLVq5YzGMhQQ5cuXT3VoGz6tuwAQauKp3z6zSZYoYVR46FYmXdPx2WdQtqyxj0IrIDp2hIULYf58c0Y0TXi4cd6oPEQJCIVCYRXSahAg78WBgXK/bFn5+t13UKeOvFdrcXS0ATSAbHQzqP0wYsQISpQowejRBgWxUzWIFMOY2lQyMSLJi/74I3E1a7IKmWjQLJ4+Na6DnZJiXAf7llkiJz03bsiILG0hjjxGCQiFQmEV0vog0qINWlq+PP2xs2c7IW/X4YSEgKurPpV4ixYtAHj99UG6tu7dpWkpwfDmnEqWGoSjI2GrV/MmYJtZ34xYs0a/n5wMBw/q35csacmIerZula8mamPkBZkKW4VCocgupjQIQ7QahIl7OtHR+mfVnTtBiIe699oIJkPTfqVK2ifsUoBx2owsBQTwNDUz7PXM+maHhAT4+Wf9e0tDbPMpSoNQKBRWIbsCIisePpTpwLXIaCdjAaHRuANByPyvrY3Oz9LEBBw9ehSAPcBDQ1uXuTRubFy8O6e+g3y2IE8JCIVCYRW8vKQzukQJvUPaEONU4Rlz4wZ07tyZoKA1VKgQT//+sj29gBia+u4Lo/P/yWTs3aGhXLlyhf/+97+6tr3aHCGWkFYdMkdACAFz5siU5fkUZWJSKBRWwc0N9u0DZ2ewMfHoabgYulUraN0afv8djh0z7nfzJtjY2PDkSW9u3JD1JRwc0tbHdjXYN155/QswEjgCHE0zhzU7d7K4ShWjtlhTq6EtJa2AePrU9FoJkOFdo0bJ/XymOWhRGoRCobAaAQEyQskUhvflkiXh44/h22/1bdoH+YsX4csvjU37S5bAhx/q38fFGXoa0qfmmAscAwYAOw3aH6brCTHWDCk1qHoHyJQbhiQmymXlGzbA+vXWu24uoQSEQqF4JtjYyBRG9vYwcaJsM4x2+uor+Xr/vjxuWHgoLUWLGjo5MnhCB1YC7xi8NyUgImNjs5i5Gfzyi/F7gxoWgPRXLFgAPXoYh8vmU5SAUCgUz4zFi2WNiHr15PvKlWVNnpAQaXaqUSN74zx6pM/YWry4Ke93caRvogaGt3+9gHAEXgXciTBwbkzI3uWzT1rT0d9/m+6nzUybz0xNSkAoFIpnhpOTfj2Elm7doEEDud+6dfpztE5qQwyzwjo42DN58mTGjh3Lz6l2qQYNDiETaezOQEB8CWwEfjMSEDsA4ehoxicyg+XL5WprUxQvnj5E1sQiwGeNclIrFIp8w6efysAgbb69UqWgUydYudK4n6GAiImBKVOm6N63bduWChU8Ut+VJxbYCjgD93S9tIvuWhJtUOv6LhB76RILp09n+/z5Rv6LHPP225kfT5tDKjERcktYZROlQSgUinxD6dLGD9nFikkBoa0tkbqo2ijh35MnxmUZPDw80kWfdgJaAXoDjv7pfHtUFNeB74HbQJStLeMXLGAX0v1tQqnJPlqT0dmzWfdNSDA2MeUDDUIJCIVCka8wLAj3wgvg4iLvr/HxGZeINqwSCmmXJ5xAiobhBm16ifIIqAS8l/o+KioKFxeZDDAeOJa6bxFTp8Lu3bL8XlbExxsLBRcXMNBu8gIlIBQKRb6jXTv5OiHVa+zsLAVHBkXojO6j6bNdaBdgzEM6psFQQKTlyZMnODs7mzXfTMlu4aCnT+H0af17IeS5t29LFenWrWfuxFYCQqFQ5DvWrIFLlyBtORZHx3QlHQApIGJj4T//kWsmMmYj0BQwrIc9DplJVjtWFMLgRhwTE2P+B7CEK1dkbWxDjh+X5fns7GRMsOFikp07ZVHwXKTQO6mfPHlC69atSUxMJDk5mQ8++IB3tVXWFQpFvsTDwzi3kyFlyxrXkgApIObNM06LlDEH07yfDlwDZATUypUriYoaC7wGNEbme3oGvPVW1n20AmHfPmibWoUvF7WKQi8gnJ2d2bdvH87OzsTGxlKnTh1ee+01SuQkQZdCocgzypZN7/ONjIQLF3Iyqn4BxooVa4Hlqe86ActyMnD2SSv1MkKjybqPlSj0JiZbW1udPTEuLo7k5GQj9VGhUBQsTPkhHjzIadCPP6Ad2KA0Hv8FdjIeex4B23NyiQJInguI/fv306VLF8qWLYtGo2HTpk3p+ixYsIDKlSvj5OSEv78/Bw4cMOsajx8/pl69epQvX55x48ZRMqdFPRQKRZ5hKm34tm1wPU1xB8Oa2FnTDdAW6TFM3fEC0Iav6UsJpIt7IPA6eh2jMJPnAiImJoZ69eoxP4Narj///DOjRo1i4sSJnDx5kubNm9OhQweuG/w1+Pv7U6dOnXTb7du3ARkXferUKa5cucLq1au5d++eyWsBxMfHExUVZbQpFIr8g6EGUauWfF29Gv7807ifds0EwKBBZIMiwDSgqIljtgggDliBzBj7NuAJVAc6IGtLFDpEPgIQGzduNGoLCAgQQ4YMMWqrVauW+Oijjyy6xpAhQ8TatWszPD558mSBDJo22iIjIy26nkKhsC6//CKE9MwK0by5fj/t9v33+v2NG4Xo2DE8w75ZbyNN3Bfsjd5vtnzwnG0WEBkZma37Wp5rEJmRkJBASEgIbbXe+lTatm3LoUOHsjXGvXv3dFpAVFQU+/fvp2bNmhn2nzBhApGRkbrtxo0bln8AhUJhdUqV0u+nDYM1pFo1/b6rK/j6Wh6Y4uPTKk3LZ0AE8JKuxUQl1QJPvhYQ4eHhJCcn4+npadTu6enJ3bt3szXGzZs3adGiBfXq1aNZs2YMHz4cX1/fDPs7Ojri7u5utCkUivxDGYMlDJMnm+5ja6tPAAhy8Vz37pZf08lJn168b99+wCRk0aJN+mtYPny+pUCEuWrShHUJIdK1ZYS/vz+hoaFmXzMoKIigoCCSrVltSqFQ5JgaNSAoSOZtcneHVatknQmQ2WDLlIExY+Sxjh3h6FF46aXMx8yK+HitX8KfXbt+MDiid2g/ytkl8iX5WoMoWbIktra26bSF+/fvp9MqrM2wYcMICwvj+PHjuXodhUJhPkOHQs+ecr+CQe2grl2lwNCWN928WZYw9fCAoga+Z3O1idhYbeqNldy/b/hwqq9sN8m8Ia1HLib1y9cCwsHBAX9/f3buNE66u3PnTpo0aZJHs1IoFPkJw6p0aXPiaTSyBoV2v0sXmQMvKMi8azg4aE3NDmmO6AXEM1pvnZ70yaesRp4LiOjoaEJDQ3VmoCtXrhAaGqoLYx0zZgyLFy9m6dKlnDt3jtGjR3P9+nWGDBmSh7NWKBT5hXLl9PtZJU3duFHmvjO12G7AgF04O68xeZ6NjQu//fYb3t4V0hwxz0qfPc+pmeSigMjzMNc9e/aYDCsdMGCArk9QUJCoVKmScHBwEA0aNBD79u3L9XnNnz9f1K5dW9SoUUOFuSoU+Zz9+4XYvdu8c9JGi44adVgULVrcZCRplSrynNq1TUWa6u9bmYWj9qakcMdZ9LB2mOvjx2Z/X9kNc9XIL0qREVFRURQtWpTIyEgV0aRQFCLSxrksXZrCe+85kpSUmK5vuXLSl1G5sqkEqm5ANNAJwZaMr4dAGqI8uQhUA4KAYZZ/BMmDB2Bmdojs3tfy3MSkUCgUecHu3fDRR/r3iYk2JCUlARvS9b11C/7913R27dKlI5ApxDdn46qlAZkjtiswEunF+MDMuRuRiyYmpUFkgdIgFIrCjVaTCA6GBw9+5dVXX0UWKP0zk7NMI1LrSiRjgy3G0UUaXcHT9CH6JYDwdK3Z5Pp141CubKA0iBwSFBSEj48PjTJbqqlQKAo8Z87A+vXQpAl069aN1157DdjD0KGb2bPHvLFqcJ5xTKcm5wG4SiVC7F/gFd7I9LwcrbZKW4DbiigNIguUBqFQPF8kJiYSFhaGr68vDx5osHTJVRFieUoR2rRJYdeuKcDU1CPpNQh3IDKDcd5HJgSsDawCfkzbYeJE+Pxzs+amNAiFQqGwAHt7e+rVq4dGo8HRMfO+mUXbP8UZ0LBrly164WCaZDKugZ2MrErREVhtqsMff2Q+yRygBIRCoVBkgKtr5sdHjMjJ6BqgJNCZFG7rWgcDc4H9QDzGLnOT5p5cXEldIHIxKRQKRV5ga5v5ceeMH/wz4S1gFtI1LUkmXre/D1iauu8IBkesOolsoTSIDFBOaoVCAXDsGEydCmFhkJwMlSrpjxUpkvF5GbMSQ+EA+ugnMPZQZCkcAHr0sGQS2UI5qbNAOakVCoUh3t5w7Zrcj4qSWWNzjuAgzXDnEH5AZkYj7Q07Eln77p933qHW99+bdTXlpFYoFIpcwHAFtmUahMlRacZB6gEp2AK/AhMzPUOrXaxavtxak0iHEhAKhUJhBoZCwc6qXlwNAhugE3Kddeahq3Gpr7aFOZurQqFQFCTSZoK1rptyPIZFiGAOUMlkz6eprz6GtVWtjBIQGaCc1AqFwhQDBsjXF1+Ur3v3wqefpu9nbs0JyZdADYP3HwBXgSrpempNTGU8PCy5ULZQTuosUE5qhUKRlpAQmf6otMy9R0oKzJwJY8fK902bwoEDYGO1R/CDwD/AOzzBHleSWIhcZX3Az4/mJ0+aNZpyUisUCkUu4e+vFw4gBUHTpvr3+/alTyeeM5oB7wBQmkhK8QofAV7Aply0cigBoVAoFFbAcFFd2gV2335rves8xZlwviYKWaEu2rqSyAglIBQKhcIKNGwIL78MgwalP9aihTRLdetmrau5or19J+RiNleVakOhUCisgI0N7Npl3Hb7tlxUp7UCbdoEZ89mXTs7a6ohq9j1wdPSdLPZQDmps0A5qRUKhbX56Sfo29c6Yz1+HEnRokXNOkc5qXOICnNVKBS5RSXTSxsswlzhYA5Kg8gCpUEoFAprc+yYfh0FzMXR0Z74+PctGsuSO7jSIBQKhSKfYhjl1LnzKSZO7GKyn7f3s5lPRigBoVAoFM8YwxIOy5cvwdu7vMl+Bw8+owllgBIQCoVC8YwpU0a/r9FkXJioXLmsx/oxXZFq66HCXBUKheIZU6wYfPIJPH0KxYunTwBoDsePw5tvWm9uhigBoVAoFHmAYYK/wED4+GO5RmLTJvPGycWF1EpAKBQKRV6j0ciyptp9yLoetuG5uYXyQSgUCkU+RFuMKKts3nfv5t4clIDIALVQTqFQ5CVaAfG//+nb0qbyAIiOzr05KAGRAcOGDSMsLIzjx4/n9VQUCsVzhHYBndbx3LmzXHndtSu4uaXvn4sLqZUPQqFQKPITW7bAtm3Qvbt87+wMly/LZIChoen7f/ll7s1FCQiFQqHIR5QoAf36GbdpHdYODsbtV65AxYq5NxdlYlIoFIoCgp3BI/3Jk7mfikMJCIVCoSggGIa0WjMjbEYoAaFQKBQFBEMBYW+f+9dTAkKhUCgKCIYCIrsL6XKCclIrFApFAaFiRemoLlIEHB1z/3pKQCgUCkUBwcEBHj2SIa82z8D+owSEQqFQFCAMa0nkNsoHoVAoFAqTKAGhUCgUCpMoAZEBKlmfQqF43tEIIUReTyI/ExUVRdGiRYmMjMTd3T2vp6NQKBQ5Jrv3NaVBKBQKhcIkSkAoFAqFwiRKQCgUCoXCJEpAKBQKhcIkSkAoFAqFwiRKQCgUCoXCJCrVRhZoo4CjoqLyeCYKhUJhHbT3s6xWOSgBkQVPnjwBoEKFCnk8E4VCobAuT548oWjRohkeVwvlsiAlJYXbt2/j5uaGRqOhUaNGHD9+3CpjWzpWds/LTr/M+mR0LLvtUVFRVKhQgRs3buTpIsO8/s3MOServpYeN9Wufq+cn1dQ/8eEEDx58oSyZctik0laWKVBZIGNjQ3ly5fXvbe1tbXaP4+lY2X3vOz0y6xPRsfMbXd3d8/TG05e/2bmnJNVX0uPm2pXv1fOzyvI/2OZaQ5alJPaTIYNG5bnY2X3vOz0y6xPRsfMbc9r8vo3M+ecrPpaetxUu/q9cn5eYf8fUyYmRa6h8lgVLNTvVfDI7d9MaRCKXMPR0ZHJkyfj+CxqIypyjPq9Ch65/ZspDUKhUCgUJlEahEKhUChMogSEQqFQKEyiBIRCoVAoTKIEhEKhUChMogSEQqFQKEyiBIQiT9i8eTM1a9akevXqLF68OK+no8gG3bt3p1ixYvTs2TOvp6LIghs3bhAYGIiPjw++vr6sW7fOonFUmKvimZOUlISPjw979uzB3d2dBg0acPToUYoXL57XU1Nkwp49e4iOjmbFihX88ssveT0dRSbcuXOHe/fu4efnx/3792nQoAHnz5/HxcXFrHGUBqF45hw7dowXXniBcuXK4ebmRseOHdm+fXteT0uRBa1atcLNzS2vp6HIBl5eXvj5+QFQunRpihcvTkREhNnjKAGhMJv9+/fTpUsXypYti0ajYdOmTen6LFiwgMqVK+Pk5IS/vz8HDhzQHbt9+zblypXTvS9fvjy3bt16FlN/bsnpb6Z4tljz9zpx4gQpKSkWlSxQAkJhNjExMdSrV4/58+ebPP7zzz8zatQoJk6cyMmTJ2nevDkdOnTg+vXrgOkiJRqNJlfn/LyT099M8Wyx1u/18OFD+vfvz6JFiyybiFAocgAgNm7caNQWEBAghgwZYtRWq1Yt8dFHHwkhhAgODhavvvqq7tgHH3wgVq1aletzVUgs+c207NmzR/To0SO3p6gwwNLfKy4uTjRv3lysXLnS4msrDUJhVRISEggJCaFt27ZG7W3btuXQoUMABAQEcPbsWW7dusWTJ0/YunUr7dq1y4vpKsjeb6bIP2Tn9xJCMHDgQFq3bs1bb71l8bVUwSCFVQkPDyc5ORlPT0+jdk9PT+7evQuAnZ0dM2bMoFWrVqSkpDBu3DhKlCiRF9NVkL3fDKBdu3b89ddfxMTEUL58eTZu3EijRo2e9XSfe7LzewUHB/Pzzz/j6+ur81/88MMP1K1b16xrKQGhyBXS+hSEEEZtXbt2pWvXrs96WopMyOo3U5Fm+YvMfq9mzZqRkpKS42soE5PCqpQsWRJbW1ujJ0+A+/fvp3viUeQP1G9WsHiWv5cSEAqr4uDggL+/Pzt37jRq37lzJ02aNMmjWSkyQ/1mBYtn+XspE5PCbKKjo7l06ZLu/ZUrVwgNDaV48eJUrFiRMWPG8NZbb9GwYUMaN27MokWLuH79OkOGDMnDWT/fqN+sYJFvfi+L458Uzy179uwRQLptwIABuj5BQUGiUqVKwsHBQTRo0EDs27cv7yasUL9ZASO//F4qF5NCoVAoTKJ8EAqFQqEwiRIQCoVCoTCJEhAKhUKhMIkSEAqFQqEwiRIQCoVCoTCJEhAKhUKhMIkSEAqFQqEwiRIQCoVCoTCJEhAKhUKhMIkSEArFc0ZCQgLVqlUjODjYquNu3ryZ+vXrWyXNtCJ/oASEokAzcOBANBpNus0w0ZnCmEWLFlGpUiWaNm2qa9NoNLrCMoYMHDiQV199NVvjdu7cGY1Gw+rVq600U0VeowSEosDTvn177ty5Y7RVrlw5Xb+EhIQ8mF3+Y968ebzzzju5Mvbbb7/NvHnzcmVsxbNHCQhFgcfR0ZEyZcoYbba2tgQGBjJ8+HDGjBlDyZIleeWVVwAICwujY8eOuLq64unpyVtvvUV4eLhuvJiYGPr374+rqyteXl7MmDGDwMBARo0apetj6onbw8OD5cuX697funWL3r17U6xYMUqUKEG3bt24evWq7rj26fzbb7/Fy8uLEiVKMGzYMBITE3V94uPjGTduHBUqVMDR0ZHq1auzZMkShBBUq1aNb7/91mgOZ8+excbGhsuXL5v8rv766y8uXbpEp06dzPyW4erVqya1tcDAQF2frl27cuzYMf7991+zx1fkP5SAUBRqVqxYgZ2dHcHBwfzvf//jzp07tGzZEj8/P06cOMG2bdu4d+8evXr10p0zduxY9uzZw8aNG9mxYwd79+4lJCTErOvGxsbSqlUrXF1d2b9/PwcPHsTV1ZX27dsbaTJ79uzh8uXL7NmzhxUrVrB8+XIjIdO/f3/WrFnD3LlzOXfuHN999x2urq5oNBoGDRrEsmXLjK67dOlSmjdvTtWqVU3Oa//+/dSoUQN3d3ezPg9AhQoVjLS0kydPUqJECVq0aKHrU6lSJUqXLs2BAwfMHl+RD7F6AnGF4hkyYMAAYWtrK1xcXHRbz549hRBCtGzZUvj5+Rn1nzRpkmjbtq1R240bNwQgzp8/L548eSIcHBzEmjVrdMcfPnwoihQpIkaOHKlrA8TGjRuNxilatKhYtmyZEEKIJUuWiJo1a4qUlBTd8fj4eFGkSBGxfft23dwrVaokkpKSdH1ef/110bt3byGEEOfPnxeA2Llzp8nPfvv2bWFrayuOHj0qhBAiISFBlCpVSixfvjzD72vkyJGidevW6doB4eTkZPQ9uri4CDs7O9GtW7d0/Z8+fSpefPFF0blzZ5GcnGx0rH79+mLKlCkZzkFRcFAV5RQFnlatWrFw4ULdexcXF91+w4YNjfqGhISwZ88eXF1d041z+fJlnj59SkJCAo0bN9a1Fy9enJo1a5o1p5CQEC5duoSbm5tRe1xcnJH554UXXsDW1lb33svLizNnzgAQGhqKra0tLVu2NHkNLy8vOnXqxNKlSwkICGDz5s3ExcXx+uuvZzivp0+f4uTkZPLYrFmzaNOmjVHb+PHjSU5OTtd38ODBPHnyhJ07d2JjY2yIKFKkCLGxsRnOQVFwUAJCUeBxcXGhWrVqGR4zJCUlhS5dujB9+vR0fb28vLh48WK2rqnRaBBpam0Z+g5SUlLw9/dn1apV6c4tVaqUbt/e3j7duNow0SJFimQ5j3feeYe33nqLWbNmsWzZMnr37o2zs3OG/UuWLKkTQGkpU6ZMuu/Rzc2Nx48fG7V9/vnnbNu2jWPHjqUTgAARERFGn1FRcFECQvFc0aBBA9avX4+3tzd2dun//KtVq4a9vT1HjhyhYsWKADx69IgLFy4YPcmXKlWKO3fu6N5fvHjR6Km5QYMG/Pzzz5QuXdoiez9A3bp1SUlJYd++feme7LV07NgRFxcXFi5cyB9//MH+/fszHbN+/fosXLgQIQQajcbsOa1fv57PPvuMP/74w6SfQ6sh1a9f3+yxFfkP5aRWPFcMGzaMiIgI+vTpo4u22bFjB4MGDSI5ORlXV1cGDx7M2LFj2b17N2fPnmXgwIHpzCitW7dm/vz5/PXXX5w4cYIhQ4YYaQP9+vWjZMmSdOvWjQMHDnDlyhX27dvHyJEjuXnzZrbm6u3tzYABAxg0aBCbNm3iypUr7N27l7Vr1+r62NraMnDgQCZMmEC1atWMTGOmaNWqFTExMfz9999mfGuSs2fP0r9/f8aPH88LL7zA3bt3uXv3LhEREbo+R44cwdHRMct5KAoGSkAonivKli1LcHAwycnJtGvXjjp16jBy5EiKFi2qEwLffPMNLVq0oGvXrrRp04ZmzZrh7+9vNM6MGTOoUKECLVq0oG/fvnz44YdGph1nZ2f2799PxYoVee2116hduzaDBg3i6dOnZmkUCxcupGfPngwdOpRatWrx7rvvEhMTY9Rn8ODBJCQkMGjQoCzHK1GiBK+99ppJ01dWnDhxgtjYWD7//HO8vLx022uvvabr89NPP9GvX79MzVyKgoNGpDWkKhSKdAQGBuLn58fs2bPzeirpCA4OJjAwkJs3b+Lp6Zll/zNnztCmTRuTTvSc8ODBA2rVqsWJEydMLlRUFDyUBqFQFFDi4+O5dOkSkyZNolevXtkSDiB9G19//bXRoj1rcOXKFRYsWKCEQyFCOakVigLKTz/9xODBg/Hz8+OHH34w69wBAwZYfT4BAQEEBARYfVxF3qFMTAqFQqEwiTIxKRQKhcIkSkAoFAqFwiRKQCgUCoXCJEpAKBQKhcIkSkAoFAqFwiRKQCgUCoXCJEpAKBQKhcIkSkAoFAqFwiT/D+sjUANna+9JAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import fractions\n",
+    "\n",
+    "resampling_factor = 1.5\n",
+    "\n",
+    "def simple_rasa(resampling_factor):\n",
+    "    rat = fractions.Fraction(str(resampling_factor))\n",
+    "    up, down = rat.numerator, rat.denominator\n",
+    "\n",
+    "    # Much faster than FFT-based resampling\n",
+    "    data_up = dsp.resample_poly(sig, up, down, axis=-1)\n",
+    "    data_down = dsp.resample_poly(sig, down, up, axis=-1)\n",
+    "\n",
+    "    # Calculate an up/downsampled version of the PSD using same params as original\n",
+    "    _, psd_up = dsp.welch(data_up, fs * resampling_factor,  **kwargs_psd)\n",
+    "    _, psd_dw = dsp.welch(data_down, fs / resampling_factor, **kwargs_psd)\n",
+    "\n",
+    "    return psd_up, psd_dw\n",
+    "\n",
+    "\n",
+    "psd_up, psd_dw = simple_rasa(resampling_factor)\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(4,4))\n",
+    "f_max = freq < 100\n",
+    "ax.loglog(freq[f_max], psd[f_max], color='k', label='original')\n",
+    "ax.loglog(freq[f_max], psd_up[f_max], color='b', label='upsampled (factor = 1.5)')\n",
+    "ax.loglog(freq[f_max], psd_dw[f_max], color='r', label='downsampled (factor = 1.5)')\n",
+    "ax.set_ylabel('Power')\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that up-/downsampling shifted the peak (oscillation) in the spectrum relative to the original data.\n",
+    "Now we compute the geometric mean of the up-/downsampled version of the data and repeat the procedure for a different resampling factor.\n",
+    "We can see that this creates a version of the original data with 2 peaks that are shifted around the original peak by a factor of x."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x111e85820>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "psd_up_19, psd_dw_19 = simple_rasa(1.9)\n",
+    "psd_up_11, psd_dw_11 = simple_rasa(1.1)\n",
+    "\n",
+    "gmean = lambda x, y : np.sqrt(x * y)\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(4,4))\n",
+    "f_max = freq < 100\n",
+    "ax.loglog(freq[f_max], psd[f_max], label='original')\n",
+    "ax.loglog(freq[f_max], gmean(psd_up_11, psd_dw_11)[f_max], label='factor 1.1')\n",
+    "ax.loglog(freq[f_max], gmean(psd_up, psd_dw)[f_max], label='factor 1.5')\n",
+    "ax.loglog(freq[f_max], gmean(psd_up_19, psd_dw_19)[f_max], label='factor 1.9')\n",
+    "ax.set_ylabel('Power')\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can compute the median, between our 3 geometric means to obtain our aperiodic spectrum. In reality we use a bit more than 3 up-/downsampling factors, but on this data its enough to get a decent spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x111dbd5e0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aperiodic_spectrum = np.median([gmean(psd_up_11, psd_dw_11),\n",
+    "                                gmean(psd_up, psd_dw),\n",
+    "                                gmean(psd_up_19, psd_dw_19)], axis=0)\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(4,4))\n",
+    "f_max = freq < 100\n",
+    "ax.loglog(freq[f_max], psd[f_max], label='original')\n",
+    "ax.loglog(freq[f_max], aperiodic_spectrum[f_max], label='aperiodic spectrum')\n",
+    "ax.set_ylabel('Power')\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But how do we get the periodic spectrum? Its actually quite simple. We just need to subtract our aperiodic spectrum from the original spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x112ae2ff0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(figsize=(4,4))\n",
+    "f_max = freq < 100\n",
+    "ax.plot(freq[f_max], psd[f_max] - aperiodic_spectrum[f_max], label='periodic spectrum')\n",
+    "ax.set_ylabel('Power')\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can do slope fits on the aperiodic spectrum or do peak detection on the periodic spectrum.\n",
+    "However, this was quite a simple spectrum and reality is usually much messsier and noisier. For instance we might get spectra that dont linearly decrease with frequency by the same value but have a deflection point (spectral knee) at which the slope starts changing. We can also deal with those using IRASA.\n",
+    "See below for how we would do this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# %% Lets check the knee\n",
+    "knee_freq = 15\n",
+    "exp = 1.5\n",
+    "knee = knee_freq ** exp\n",
+    "duration=4\n",
+    "overlap=0.99\n",
+    "\n",
+    "sim_components = {'sim_knee': {'exponent1' : -.0, 'exponent2': -1*exp, 'knee':knee }, \n",
+    "                  'sim_oscillation': {'freq' : 10}}\n",
+    "\n",
+    "sig = sim_combined(n_seconds=n_seconds, fs=fs, components=sim_components)\n",
+    "\n",
+    "# %%\n",
+    "max_times = times < 1\n",
+    "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
+    "axes[0].plot(times[max_times], sig[max_times])\n",
+    "axes[0].set_ylabel('Amplitude (a.u.)')\n",
+    "axes[0].set_xlabel('Time (s)')\n",
+    "freq, psd = dsp.welch(sig, fs=fs, nperseg=duration*fs, noverlap=duration*fs*overlap)\n",
+    "axes[1].loglog(freq, psd)\n",
+    "axes[1].set_ylabel('Power (a.u.)')\n",
+    "axes[1].set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 100), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2.5, 0.05))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
+    "axes[0].plot(irasa_out.freqs, irasa_out.periodic[0,:])\n",
+    "axes[0].set_ylabel('Power (a.u.)')\n",
+    "axes[0].set_xlabel('Frequency (Hz)')\n",
+    "axes[0].set_title('Periodic Spectrum')\n",
+    "\n",
+    "\n",
+    "axes[1].loglog(freq[1:], psd[1:], label='psd')\n",
+    "axes[1].loglog(irasa_out.freqs, irasa_out.aperiodic[0,:])\n",
+    "axes[1].set_ylabel('Power (a.u.)')\n",
+    "axes[1].set_xlabel('Frequency (Hz)')\n",
+    "axes[1].set_title('Original + \\n Aperiodic Spectrum')\n",
+    "\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ch_name</th>\n",
+       "      <th>cf</th>\n",
+       "      <th>bw</th>\n",
+       "      <th>pw</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>9.75</td>\n",
+       "      <td>1.188926</td>\n",
+       "      <td>0.501843</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ch_name    cf        bw        pw\n",
+       "0       0  9.75  1.188926  0.501843"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# %% get periodic stuff\n",
+    "irasa_out.get_peaks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Knee</th>\n",
+       "      <th>Exponent_1</th>\n",
+       "      <th>Exponent_2</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>Knee Frequency (Hz)</th>\n",
+       "      <th>tau</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.610455e-15</td>\n",
+       "      <td>62.715188</td>\n",
+       "      <td>0.047433</td>\n",
+       "      <td>1.469384</td>\n",
+       "      <td>knee</td>\n",
+       "      <td>14.093899</td>\n",
+       "      <td>0.011292</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Offset       Knee  Exponent_1  Exponent_2 fit_type  \\\n",
+       "0  3.610455e-15  62.715188    0.047433    1.469384     knee   \n",
+       "\n",
+       "   Knee Frequency (Hz)       tau ch_name  \n",
+       "0            14.093899  0.011292       0  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aps = irasa_out.fit_aperiodic_model(fit_func='knee').aperiodic_params\n",
+    "aps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %% get aperiodic stuff\n",
+    "ap_f = irasa_out.fit_aperiodic_model(fit_func='fixed')\n",
+    "ap_k = irasa_out.fit_aperiodic_model(fit_func='knee')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mse</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>R2_adj.</th>\n",
+       "      <th>BIC</th>\n",
+       "      <th>BIC_adj.</th>\n",
+       "      <th>AIC</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.014386</td>\n",
+       "      <td>0.887723</td>\n",
+       "      <td>0.887157</td>\n",
+       "      <td>-13.429702</td>\n",
+       "      <td>-19.775835</td>\n",
+       "      <td>-21.412631</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000120</td>\n",
+       "      <td>0.999062</td>\n",
+       "      <td>0.999053</td>\n",
+       "      <td>-30.116565</td>\n",
+       "      <td>-42.808830</td>\n",
+       "      <td>-46.082423</td>\n",
+       "      <td>knee</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mse        R2   R2_adj.        BIC   BIC_adj.        AIC fit_type  \\\n",
+       "0  0.014386  0.887723  0.887157 -13.429702 -19.775835 -21.412631    fixed   \n",
+       "0  0.000120  0.999062  0.999053 -30.116565 -42.808830 -46.082423     knee   \n",
+       "\n",
+       "  ch_name  \n",
+       "0       0  \n",
+       "0       0  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([ap_f.gof, ap_k.gof])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "f, ax = plt.subplots(figsize=(4,4))\n",
+    "sns.lineplot(data=ap_k.model, x='Frequency (Hz)', y='aperiodic_model', ax=ax)\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyrasa",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/examples/custom_fit_functions.ipynb b/doc/source/examples/custom_fit_functions.ipynb
new file mode 100644
index 0000000..7abbd7a
--- /dev/null
+++ b/doc/source/examples/custom_fit_functions.ipynb
@@ -0,0 +1,313 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Custom functions to model aperiodic signals\n",
+    "\n",
+    "Aperiodic changes in a power spectrum can be modeled using a variety of different linear models. This can be easily accomplished using scipy's curve_fit function.\n",
+    "`PyRASA` aims to give users both some preset models that are similar to what has been implemented in specparam, as well as ability to fit custom models.\n",
+    "The preset models are called `fixed` and `knee` and can be entered to the compute_slope method in the `IrasaSpectrum` returned by the `pyrasa.irasa` function.\n",
+    "However, users can also import the class `AbstractFitFun` from `pyrasa.utils.fit_funcs` and inherit from it. \n",
+    "Below we will illustrate how this is done and how the resultant child class can be used as a Custom fit function to model aperiodic signals.\n",
+    "\n",
+    "\n",
+    "First we simulated a signal with a spectral exponent of 1.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.signal as dsp\n",
+    "from pyrasa.utils.aperiodic_utils import compute_aperiodic_model\n",
+    "from pyrasa.utils.fit_funcs import AbstractFitFun\n",
+    "from pyrasa import irasa\n",
+    "import numpy as np\n",
+    "from neurodsp.sim import sim_powerlaw\n",
+    "import pandas as pd\n",
+    "\n",
+    "n_secs = 60\n",
+    "fs=1000\n",
+    "f_range = [1.5, 150]\n",
+    "exponent = -1.5\n",
+    "\n",
+    "sig = sim_powerlaw(n_seconds=n_secs, fs=fs, exponent=exponent)\n",
+    "freqs, psd = dsp.welch(sig, fs, nperseg=int(4 * fs))\n",
+    "freq_logical = np.logical_and(freqs >= f_range[0], freqs <= f_range[1])\n",
+    "psd, freqs = psd[freq_logical], freqs[freq_logical]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to simply overwrite the func method and enter it as a fit_func to either the `compute_slope` function imported from`from pyrasa.utils.aperiodic_utils`.\n",
+    "Or we can use the method of the `IrasaSpectrum` returned by the `pyrasa.irasa` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class CustomFitFun(AbstractFitFun):\n",
+    "    log10_aperiodic = True\n",
+    "    log10_freq = True\n",
+    "    \n",
+    "    def func(self, x: np.ndarray, a: float, b: float) -> np.ndarray:\n",
+    "        \"\"\"\n",
+    "        Fixed fitting function.\n",
+    "        Use this to model aperiodic activity without a spectral knee\n",
+    "        \"\"\"\n",
+    "        y_hat = a - b * x\n",
+    "\n",
+    "        return y_hat\n",
+    "    \n",
+    "\n",
+    "slope_fit = compute_aperiodic_model(psd, freqs, fit_func=CustomFitFun)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.246405</td>\n",
+       "      <td>1.480257</td>\n",
+       "      <td>custom</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a         b fit_type ch_name\n",
+       "0 -1.246405  1.480257   custom       0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_fit.aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_fit_2 = irasa(sig, fs=fs, band=f_range, psd_kwargs={'nperseg': 4 * fs}).fit_aperiodic_model(fit_func=CustomFitFun)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.237573</td>\n",
+       "      <td>1.483932</td>\n",
+       "      <td>custom</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a         b fit_type ch_name\n",
+       "0 -1.237573  1.483932   custom       0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_fit_2.aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mse</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>R2_adj.</th>\n",
+       "      <th>BIC</th>\n",
+       "      <th>BIC_adj.</th>\n",
+       "      <th>AIC</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.006865</td>\n",
+       "      <td>0.979519</td>\n",
+       "      <td>0.979450</td>\n",
+       "      <td>-19.046324</td>\n",
+       "      <td>-25.395720</td>\n",
+       "      <td>-27.823447</td>\n",
+       "      <td>custom</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000361</td>\n",
+       "      <td>0.998907</td>\n",
+       "      <td>0.998903</td>\n",
+       "      <td>-37.861877</td>\n",
+       "      <td>-44.211273</td>\n",
+       "      <td>-46.638999</td>\n",
+       "      <td>custom</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mse        R2   R2_adj.        BIC   BIC_adj.        AIC fit_type  \\\n",
+       "0  0.006865  0.979519  0.979450 -19.046324 -25.395720 -27.823447   custom   \n",
+       "0  0.000361  0.998907  0.998903 -37.861877 -44.211273 -46.638999   custom   \n",
+       "\n",
+       "  ch_name  \n",
+       "0       0  \n",
+       "0       0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([slope_fit.gof,\n",
+    "           slope_fit_2.gof])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/examples/hset_optimization.ipynb b/doc/source/examples/hset_optimization.ipynb
new file mode 100644
index 0000000..1bc4520
--- /dev/null
+++ b/doc/source/examples/hset_optimization.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optimize your hset\n",
+    "\n",
+    "IRASA - as an algoritm - is very appealing, because its so simple. In the end we are just up/downsampling signals, computing psd's and averaging them.\n",
+    "This means that we only have a single hyperparameter to set when running a model, the set of up- and downsampling factors. \n",
+    "Correctly specifying the hset is very important to avoid poorly specified models (see also irasa_pitfalls.ipynb and improving_irasa_models.ipynb).\n",
+    "In both examples we looked at rather simple spectra, where power decreases with the same exponent across the whole spectrum.\n",
+    "Here we consider also spectra that contain a spectral knee and introduce an approach to improve your hset specification. In the presence of spectral knees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from neurodsp.sim import sim_combined\n",
+    "from neurodsp.utils import create_times\n",
+    "import numpy as np\n",
+    "import scipy.signal as dsp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from pyrasa.irasa import irasa\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# %% Lets check the knee\n",
+    "fs = 1000\n",
+    "n_seconds = 60\n",
+    "knee_freq = 15\n",
+    "exp = 1.5\n",
+    "knee = knee_freq ** exp\n",
+    "duration=6\n",
+    "overlap=0.5\n",
+    "\n",
+    "sim_components = {'sim_knee': {'exponent1' : -.0, 'exponent2': -1*exp, 'knee': knee}, \n",
+    "                  #'sim_powerlaw': {'exponent' : -1*exp}, \n",
+    "                  'sim_oscillation': {'freq' : 10}}\n",
+    "\n",
+    "sig = sim_combined(n_seconds=n_seconds, fs=fs, components=sim_components)\n",
+    "times = create_times(n_seconds=n_seconds, fs=fs)\n",
+    "\n",
+    "\n",
+    "max_times = times < 1\n",
+    "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
+    "axes[0].plot(times[max_times], sig[max_times])\n",
+    "axes[0].set_ylabel('Amplitude (a.u.)')\n",
+    "axes[0].set_xlabel('Time (s)')\n",
+    "freq, psd = dsp.welch(sig, fs=fs, nperseg=duration*fs, noverlap=duration*fs*overlap)\n",
+    "axes[1].loglog(freq, psd)\n",
+    "axes[1].set_ylabel('Power (a.u.)')\n",
+    "axes[1].set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When things are specified correctly we can rather unproblematically regenerate our simulated aperiodic model parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Knee</th>\n",
+       "      <th>Exponent_1</th>\n",
+       "      <th>Exponent_2</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>Knee Frequency (Hz)</th>\n",
+       "      <th>tau</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.481576e-16</td>\n",
+       "      <td>63.175767</td>\n",
+       "      <td>0.009849</td>\n",
+       "      <td>1.516753</td>\n",
+       "      <td>knee</td>\n",
+       "      <td>14.855539</td>\n",
+       "      <td>0.010714</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Offset       Knee  Exponent_1  Exponent_2 fit_type  \\\n",
+       "0  4.481576e-16  63.175767    0.009849    1.516753     knee   \n",
+       "\n",
+       "   Knee Frequency (Hz)       tau ch_name  \n",
+       "0            14.855539  0.010714       0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Jkydx7Ngxs7Zvv/0Wrq6u6N69OwBgxIgRuHDhAry9vdGjRw+LW30LTt59993466+/TEHJ6Ouvv4aTkxPuuusuAIapotr6eTPGKbDFixfXuX+1FQ49deoUgKqQY/Tnn3/i2rVrpsc6nQ6rVq1CWFgYQkJCABi+lydOnEBYWJjV76XxnLGxsSgpKblpmQGtVovi4uI6fa4WLVogPT3drK9lZWXYsGFDnc5D1FRwxInoDpSZmYmff/4ZQ4cOxejRo60eM3/+fHz99deYO3eu6eqqoKAgjBo1Cm+//TYCAwPxzTffYNOmTXj//ffh5OQEAJg+fTpWr16NAQMG4MUXX0Tnzp2h1+uRlJSEjRs34qWXXkKvXr3q3Oe33nrLtOZn1qxZ8PLywsqVK/H777/jgw8+gLu7u+n9v/zySwwfPhxz5swxXVV3+vTpm75H//798eijj2LOnDm4du0aRowYAa1WiyNHjsDJyQnPPfdcra/t0KED7r77bsTGxiIsLAwlJSXYt28fPv74Y/j7+2PKlClmx/v4+GDQoEF48803TVfVnT592qwkwezZs7Fp0yb06dMHzz//PMLDw1FSUoLExESsW7cOS5YsQUhICCZMmIBly5Zh2rRpOHPmDGJiYqDX67Fv3z60b98e48ePBwB06tQJW7duxa+//orAwEC4uroiPDz8ht+TcePGYdasWRg/fjxeeeUVlJSU4JNPPoFOp7vp95OoSZJ6dToR2Z/xiqa1a9fWesySJUtEAOLq1atFUTRcVTd8+HDxxx9/FDt06CBqNBqxRYsW4rx58yxeW1BQIL7xxhtieHi4qNFoRHd3d7FTp07iiy++aHYlGQAxLi7O6vujxlV1oiiKx48fF0eOHCm6u7uLGo1G7NKli7hs2TKL1yYkJIj33HOP6ODgIHp5eYlTpkwRf/7555teVSeKoqjT6cT58+eLHTt2NPW9d+/e4q+//lrr90oURfGzzz4Tx44dK7Zq1Up0cnISNRqNGBYWJk6bNk1MTk62+GxxcXHiokWLxLCwMFGtVovt2rUTV65caXHe69evi88//7zYsmVLUa1Wi15eXmJkZKT4r3/9y+zqteLiYnHWrFlimzZtRI1GI3p7e4uDBg0Sd+/ebTrm6NGjYt++fUUnJycRgDhw4EBRFKuuqqt5JaTRunXrxK5du4qOjo5iq1atxIULF9Z6VV3Nf89Lly6JAMQPP/zQrH3Lli0iAPF///vfDb+vRLcbVg4nIgCGKZuOHTvit99+k7ortz1BEBAXF4eFCxdK3RUisjOucSIiIiKyEYMTERERkY04VUdERERkI444EREREdmIwYmIiIjIRgxORERERDZicCIiIiKyESuH34Ber8fVq1fh6upq835bREREdHsRRRH5+fk2babN4HQDV69eRWhoqNTdICIiokaQnJxs2kuyNgxON+Dq6grA8I10c3OTuDdERETUEPLy8hAaGmr6vX8jDE43YJyec3NzY3AiIiK6w9myLIeLw4mIiIhsxOBEREREZCMGJyIiIiIbMTgRERER2YjBiYiIiMhGDE5ERERENmJwIiIiIrIRgxMRERGRjRiciIiIiGzE4ERERERkIwYnIiIiIhsxOBERERHZiMGJiIiIyEYMTkREREQ2YnAiIiIishGDExEREZGNGJyIiIiIbMTgREREsnb2Wj6Kyiqk7gYRAAYnIiKSsRNXcjFk/na8/L9jUneFCACDk1Xx8fGIiIhAVFSU1F0hImrSUrKLzb4SSY3ByYq4uDgkJCTgwIEDUneFiKhJ0+lFs69EUmNwIiIi2arQ6wEwOJF8MDgREZFsGQNTBYMTyQSDExERyVYFp+pIZhiciIhItqpGnPQS94TIgMGJiIhkyzTipOOIE8kDgxMREcmWTmcYaeIaJ5ILBiciIpItrnEiuWFwIiIi2eJVdSQ3DE5ERCRbHHEiuWFwIiIi2eJVdSQ3DE5ERCRbxhEn5iaSCwYnIiKSrQrTVXVMTiQPDE5ERCRbxqk6vQjouc6JZIDBiYiIZKv61XQ6kcGJpMfgREREslX9ajpeWUdywOBERESyVX1tE2s5kRwwOBERkWyZjThxvzqSAQYnIiKSrYpqYYlX1pEcMDgREZFscY0TyQ2DExERyVb1dU1c40RywOBERESyxREnkhsGJyIikq3q65oYnEgOGJyIiEi2dJyqI5lhcCIiItmq4FQdyQyDExERyZb5iBPLEZD0GJyIiEi2qtdx4ogTyQGDExERyRa3XCG5YXAiIiLZ4honkhsGJyIiki2zNU7cq45kgMGJiIhki2ucSG4YnIiISLZ4VR3JDYMTERHJVvWwpBc54kTSY3AiIiLZ4honkhsGJyIiki1eVUdyw+BERESyxb3qSG4YnIiISLY44kRyc8cHp99++w3h4eFo06YNvvjiC6m7Q0REdcARJ5IbldQdaEgVFRWYMWMGtmzZAjc3N3Tv3h1jx46Fl5eX1F0jIiIbVOiqrqrTsRwBycAdPeK0f/9+dOjQAcHBwXB1dcWwYcOwYcMGqbtFREQ24ogTyY2sg9P27dsxcuRIBAUFQRAErF271uKYRYsWoWXLlnBwcEBkZCR27Nhheu7q1asIDg42PQ4JCcGVK1cao+tERGQHXONEciPr4FRYWIguXbpg4cKFVp9ftWoVpk+fjn/96184cuQI+vfvj9jYWCQlJQEARCvF0gRBaNA+ExGR/bCOE8mNrNc4xcbGIjY2ttbn582bhylTpmDq1KkAgAULFmDDhg1YvHgx5s6di+DgYLMRppSUFPTq1avW85WWlqK0tNT0OC8vzw6fgoiI6kMURY44kezIesTpRsrKynDo0CEMGTLErH3IkCHYvXs3AKBnz544ceIErly5gvz8fKxbtw5Dhw6t9Zxz586Fu7u76RYaGtqgn4GIiGpXMyjpuOUKycBtG5wyMjKg0+ng7+9v1u7v74+0tDQAgEqlwscff4yYmBh069YNr7zyCry9vWs958yZM5Gbm2u6JScnN+hnICKi2tVcDM4RJ5IDWU/V2aLmmiVRFM3aRo0ahVGjRtl0Lq1WC61Wa9f+ERFR/dQMSlzjRHJw2444+fj4QKlUmkaXjNLT0y1GoYiI6PZjOeLEOk4kvds2OGk0GkRGRmLTpk1m7Zs2bUKfPn0k6hUREdmLxYgTp+pIBmQ9VVdQUIDz58+bHl+6dAlHjx6Fl5cXmjVrhhkzZuDRRx9Fjx490Lt3b3z++edISkrCtGnTJOw1ERHZQ0WNESaucSI5kHVwOnjwIGJiYkyPZ8yYAQCYOHEili9fjnHjxiEzMxOzZ89GamoqOnbsiHXr1qF58+ZSdZmIiOyEI04kR7IOTtHR0VaLWFb3zDPP4JlnnrHr+8bHxyM+Ph46nc6u5yUiItvVXAzOESeSg9t2jVNDiouLQ0JCAg4cOCB1V4iImizLEScuDifpMTgREZEssY4TyRGDExERyRLrOJEcMTgREZEsWVxVxy1XSAYYnIiISJYs9qrjVB3JAIMTERHJUs01TixHQHLA4ERERLJkUY6Aa5xIBhicrIiPj0dERASioqKk7goRUZNVc40TR5xIDhicrGAdJyIi6VmucWIdJ5IegxMREckS1ziRHDE4ERGRLNVc08Sr6kgOGJyIiEiWOOJEcsTgREREssQ6TiRHDE5ERCRLFpXDGZxIBhiciIhIljjiRHLE4GQF6zgREUnPuKZJo1SYPSaSEoOTFazjREQkPeMIk1alqHzMOk4kPQYnIiKSJeMIk1bNESeSDwYnIiKSJZ3OMMKkVSkNjxmcSAYYnIiISJYqakzV1dz0l0gKDE5ERCRLxhEmjWmNE4MTSY/BiYiIZKlqjZPS7DGRlBiciIhIloxTc7yqjuSEwYmIiGTJGJRMa5w44kQywOBERESyVLU43DBVp2dwIhlgcLKClcOJiKSnYx0nkiEGJytYOZyISHo1yxHwqjqSAwYnIiKSJV2NqboKvQhRZHgiaTE4ERGRLFXUWBwOABx0IqkxOBERkSzVXOMEVIUpIqkwOBERkSxV1XFSmtq4zomkxuBERESypKuxOBzglXUkPQYnIiKSpZpX1QGAjhv9ksQYnIiISJaMI05qJUecSD4YnIiISJZEGEKSQgBUCgEA1ziR9BiciIhIlkwX0AkCFMbgxDpOJDEGJyIikqXqI06VuYkFMElyDE5WcK86IiLpGWflhMr/AQBzE0mNwckK7lVHRCQ9Y0hSCIAgmLcRSYXBiYiIZMk4LScIgKIyORmn74ikwuBERESyZIxIVRN13KuOpMfgREREslR9xAlcHE4yweBERESyZFocLgjVpuqIpMXgREREsmQMSeaLwxmdSFoMTkREJEvVp+oEU5t0/SECGJyIiEimqsoRcKqO5IPBiYiIZElfbXjJOFWn55ATSYzBiYiIZEmstjgcrBxOMsHgREREsmR9rzoJO0QEBiciIpIps73qOFVHMsHgRERE8lR9rzrTdXVE0mJwsiI+Ph4RERGIioqSuitERE2W3myvOkMbB5xIagxOVsTFxSEhIQEHDhyQuitERE2Waa86QahcIM6pOpIegxMREcmSacSpWhtjE0mNwYmIiGSpejkChcLYxuhE0mJwIiIiWTLbqw7GqTrp+kMEMDgREZFMme1VZ5qvY3IiaTE4ERGRLFWfquMmvyQXDE5ERCRL1ReHc5NfkgsGJyIikiWzveqMlcO5yIkkxuBERESyZBxxUlTlJo44keQYnIiISNYECFVTdUxOJDEGJyIikiWx+l51pi1XmJxIWgxOREQkS6btVarVcWJsIqkxOBERkSxVFcAUqo04SdYdIgAMTkREJFPVyxFwk1+SCwYnIiKSJ2sFMCXrDJEBgxMREclS9b3quMkvyQWDExERyZK++l51YDkCkgcGJyvi4+MRERGBqKgoqbtCRNRkme1VZ1wczsk6khiDkxVxcXFISEjAgQMHpO4KEVGTZW1xOEecSGoMTkREJEtVBTCrFodzqzqSGoMTERHJklh9jRMrh5NMMDgREZEsGSOS2V510nWHCACDExERyVTV4nBU1XHiiBNJjMGJiIhkSW91qk7CDhGBwYmIiGTKfK86TtWRPDA4ERGRLJktDq9s4151JDUGJyIikiWzcgScqiOZYHAiIiJZMiuACU7VkTwwOBERkSyZyhFwk1+SEQYnIiKSJbO96rjJL8kEgxMREcmS+V51hjZu8ktSY3AiIiJ5MlsczhEnkgcGJyIikiW91XIE0vWHCGBwIiIimaq+Vx03+SW5YHAiIiJZqj7ixE1+SS4YnIiISJa4yS/JEYMTERHJkvledZVtzE0kMQYnIiKSJbO96jhVRzLB4ERERLJktlddZRs3+SWpMTgREZEsWS2AydxEEmNwIiIiWTJlJF5VRzLC4ERERLJkNlXHOk4kEwxOREQkO9UDkgBwk1+SDQYnK+Lj4xEREYGoqCipu0JE1CRVD0gccSI5YXCyIi4uDgkJCThw4IDUXSEiapKqXz1XvRwB96ojqTE4ERGR7FTPR0K1cgTMTSQ1BiciIpKdmiNOCk7VkUwwOBERkexUz0eGOk5cHE7ywOBERESypjCbqmNyImkxOBERkezUtjicI04kNQYnIiKSndrKEfCqOpIagxMREclOzc18OVVHcsHgREREslM9HpkXwJSkO0QmDE5ERCQ7or7qvlBtk18iqTE4ERGR7FSfkjOUIzDc13ORE0mMwYmIiGSn5uJw4yonxiaSGoMTERHJTu2VwyXqEFElBiciIpIdi73qTOUImJxIWgxOREQkO8aAZAxMAqfqSCYYnIiISH4qE5LxWjqFqZAToxNJi8GJiIhkx3jxnLEMgXHLFV5UR1JjcCIiItkxliOoWb6JlcNJagxOREQkO8YZOeNIk4Kb/JJMMDgREZHsmBaHVz7mJr8kFwxOREQkO2LNNU7Gdk7VkcQYnIiISHaqpuoMXxWmCpjS9IfIiMGJiIhkx7Q4vPKx8SsLYJLUGJyIiEh2apYjALdcIZlgcCIiItkRRfMKmKar6iTqD5ERgxMREcmOMSDVXBzOqTqSGoMTERHJjlhzrzpO1ZFMMDgREZHs1CxHoKhZQpxIIgxOREQkO/oam/xyqo7kgsGJiIhkx2KvOm65QjLB4ERERLJTc686Vg4nuWBwIiIi2am5V51xjRP3qiOpqer7wuTkZCQmJqKoqAi+vr7o0KEDtFqtPftGRERNlMVedbyqjmSiTsHp8uXLWLJkCb777jskJydXFSgDoNFo0L9/fzz55JO47777oFBwMIuIiOqn5l51VdfUMTmRtGxONy+88AI6deqEc+fOYfbs2Th58iRyc3NRVlaGtLQ0rFu3Dv369cObb76Jzp0748CBAw3ZbyIiuoMZ1zKZyhEouDic5MHmESeNRoMLFy7A19fX4jk/Pz8MGjQIgwYNwltvvYV169bh8uXLiIqKsmtniYioaahtLRPLEZDUbA5OH374oc0nHTZsWL06Q0REBLByOMkXFyIREZHs1Nyrjpv8klzYNTj985//xOTJk+15SrsYM2YMPD09cf/990vdFSIisoHFiFNlO6fqSGp2DU5XrlxBYmKiPU9pF88//zy+/vprqbtBREQ2qq0cAYecSGr1ruNkzVdffWXP09lNTEwMtm7dKnU3iIjIRjX3quNUHcmF5Guctm/fjpEjRyIoKAiCIGDt2rUWxyxatAgtW7aEg4MDIiMjsWPHjsbvKBERNRpTnUDBvJ1TdSS1eo04zZ49+4bPz5o1y+ZzFRYWokuXLnj88cdx3333WTy/atUqTJ8+HYsWLULfvn3x2WefITY2FgkJCWjWrBkAIDIyEqWlpRav3bhxI4KCgmzuCxERyYPeYqqOdZxIHuoVnNasWWP2uLy8HJcuXYJKpUJYWFidglNsbCxiY2NrfX7evHmYMmUKpk6dCgBYsGABNmzYgMWLF2Pu3LkAgEOHDtXjU1gqLS01C2B5eXl2OS8REdWNsQBm1VSdsZ1IWvUKTkeOHLFoy8vLw6RJkzBmzJhb7pRRWVkZDh06hNdff92sfciQIdi9e7fd3sdo7ty5eOedd+x+XiIiqqOaI06VzZyqI6nZbY2Tm5sbZs+ejTfffNNep0RGRgZ0Oh38/f3N2v39/ZGWlmbzeYYOHYoHHngA69atQ0hISK3bwcycORO5ubmmW3Jy8i31n4iI6kdfc686gUNOJA92vaouJycHubm59jwlgGr/h6kkiqJF241s2LDBpuO0Wi20Wm2d+kZERPZnmqozFcA0byeSSr2C0yeffGL2WBRFpKamYsWKFbj33nvt0jEA8PHxgVKptBhdSk9PtxiFIiKiO0fNcgTGoSe9XpLuEJnUKzjNnz/f7LFCoYCvry8mTpyImTNn2qVjgGFj4cjISGzatMls7dSmTZswevRou70PERHJS22VwzniRFKrV3C6dOmS3TpQUFCA8+fPm5376NGj8PLyQrNmzTBjxgw8+uij6NGjB3r37o3PP/8cSUlJmDZtmt36QERE8lKzcriC5QhIJuy6xqk+Dh48iJiYGNPjGTNmAAAmTpyI5cuXY9y4ccjMzMTs2bORmpqKjh07Yt26dWjevLlUXSYiogZWtcYJZl/1DE4kMbsGp0WLFiEjI6NOdZyio6OrKsTW4plnnsEzzzxzq92zWXx8POLj46HT6RrtPYmIqIqpcHiNcgS8rI6kZtctV1avXo3ly5fb85SSiIuLQ0JCQq1lC4iIqGHVulcdcxNJzK4jTn/++ac9T0dERE2UcSbCWIYApqk6JieSluSb/BIREdWkr2WqjrGJpHZLI04JCQlISkpCWVmZWfuoUaNuqVNERNTUme9Vx01+SS7qFZwuXryIMWPG4Pjx4xAEoVq9DcMPNhdVExHRrdBblCMwPGZuIqnVa6ruhRdeQMuWLXHt2jU4OTnh5MmT2L59O3r06IGtW7fauYtERNTUmEaWapQjuNlV2EQNrV7Bac+ePZg9ezZ8fX2hUCigUCjQr18/zJ07F88//7y9+9jo4uPjERERgaioKKm7QkTUJBnrOClMlcM5VUfyUK/gpNPp4OLiAsCwn9zVq1cBAM2bN8eZM2fs1zuJsBwBEZG0qsoRVC4O5ya/JBP1WuPUsWNH/P3332jVqhV69eqFDz74ABqNBp9//jlatWpl7z4SEVETYypHUPnnvcBNfkkm6hWc3njjDRQWFgIA5syZgxEjRqB///7w9vbGqlWr7NpBIiJqesSaI07Gdo44kcTqFZyGDh1qut+qVSskJCQgKysLnp6epr8KiIiI6qvmXnWsHE5yYbfK4V5eXvY6FRERNXEWe9UJ5u1EUrF5cfi0adOQnJxs07GrVq3CypUr690pIiJq2mruVcepOpILm0ecfH190bFjR/Tp0wejRo1Cjx49EBQUBAcHB2RnZyMhIQE7d+7E999/j+DgYHz++ecN2W8iIrqD1dyrjpXDSS5sDk7vvvsunnvuOSxduhRLlizBiRMnzJ53dXXF4MGD8cUXX2DIkCF27ygRETUdtU3VcZNfklqd1jj5+flh5syZmDlzJnJycnD58mUUFxfDx8cHYWFhd8zC8Pj4eMTHx3PrGCIiiVgWwDS2E0mr3ovDPTw84OHhYceuyEdcXBzi4uKQl5cHd3d3qbtDRNTk6E0JybhXHafqSB7qVTmciIioIVVN1Zl/5V51JDUGJyIikh2LqTrTlitE0mJwIiIi2bHcq45TdSQPdQ5OoiiaFoUTERE1iJp71VU286o6klq9glObNm2QkpLSEP0hIiLiiBPJVp2Dk0KhQJs2bZCZmdkQ/SEiIqpaBG7aq66yXZruEJnUa43TBx98gFdeecWiCCYREZE9GEecjGUIjCNPvKqOpFavOk6PPPIIioqK0KVLF2g0Gjg6Opo9n5WVZZfOERFR02SMR6a96rjJL8lEvYLTggUL7NwNeWHlcCIiaVnsVWds52QdSaxewWnixIn27oessHI4EZG0LPeqM3zVMzeRxOpdx+nChQt44403MGHCBKSnpwMA1q9fj5MnT9qtc0RE1DQZR5ZYOZzkpl7Badu2bejUqRP27duHn376CQUFBQCAv//+G2+99ZZdO0hERE2PRTmCynbGJpJavYLT66+/jjlz5mDTpk3QaDSm9piYGOzZs8dunSMioqap5l51CtYjIJmoV3A6fvw4xowZY9Hu6+vL+k5ERHTL9LUsDmflcJJavYKTh4cHUlNTLdqPHDmC4ODgW+4UERERUL1yuOExYxNJrV7B6aGHHsJrr72GtLQ0CIIAvV6PXbt24eWXX8Zjjz1m7z4SEVETI9bcq45brpBM1Cs4/fvf/0azZs0QHByMgoICREREYMCAAejTpw/eeOMNe/eRiIiamKqyA+aLwzlVR1KrVx0ntVqNlStXYvbs2Thy5Aj0ej26deuGNm3a2Lt/RETUBImmLVcMXzniRHJRr+B07tw5tGnTBmFhYQgLC7N3n4iIqIkzjiwJNTb5JZJavabqwsPDERwcjIceegifffYZzpw5Y+9+SSo+Ph4RERGIioqSuitERE1S1V515pv8cqqOpFav4JSamoqPPvoIbm5umD9/Ptq3b4/AwECMHz8eS5YssXcfG11cXBwSEhJw4MABqbtCRNQkWexVx01+SSbqFZz8/f0xYcIELFmyBKdPn8bZs2cxdOhQrF69GnFxcfbuIxERNTGWe9VVtrMgAUmsXmucCgoKsHPnTmzduhXbtm3D0aNH0b59ezz33HMYOHCgvftIRERNjMVedeAmvyQP9QpOnp6e8PLywqOPPoo33ngD/fr1g7u7u737RkRETZTFXnWcqiOZqFdwGj58OHbu3IkVK1YgOTkZSUlJiI6ORvv27e3dPyIiaoJqliNQCNzml+ShXmuc1q5di4yMDGzatAn9+vXDn3/+iejoaAQEBGD8+PH27iMRETUxYo1yBMavnKojqdVrxMmoc+fO0Ol0KC8vR2lpKdavX4+ffvrJXn0jIqImylSOQDCvHC5yro4kVq8Rp/nz52P06NHw8vJCz5498d133yE8PBxr1qxBRkaGvftIRERNjOWIU2XlcKk6RFSpXiNOK1euRHR0NJ544gkMGDAAbm5u9u4XERE1YbUtDtdzro4kVq/gdPDgQXv3g4iIyMRirzpjuyS9IapS7zVOOTk5WLp0KU6dOgVBENC+fXtMmTKFZQmIiOiWWe5VZ6qASSSpeq1xOnjwIMLCwjB//nxkZWUhIyMD8+fPR1hYGA4fPmzvPhIRURNlMVXHxeEksXqNOL344osYNWoU/vvf/0KlMpyioqICU6dOxfTp07F9+3a7dpKIiJoWfc296sDF4SQP9V7jVD00AYBKpcKrr76KHj162K1zRETUNIlV9Qiqf2HlcJJcvabq3NzckJSUZNGenJwMV1fXW+6U1OLj4xEREYGoqCipu0JE1CQZ96pT1CiAyU1+SWr1Ck7jxo3DlClTsGrVKiQnJyMlJQXff/89pk6digkTJti7j40uLi4OCQkJOHDggNRdISJqkizLEXCTX5KHek3VffTRRxAEAY899hgqKioAAGq1Gk8//TTee+89u3aQiIiantrKEXDAiaRWr+Ck0Wjwn//8B3PnzsWFCxcgiiJat24NJycne/ePiIiaoJqVwxWmyuFMTiStOk3VFRUVIS4uDsHBwfDz88PUqVMRGBiIzp07MzQREZHdGEecBKFmOQKJOkRUqU7B6a233sLy5csxfPhwjB8/Hps2bcLTTz/dUH0jIqImqmYBTG7yS3JRp6m6n376CUuXLsX48eMBAI888gj69u0LnU4HpVLZIB0kIqKmx1SNoMbicMYmklqdRpySk5PRv39/0+OePXtCpVLh6tWrdu8YERE1XRaLw1nHiWSiTsFJp9NBo9GYtalUKtOVdURERPZQc3G4YOU5IinUaapOFEVMmjQJWq3W1FZSUoJp06bB2dnZ1PbTTz/Zr4dERNTkGKOR8Wo60ya/MIw6CYKVFxE1gjoFp4kTJ1q0PfLII3brDBEREWC5mW/1oKQXRSjA5ETSqFNwWrZsWUP1g4iIyMSiHEG1oMSJOpJSvbZcISIiakjGESfT4vBqv624xImkxOBERESyU1WOwPwrYDmNR9SYGJyIiEh+jOUIFOZ1nIikxuBERESyY6ocXvlYUS03ccCJpMTgREREsnOjxeGcqiMpMTgREZHsWOxVV33ESYL+EBkxOFkRHx+PiIgIREVFSd0VIqImyXKvumrPccSJJMTgZEVcXBwSEhJw4MABqbtCRNQkiTXLEZhN1UnRIyIDBiciIpKdqjVO5l8NTzZ6d4hMGJyIiEh2TFN1psXh1Z9jciLpMDgREZHsWJYj4FQdyQODExERyY5FOQIuDieZYHAiIiLZsdirTuAmvyQPDE5ERCRb1UeajPc54ERSYnAiIiLZMYaj6mubBNNzTE4kHQYnIiKSHWvbqhin6xibSEoMTkREJDvWRpwUnKojGWBwIiIi2am5Vx1QVT2cm/ySlBiciIhIdmruVVf5wOw5IikwOBERkezU3Kuu+n0uDicpMTgREZHs1NyrDqgafWJuIikxOBERkezU3KvOcL/yOQYnkhCDExERyU7NveqAqivsuMkvSYnBiYiIZOdGBTC5yS9JicGJiIhkR7RSjgBcHE4ywOBERESyU7XGqapNwcrhJAMMTkREJDtVBTCtLQ5ndCLpMDgREZHsmMoRVGsTajxHJAUGJyIikh3re9Vxqo6kx+BERESyY3WvOsH8OSIpMDgREZFsKaxcVsfcRFJicCIiItmxXgDT8JXBiaTE4GRFfHw8IiIiEBUVJXVXiIiaJFM44lQdyQyDkxVxcXFISEjAgQMHpO4KEVGTZAxH5pXDhdoOJ2o0DE5ERCQ7VgacuMkvyQKDExERyY+xHIHCWjkCJieSDoMTERHJjrXF4VXPNW5fiKpjcCIiItmp2quOW66QvDA4ERGR7FgrgMnK4SQHDE5ERCQ7Vveq44gTyQCDExERyY61veq4yS/JgUrqDhA1ZSXlOvxy9CpyisugVCigUghQKQWoFAKUCgXUSgFKheGxSqGAUll1X6UU4KRRon2Am9mVR0R3ApFTdSRTDE5EEtl6Jh2zfj6JpKyiWzpPRKAbXhkajuhwX7OFtI0ht7gcx1Ny4euqRaiXI5w08vtPyqWMQizeeh6P3NUcnUM8pO4O2cgYjsz2qjNWDudldSQh+f1XjugOl5Zbgtm/ncS642kAgAA3B/Rp7Q2dXkSFTkSFXg+dXkS5TjS06fWV7WJlu76yXcS1vBIkpObh8eUHENXCE68MbYeeLb0a5XMcTMzCMysPIz2/1NTm7axBiKcjQrycEOrphFAvR4R6OqFnSy84qJWN0q/qTqXm4dGl+5BRUIbkrGJ89+Rdjd4Hqh9r26qYpuoatytEZhiciBpJhU6Pr/ZcxryNZ1BYpoNSIeDxPi0w/Z62cNHW7/+K2YVlWLLtApbvTsSBxGw8+NkeDGzri1eGhqNjsHv9O6srB0ryAAc3QKk2e0oURSzfnYh//34KFXoRPi4alOtE5BaXI7OwDJmFZTiWkmv2mhbeTvhmai+EeDrVv0/V3v+bvZeRW1yOR3u3gLuj2upxR5NzMPHL/cgtLgcA7E/MQm5ROdydrB/fEE5cycUrP/6NuJgwjOgc1GjveycwLQ63NlXH5EQSYnCiW1ZSrsPVnGKk5pbgSk4xUnNKcDWnGJn5RQjzd0evVl7o0cILbg6N9wtLbg4nZeONNSeQkJoHAOjezANz/tEJEUFut3ReT2cNZg5rj8n9WuLTv87h+/3J2Hb2OradvY5hnQIw455wtPZzMRxcXgxc2gFkXQRKcoDiHMPXktyq+8WVj8sLDa9RqACvVoBPW8CnLUo9w7DwbyWWnVGhAk4Y0TkQ79/XGc5aFfJKypGcVYTkrGKkZBcZ7mcX41hyDhIzi/DAkj1YObUXWvm63NJnXrT1Aj7ccAYA8MXOS3g2pjUeuau52YjW3ouZmLL8AArLdOjezAPZReW4lFGIrWfTMbpr8C29f118uOEMTqXm4ZX//Y2IQLcbfvYvdlyEm6MaD/YIbbT+yZne2uJwXlVHMsDgRDdUodMjPb8UV3OKcTW3BKk5xab7xrCUU1iC5sI1hAvJaKdIQjshGaOEJDQX0lGcqMG1vZ44BQ+UOPhB6xkEL//mCAltASefEMAlAHANALSu5n9a3iFyisrw/voz+P5AEkQRcHdU4/XYdhjXI9SuC7r93Rww5x+d8ET/Vliw+RzWHr2CdcfTsPvEebzWKgmjHY/A6fLWqkBkK30FkHHWcAOgBfASgJccgEKtL5zKIyBsbAloXeGmcUEHjTM6aJwBd1fA1xnQOCOz3BlP/1qA/RkiHvxsD76e3KvegfH7/Umm0BTk7oCruSWY8/spLNuViJeGtMXorsHYce46nlpxCKUVevQJ88Z/H+uBhVvOY/HWC9h8qm7BqUKnR1peSb1Gys6nF2Db2esAgOJyHaavOorVT/eBWml5MXPC1TzM+f0UAMP6nfE9m9X5/arT6UV8tz8Jzb2d0L+N7y2dSzqWi8ONm/wyNpGUGJyaMFEUkV1UbghClTfTqFFlSLqWXwpdtYWY3shFuCIZ7YRk3CMkIVyRjLbaFDgKZVbfwxmlaCWkoRXSgLLTwDUYbn/X6IvaCYJrQFWQMt5qPta6SROwRBEoza8clck2jMyU5hv65NPWMKVldriInw5fwf+tO4XMQsP35v7IEMyMbQdvF22DdbO5tzPm3+uL17y3I+vQGrQpOgb1FZ3peZ1rMJShUYCjJ+DoATi4Aw4elfcrHxvva92A/FQg4yzOnDiMo0f3oZnuCtoor8IHOXAuvQ5c2ma43YA3gB8AFDo64VxZIM5/Hgz3yF4Ibt3F8L3zbAmoNDf9bJsSruGfa44DAJ6JDsNLQ8Kx+lAK5m06iys5xZjxwzEs3noBiZmFKNeJGNTOD4se7g4HtRKD2/tj8dYL2HomHWUVemhUtlVimfHDMfxy7Co+ndANI7vUbapt+e5LAICoFp44e60Af6fk4j+bz+HloeEWx+6+kGG6/8baEwj1ckLf1j51ej+j/JJyPP/dEWw5cx1OGiUOvjFYlov2b8ZqOQJu8ksycPv9v4lsVlhagdTcYlzJqRwpMo0SFeNq5XRaaYXe6mu1KEMbIQV9FUlor05BJ/UVtMFleOhzrB4vqhwh+LUD/DsAfh0A/wjAJxwoLwLy05CTnoTkpEvIvpaEsuyrcCq9Dj8hB35CNtyEYgjlRYYppKyLN/5QKscbByvjYwd3y4AlikBZYdWUVHG2+ZSVMRBZbcsFRB1q5RpoCAG+4UjXNseSkyr8etUVmXBHGz9XzPlHR/Rq5X3jz1ZfoghcPwOc/g04/Ttw9TACAAQAgAAkqVpgbUlXbNBF4aIuDKOaB6OdlyvC/V3Rxt8VPi6aWq/G07mFYMH+Iny6twRABLo388CihyMBTQmQcc4wEpWbDJQVGL63ZYVAaUGNx/lA/lU4i0XoqriArrgAHNoOHKp8E0EBOPsCLv6Gm6u/4d/RxR9w8QWUGpy7XoQfNp5DfwHoF+6HqW2cIaQW4cEOLTCqy0As230Zi7aex7n0AgDA8E6BmD/UC5oT3wGJO9E99Ri+dnTCnrLWOLNPh05R0YDmxqNImxOu4ZdjVwEAs39LQHS4L1xtnG7OKSrD6kNXAAAv3tMW2YXliPv2MBZtPY+B4b6IamG+gH/XeUNw8nHRIqOgFNO+OYQ1z/RBaz9Xm97PKCW7CFOWH8SZa/kAgKIyHf46nX5brq+ytled8efU2sJxosYiiJwsrlVeXh7c3d2Rm5sLN7dbW4tib+U6PdKqTZddqQxEqTlVI0bGRbE3IkCPri456OmYig6qK2gtXkZQ2UW4FyVDgLVQJQBeLc0Dkn9HwLMFoLD9qqn0/BLsv5SFfRezcOziFeReT4EfcuAvZJsCVZhjAVpp8+En5MC57DoUpXm2f4NUDoYA5ehp+EVuDEn6CtvPYY1SaxiRcfQENC5AbgpQkFbr4aUqV6gD2kHhG24Ikr7hhoDl0cz275coAqV5lSGuxi3rEnB2PZB5vtoLBCC0F9BuuOHmHYZd5zPwwYYzOJacY3F6Tyc12vi7oq2/C9r6u6KNn+G+UiHg+e+PYnvldNPE3s3xr+ERNo/WmKkoBbIuojTtFH7evBWq7AtorUhFhDoNqoo6Th/WpHUHPJuhzK0Z/i7whJMuF+1Lj0HISar9NQoVENAZcAsyBGxBafj3EBSArgwVpcU4eikViopilEKDFRWDEdxnPP41ooPZaYrKKvDv308hyMMRTw8MM02/Ltl2Ae/9cRrtAlzxxwv9IQgCXvrhGFYfTkGIpyM2TB8A58oLAsp1enR9ZyMKy3RY80wf/Pv3Uzh4ORuhXo5Y/0LVcbZ4YMluHEjMhp+rFl1DPbAx4Rru7RCAJY9G2nyOLafT0dLHGS18nG1+TUPoOnsjcorKsXnGQNM6vWH/2YGE1Dx8NbknBra9XacgSY7q8vueI04ypNeLyCgsRWpOSY0RI8NIUWpuMdLzS20arnZ1UCHI3RFBHg4IcylFB2UKWukTEVByER7556DJOguhvBDIt/JiJ+9qAakyJPm2AzS3/h9UP1cHjOgcVPmXcEdkFpQagtSlLOy+mInTafmGPlXrVztvFe4O0aOXbzk6uRXBU59tmErKv2b4WlD5tSQXqCgBshMNt5oU6qrpKGMIqj5dZbWtcmpL7Wh5vuIcHDi4D5u2b4dXcSJaC1fQWXsNvro0aCvygZQDhlt1KgfAuw3ga1h0DVGsNgJWMyDl3Hi0CwCUGqBVtCEohQ8DXPzMnu7b2gdrw7yx7ex1HEjMwtlrBTh3LR+Xs4qQXVSO/ZeysP9Sltlr1EoB5ToRDmoF5o7thDHdQm7chxtRaQG/9tD6tceo9qPx3HdHsCnhGlTlwOJ/hOCeUBj+HQvSKv8drwEF11Caew1nU7Oh0+ngohHQytsJCoiG70dRluH40lwg7Tg0acfRo/p7KlRAUHegRT8gpAdOnzqOC4f/RE/lefjqs4Crhw03a90FDOeqzIi9NQn4e/9vSAn8P4RExgIw/P90xqpjWH/SEJwTrubh4we7QKUQ8PXuRADA5L4tTaMkb4+KwN6LmUjJLsYvx65iQuU6pr9TclBYpkNzxxJ08VXg88d6YOSnO5GcVYyv9iTimejWNn2LD13OwoHEbGiUCqx+ug9yi8uxMeEatpxJR0FphU1Xbm49k47Hlx9AhyA3/P58f5vet6EYazWZXVVX+e/Bv/dJSgxOEknJLsK5awW4mlt1FZoxGKXllqBMZ30KrTqNUoFADwcEuTsi0MMBwR6OCHR3RLCrgBb6ZPgVX4Bj1mkgPQG4dhK4fM36iVQOhpEQ/46AX4QhIPl1MPzybaT1RN4uWsR2CkRsp0AAhqmO/ZeysPdiFvZdykRCah5OZ1bgdCYQDyUAVzTz8kevln1wVytv9GrlVbWAt7wYyK/8BVyUZVh/VD0EaZzt9rmu5BTjnV/OY2NCBYA+CPa4G2+NjIBfhwCgvMQwEpRxBrh+tupr5nlDsLt23HCzlcqxMsB5Ak5ehs/j5AO0HAC0ucewwP4GBEFAdLgfosOrQlVxmQ4Xrhfg7LV8U5g6m56P5KxilOtENPNywpJHIm/56r/qHNRKLHq4O17+3zH8fPQqnlyTglBPJ7g6OMLNoS3cHCPg6qCGm5MaW1PScbG4EK39XPDjtN5QONVYC1VWBOQkVYXk7ERA7QA07wc0uwvQVl3F1qzVUIw+1AWlJTpsntIKrUtPV45C6gBRb7jpdbhaoMN/tqegWK/BE3d3QCdFIkq2f4LOiovAr+MhnoyGEP1PfJTgjvUn06BWGn6Wfj+eiusFpfhH12BczS2Bl7MGo7pWTZG5OqgxsU9z/N+60/j+QLIpOO06dx1TlOvwOlZB8VkQvKbtwMtD2+LFVcfw2baLeOSu5hZXpBrLUPi7OeDxvi0gCAI+22aY5h7TLRihXk4IEUW08HZCYmYR/jx1zaZF8Sv3GUbpTl7NQ15JuaRXwhqjkdlUnXFxOHMTSYjBSSIr9lzGZ9trX88jCICfqxZBHo6mEaNAd0fD48r73k4qKHKTKoNRAnDtBHA2Aci8UPsIhWcL84Dk39FwuXkdptkag4eTBkM6BGBIhwAAhgrVBxMNI1L7Lmbi+JVcJGUVISmrCP87lAIACPZwRK9WXrirpTfuauWP0NAWdaqkLYqGopOlFTqUVuhRUm74WlquR2mFDiWVX0sr9Cit0OPS9UJ8tv0Cisp0UCkETOnfEi/c3aZqIa7aAQjoaLhVp9cZfsFnnDWsTco8bxgdMYYiqzcP66Ndt8hRo0THYHeLmk+FpRW4klOM5t5O0Krs/7OhViow78GucNGqsHJf0g2rpwe6O+DryT3hUTM0AYZ1Sn7tDLebcNKo0K+1D/48nY71yWo8O2isxTE6vYinF+3CsYrWGNE5EJ3u7g4AyGzzCDZ//gomCJugubgVuLgVg/WtkagYhsH/mIIATxc8teIQ9l/KxIVLl9BVuI6pYUo47D1uCPCeLYCgbhjbsS0+WC/gWHIOTqXmob1rCfofeAbd1AcNSSHnMrD5bYwaNg/xWy7gfHoBvtx5CdMHtwVg+Bn99e9UvPPLSdNFB6UVetwT4Y9Npwx/GD0xoCUAQ1Ae0TkIC7ecx6/HUm8anNLzSvDX6XTT4+MpufVeoG4PN1wczuvqSEIMThIJ83VB+0A3BLk7IMjDfMQoyMMB/m4O5pctF2VVjRxdPGm4n37KsAjXGkdPy4Dk287sL/DbibujGne398fd7f0BGK4cOng5G/sqR6SOp+TiSk4xfjp8BT8dNizKDXR3QNdQDwgCKsNPtTBUSziqz04OPVt44d1/dER4gI0LeRVKwDvMcAuPrfsbNgJnrQpt/eu2MLmulAoB/x7TCdMGhiE9vxR5JeXIL6lAXnG56b4oAg/3aoYgD/uExsER/vjzdDo2nUrHs4PaWDz/zd7LOJaSC1etCrNGRJjag0OaIav/uxj0VyxedvgFw/Tb0F1xHos0nwC71gK+7XDQKxH67MtwRGUl9bOVt2p8BAX2OfkivwxwXa6BiBx0K81HiahGadeJcD/2BXDwSyg7PYjpg9vg2W+PYOmOS5jYuwWKynV4c+0JU7gJcHNAWl4J3l9/Gj8fvQJRBAa39zdbUD6iSyAWbjmP7Wev33QE6X+HUsyuoD2anCNxcLJSjoAFMEkGGJwk8mBUKB6MslLorqLUMAqRVBmSjGEpP9X6iZQawzRb9XVIfh0MC6PvwLpIRq4OasSE+yGmctqpsLQChy5nY9+lTMOC85QcQ0mF3NoXbt+MVqUw3NRKaFUKOFR+NdyUcNIoEdspEPd1D270PeLuJKFeTgj1uvWK4ra4u53h5+VYcg7S80rg5+Zgei4ps8hUI+rV2HZmzwHA09FhWH04BdOzp2AO7sc7gbsxrOR3wyL0nCQYi0zoIaBY6wtn/zDDRQAufkDGeSD1KJCfCu+Ka/BWAMZ8dUofine1L2HlPyYBilLgyArg1+cx7MkdaBfgitNp+Xh65SEcT8lFYZkOGqUCcTGt8XR0GD7eeAafbb9oWBMIYNrAVmZ9Dvd3RWs/F5xPL8Bfp9Lxj27WR530ehGrDiQDMOx9mJCaZ7qQ4Hp+KTRKRaNWXAes71VnvMet6khKDE5SEUXD2gxjMDKGpIxztU+zeTSzMs0WBij5z+isVWFAW18MqLzSprhMhyNJ2TiVlg+1UqgRfJQ1AlFlm7racyoFw9AdyM/NAV1CPXAsOQd/nk43rTNKzyvBI0v3oaC0At2aeeBhKwUoHdRKvD2yA6Z+fRDBIc0w6MlxEPABcOpXQ9kNz+aAR3Mo3EPgrKqlVld+GnTZyXh6xQFkFpQgwNMZG7MCMKpj5bTykHeBsxuAjLNQbHkX/+wzBk/8lIm9Fw0L93s098R793UyjSq9dm87pOeXYs2RK+jR3BM9apQ5EAQBg9r54Xx6AfZdyqo1OO29mImkrCK4alV4LbYdJn65H0eTc5CeX4J75m2Hl7MGf84YWGvR1p+PXsGrP/6NxY90x6B2/jb9W9yM1b3qWDmcZIC/caXyx6vA/s+tP+fgbn2azUFeJRHkzFGjRJ/WPugj4VQDydM97f1wLDkHmxOuYULPZsgpKsOjS/cjKavItBi+toAwOMIfW16ORqC7Q+UWL05Al3G2v7lrAJSuAWjf0x3/+fMcUHkhY9/WlTW+HD2B2PeBHx8H9izEACzEGQfgIoKR0Gcehg3ubehbwXVg/WtQ5CThY4/meKazJ4IddcD38UDhdcMFEhWlgEqLJ/UuCFcrcfF8D6AwCHD2ttgIbvs5Qx2poR0DENXCE0qFgPT8Uny04Qxyi8uRW1yOixmFVdv31PDlzksordDjm71JdgtOpjVO1f4tTHvV2eUdiOqHwUkq3m0Ml8X7hlcGpMqpNr+IqtoyRGR3gyP88dHGs9h5PgPX80sx9WtDwUg/Vy2+mdIL/jWm6GpqaYf6Rg/0CMEnf50zhYM+YdUCfocxhtHno98BhemArgytcAWtDkwGmn8JeIQC344Hcg1XwClSDsBytVYVHwD3KQEU7YD40X8gqBwMwco9FJj8B+AegsNJ2QAM6/WcNIb1badS8/DDwRTTeY4m51gNTqm5xaZNnXdfyEBJuc5s38D6MmW7am2C6TlGJ5IOg5NUuj0C9HjcYud5ImpY4f6uCPF0REp2MUYv3ImruSXwcFLjm6m90My7cdZahXg6oV9rH+w4l4FWvs4IcK8W1gQBGPSG4SaKQEE6sOZJ4OJW4PsJhrIU5YWGafro1w3rH3NTDOUo3IIAZz9A7WTYxqaiFCjKxPJfNqFn+QFEKC4bphUBQ/Ba+zTKH16Dv1NyAADdm3sAALqGeuBUqnnB2aPJ2bg/0lDL61peCSZ8vhd3hXmjXbWLIkrK9dhzIRMx7czriNWHaG2vOm65QjLA4CSVm2z3QEQNQxAEDG7vj+W7E3E1twTOGiW+erxng19FWNPTA8Ow50ImHuxh5SIRI0EwbEHz8I/A7zOAw18bQlOL/sCDXxvqedng4Mm2ePvvVLw50AtT7goEijKB5SOAS9txfdMClJS3h5uDCq18DCNKXUPdcebAWeTBCc7BHXAsJRdHq1WdX7HnMi5mFOJiRiG8nA1lIpw1ShSW6bDlTDpi2vlBFEXsOp+J9oGu9dqfUW+tHAE3+SUZqMfeCUREt7ehlfXBNCoFvpgYhS6hHo3ehz6tfXD63XsxbWDYzQ9WqoGRnwCj44G7ZwGP/GRzaAKA7s08AQA705SGmlLBkcCQOQAA/wPvY4bqB0zwT4KioggoL8G9iR/iJ+3b2Kh5Dcu8VyAY13E6NQ8l5TqU6/RYdTDZdO6synpSL95jqDX11+l0iKKI/x1MwSNL92HWzydt7md1otW96ozP1euURHbBEScianLuauWFeQ92QZiviyShyUilrMPfroJgmOKvh+7NDcHpSHIORFE0XMHXYzJwbhOUZ//A86q1wLW1wP+9Aji4w70kBwCgEER4nfkeuxy+x3XRHfn/G4aj4S/ien4pfFy0CPZwwLGUXIT7u+KhXs3wwfozSMk2rHlatNWwf+JRK/sj2sJUOdxKAUxu8ktS4ogTETU5giBgbPcQSUNTY4oIdINWpUBOUTkuXC/A3HWn8MSKQ8gZ8V/MVcfhZ10flDr6ARAN29A4ehmmBydvBJr1hh4K+Aq58D37HbqtG44o4TTGRYXgkwndMKidH14ZGg4njQqDIwxrmx5dug+JmYa1VFdzi1FSfpO9Fq2oceGf4T6n6kgGOOJERHSH06gU6BTsjoOXs/H0N4dxLt2w48DkglIczu8LQeiLv18YAm1FDpB5DvBrbyiNAACT1+O/m49j91+/4t+a5QjRXcNyzfvIa3MvAr2d8eWkKNP7zB7dEUeSDMVnjUQRuJRRiPaBtpdTqX7VXPU1Ttzkl+SAI05ERE2AcbruXHoBBMFQGf9wUg4Aw5WGrg5qwMUXaN6nKjRV6tgiENv0XTCkZC726dvBWShF4F8vGEomLOkHHFoOAPBx0WLxI5FwUVXAR6tHK19D6YaL1wvr1NfqlcG5yS/JDYMTEVET0L2Zh+n+7NEdMe/BrlXPNfe0fEE1XUM94O2sgaOzG8pHLga0bkDKAWDtNCDtOPDbDCBxl+FYx+s46vYy9rq9joGBegDAhevW99RMyS5CuU6PxIxCDJ63DQs2Gzb3qz6iZLUcASfrSEKcqiMiagJi2vnhwR4h6N7ME+Mrt5RJzAzH4q0XMKpL0A1f66xVYdurMVApBENxS81HhtpSKgdD4d4rh4AfJwND/w38ORuqIsNGxJNV72E5nsNFK8HpWHIORsfvwsC2vvBy1uB8egEWbD4Hb2cNxkVVbXljvjicI04kPQYnIqImQKtS4oP7u5i1xcW0xjPRYTbty+iirfbross4wKuVYTNxJy/gv4OA66eB1VMMz3s0AwozEJq9Dwe1CdCfdQB2Pgvc9TRQuY/fzvOGbV62nb1u9j5v/5qA/m18TY/NF4cbcJNfkhKn6oiImrB6b2YdGmXY/kXjbLgCL/JxIKg7ENAZeGQNMOxDAIC3kA9f/XVg81vAJ92AnQuAsiKk55WYne6uVl5oF+AKnV7EscpK5kCNApjc5JdkgCNORER0azxCgZELzNt8WqM8oBvGLtyKcFzG+x4/Q5l3xRCgLm5Fiu51s8PjYlrjx0MpOJ2WjwvVFpNXj3Xc5JfkgCNORETUINSBHZDvEYEfdQOxf/RfwKiFhn30Lm7BPamfQ40KzHuwC1Y/3Rv9W3ngocIV+Fi9CMnXqqbvrE3VccSJpHTHB6fk5GRER0cjIiICnTt3xv/+9z+pu0RE1GQY9wBctCMF5V0eBkb+BwAwvmw1jmmfwKD0rxDpC+CbseiVvBT3KXei+YWVAAz73zmolKZzGUPUa6uPY1flGimixnbHByeVSoUFCxYgISEBmzdvxosvvojCwrrVFCEiovp5blAbOKqV2HEuA6+vPg5dxweQ3+8NZIqucBJK4bH3A+DT7sCl7RAFQ0h6TP8znFGMbs08oVBYXlUHAB9uOHPD903JLsIXOy4iOasImxOuIa+kvGE+IDU5d3xwCgwMRNeuXQEAfn5+8PLyQlZWlrSdIiJqIjqFuGPhQ92gEIDVh1Pw3HeHcaz54+hRuhj/UU82HFScDXg0Q8nkP3FRHwAvoQDPqtaa1Z4CzNc7nUnLh/4Gl9e9+uPfmPP7KfT/YAumfn0QH66/cdAispXkwWn79u0YOXIkgoKCIAgC1q5da3HMokWL0LJlSzg4OCAyMhI7duyo13sdPHgQer0eoaGht9hrIiKy1d3t/fHphO7QKBVYdzwNr/x4DCIU2O8/Dhj7XyBqKjD1LziGdsNSjWEj46dVv2JU8VqgJA+4tAMoyjJb71RcrkNSVhFOXMnFtBWH8Muxq7iUUYiyCj2yC8uw+0KmWR/OpOU34iemO5nkV9UVFhaiS5cuePzxx3HfffdZPL9q1SpMnz4dixYtQt++ffHZZ58hNjYWCQkJaNbMUCQtMjISpaWlFq/duHEjgoIMhd0yMzPx2GOP4YsvvmjYD0RERBaGdw6EWingyRWHTHvZNfNyAjo/aLhVSgy4B58knsfzqrVofeT/gCP/Z3gioBOUTvPMznn96DokHf0TWzKGYv3JNABAz5ZeeLCH4Y/jNn4uGN+zGd79LQFXcoob4VNSUyB5cIqNjUVsbGytz8+bNw9TpkzB1KlTAQALFizAhg0bsHjxYsydOxcAcOjQoRu+R2lpKcaMGYOZM2eiT58+NzyuegDLy8ury0chIqIbuCfCH+0D3XAq1fDf1lAvJ4tjWvm4YN75B+Dk7IapqvVAoaEKOdKOI9Llf1iH6MojRXTd9QyixDI4q89DDwE/6AZiW2I3Q3VzAMM6BWJE50C8+1sC0vJKUKHTQ6W8+URLfkk5NCoFtNUWphMZST5VdyNlZWU4dOgQhgwZYtY+ZMgQ7N6926ZziKKISZMmYdCgQXj00UdveOzcuXPh7u5uunFKj4jIfgRBwOS+LUyPm1kJTlEtvQAIuN7lGWBGAjD9BDBiPgBgXP4K9FUcx5PKXzFMsQ9qsQwAcK/yAIYp9+NdzdeAqMf2ymrkg9r5wddFC41SAZ1eRFqNopvWFJRWYMAHWxD94VacvcbpPbIk6+CUkZEBnU4Hf39/s3Z/f3+kpaXZdI5du3Zh1apVWLt2Lbp27YquXbvi+PHjVo+dOXMmcnNzTbfk5ORb/gxERFRlZJcg+Loatl1pF+Bq+XznQPwc1xczhrQFlGpDcc3uk4Dm/eAiFGOlZi7+qf4OizSfWLw2FNdwl+IUAECjVKB9oBsUCgFBHg4AgJRs69N1oiji1R+P4aUfjuHi9QJkF5UjNbcEM344ap8PTXcUyafqbFFzSwBRFG3eJqBfv37Q6/U2HavVaqHVauvcPyIiso2DWonvnrgLKdlFaO1nGZwEQUCXUA/zRoUCmPAtjv/fQHRSXDJ7ao3PUxgx5F6oE34Ejq7EQ8o/sUffAe0DXaFRGcYGQjydkJhZhCu1BKfMwjL8cDAFANAx2M3UfiGdpWvIkqxHnHx8fKBUKi1Gl9LT0y1GoYiI6PbQ2s8F0eF+dXuRgzvKHvsd84I+Rl6/N0zNY8ZNgbrtIKDnEwCAkcq9eE31HToZA9DJtfjo+lMYodhT64hTclaR6f6+i1XlaorLdSirsO0Pb2o6ZD3ipNFoEBkZiU2bNmHMmDGm9k2bNmH06NES9oyIiBpbZOtgRLaeCpSXAFe2A45egE9bw5NB3ZDU/XU0O/wenlb9itwLJ4HP3YGrRxAA4AP15/h31ggAbSzOm1QtOO2+YF6RPLe43DS1SATIIDgVFBTg/PnzpseXLl3C0aNH4eXlhWbNmmHGjBl49NFH0aNHD/Tu3Ruff/45kpKSMG3aNAl7TUREklE7ABN/tWh2vfslvLcvEa+rv4d7wUWgoOo5J6EUvS7GA2WdAY35ovTqI1F5JRVmzzE4UU2SB6eDBw8iJibG9HjGjBkAgIkTJ2L58uUYN24cMjMzMXv2bKSmpqJjx45Yt24dmjdv3mB9io+PR3x8PHQ6XYO9BxER2Zenswbldz2PXRdL0EdxAkKvaUCbe5B3Zhvc1j+PUSW/oGLeLqiGvI31mnugVSsRE+5nNlVXU24xt2ohc4LIbaZrlZeXB3d3d+Tm5sLNze3mLyAiIvkRRaxYPAcD075CM4WhVMH75eNRDiVmjIzCE8fbYdcF61txLZsUhZh2dVyPRbeduvy+l/XicCIiolsmCAiKeRIxZfPwo2oEAOA19fd4Q70STuun44XU19FbcdLsJUHuhhIGHHGimhiciIjojtcu0A06KPHP4gk41voZXNQHoFA0rF3qqT+Kler/wyTfc6bj2/gbSiUwOFFNkq9xIiIiamgBbg5QKQSU6YAvlA/i17J+AIC2QjL+qfoW0cpjeDv/LYx074VE954oF72wDeFmwWnLmXSsPpSC/xvbCW4Oaqk+CkmMwYmIiO54SoWAIA9HJGUVYce566b2s2Ionip/Eb94LEF4/l5Elu5DZPo+AMCfihnILW4JACgp1+HxZQcAAJ1D3PHkgLDG/xAkC5yqIyKiJiHUyxEAkFNkGEXq2dILAFAKDa4M/xroMNbs+I/US9A7cRGQvB+/HL1qauf0XdPG4ERERE1CiId5/abZozsgxNMRIZ6O6BPmA3Q33wjeXSjC4IwVKP9qDJbvTjS1p+eVNkZ3SaY4VWcF6zgREd15jCNOAKBVKdDGzxUbXxwAAQIc1Eqg5UCgywRA44Kk9Ew0u/wTAEBdUYC8tPPoo0jHcMU+vHdoAlr7ueCpgVXTden5JSjXiQj2MLzHZ9suQC8CT0dzSu9Ow+BkRVxcHOLi4kx1HYiI6PYX4lk14jSicxCUCgFOmmq/BhVKYMwSAEDWnj8RkrgGCsFQ6nCK8g88qNwKZ6EU6aIH5v7hhCEdAuDqoMLvf6di7h+noNOLOPive5BbXI65f5wGAPyjWxAC3asCG93+OFVHRERNQohnVYC52UhQ6+4DMari/7CkwlD36XHVBjgLhim6F9WrES4k4UJ6AWb8cAxv/XISJeV6lOtEHL+Six3nqxafn7iS1wCfhKTE4ERERE1Ct2aeuK97CGbGtkNrP5cbHuuiVeFfk8dhuWIMspU+AACd1h3ZouF16zQzkX3xELafvW72unPp+dh5rmqj4BNXcu38KUhqDE5ERNQkKBUCPn6wi9napBvpHeaN7bPGwvOp34GuD0M58Wc8V/4cUkUvKAURPU+8gzDhCtSo2hj4TFo+jiTlmB6fvMrgdKdhcCIiIqqFRqUA/NoB/1gEBHVDq14jMLr0XeSJjmhechp/al/BDu0L6ChcBADsv5SFtLwS0+svZRRK1XVqIAxORERENpo9uiNWTB+NDyrGm9oChGys9PoSGpTjYo2gZKwZdTML/zqHx5ftR1mF3q79JftjcCIiIqqDAHcHfKMbjKllL+GB0lnIV3nCvfAiftG8ge7CWcxTL8I+p+m4X7kN2UVl0OtFq+e5lFGId39LQHpeCT7aeBZbzlzHhpNpjfxpqK5YjoCIiKgO3BxUUCsV2KyLBABsa/UyRpz9F9opkvGT9m3DQXrgI/VnCKu4ipJdiXDq8xSgNP+VO+6zPUjPL8XxagvIC0srQPLGEScr4uPjERERgaioKKm7QkREMiMIArydtabHmc2HA32nmx6XimoUOgQAAJ5W/QqnP/8JJKwF0k8BYtXoU3q+obzB/ktZpjbrY1MkJwxOVsTFxSEhIQEHDhyQuitERCRD3i6aqvuuWuCedzDaZSUWVIzF/WVvYX/sOmxR9q56weopwKK7gD/fueF5K2qZ1iP5YHAiIiKqI2+XqhEnn8r7WhdvLKi4H8fFVvD18cECzzfwVNmL5i/cOR/IvFDreQtKOFUndwxOREREdeTtXDXi5FM5+uTmWLWGyd/NAR5OGhzQh1u++NPuOLT+a6vnzSup/So8URQhihyRkhqDExERUR25OVSFJON6p+qzbN7OGuhFEVlww9vlj2Gzrhu26zqZnm+751Wr580rth6cyir0uHfBDkxaxiUkUuNVdURERHUkCILpvrujGgDMajApFAIuZxYBAJbr7sVy3b3wQzb2K+MAAK5CMUKEdKSIfmbnzbMyVafTi3j+uyM4cy0fZ67lo6RcBwe10u6fiWzDESciIqI6qj5lplAYQlSZzrx45czYdmaP0+GJEaVzTI93aqdjtGKn2THWRpwW/nUe66vVd7peeTUeSYPBiYiIqI7CA9ws2l4ZaljPNKlPCwDAvR0DcOytIUh8bzh+eqYP2ge64aF/jMYXFbGm1/xHswirNW+hj+IEACDXSnD67e+rZo+vVdvSRRRFHLqchQLWf2o0nKojIiKqowd7hCA9vwR9W/uY2qJaeOHIm/fAw8kwdScIgmkar3szT/zxQn8AwOKCtzBo892YqvwdD6m2IFJxDt9q/g8H9G3xW/5YAH1N5zx0ORvn0gvM3ttY/ynhah5+OpyCL3ZeQt/W3lg59a6G/MhUicHJivj4eMTHx0On00ndFSIikiGVUoHpg9tatHtWu9quNo4ODrgoBmGRbjQeUm0xtUcpziKq5D3gyhBUBHRDSnYx7lu82+L11/JKUFymw7BPdpjadp3PrOcnobpicLIiLi4OcXFxyMvLg7u7u9TdISKiO4iLg2EUKkX0w2pdf/RWnESQUFU9XL/vc8y7GIpTWQDQzeL11/JKkV1UZtF+JacYwR6ODdVtqsTgRERE1IhctFVXxL1U/jQA4Om2uWh+cRXGq7ZC8ff3eBWATi2gb+knSIO32etzisrM9rcz6vveX1gxpSf6t/Ft0P43dVwcTkRE1IictZZjFu0jo/G2bhKKxaqpPqUgoq/ipMWxCal5eGrFIavn/mjjWft1lKxicCIiImpENYNTzxZeGNrBH1oHZ7xY/gy26Lrgumi4am+O+kuMVuyEElVrbv9OsRxtMqqtgCbZD4MTERFRI3KpFpwc1Ur8MK03tCol3BxVWK/vicfLX8PUspcNzwtl+I9mEd5T/RdfPNodbYVkuKKo1nPnWFn7RPbF4ERERNSIqgcnB3XVr2G3ykXjAHBMDMNRfSvT4wdU2xG57wVs1L6GHdoX4A3ro045HHFqcAxOREREjaj6VJ1SUbV1S/XgBAiYVvYinip7EcsqhgIAPJM2AAA8hEKMVe6ANaIIFJVV4Hx6PhKu5tm/88TgRERE1JicNVVX1WUUVE2tOWvN959Lgzc26KMw/vUvAP9OZs89oVqHR5SbMFJhWefpQnohBs/bjmGf7EBeCUeg7I3BiYiIqBGplAqoKkea2vq7mNqrb/DbJdTDdN/RxQ0YvdBwjOiEJL0v/IQczFEvw6eahfBAvtn5d57PMN0fvXAX9HoRZD8MTkRERI1s26sxGNklCM/f3cbU9nifFvBy1mDxw93hpDYffUJQV+DJbXhANwfLdfeaPdVekQRf5EADw+jS++tPm567lFGIixnmW7bQrWFwIiIiamTBHo74dEI3jOgcZGqL7RSIQ28MRmynQOhFK6NEQV2R5dgcP+v6IE30NDVPVv6BndrnsUAdb/W9isv0KK3QYf2JVLMNgql+GJysiI+PR0REBKKioqTuChERNSGCYJjCs5abAMDdUY1MuOOu0oVY7/cEAOAe5WFohQoMU+7HO6pleFH1P7PX/Hb8KpbvSsS0bw6j1//9ieIyHbacScehy1nW3oJugluuWMG96oiISEqezmqr7W4Oxl/bAo75jca96f81e36iahMAYLeuI/aJ7QEAn227aHbMjnPX8WRl5fFLc4eZwhrZhiNOREREMvPG8Ah0DfXApxPMN/l1d6wKVDonH2DYR1Zfv0r7Lvopjlt97slq27UUlelQodNj+9nryOcVeDZhcCIiIpKZUC8nrI3ri5Fdgsza3aoFJweVAoiaiuTRq62eY7rLn1CjwupzRtlFZfh8x0U89uV+TPnq4K13vAlgcCIiIrpNVB9x0qqVgCAgtNtgoM/zAIBZ5RPxbNlzAIAeZfuxVxuHLsL5Ws+XXViOVQeSAQD7LxnWPJVV6LHt7HUUld04dDVVDE5ERES3CbPgpKr2K3zwO8DMFHytG4r1+ihcFb0AAN5CPl6qsVi8uuyiMlNNKQD46XAK2r7xByZ+uR/Pf3fE/h/gDsDgREREdJuovi2LtnqtJ4UC0LpidNcgVECFH7p/h7J/GBaO91YkYIRiD15U/Q9+yDY7nyE4VUWBGT8cM93ffCrdrCYUGTA4ERER3SZqHXGq9N7Yzvh6ck88PTwKmq4PQu/kC7Wgw0LNp3hBtcY0+hTs4QgAyC4sg0pZ+1V1i7desPMnuP2xHAEREdFtonW1LVoqdJbFnhw1Sgxo62t6LDh5AUXXTY+HK/eihSINguCD8ZiGrKJyqJQcQ6kLfreIiIhuE91CPdDc2wkA0Cn45nUGBV2p2WMXoQS9FKfRs3gnhigOIqfGGidrsgrLUFahN2tLzyvBpYzCOvb+zsARJyIiotuEIAj444X+SMwoQkSQ281fUF61xcpZRSu01VcVw1yiWYAj5w7jnMsrNzxF93c3oZWvM166Jxxf7U7EfyZ0Re+5fwEADr4xGD4u2vp9mNsUR5yIiIhuI04alW2hCQDa3AMAEN1DEfbQPMDFH3DyNj3drWA7hqYuwb2K/Tc8zcXrhYj79jD2J2bhzbUnTe0JV/Pq/gFucxxxIiIiulMNeRfwbA6h0wNQerYAXj4LlBWh+JNecCxIAgBMEn7HJA3wbNlz2KHvhFy43PCUlzOrpugKSpterSeOOBEREd2pHD2BAa8Ani2q2jROODduG6aWvWR26ELNp/hD+zqcUIIbyS+pCkvX8m587J2IwcmK+Ph4REREICoqSuquEBER2Z2nswO26zsjSe9r1h4kZOEV1aobvjatWli6nl96gyPvTAxOVsTFxSEhIQEHDhyQuitERER25+msQRnUeLj8n5hWNt3suYeVmzFcsRfPKX+CA24cjPKqbQxcodPf4Mg7B9c4ERERNTHOGkPV8WTRH8miP87qg9FWcQUAoBF0iNd8AgDwF7LxRsWUWs+TV2yYtpv503H8cSIVG18cAD9XhwbuvbQ44kRERNTECIJ57aZJZa/h24oYHNK3MWsfqjyIZ5Q/Y4xih9Xz/HLsKnR6Ed/tT0JOUTmW7rjUYH2WC444ERERNXFX4YN3hWmYrv8akYpzpnZfIRevqg1rnq6XeSBF9EGiGGj22s2nrpnu55Xc+VfZccSJiIioiZvYuzl+f74fwsPCaj3mG81cbNW+hEGKw2bt2YVlpvvf7U9CSnZRg/VTDhiciIiImqAP7u+McH9X/PpsP8wa2QGtfF0QPfQ+6EUBf+ii8Eb549Zfp/7c7LGyxpYtvx5LbbA+ywGn6oiIiJqgB3uE4sEeoeaNQV3xx7Bd+PpIDvYlZqO1cAVjlTvgJhSbDikWzbdYSc01r+WkVd3ZYzKCKIqW2ysTACAvLw/u7u7Izc2Fm5uN5e2JiIjuAB9vPINP/zoPAOitOIkQ4To+rBxtOqlvjgfLZqEQjhave3FwW7wwuA3ySsqRWVCGlj7Ojdrv+qjL7/s7OxYSERFRvTiolab7e/QdsE7Xy/S4g+IyvtX82+rr8itrO438dCdiPtqKs9fyG7ajjYzBiYiIiCzUnHKrObrURXERU5Tr4ItsAGLlrWpLlsuZhkXiv/99Z615YnAiIiIiC9bWKs0pf9js8Zvqb3DAIQ6JDg/jS/WHAIBVB5ORmFG1EXB2URnuJAxOREREZMFZW3X9mEZpiAtf6IYjrGQFftb1sTh+kPIoApAJAIj+aKup/ddjV7Fo63mcuJLbsB1uJAxOREREZCEm3A/ujmq4aFXYM3MQfnuuHwBAByVeKH8W5yYesXjNPcpDUKECnYULME7dZReV44P1ZzDi0534YsdF3O7XpLEcAREREVnwdNbgr5cGorRCD28XLZw0VZHhy0k90KalP4qbRcMxaaupfaxyF55VrYW/kIO3yifiK91Qs3PO+f0UAtwdMKJzUGN9DLvjiBMRERFZ5e2iRZCHYVG4o6bqKjs3B7XhjntVHSi9oEQ3xTn4CzkAgHfUX0GAHgL0Zuc8nXp7X2XH4EREREQ2+XRCNzw/qDUim3sCAPQDX8NZfTDeLX8YOe4RFscvUS/AUe2TlVfeGVToOVVHRERETcDILuZTbA5eIRhSZriaLqbzMPTbbn7V3VDlQQDAKOVuLNUNBwAs2XYBCgF4ZWg49KLlli1yxxEnIiIiqpfqoafIvwe26TpbPW6SciM8kWd6vGjrBTz77RF0fGsDjiXnNHQ37YrByYr4+HhEREQgKipK6q4QERHJWoinYQ1Uz5ZeeLp8Oh4onYXHy14xOyZUcR0bta+Ztf1+PBXF5To8unSfqe1wUjYmfL4XJ6/Kt3QB96q7Ae5VR0REdGMl5TqUluvh7qRGi9d/BwC4ogjHHaZaHPtu+cNYqRuMEphvFJz4nmEaz/j6YA9H7Hp9UAP3vAr3qiMiIqJG4aBWwt1JbdZWAAerx76pXonTDo9btNccw0nPL7FfB+2MwYmIiIjsSqwWL9ZaqTL+T9VKvKf63FSqYMe5DEz96oDpeWPNqMLSCsStPCyr/e54VR0RERE1mG26LviHcrdZ25Mqw5Tc17ohSBBb4LEv95s9n1tcjkVbz+OD9WcAGNZDDe88vHE6fBMccSIiIiK7e1P/FK63+gd+1feu9Zh12n8iCBlWnzOGJrlhcCIiIiK7W41B8H3sK1RAhdP60FqP2+3wPFxRZFauQM4YnIiIiMguHuvd3KKtQ5Ab4sqfR0FoDDBlMz7puw9bdV3MjjnuMBXbtDPghsJaz33f4t24eL3A7n2uKwYnIiIisot3RnUw3TdeKLfmmb74duZjcJmyFgiNwujuodis727xWjehCC2F2heBH7qcLYvpOwYnIiIisgtBsNw+RaNSwN+tqjxBc29nOAd3sDgOAFoJqRabAldXpqv9ucbC4ERERER207e1NwBgfM/a1zVleHVDhmhZaHK+ZjF2aZ9HWyHZ6us8HNVW2xsTgxMRERHZzZJHIvHfx3rg9dh2tR6jF1QYVPoRupcsQY7obPZckJCFR5WbrL4us7DMrn2tDwYnIiIishtXBzXuifCHVqWs9RgBQB5ckAU3bBy5D4nO5ovFH1VtRqLDQxZrnjILSxuiy3XC4ERERESNqvoGKw/2CAXunmX1uM2al+GAUtO6p8wC6UecWDmciIiIJNWi+2D0+99/kCZ64i7FKXyjmQsAUAoiTjs8jnW6nvhD1xPFhc4Q9dEQFLWPZjU0BiciIiJqVF7OGou2MpcQVOSXYqe+k8Vzw5T7MUy5H3ooUaF/DWoJ58s4VUdERESN6vlBbdCvtQ8+fqBqbdO6F/rjo8rHV0Rvq69TOLhCfYO1U41BEEVRvPlhTVNeXh7c3d2Rm5sLNzfLyyaJiIjIforLdGg/az08kI8QRSb6CMfxT/V3VQd4NAOmH7f7+9bl9z1HnIiIiEgWHCrn4HLgijSntvhcNxJ3l35YdUApt1whIiIiAmCoPP7Ls33x7dRe8HHRAgAuiMFVBxRnSdSzKgxOREREJBudQzzQp7UPzBYStR9l+BoxWpI+Vcer6oiIiEh29NWT04NfA5e2AwGWV9w1NgYnIiIikjdBAFoNlLoXADhVR0RERDL05ogIAMBTA1tJ3BNzHHEiIiIi2RnQ1hfH3x4CVwe11F0xwxEnIiIikiW5hSaAwcmq+Ph4REREICoqSuquEBERkYywcvgNsHI4ERHRnY+Vw4mIiIgaAIMTERERkY0YnIiIiIhsxOBEREREZCMGJyIiIiIbMTgRERER2YjBiYiIiMhGDE5ERERENmJwIiIiIrIRgxMRERGRjRiciIiIiGzE4ERERERkIwYnIiIiIhsxOBERERHZiMGJiIiIyEYMTkREREQ2UkndATkTRREAkJeXJ3FPiIiIqKEYf88bf+/fCIPTDeTn5wMAQkNDJe4JERERNbT8/Hy4u7vf8BhBtCVeNVF6vR5Xr16Fq6srBEGQujtmoqKicODAgTu2D/Y6962cpz6vtfU19jguLy8PoaGhSE5OhpubW536eTuR+me9od9f6p/1hvw5t/XYmx3TFH7Wpf45b+g+SP1zfrPXiqKI/Px8BAUFQaG48SomjjjdgEKhQEhIiNTdsEqpVEr+H5CG7IO9zn0r56nPa219jT2Pc3Nzk/xnoSFJ/bPe0O8v9c96Q/6c23qsree7k3/Wpf45b+g+SP1zbstrbzbSZMTF4bepuLg4qbvQoH2w17lv5Tz1ea2tr7H3cXcyqb8HDf3+Uv+sN+TPua3HSv1vLAdy+B7wv+m24VQd0W0qLy8P7u7uyM3NlfwvVaKGxJ91khOOOBHdprRaLd566y1otVqpu0LUoPizTnLCESciIiIiG3HEiYiIiMhGDE5ERERENmJwIiIiIrIRgxMRERGRjRiciO5Qv/32G8LDw9GmTRt88cUXUneHqEGMGTMGnp6euP/++6XuCjURvKqO6A5UUVGBiIgIbNmyBW5ubujevTv27dsHLy8vqbtGZFdbtmxBQUEBvvrqK/z4449Sd4eaAI44Ed2B9u/fjw4dOiA4OBiurq4YNmwYNmzYIHW3iOwuJiYGrq6uUneDmhAGJyIZ2r59O0aOHImgoCAIgoC1a9daHLNo0SK0bNkSDg4OiIyMxI4dO0zPXb16FcHBwabHISEhuHLlSmN0nchmt/pzTiQFBiciGSosLESXLl2wcOFCq8+vWrUK06dPx7/+9S8cOXIE/fv3R2xsLJKSkgAYdvquSRCEBu0zUV3d6s85kRQYnIhkKDY2FnPmzMHYsWOtPj9v3jxMmTIFU6dORfv27bFgwQKEhoZi8eLFAIDg4GCzEaaUlBQEBgY2St+JbHWrP+dEUmBwIrrNlJWV4dChQxgyZIhZ+5AhQ7B7924AQM+ePXHixAlcuXIF+fn5WLduHYYOHSpFd4nqxZafcyIpqKTuABHVTUZGBnQ6Hfz9/c3a/f39kZaWBgBQqVT4+OOPERMTA71ej1dffRXe3t5SdJeoXmz5OQeAoUOH4vDhwygsLERISAjWrFmDqKioxu4uNSEMTkS3qZprlkRRNGsbNWoURo0a1djdIrKrm/2c82pRamycqiO6zfj4+ECpVJr91Q0A6enpFn+dE92u+HNOcsXgRHSb0Wg0iIyMxKZNm8zaN23ahD59+kjUKyL74s85yRWn6ohkqKCgAOfPnzc9vnTpEo4ePQovLy80a9YMM2bMwKOPPooePXqgd+/e+Pzzz5GUlIRp06ZJ2GuiuuHPOd2OuOUKkQxt3boVMTExFu0TJ07E8uXLARgKA37wwQdITU1Fx44dMX/+fAwYMKCRe0pUf/w5p9sRgxMRERGRjbjGiYiIiMhGDE5ERERENmJwIiIiIrIRgxMRERGRjRiciIiIiGzE4ERERERkIwYnIiIiIhsxOBERERHZiMGJiIiIyEYMTkREEikrK0Pr1q2xa9cuu573t99+Q7du3aDX6+16XiJicCIiO5k0aRIEQbC4Vd/Elcx9/vnnaN68Ofr27WtqEwQBa9eutTh20qRJ+Mc//mHTeUeMGAFBEPDtt9/aqadEZMTgRER2c++99yI1NdXs1rJlS4vjysrKJOid/Hz66aeYOnVqg5z78ccfx6efftog5yZqyhiciMhutFotAgICzG5KpRLR0dF49tlnMWPGDPj4+OCee+4BACQkJGDYsGFwcXGBv78/Hn30UWRkZJjOV1hYiMceewwuLi4IDAzExx9/jOjoaEyfPt10jLURGg8PDyxfvtz0+MqVKxg3bhw8PT3h7e2N0aNHIzEx0fS8cTTno48+QmBgILy9vREXF4fy8nLTMaWlpXj11VcRGhoKrVaLNm3aYOnSpRBFEa1bt8ZHH31k1ocTJ05AoVDgwoULVr9Xhw8fxvnz5zF8+PA6fpeBxMREq6N70dHRpmNGjRqF/fv34+LFi3U+PxHVjsGJiBrFV199BZVKhV27duGzzz5DamoqBg4ciK5du+LgwYNYv349rl27hgcffND0mldeeQVbtmzBmjVrsHHjRmzduhWHDh2q0/sWFRUhJiYGLi4u2L59O3bu3AkXFxfce++9ZiNfW7ZswYULF7BlyxZ89dVXWL58uVn4euyxx/D999/jk08+walTp7BkyRK4uLhAEARMnjwZy5YtM3vfL7/8Ev3790dYWJjVfm3fvh1t27aFm5tbnT4PAISGhpqN6h05cgTe3t4YMGCA6ZjmzZvDz88PO3bsqPP5iegGRCIiO5g4caKoVCpFZ2dn0+3+++8XRVEUBw4cKHbt2tXs+DfffFMcMmSIWVtycrIIQDxz5oyYn58vajQa8fvvvzc9n5mZKTo6OoovvPCCqQ2AuGbNGrPzuLu7i8uWLRNFURSXLl0qhoeHi3q93vR8aWmp6OjoKG7YsMHU9+bNm4sVFRWmYx544AFx3LhxoiiK4pkzZ0QA4qZNm6x+9qtXr4pKpVLct2+fKIqiWFZWJvr6+orLly+v9fv1wgsviIMGDbJoByA6ODiYfR+dnZ1FlUoljh492uL44uJisVevXuKIESNEnU5n9ly3bt3Et99+u9Y+EFHdqaSNbUR0J4mJicHixYtNj52dnU33e/ToYXbsoUOHsGXLFri4uFic58KFCyguLkZZWRl69+5tavfy8kJ4eHid+nTo0CGcP38erq6uZu0lJSVm02gdOnSAUqk0PQ4MDMTx48cBAEePHoVSqcTAgQOtvkdgYCCGDx+OL7/8Ej179sRvv/2GkpISPPDAA7X2q7i4GA4ODlafmz9/PgYPHmzW9tprr0Gn01kcO2XKFOTn52PTpk1QKMwnERwdHVFUVFRrH4io7hiciMhunJ2d0bp161qfq06v12PkyJF4//33LY4NDAzEuXPnbHpPQRAgiqJZW/W1SXq9HpGRkVi5cqXFa319fU331Wq1xXmNl/M7OjretB9Tp07Fo48+ivnz52PZsmUYN24cnJycaj3ex8fHFMxqCggIsPg+urq6Iicnx6xtzpw5WL9+Pfbv328RDAEgKyvL7DMS0a1jcCIiSXTv3h2rV69GixYtoFJZ/qeodevWUKvV2Lt3L5o1awYAyM7OxtmzZ81Gfnx9fZGammp6fO7cObNRlu7du2PVqlXw8/Or13oiAOjUqRP0ej22bdtmMRJkNGzYMDg7O2Px4sX4448/sH379hues1u3bli8eDFEUYQgCHXu0+rVqzF79mz88ccfVtdRGUfUunXrVudzE1HtuDiciCQRFxeHrKwsTJgwwXT118aNGzF58mTodDq4uLhgypQpeOWVV/Dnn3/ixIkTmDRpksV01KBBg7Bw4UIcPnwYBw8exLRp08xGjx5++GH4+Phg9OjR2LFjBy5duoRt27bhhRdeQEpKik19bdGiBSZOnIjJkydj7dq1uHTpErZu3YoffvjBdIxSqcSkSZMwc+ZMtG7d2myK0ZqYmBgUFhbi5MmTdfiuGZw4cQKPPfYYXnvtNXTo0AFpaWlIS0tDVlaW6Zi9e/dCq9XetB9EVDcMTkQkiaCgIOzatQs6nQ5Dhw5Fx44d8cILL8Dd3d0Ujj788EMMGDAAo0aNwuDBg9GvXz9ERkaanefjjz9GaGgoBgwYgIceeggvv/yy2RSZk5MTtm/fjmbNmmHs2LFo3749Jk+ejOLi4jqNQC1evBj3338/nnnmGbRr1w5PPPEECgsLzY6ZMmUKysrKMHny5Juez9vbG2PHjrU6hXgzBw8eRFFREebMmYPAwEDTbezYsaZjvvvuOzz88MM3nC4koroTxJqLA4iIZCw6Ohpdu3bFggULpO6KhV27diE6OhopKSnw9/e/6fHHjx/H4MGDrS5evxXXr19Hu3btcPDgQasFSImo/jjiRER0i0pLS3H+/Hm8+eabePDBB20KTYBh7dQHH3xgVozTHi5duoRFixYxNBE1AC4OJyK6Rd999x2mTJmCrl27YsWKFXV67cSJE+3en549e6Jnz552Py8RcaqOiIiIyGacqiMiIiKyEYMTERERkY0YnIiIiIhsxOBEREREZCMGJyIiIiIbMTgRERER2YjBiYiIiMhGDE5ERERENmJwIiIiIrLR/wMtvAHuAIn15QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 50), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2., 0.05))\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(6,6))\n",
+    "ax.loglog(irasa_out.freqs, irasa_out.raw_spectrum[0,:], label='psd')\n",
+    "ax.loglog(irasa_out.freqs, irasa_out.aperiodic[0,:])\n",
+    "ax.set_ylabel('Power (a.u.)')\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "ax.set_title('Original + \\n Aperiodic Spectrum')\n",
+    "\n",
+    "f.tight_layout()\n",
+    "\n",
+    "irasa_out.fit_aperiodic_model(fit_func='knee').aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mse</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>R2_adj.</th>\n",
+       "      <th>BIC</th>\n",
+       "      <th>BIC_adj.</th>\n",
+       "      <th>AIC</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000152</td>\n",
+       "      <td>0.997741</td>\n",
+       "      <td>0.99771</td>\n",
+       "      <td>-27.333955</td>\n",
+       "      <td>-40.019592</td>\n",
+       "      <td>-42.149085</td>\n",
+       "      <td>knee</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mse        R2  R2_adj.        BIC   BIC_adj.        AIC fit_type  \\\n",
+       "0  0.000152  0.997741  0.99771 -27.333955 -40.019592 -42.149085     knee   \n",
+       "\n",
+       "  ch_name  \n",
+       "0       0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "irasa_out.fit_aperiodic_model(fit_func='knee').gof"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, sometimes we might feel the need to drastically increase our hmax, because we have a very broad oscillation that we want \n",
+    "to remove from our signal. In this case we start running into problems when trying to recover our simulated Knee Frequency.\n",
+    "This comes from the matter that the exponent before and after the knee get mixed up when up-/downsampling the signal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Knee</th>\n",
+       "      <th>Exponent_1</th>\n",
+       "      <th>Exponent_2</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>Knee Frequency (Hz)</th>\n",
+       "      <th>tau</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.958207e-14</td>\n",
+       "      <td>67.992202</td>\n",
+       "      <td>0.210813</td>\n",
+       "      <td>1.347045</td>\n",
+       "      <td>knee</td>\n",
+       "      <td>10.865906</td>\n",
+       "      <td>0.014647</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Offset       Knee  Exponent_1  Exponent_2 fit_type  \\\n",
+       "0  1.958207e-14  67.992202    0.210813    1.347045     knee   \n",
+       "\n",
+       "   Knee Frequency (Hz)       tau ch_name  \n",
+       "0            10.865906  0.014647       0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "irasa_out_bad = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 50), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 8, 0.05))\n",
+    "\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(6,6))\n",
+    "ax.loglog(irasa_out_bad.freqs, irasa_out_bad.raw_spectrum[0,:], label='psd')\n",
+    "ax.loglog(irasa_out_bad.freqs, irasa_out_bad.aperiodic[0,:])\n",
+    "ax.set_ylabel('Power (a.u.)')\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "ax.set_title('Original + \\n Aperiodic Spectrum')\n",
+    "\n",
+    "f.tight_layout()\n",
+    "\n",
+    "irasa_out_bad.fit_aperiodic_model(fit_func='knee').aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So how do we set the hset to avoid this issue. Here I am introducing a way to optimize the hset. \n",
+    "For this we can take the absolute periodic spectrum, which contains apart from the oscillations also in a way the model error, as we get the periodic spectrum by subtracting the \"aperiodic spectrum\" from the original psd. So we first need to get rid of the putative oscillations to get a good estimate of the model error. We do this by simply zeroeing oscillations or \"peaks\" in the periodic spectrum.  The residual absolute spectrum is then averaged and the resultant mean squared error can be used reduce the overall error between the original spectrum (blue line) and modeled aperiodic spectrum (orange line).\n",
+    "Below we can see that the good model has a much lower \"error\" than the bad model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def get_aperiodic_error(irasa_out, peak_kwargs=None):\n",
+    "\n",
+    "    if peak_kwargs is None:\n",
+    "        peak_kwargs = {}\n",
+    "    #get absolute periodic spectrum\n",
+    "    squared_error = np.abs(irasa_out.periodic[0,:])\n",
+    "\n",
+    "    #zero-out peaks\n",
+    "    peaks = irasa_out.get_peaks(**peak_kwargs)\n",
+    "    freqs = irasa_out.freqs\n",
+    "\n",
+    "    for _, peak in peaks.iterrows():\n",
+    "        cur_upper = peak['cf'] + peak['bw']\n",
+    "        cur_lower = peak['cf'] - peak['bw']\n",
+    "\n",
+    "        freq_mask = np.logical_and(freqs < cur_upper, freqs > cur_lower)\n",
+    "\n",
+    "        squared_error[freq_mask] = 0\n",
+    "\n",
+    "    return squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_99188/150655870.py:12: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  plt.legend()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x12ae33a10>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax =plt.subplots(ncols=2, figsize=(10, 5))\n",
+    "\n",
+    "ax[0].plot(irasa_out.freqs, get_aperiodic_error(irasa_out), label='good')\n",
+    "ax[0].plot(irasa_out.freqs, get_aperiodic_error(irasa_out_bad), label='bad')\n",
+    "\n",
+    "\n",
+    "df2plot = pd.DataFrame({'good': get_aperiodic_error(irasa_out),\n",
+    "                        'bad': get_aperiodic_error(irasa_out_bad)}).melt(var_name='kind', value_name='error')\n",
+    "\n",
+    "sns.pointplot(df2plot, x='kind', y='error', ax=ax[1], hue='kind')\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, you don't need to calculate it by hand. The IrasaSpectrum object that gets returned by irasa has a method called `get_aperiodic_error`. \n",
+    "That is taking your model specifications alongside some information for the peak fitting\n",
+    "to compute the error of your aperiodic spectrum. You just need to specify your kernel size and are good to go :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neurodsp.sim import sim_peak_oscillation, sim_powerlaw, sim_knee, sim_oscillation\n",
+    "height = 2\n",
+    "freq = 10\n",
+    "exp = 2.5\n",
+    "fs=1000\n",
+    "n_seconds = 60\n",
+    "\n",
+    "peak_params = {'freq': freq, 'bw': 2, 'height': height}\n",
+    "sig_ap = sim_knee(n_seconds=n_seconds, fs=fs, exponent1=-.0, exponent2= -1*exp, knee=knee)\n",
+    "sig = sim_peak_oscillation(sig_ap, fs=fs, **peak_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "duration=4\n",
+    "overlap=.5\n",
+    "\n",
+    "hmax_list = [2, 3, 4, 5, 6, 7, 8, 9,]\n",
+    "\n",
+    "irasas = []\n",
+    "for hmax in hmax_list:\n",
+    "    hset_info = (1, hmax, 0.1)\n",
+    "    irasa_out = irasa(sig, \n",
+    "                        fs=fs, \n",
+    "                        band=(.1, 50), \n",
+    "                        psd_kwargs={'nperseg': duration*fs, \n",
+    "                                    'noverlap': duration*fs*overlap,\n",
+    "                                },\n",
+    "                        hset_info=hset_info)\n",
+    "\n",
+    "    irasas.append(irasa_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EP7t372b79u0+ZV988QV+fn50794dgOuvv57Dhw8TEhJCz5496zzONuHkoEGD+O2337yB0nELFy7EYDBw5ZVXAp6uovrqeSbHu8DeeeedRtevvsShe/fuBU4EOcetWLGCgoIC77bL5eLrr7+mTZs2xMbGAp73cteuXbRp0+aU7+Xxaw4bNgyr1XrGNANarRaLxdKo15WYmEhhYaFPXe12O0uXLm3UdQThr0C0OAnCJaqkpITvv/+eoUOHcsMNN5zymNdee42FCxcyZ84c7+yq6Oho/va3vzFr1iyioqL47LPPWLZsGS+++CIGgwGAqVOn8u2339K/f3/+9a9/0aVLF9xuN5mZmfz66688/PDDXHHFFY2u89NPP+0d8/PUU08RHBzM559/zk8//cRLL71EQECA9/7z58/nuuuu47nnnvPOqtu3b98Z79GvXz/uuOMOnnvuOQoKCrj++uvRarVs27YNg8HAP//5z3rP7dixI4MGDWLYsGG0adMGq9XKhg0beOWVV4iIiODuu+/2OT40NJSBAwcyc+ZM76y6ffv2+aQkmD17NsuWLaNPnz48+OCDtG/fHqvVSnp6OkuWLOHdd98lNjaW22+/nY8//pgJEyawf/9+BgwYgNvtZsOGDaSkpHDbbbcB0LlzZ1atWsX//vc/oqKi8PPzo3379qd9T2699VaeeuopbrvtNh599FGsViv//ve/cblcZ3w/BeEvp6VHpwuC0DyOz2havHhxvce8++67MiB/++23six7ZtVdd9118jfffCN37NhR1mg0cmJiovzqq6/WObe6ulp+8skn5fbt28sajUYOCAiQO3fuLP/rX//ymUkGyJMmTTrl/ak1q06WZXnnzp3yiBEj5ICAAFmj0chdu3aVP/744zrn7tmzRx48eLCs0+nk4OBg+e6775a///77M86qk2VZdrlc8muvvSZ36tTJW/fevXvL//vf/+p9r2RZlt977z35pptuklu3bi0bDAZZo9HIbdq0kSdMmCBnZWXVeW2TJk2S3377bblNmzayWq2Wk5OT5c8//7zOdYuKiuQHH3xQbtWqlaxWq+Xg4GC5R48e8owZM3xmr1ksFvmpp56S27ZtK2s0GjkkJEQeOHCg/Oeff3qPSUtLk/v27SsbDAYZkK+++mpZlk/Mqqs9E/K4JUuWyN26dZP1er3cunVr+a233qp3Vl3tz/Po0aMyIL/88ss+5StXrpQB+b///e9p31dBuJiIzOGCIHglJibSqVMnfvzxx5auykVPkiQmTZrEW2+91dJVEQShCYkxToIgCIIgCA0kAidBEARBEIQGEl11giAIgiAIDSRanARBEARBEBpIBE6CIAiCIAgNJAInQRAEQRCEBhKBkyAIgiAIQgOJzOFn4Ha7yc3Nxc/Pr8HrbQmCIAiCcPGQZZmqqqoGLaQtAqczyM3NJS4urqWrIQiCIAhCM8vKyvKuI1kfETidgZ+fH+B5M/39/Vu4NoIgCIIgNLXKykri4uK83/mnIwKnMzjePefv7y8CJ0EQBEG4hDVkSI4YHC4IgiAIgtBAInCqx7x58+jQoQO9evVq6aoIgiAIgnCBEEuunEFlZSUBAQFUVFSIrjpBEARBuAQ15rtejHESBEEQhCYiyzJOpxOXy9XSVRFOolQqUalUTZJWSAROgiAIgtAE7HY7eXl51NTUtHRVhFMwGAxERUWh0WjO6ToicBIEQRCEc+R2uzl69ChKpZLo6Gg0Go1ImnyBkGUZu91OUVERR48epW3btmdMcnk6InASBEEQhHNkt9txu93ExcVhMBhaujpCLXq9HrVaTUZGBna7HZ1Od9bXErPqBEEQBKGJnEtLhtC8muqzEZ+wIAiCIAhCA4nASRAEQRCERpk1axbdunVr1DmpqalMnTq1xetxrsQYJ0EQBEEQGuWRRx7hn//8Z6POWbRoEWq1uplqdP6IwEkQBEEQhAaRZRmXy4XJZMJkMjXq3ODg4Gaq1fkluuoEQRAEoYm53TIl1bYWe7jdDV8UxGaz8eCDDxIeHo5Op+Oqq65i06ZNAKxatQpJkli6dCk9e/ZEq9Wydu3aOl1kTqeTBx98kMDAQEJCQpg+fTp33XUXN954o/eY2l11iYmJPP/884wfPx4/Pz/i4+N5//33feo2ffp02rVrh8FgoHXr1sycOROHw3FWn0lTES1OgiAIgtDEymrs9HhueYvdf8uT1xBi0jbo2GnTpvHtt9/yySefkJCQwEsvvcTQoUM5dOiQzzFz586ldevWBAYGsnr1ap9rvPjii3z++ed8/PHHpKSk8MYbb7B48WIGDBhw2nu/8sorPPvsszzxxBN88803PPDAA/Tv35/k5GQA/Pz8WLBgAdHR0ezcuZN7770XPz8/pk2b1sh3pOmIFidBEARB+Isym8288847vPzyywwbNowOHTrwwQcfoNfr+eijj7zHzZ49m8GDB9OmTRtCQkLqXOfNN9/k8ccfZ+TIkSQnJ/PWW28RGBh4xvsPHz6ciRMnkpSUxPTp0wkNDWXVqlXe/U8++SR9+vQhMTGRESNG8PDDD/Of//ynKV76WRMtToIgCILwF3X48GEcDgd9+/b1lqnVai6//HL27t1Lr169AOjZs2e916ioqKCgoIDLL7/cW6ZUKunRowdut/u09+/SpYv3uSRJREZGUlhY6C375ptveP311zl06BDV1dU4nc4zLsLb3ESLkyAIgnDeybLMloxSDhRUtXRV/tJk2TMWqvbyMLIs+5QZjcYzXutU1ziT2rPsJEnyBlvr16/ntttuY9iwYfz4449s27aNGTNmYLfbz3jd5iRanOoxb9485s2bJ1a4FgRBaAYPfLaVX3bnA3D3Va148rqUS2pttyCDhi1PXtOi92+IpKQkNBoNv//+O6NHjwbA4XCwefPmBudcCggIICIigo0bN9KvXz8AXC4X27ZtO6ccS3/88QcJCQnMmDHDW5aRkXHW12sqInCqx6RJk5g0aRKVlZUEBAS0dHUEQRAuGbtyKrxBE8BHvx/F5nQx+2+dUCgujeBJoZAaPDi7JRmNRh544AEeffRRgoODiY+P56WXXqKmpoa7776b7du3N+g6//znP5kzZw5JSUkkJyfz5ptvUlZWdk7BcFJSEpmZmXz11Vf06tWLn376ie++++6sr9dUROAkCIIgnFcbjpbWKftsfSYBejWPDk1ugRr9tb3wwgu43W7uuOMOqqqq6NmzJ0uXLiUoKKjB15g+fTr5+fnceeedKJVK7rvvPoYOHYpSqTzret1www3861//YvLkydhsNq677jpmzpzJrFmzzvqaTUGSG9IJ+Rd2vMWpoqKixQekCYIgXAomfLrFp8XpOLVSYsMT1xBsbFg304XEarVy9OhRWrVqhU6na+nqtDi3201KSgq33HILzz77bEtXBzj9Z9SY73oxOFwQBEFodtU2J9uzyqmxO9mUXrfFCcDhktmaUXaeayY0hYyMDD744AMOHDjAzp07eeCBBzh69Kh33NSlRHTVCYIgCM1q+Z4CHv7vdiosDiQJTtfPsTWzjGs6RJy/yglNQqFQsGDBAh555BFkWaZTp04sX76clJSUlq5akxOBkyAIgtBsvtmSzfRvd+A6tgTImQaHbM0ULU4Xo7i4OP7444+WrsZ5IbrqBEEQhGbxzZZsHvnvdm/QdCpale/X0I7sCpyu0ydNFISWJAInQRAEocnV2J0887/dZzzu/v6ta53nYr9IiilcwETgJAiCIDS579NyqbI6z3jc0E6RxAcbfMq2ZpY3U60E4dyJwEkQBEFoUrIs8+k63wzPvRJPnRMoOdKf7vGBPmXbxDgn4QImAidBEAShSW3NLGdPXqVP2cTUJJ68zneG1bUdI1EqJLon+AZV20SLk3ABE4GTIAiC0KQ+X+/b2hQXrKd/uzDu6pPIiK7RALQKNfLote0BuCzON3A6Wmym1NyyC7kKQn1EOgJBEAShyZSa7fy4M8+nbMwVCSgVEkok3rz9Ml64qTM6tRLlsXXpkqP80KoU2JwnZtMdLqom2Bh8Xuv+V5Wamkq3bt14/fXXW7oqFwXR4iQIgiA0mZ935WE/KQDSKBXc3CPW5xijVuUNmgDUSgUxgXqfY/IrrM1bUeGi9sEHH9CvXz+CgoIICgrimmuuYePGjefl3iJwEgRBEJrMyn1FPtuDO0YQYtKe8bxwf99jCipF4CTUb9WqVdx+++2sXLmSdevWER8fz5AhQ8jJyWn2e4vASRAEQWgSNqeLPw4V+5RdkxLeoHMj/X0XXRUtTueX2+1m2rRpBAcHExkZyaxZs7z7JEnivffe4/rrr8dgMJCSksK6des4dOgQqampGI1GevfuzeHDh73nHD58mBtuuIGIiAhMJhO9evVi+fLl3v379u3DYDDwxRdfeMsWLVqETqdj586dZ6zv559/zsSJE+nWrRvJycl88MEHuN1uVqxY0TRvyGmIwEkQBEFoEhuPlmJxuLzbkgT924Y16NyIgFqB06XQ4iTLYDef/8eZ1rU5hU8++QSj0ciGDRt46aWXmD17NsuWLfPuf/bZZ7nzzjtJS0sjOTmZ0aNHc//99/P444+zefNmACZPnuw9vrq6muHDh7N8+XK2bdvG0KFDGTFiBJmZmQAkJyczd+5cJk6cSEZGBrm5udx777288MILdO7cudH1r6mpweFwEBzc/OPixOBwQRAEoUnU7qbrGhvYoG46qNvidEl01Tlq4Pno83/fJ3JBY2zUKV26dOHpp58GoG3btrz11lusWLGCwYMHAzBu3DhuueUWAKZPn07v3r2ZOXMmQ4cOBWDKlCmMGzfOe72uXbvStWtX7/Zzzz3Hd999xw8//OANsCZOnMiSJUu444470Gg09OjRgylTppzVS37ssceIiYnhmmuuOavzG0METoIgCEKTWLW/0Gd7QPuGddPBqQInW5PUSWiYLl26+GxHRUVRWFh4yv0REREAPi1DERERWK1WKisr8ff3x2w288wzz/Djjz+Sm5uL0+nEYrF4W5yOmz9/Pu3atUOhULBr1y4kSaKxXnrpJb788ktWrVqFTqc78wnnSAROgiAIwjlLLzZzpNjsUzYwueGBU3jtMU6VVmRZPqsv0guG2uBp/WmJ+zb2FLXaZ1uSJNxu9yn3H/9MTlV2/JxHH32UpUuXMnfuXJKSktDr9YwaNQq73Tc/1/bt2zGbzSgUCvLz84mOblwL3dy5c3n++edZvnx5neCvuYjASRAEQThntVubQk1aOkb7N/j8yFpjnOxON+U1DoKMmiapX4uQpEZ3mV0q1q5dy9ixYxk5ciTgGfOUnp7uc0xpaSljx45lxowZ5OfnM2bMGLZu3Yperz/FFet6+eWXee6551i6dCk9e/Zs6pdQLzE4vB7z5s2jQ4cO9OrVq6WrIgiCcMFbfcB3fFNq+zAUioa3FoX7aanduHRJDBD/i0pKSmLRokWkpaWxfft2Ro8e7dOCBTBhwgTi4uJ48sknefXVV5FlmUceeaRB13/ppZd48sknmT9/PomJieTn55Ofn091dXVzvBwfInCqx6RJk9izZw+bNm1q6aoIgiBc8PblV/ls92sb2qjz1UoFIUbfgeQicLp4vfbaawQFBdGnTx9GjBjB0KFD6d69u3f/woULWbJkCZ9++ikqlQqDwcDnn3/Ohx9+yJIlS854/bfffhu73c6oUaOIioryPubOnducLwsASZbPYt7iX0hlZSUBAQFUVFTg79/wZmdBEIS/CqvDRcpTv/jMgl/yYD86NKKrDuD6N9eyK+fE4sAv3NSZ2y6Pb6pqNiur1crRo0dp1arVeRmgLDTe6T6jxnzXixYnQRAE4Zxkl1nqpA6KC27YOJWTRfiJmXXChU8EToIgCMI5ySqt8dkONmrw06nrObp+l2QSTOGsmEymeh9r165t0bqJWXWCIAjCOcmsFTjFBzd+OjxcokkwhbOSlpZW776YmJjzV5FTEIGTIAiCcE6aK3AS69X9dSUlJbV0FeoluuoEQRCEc9JUgVPtrjrR4iRciETgJAiCIJyT2mOczjpw8vdNR1BitmNzuuo5WhBahgicBEEQhLMmy3KdFqe4JuqqAygUM+uEC4wInARBEISzVmK2U2P3bRWKDzm7wClAr0ar8v1aEjPrhAuNCJwEQRCEs5ZR4tvapFZKp2w5aghJkogN8s3/dLTWwsGC0NJE4CQIgiCctdrjm2KDDCgbsUZdbW3CTD7bhwubf+2xv7rU1FSmTp3a0tW4aIjASRAEQThrTTWj7rikcN/A6ZAInIRTWLRoET179iQwMBCj0Ui3bt349NNPz8u9RR4nQRAE4aw1e+BUJAInoa7g4GBmzJhBcnIyGo2GH3/8kXHjxhEeHs7QoUOb9d6ixUkQBEE4a80dOGWV1mB1iJQEzc3tdjNt2jSCg4OJjIxk1qxZ3n2SJPHee+9x/fXXYzAYSElJYd26dRw6dIjU1FSMRiO9e/fm8OHD3nMOHz7MDTfcQEREBCaTiV69erF8+XLv/n379mEwGPjiiy+8ZYsWLUKn07Fz584z1jc1NZWRI0eSkpJCmzZtmDJlCl26dOH3339vmjfkNETgJAiCIJy12mOczjYVwXG1xzi55Yt3gLgsy9Q4as77Q6694nIDfPLJJxiNRjZs2MBLL73E7NmzWbZsmXf/s88+y5133klaWhrJycmMHj2a+++/n8cff5zNmzcDMHnyZO/x1dXVDB8+nOXLl7Nt2zaGDh3KiBEjyMzMBCA5OZm5c+cyceJEMjIyyM3N5d577+WFF16gc+fOjX6fV6xYwf79++nfv3+jX3tjia46QRAE4axYHa466QLOtcXJqFURHaAj96TlVg4VVpMS5X9O120JFqeFK7644rzfd8PoDRjUjfscunTpwtNPPw1A27Zteeutt1ixYgWDBw8GYNy4cdxyyy0ATJ8+nd69ezNz5kxvt9iUKVMYN26c93pdu3ala9eu3u3nnnuO7777jh9++MEbYE2cOJElS5Zwxx13oNFo6NGjB1OmTGlwnSsqKoiJicFms6FUKnn77be99W1OInASBEEQzkp2mYXajRtxwfpTH9wIbcJNdQInoXl16dLFZzsqKorCwsJT7o+IiADwaRmKiIjAarVSWVmJv78/ZrOZZ555hh9//JHc3FycTicWi8Xb4nTc/PnzadeuHQqFgl27diFJDZ+R6efnR1paGtXV1axYsYKHHnqI1q1bk5qa2piX3mgicBIEQRDOSu1uumCjBj+d+pyvmxRuYu3BYu/2xTpAXK/Ss2H0hha5b2Op1b6fmyRJuN3uU+4/Htycquz4OY8++ihLly5l7ty5JCUlodfrGTVqFHa73ec+27dvx2w2o1AoyM/PJzo6usF1VigU3sWAu3Xrxt69e5kzZ44InARBEIQLU1MttVJb7QHiF2suJ0mSGt1ldqlYu3YtY8eOZeTIkYBnzFN6errPMaWlpYwdO5YZM2aQn5/PmDFj2Lp1K3r92bVayrKMzdb8S/SIwEkQBEE4K7UDp4SmCpxqDRA/UmzG5ZbPKbGmcH4lJSWxaNEiRowYgSRJzJw506cFC2DChAnExcXx5JNPYrfb6d69O4888gjz5s074/XnzJlDz549adOmDXa7nSVLlrBw4ULeeeed5npJXiJwEgRBEM5KU6ciOK5NrRYnu9NNVmkNiaHGJrm+0Pxee+01xo8fT58+fQgNDWX69OlUVlZ69y9cuJAlS5awbds2VCoVKpWKzz//nD59+nDdddcxfPjw017fbDYzceJEsrOz0ev1JCcn89lnn3Hrrbc290tDks9m3uJfSGVlJQEBAVRUVODvf/HN6hAEQWgu176+hn35Vd7tl/7ehVt6xZ3zdWVZ5rJnl1Fe4/CWzR/bk4HJEed87eZitVo5evQorVq1Qqc7u7X6hOZ1us+oMd/1Io+TIAiC0GiyLDfbGCdJkmhVq3Wp9mLCgtBSROAkCIIgNFqJ2U6N3Tejd3xI0w2ETgwRgdNfmclkqvexdu3aFq2bGOMkCIIgNFrtQEatlIj0b7ouqoRaQVh6ycWZPVw4O2lpafXui4mJOX8VOQUROAmCIAiNVjuHU2yQoUlnvYkWp7+24/mZLkSiq04QBEFotOYa33Rc7RanrNIanC53PUcLwvkjAidBEASh0eqmIjj3pVZOVrvFyemWyS231nO0IJw/InASBEEQGq1u8sumzbEUaFDjr/MdTSLGOQkXgr9E4DRy5EiCgoIYNWpUS1dFEAThklB7jFNTd9VJklQn4WWGCJyEC8BfInB68MEHWbhwYUtXQxAE4ZJgdbjIr/TtNmuqrOEnS6jVXZcuBogLF4C/ROA0YMAA/Pz8WroagiAIl4TsMgu115yIa+IxTgCJtQaIixan5pGamsrUqVNbuhoXjRYPnNasWcOIESOIjo5GkiQWL15c55i3337bmyK9R48eLZ78ShAE4a+sdjddsFGDn07d5PcRLU5CQ3z11VdIksSNN954Xu7X4oGT2Wyma9euvPXWW6fc//XXXzN16lRmzJjBtm3b6NevH8OGDSMzM9N7TI8ePejUqVOdR25u7vl6GYIgCH8ZzZ2K4LjaLU6ZJTW43GJ5VeGEjIwMHnnkEfr163fe7tnigdOwYcN47rnnuOmmm065/9VXX+Xuu+/mnnvuISUlhddff524uDjeeecd7zFbtmxh165ddR7R0dGNro/NZqOystLnIQiCIJxQNxVB8wROtVuc7C53nbFVQtNwu91MmzaN4OBgIiMjmTVrlnefJEm89957XH/99RgMBlJSUli3bh2HDh0iNTUVo9FI7969OXz4sPecw4cPc8MNNxAREYHJZKJXr14sX77cu3/fvn0YDAa++OILb9miRYvQ6XTs3LmzQXV2uVyMGTOGZ555htatW5/7m9BALR44nY7dbmfLli0MGTLEp3zIkCH8+eefzXLPOXPmEBAQ4H3ExZ37St+CIAiXkubO4XRcqEmDQaP0vfdF1F0nyzLumprz/pBrD0BrgE8++QSj0ciGDRt46aWXmD17NsuWLfPuf/bZZ7nzzjtJS0sjOTmZ0aNHc//99/P444+zefNmACZPnuw9vrq6muHDh7N8+XK2bdvG0KFDGTFihLe3KDk5mblz5zJx4kQyMjLIzc3l3nvv5YUXXqBz584NqvPs2bMJCwvj7rvvbvTrPRcX9JIrxcXFuFwuIiIifMojIiLIz89v8HWGDh3K1q1bMZvNxMbG8t1339GrV69THvv444/z0EMPebcrKytF8CQIggDsyqngg7VHWL2/yKe8qXM4HSdJEjGBeg4WVnvL8ioszXKv5iBbLOzv3uO837f91i1Ihsa1Anbp0oWnn34agLZt2/LWW2+xYsUKBg8eDMC4ceO45ZZbAJg+fTq9e/dm5syZDB06FIApU6Ywbtw47/W6du1K165dvdvPPfcc3333HT/88IM3wJo4cSJLlizhjjvuQKPR0KNHD6ZMmdKg+v7xxx989NFHp13Trrlc0IHTcZLku/6RLMt1yk5n6dKlDT5Wq9Wi1WobfLwgCMJfweb0Um7/YD0OV93WjOYa4wQQVStwyi2/eAKni0mXLl18tqOioigsLDzl/uONGSe3DEVERGC1WqmsrMTf3x+z2cwzzzzDjz/+SG5uLk6nE4vF4jM+GWD+/Pm0a9cOhULBrl27GvTdXlVVxT/+8Q8++OADQkNDz+r1nosLOnAKDQ1FqVTWaV0qLCys0wolCIIgNJ9vtmSfMmgK99PSIyGo2e4bE6jz2c6tuHjGOEl6Pe23bmmR+zaWWu07K1KSJNxu9yn3Hw9uTlV2/JxHH32UpUuXMnfuXJKSktDr9YwaNQq73e5zn+3bt2M2m1EoFOTn5zdobPLhw4dJT09nxIgR3rLj91WpVOzfv582bdo06HWfjQs6cDredLds2TJGjhzpLV+2bBk33HBDC9ZMEAThryWrzHdskVopMaRjJI9dm4xG1XzDZaMCfIOAi6nFSZKkRneZXSrWrl3L2LFjvd/d1dXVpKen+xxTWlrK2LFjmTFjBvn5+YwZM4atW7eiP0Pgl5ycXGcA+ZNPPklVVRVvvPFGsw+vafHAqbq6mkOHDnm3jx49SlpaGsHBwcTHx/PQQw9xxx130LNnT3r37s37779PZmYmEyZMaMFaC4Ig/LXk12rpefHvXbipe2yz3zc60PdLNE8s9HtRSEpKYtGiRYwYMQJJkpg5c6ZPCxbAhAkTiIuL48knn8Rut9O9e3ceeeQR5s2bd9pr63Q6OnXq5FMWGBgIUKe8ObR44LR582YGDBjg3T4+MPuuu+5iwYIF3HrrrZSUlDB79mzy8vLo1KkTS5YsISEhoVnrNW/ePObNm4fL5WrW+wiCIFzoZFkmr1bgFBPYPDPpaouu3VV3EbU4/ZW99tprjB8/nj59+hAaGsr06dN90vssXLiQJUuWsG3bNlQqFSqVis8//5w+ffpw3XXXMXz48Bas/elJ8tnMW/wLqaysJCAggIqKCvz9/Vu6OoIgCOddpdVBl1m/+pStfjS1Tp6l5pBebCZ17iqfsh2zhuDfDJnKz4XVauXo0aPeVS6EC8/pPqPGfNdf0HmcBEEQhJZXu5sOIML//AQHkQF17yO664SWJAInQRAE4bRqB07BRg06tbKeo5uWTq0k1KTxKRPddZc+k8lU76Ol16tt8TFOgiAIwoWtduAUeZ5am46LDtRTXH1iGnvuRZQEUzg7p0tsGRMTc/4qcgoicBIEQRBOq/bA8FN1nzWnqAAdO7IrvNuixenSl5SU1NJVqJfoqqvHvHnz6NChQ71LswiCIPxV5Ff6BirnO3CqnZIgV4xxElqQCJzqMWnSJPbs2cOmTZtauiqCIAgtqnZXXdR57qqrnfpAtDgJLUkEToIgCMJptXxXXa3ASYxxElqQCJwEQRCE08qvrNXiFHB+kl8eVzsJZn6FFbdbpCAUWoYInARBEIR6WR0uymscPmWRAdrzWofaY5wcLpmiatt5rYMgHCcCJ0EQBKFep0p+GXmeW5zCTFo0St+vq6zSmnqOFoTmJQInQRAEoV61xzf5aVWYtOc3k41CIREb7BusZZSIwKmppKamMnXq1JauxkVDBE6CIAhCvVo6FcFxCcEGn+0M0eL0l7ZgwQIkSarzsFqbP1WFSIBZj3nz5jFv3jxcLldLV0UQBKHFtPSMuuM8CwoXebczS8wtUg/hwuHv78/+/ft9ys7HAsuixakeIo+TIAgCFLTwcivHxddqccoULU5Nyu12M23aNIKDg4mMjGTWrFnefZIk8d5773H99ddjMBhISUlh3bp1HDp0iNTUVIxGI7179+bw4cPecw4fPswNN9xAREQEJpOJXr16sXz5cu/+ffv2YTAY+OKLL7xlixYtQqfTsXPnzgbVWZIkIiMjfR7ngwicBEEQhHrVbnGKaqEWp4sxcJJlGYfNdd4fstz4VA2ffPIJRqORDRs28NJLLzF79myWLVvm3f/ss89y5513kpaWRnJyMqNHj+b+++/n8ccfZ/PmzQBMnjzZe3x1dTXDhw9n+fLlbNu2jaFDhzJixAgyMzMBSE5OZu7cuUycOJGMjAxyc3O59957eeGFF+jcuXOD6lxdXU1CQgKxsbFcf/31bNu2rdGv+2yIrjpBEAShXrVzOJ3vGXXHJYT4Bk7F1Xaqbc7zPlC9MZx2N+9PWX3e73vfG1ej1iobdU6XLl14+umnAWjbti1vvfUWK1asYPDgwQCMGzeOW265BYDp06fTu3dvZs6cydChQwGYMmUK48aN816va9eudO3a1bv93HPP8d133/HDDz94A6yJEyeyZMkS7rjjDjQaDT169GDKlCkNqm9ycjILFiygc+fOVFZW8sYbb9C3b1+2b99O27ZtG/XaG+vC/Y0TBEEQWtyF0uIUV6vFCSCzpIYO0f4tUJtLT5cuXXy2o6KiKCwsPOX+iIgIAJ+WoYiICKxWK5WVlfj7+2M2m3nmmWf48ccfyc3Nxel0YrFYvC1Ox82fP5927dqhUCjYtWsXkiQ1qL5XXnklV155pXe7b9++dO/enTfffJN///vfDX/hZ0EEToIgCMIpOVxuimslmoxooTFOOrWSSH+dTwtYZumFHTipNArue+PqFrlvY6nVap9tSZJwu92n3H88uDlV2fFzHn30UZYuXcrcuXNJSkpCr9czatQo7Ha7z322b9+O2WxGoVCQn59PdHR0o+sOoFAo6NWrFwcPHjyr8xtDBE6CIAjCKRVW2ag9XKalWpwA4kMMtQKnC3tmnSRJje4yu1SsXbuWsWPHMnLkSMAzHik9Pd3nmNLSUsaOHcuMGTPIz89nzJgxbN26Fb2+8d3BsiyTlpbW4PFR50IMDhcEQbjUOO3wTl94fwC4zz6lSn6txXS1KgWBBnU9Rze/2gPERRLMC1dSUhKLFi0iLS2N7du3M3r0aJ8WLIAJEyYQFxfHk08+yauvvoosyzzyyCMNuv4zzzzD0qVLOXLkCGlpadx9992kpaUxYcKE5ng5PkSLUz1EHidBEC5auVuhYJfnef4OiL7s7C5TXnd8U0PHoDSH2kkwL4aZdX9Vr732GuPHj6dPnz6EhoYyffp0KisrvfsXLlzIkiVL2LZtGyqVCpVKxeeff06fPn247rrrGD58+GmvX15ezn333Ud+fj4BAQFcdtllrFmzhssvv7y5XxqSfDbzFv9CKisrCQgIoKKiAn//C7cvXRAEwWvNy/Dbc57ng2dD34bNVKrtxV/28c6qE7l5rkoK5bN7rmiKGp6V79NymPJVmnc7IcTA6kcHtFh9Tma1Wjl69CitWrU6L0kYhcY73WfUmO960VUnCIJwqUn/48TzI2c/HX5bZpnPdte4gLO+VlPwZA8/IbvMgtst/vYXzi8ROAmCIFxKnHbI2nBiO3Odp6yxl3G52ZFd4VN2WVzQudbunIT5aX22XW6ZKpuzhWojNCeTyVTvY+3atS1aNzHGSRAE4VKSuw0cNWAIASSoKYaczZDQp1GXOVBQTY3dd4xnt/jApqvnWQjQ1x2YXmlxnLJcuLilpaXVuy8mJub8VeQUROAkCIJwKcn43fMzoQ8o1LB7kae7rpGB07Ys3266+GADoSZtPUefH0aNEqVCwnVS91yFxUFcC9ZJaB5JSUktXYV6ia46QRCES0n6scApsR+0PpZ88eiaRl9mW2a5z/ZlLdzaBJ68SLVblyosjhaqjfBXJVqcBEEQLhUuB2QeG9+U0Bc0xwZTZ28Cu/nEdgNsrTUw/LK4wCaq5LkJ0KspNZ8YsyUCJ+F8Ey1OgiAIFwOHBda+Ajv+S5103sflpoHDDPogCO8AQYkQEA9uB2Ssa/CtymvsHCnyzcrdPaFlB4Yf5y9anIQWJgInQRCEC13xIfjwGlgxGxbdA1/cApV5dY/zjm/qCwoFSBK07u8pO9rwtARpWeU+21qVguTICyOPneiqE1qaCJzqMW/ePDp06ECvXr1auiqCIPyV7f4O3k/1ZALXB4NSAwd/hbevhJ3f+LY+ecc3XXWirFWq52cjAqfa45s6xwSgUV0YXxcicBJa2oXxL+ECNGnSJPbs2cOmTZtauiqCIFygPtz5IdPXTMfuanyepDOSZfj5MfjvWLBXeVqRHvgT7l8DUV3BWg7f3g1fjYGydHA5IXO951yfwKmf52feDqgpbdCtt9VqcboQBoYfF6D3HZorAqdzl5qaytSpU1u6GhcNETgJgiCchd3Fu3lj6xssObqEVVmrmv4G6Wthwzue51f9C+78AfyjIDwF7lkBqU+AQgX7f4J5V8APk8FeDbpACO944jp+kRCWDMhnnl1XuA93WRZptQeGx18Y45tAtDgJJ5SXlzNp0iSioqLQ6XSkpKSwZMmSZr+vCJwEQRAaSZZlXtvymnd7Wcaypr/J8SCn8y1wzSxQntTSolRD6nSY8Du06g9OK2z/0rPv+Pimk7Ud4vm5dWH99yvLgPf645p/LdVW3xa0C6vFyTdwqhSB01+S3W5n8ODBpKen880337B//34++OCD85IcUwROgiAIjbQudx0b8jcgIQGwJnsNVqe1aW9yfLzS8a62UwlP8bRE3bwA/I99YaSMqHtcr7tBUsDhFVC479TX2vcTuGyoq7LpKe33FkcF6IgK0J/da2gGtQOn8hoRODUFt9vNtGnTCA4OJjIyklmzZnn3SZLEe++9x/XXX4/BYCAlJYV169Zx6NAhUlNTMRqN9O7dm8OHTywIffjwYW644QYiIiIwmUz06tWL5cuXe/fv27cPg8HAF1984S1btGgROp2OnTt3nrG+8+fPp7S0lMWLF9O3b18SEhK46qqr6Nq1a9O8IachAidBEIRGcMtuXtvqaW0akzKGSGMkNc4a/sz9s+lu4rBAzhbP84S+pz9WkqDjSJi82dMC1fW2uscEJUL74Z7nG9499XUO/Ox9OkS52fu8Z2JwIyre/C6mrjpZlnFYref9IdeXruI0PvnkE4xGIxs2bOCll15i9uzZLFt2oiX12Wef5c477yQtLY3k5GRGjx7N/fffz+OPP87mzZ7fl8mTJ3uPr66uZvjw4Sxfvpxt27YxdOhQRowYQWZmJgDJycnMnTuXiRMnkpGRQW5uLvfeey8vvPACnTt3PmN9f/jhB3r37s2kSZOIiIigU6dOPP/887hcrjOee65EAkxBEIRG+OXoL+wr3YdJbeK+LvcB8Nnez1iWsYyB8QOb5ibZm8BlB78oCG7dsHM0Bog8zRfOlRNh34+w/SsY9BQYTgqILOWQcSLwG6rYzHP8A5DolXjhjG+CiyuPk9Nm4993jTrv933wk29Q63SNOqdLly48/fTTALRt25a33nqLFStWMHjwYADGjRvHLbfcAsD06dPp3bs3M2fOZOjQoQBMmTKFcePGea/XtWtXn9af5557ju+++44ffvjBG2BNnDiRJUuWcMcdd6DRaOjRowdTpkxpUH2PHDnCb7/9xpgxY1iyZAkHDx5k0qRJOJ1OnnrqqUa99sYSLU6CIAgN5HA5+Pe2fwMwrtM4gnRBDE7wfLGszlrddLPr0v/w/Ezo62lRagoJfSCyCzgtsOVj332HloPbiTMgEYusIU5RRIrkaRnodYG3OFVaHbjdjW9hEXx16dLFZzsqKorCwsJT7o+IiADwaRmKiIjAarVSWVkJgNlsZtq0aXTo0IHAwEBMJhP79u3ztjgdN3/+fHbs2MHWrVtZsGABUgN/391uN+Hh4bz//vv06NGD2267jRkzZvDOO+807oWfBdHiJAjCRSmvwsK2zHK6xwcRGdC4v67P1n8O/Iec6hxC9aH8I+UfAHQL70aYPowiSxHr89bTP7b/ud8o41jglHiGbrrGkCRPq9PiCbDxA+jzoGeQOcB+Tzfd0fBrOFKaxlDlZoYqN5GjakP7CL+mq0MTCDRofLZlGapszjoB1YVApdXy4CfftMh9G0ut9n3/JEnC7Xafcv/x4OZUZcfPefTRR1m6dClz584lKSkJvV7PqFGjsNt9/7jYvn07ZrMZhUJBfn4+0dHRDapvVFQUarUapVLpLUtJSSE/Px+73Y5GoznN2edGBE6CIFx0duVUMObDDVRYHGhUCr6b2IeO0QHNek+zw8z7O94H4IGuD2BQGwBQSAoGxg/k6/1fszxj+bkHTg6rp6sOPAv1NqVON8Gyp6AqD3Yvhi43e9a3O+QZy7JK7sE+l4qhys0MUWxhR+JEFIomavFqIqcKkCotjgsycJIkqdFdZpeKtWvXMnbsWEaOHAl4xjylp6f7HFNaWsrYsWOZMWMG+fn5jBkzhq1bt6LXn3kyQt++ffniiy9wu90ojs0iPXDgAFFRUc0aNIHoqhME4SJjsbuY8tU279gWu9PN68sPNvt9P9n9CaXWUhL9ExnZdqTPviEJnun+v2X9hsNdd8yNLMvkm/MbdqOcLZ70AsZwCEk653r7UGnh8ns9z395DAr3YTn8O1grwBDCd0VRrHBfhlNW0EGRwYCImqa9fxMwapQoawVzF/I4p7+qpKQkFi1aRFpaGtu3b2f06NE+LVgAEyZMIC4ujieffJJXX30VWZZ55JFHGnT9Bx54gJKSEqZMmcKBAwf46aefeP7555k0aVJzvBwfInASBOGi8uIv+zhcawHalfsKKam2Nds9iy3FLNi9AIAHuz+IWuHbutE9ojtB2iAqbBVszt/ss0+WZZ74/QkGfzOYFZkrznyzk7vpmmp808mumODJPF5TTPUHw1jz6f8BkB58FXsLayjHj43uZABS3Rua/v7nSJKki2pm3V/Va6+9RlBQEH369GHEiBEMHTqU7t27e/cvXLiQJUuW8Omnn6JSqTAYDHz++ed8+OGHDUpiGRcXx6+//sqmTZvo0qULDz74IFOmTOGxxx5rzpcFiK66es2bN4958+adl6mNgtDcLJYsDh1+ieCgPsTE3N4s91h9oIh3Vh0ixKRlxvAUogObPvfPmgNFLPgzvU650y3zfVou469q1eT3BHg77W0sTgudQztzTfw1dfarFCoGxg/k24PfsixjGb2je3v3fbX/K3488iMA3x74lkHxg05/s/STFuptDjp/uGMxlg+vw1S6h6FKzzIsLxxp5V32bqm7F32Ue4jJ/w2Y1jz1OAcBejWl5hNjZUTgdG5WrVpVp2zx4sXe57XTGyQmJtYpS01N9SlLTEzkt99+8znm5NagO++8kzvvvNNnf48ePbDZGv4HUO/evVm/fn2Dj28qosWpHmKtOuFSUVa2kU2bR1JYuIR9+5+kqGj5mU9qpB3Z5YxfsIn1R0r5aUcet72/3ueLrSmU19h59Jvt9e7/Zkt2g6/ltttxm81nPhDYV7qPbw54Bvg+3PPhemf9HJ9dtyJzBS635w+uXcW7eGnTS95j1uWuo8JWUf/NnHbI2uh5fvJ6c03NEMw7cXPZ744FwCarWOs+MUPqV1dPABRZ66G6qPnqcZYuppQEwqVHBE6CcAnLyf2abWl34HCUoVL5A7Bn7yPU1KQ32T2cLjePfbsT10lTwjNLa5jw6RZszqZpsZVlmRmLd1FQWf9fo3vyKtmTW3na67jNZorfe5+D/fpzaPAQ7FlZZ7zvnA1zkJEZljiMHhE96j328qjL8df4U2otZWvhVipsFTy86mGcbieD4gfRNqgtTtnJb5m/1XsNcrd50gUYQo6tL9c83G6Z/+y1MsY+g2WuHrzqvBkzJ1oI8wgh33hsfbuTEmNeKERX3aXPZDLV+1i7dm2L1k101QnNymYroqJiKxUVWyiv2IrTWU1IcF9CQlIJDLwcpbLx02aFM3O7nRw6/AJZWZ58PeHhw0lJfp607eOpqNjKzl2T6NnjG5TKc+9O+/iPdPbk1Q1YNqaX8sSiXcy9uUuDc7PU54ftufy0I8+nrF/bUA4VVpNXcWKpk2+3ZtMhukOd8902G2VffknJ+x/gKi31luc88giJn32GpD71jKyl6UvZWrgVnVLHQz0fOm0d1Qo1qXGp/HD4B35N/5WFuxeSa84l1hTL7L6z+WrfVxwsO8jSjKV1Bpd7ZZzUTdcc45uO2ZxRRn6lFQjgXsfDpzymps0w2LEP9v4I3e885TEtRQROl760tLR6952P9ehORwROQpNxu52YzQeoqNhKefkWSss243Dk1jmupuYgWdkLUCh0BAf1ISQklZCQq9HrY1ug1pceh6OSXbsfpLTU81dZq1ZTaZU4GUmS6NTpTTZu/BvV1fvYt38mHVJePqegJqu0hleXHah3/7dbs0kKN/FAapuzvkduuYUnF+/yKQvQq3mxh4ndL89nc5lMtimUHL9w1vxhxn5tezQqT24X2W6nfNEiit95F2dBAQDq+HiC77qTojf+jXX7Dor+/SbhD9cNiixOC69seQWA8Z3HE2mMPGNdhyQM4YfDP/DfA//FJbvQKDS8mvoq/hp/hiQM4c1tb7IhdwPl1nICdYF1L3B8fFNzdtMB/9te99/lyRQSRPT6O+x4DY6sBFsVaC+cfE4Bet+vLhE4XXqSkpp4RmkTEoGTcNYcjkoqK7dRXr6V4pINmM07kWXfhU5lGczmIKoqw6isDMPtVhIUlEtQcA5arYXikt8oLvF0XRgMSYSGXH2sNaonCkXz5uK4FNXUHGX7jvuoqTmCQqGjQ4e5RIQP8+7XaSPp3OnfbEu7k/z87wgI6E5szOizupcsy8z8fhcWx4nuOEkCrUqB1XFi2vGLv+yjVaiRazudCDxcTidulxO19vQ5btxumUf+u50qq9On/KU+IZj/+QCxRUXUDrcPLpmDvnUrNIkJWHfuwpHtGfukio4ibOJEAm64AUmtRhUaRs6UKZR8+CHG3ldi7NPH5zrzd80n35xPtDGacR3H0RC9o3tjVBsxOzzjpx674jFSQlIASAxIJDk4mX2l+/gt6zduanuT78kuB2Qem8XWXAPD8XStLtnp23p3++XxLN9bQFGVpyt0eOcojLGdILgNlB72ZBbvWE8rWQsQLU5CSxKBk9AgsixjsaRTXrGF4qINlJVvxunMrHOc06mmqjKUysowaioj0VS0IsQRSrzbnzBFICjgaF4+6coCzH4ZBAXnEByci79/ETU1h8isOURm1kcolUaf1iidLur8v+iLTGnpH+zc9U+czgq02ki6dHkPf79OdY4LCrqSNq0f4dDhFzlwYDZ+pg4EBHSrc5wsy9gOV+Aqs2LoHo6k9B0S+eOOPFbt9x04POaKePq1DWPCZ1s4edLNv75OIzaoN51iAji8ZSO/vvcmTrudq+96mIROndCZNGh0yjqtXx//mc6fh0t8ykYnGWnz8hM4iorQtm3Lcr/WqHOziK0uItJcgspmxbZ3L7a9ewFQhoUSev8EAm+5GcVJifH8hw7BfOutlH/9NTnTp9P6++9RBXuWF8mtzuXjXZ5uzod7PoxO1bAkhhqlhkHxg/jh8A9c3/p6RrX1XadsaOJQ9pXuY2n60rqBU952cJhBHwThdbsbm8q6IyWU1Bq4f3//1vxrcFs+XZeBn07FP65M8ETBKdfDH294uusu5MCpRgROwvkjAifhlFwuC5WVOykuXk9R8QYsll1AdZ3jLBY/KivDqKoIR1mRiH9VAqHuQFKMwYRFRaBNNqGOMqKOMqEK1YNbptWRCix7Syjek8PhIzmkZxSyR11AYFAewcE5BAXnotGYKSpeRlGxJ6OxyZRMSLCnNSog4DIUigsvS3BLysr+lIMHn0WWXfj7d6NL53fQasPrPT4+/l4qKtMoKlrKzl2TuLzXD2g0Id79tiPlVPyagT392LpTG/MJvj0ZVbAngKiocfDM//b4XDPcT8u0a5Px16l57Npk5vy8z7vP4nAxYf7vPOy/jyN/rvSWL3v3edSmv6FUJ6JQSuhMavQmNTqTBqcKthwq5nq3GrUsoQb8JIhbV8Tq6HtwJehRmPxQ9wjmmV2eIF7pdhFrLeOrYVFocrNRmEwE/G0EinoyEUc8Np2aLZuxHzpM7uOPE/fOO0gKBXM3z8XmstErspd3tlxDTes1jatjr2ZA3IA6geCQhCG8sfUNNuRtoMxaRpDupAV0d3937MPpA4rmm7dTu5uuS2wAiaFGAB4e0t734ORjgdPBXz0z/lQXRiuwaHESWpIInAQArNY8Sko2kpf/O5UV23DLGUiSb5ZXl0tJdXUIlRVhuCvi0Je3IdQRTZfAMCJio9B18EcVZcSqslOQl8/+jGxKd+dTuboAc3kxNnMpstuJxhCEKSicgLBIItvG0F7RFneJncN5maQXFHJQUYrRr/RYa1QOfn7FVFfvo7p6HxmZ76FS+REc3O9YINX/tAHCpc7tdnDg4LPk5HwOQGTEjSQnP19n0L3VamXnzp3o9Xrat2+PWq2mQ8qLbDIfoKbmKLt3/4tu3T7GnmmmclkGtkPlnhNVEpJSgT2rioI3thJ0UxKGruG88Ms+imslnJz1t4746zxfaPf1b82hwmr+eyxFQFxNFoMyV3LE5enCUmp7oFCW4ag5gqN6MRiHg6YdNRV2airsgOe4jiipzawOgePfmxYXinXFJJiUZEguXAolGYZQfg1ox13Dhpzx/VPo9cS88irpN9+MefUayj79lENDUliWsQyFpGB6r+mNHgMWoA1gSOKp7x3vH09KcAp7S/eyInMFo9oda5Eq3Eve5g/4MiiQUR2GE9+oOzaczenil12+GcxHdDnN2mAxPcEUAdUFkL4GkurmsGoJQbXWqzvexSgI54MInP6C3G4H5RU7ycleQ0nRRhzOfShUvrllJAlsNj2VleHYK6JRl7cmyNKWNsExhISHYNPbKfEvoaAsj7TiTZiPFGGtLsVpLwf59H/9WSpKsVQcpigdDp1UrlcFERfQic7GFKqdbjIqitmRUYZSbSHoWGtUcEgeUEVh4RIKCz3ZZf38Onq79AL8uyFJdb9sz6fjX05fbszkYEE1wUYN3eOD6J4QSPf4INqEmZpk/S+Ho5yduyZTVrYOkGjT5lES4u/z+aJ3Op1s3ryZ1atXY7FYANBqtXTo0IEuXbrQqeM8Nm/5O6Vlf7Br8RMEbbjec6JSQtFez76qjVSVlNDJrzfaKg2lX+4ne2sB3+337aYdlBzOsJPGMEmSxP+N7ExWYRn6bUvoVOVpnZIUgaiNQ+nQrwf9bmvD0nde58C6tThrltD7xlgMba5g6ZZc1u8rwmlxopYlHJKMS3YzNn8doVn7UGlVxDz1BMa2rdj001EOby3iJouW93U1mI811HyzJZu7+iQ26H3UtW9H+GPTKZj9LAUvz2WhORaMcHO7m2kf3P7MF2ikoYlD2Vu6l6XpSz2BkyyT/eODjI8MIU+lIqt0C6/xjya/L8DvB4uprDVe7Loup+kGVyig/XDY8jHsWnTBBE61k6sWVllxuNyolSLDjtD8JLl2+k/BR2VlJQEBAVRUVODv79/S1TkrVmsxWekryc/+nRrbbhSaLBRK3/88ZVmiujoIS0UkiopEDDXtMLki0big0lJCYWU2FVXZOGwVZwyMACSFEbUuEL1fCKbgMALCIwiOiUSj01KclUdpbh5VJQVYKotxWMuQ3b7JCJWSinBdPGHG1rj8gshRV5GjKMEtufDzKyEoOJfQ4FyMfsU+56lUgYSE9PMEUsH9fLqfmtvRYjNfbTzCrzv3oHAXEagrx6Q2U2YNpLAmjCJLCC5Zhb9ORbf4ILrHewKpbvGB3paaesmyZ8ZVRRYoVJhdRWyv/ASLqwSlpKVj8DjCjJeBpASFCrekYHd6ESu2HKC8yrPeWEiAH06Xk4pqi/ey/loVySGFaJN+AiBm2z/RVOrZVrSG9JITwbSERMfAvnQI7IMkSeS4bcyU7ByQZAwaJcseupqYWl9mmbt28PPbr1Fd4hkHpdR2Q6XvxzoddBsWz6PXpuB2u1j2wdvs+m0pAH8E92ZrrfFWkuxm9u5v6HloI5JOR/xHH2Lo4cmnZLc6WfTyFkpyzOQq3XxlsuE6Fjcundqf9pENmwkmyzLZ//wn1ctXkBsEy/sYmBh1C8qiMhz5+Tjy85BrLASNGUPIffcinUNXWlZVFsMXDUchKVh5y0rMaV9y9+63yFN5/o7Vq/SsvW0t2mZI1fHk4p18tv5E0NszIYhvHuhzmjOA9D9gwXDP8zu+gzYDm7xejVVqttP92WU+Zb9PH0BskKGFauRp1T169CitWrVCd5Et7Juamkq3bt14/fXXW7oqzep0n1FjvutFi9MlxuVykntoI9kZq6isScOtPoJGX+bZqQDVse82h0ODuTIcV0UcqvIEFKXhuCpqsFVnUmHLxikfPe19JIUJtS4QnSkYo38w/n7+BBj1BOkUBEt2VJXlOEuKcRWX4Nx1EGdJCa6SEmS3m1ZRUbSPi0UdG4e6Yw/UsbFYTKEUW2QKC4opzcmjsqiQ0ooi8svSkIsrCNIE093YBsk/mkJXBNmVEWRmdEWttniCqMBcAoNzgXIKCv5HQcH/AAm9tj1h4QOJiBiMn18nJOnsv/BcLgs2Wz5WWz42WwE2az4Wax7ZxRmUVOagppie2iou73Xqv0XcskSJJZjCmlAKasLYdSCM39LCKLSEEeiXQLe4MLrHB3FZfOCJVilZhkMrYNXznsVfgeIgNbtS/HCpFOisLrruysdU85z3PkeJZRn9yMXTAmTCzAD+pFvFbiRkMokhTe7JHlpTaXOyMTeY1vr2xMTsJ7PjO+xb3BpHmRIFbtr5FxOlr+JodRB7ytdQYM3gyrARxKj8+EBWsbR6D65eMYQbTryvDquVNV8sIG2pZ5kRpTYQhXowkjqWZXoH27Uu/lh1hHB/PRaHiy/KOxIbUECPijT6lq5D67KyLugKT7OnLDNx9w/0PLQRVCpi33jdGzQBaHQqhk3ozH/nbCa6xsk1FjVL9Q6QPKkQnhie0qDPVpIkto7vTfCGFUSXwZ0/1VDNgjrHFb3+OpZt24h+8QWUgYENunZtcX5xdAjpwJ6SPSxIe5ef93xOvkpFojoAs0pDkaWIDXkb6B/b/6yufzprDvj+oTEoJeLMJyX2hZ7jYfN8WHQ/PPAHmFq2ezzIoEan9p29mVtubdHASTi/UlNTWb16dZ3y4cOH89NPPzXrvUXgdBGTHS6KD6WTfmQlpeYtOFQHUBtzUantoALVSUFzjTkAe0U07tJIXIVBOArcmGsKMTvLgPI615YUJlSaQLRaPwxaI0a1Fn+ljL/dQoC5BFVpPu6cvThLS8Hp23p1mgUlAHBkZeHIygLW+ZQrgRiTicS4ODSxsajbxaKM7YktMIZyt47iSguluXnoCkvpYgkFnYoynYPsfBOFBW0AN/7+xYQF5RIUmIU+oByLbR+ZWfvIzHobl02DqzIOnbILQUF9CIlMIqpdCkqVCqezAputAKstD5v1WGBky/cJlJzOU78yBRB20h8vTreCclsgZdYAapwGgrTlhBuK0KnshBlKCDOU0JH9PtdwyxLFlmDyjoaRtjuMSnsEnbRuhlatJaFkLwoZZLWBrJQ2HAzIAwkCrXo6F0SgCQICXRTYtCyvSuKgPQwADQ76ag/QW7MPjWwHdwhOYvG33kjPmu50QyZTUcwexSGOHu6On6kU/4AiEoYXkrNnKFekJNClfSvU5jy67/0f1oNryajaz5HKvYTo/kGUoT3DTZ3J3XKID1feTXSnZOI6dGbbLz9SXuCZ7m4K7YHD2RuFWstinY0DqhOpC57+Ybf3eWZwb2wKLX3KNtCzYhsat53VIf14pPAPBh3y5DaKnvM8pquvrvP+B4QZGHJPR37493a62FUUKN2kaV0s2prDtKHtUTWg+2Zl5kqe2vUSrUcqeWRDGPFRyaiiIlFHRqGOikQVGYn9yFEK5syhevVqjv59FDFvvIG+U8e6F5NlsJSBIbje+w1NHMqekj18vP9LUCpIdEvMH/Ef3t/9MV/t/4rfMn9r8sApvdhMZmmNT9nV7cIadvLQ5yFzPRTuge/uhzHfNusA9jORJInoQD1HTlrsOa/CcpozhEvNokWLsNtPzA4tKSmha9eu3Hzzzc1+bxE4XQRkWcZd5aAyvZj0I5soKF+PVbUfpV82elMpkl5GpT/xYbpcKiyVYThKI3EUBmHJUVBdVoLDbQXygQIkhQml0g+DPhSdpMbgljFZazCVl+BXkIHOegSJelpOgNqrkCkCAlCFhKAMCUEO8scV5Ic9QI/FX0uNn5pKo5Jyo4wkKYgz64gsB7/iGty5edizsnFkZeEsKsJdXe0zldx7fSBckoiOiPAEVbGxKGLisAXGU23pRG5RMXnWQrIq1ByuDIeMbmg0NYQF5hEamI0xJAel1o4y7DBuDlPs/o6s3Vp2HgSNyYWkcNIgkp5KexB5lUYqbX6YbX5YbQasNiNOuw6XTYfCqUYv2TEpHChlmXKdmp1qHSUuN9U4MOjMhBmKiTAWEa73/NQq7YQbSgg3lNCJE7PRjgBH5BAcFh1lcjjhRs8SIdFRt9C+/TMoFBoqKipYtWoVaWlpyLKMQqGgR48eXH311ZhMJgDsudVU/5lLzdYCzwcIlLqyOJizDLO9CINSRbG2L4buSzGZyglM2MT/tqn5dU8pHTt2pOfAf/Nk9V78HMu51riJZJ6gzDYCpDFEG5II0kSwfudPrNzkWXDTGBiC2jQEqzkKnUnFdRO74l9UXieR5cm2BHbHqtAyoGQNXap2M1RZRNd1nuA6YsYMAkaMqPfc+A4htB8Sy4FfsxloUVOklMmptrHmYBEDk0/fqrKlYAuPrnkUt+ym09Uj6fP47FMOCDdefjn6rl3IfnAKjqwsMm6/nYgnnyTwlptPHO+0w6J7Yc9i6HUvXDsHlHW7YYckDOG1La8B0MruYP7VrxPqF82A+AF8tf8rVmWtwi27UZxDC2ltqw/4po4I89OSEtXApJZqPYyaD+8PgMO/wfYv4bIxTVa3sxEd4Bs45ZSLwOlcuN1upk2bxocffohGo2HChAnMmjUL8ASq7777Lv/73//47bffSEhIYP78+YSFhXHPPfewadMmunTpwmeffUabNp5kt4cPH+ahhx5i/fr1mM1mUlJSmDNnDtdc4xknt2/fPrp3786HH37I6NGeXHKLFi1i9OjRbNq0ic6dO5+ynscFB/v+YfLVV19hMBjOS+Akxjidwfke4+SyO6g4lEf5gTxyjmRSLu/D7ncEKSALvX8+Gm3d/xxsFhO28nBsRYGYs1VU5NQARhQKE2pZi84FOosNo9WCn7mSAHMJelsZCtldtwLHSRIE+uMO8scZYPQGQdUmFZUmBeUGmWK9k2KdgwKNhTJ3NZX2Sqrt1cj1BFy1KSQF0cZoEgISSPRPJFEbTasaI1EVCoxF1Tizc7Bne4Iqe3Y2ck3Naa8n6fQo2/agML4D6QYNmYoybJLz2MtxEexXSmRgIcago2gDyuqc77QqkeRAtPpoNIYY3O4A7DYDFdUajua7ySmQke1uDJITpXT2/2xckosqbQ1lKgf5sotCVCglFZdp8hmu20kbXQmyVqJGr6Jar0ahPPE5uWWJJRm3cPfQaSSF6Pj9999Zv349zmOtfikpKQwaNIjQ0FDcdheWHUWYN+Rjz6ryXiPfepQdJWsos+ej0RvoePUgug4ZTkhMHKVl69i27U7ATXZ2KkePxHnPK3Cb2OcMJ8MdhAYHz3bM5waOUrq3O053LLLsJtu8hnLFLgpVd2O1qPAL1HLt/Z0ICDOALPPK0gN8uSkTCahA5uSV7FKi/PlHrxja/P4f/ty8FlmSCKs003fAtbSZNv2M76ssy8x8bDVRFW7Mksynfjb6d41k3pju9Z6zv3Q/434ZR5WjitS4VOb2f5XyGhdhJm29A/hdlZXkPvY41cdWfg8YOZKoZ2cjyU74z51wcOmJgxP7wS0L67Y+pf/OnKUPcFi28kJEKqEjPwDA4XLQ/+v+VDuq+XTYp3QL73bG191Qdy/YxIp9hd7tm7rH8Ootjbz+yudh9YvQ8Sa4+eMmq9vZmPbNdv6z+cTCzndcmcCzN9bNW3a+nGr8jCzLyI7T/D/bTCS1olGzQVNTU9m2bRsPPfQQo0ePZt26dYwdO5alS5cyePBgJEkiJiaGV199lW7dujF9+nTS0tJo3bo106ZNIz4+nvHjxxMYGMjPP3vWNty+fTvr16+nT58+6HQ6PvnkE1555RX2799PfLxn3ujbb7/NE088wfbt21Gr1XTu3JmZM2cyderURr/mzp0707t3b95///16j2mqMU4icKrHvHnzmDdvHi6XiwMHDjR54FRdWsqBNRswHy3DVWrFbnNj01VjD87EGZiOJiAXk18JCoXvPzq3W4G1MhhLcQjmfH8smX6oy3XoHU6MVit+lkr8q8swWEtQun3bhWSFAoe/HluAHoufhiqTkkqjRJnREwQVam3ka60U6GxUGcB9DjO/dEod/hp//LX++Gn88FP7ocCAxWGnxJZLrjmLGmf9q9NrlVri/eNJ9E8kwT+BBL94EuUQoioU6ArKcWTnYM/OwnGstcqRnw9u3/dKNoRQ3K4H2THRZJqcWKUTg9qNGgsxhiosDgNmu5pquwo7SmjES3YgY1fasavMWJQWLEorNaoaapQ1WFVW/P38SQhKwFHhwFnmRF2tJsAagNZdd9CvVWGlTFtGqbaUUm0p5ZoyHLIWpzMAP/SEKZWEKGUyqmLJqUigAyp6qsy47J5p2PHx8QwePJi4uDgcBWbMG/Ixby1AtnpCEzdusqv3c6ByMyW2XELjEug29DpS+g1Ao/Md1H30yLscSX8ZCTVq52z2HrSSU3gU6ViwaHerKZeiuG/YAEIDA5Ccdli9FzmrcQ3Yxbj5P4WNuK7hjLk8jqR9Gyl+49/Y09Mp9DOwtVUk7mP/+Sd0uYxuQ66jdY9eKBT1z5pcuPYoh788TLhbQZ7SzbcBDtY9OYhAQ938Q9lV2dz5850UWYroHt6dZ698gwc+3cnu3Erahpv46r4rCTGdeoC27HZT8tFHFL32OrjdBN91BxGxmzytMSodXPUv+PNNsFdDUCLc/hWEp0BNKSx7CrZ96rlQQBzctxqMJyYxTFszjZ+P/sy4TuN4qMfp18drKJvTRbdnlvlkeX/jtm7c0K2Ra34dWQULb4DAeJi6s0nqdrZeX36A15cf9G4PSg7no7G9Wqw+p/pSdttd5D7153mvS/TsPig0DZ9dnJqaisvl8lk89/LLL2fgwIG88MILSJLEk08+ybPPPgvA+vXr6d27Nx999BHjx48HPC0+48aN887ePZWOHTvywAMPMHnyZG/Z9ddfT2VlJRqNBoVCwdKlSxudAmTjxo1cccUVbNiwgcsvv7ze48Tg8GY2adIkJk2a5H0zm9r2r9eQX7EbW+BhpA5Z+PkXYtBXU3too9Omw1ISij03ANVhPX6HZMIqyjFYC1A5j+JWStT4qakyKakwSqQHuSnWOygzyFQYFZQbocIoUWGEaj3Ikg04U84Tzy+tn9oPf60//hpP8OMNhOorP/5c44/NIbE9q4ItGWVszSxjbWZZrWnQMiaDhcCACvTGUlTaEtzKQizkU+nMx+aycbDsIAfLDtapnb+kJtGkJaGdgoRWLhJsNSRUm4kurEJZrcRRrcRhVmKvtqDPyCNyj4qusoHSpB5kJySQ6a/AbIcD9pMCBslbLfRoMMga9LIWg6zBgPakbS0GNOhlDapT5BjyUkqev/o0StThBtQJRtRRRmxhkG7P5kj2UfKPHKAivxiHzYjOrSPKEkWU5cTU8Cp1lTeQKlUUk6WoJFpVww0KNSanCZcdqtTV5EXnkhWWiXttJd1z2hJecuIfvVVhYX/xBo5W7cQmW2jV7Uou6zkeQ2ArLFV2tvycS02FDXOFDXOFHXOFDZu5LTF9LsMvdhtm28u49z9JiPNyrPp8rIY8NEo74WSy+OeFaK2h6GuiUTn8iVY76apXom1gwB2KgtcwoHdWU/nwfeTu8aQsUAYF0fmBCbS9vCcbvv8vR7ZtJmPHNjJ2bMMvNIyu1wyj86ChGPzr/rv8W48Yrlmyg9vK9US5VAyzlfLyl5/Tq2sEoRH+BOmDCNAGIEkS9y+7nyJLEW2D2vJ66hvcu2APu3M9CT8PFlbz9qrDzLz+1Bm8JYWC0HvvRRMTQ85DD1P6yafoepcSkGSE0V9Dq36Q8jf48jYoS4cPr4Hek2HzR2A+1mXWYxxcMwv0gT7XHhg3kJ+P/szKzJVNFjhtSS+rszROv7YNHN90sujugATlmVBdBKazuEYTiQ7wDfhFV9256dKli892VFQUhYWFp9wfEeHp/j65Oy0iIgKr1UplZSX+/v6YzWaeeeYZfvzxR3Jzc3E6nVgsFjIzfVOZzJ8/n3bt2qFQKNi1a9dZrZ350Ucf0alTp9MGTU1JBE4txBG2i8D2vk3dsgyO8gDkLC3SUQf2LDOVdjcVxiLKjUWeAKgXlB8LhCoMSsw6QHLjHbgCeKIACZWk8gY4rU4T5Php6gZCJrUJ5Wn+svett8zRYjNbDpaxNTOPbZl72F9Q5bPkhg4bbaRiwqVygqkiSKoiyFZFUFE1QcVVBFHtKaMKP6maarWDdLWKDLX62E/P8zyVikrZwQ6Xgx3g+Q1WAUY/iPAjwukk0QUJaEhQGUjUBJGgCydeEUIbi4auVTXYiu1kVRrIdWpQV9vQ2mSMLiUqyQ+3IQ6HPhxJsiPJZhSyFQWglJQoJBmFwoFT5aZG6UKj0qJxq1C4Jd+3H8AlI7tcyFYXtkr7iYSSQIQKYtUyansVaikHhTKTkrZ9yIm5hpzSGrKzsykrK8PP4Yefw4+E6gQA3MgojkV4FoWVvUF7sGvMDCu6imsOXUmAyzOeyYWLg+595BXuotCSDoDZPwR/xY3kpUeSl14D7KZ+EgVbx6EPzkNtzCfkqrfJOtoPR3U49qoUjDoHSn0eNZRh0xdh0xehkf3AGUe5M5yklCD6OGejzFzl+YYe+n9wxX3eX01JkpAdLoo+3Ig9w4llJ8iqbiiMmQSPG0vw2LEoTZ5M1iOTn6aiMJ/ty35m58plVBUX8ftXC1n3zRe0692P5EGDKAt2sadkj/dhSz7K8oq2XLfnAdqYo2AT5GyCDMlMuf4oZfp8ygz5KPVBJAcH80a/l3l/dT6bM3y7b5fuzufJ61JO+x+5/8C+WC/3o2RjFXkbg9DeORtdq37HPugOcO9K+O9dkL4WVr/gKQ9tDyPegITep7zmVTFXoVKoSK9M50jFEVoHtD7NZ9Uwqw/6jm/qEhNAsPEssoDr/CGsPRTtg5zN0H7Ymc9pJrVzOeVVWOs5suVIagXRs8+Q7qGZ7ttYarXvWDxJknCf1Ip/8v7j/yZOVXb8nEcffZSlS5cyd+5ckpKS0Ov1jBo1ymdAN3i69MxmMwqFgvz8fKKjT5OQ9RRqamr46quvmD17dqPOOxcicGohQZ2uJD9jIeXlWgoq4YjFySFZQaHejiXQDt0l6K5Cr9L7BDn+Gn/aNrAVSK/Sn1X0fiZmm5PtWeVszSxja2Y52zLLcNWUEyMVEyMVc4VUzE3KYmKkImKPlYVKlY17f5wQ7XDRATXlso5S/CiX/SjASIZaTbZaQaHaTanGTrW6BpumClllpUClokAFG3AD1eCqBnMWCpSE66OJT0wg6bJWtAlMxKAM5Lu9Wzh6YAtRRem0LqqhdQ7EFeuwmq4kJ+ZqanSRyK5cXLadyLa9uKUTrXVqpUSbdq1pP3gECT37o1IokR1uZJeM7HR7HlYXjnwz9txqHOmFOAqsyE41dmckdo7lxnGCtAcSiwy0izahvuwKHMFKiuRycovzycnJITs7G6vViqxQscMWTrArlvE1Q+jJif+4rBobR5y7OJSxHovz2PI46lDU2iGEKD2pCWTZTo2mkkpdJS6djYAgA1HhYbSJTiA+MhpjgBZjoBatQUVpRWs2brqJ4NBDBIeeSFWqVAVjMrZCIoLCQomjR2uorjZSai2j2qBHVaInT+6D1h6OpiwLzQffolm4Co0hBrXFgtpsRl1RjrxvP9q216LpcCOa1qmYrh5O8NguKI2+/4EHhEfSf8w4+tw8hp2/r2Djz99RnZnL3rUr2bt2JWlJ5aS1853xmGXI48eEJbQr60iw3Y9gewAqt5rQmhhCa2LgpOXvvlu/B1mSuU2hoVzpRqUsZa3aQHaZhQMF1fXngkr/HX58iLDE/VizwzHnqsj+vw9oddmQE6kKjCGe3EdLZ8COr+HKBzzdeKr6czSZNCauiLyCP3L/YGXmSlp3boLAqdaagv0bOpvuVGJ6egKn7JYOnHy7WiosDqptTkzaC+drTZIkpEZ0mV1K1q5dy9ixYxk50rPGYXV1Nenp6T7HlJaWMnbsWGbMmEF+fj5jxoxh69at6OtZIulU/vOf/2Cz2fjHP5onaeypXDi/YX8xcYndWV51H/5RAURo/EnS+DGqdkCk8Ud9ihk555Msy2QUm9l96DCZR/dTlnMYKjKJppj2UjGDjgVG/rrTD9wGqJL1FBBMiexHmexHmWyijFo/ZT/K8KNU9qMSAzKn+MupvvybSjMKdQkKbREKTbHPw61wkG/JIt+SxcaC333PC4XcUNicrMZti8BlicW/zERi3nZSrTqClLGUGwciGwYgu3KRzGm4HAdx4Gbf3sPs2/s6KvlV4jUySW3jSRpyPbqeQ5DUno5XjToD4+E5UPYjslqBkxgcsaNxhAzDXqbCkVuN2+zEWVCDs6AGtnmqZQTaaSSStWGg0FGlLkVdo0GyR6NS6o99Pm6sRXvJKtvMDlUermPdZGopHMk0EIXK89dbSMlO4rJ+I6j8ABJwNELi9w4Sv4RLlLgkyILwknB6RfaiV0QvonUdeevXCnKLJtMvZj0RhkIiDYUE6ipxOUupqCj1vDYttE8+/rsCNpuR8rIoMnKSqdFGQGStGW1+JsDzpe3XujXDdXoihkdRsawYe1YNhW9tI+TOjmiiPC1ODpeDncU72ZC/gQ15G9hetB1nJychsRpSMvxIyjHR7VAgUW3b0+qynnQM6UjbgPbc9NYu9ldY2a8AdCBpnQxrHcjUy1tRXWihMKeCqkIb5YUW7DVO/GQJP5eSOJcSiKCbrYIa0zr+3B5C+8gra/0i58OvM2HnfwCQ/MKIeWcBRyc/gyMri5yHHyHu/feQlMe+MJVqGP4SDHvR0wLXAAPiBngCp6yV3N357gadU5/CSiv78qt8yhqchuBUYntA2meeFqcWFBVQ98s1r9xC24gGzhQUmlVSUhKLFi1ixIgRSJLEzJkzfVqwACZMmEBcXBxPPvkkdrud7t2788gjjzBv3rwG3+ejjz7ixhtvJCTk/CU7FoFTCwnWBfNwr0dauhoebhdU5UF5FvaSDPKzDlCVfwS5PAuTJZcIuZhE6aTm1Xp+a0pkP3LkUO8jXwpDGRRPcExbEtu0p3ObBJKCDETanBRWWimssmGqsqGtsqGosuKutGGrslFdZUWqsiE3dsVzlxG3y4jbWnulLzeSqgqF5nhAVYRCW4ykqsJtC8dlicVtjcVljQZZg0apoE/nSMaMTaBXYhCSJFGWb2bHsqPs3yjhUMWglGXU9qNoKv7AIhVgVyk44oAje3L4befbRFS9TILCRnywCkVFFm6HhNsRjFsbjlsdhnv9Glw1P+OuNiNbLEi6QBQBcSgD4lAExnt+GsOQ7UpcdgNgwMCxIEQJbmsFtozfOVS9gwMB4NQoAAmFIhyloT9KdTwK2Um8OpO2IaUEddKi0N+AecN6zH/8SasCF60KZO5YCVmtTCxrZ+WP5AJ+qvmJn478dOxd88dlbM2X+TEgxxEXbODu3tGo3KVoqnIxHd2Htjwb9DU4I2QwgE5nJjLqEJFRh7AVh1KRk4y5JBSHvRq7UoNDZcCmNuBwy1T5+/OjyUi/yFJcN9kJ/1FGXwY5b23k+07r+NOURnplOhan79iVSGMkV7S5giuuvwKWH+TAb7+RsLaGMdf/Hf9QT2LGF/+u4J5PNmN3ef6jliVYcrSYcoXMB3f2pLs2AafLzegPNrDjSBVd5CKmSr+ichs5bO1LqTMeZeVAEn/5DkfFAtR974XIrrDxfc/MMnsVIHkSQw58EqUhmNi33iT9ttsx//EHRa+/TvjDD/v+GjZmllNcKs9teI4dRTsothQTqg9t8Lm1rTnom/TST6eiW1zgWV+PmJ6enzlbPRMyWiifk16jJNioodR84v+m3AqrCJwuEK+99hrjx4+nT58+hIaGMn36dCorT/Q8LFy4kCVLlrBt2zZUKhUqlYrPP/+cPn36cN111zF8+PAz3uPAgQP8/vvv/Prrr835UuoQs+rO4FJYcgWnDSqyPct1lGd5f8oVmbhKM1BU5aGQT5/HyC1LFBJIthzmExzlyKFkH/tp8gugx0lrsnWKCUCnPvtmapvTRXG13RtkFVbZKDrpeWGVlcJKG8XVNtxN8FucEGJg9OXxjOoRW+9sKrvFyb71+exclU15gaeVTZZlgtX7UFb8QbGlDKvyxGtWutwY7A60ThdahxOdw4XW6UTrcKF1uNAde66UZVCrURoMKEwmFEYjClMQioA4FIYI0ISCFICkdqMIqeFA9T7S9qThdBzrOlSGo9b3QaFqRY0CYi8PZ+TN7dGb6o5jcZaVUbV0KZU//kTN5hOtBrJCwa7YEFa2l9jcqRyrzvevQ6VLpvshmat3eX6qju12KmBbG1jfXaK4nczlAS666l0cHyNe4JBYXaViU40Kh+wpNLoM9Mnri7/DH4vSwpqoNaBw80T2PVxW42nC+iTsB74K+YUgXRCXR13OFVFXcEXkFcT5xXm7oJ0OB189NY2CIweJSmrPrc+8gFLlaaVde7CI+xZu8RkUDXBZfCALxl7O+2sPM2/lYYYqNvKq+h2Mko1cKYL7LVPoZTERYvOkYghSZjM48DXC/MvB4mlpI6YHDJ8LMb6pDiqXLCHnIU/AFPvuO/ilpp7y96ghbv/xdnaV7GJW71n8vd3fz/o6d87fyJqTcjgN6xTJO//ocZozzsDlhBfiwFEDkzZ6xjy1kOvfXMuunBNfxnNu6sztlzfXEsmndzEvufJXIdIRnCcXReBkN58UEGX4BEdUZHm6Fs6QW8khK8mTg8mRw8jhRDB0PFDKk0NwnNTUpFJIdIj29y4R0j0+iNig5hlTdSYut0yp2e4JpKpsFFXavM8Ljz0vqvY8tzlrBQMKicEpEYy5Mp6+bUIbvPiu7JbJ2lfKzpXZpO8q8b69/mE64tpZsGWu5Mi+XdTYG5ZYU2swYgwKxhQUhDEoBGNgEKagYIyBQcfKg9GZ/Ni7diUbFv+XmopyACRlCCpdbxTqthQpZTbrnOxTu3BJMP3aZCZc3fq0n4kjP5/c734g/T+LCc87scyOXaFiU0wcv7fzozrEwY051XRMy0dnPtEKWBzvz8ErYznUKxKr34lA0+ayoXCW0Zp02quK0Eqe99zslvijWsnvVSoq3Qq0Li398/rj7/DHoXZQ3aWa0OBQrt7fidYHPC0srnZaIga2QxsfgFTPZ1NRmM+nj03BZjbTffgNDLjrXu++zemljPt4E1U2388hIcRAZkk1U1XfMkX1HQCbFF1InPA1qW/twGx3keBQ8LcaBTpZjQInV5i+oFvoahSDn4bL7qy3paVgzhxKP1mIpk0bWv/w/Ykuu0Z6f8f7vLntTXpF9uLDIR+eVTLMndkVjHjLt2v6pb934ZZecfWc0UAfD4eMP+CGeXDZ+RtbUtu9CzezbE+Bd/ufA5N4eEjLBHIicLrwicDpPGnxwOn48g21Wosozzjx/PhfwadhQ0u2HEK2+0Qr0cmtR4UE4T7VeKJjQk0aLosPovuxxWm7xAaiv8gGPcqyTKXVSdGxliqb003HGH/C/c7tP7mKohp2rs5h7x952C2eL2iVVkn7K8KJay/hdlVhriijpryMmooyzOWlmMvLjpWV4qw1y6QhJEWAJ2DSJBPTIYRlLgtLCsrq5KG6pWcsz93YGY2q7mfrcst8tj6DuUv3U2VzElNdxNXZ27g6O4346sI6xwOowsLw/9sIAm64AV27dmesp9NZRW7eN2RlfYLV6sl6LsngX2QjIsdKQPgwPi3rTlFpBSaTibFjxxIaGop5Yz5l3x8Cl+e/J4W/Bn3HEPSdQtEmBiApfV/ooc0b+P5lT46ZEQ89Trsr+nr37cqp4M75G326dEzU8KrqbYaotgIw3zWc7ve8SbeEUCZ8uoVfducDoHPDnWo/Ako8n+sVwyLpecOpUxR439eqKg5fMxhXRQVRzz9P4E0jz/g+nUpGZQY3Lr4Rp+xkfKfx3NJmAt9uySbYqGFUj9gGteae/FrA8+/49+kDz6klGPCM8frz356UCiNeP7drnYNZP+xmwZ/p3u2/d4/llVu6tkhdRODUtI6venAqP//8M/369Wv0NUXgdJ40e+DkdnvyupRnQkVm3dai8kxPEr0zkLX+WIwxFCnCOeIIZke1P/ssgd5Wo1L8aGh2R6VCIjnSj+7xQfRI8ARLccEt05p0MbFbnRzYWMCOldmU5dWf3PNksiyDbEOWzchuM7jNyHI1stuzLcvHytzVgAMkP1T6K9AYO5PSO4aug+IIijTicLl56vtdfLkxq849ercO4d1/9CDAcGKiwY7scmZ8t4udOadYf0+WGaavYjJH0a5ZgbOkBL9BgwgYeSPG3r2RVI0fGinLLoqKlpOZNZ+KCk/3oNIlc+XmMlw2A5/ox1NoUWIymbjrrrsICwvDnlVF9R85WPaWIttOdLcpjCr0HULRdwpB2yYQ6VhQuPqz+Wz+3yI0egP/eOF1giJPTGs+VFjFPz7cSH5FDTdblpJUuh+N5OKmhN084x5Lh2H3c08/z+y1/2zOYto3O7znGtVKFlyVzMbvjqAzqrlrTh9UZ/ijoWT+xxS+9BKq6Cja/PwzCm39s+hO5/tD3/PkH0966lE1ivxsz/ii2y+PZ85Np1+S4mBBFYNfW+NTNu3a9kxMTTqruvjY870nS3pkZ5jw+5mPbybvrznM80tOLFHUu3UIX9535WnOaD4icGpahw4dqndfTExMo2beHScCp/Ok2QKnnK3w7T2esUeuMyWkBIxhnizDgXEQGE+lNooD1kC2VPixplDHpjyXdyBsYwUZ1J6WpGNBUte4AAwaMW/gbMmyTM7+MnaszCZ9ZwlyUwzAAmTZgd7fQNcBsXTsH1Nn/JIsy3y49ijP/7yX2v+qW4cZmX9XL4KMGl75dT+frs+ocwyAv07F9GHJ3N4rHoVCQpblJg+YKyt3sHffE1RX7yWhOpKkrbswo+cT6RYK5WBMRiN3jR1LWJhn5pfsdGM9VI5lVzHWPSW4a050u0k6FUE3tsHQLRyX08l/n32CnH17CEtsze3PvoxacyJg2b30K9Z/OZ9yy4n3LSsggeq+4/jgzh7e11lcbaPX/y33eX8WjO1F5qeHqCqxkjqmPR37nT7jtttq5fDQa3EWFBDx+GME33XXWb9fH+z4gH9v+zeyLGHNGYOzqhMKCdY/Mei0raX/+jqN77bleLf9dSr+eGwgfrommKlbkQOvdQBJCY9ng6Z26t7z43/bc/nnl9u82wkhBlY/OqBF6iICpwufyBx+sdOYoPSw57mkAL9oT1B0UnDkeR6P3RjNnmIHWzPK2JJZxrYtZeT6JHtr+OwzhQTtIvzonhB0bCB3EIkhBtGa1IQkSSI2OZjY5GDcbtkTOMl41vCTjw2Hko+1Nh1/DiDLJ76sTz7++GGyjN6kRqE8dZeqJEnc27818SEGpn6V5jMo+kiRmZFv/4FSoaC4+tSB+k3dY3hieAqhJw2Mb47fC3//LrRp/RDbd9xLTqCZxDv+g3H5/3FX3tcsZBQFZljw4bvcNf4+wiMikFQK9MnB6JODkV0ytqPlWHYWY9ldgrvaQenX+0EhYegSxnVTpvHp9CkUpR9h1YIPGHzfZCoObmbtu8+zP9sOaFBJLpwmI1RZia7M5vbUCJ/XGWrS0i0ukG2Z5d6y3/YXctOAWP745hDbf8umw1XRp31vFDodoZMnkT/zKYrffY+Av4/yJvVsrHs638Nnm7dTqlyNLvorbIXXITuNvPZHBUM7RmN1Walx1GB2mFFKSka2HUlRhcwP23N9rjO2T2LTBE0AATFgioDqAija6xks3wJOlQTT7ZYbPFZREM6GCJxaSlAi3PWjJ0Dyj/ZZQb2wysrWjHK2Hihja0YRO3MO1RnU3FABejWXxQd6g6SucYEXVIK4S51CIcF5/k98aMdI/nN/b+7+ZBOFVSeCpLJ60jskhZt49oZO9G5z/vKghISkYjC0oqbmKHmaPOLuXYVx53+5c/mLLKy6kgJbOJ+89wZ3Xd+X8O7Xec+TlBK6pCB0SUEE3pBE2aKD1GwuoPSr/UhKBX4dQ7nun4/yzfMz2bHiFyyH13EkowyXrABkOrb247K7Z/B9joHq7+ehzDnA9sVfEjdlmk/9rkmJ8AmcVuwtZMaD7dn441HK8sxk7S0lvsPp36/AkSMp/Wg+9vR0ShcsIGzypLN6r3IrrGQeHIo2pgi13x50kd8D8GOB51Hbj7v3U5w1ENdJLZ0GjZJxfVud1f3rFdTKEziVZbRY4BRTK3CyO92UmO2E+Z1d16ggNIT4Bm0pKg206ofD5WZfXhVbMkrZmunJxp1ddnZrLkkStAv3o3tCoHcgd+tQo/jr6y+oc2wAiyf15e5PNrM379RZ23VqBf8c2JZ7+7U+5eDx5iRJCuLixrN//0wysxYQG3sHUtdbMXb4G3etfZuFa9PJd4ew4IfVjN/6CaFDH4U43wVcJYVE0E1tkZ1uLGlFlHyxl9C7OpLQoQO9r0hi3fqDHEyvABTEh0hcPf5BwnsOAeC+JChMmMynj01h/59r6DH8BqLanpiNNSglnJeX7vdu55RbOFJhIaVPFDt+y2b7iqwzBk6SSkXY1CnkTP0XpfPnEzT6dlTBwY1+rxZvy0GWFVhzbkMOW4pCVwC4QHLTIcqEv04PspZtmcW4dPvZXvEr5oLunPzf+5gr4gk6myVWTicwHrLWe8ZhtpAwPy0qhYTzpCAxt9wiAiehWYnAqQWkZZXzy658tmaWsSO7HKvj7FqT/HSqYwGSJx1At/hA/JuqKV646EUH6vnvhN48+OU2ftvnO0tuUHI4s/7WkbjglhmbAhAVOZLDh1/Bas2iqGg54eFDQa3HMPBh7uySxcL5H5BfY+DT7Bju/ugG/Nv1gwEzIOrEYqOSQiL45vaUOt1YdpVQ/MlOQgPe5Erzr1QEtKPMHcyVN46i1XX31OlaC09sTcerB7F71XJWf/YRt8560XtM+wg/YgL1PgvHrthbwB0D4tixMpvM3aWU5pkJjjp995vfkCHoOnbEuns3Je+9R8TjjzfqPZJlmUVbs49taLAVjvDZ37t1Eg8Pacet762nPL0IY9KLKNSVqPx246zyzC7TqBTege9NKsizhmJLBk5KhUSEv87nc8qrsND1XBJ8CsIZtEzK17+4dYdLeHf1YTYeLW1U0JQUbuKWnrG8cFNnfv1Xf7Y/NYSF4y9n6jXt6N8uTARNQh0mrYoP7uzJ1GvaEuanpWtcIO/d0YMP7+rZokETgFKpJzZmNACZWR/57DOExvGPidMIDvSnggA+4yYsB1bCe/3gP3dB0YnWIEkpEXxVNTrjAXBJlJTeh0PXl2H3T2b0+0toff299Y5H6nvrP1BptOTs28OhjetOXFOSuCYl3OfY5XsLCQjT06qLJ8fUjt/qzmCsTVIoCHvoXwCUffEljpycM5zha0d2BYeL6p+huTgthx+257IxvRRQ4ij3tMqpgzYAnlbF527sRIR/MwxWDjyWaLI8o+mv3Qi1u+tyyi+8xX6FS4sInFpA9/jAMx5j0qq4KimUBwcm8fG4XqQ9NZjlD13NS6O6ctvl8bSL8BNdcEKDKBUSU69px6YZ1/D9pL4M7Rh5wUwGiI29A0nSUFGxhYqKNJ99JpOJO+4ah8lkopBQvjTc7UnCumcxvH0lLLof0v+A/45DWjCYEOd0tMrtyOgots7AHn4TKE6fNsAvOJSeIzx5ltZ88TEu54lxYINSfNfZ255dTlGVjW7XeJJH7l+fj7X6zBMzjH36YLjiCmSHg5IFnzTgXTnB29p0vL46306C7DILTyza6d12lF8OsoTKeIQ5t4azccY13NLzHJNd1scbOLVcixNAVK3FfnPLz26ow19ZamoqU6dObelqXDRE4NQCusQGoqoV9LQONfL37rH838hO/DK1H9ufHsJn91zBQ0PaM6B9OIGGJh6fIAgXAK02nMgIT/dTZtb8OvuDgoL4xz/+gVarJbNGy7et5uBufz3IbtjxFSwYDrsXARLSZbcS8tCNaFoFINvcFM3fhT33zDnQeo24CUNAIOX5eWxf9rO3/IrWwRhPytcky7ByfyFRSYGExplwOtzs/v3MLUiSJBFyryebecW33+KqqjrDGR52p5v/7cjzKRvftxVJ4b6JAc32E7MnZWcAHYN6A5Dp+K15W6EDT+qqa8GsNnVn1onA6a/i9ddfp3379uj1euLi4vjXv/6F1dr8LY4icGoBeo2SW3vFMWlAG+aP7cm2mYP57ZFUXrmlK2OuSCA50h+laE0S/iLi4scDUFT0CxZL3UAkMjKS22+/HaVSyb6jufxkug35nt8gabDngNapMGEt3DgPRUgsoWM7oIn3Q7Y4Kf5oF+4zLBat0Rvoe4tn2ZB133yJ1ewJtrQqJf3bhZ04UJZZsbcASZLoNsjTirNzZTauBsx4Nfbtg6ZNG9w1NVQsWnTG4wFWHyjyyXYOnpQRIy+rP4fUVUmhTO5xBwDfH/6+zgLJTco/xpNKxWmFejLNnw+1AyfRVffX8Pnnn/PYY4/x9NNPs3fvXj766CO+/vprHm/kOMKzIQKnFvJ/Izvz6NBkBiZHNP1sF0G4iPiZkgkO6ossu8jOPnVXVmJiIn//u2eh2y1btrDqYCX84xt4LBPu/N6TwfoYhVZF6PhOqML0uM0Oqjfmn/KaJ+s0YDAhsfFYq6vY8N1/AHBYrfTRFnFVyR+Mzv6Kienvk7PlT6wOF0k9IzD4azBX2Dm05cxBgyRJBN/hCWhKP/sc2eU6wxl1u+l6JgSREGLkb12jT3m8UiHx9IgO9InpQ4wphip7Fb8c/eWM9zlrKo0n/xy0aHdddIBvV12e6Ko7K263m2nTphEcHExkZCSzZs3y7pMkiffee4/rr78eg8FASkoK69at49ChQ6SmpmI0GunduzeHDx/2nnP48GFuuOEGIiIiMJlM9OrVi+XLl3v379u3D4PBwBdffOEtW7RoETqdjp07T3Q/12fdunX07duX0aNHk5iYyJAhQ7j99tvZfNLC5c1FBE6CILS4461OOblf43SeuiurQ4cOXHedJ6fT6tWr2bRpE+gCTnmsQqfCL9XTKmT+Mxf5DFn1FUol/f8xDoBtP//Af2Y/wby7b6Pwm7e4rHIHIY4ylLiJrzjI+iMlKFUKOqd6Wn52/JZFQxZgCLjhbygCAnBkZVG9evVpjy2vsbNir29ANrK7535xwQYuT6yb1uDO3gm0jfBDISm4ud3NACzcs5C86rw6xzYZ78y6lhsgXrvFqbDKhs155sD0/9k77/AoqvWPf2Z7em+k0BISAoTeqygiSBcbKoJg79eueO2/67VguTYUsWABpQmIICC99wChhHTSe99sm98fk2xYkkASghTP53n2ye7MOWfOLJr55j3v+b5/B7IsYzKZ/vZXcwqCfPfdd7i4uLBr1y7eeecdXn/9ddauXWs//8YbbzB16lQOHjxIVFQUU6ZM4f777+eFF16wi5VHHnnE3r6srIzRo0ezbt06Dhw4wMiRIxk7diypqYrIjoqK4r333uOhhx4iJSWFjIwM7r33Xt5++226dDl3OSGAQYMGsW/fPnbv3g1AYmIiq1atsv+OuJgIOwKBQHDJ8fEegrNzOBUVp8jI+JWwaiF1Nr1796asrIxNmzbx+++/4+zsTKdOnept69zVj+I/krCWmKg8nIdzN/9629XQtlsvwjp3JfXIIdKOKrXq3Hz9iNcGk1qppk/RPnxNefwZl82wSH86DQ5m76oUclJKSYsrIKzTuX2dVE5OeN08mfy5X1Pw/Xzchg9vsO2Pu1IdSijp1CrGdKmNNE3oHly9k07B20XHE9fVFl2eGDGRrw5/xamiU4xZOoY7ou9gZpeZuOtauN6mZxikbLushBNAdnEVYT6XdtcogNls5v/+7//+9uu++OKL6HRNW8mIiYnhlVdeASAiIoJPPvmE9evXM2KEsiQ+ffp0brnlFgCee+45+vfvz8svv8zIkSMBePzxx5k+fbp9vK5du9K1a23B5TfffJOlS5eyfPlyu8B66KGHWLVqFXfddRc6nY6ePXvy+OOPN2q+t912G7m5uQwaNAhZlrFYLDz44IM8//zzTbrv5iAiTg3w6aefEh0dTe/evc/fWCAQXBCSpCIsVPmlm3b6W2w2S4Nthw0bRs+eilP1kiVL2LFjR70JoZJGhWt/RWyUbk0/71/hkiQx8oHH6TpiNMOn38/0D+Zw7yfzaDdhOgfdFe8oD0sp6w+lYLHacHLT0XmIEgXaviQB2zlqEhaWm9iekIdu8q2gVlOxcyfGEyfqbWs0W/lmW7LDsRs6BzoUab6pZzAxIUq0TauWeOemGDycas97G7z59oZv6R3YG5PNxDdHvmHMkjEkFzuOe8FcBjvr3A0ahyR+gAyRIN5kYmJiHD4HBQWRk5NT7/mAAGXH6ZmRoYCAAIxGIyUliuFueXk5zz77LNHR0Xh6euLq6srx48ftEaca5s2bR2xsLPv37+fbb79t9I7fjRs38tZbb/HZZ5+xf/9+lixZwsqVK3njjTeaduPNQEScGuDhhx/m4Ycfthf+EwgEF5fAwAkkJL6P0ZhObt6fBPiPrredJEnceOONVFRUcOzYMdasWcOGDRvo1q0bffr0wdfX197WpW8gJRtSMZ8uw5RSgr7Nuf9fdvfz57qZDzkcu7FLEP9dfZxStStu1jLURZlsT8hnSAc/eo1uw7EdmeSnl3FyVxZR/YPqjHkkvZibPt9OlcVGax9nvhl2DVXr11Ewfz6t3nyzTvulB9Lr1BO8b4ijgaVeo2bpQwM5kFpIiJczgR51fZqivKP4+vqv2ZK+hf/u/i+ppaksjl/MU72eOud30CQ8L70JpiRJtPJ0Ij6ndgfl5WJJoNVqefHFFy/JdS+0jyRJ2Gy2es/XiJv6jtX0eeaZZ1izZg3vvfce4eHhODk5MXnyZEwmxw0Phw4dory8HJVKRVZWFq1a1Z/DdzYvv/wyd911FzNnzgQUEVdeXs59993HSy+9hEp18eJCIuIkEAguC9RqAyHByu621NS61gRnolKpmDx5MjfeeCO+vr6YTCZ2797NJ598wvz58zl58iQ2mw21qw6X7spfx2VbmmY+WUOYjzNdQzzI1StLcb5V+ayoLqBrcNXS8wZFPOxanojFVDe35qP18fZakyn5FfwRORSAkhUrsRQWOrS12mS+2pzocGxQuC+dg+sKPrVKolcb73pFUw2SJDEkZAj3d70fgAM5Bxp1z42mJuJUeGlNMH1cHZeliisbX/j8YiJJEjqd7m9/XQ4+bVu2bGHatGlMnDiRLl26EBgYSHJyskObgoICpk2bxksvvcT06dO54447qKxsnOitqKioI47UajWyLDcrx6spCOEkEAguG4JD7kCl0lFScoCi4n3nbKtWq+nduzcPP/wwd911F5GRSq25hIQEfvrpJ/73v/+xb98+XAcqf8FWxuVjKWjeVvUxMa3I0ymRLF9THmuOZmGqFkMxw0Nw9dZTVljFobPcxK02mZ2J+Q7HPst1QduxI3JVFUULf3E4tzYum8Q8R6fws6NNzaG7f3cA4vLjqLJWnad1E6gRTsVpYGte6aiWwO0sv6pSY8NLvYK/h/DwcJYsWcLBgwc5dOgQU6ZMcYhgATzwwAOEhoYya9YsZs+ejSzLPP30040af+zYsXz++ecsWLCApKQk1q5dy8svv8y4ceNQq89tfHuhCOEkEAguG/Q6XwIDJgDnjzrVIEkS7du35/bbb+exxx6jf//+6PV6CgsLWbFiBSVaI/oIT5ChbHtGs+Z1Y0wQebrqiJMpnxKjhS3xuQBotGr6jW8PwL7VKVSW1i5FHMssqfMQLzfbONJvFAAF339PwU8/Yc7ORpZl5mxWtnNrbBY65yXwcOY2ehkvfFdciGsIvk6+mG1mjuYdveDx7LgHg6QGqwnKsltu3CZytqN6qfHyiDj9k/nggw/w8vJiwIABjB07lpEjR9KjRw/7+e+//55Vq1Yxf/58NBoNzs7O/Pjjj8ydO5dVq1add/xZs2bx1FNPMWvWLKKjo5kxYwYjR45kzpw5F/O2AJDkix3TusKpyXEqLi7G3b2Fd6QIBII6lJWdZNfuUYCKAf3X4+QU1uQxTCYTX3/9NdnZ2UyePJn22lbkf3MUSa8m6IU+qAxNT++8Y/bv9Nj1ORZJzRetZzK+ewgf3qZEcmSbzK9v7yU3tZQu14Qw5FZlh9vcLYm8+fuxOmOFuar5etNsLGm1ESprZDS/yoG0Lc6kS34izhYlMqRtHUb4mjVNnu/Z/Gvjv1ibspYnejzBjC4zLng8Ox92UXKc7lkDYf1abtwm8Oryo3y7Pdn++dZeofx3ckzDHS4CRqORpKQk2rZti8FwEWoDCi6Yc/0bNeVZLyJOAoHgssLVtQPe3oMBG2lpTavtVoNOpyMkJASArKwsDBFeaPyckKuslO9tXmTkuj5RmCQtGtmKl7mItXHZGM1KTpOkkhgwSYk6Hd2UTlF2BUCdZboaUsusnJw1G/9nnsape3eQJNQn4rjt5F/0zT6Gs6WKUr0rSBLmlFQs+fWP0xS6+XUDLkae06VPEK8TcaoSESfBxUMIJ4FAcNkRFqpERDIyf8VsLmnWGDVbprOzs5FUEq6DFOuAsu0ZyOewDmiIG2OCz1iuy6PcZGXjidrt2iFR3oR18sFmk9m5LAGrTWZ3UkFDwzHncBHe99xDm59/wvjzcj7qNpm1ob34utONPDLsSY78bwG6dkp+U+Wh2CbP92xq8pwO5h7EJrdgPpLnpTfBrLtUJ3KcrnRcXV0bfG3ZsuWSzk3YEQgEgssOb+9BuLhEUF4eT3b2CkJC7mjyGGcKJwDn7v6UrEnGWmDEGJePU2ffc3Wvg7+7AY1fCJzOws+Ux0k6sOJQJjd0rrUgGDCpPWlx+SQcyMV9TwYl53iAx54uZk9yIS56NdN+S6S4TT9Wt1GWujyctNzatzXFMTGYEhKoPByL2/BrmvoVOBDlE4VBbaC4qpjk4mTaeV540jlwWeysO7uY8bm+d8GVwcGDBxs8FxzccL3GvwMRcRIIBJcdkiTRKkhxKc7MWtqsMWqEU0lJibJ1WafGpa8ickq3Ns+aILyjkrvkW6Usna0/nk15Ve1D2ifYlci+gQAc2ux4jdY+zoR6O7pc/+ePY9w5d1ed7fPTBrTBRa/BqauSp2NsgYiTVqWli59iWNiiy3V2E8xLGXE6e1edWKq70gkPD2/w5eRU1y3+70QIJ4FAcFkSEDAWSVJTUnKA8vLE83c4C4PBgKenJ1AbdXLtHwRqCVNyCabT9dfEOxdD+iklJHxNeSDLGM021h93rCkXWW2CaUopR3XGiuCA9j5MH9DWoe2B1CIKKxwf8gPa+/DgMCVfylDtzFx55AhyC2z3vyh5Tr4Rys/c+p3Q/w7EUp3g70QIJ4FAcFmi1/tVJ4lDVjOjToGBgdX9swBQu+txjvEDmhd1atchHBkJZ5sRF6uSAF5jhllDq3APDK5aNBaZUEvtr9h+7Xy4pXcobvqGMyT6tvVm7t29MGgVHxpDhw5Iej22khJMyRce0anJc2pR4eTfEZAUO4Ky3JYbtwkIOwLB34kQTgKB4LIlKHASAFlZy5CbkdB8dp4TYE8Sr4zNw5RZXm+/htDq9Gh9FDHma8oDYNOJXErOeFCr1Cq8OihO3x3MtUZ8fdv64KrXcHvf+u0VerfxYt603jjrakWApNViiI4GwHj4wpfruvp3RUIitTSVvMq8Cx4PAJ0LeFfnS2UfaZkxm8jZS3VGs81uUCoQtDRCOAkEgssWX99r0WjcMFZlUFS0u8n96xNOumBXDNE+YJMpWHAc2Vy3TMq5aB0erszNpOQ5maw21h51tDjI81LET4RZjSRDG5/aenLTBrRBrXIsidGztRffTO+DSz3RKKfq4qotsbPOXedOe09lGfBQzqELHs9OQCflZ3YLmms2Afd6fLlE1ElwsRDCSSAQXLao1Qb8q4v9ZmYuaXL/mqW6nJwcrNZageQ1KRyVqxZLdgXFfyQ3acxW7RXh4WeqjdisiHVcrttTXk6lJOMiS4RYVPRr51Pb39OJf43oYP/cp603307vjWsDS3g1CeKVsRcunOAiLdcFKrlYl0vECUSek+DiIYSTQCC4rKlZrsvJXY21Oq+osXh6eqLT6bBareSfYSKpdtXhfbMiXsq2Z1B5omG/pbPxb6MsS9VEnAC2xudxNKMYAJtNZndKIfFaRah1MKsdhBPAQ8Pas/LRQfx8bz8W3tev3gd/DYbqiJPxxAlsVRdeZ66rn5LgHlcQd8Fj2bFHnC6NcDJoVWjOiuIJ4dR4hg0bxhNPPHGpp3HFIISTQCC4rPHw6ImTIQyrtZzc3LVN6qtSqfD39wccl+sADJHeuA5QCgAX/noSa5mpTv/68Gut7IzzNBehsSnLQRabzL3f7SWn1MixrBKKK82cPEM49Wnj5TCGJEl0Dvagf3uf81ay1wYHo/b2BrOZqmN1y7c0ldbuimHl6dLTFzyWnYDOys/cE2D9+5fIJEnC3UlYEvyTMJvNvP7667Rv3x6DwUDXrl1ZvXr133JtIZwEAsFljSRJBAZNBJrn6XT2zroz8RjVBk2AM7YyM4WL42lM6U4XTy9cPL2QAJ8zok4ZxUbun7+PzSeVJbwUjQ0jMq6yBPmNE2X1IUkSTjW2BLGHmz1ODSFu1aVoyrMwt5TI8QwDvbtS7DcvvmXGbCLKzjqZOdrZ/Kh9i7KypttNCK4cZs2axZw5c/jf//5HXFwcDzzwABMnTuTAgRYuKVQPQjgJBILLnqDACQAUFGzDWFVXAJ2L+hLEa5C0arxviwK1hPFYAeW7Gje2X/VyXS+3SofjB1KLmL1W8TOySXCqOuqUsN/R66mpGFowz8nH4IOTxgkZmYzyjPN3aAySdMmX69wMGnpI8YxU72Wg+ihtDr1/SeZxpWKz2Xj22Wfx9vYmMDCQV1991X5OkiTmzJnDmDFjcHZ2pmPHjuzYsYNTp04xbNgwXFxc6N+/PwkJCfY+CQkJjB8/noCAAFxdXenduzfr1q2znz9+/DjOzs789NNP9mNLlizBYDBw+PD5/0CYP38+L774IqNHj6Zdu3Y8+OCDjBw5kvffv/j/7kI4CQSCyx4npzA8PHoBNrKzfmtS33MJJwBdkAseo5Tlt+LfEzHnnD+Pqma5blQrG8Geji7GZmtt1OqkThFOiQdym1UfrwanGCUvqSWEkyRJBLsqlgwtu1x3iYWTXstY9Q7754ikHyBl+yWZSw2yLGO1Vvztr8ZETs/mu+++w8XFhV27dvHOO+/w+uuvs3Zt7dL4G2+8wdSpUzl48CBRUVFMmTKF+++/nxdeeIG9e/cC8Mgjj9jbl5WVMXr0aNatW8eBAwcYOXIkY8eOJTVVKQYdFRXFe++9x0MPPURKSgoZGRnce++9vP3223SpjrCei6qqKgwGg8MxJycntm7d2uR7byqiVp1AILgiCAqcSHHxXjKzlhIWdt95c4NqqBFOpaWllJeX4+LiUqeN64BWGE8UUBVfRMGC4/g/1A1J0/Dflf7VwqkkPYWvH5nGTZ9tp9xU19YgWWNDo1dTVlhFdnIJge08GjXns3HqouQQmVNTsRQWovHyOk+PcxPiFsKpolMXJ88p69IIJ3e9xI3qXQAct4USpUqDTe/A1GWXZD4ANlslGzedXwS0NMOGHkatdm5Sn5iYGF555RUAIiIi+OSTT1i/fj0jRowAYPr06dxyi1IG6bnnnqN///68/PLLjBw5EoDHH3+c6dOn28fr2rUrXbt2tX9+8803Wbp0KcuXL7cLrIceeohVq1Zx1113odPp6NmzJ48//nij5jty5Ehmz57NkCFDaN++PevXr+e3335z2D17sRARJ4FAcEUQEHAjKpWO8vJ4Sssa7xek1+vxqhYaDUWdJJWE980dUDlrMGeUn9dVvGapLi81hQ7+Lnx0W3fq03Ehvs60jVGKCV/Icp3awwNdmzYAGBuxjHE+QlyVPKfTZRdBOF0iL6eutjj8pSKKZBdes0xVDuYnnLuTwE5M9e7NGoKCgsjJyan3fM0fI2dGhgICAjAajZSUlABQXl7Os88+S3R0NJ6enri6unL8+HF7xKmGefPmERsby/79+/n2228b/QfRRx99REREBFFRUeh0Oh555BGmT5+OWq0+f+cLREScBALBFYFG44av7whycn4nM3MJ7m6dG903MDCQwsJCsrKyaNeuXb1t1O56PG5oS+GSeCoO5OA+LLTB8byCWqHR6TFXGSnKyuK66GCeuyGKt/847tCub1tv2rf3I35PNgn7cxlwU3ijHwxnY4jpgik5mcrYw7gOGdKsMWqoSRBv0YiTvfRKFpTngYtvy43dCPqUbwRgtbU3STZlQwAl6WC1gPrSPOpUKieGDb1woduc6zYVrdZxV6IkSdjOqI945vma/4brO1bT55lnnmHNmjW899579sK8kydPxmRy3Chx6NAhysvLUalUZGVl0apVq0bN18/Pj2XLlmE0GsnPz6dVq1Y8//zztG3b9vydLxARcRIIBFcMQYHK7rrs7BXYbI3fEXa+PKcanLr4glrCkl2BObvhciwqlRrfMGVbf26KUoD4/iHtmNQj2KHduK7BhHXyQaNTUVpgJDe1+Tu9avOcLtzx+6JEnPSu4F390MpsQVfyRtK+bB8Aa2y9ycELK2qQrVCa+bfPpQZJklCrnf/2V3PFeUuyZcsWpk2bxsSJE+nSpQuBgYEkJyc7tCkoKGDatGm89NJLTJ8+nTvuuIPKysr6B2wAg8FAcHAwFouFxYsXM378+Ba8i/oRwkkgEFwxeHsPRqfzxWwuIL9gc6P7NVY4qZw0GCKUZb2K2HPXcvNvrUSuclOSAOUh+d+bYnhqRAeGRfrx35u6MCjCF61OTevOigFm0qHm14ezO4gfikW2XVgdtjMjTs1JJG6Q0H7Kz8QNLTdmY6gowMuYBsB+WwQ2VOSrlWLOFKf9vXMRABAeHs6SJUs4ePAghw4dYsqUKQ4RLIAHHniA0NBQZs2axezZs5FlmaeffrpR4+/atYslS5aQmJjIli1buOGGG+w7Ay82QjgJBIIrBpVKQ2CA8hdlVmbjPZ1qvJxyc3PPmzzqVJ2TVHk495yiomZnXU5yov2YVq3i0Wsj+HZ6H27tXVvMN6yTIpzSjjXeofxsDFFRSM7O2IqLqYo/1exxAFq5KsshZeYyiquKL2gsByKuU37Grzt3u5YmXYk2JdoCKcYVgGxVtXAqEsLpUvDBBx/g5eXFgAEDGDt2LCNHjqRHjx72899//z2rVq1i/vz5aDQanJ2d+fHHH5k7dy6rVq067/hGo5FZs2YRHR3NxIkTCQ4OZuvWrXh6el7Eu1IQOU4CgeCKIjBwIqlpX5Obtx6zuQit1vO8fWpKr5hMJvLy8uwRqPpwivahUC1hyanEkl2BNrDuLjyoTRDPPUM4NURoR28AcpJLqKowo3duuMRKQ0haLc7dulG+fTsVe/dgiOxw/k4N4KRxws/Jj9zKXNLL0vE0eDZ7LAfaXQOSCnKPKYLFs+E8sRbltLId/qAcbj+ULvvRBaA4tf4+AjsbN26sc2zZsmX292f/AdGmTZs6x4YNG+ZwrE2bNvz1118ObR5++GH7+6lTpzJ16lSH8z179qSqkWWFhg4dSlxcC5YNagIi4iQQCK4o3Nw64uoahSybyM45/1+moCyjNXq5zqDB0KF6ue5ww0trftU5TmWFBVSUnDtq4+ZtwCvQGVmG0ycKGzXn+nDu3UuZV7VvzoVQs1yXVtaCERlnbwjprbw/1bTyOBdEuvJ9HLDVCqdUa3Vyuog4CVoYIZwEAsEVR2B1knhW5pIm9Gm49MrZOMUoyzyVsQ0v1+mcnPEJUZbjkg/uO++YIdVRp7RjFyCcelULpz17Lzg3yZ4g3pI76wDCFd+fv225TpZrI05nCKcki/J9U9zC9yf4W3B1dW3wtWXLlks6NyGcBALBFUdgwDhARXHJASoqkhrVp7ERJwCnjt6gkbDkKst1DdGh30AATuw4/y/ymuW6tLj887RsGENMDJJOhzUvD9NZO5SaykWxJACIqBZOSZvA0vwafY0mPwGMRdjUeo7LtXllyVYlr0wkh1+ZHDx4sMFXr+o/IC4VQjgJBIIrDr3eHx+fwQAkJX/aqOhLUyJOynKdInQqYnMbbBfZX/FTSj50AGNZ2TnHDO7giUolUZJnpDi3aVuu7fPS63GqNiK80OU6u3BqSUsCgMAYcPEHUxmk7jh/++ZitcCW92HlE8pH/y6Yz0jbTZfPWKpryZ2Dgr+F8PDwBl9OTk33qWpJhHASCARXJGGhMwEVWVlLSU757Lzt/f39AcXRuOw8IgfAuWZ3XWxeg8LMJyQU37A22KwW4vecuy6azqAhoJ07cGG765xq8pz27Gn2GHARl+pUKgiv2V33Z8uOfSZHl8D61yFZifap2g9Do6r1L8qUfZCRwFIJFc2P8gkEZ3PVC6e0tDSGDRtGdHQ0MTEx/Prrr5d6SgKBoAXw9h5Ahw7/BiAxcTaZWcvO2V6n0+HtrUSRGrNcZ6hZrsurxJzV8HJdZH8l8nViexOW6y5AONnznFoo4pRVnoW5CWaijaKDUr+M/fOh5CIZUB5ZrPyMngC3L0Q97Dna+NbugDSjoVJfY0nw9+2sO9urSHD50FL/Nle9HYFGo+HDDz+kW7du5OTk0KNHD0aPHl1voU+BQHBlERpyF0ZjOqmpX3Hs2PPodf54ew9osH1gYCAFBQVkZWXRvn37c46t0ivLdca4fCpjc9EF1f87I3LAYLYtnE/qkUNUlBTj7N5wId/QaG92r0gi/UQhNqsNlbrpf7s6d+8OGg2WjEzM6elog4PP36kefJ180av1VFmryCrPItStBa0DosZAqx6QsR9WPQ23/kC9xfyaS0UBnFqvvL/mRfCLBKBDgCuncmqjifkaf5yrciB5K7Tq3rJzOAudTodKpSIjIwM/Pz90Ot1l4eAtUOwUTCYTubm5qFQqdDrdBY131QunoKAggoKCACVU7+3tTUFBgRBOAsFVQnj7ZzEaM8jJ+Z3Yww/Sq+cvuLpG1ts2ICCAuLi4RkWcQFmuM8blU3k4D/frW9f7IPQKbIV/2/bkJCUQv2s7XUeManA8/9bu6J01VFVYyEkpJbBdwyKrIVTOzhg6RWM8FEv5nj14NlM4qSQVwa7BJBYncrr0dMsKJ7UGxn8Cc4bA8ZUQ9xt0mtBy4x9fCTazUljYr/bfOtzfDajNYTtFGKEcgbUvQ8o2uO0nUF2cIrAqlYq2bduSmZlJRkbGRbmG4MJwdnYmLCwMlerCFtsuuXDavHkz7777Lvv27SMzM5OlS5cyYcIEhzafffYZ7777LpmZmXTq1IkPP/yQwYMHN/lae/fuxWazERr6N5myCQSCi44kqYju+C4mUy5FRbs5eOgeevVajEEfWKdtU3bWQc1ynUpZrsssR9fKtd52kf0Hk5OUwIntm88pnFQqieBILxIP5JJ2rKBZwgmU5TrjoVgq9u7F86zfl00hxC1EEU4tnSAOENAJBj8Fm/6rRJ0irgedc8uMXbNM13mSw+EIf8d/nzfNd3DNoEjY8RmcXA0H5kPPaS0zh3rQ6XSEhYVhsVjO61Av+HtRq9VoNJoWiQI2WzilpaWRnJxMRUUFfn5+dOrUCb1e3+RxysvL6dq1K9OnT+emm26qc37hwoU88cQTfPbZZwwcOJA5c+YwatQo4uLiCAtTtp425Db6559/2ist5+fnM3XqVObOndvkOQoEgssbtVpPTJfP2bvvFioqEjh0aAY9eyxAo3FzaHdm6RWLxYJGc+5fgSq9BkOkF8aj+VTG5p1TOG356VvSjh2hrLAAVy/vBscM7ehtF069b2xeJXfn3r0p+HoelXsuMM/pYiWI1zD4Kdj3HZRlKWVR2jb9D946lGZDUnWdwk5nCacAx3+fhBKJ0kEv4ebiD2tegL/eVPoY3GsbFZ+GZQ9Ctzuh660XPD1JktBqtWi1TXeHF1wZNClelZKSwgsvvECbNm1o06YNQ4cOZdSoUfTq1QsPDw9GjBjBr7/+2qQErFGjRvHmm28yadKkes/Pnj2bGTNmMHPmTDp27MiHH35IaGgon3/+ub3Nvn37OHLkSJ1XjWiqqqpi4sSJvPDCCwwY0HD+Q03bkpISh5dAILj80Wo96dZ1HjqdL2Vlxzl8+BFsZyU9e3h4YDAYsNls5OU1ruBuze66inPUrvPwDyAoIhJkmZM7t51zvJoE8ezEEkxGS6PmUGdOPXqAJGFKScGck9OsMeAiejnVoNFDaB/lfcaBlhnz8C8g2yC4F3g7Cs+2vi6oVY4RhVM5ZdB7JviEQ3ku7PwMCpJgdjSsfwMWTFGE2NL7WmZ+gqueRgunxx9/nC5duhAfH8/rr7/O0aNHKS4uxmQykZWVxapVqxg0aBAvv/wyMTEx7LnArbIAJpOJffv2cf311zscv/7669m+/dxbf2uQZZlp06YxfPhw7rrrrvO2/89//oOHh4f9JZb1BIIrByenELrGzEWlcqKgcCvHj7/kIHbOLL3SGD8nAEOUD2hUWPONmDPKG2xX4+l0PjNMDz8n3H0N2Gwy6SeLGjWHs1G7u6OPigKg8gJ21wW7KvlRaaUX0SSyVXflZ0sIJ1mGgz8p77vfUee0XqOmtY/jcmB8ThlodDDoSeVAwgbFyqAkHba8B5mHHMcXCM5Do4WTTqcjISGBRYsWMXXqVKKionBzc0Oj0eDv78/w4cN55ZVXOH78OO+88w4pKSkXPLm8vDysVmudgpwBAQGN/qW3bds2Fi5cyLJly+jWrRvdunXj8OHDDbZ/4YUXKC4utr/S0oTrrEBwJeHu3oUunf8HqMjMWkxu7hqH803Nc1Lp1ThFKbXr8r4+TNGKBMzZdQVUh/4DQZLIOBFHSV7DppkAodGKq3Va3AXYElT7ORUvX4E5u3lRpwjPCABOFJ4go+wiJTS3pHDKOAA5caAx1Fmmq+HsPKf47FLlTU0NvaxYyIytf3zh9yRoBI0WTu+++y5+fn6Najt69GgmT57c7EmdzdnJXLIsNzrBa9CgQdhsNge79i5dujTYXq/X4+7u7vASCARXFr6+19CmzYMAnIx/E6u11oepqcIJwP261qg99dgqLJRtyyD7g/3kfH6I8n3Z2ExKErCbty/BkdHKNXduPed4oR0VIXYhfk4u/foDULZxI6eGDSP1nnso/u03ZHPjPZlC3UPpG9QXm2zj5+M/N3su56RVN+VnYRJUnlWnz2KCpnjr1ESbosaAk2e9TSL8HfPaTmZX2xP4hIPOFcwVcPIMMS2d8RgsaFz5HsE/m8vaANPX1xe1Wl0nupSTk1MnCiUQCARn0qb1gxgMwVRVZZKcXJsTeWbplcYWytUGuhD4bG98pnXCEO0DKjCllFD460my39+LpVjZnBI5oNoM8zzLdSGRXkgSFGVXNDvq5DpsKEFvvYVTz54gy5Rv30HGc8+TOHEi5Tt3NXqcqdFTAVh8cjEV5oaNPpuNkxd4VeciZRysPV6eDx90gi8GKfXmQPmZHVf/OMYSOFxtYFzPMl0N7f0drWZOF1bfk0qtlIMBxU0c4LGD8PAeaFOdtF6Q2Lh7EvyjaVHh9OKLL3LPPfe02Hg6nY6ePXuydu1ah+Nr1649b5K3QCD4Z6NWOxER8RIAKalz7cWA/f39kSSJiooKduxofC01SSXhFOWN79Rogp7vi/vI1qjddViLTRQvVx78HfoORJJUZJ06yd6VSzm45nf2/7GC/at+4/Bff2K1KNEgvbOWjgOVzStrvj5CSV7Ta9dJKhWeN02izY8/0H7tn/g++ghqb29MpxJInTaN9H/9C3MjomqDggfR2r01peZSlp1a1uR5NAr7ct3+2mNHl0B5DuQcha+Gw89T4H89Yc5gx7yjGnZ+BsYi8ImAtkMbvFSgu2Mds+ySM3Zc18wDwCNUSS73Da9NMq8RTlZz0yJhgn8ULSqc0tPTSW5ixe6ysjL7EhpAUlISBw8eJDVVscj/17/+xdy5c5k3bx7Hjh3jySefJDU1lQceeKAlpy4QCK5C/Hyvx9t7MLJs4mT8G8iyjFarZeDAgYBiWbJ69eoml2JQu+twvyYM33s6g0qi8mg+lXH5uHh6EdqpMwCb5n/N+nmfs+HbOWz47iv+nPMxO5f8Yh9j8K0R+Ld2o6rcwqovDmM2Nd/3Rxcait/DD9P+j1V4TZkCKhUlq/4gefLNWM+zM1glqbijoxLB+fHYj9jkiyAY6stzqokeGTwUQXTid0AGmwU2v+vYvzwftn+ivB/+0jlNLAM9DA6fy6oslFVV716sWTYECOpa+74mIlaYBKZyJQr2aW+w1LW5EQhaVDh99913/PXXX03qs3fvXrp370737sr/WP/617/o3r07//63UoPq1ltv5cMPP+T111+nW7dubN68mVWrVtG6deuWnHodPv30U6Kjo+ndu/dFvY5AILh4SJJEZIdXkCQt+fmbyMtTynRce+21jBgxAoCdO3eyaNEizE3IDapBG+iC22BlZ1rRbwnYqqwMuXMGkQOGENF3AB36DSKy/2DadusJwP5Vv2GsLjCs0aoZ9UAXnNy05J8uY8P3xxq9dNgQag8PAv/9Mm0X/Yo2LAxLbi55n31+3n7j24/HTedGamkqm09vvqA51ItdOB1UfhYmQ9ouQIL7N0P/R6D7nXDL98qxYysg51ht/62zwVSqLLV1HH/OSwW6G+ocyyo2Os4DHEWUdzvlZ0Ei7Pwcco9D/inFe0ogOAtJvtD/U69ySkpK8PDwoLi4WCSKCwRXKKcS3iUl5QsMhhD69V2DWq08XA8fPszSpUux2WyEhYVx22234ezcNHdrm8lK9gf7sBZW4To4GM8b29VpI9tsfP/so+SlpdDvptsZeEttjk5GfBG/fXAAm01mwKRwul8fdmE3W03Zlq2k3XsvaDS0W7Ecfdtzm23O3jubb45+Q6+AXswbOa9l66wZS+DtamuXp0/B/u/grzeUJbe7lzu2/WWqUqKl82SY/LUSAXo3AszlcMciiBhx3svFvLqGkjM8sn6c2ZeB4b7K8tvbYYoIu2MxRFynNMiMVZYIAfTuUFUdpRv2Igx7TnlvNkLCX8r11cLc8mqjKc/6ZkWcXn/99XO+BAKB4HKibZuH0esDMRpPk5L6pf14ly5duOuuu9Dr9aSmpjJv3jwKCwvPMVJdVDo1nhPCASjblo4pvaxOG0mlov/k2wHHqBNAqwhPBt2i2ALsWHrqgnbanYnr4EG4DB0CFgs577x73vZTOk5Bq9KyN3sv61LXtcgc7BjcldwkUMql1CzTdbm5btvBTys/jy6BvFNw4g9FNHm2hvDrGnW5s5fr7BEnlQpGvgk9pkK7M/KkzjTSrCoBVbWjfPIZSf7rX4cFtyu5VoJ/NM0STkuXLnV4/fLLL/z3v//l/fffZ9myZS08RYFAILgw1GpnIsJfBCAl5QsqK2v92dq2bcs999yDu7s7eXl5fP3116xYsYJ169axbds29u/fz7Fjx0hLS2swF8op0hunGF+wQeHSeGRb3UB+RJ8B+Ia2xlRZwf4/fnM413loMFEDgpBlWPdNHJVlpha574DnngONhrINGyjbdm5H80CXQO7prGzueXv325SbGzb7bBY1DuKrn1OWwtQ66Di2brugGOgwSnEH3zobDi9Sjne5GRoZBQs4a7kuq8RY+6HnNBj3P8eokd4NXKrtdrQuMPkb5X3abiXSJMtwdKly7NT6Rs1BcPXSLOF04MABh9eRI0fIzMzk2muv5cknn2zpOQoEAsEF4+8/Gi+v/thsVZyMf9PhXEBAADNnziQgIICysjL27dvH1q1bWbt2LcuXL2fhwoV8/fXX/PDDD1RW1r8DznNMeySDGvPpMsp3ZtY57xh1Wo6xvDbqJEkSQ27rgFegMxUlJjb+eOKC850A9O3a4TVFuWbO228jW85d4mVml5mEuIaQU5HDpwc/veDrOzB8FvSaAS7+yufOkxv0YmLoM8rPQwvgVPWu6vqiUw1wdp5T9pnCqSFG/h/0ugeeOKwIOtdAsFbB6T2QeRBKqw1CT+9Vdt0J/rG0WHK4u7s7r7/+Oi+//HJLDSkQCAQthiRJdOjwCpKkIS9vHXn5Gx3Ou7u7c8899zB27FiGDRtG3759iYmJISIigpCQEDQaDYmJicydO5f8/LoO02p3HR43KEs+xWuSsRTV3ZFVE3Wqqihn/yrHqJNWp2bEPZ1QqSQSD+RyfEfjqiOcD7+HH0bt4UFV/ClOXX89OR9+iKmB3c8GjYGX+ikWDj8e+5Fj+cfqbdcs3FvBmNnw1HF4eDeM+aDhtsE9of21IFuVXXYBXcA/qtGXOjvi1CjhFHOLMicXHyWyVbOUt2cuHF9V285cDlkNV58QXP206K66oqIiiouLW3LIS4bYVScQXH24ukQQGnI3ACdPvEZlZbrDeb1eT8+ePRk2bBijRo1i0qRJ3HHHHcycOZMZM2bg7u5Ofn4+X331FYmJdc0SXfoEogtzQ66ykvdVLJYixwe2pFLR76b6o04AfmFu9BmniK8tC09SnNt0f6ezUXt4EPTWm6jc3LBkZJL/xRwSbhhF1htv1hvVGhQ8iJFtRmKTbbyz550Lvn4dVGrwiwRt3d1vDgx5pvZ9l6ZVogg4O8eppBm2AgMeVVzF45bB7uq8OE31uKk7mz6e4KqhWbvqPv74Y4fPsiyTmZnJ/PnzGTJkCD//fJGs+y8BYledQHB1YbGUsXPXSKqqstBoPOncaTY+Pg0bKp5JaWkpCxYsID09HZVKxejRo+nVq5fj+AVGcr+KxVpYhdpTj9+9XdD41JoyyjYb3z3zCPmnU+k/+XYG3Ozogm2zySybvZ/MU8UEtvNg4lPdUanr/o0ryzKnjxXiHeyCi4f+vHO3VVVRtn49RcuWUb5lK8gyXnfdRcCLL9TZQZdVnsWIRcrutb9u/gs/58aV22pxFt2jiJR7/wK3wEZ3WxuXzb3f1xY/DnQ3sPPFa5t+/VXP1IomzzDofBNs/QA6joNb5zd9PMFlS1Oe9c0STm3P2taqUqnw8/Nj+PDhvPDCC7i5uTXQ88pDCCeB4OqjsvI0h488QmnpYUCibZtHadv2ESSpYWPFGsxmM8uXL7cXC+/bty8jR45EpaoVN5biKvK+OowlrxKVuw6/mV3Q+tfaHJzYsZWVH76NzsmZu97+CM/AIIdrlORVsuDN3ZiNVvqMbUvvG+taCRxYm8r2xacICvdg0tM9m3T/RUuXkfnCCwD4zJyB31NP1RFPU36fwuG8w7w24DUmRdRfUPdy5fDpYsZ+UlsvUK2SOPnmKNSqJlosGIsV8eQRCgMfU7yl5o1U8rSePtnoZHXB5c9FtyNISkpyeCUkJLBz507+7//+76oSTQKB4OrEySmEXj0XEhw8BZBJSv6Yg4dmYDKd3wpAq9UyadIkhg8fDsCuXbv4448/HJa9NB56/O6PQRPgjK3ERO6cWEwZtctyHfoOIKBdBKbKCha9NYuyAsecKXdfJ4be1gGA3SuTSDyY63A+N62Unb8pZV4yTxVTlN20GnOeEycQ+OqrAOTP/Zr8OV/WaTMkZAjAxTHEvMgEnBWBs9pk8sqasVxn8IBJX8K1LyvvW3UHtV4pFSPq2v1juayL/AoEAsHFQqXSExX5BtEd30WlMlBQsIXde8ZRXFJPnbSzkCSJIUOGMHHiRAD27NlTp2qC2k2H330xaINdsZWbyf3qMFWJSg6opFIx4dmX8QwIojgnm8X/928qy0od+nfoG0jnIcEgw9p5R8lJUUwZLSYra78+is0iQ3XA48SupieSe912KwEvPA9A7iefYEpJcThfI5y2Z2zHZG0Ze4S/C18XPZqzokt2L6cLQaOH4B7K+9TG1zkUXF20qHD67LPPhAGmQCC4oggKmkTvXktwcmpDVVUm+/bdSvypt6ky5Z23b9euXRkzZgwAW7ZsYevWrQ7n1S5a/GZ2URLGKy3kzj1M6bZ0ZFnG1cubybPewMXLm7y0FJa+/SomY20yuCRJDL41gtBobywmG79/FktZoZHtSxIozKrA2V3HkFuVqNTxnZn1ekedD++778ZlyGCwWMj9yDF3taN3R/yd/Km0VLI3a28DI1yeqFQS/m6OUaesxuysawxh/ZSfNcJJFN/4x9Giwmnx4sV8++23LTmkQCAQXHRcXSPp03sZfn4jkWUzqalfsX37UE6efIOqquxz9u3VqxfXXac4Wq9bt469ex1FhspJg+/MLjh18wObTPGKRAp/OYnNZMXDP5DJL72BwdWNzPgTLH///7CcUTNPpVYx8t7OeLdyoaLYxJL39nN442kArr27Ix0HBKFz0lBWUEVGfFGz7t2/2nuvZNUqKo8etR+XJInBIUoZkk2nNzVr7EuJf3MsCRpDWH/lZ+pOSNwI/xcM+0Wi+D+JFhVO69evr3eL7pWIsCMQCP5ZaDRudOn8KV1jvsLdvSs2m5G009+ybfswjp/4dx3rgjMZNGgQgwcrImPlypX2xPEaVDo13rdG4jGmHaig4kAOuZ8fwlJgxDe0NZOefxWt3kBK7AH+mudYlFfvpOHGh2JwctNSmq88/GOGhxDWyQeNTk14T8VQ8ng9ppuNwdCxI+7VUbPcDz50ODc0RNltuOn0JmRZJrE4kbmH51JiKmnWtf5Ozo445beQG7vigC4pRYC/H6/4Oq18omXGFlwRiBynBnj44YeJi4tjz549l3oqAoHgb0KSJHx9h9Or52K6df0WD49eyLKJ9PQf2bFzOJmZSxrsO3z4cPr0UcqKLFmyhM8//5yff/6ZP/74gx07dpCYmIjrwFb4zuiCykWLObOc7P8doHx/NoHhHRj31IsgSRz+60/i9zjmz7j7OjH6oRj0zhoC2rrTf2J7+7nIfso2/YT9uZirrM26b7/HHwOtlvKtWynfuct+vG9QX3QqHell6XwZ+yW3rbyNj/Z/xNu73m7Wdf5OfFx1Dp8LK1pIODl51S007N6qZcYWXBE0y46ghri4OFJTUzGZHP+DHDdu3AVP7HJB2BEIBP9cZFmmqGgXSUn/o7BoJyqVjp49f8XdrXO97W02GytWrODAgQP1nh80aBDXXXcdlqIq8n88hjlNSQg3RHrhOSmC7St/Ys/yxRjc3Ln73U9w9fJ26G82WVFrVKjOSHyWZZkfXt5BSZ6R66ZHE9m3fr8jq8WGJFGvJxRA1htvUvjjjxhiYmizcIHdnuCBdQ+wLd2xzp1G0rBq0iqCXIPqG+qy4J3Vx/lsY4L985iYID6Z0qNlBk/bA1+fUXDYPxoeEsniVzJNedZrmnOBxMREJk6cyOHDh5Ekyb4Nt+Z/NKu1eX/1CAQCweWEJEl4efXD07MPsYcfIC9vPYcPP0yf3r+h1XrWaa9SqRg/fjyDBg2ioKCAoqIiioqKyM/P5/jx42zdupWwsDA6dOiA/wMxlG5Op2RdCsYThWTP3ke3G0aQ3OYAucmJrPniIyY9/6qDv5JWV9dnSpIkIvsFsWdlEid2ZtYRTrIss31JArHr07DZZCQJNHo1ET39GXBTOHpnpdit74MPULRkCcbYWMo2bsTtmmsAZbmuRjjN6DyDQ7mH2Ju9l/nH5vNs72db6qtucbxdLlLECSC0N3S4AU6uVj5X1C3BI7h6adZS3eOPP07btm3Jzs7G2dmZo0ePsnnzZnr16sXGjRtbeIoCgUBwaZEkFdEd38XJEIbReJqjcU8jy7YG2/v4+BAREUHv3r0ZMWIEt912m30Zb+nSpRQXFyOpVbhfE0rA4z3sZVpKfktiROfpaLQ6kg/u4+CalY2aX41YSjteSFlhbRK0LMtsW3yKg2tTsVXvupNlMButxG3L5OfXdpF8WNk9qPH1xfuOKQDkffKp/Q/iCeETmBo9lc+u/Ywnej7BjC4zAFh0chHFVZdviS0vZ0fhVFDewoV5J8+DW6qTwivyxe66fxDNEk47duzg9ddfx8/PD5VKhUqlYtCgQfznP//hsccea+k5CgQCwSVHq/WgS5dPUKl05OdvICXliyb1v/766wkKCqKyspJff/3VHpnX+jvj90BXPEa3BQmsR8sYOeQBADb/8A35p1PPO7aHnxNB4R4gw6L/7uPIptNYLTZ2LU/k0Lo0AIZOiWTGe4OZ9vZAxj3eDQ9/J8qLTfz+aSy/fXiAg+tSkcZMAWdnjEePUrZhAwBOGiee6f2MfYfdwFYDifCKoNJSyS8nfmnSd/B3UifiVN7CXlQ6l9pcJ5sFqi7/hHlBy9As4WS1WnF1dQXA19eXjIwMAFq3bs2JEydabnYCgUBwGeHm1onIDopXXULiBxQUbDtPj1o0Gg0333wzer2e06dPs379evs5SSXhNiQEzzHtAHBNdKJH9GgsZhPzn3uMJW+/Suz61ZQXFTY4/sDJEbh66SkvqmLTzyf57oVt7PtDMbUcclsHOg8JxuCqxcVTT2hHb26d1Ydu14WCBKePF7Jt0Sl++fA4x695CRnI/d8n9RYBliSJ6Z2mA/DN0W94bcdrfHf0O7LKm27CeTHxOks4FVSY6r2fC0LrBFoX5b1YrvvH0Czh1LlzZ2JjYwGlTtM777zDtm3beP3112nXrl2LTlAgEAguJ1q1upmgoJsBG0eOPoHR2HgbAG9vbyZMmADA9u3biYuLw2w22x/orgODcR2o7NCKMMXQoW1frBYLSQf2svbLT/jigams/uxDbLa6eaQBbdy58/X+DL61A84eOipLlaWpgZPD6TIspE57rU7NwMkR3PFqPwbcFE5oRy9UKomMck8KWvWk6tgxcj/4kMzXXiP5ttspWrrM3veGtjcQ4hpCqamURScX8d7e93h28+WV7+R91lKdyWKjwnQR8m+dfZSfFecv1yO4OmjWrro1a9ZQXl7OpEmTSExMZMyYMRw/fhwfHx8WLlxor+F0NSB21QkEgrOxWo3s3XczZWVxuLt3pVP0bJyd2zS6/x9//MGuXbscjmk0GnQ6HTeMvIFWhzQY4/JROWvQTgwg8dReTu3ZQVZCPADdR41l+LT7GxzfYrJyfGcWemcNEb0CGj2v7UtOceDPVDz1FXRf8wxnFi1RubkR/td61NX1SHMrctmZudPu7QSw/ub1+Dv7N/p6F5NSo5kur/7pcGzLs9cQ6u3cQI9mMmcoZB6EKb9Ah5EtO7bgb+OiF/kdOXIkkyYp1bLbtWtHXFwceXl55OTkXDWiSRhgCgSChlCrDcR0+RSNxo2SkkPs2DmCo3FPU1GR1Kj+I0aMIDIy0uGYxWKhoqKClb+vRDM6CG2IK7YKC5YVuXTvO5o7/u8DxjzxHAAH/ljBvt9/a3B8jU5N5yHBTRJNAN2vD0OjV1NU5UxJ7/EYOnXC++6p6Fq3xlZaSuHPC+xt/Zz9GNt+LI/3eJwY3xgANqZtbNL1Liaueg1atWO9uoKWznOCMyJOYqnun0KLGWB6e3s7bJu90hEGmAKB4Fw4OYXRs8dCfH2GAzayspayY+f1jRJQGo2G22+/nVmzZvH888/z9NNP88QTTxASEoLJZGLVn3/gMzUajb8T1hITuV8covJYPpH9BzN4yjQANs6fS/yu7S17T646YqqX9ZIjJ9Hm118JeOEFfB96EICC777DZqxbumRY6DAANqRtaNH5XAiSJNXdWdeSlgQ1COH0j6PRwumBBx4gLS2tUW0XLlzIjz/+2OxJCQQCwZWAq2skXbt+Re9eS+sIqAMH7iYzcykWS3mD/TUaDQaDAVdXVzw9PRk3bhwqlYqTJ09yPDUe/we7oQ/3RDbZyP8+jtKt6fQaO4muI0aBLLPqf++RfjyuRe+p+4gwtHo1+afLSDyUC4D76NFog4Ox5udTtHhxnT7XhCqeT7syd1Fubvh+/24u+s46EMLpH0ijhZOfnx+dO3dm1KhRfP755+zZs4f09HTy8/M5deoUy5cv59lnnyUsLIwPP/yQmJiYizlvgUAguGxwd4+pI6AKCrcSd+xptmzty5GjT5Kbt57KyjRstob9hPz9/e0171atWoVRNuE7vRMuvQNBhuKViZSsSWH49Ado16M3FrOJX15/kV3Lfq03Ybw5GFy1xFyjRJ32rExCtslIWi0+MxX/pvyvv0Y2O95De8/2hLqFYraZ67iMX0rqejkJ4SS4cBotnN544w3i4+MZMmQIX3zxBf369SMsLAx/f38iIyOZOnUqiYmJzJ07lx07dtClS5eLOW+BQCC47KgRUP37/UXbtk/g5NQGm62S7OzlxMbex/Ydw9iwMZpt2wazb99tnIx/C5Mpz2GMwYMH4+fnR0VFBWvWrEFSq/CcFI7HqLYAlG06jSmxhBsff5bw3v2xWS1s/fk7fnntBYpzWsYSoNuIMLQGNfnp5SQcUKJOHpMmofb1xZKRSfHyFQ7tJUmyR50aynOyyTbSStNa3hLgHFxU9/AanKvL4ohddf8YmpTj5O/vzwsvvMChQ4fIz89n//79bNu2jRMnTlBYWMiiRYu4/vrrL9ZcBQKB4IrA2bk17do+Sv9+6+jVawkhIVNxdm6LSqUDbBirMigq3kNa2jy277iWlNS52GzKQ12j0djrfR46dIhTp04hSRJuQ0Nw6a/UhitcEo9G0jHuqRcZ+eAT6JycSD8ex3fPPMrp40cveP4GFy1dh4cCsOd3Jeqk0uvxueceAHI//QRbVZVDnxrhtDl9Mxabpc6Ys/fOZvSS0axNWXvB82ssXi5ah88t7h4OIuL0D6TZyeGenp507dqVfv36ER4eflUlhgsEAkFLIEkSHu5diezwCv37rWPY0KMMGriTXj0XER39Pm5unbFayzh16j/s3DWKvLwNyLJMaGgoffv2BeC3336jrKwMAI8b2qL21GMtrKJkTTKSJNF52HVMfecTgqOiMRsrWfPZh5ir6iZwN5Wu14aic9JQkFHOqf05AHhNuR1NYCCWjEwKf/zJoX03/2546j0pripmftx8h3PZ5dn8dFxpv+n0pgueW2M528vpouY4FSSB+cK/d8HlT4vtqhMIBALBuZEkFXq9Hx4e3QkKnEDvXkvpGPVfdDpfKiuTORQ7kyNHH8NsLmH48OH4+vpSWlrKokWLsFqtqPRqvCZFAFC2I4OqZKVWnId/ABOfexVXH1+KsjPZ/utP55pGo1CiTtW5Tr8nY7PJqAwG/KrLauXNmYO1uLZWnUalYVqnaQDM3jebL2O/tJ/79ui3mKtzuw7nHb7guTWW+tzDW5xW3cE1AMqyYNN/W358wWWHEE4CgUBwiZAkFa1aTaZ/v3WEhd2LJGnIyVnF7j1jqDQe4dZbb0Wn05GcnGwv0WLo4IVzzwCQoXBxPLJZKTasd3ZmxMyHAdi3cpndLPNCqIk6FWaWk7BPiTp5jB+HPiICW3Ex2f95m9yP/0fqPfdQtGQp93S+h4e6PQTA/w78j9d2vEZycTKLTi6yj5lUnESpqdThOodyD7Eve98Fz/ds/pZddXpXuHG28n77x1BVeu72giseIZwaQBhgCgSCvwuNxo2I8Ofp2fMXnAxhGI3p7N9/O2VlCxg3bgxQW6IFwPPGtqjctFhyKylYdBJrmSII2vXoTdTAociyjT+/+AirpW6ukcVs5tDaPzi2bRP56Wnn3I2nd9Yq9exQXMVNlRYktRq/p/4FQPGyZeR99hnl23eQ9frrWHJyebDrgzzV8ykAFp1cxITfJmC0Guni24Vg12AAjubX5mElFCUwbfU07llzD3H59VsrmKymOmKrMZy9q+6iJIcDdBwDenel2G9J40vwCK5MmiycZFkmJSWFysrKizGfywZhgCkQCP5uPNy70qfPcgIDxiPLVhKTPsQmz2HAgP4ALFu2jNzcXFTOWrwmKEt2lYdyyXp3L6Wb0pDNNq6Zdh8GN3dyU5PZs9zRc0mWZf6c8zHr5n7Kqo/f5dt/Pcgn025l2btvknRwH7LNVmdOXa8Nxd3XQFlhFVsXKVEs16FDcR89GrWPD26jbkAfGYlsNJL36acATOs8jbnXz6WdRzussiLM7ou5j86+nQE4kncEUHbavbbjNSw2CzbZxn92/afOrrviqmJuWXEL1y+6nrxKxx2I56PurjozNttF2tXn4qf8LM+9OOMLLhuaJZwiIiI4ffr0xZiPQCAQ/KPRaNzo1Gk20R3fQ5J05OauplPnMtq0aYPJZOKnn36iqKgIp04++N7bBW0rF+QqK8V/JJP1/l60lRquufteALb98gN7Viyxi5FdS3/h2JYNSCoVQeGRaHR6zFVGEvbuZMl/XmHeE/dzbJtj8rbOoOHauzuCBMe2ZZJ8OA9Jkgie/T4dtm0l5IMPCPz3ywAULV5MVaLimt43qC+Lxi5iVt9ZPN/neYaGDKWLr2JTczhXyXNaEr+EAzkHcNI44aRx4mDuQVYmrrRf22Kz8MymZ0goTqDMXMaOjB1N+i7PznGy2mSKKi/CzjoQwukfRJOFk0qlIiIigvx8sfVSIBAILhZBQRNp1/ZRAE6d+j/Gj78GT09PCgsLmTdvHvn5+Rjae+L/SHe8bu6A2l2HtaiKgkXxRA0YStcRo0GW2fzDPFZ/9gFxWzawbaGy2+26GQ8x5a33efS7X7jrvx/TfdRY9M4uFGVn8sf/3ifj5HGHubSK8KLrNcqS3cYfjlOcW+Fw3rlnT1yvuQasVnI//NB+XKvWcmvUrdzR8Q5lB+AZEae8yjxm71Nygx7t/ij3xdwHwPt73+d4wXEqLZW8uv1VdmTWiqU9WU1bAfB309epV5eQW9akMRqNqxBO/xSaleP0zjvv8Mwzz3DkyJGWno9AIBAIqgkLuxdX12gsliJOn36X6dOn4+PjQ0lJCfPmzSM7OxtJJeHSMwD/R7oj6dSY00qp3J/DtTMe5Jpp9yOpVMRt/os/PnkfgJ43jifmuhsAUKnU+Ldpx/Bp93P/59/Rof9gZNnG6s8+qGNp0G9COzwDnCkvNvHDyzv59e29HPorjapKJY/K78knQJIo/fNPUqZPp/DXX+vUtevo3RGVpCKnMofnNz9PqamUjt4duT3qdqZGT6WtR1vyjfnc/vvtjFs2jt8SlELGN0XcBMDe7L1N+v60ahVtfV0cjp3MvkjJ2yLi9I+hWcLpzjvvZPfu3XTt2hUnJye8vb0dXgKBQCC4cFQqLdEd/6vstsv9g6qqnUyfPp2AgADKy8v59ttvSU9PB0DtrsP9ujAAilcnI1da6DFqLDe98DoGF1dASR4fcuc99V5LazAwYubDuHp5U5iZztYFjl5MGp2aUQ90ISTKC0mCnOQStv4Sz3cvbGPLLyexBbbB5z4lalSxYydZL/+b9KeedhjDWetMuGc4ALuydqGSVLwy4BU0Kg06tY5vb/iWa8OuxWKzkFWeRYBzAJ9f9zlP93oalaQirTSNrPIsThaeJL0svd77KK4qpriq1iYhIsANAElTiKHVz+xMb/ndewC4+Cs/y3IuzviCywZJbob//XfffXfO83fffXezJ3S5UVJSgoeHB8XFxbi7u1/q6QgEgn8gCQnvk5zyGTqdL/36rsFs1vHjjz+Snp6OVqtlwoQJdOrUCdlqI/uj/VhyKnEd0ArPce0BKMnNIeXIQaIGDEGrN5zzWkkH97HkP68A0GvsJHJTksiMP0FEnwFcN/MhNDodFSUmTu3L4cjmdAozlaK+vqGu3PxCbywZ6ZT8vorcjz4Cm422vy3DEBlpH//V7a+yOF5JWr+z45081+c5h+vLssyalDUkFSdxZ8c7cdMpwuf2lbdzJP8IU6KmsPDEQlx1rvw2/jd8nBQDyhJTCXMOzeHn4z/jZfBixYQVOGud+WhdPB+sO4FT6Dw0rvFoZW+237kGg+bc30OT2f0VrHoaosbAbaLI/ZVGU571zRJO/ySEcBIIBJcam62KXbvHUVFxCj+/6+nc6UPMZvjll19ISEgAYOjQoQwdOhRTYjF5c4+ABP6P9UAX5HKe0evy55yPOfzXn3WOB3WIYvxTL+Hi6QUoIictroA1c49iqrRw3bSORPZTysKcfvJJSv9YjceECbR6+z/2MZbGL+Xf2/9NoEsgv43/DWetc6Pm9P7e9/n26LcOx8a3H8+bg97EarMybfU0DuYetJ/79NpPGRIyhD8OZ/LIbz/hHFrb96FuD/Fg1wcb+W00krjf4JepENoPZqxp2bEFF52mPOub7eOUkJDArFmzuP3228nJUUKTq1ev5ujRC6+TJBAIBIJaVCo90R3fRpLU5Ob+yb79dyBTyJQpU+jXrx8AmzZt4tdff0UV5oJTF1/FIHNJvN3jqSkMvWsmEX0H0L5XP66Zdh+jH3sGvYsLmSeP8+NL/yIz/gSglJQJ6+RDzxtaA7BzeSIWs2I/4DN9OgDFv/+OObt2+WpM+zE80eMJvrjui0aLJoDegbWeejVRqN8SfmNP1h5+OPYDB3MP4qp1tber2YHXzt8Zvf/vAFgrFSf0rw9/zd6spuVLnRd7jpNYqrvaaZZw2rRpE126dGHXrl0sWbLEXkcpNjaWV155pUUnKBAIBALw8OhO15iv0Gg8KCk5wJ494ykp3c8NN9zAuHHjUKlUHDt2jN9//x2PG9vZE8WzP9xPZVzTdkHrnZ0Z968XmfDMLHqMGkfHgUOZ8ub7eAYGUZqXy88vP8PG7+faE8hjrgnBxVNPWUEVRzYpuUdOMTE49ewJZjOF87/HkpuLOScHrUrLjC4zaO/Zvklz6u7fHY2kAeC1Aa/ZE8ZnrJnBh/s+BODpXk9za+StAOzM3AlAlikWtT4X2epERepMLGURVFmruG/tfWxKa8G6eXbh1DSvKcGVR7OE0/PPP8+bb77J2rVr0elqfTKuueYaduxoms+GQCAQCBqHj89Q+vRehqtrFCZTHgcO3Elm5hJ69OjBHXcoW/4PHTrE8dPx+D0QgybAGVuZmfzv4yhYdBLZ3LBL+PnwbhXClLdm03HwNciyjX2/L2P+c49TWVqCRqemz9i2AOz9IxljmeKV5DN9GgD5c78mfvAQTg0ZSuZrryGbm+6l5KZz472h7/H6gNcZ0XoET/Z8koHBA5GRscgW+gf1Z1LEJPoG9kVC4lTRKXIqclh2agkA5uIeYDNQeXoqEa79MdvMfHrw02Z/H3WoEU5VJaLY71VOs4TT4cOHmThxYp3jfn5+wt9JIBAILiJOTmH06vkrAQFjkWULx0+8TGVlKu3bt2fQoEEArFy5kkoXKwGPdMd1cDBIULE3m8LfEi7s2q5ujH7kKSY+/4p9993mH78BIKpfIF5BLlSVW1j56SFMRguu11yDITpa6SwpfkpFPy8g7f77sRQUNPn617a+lokRyrPHQ+/BF9d9wR+T/uCNgW/w/rD3kSQJT4Mn0T7KNVclrmJj2kYAzEXVS32ylrbSFADiC+MxWlpI5Bg8QF0dSBCWBFc1zRJOnp6eZGbWrcdz4MABgoODL3hSlwOiVp1AILhcUaud6RT9AV6e/bDZjBw//jKyLDNs2DBatWqF0WhkyZIlyGrwvLEdPnd3sounitgLf6i3696bMU88D8CRDWtJizuMSq1i5MxO6J01ZCeV8McXh7HaoM0vC4nYuoWow7GEfPoJkrMz5dt3ED90GGmPPELlkQvLiw1xC2FC+AR73hNA/1ZKiZqPDnyERbbgr4vAVhVoP5+ao8PXyReLbOFYwbELur4dSRJeTv8QmiWcpkyZwnPPPUdWVhaSJGGz2di2bRtPP/00U6dObek5XhJErTqBQHA5I0kSUVFvolLpKCjcSlbWMtRqNTfddBNarZaUlBS2bdsGgFOUN27DFOfvwiWnsBTVRllkq1ynPlxjCI6Kthtprv3qUyxmMz7Brox9tBtavZrTxwv59T97ObYrBzy8kDQa3K69ljY//oChUycwmylbt57UGTOwVVSc52pNY2SbkWgkDRabYs55fchkh/PHs0rp7KO4mMfmxtqPy7LMvux9VFmrmndhF1/lp8hzuqpplnB66623CAsLIzg4mLKyMqKjoxkyZAgDBgxg1qxZLT1HgUAgENSDs3Nb2rZ5DID4U29hMuXj4+PDqFGjAFi/fj1btmxBlmXcrwtDF+qGbLRQsOAExvhCChaeIOPV7eR8dghLUdPFwuAp03Dx9KIw4zQrP3ybhH278Qk2MPqhGHQGNQUZ5WyYf5yfX9tFebEyvqFjR9ouXkTb335DGxKCrbiY4t9/b7kvBYjyjmLDLRtYcOMCfhr9E3d2nuBwvtRoobVrRwD2Z+9nwfEFJBYnsiR+CdNWT+Pxvx7HJtcteHxenBVPKSpEysrVzAX5OCUkJHDgwAFsNhvdu3cnIiKiJed2WSB8nAQCweWMzWZmz57xlJWfIDBwAp2i31dMJNesYedOZWdZt27dGDNmDBSbyf7oALKpbpK4ylWLz13R6Fs37ffcyZ1bWfHB2/bPLl7e3PbqfzG4+RK3LYND69OoKDYRFu3NmEe6Iqlqa8flfz2PnHffRR/dkbaLFyNJUn2XuGBkWabb62spPqPA79PjJeacrDXfbO/RHledK4dyDwHwXO/nuDP6zqZdaPFMOPwrjPw/6P9wi8xd8Pdw0X2c4uPjAWjfvj2TJ0/mlltuuSpFk0AgEFzuqFRaoqL+D5DIylpGZqYiQG644QZGjRqFJEkcPHiQ+fPnU64x4TUxHCSQDGpc+gbiM60T2kBl913ul7FUHGiaD1GHfoO47fV36TZyDC6eXpQXFrB35RIMLlp6XN+a8Y93R61VkRpXQOyG0w59PSZNRNLpqIo7hjE2toErXDiSJNExyM3hWEVpK4fPCcUJdtEE8NH+j6i0VDbtQk7VJcdExOmqplnCKTIykuDgYKZMmcKcOXM4ceJES89LIBAIBI3Ew6MbYaFKDbq4Y89yKuFdZNlG3759mTJlCjqdjpSUFD755BP2lBzD56nutHqpL14TI3CK8sbvwW4Yon3AKlOw6CTmrPImXT84siPX3vMANz72DABHN/+FsVzx99M7m+g+wgOA7UtPkXK0VlRovLxwr15WLPzp5wv+Hs5FxyDHKEJCtoWO3spynY/Bx368d2Bv3LRuGK1G0kvrr4fXIGKp7h9Bs4RTZmYm7733Hu7u7nzwwQd07NiRoKAgbrvtNr744ouWnqNAIBAIzkN4+PO0bq2UEUlJ+YLYww9gsZQRERHBzJkzCQ0NxWw2s2HDBub8MJe/Nm/k8OHDZGVlgVbC586OGDp628WTbGt6FkdIdBd8Q1tjqari6MZ1lOTl8N0zj7Djl9dpFQE2i8zK/x1i008nMFcpy4Vet98GQMkff2DOyGi5L+Qsos8STnGZJbwz5B0+GPYB3436DgllmXBU21EEuym7wxsqJNwgzjURp6ZbLQiuHJolnAICArj99tv54osvOH78OCdPnmTkyJEsXryYhx8W67oCgUDwdyNJKsLbP02n6NmoVDry8tazb/+tmEz5+Pv7c88993DTTTfh5uZGUVERW7ZsYfHixXzxxRf89NNPyMh4TQxHMqgxny6jbGsTRQPKkli3kWMAOLjmd1bM/g+VJcVYzWb0+oN0GaaUPDmyOZ0/v1ZsCAxdu+LcuzeyyUT2f/7T4NgXytkRp7SCSnz0wVzX+jpau7dmZpeZ9A3sy6g2owh2ba5wqok4CeF0NdMs4VRWVsbq1at5/vnn6d+/P126dCE2NpZHH32UJUuWtPQcBQKBQNBIAgPH06PHAnQ6P8rKjnPg4N2YzUVIkkSXLl149NFHGTWqNz17diM0NBS1Ws2pU6c4evQoanc9nqPbAVD8ZwrmvMomWxVED74GvYsLRdmZZCXEozU4AXB820a6Dndn7GNdUakkkmPzSD2ajyRJBLw8C9RqSteuo2zz5hb/TgAiAlzRqByTz49lltrfP9bjMeaOnIurzpVWrkr+U/MjTmKp7mpG05xOXl5eeHt7c9dddzFr1iwGDRqEh4dHS89NIBAIBM3Aw70rPbr/yP4DUygrO8aBg3fTvdt8KiqTSEyYTVn5Vvz8Ixg69B0OHChkw4YNrF+/no4dO+LcO4CKQzlUJRST/V51IVyVhMbbgLaVCxo/Z2wlJiz5lchWGW2AMxp/ZyS1hM1oRaVX03noCPatWgaSxNgnnmPfqt9IiT3A7mWLGHHfI3QZFsKhv9LYuugUt0V5YejQAe+pUyn45hvS//UUKnc3NL5+hH31JeoWerboNWrC/V05nlUrlg6nF9OnrXedtjURp4yyJi4dihynfwTNijjdeOONWK1W5s+fz/fff89PP/3EsWMt5L4qEAgEggvGxaU93bvNR6v1prT0CDt3jWDv3kkUFG4FoLw8nr37JhPUajdubk4UFRWxe/duJEnCa1IEKhdt7WA2GUteJZWxeZSuT6V8TxZVicWYUkoo351F8cpEin5LoGRNMkXLE+jkPpDQTjFce8+D+FtD6BcxHgmJI9V5T71ubIPeRUNhZjlxWxVx4vvww2iCgrCVlWHJyMQYG0vhzwta9DvpEuwowo6kF9fbrqGlOlmWic2NpdBYWP8FaoRTZQE03+lHcJlzQT5OsbGxbNq0iU2bNrFlyxYkSWLYsGEsWNCy/7FfSoSPk0AguJIpLT3G/gN3YrEUASqCgiYREnIXqSlfkZ2zEgCNpgsb/uqKweDEY489hrOzM7LVhlxlRbbJyBYblpxKTBllWPONqNx1aHwMSGoJc1YFlpwKkACVRGWs4prtO6MzpvQySlYnA5CgPczek6vwahXC5BdfJ+Woic0LTqJ31nD7K31x8dBjyc3FePIkxqNx5M6ejcbfn/B1a5HOKCZ/IXy/I5l//1Zb4iXc35V1/xpap118YTyTlk/CXefOttsV93WT1cSr219lReIK+gb1Ze71c+tewGyEtwKU98+nKvXrBFcETXnWN2uproaYmBisVitms5mqqipWr14tcpwEAoHgMsLNrSM9ey4gO3slgQHjcXFRcpg6d/4Iv5yRHDv2HBbLYcIjgjgVH8CmTZsU/ye1Csm5dlFC42nA0MGr7gW6On4sdIqnfFcW+T8cQ66qNdpsL3Ul2fcIeRmp/Pzy00x84XV8Q13JSytj008nGPVAFzR+frj6+eHSuzcF87/HkpNDyZo/8Rg7pkW+i85nRZwScssor7Lgond8FNZEnEpMJZSaSnHTuTEndg4rElcAsCtzF7IsI0kSJaYS3LRuinmn1gBaFzCXK8t1QjhdlTRrqe6DDz5g/PjxeHt706dPH37++WciIyNZunQpeXmiRo9AIBBcTri6RNC+3ZN20VRDgP9o2rZ9HIDg4B1oNEZ27drFqlWrsFrruos3Bs8x7dAEONtFk9s1oWhDXMFk44be9+MTEkZZYQF/fvERw6d2RKWSSDqUR/yebPsYkk6H95QpABR8912zaunVR3SQO+ozEsRlGa6bvYm9yY674Jy1znjpFZF4KPcQVdYq/kz+06FNbmUu61LWMejnQXwf9/0ZnauX607+CeYmGmgKrgiaJZx+/PFHIiIi+P7778nPz2fPnj289957jBkzRixnCQQCwRVEaMjduLpEYrOVMmBgJgC7d+/m+++/p7y8aUaYAJJWjc+UKLQhrrhdG4b79a3xmhgBEpiPl3DjoEdx0rmRnRhPVVkKvW5sA8DGn05wfGemXSR53norkk6H8cgRCn/40eEa5qwsLIW1eUZVSUnIjRB6Bq2aCH9Xh2OZxUamzN1FQbnJ4XiAi7Lk9uC6B7lt5W0klySjUWnsZplJxUl8GfslMjI7MnbUdqzZWbf6Odj03/N/YYIrjmYJp7179wqhJBAIBFcBKpWWyMjXAZDlrUycGGN3Gp83bx6VlU2PmmgDXAh4pDseI1ojSRK6YFfchoYCULUrjxtD7yPIqR37fl9Gjxta0yrCE7PRyvpvj7HmqyPYrDY03t74PvIIANlvvUXRsmWAYpR56trrSBo3HmtREXlzviRx1Gjy537dqLmdnSAOYLLY2J7guFrS2r21/f2polMA9AroRWffzgAsT1jOsQJlU1RqaWptxzOiTKf3fkWFuaJR8xJcOTRLOAEUFRXx/vvvM3PmTO69915mz55NcXH9OxSuRD799FOio6Pp3bv3pZ6KQCAQXFQ8PXvRKugWACoq5zB9+u24u7uTn5/PokWL6l22k2UZm83W6Gu4j2yNz9RoNAHOqG0auvtcy6m9uyjJzWL8E93oN6EdKrVEwv5cUo4qS2c+987Ea+pdAGQ+/wJJN99C+tPPgNWKJTeXjBdfIu/TTwEoWb26UfOICak/7+h0YSXLD2Xw8I/7+WpzIlOi7uS6sOsY136cvc3QkKG09WgLwIqEFfbjGWUZmG1m5h2Zx92uNopVKmL1OsYEevPv7f9u9HckuDJo1q66vXv3MnLkSJycnOjTpw+yLLN3714qKyv5888/6dGjx8WY6yVB7KoTCAT/BMzmQnbuGoXJlIuf7wh8fV/mm2++xWw2069fP2644QZ7W6vVyoIFC8jJyeH2228nMDCwwXELCwsxGo0EBQUBYKuykvHGTrDYWJP+DW2u6c219yilYrb+Gs+h9WmE9/Rn5L1KZEe22cj5738p+PEnsFgAcBkwgPLt2+tcK2L7NioPHEA2W3C/YWS98ymuNDP8vY3kn7U0dzavjI1m+sC2VFmruGn5TWSXZ/PbhN/YmbmTV7a/Ym8nISEj89m1n/HQ+ocAeMHqxl+mXHY5GQA4NPUQKqnZcQrB30BTnvXN+pd88sknGTduHMnJySxZsoSlS5eSlJTEmDFjeOKJJ5ozpEAgEAguIVqtFzFdvkCl0pGbt5ZK4y9MnDgRgJ07d7J79257202bNhEfH09xcTE//PADBQX1lxgxmUzMnTuXOXPmcOqUstyl0qtxilISr0Ndojj45yritmwAILKvIsCSYvOoqlREkqRSEfDCC4SvX4/fE4/j/8zThM79Co/x42omjqZauBX98gunH3mU9CeeoCopqd45eThpWfHoINwM595UvjZOSVbXq/X8OPpHlk9YTivXVvaIE0CkVyThXuEAzNo2y378SPgQMjRq++fk4mQeWvcQIxeNpMxUds7rCi5/mp3j9Nxzz6HR1P6Hp9FoePbZZ9m7d2+LTU4gEAgEfx8eHt2IinwLgOTkT/DxSWLYsGEArFq1ig0bNpCcnMyWLVsAcHNzo6ysjPnz51NaWlpnvP3799sTzBcvXkxRUREATjF+AIT79gBZZvWnH3Bs60Z8Q13xCnTGaraRsD/HYSxtgD++DzyAz4wZSCoV/s89h+t11xL0yr9xr46G5X7yqd14smTFChqilacTr47tdM7vIiG3VuB46D0IclUiZm3c29iP3xp1K63dlFyoAmOteFyR9Dtp2loD0V1Zu9iSvoWM8gy2Z9SNlAmuLJolnNzd3UlNTa1zPC0tDTc3twuelEAgEAguDUFBkwgLmwlA3LFn6dHDl8GDBwNKpGn+/PnIsky3bt2477778PT0pLCwkC+//JIjR47Yd8VZrVZ27FB2m+n1eiorK/n111+xWCwYoryRtCp0Fj29B45Hlm2s+t97bPphHuG9fAE4uTvLYV5Wq40tC0+y8acTyDYZjbc3oZ98gufkybgMHKA0ql7KAyhevuKcNgYhXk7n/B6yS6ooNZrrHPcyeDGg1QDCPcO5se2NhLqH2s9FekXWuyR3Zj7UptObuGfNPVz7y7UkFCWccw6Cy5NmCadbb72VGTNmsHDhQtLS0jh9+jQLFixg5syZ3H777S09R4FAIBD8jYS3fxYf7yHYbEZiDz/AoMFdGTNmDJIkYbVa8fLyYtSoUbi5uXHXXXfh7e1NaWkpixYtYv78+eTl5XH06FGKi4txcXFh5syZGAwG0tPTmT9/PuVVFRg6Ktv2Y9oPp8fo8QDsW7mUE1s/RZYtpJ8oYsl7+9i5LIGsxGJWzzlC7IbTHN2cTvJhxx1wzj17QnWER9euHSoXF8ynT1O5f3+99yebzfj+/gttis9diy4ht347hjkj5rBk3BKctc6EuIbYj98VfRe+Bl/751FlSv/DeYftx5YnLGdP1h5yKnNYk7zmnNcXXJ40Szi99957TJo0ialTp9KmTRtat27NtGnTmDx5Mv/9r/CtEAgEgisZSVLTqdNHODu3o6oqk8OHH6RHjy7ccccdREdHc+utt6LX6wHw8fHhwQcfZNiwYajVahITE/n8889Zs0YRBX379sXPz49bbrnFbnMwZ84cClop0aGyrRl0tvZn4p0vondxIScpHp8gpUZc5qli9q1OYfE7+0iOrRVLB9Y6rnionJ1x6dNHmc+Me3C7/noAcmZ/QFV1bhWAzaQkhBcuWIjp0495dv/P5/weEnIazkeSJMVIM8StVjiNaD2CKR2nICHxtFsnHi8sOuf4xwuOn/O84PLkgmrVVVRUkJCQgCzLhIeH4+zs3JJzuywQu+oEAsE/lYqKJPbsnYjFUkpIyFQiO7xyzvYFBQWsWrXKngiu1Wp58skn7c+GvLw8Fi5cSG5uLhqNhpE+fQlKUXaeIUFep0LWr/gSv9ZtGfuvt8lKLCblSAEpR/ORgLCOpzm5KxW1vi+DbokgLa6A8F7+RPULwpydg/HoEVyvuQbjkaMkT5kCZjNIEu6jRiFbrZSuWYPvo49Qtv4vjHFxANw//Gn8KwtJ8Aim0OD4O/6hYe159oaoc96zLMt8EfsFnXw6MSRkCLIsU2Yuw+3gAlj1NLe0i+SYXL8XVpBLEH9O/rPec4K/l6Y865sknCoqKnjmmWdYtmwZZrOZ6667jo8//hhfX9/zd75CEcJJIBD8k8nL38ihQzMAFX37rMTVNfKc7WVZ5tixY+zYsYNu3brRs2dPh/NVVVUsWrSI+Ph4JElizLWjaZ3ogjEuH5WPnsWx72EyVXDrq28T0lGxJLCYrZQXFjH30bsB0LlPR6VWdua5eRuY+n8D6szDeOIkeZ9+SumfZwkTtRrO8KUq1LviVVXG9sBOvNFvukPT66MD+HJqr0Z9T3U4uQZ+uoV5wRF8oKtyODW903S+OfoNAFtv24qHXtS0u9RcNDuCV155hW+//ZYbb7yR2267jbVr1/Lggw9e0GQFAoFAcPni6zMMf79RgI34+P87b904SZKIjo5mxowZdUQTKInit912G926dUOWZVZv+hPXca1RuWmx5VcxOPpmAA6sXmnvo9GqyUk+UTuIrbauXWmBkfIiR2ECYIjsQMjHH9F26RLcR4/CffRonPv1s4smVfXD0atKWY4bkHW0zhhn7qxrMn5KpGpk7mn7oef7PM+49uN4sNuD9kLCYrnuyqNJwmnJkiV8/fXXfPnll3z88cf8/vvvLFu2rNnFIAUCgUBw+RMe/iySpKOgcCv5+RsveDy1Wm0vFG82mzmRcgqvCYofkm9JIK4aL+J3b6cou3ZnXfrxWmET3gNu+3cffIKVunNZiQ1XrTB07Ejw7NkEz36foNdeRapOIg948QUsakcvJ7+KQh45uJi21UnjKfkVmK2Nd0d3wDMMdG4Emyr5sPvTfHjNh9zR8Q7eGvQWThonon2iAfjkwCeUmupaOQguX5oknNLS0uzbUgH69OmDRqMhI+PcOxMEAoFAcOXi5BRGaKiyTBZ/6j/YbHW36TcVSZKIiYkBIDY2FqdOvhgivUCGHu1GIttsrJv7qT3CdfpYnL1vSW4qPq1cCWynRI3OJZzORNe6NcEffYjfE0/gMW4cxRNuJ94j2H7+8bjl3Ji8gycO/AKyjMUmczK7maJGkiBAEUfXSi5cG3atw+mZXWbipnPjYO5B5h6ey7qUdZisJk4VnkKWZVYkrGDYwmEczDnYvOsLLhpNEk5WqxWdTudwTKPRYDnDO0MgEAgEVx9tWj+EVutNRUUCh488QmVlGrIsk1+wlaNxT3My/k3y8v7CYmn88laNcEpMTKS0tBRTjDPZUhFBtEGvcyEl9gBxm//CVFlBTnKt51F2UgKyzUZgeyU3KCuxpNHXdBs+HN8H7kdSqejxyrP8d+KLHPZpB0CPTEWcdSg6TWRhKs7mSkr+9TiFCxY0enwH/KOrJ1x3GTDaJ5pnej0DwLwj83hy45P0/KEnE5dP5Luj3/Hi1hfJN+bz/Jbnm3dtwUXj3J7zZyHLMtOmTbNvQwUwGo088MADuLi42I8tWbKk5WYoEAgEgkuOVutOh4iXiTv2NHl56ygo2IzBEEpFRa2gSUv7BpXKiajI1wgKuum8Y3p7exMSEsLp06dZuXIlCQkJWPUWJlb15ZqBd7N6w2ds/O4rrBYzss2Gm48flWUlmI2VFGSmE9jWB4Cc1BKsZhtqbdMcdpx1Gv54YjDH0lbB+kSkM9JOxiRtJ94zBM/Du8hLT8TrttuaNDYAAdXu5Dlx9Z4eFDyo3uPv73vf/j6jTKzoXG406b+yu+++G39/fzw8POyvO++8k1atWjkcEwgEAsHVR2DgOPr0Xom310BsNhMVFQmo1c6EBN9Fq1a3YTCEYrNVEnfsWY6f+Ddmc9F5k8lrok4nTpzAYrEgAyfVmXiX+BLYtgPG8jLWfvUpACHRnfFv0x6A7MRTSFIpemewWWRSjuSTsD+H/X+mOHg+nQ93g5bwXp3tn+Vqf6ah6Qe5MUlxPrfk5mJpoB7fOakRTtn1Cyc/Z7/zDiEjsyltE2/tfAtzCyyRCi6cC/Jx+icg7AgEAoHAEVmWyc/fSKXxNIEB49Fq3auP20hK/oSkpI8B5dGiUulwcelAaMg0AgLGoFJpHcYqLy9n9uzZWK1W2rZtS1JSEk7ouN04EI2/ExsSfyQrV4lqjbjvEfLSUjjwxwq8goIpzEzH1acjFtuoOnPsel0oAyeFI6mk895P6caNnH5A2SEuR0ZzKN9Et7xTDm3CvpmHS//+TfuiSrPg/UiQ1PByLqjUdZrMPTyXj/Z/RLhnOKeKTiEhIVP/Y/nlfi9zS+QtTZuDoFFcNDsCgUAgEAgkScLX9xpCQ+6yiybluIp2bR+ja8xXGAxK0rXNZqK09Ahxx55mx47hFJccchjLxcWFu+66iylTpnDnnXfi7OxMJSbSnYqw5VQx1H0yvQJGYtC4Eta5G4HtIgAozFTcxcsLTiDbKkECvzA32sQovoKH1qWx7IMDnD5ReN770bdrZ3/v2aMr33SbgBVHwWU8ceLsbufHxU8RTbIVynLqbTK903QWjV3EknFL2H3Hbt4f9n697cCxkLDg0tGkHCeBQCAQCM6Hr+81+Ppeg9VahcmUTXb276Sd/hZjVQaHDs2kd68lODnVFsdt06aN/X1MTAw7d+4kNbySCJMXxhOFtHfuRnuv7rhqPAgM72BvqzU4YTZW0mOEje6jBuHkqmxeOrEri7/mHyMjvojfPjjAuCe6ERrl3eB8tcHBSFotstmMc9cYPLL8WZ40iIkJW0jwaEX74gyqjjdDOKnU4BoApRnKyz2oThO1Sk2kt2Iq6qRxoldAw4abhcbzi0DBxUdEnAQCgUBwUVCr9Tg5hdGmzYP077ceN9dOmM0FHDw0E7O5/p1w3bp1A+BkYjy6m8LwndEZTYAzVMkULIrHKzCY62Y+xI2PP0uvMRMAyEk+aBdNAJF9A7nz9f6EdlTcxZMOnjvnSVKrcRk8GJW7Oy79B9AjzIsvO4/jrutn8VPkCACKly0j9Z57yJg1i8rY2MZ/CTViqSSzUc29DF4NnksvS2/8dQUXDSGcBAKBQHDR0Whcien6JXp9IBUVp4iLe6rexPHAwECCg4OxWq0sWrQITVs3fKdGI+lUmJKKKd+ZSdcRo4kaMITw3krOUcqhA5iNRodx3LwNRPVXREtOyvntCkI++pCIDX+hDfCne5gnSBJ5zp4kerSytynfvoPiRYtJf+rp8ya9106kWjiVNk44AdzZ8U5AyWnyNtRGyoRwujwQwkkgEAgEfwsGfSBdY75CknTk5f9FZubiettNnDgRvV5Pamoqy5cvJz4nGctgJRJT9HsiZdszkGUZv9Zt8QgIxGI2kXRoX51x/Fsr+Vd5aWVkxBcy75ktHNtev4CRtFpU1bY63cNqoz7Zzl4Y1UpCu7ZrNySDAXNaGuVbt1GyejWy7TzO4u7Vwquk8bYCT/V6igU3LmByh8n8OvZXHur2EKAIJ7Gf69IjhJNAIBAI/jbc3KJp3+4JAE7Gv4HRWFdQ+Pr6MmnSJEBxFV+4cCHf7VhESYQKrDJFyxMoWKDkHNVEnY5uWo/VYmHRWy+z8LXnsVosePg7oXfWYLXY2PDDCSpLzRzb1rCAKcmrZNns/VSdLqeVhwEAWVLxQfdbWRgxnEd7zsB54EAA0u69l/QnnqRo0aLz3HDTI04alYZOvp1QSSr8nf25p/M9AFRaKimqKmr0OIKLgxBODfDpp58SHR1N7969L/VUBAKB4KoiLGwmHu7dsVrLiDv2LLJcN2oTGRnJ5MmTad++PV5eXthsNjabD+M0KpR92kRiD8dSsT+HmGtvAEkicd9utvz0LSmxBzgdd4TEA3uQJAm/MDcAirIrAMhJLcVqqT9KdHRLBukni9j3RwrdW9dGnTaHdOPbTqM5VlBFQY8BDn2KFiw89802I+J0Nnq1Hn9nfwBSSlKaPY6gZRDCqQEefvhh4uLi2LNnz6WeikAgEFxVSJKa6Oh3UamcKCzcQXLK59hsFhKTPuZo3FNUVp4GoHPnztx1113MmDEDg8FAVlYWX+/6hQPqJDZrj5Gx4hgebn6E9+oLwL7fl9mvcWTDWqB2ua4Gq9lG3un6y8JkJyu5UNlJJXRr5Y5ahr5GDcGW2kflqfZdQVX72RgXR+XRo4pJZmE9u96aEXGqjwgvxYbhZOHJCxpHcOEI4SQQCASCvx1n57ZERr4KQGLih+zbfytJSR+RlbWMXbtHkZL6FVUmZTecq6srI0You9sqKpTIkSzJnDKnU/x7Ir3H1ZZ3cXJThFLSgb2UFRbg38atzrWzk+oWBZZtsj2J3Gqx0cXJiUnlOoYYtdxQUWvaebJCRchnnxL09n9wH60Yb6bePY34IUNJGjceW2Vl7ZhWK5lfLKXghEujd9U1RJRXFADHCo5d0DiCC0cIJ4FAIBBcEloFTSYwcCJgo6TkIGq1M+7uXbFaKzh16m22bu3H/gN3YbVW0L17d3r27EnHjh0ZMmQIAPHqTCoO5eLv3YbQ6C4ADLnzHlp16Ihss3Fo7SqHiFP7HkqJkzOLApcVVrH844Ps/C0Rs7G2Vt2+b0/QxqI4fXvbVGirc7ITcspwGzYMzwkT8H3kUbQhIdjKykCWseTmUpWQaB+jYu8+ipavIfuQO7aKMjDWFWyNJcpHEU4nCprhJyVoUYQBpkAgEAguGZEdXqOyMg2rtYxO0bNxcYkgI2Mh6RkLKS09TGHhdnJz1xEYOI6xY8dSUZHEkaPP0iGylJMn+pFPKS5bTjP2qRfJS01WBJQsk3HyGDsXLyA3JZmeN0xCpXEisJ07CftzSY3L56fXdhHZN4DCrArS4gpIizvLlfuszWv+VhXpGhsJubXLfPp2bWm/ZjWVh2JJf/xxLLm5FP7wA9mpqQS89CKVsdUu6TaJynwtLgVJ0Kpbs76nmojTycKTWGwWNCrx+L5UiIiTQCAQCC4ZGo0LPXssoG+f33F1jUSSVAQH306f3sto3VqpH5ebp+Qr5eVtYPeeCZSW7icgIB69vox4dSbl+7JRF0KrUMWBu9PQa+k/6jY0ah0Je3diLNlGnzFtCWjrARJUlVsozCxn57JETuzKcphPSFRtUnhE7wC8w5WIlb9VKcGSWlBBlaU2MiWp1UidY0iP7A4oRpmV+/eTfNNkKvfWWiRU5OqgMKnZ31OYexiuWleqrFUcyxfLdZcSIZwEAoFAcEmRpPoL8fr5XgdAfv5mKivTOXzkYazWMqiuI+ftnU6CNhubxUrOpwfJfGsX+T8dI//7Y4Qdb82EAU8BcGjdaipKitE7aQhq74EkQVB7D+UiMngGONuv2XFgEH5hbngGODPktg4Et1Pa+VmVx6VNhpT8Cod5PvrzflYU6jibsk2b7O8rcvVU7VhByfcN16I7FypJRf9WivXCd3Hf8dC6h/gq9iusNut5egpaGhHrEwgEAsFlibt7DDqdHyZTLkeOPILNVoW7e1d8fYaTmPQBfv6ZZGZGku5WQmurL9YKM/uOHgRkOhKCOt1GaNvOpCUdYdvC+XQcOIzRD3aiKCuXtV++i7t3Jyorohh1fxf2rEoiJ7mEsGgfInoFINtkVGoVwW08OEwa/tbaOENCThkdApSk89zSKtYdy6Gvq98576UyT0v63M1UFe1AG9oap2smN/n7GBIyhLUpa1mTvAaALelbsMk27u96f5PHEjQfIZwEAoFAcFkiSSp8fYeTkbGQklKlPlzbNo9icAohMekD3N0zUanMpIZX0O+Wfvy+eAV7jyjJ055eXrTKc6V357GkJR0hdt1qYtetpuuIUWh0OnKSE/EMrGTGe/ei1qq4fkYnh8iXpFbe+4a6AuBnlZBkcJUlYhclcOjXBLb62PD3cAIgzc3f3tcU1Rnf8DaUrFyJISaGqrijyBaoKlLEl+nwrmYJp8HBgzGoDRitteVl9ufsb/I4ggtDCCeBQCAQXLb4+Y4gI0MxmXR1jcbHZxgAToYwKo2peHplceKEnl9//ZW4uDh7v53yCSbQHaccJ6IHXUPGqeMUZWVyZOM6dAZF7BRlZVJVWYqz1qPB5UJ3HydkjYTGAoOMGrpVaTCUmADQlpr4S18KQJZzbU25MjdvYv7zf7j064tT9+6k3XsP5owc+3lL7rmLDjeEj5MP80fPxypbKTIW8cC6B8irbN5YguYjcpwEAoFAcNni5dUftVrJQWrT5kEkSUKSJHx8rwEgNDQbm81mF03XX389zs7OFJQXcUybgSWnkmsn3Mc9H35JYHgHrGYzlaW1dgSZ8efe3i+pJMIHKSaW/aq0GJAolZQtd/2NWnTVu+9sKrW9T0pwBJJWi+fkyejbt0cbHOowpjkru9nfR5R3FJ18OuHj5ANAfmV+s8cSNA8hnAQCgUBw2aJWG+jc6SPCw1/A3+8G+/EA/9EAuLsfIyzsECEhwcycOZMBAwZwzTWKqDpqOI2MTNnOTCRJoufo8bUDV0eYMuNPYLVY2PbLD6z86B0sJlOdOYy4uQNVHsoCTabaxjduRgpUNlxkia5VtQs3zwx6kPlR17MhcrBDf21omMNnS149DuNNxMegCKfCqkKRIP43I4STQCAQCC5rfH2H0zpsJpJU+8jy9OxF+/bPAtC6TSy9e+/G3V0RJN26dUOv11NiLidTVUTFwRyspSbatuuBh38AkqQiZvhIABL37+aX115g5+IFnNi+mfg9O+pcX61WMebhrvzhZOJXlyqqVLBbb1GuZVJTHYDiiG97foq6nqRCo0N/batgh8/mwnIAZJsN4/HjyNamCx8vgxcSEjbZRmHVhQsxQeMRwkkgEAgEVyRtWt9PRMQsQEVe/l/s3XcTxcUH0GhUdO2Wj7NzEQluuWCVyf7fAXI/OsjYPo8x5c336DbyRgByU5LIOFnrixS/c1u914oIdSffX0tV9VPzmM5KpSTjaVPR3uz4KE3Nr8Bqq3XQ1AY7CidLdY5UxnPPkzRhIsW/LW/yvWtUGrwMiueUWK77exHCSSAQCARXLGGh0+nXdw2enn2RZSvp1a7jev1SIjrsINGSiRkLtmqxYj5agluxB7oEFR5aX/s4ox9RPJ+SDu7DbDTWuY4kSfRpW5sAbpEgVqdEnQYatXhbJXyqTTJNVhtZJbVjaFu1chjLUgHlW7dQsmIFAGUbNjTr3u15TkYhnP5OhHASCAQCwRWNi0s72rV9DIDc3D/JyFgAgJtbPjYqOOyfha61O04xilAq/PUkpX+mMqz9FFw8vZjy5vtEDRqGR0AgFlMVSQf31nudM4UTwB69hSqVjL9NxYxSA9NL9Qyo1OBnlVj1n32c2qfspNMGtzprJIncT/53xuez6rs0kpo8JxFx+nsRwkkgEAgEVzyenr3R6fywWIopLT0KgCTJeHhks7/kBEk9jHhNikDtXuvwbahyYuZ/vyIoIhJJkojoMwCAIxvW1nuNvm19HD5XqiD8uhD7ZwmJgVVappUaMBWbWPPVEQC0AQGgcnzcVh48bH9vzsmhOYiddZcGIZwEAoFAcMUjSWr8/W+oc7xzZy0Af/zxBxu2bcLvoa74PdgVXZji/G08UZtYHXPtSCRJRdLBfaz98hO+euQeMk4et59v7+dChL+r/bOvq44RY9pz3bSOpMW4ckpTN8nbXGVF0mpxHTYMbUgIhoC6pVks2c0TTv7Oiunm6bLTzeovaB5COAkEAoHgqsDf/0b7+wD/MQC4uKQxeLBiD7BlyxYWrV4GgXoMHZVlt7JtGWS9v5eSv1LxCgqm4+BhAMSuX01Jbg67f1tkH1OSJD6+vTuDI3zp08abz+7oiZNOQ2S/IIIjvchT2+rMKSuhGICQTz+h/ZrVGNrULc1iyc3FnJ6OLDdtya6TTydlrrmxTeonuDCEcBIIBALBVYGnR0+8vAbg4dGD8PDnASgrP87gwd2ZOHEiarWa48eP880336AOVyJOlpwKLLmVlPyZQumWdLp7XounvrZ8SuL+3ZQX1UalOga5M39GX355oL9DzlO4vyv56rrCZ/nHB9m1PFEx7lSr0Ue0t59TubkpS3hWK6euvY7iJUuadL9d/boCcLLwJJWWyib1FTQfIZwEAoFAcFUgSSp6dJ9Pr56/YjAE4eLSAYDCop3ExMRwyy1dcHHVkpWVxYGUo6i99ACoXJTlvOLfE7EcLuH6bjMZ/8zLBEVEIttsxG05/663iABXClR1I04Ae/9I5r55u5ny1U6ygjrYj+vatEHjU5s3lfnSrCbdb4BzAP5O/lhlK3H5cefvIGgRhHASCAQCwVWJt/dAAAryt5Ce8TOn05+if78UAHbs2IHbze3xGNOOwKd7ofF3sveTcqy0jepO52tGKG1//YmDf67CWF7W4LVa+7hQoq39XCLZCOxVbXcgw/Gj+WxPyOfZU572Nmo3NyXqVI3Gr+4y3rmQJIlo32hAiToJ/h6EcBIIBALBVUlNQeC8/I1kZSrLYGbLNry8nCgvL2df2hFcB7ZC5aTB/+FuBD7fB22QCwDGYwVEDx5OWOeumKuMrP/6M764704S9u2u91patYpWfi72OnZ5apmnTqVhCVEEWbBVedwekQPsfWwlBZhSU+2fZbOZsk2bsBYXY8nPp3DBAsq2bCXztdewlZfXe90gF6WOXk5F8xLMBU1Hc/4mAoFAIBBceXh59kalcsJkysFkUoSFzWakVy9YuxY2bNhAQkICI0eOJDg4GJVeg1MnH8yZ5VQcykXf3pObXnqd/b//xsG1qyjOzmL/qt9o3aUbJ3ZsoSg7k74TbkGjU3bKRfi7UpBYgJtFTaFKEVAb84u5Dh0DjBoy1DZStDZkVwmpTCY92I/KQgvB6acAsBYVkXb/A6jc3LCVljrci6RSE/hy3aW8AGdFiGWXN79wsKBpiIiTQCAQCK5KVCq9fblOQXH21hv2M2zYMDQaDampqXz11Vds3LgRAKfOyvJa1akist7ZQ9HiBHqMHM/Ns94EIPXIIeY8MJXVn33AzsULOLR2lX30qEB30jRKntPpamuC9OqddiokbinX08+oYWf/WwjoU0yaSzazom9mVZt+DvM+WzQBlG3eXO891lgSZFcI4fR3IYSTQCAQCK5afHyG2t+Hhk4HVBQX76VHDx8effRRYmJiANi4cSPHjx9HE+CMS59AJXFcgop92RQsOIGHfyCtOnQEcMh1Sti7y/6+Uyt3dugtfO5eyUmtIpxy1TJZZ9gUDDZqqTQPISuwH7fpNvKEx2IWdbuGIrfaHXouA/rXuQ9zVhbWsrrLdYEugYCyVGcrLyfr9Teo2Fu/87mgZRDCSSAQCARXLb7VeU4AoSFTCfAfDcDRuKdASqZ3n3IGDDyFl1c6S5cuZc+ePbiPb0fQc33wvaczAMbj+dgqzEQNqhVho6pr250+fhRjmSKkOgW7gwSBlQnclzKP6NI4ZAnmu1XxkUelPf8J4IRR8Za6TbORezW/41laYD8XNm8e4Zs2Ot6I2UzFbkWkWYuKsFXX06tZqisoySJ79mwKf/qJlDvvutCvTXAORI6TQCAQCK5aDIZWRHd8F5BwcgolMvJVior2UFGRwN69kwBQq6FTZ4n4+ApWraoiKSmJm2++GUOEF5oAZyzZFRhPFtJ52HVkJ5wiLKorkT0GsTvkV/JPpxK3ZQOmygpUajVeJiOjcv4EoEfRQeLclF1vJgl+cDPS3qzm+kodGaYuVFg9cFYXM1h1mMXtBjE+cStu118PVJdpOYuKXbsp27KFogULMcR0oc2CBfg7+9P9lI1HV5RRZPzJ3rbghx+pOnGCwNdeRVKJGElLIslNtSr9h1FSUoKHhwfFxcW4u7tf6ukIBAKB4AIpKNjGodiZqFR63N27oZK05OX/BcCJE0PIyW7N8OHDGTJkCMV/JFG66TRO3fxwGxJC+e4syvdmodKrSW6TwI6VCxzGLlc74WJVzCgrVE583XpanevfWaonyKoixSmFdzyeAGBg2YfcrivjoX/fi8pgACD/m2/J/fhjvG69lYJvv60zTtg383Dp35/N/TrhV1S/h1Tol3NwHTIEgKr4eLJeex2/f/0L5x7dm/PVXbU05VkvZKhAIBAI/lF4ew9kyOADDBm8n+7dviUm5ktCQu4GIDJyB76+yfz113p27NhhL81SeTCXnI8PUL4zEywytnILUd59iRwwBEmqfZS6WCuxVSehO9sq0VuNda5/UGcBwMcYRqw1HID7Db+zJaynXTQB+EyfRtSB/XhPn1bvfRT+vABrSUmDogmUci41ZM56mYq9e0mZMsV+TLbZmlzq5Z+OEE4CgUAg+MehLgsH2AAAQPRJREFUVhvsgkeSJDpEvISv73WAmY7RW+jcZR1r1vxBYkUGKrdqZ0u1hFMXX1wHtAKgck8u1914Lw989QOPfPML7n7K8tomv6GUaBRjy2H5W+hVuA/fqjz7teN0VoolG66yxF8VkwGYqllLv4Kl9c5VGxCAITra/tn3oQcBqNi7l6oTJ855n7byCvt7c2bmGcfLsRmNJN44hrQZM8/7fQlqueqFU2lpKb1796Zbt2506dKFr7766lJPSSAQCASXGZKkpnOnD2nT+kFUKh1eXlm4uhZy/MRxfKd3xvvWSFq91BefOzriMaYdGj8nZKOVvLlHMO8oQO/szC3//v/27jtOqurg//jnTp/Z3dm+yzaW3vsCKoKCHXssIc0S0TwmJI/Gx+jPJCZ5EvP4xMRooogm1scUe0mMDRUFMdKrKHVpW9nep97fHwOzjDMrCwK7wPf9es3Lufeee+fcfTnO13POPed/+MrtP+fu/3c9qXlFAAxp3cIpDUu5ouJlHGEfAGEDVjojT93VdEzk9dBkAKb4FnXZ+lM490G8F16I9+KLyLhuNlithOrqaFm4CIBdWYnvK1ideJqC1mXLaHn/A/ylpbR+9BFmMHjIf7sTzXEfnDweDx988AGrV69myZIl3H333dTW1vZ0tUREpJexWt0MHHgrGelTAUhNrWLHjh048pPxjM/B4om0PBkWg8xrRuIclAaAb1tjpHxOLgMmTGJ0YRoTxw+PubbdDNK3bVdkwzT51BEkjEleyMo837cAGBneRX1TZA6nysYOvvrwvxl+55vc8dI6rLl9KPjdbym45x6syUk4B0W6+BpeeAGAZUMMnvr5yaReemnM5wYqKgEINTcTrO6cXdz36af4tm6JbocSzB0liR33wclqteLxeADo6OggFAqpP1dERLqUln5S5J9pVTQ0NFBRUcGe/cYKAdiz3KReMACAQGVr3O9Kak7nU3EbkocB0L9tO1Pq/s0NO58g2V9F6d7JMge05/FS/c94as/TrH75Y9j4Bo6HJhDa8W/aAyH+vnQnb35SGXP9fV13ofp6AD4rNPjEVYN77JiYcoHKyHlty5bF7O/YtImOteui26GGhm7+daTHg9PChQu56KKLyM/PxzAMXnnllbgyDz30EP3798flclFSUsKiRYsO6jMaGhoYO3YshYWF3HbbbWRlddGmKSIiJ7z0tEhwSk2rBsI88sgjzJ07l9WrV8eUs2e7wWJgdoQINfpoW11N3XMbMQMhhk+dTubIEt7OOoMNKZHgNLh1KyWNq3GFfVxc9RrLHZGB40MCNip8kafcNi6BxqdvIsNXxh8dD0Y/685X1sd8tnu/p+JsZ0xj9QCDspYyrIUFMeWCFRWYwSCVv/wVAI7iYgB8mzbj27o1Wi7c2Hiof64TTo8Hp9bWVsaOHcuDDz6Y8Pizzz7LzTffzE9+8hNWrVrFtGnTmDlzJjv3WxixpKSEUaNGxb3Ky8sBSEtLY82aNZSWlvK3v/2NqipNTS8iIomlpIzAak3GavWTnFwf3f/qq6/y+uuv09TUBIBhs2DPiSzi69vUQN0zG2lbWU3b6j043B5O/95tbEwZSqUzlzaLGyudT7+5wn6SfZ+xce8M460WH25LA2HTxsrWy2gM9iHF7BzYXdvqj6lj2iWXkHf33eTf8xv6//6PuO0eAuEAH5qbY8oFysup/fOfCVZWYklJofDheQD4S0sJ7N4dLRdScOq2Hg9OM2fO5K677uKyyy5LePz3v/89s2fP5vrrr2f48OHcf//9FBUVMW/evGiZFStWsH79+rhXfn5+zLVyc3MZM2YMC7tY8wfA5/PR1NQU8xIRkROHYVhJT4sM2E5PLyc9vYxx4+sxTZOlS5fy9NNPEw5HQpCtTxIA9S91Bhb/rsh4oX6ZHkbmezENCx9mdC6jsm+6gvyOSt5wd/APj59HU8LUJa8CYEP7OfylZh6ftJ8XU6/d9Z1BynA4SPvKpaRefDE2l4uJuRMB+PGW+wgbsfez5w9/BMAzcSKOfv1wjR4N4dgpDBScuq/Hg9MX8fv9rFixgnP2zqS6zznnnMNHH33UrWtUVVVFw09TUxMLFy5k6NChXZa/++67SU1Njb6KiooO/QZEROSYlJ19LgD9+u9i1Oj3SEl5jSuvnIjb7WbPnj2sWxcZH2TfG5z259u+t0XKMHj0mol86+S+OIZOYl3KCOrs6bybNR2A4S0buXHHI/gDW/AbsM7qjrnO6uavY9lv6NSKHfV05ZT8SDALWQ1q9s7fmDZrVkyZpJNPwjAMcu+4I+78UIOCU3f16uBUU1NDKBQi93NTz+fm5lJZWdnFWbF2797NaaedxtixY5k6dSrf//73o4s6JnLHHXfQ2NgYfe3atetL3YOIiBx7cnLOw2JxA51PYXu9tUyZMgWABQsWUFVVhT2vMzi5R2YCEKxuo/K+FXRsqicv1c1dl47mvq+N5/2s0/lr4dco9fSP+awza97HGg6yp8PNeM/z0f1h7JzWYYO94an+c911+zu9sHMdvcqMyE+7Z+LEmDJJ0yIziLuGD4s7Xy1O3derg9M+hhHb7miaZty+rpSUlLB69WrWrFnD2rVr+e53v/uF5Z1OJ16vN+YlIiInFpstmZyc2K6ypua1TJ48mZSUFBoaGnj44YfZGa7GPS4b73n9yPjWcKypDgCCVW00v78LMxim7tmNuN/YQfLe67itLoqSOsOLO9zB93b8mTPL36WtdSmXpP+MaSmROQcn+eyM9VsBaPWHuqxvX29fnp75NAB/mW7g/Y/ZpJx1Joa7sxXLOSAS2Cxud9z5Ck7d16uDU1ZWFlarNa51qbq6Oq4VSkRE5HAqyP9azHZT0xqcTiezZ89m8ODBmKbJy6++guXcXLzTizAMA/fo7Gh5/65mGl7bRtuqasxP63iUZC7Gzh/wMCXnEgYkx/d+rGnIIxDYzZik1zkp+S8ATPbZMExo9X3xJJXjcsaRYk9hex8D37cvw+J2k3/33RguF4UPPvCF52o6gu7r1cHJ4XBQUlLC/PnzY/bPnz8/2lwqIiJyJKSlTWTSxFc4+aS3AWhrKyUQaCQtLY1Zs2ZRUFBAR0cHH3zwwd7jbbSPc5L9nTFgNTAD4cjadnsVYuE23Awk0oLkypyB5/KbKXf24b3M01njHQ3A0trI2NqxSf8kZPhJC1sYFgwS2N3KpqVfPEwlNynSqFDZGinnPe9chq1eRcpZZ8WUy/zOdwBwDIjMRbVvPig5sB4PTi0tLaxevTo6P0ZpaSmrV6+OTjdwyy238Oijj/L444/z6aef8sMf/pCdO3dy44039mCtRUTkROD1jiYpaSBud18AVq2+mrq6xRiGn6nTIrN3l5aWYpomf/3rX3nk0T+zovITHIUp0Wu4x2aT//NTeJnYMUopVgeewkG8mP8VPvGOYEXqOExgd1sqa+r7YMPPcOeHAEwNV5O2vJH5j2/g548sp6KxPWF99wWnqrYvnnYn+z9/QL8XXyD39tsACGpFjW6z9XQFli9fzowZM6Lbt9xyCwDXXHMNTz75JLNmzaK2tpZf/vKXVFRUMGrUKF5//XWK907idaTMnTuXuXPnEgp13acsIiInBq93LO3tO2luXs+q1Vdjt6cTCNRTUDCZsrKhrFq1irKyMgDefvttPP1nkLW3bSJpUi4Wt42H6OArOKLX7GdaqGnv/I1ptSVT6unHgLbtvFM5GNM0yHBsJxxqJNWXEy0XWFvPD1uW8+SNJ+NKssfUs4+nD9DZ4tQVw2bDPXIkHRs2ABCsrfnC8tLJMLX+yBdqamoiNTWVxsZGDRQXETlBNTd/Sun2BzDNEDU170T3m6aFtWvOpqkpJ6b82H4jmfRZJMQU/M9UDItBv//3LyZjZRp2zsSOd+98Tv9NG/OJjF9KCzQwq+wFHGaA/kl1tIdTqGy340j5GhZb7NyEFpvBubNHMWB857iqR9c9yh9W/oGZ/Wdyz2n3HPC+AlXVbDn9dLBaGbZuLYalxzuiesTB/NafmH8hERGRg5CSMpwxox9i7JhHGDP6YYYPu5ucnAswjDBFRZHlUAzD4Ky9Y4mqOmpJvaA/OT8Yj2GJBKQzh+WwlBD30sHHdA70nkJnq1GDPY2X8i4BoLQ1g8r2yLFwIH5qnHDQZOm/SmP2DU2PzFO4sW5jt+7LlpEeeRMK6cm6blJwEhEROQjZ2WeTn/9V+vebA0BaegUWS4ALL7yQ0aMjA7wrKyt5et0/+Hjryuh5350+kDRPJAit6Z+EY2AqACP2Dhbfp8aRScASO5ImHI6MQVrmDMTsr93dQkt9R3R7WEZkmoPtTdtpDyYeB7U/w27HmhqpR6hG3XXdoeAkIiJyCJKShuByFmCxhLnwwkGUlJTg9XpJTo7M2FRdXc17773H2rVrCYfDTOyXwaLbZrDwRzO4/zuTyfrWCAAKsJBK59yEpmFhlyuyWK/fEglaZqiWNsNkiTPI80k+nkjpILkgMvnm8jd2sG/UTZY7iwxXBmEzzOb62HXrumLdu/C9Boh3j4KTiIjIITAMg6zsMwBwuT6N7ktOTsZm8zFm7Jvk5W3kpZde4oEHHmDt2rWkuOz0zfRgGAYWt41gWmSw+PDPtTotSZtMbcF43siJLDlmCVfxYkoZHZYw2+1haqwmqSPSAPhkYRmfflQR/fx93XWb6jd16z5sGRkABGsUnLpDwUlEROQQZWedDUBF5Yt8+ukdmKbJiBEjyMraSWrqHgYM3IzD4aC+vp6XXnqJ9evXx5wfyvUA8ENcDNnvJ7nGmUXHyZez01WE1QgTMi1cX/5/3Lx9Hv+x4zGm1X6Ic0gKE87dO03C2zsJhyOtTsPKpvD1lXeyfUv3liazZkWWignVKTh1h4JTF+bOncuIESOYNGlST1dFRER6qfT0KRQXfxewUF7xHGvWzsZi/SlDh0bmUbJY6rniSjsnn9wCwD//+U+am5uj54cm96GaMAVYuA8PE7ByOXbGYCUn2YnFMHA5wwA0BlwETQuOsJ9xTeto8QUpmdkPp8dGQ1Ub6z/YTXNdB84lfUn1ZeH6x3BCwci5NbtbqN7RlPAebJl7u+rU4tQtCk5dmDNnDhs2bGDZsmU9XRUREemlDMNg0MBb6VccmZS5tvYDfL4ywmbn0267dj2M3fEyxcXg8/lYt24dpmnS3NyMmWpwFS18QpBULPyRJH6Im4dI4vJl9cwnBVvq+ISf3VJfh8Nlo2h4pKtt0bOb+b8ff9RZNwwqtzUSDoV59q6lPH/3cnzt8cu22DL3dtVpLqdu6fEJMEVERI51ffvewO6ypwkGm7ss06//Iqqrx/D222+zYcMGdu/eDUCmZRC3huEPJDFkv7FO7vYQYHCe53z8fc/g7fInObPPv/mgqj+NATd1j/6UT9230mfgQLasqE74mbu21JKRnxTdbqnrwFmQHFPGmrm3q04tTt2iFicREZEvyW73MmrUg/Qtmt1lmXB4OyUT/4HH0xANTQD5lkaagf+klbto51pa4s51WF2ckjuQsMdFqqNz+oE3H7oPqzV+eRWfrQ2ATzZsw9fa2crU3hKIK2vb91RdXd2Bb1QUnERERA6HzIypDB7844TH0tMjC9MbBqSnlzNixAjOPPNMALyGD4AW4E0CbCEcc66PyKDvTMfZhII/Jd3eGYTCoRAf/u2PuLyxXXCesZF18Rp3+GPCUlujL65u0afq1FXXLQpOIiIih9GoUQ8C0L//TRQUfIPBg3/KhPFPM2jg7ZHjo22cd97w6JqrXksHELv62Y9pY6N3Dw86PuQbRhVte497zSIynEOi5VKzc2ltqGOZbxm7rJ3r3pUl5eGztuMMeNi2rrNFaulrpTxz11K2rurs2ovO47SnBq3CdmAKTiIiIodRbs5MppyykH7F32PY0F/Rt+jbAKSlTQSgrW0Jy5ZfimGsAiDZ8HOtazmn2kqZ5VxFX0sdCwmyyL8Wl8XHdMdG7qQtev1Ux5jo+1NsCwEorF1Dx37h6+k1texIi8wt9eniiuj+xup2ane38OYj66Mzjtv2jnEiEKD2z48e7j/HcUfBqQuajkBERA6V212A5XPLpqSkjIzZrql9BZfLFd0ebKvBbQQ5w7EVG52tR24jyHIC/AetALhsYzgpczezitcwKKUWqxHGHWij1twBQLNhgmGw3bsNAF9ziES2rY50zVncblyjRgHQ9K9/fZnbPiEoOHVB0xGIiMjhZLE4yciYFt2uq1tIfsFKLJb4Aduj7bEDvq9wrqGfYx0N+DHxMDgziVxXMn7zEsakReZnGtjwLm+6mvhrSmQcU6Wz4QvrU7pmT/R93l2/AiC4Zw+maRKoTvyUnig4iYiIHDUjht/D2LGP4XDkAFBQsILi4rVx5UZZy2O23UaQbEsrKy2RJ9/+FvhvqvwP0Ri8jjEZZ+O1d5ASaqFP0wc0WyJddo0W8Fnb4q69T8WWRvwdkUHltuxsAEJ1ddQ8OJctp51O42tqfUpEwUlEROQocTpzyMqcTmHht6L7MrN3sv/g8KBpwbp3Oz9/G+PG/wuHI9JNt8uI/PNKnOybirEjfAbnF0TWvBvcupUMfx1ZvhrMYDI1SWUJ62EYEAqG+fPNC2mqaceang62yPVq5s4FoOruuw/fjR9HFJxERESOsn7F3+PUKYuwWJy4XS1scEQmpXzfP4Cd4bRouYGDFpOSUkf/ASsBaLDEz/EEYBo/IsVTiJUw3yx7lq+XP8/F6+vZ4ylNWN5q6/z5L11TQ+maWqr7T48pY+/T59Bv8Dim4CQiInKUGYaBy5UfHfPUJ3s7H7rSuXryY/i9zXvLdA7qLihwAhAyYrveqi8fQDuNgI0RqSNijvVpbWfYp+sJ+TfGff74c4uj78s21fPGI+tYX/AVmpMKovttOTlf7iaPUwpOIiIiPSQ7+2wAxmev49JBb1Ds3c0NJY9jdQbweBr3K1kPQJLRzr+ITCNQRZjtVpN/GhsIEqKfczBWw8YWT3/mZ50RPbOJ+QyaGBuCJpzblzEzCoFIi9M+VbmdT5KH29sP670eLxScREREekh21pmETQt9vWUMz9gc3T9p0itMmty5dlwgUI03NYzVMFlk28ijdPAL2tmybTvldj9/cX5Ai2HjqgHZvJlzNhuTB7M9OdJ65Gz1UVH1Iv6WV6MTXNrsViZe0C+uPvXpQ6PvQ02NccdFwalLmsdJRESONLs9nWrf8Jh9DR1e7JYOQqGPY/ZPPa0fYRMG2WpYaCknZC2jbf07JFuCBA2T3dZafOELyDRaMA0LZ+TtwGpzYsVK7dplhANbMcMN0eu5kx0kpTpiPqPDmRZ9H25sOuz3ezxQcOqC5nESEZGjYXd759xOjb4Ublv039QHO2cHt1o9ALgdq6hOiqxjN9m+i/G22Cfmqo0GwmTyMNkMwsJ0o4XLCn/AhMyzomX2D04AKZmumO2Aw8vqMXMoy59KqLn5sNzf8UbBSUREpAdtaJwafV/RmkvItGLNuo9x455iYsnz9CueA0D1nte5cuJfcXhrcRlB7EaYPeEk3vEPBmCTrZJdllr64OJnuAkEv4XFsDLIOz56fTPUwJgzC7nnzc+4ZO5iypo7oses9kgkqMsYwcYhXyfc3IwZjl1wWBScREREetSO2g5+veSHfFY3iOc2XQrA2SMLycyYSmrqBJKTh8SUHzL44+gTdxstXgb22RDdfs++jjAmA7ASNAuj53jtkfXoNmW8w5aCdh777F7W1/+bv7Q3YdgMplw2CHeyPeZzduWfTri5mea6DtYu2EXQn3jplhONgpOIiEgPmjk6j22N/fnt8v9kR1NfxhSmkuzsXOcuKSk2OKUn1ZFX9Akbg9lcP/Exrh71DME+lQAEjBANeyfJDJMaPSfbFQlReXvgvpV3kZS8BE/hU1TbTO5JbmP8OX1pqffFfM7mwVdS+Vk1bz6yjkXPbubfL289Ivd/rFFwEhER6UEXj82PBiWHzcI9V4yJOe5y5Uff72qNPPU2sN8aLj75KbI9kSfvirK3YQ1Hgk+1pfNpOCuRQJXqiCyp4vZb+drHLXz9nSJOX52FJ9gaLZs/OC2ubhsW7aZ6R2Ss09oFu3nzT+v5+NUTO0ApOImIiPSg4XleXv/Padw3aywLbp3OsD7emOOGYWHQwNvJzj6Htyt+RF1HGgA5ns7pCuxWOy1mJHxtt35KGBO7bR5224uRa7gz+CQv8gSdM2DFYhr0r0hi9q7/IzUQCVpnXjOcSRf0Y9jJnTOGb9rSuRQMwNaV1ax4YwfhcOz+E4ntwEVERETkSOqb6aFvpqfL48XF3wHAtWIZn9YO4dSCpTHHs1McPBocwtnWrey2GKxxPsInRl88ZphLg2FS7bmszi5iZEWktWhAyljqfOU0+PcwuHULAN4sN5MvGoC/I0ho51Y2lyd1WZ/WBh9Ojw2LxcDmsH7Z2z+mKDiJiIgcI5KcNt7ZdnpccEq2VVNJKh8Fipli38EKI/KkXYcBZZZ6isKZ/KPhCsL9wjT4q8hw5hEMB3hxx+/xGw5M08QwDAAcLhsnnZvP5ie6ngCzsbqN+U9sIOgLcd6NoykalnHkbrqXUVediIjIMSLZaWVncxG3vP+rmP2h4B7unvpLdpJCTTi25WqDdRcmka41i2Ehw5kHgM0SeYrOYfppag/GnOMp/OIFfiu3NdHW6MffEeIf96/m0VsWUlV6YkyYqeDUBc0cLiIivU2SI9JR1OhPpdkf25WW6a5nasES/h3oB4DV6sdiCbLLWssWS2XC6xUlDcMdamdPS+wTddbsbIp3vNllPWrLWmK2fW1BXvjNclobfF2ccfxQcOqCZg4XEZHeJmm/aQp+v+J7rNkzMub4+f3fp9FwsDBUxMlTnmPChH8AsNFWzkL7IiodD+IwPmGnpYZmo50pOZdwRqCNXdt3xFzH4nAwqOEjJi/7Ned+JZOB47Njjn8+OO2z5B/bDsdt9moKTiIiIscIp73zZ3tncxF/XPUfuPo8yqBBd+Bw5JDqrOO3MzfiSmnAYpi4Pa0YRohKSwObrH7esfRnl/Np3nas4U37agCyHZmsvPd25n3nWzx9+03U7NwOgCM3m+TWcpKXvErRiNgxTPWVbTHbFmtkfFTp2prj/ok7BScREZFjRE2zP27fiOJTKe57PQP63wRAhmUhdmsgenzqtL+RkbEbgA5c/JOzAWi0RMJPMBy5ZltjA9Xbt/LJwvcACLdEWpXq//Z3Mpa+wKTzCpk2K3Yyzn3GnlmE1WahoyVAS11HwjLHCwUnERGRY0R+mituX3pSZH6mjIzImndB/xbSnQ0xZUaOWpDweiYmNouD1Amnk54fmV28ZtcO/MEwyaefHi3X8OeHKf7sZfqOSPz0nMfrwLV3yZaO1kDCMscLBScREZFjxEVj87EYndtXnVwcfe9yFWCzpYIZ5PLB/4w790LeoYS15FEV3ddBgOKkETSOHUj6JScDsH31Cqb86Gl+kTIx5vzWxR+SnOGMu65hMRhUkhsNTu0tCk4iIiLSC+R6XTx13WTOHpHLdaf259Zzh0aPGYZB8t517Tz2+O4yV7iZC3mXCSPfYOzod4Aw7YafdGcuYz9u4Y01b+OyJgNwSeU/WVu6i8WjBlOd4qE0K5WQ3Y7NbsXjdUSvOXpGIdfdM5XkdCeupL0tTgpOIiIi0ltMG5zNn6+eyM8uGkGq2x5zLC39pC7P+0HgB7zpHEdTphVvegUeTyMvOZfgJ8iwjv78rOK7nF7wNayGDU+4g8srX6XRGmb5gDw+LcjiM3tk0Hdan0h3oRluwWbviLY0uff+c/GLWzDN43eAuIKTiIjIcaK47/VkZZ2V8FiHx8Bx0u7odlJyPQBPOz+gymgAIM2aiTd3dMLzqwizedm/KV12F0HfGnyNf+Lfz/0kGpL2tTi1N/lZ+PdNx214UnASERE5TthsKQwf9uuEx+469X9itpOTIsHJNOCfzhUECWFikpqSmfjaFguvP/A7zHCQYNu7kZ2mia+tFQBfW2cX3fqFZWxfV5voMsc8BScREZHjiMORxSe1ww9YLiWlJmb7Sdf7LLZ9Rh8j8XIrNiAcDMXtb2+KrGnnSnbE7N+xriau7PFAwUlEROQ48/C6Oby4+cK4/W9tP4Mff/hTwqZBalo1Xm9VzPHPbOXkB3K5vN9/keMqjjlWFQ4QDsWuaQfQ1hgJTiUzY8u7UxxxZY8HCk5d0Fp1IiJyrLJaLLy9fQYvbr6QxeWT2dlUwO2LfsZzmy6lqi2HJbsjUw0MGLACr7cK634TZu621GIzbMzI+xp57gEYGF19DABtzZHgZHeGmXheLeFQpAvweH26zjCP19Fbh0lTUxOpqak0Njbi9Xp7ujoiIiIHNOFX86lrjZ9lfB+vo4n7pt4JtkgEsDbbKP90HJs6hpMa9pBuJjEpOIhU00NbsBkTkw+rXqTBXx13rbNv+D5jzjqPBU/9mZWvv4rV5sSeMoeBE7I57zuJB5r3NgfzW68WJxERkeOMxfjiVqImv5dQvSe6HUoJkjtpOXZ7O42WNrZb9/CufR0AHlsKSTYvYzNmADA0K49v/Pre6LltTY3sWLuala+/GrlW0AdEWpxa6utoqKw4rPfW0xScREREjjM2yxcHJ4A9LTmxOwzI77M5ullnaYk53Mfdj1O31TJ58EjyBg1l0iVXAFC1bTMv/Pqncddva/bzyI1X89hNN9DR2hJ3/Fil4CQiInKcsXYjOG1oHhC3r2//NWRm7Yhul1nqMOkc0ZM38QeEmiIhKDktHYAtyz5OeP22hj3R96vf+hftLc3dq3wvp+AkIiJynLFb44OTwxb7k/9+U+KHn/rkbo2+f8OxipW20ui2NbWIUJMb0zRJzszq8vNDgVI6Wsui24uffZpn7vxRt+vfmyk4iYiIHGcsCVqckhzWmO2ytnweWXsNq6tH0dDh5aHV1wGQll6B1do5sHyVrZSFtg0E2DuHk20y1Q+uwvOeQYo9I+HnB1pextf0Xsy+uvLdCcsea2w9XQERERE5vBKNcfI4bNS3xU4RsLSyhKWVJXu3TAJtbuyedor7rWbb1kmwdyqCTbYKtlv3kBlO4czAaCiLzBY+IfMsPqh8LnElzLa4XW1NjXi8qYd8X72BWpxERESOMzZL/M+753MtTvEM/HtSACgo2EhR33XUGE42pW4CwG8EqbDWs93aOSVBpjMfm9H9iS4fufFq6srLDlywF1NwEhEROc7YEoxxykw+cMCpq8yNvh9YuJpxfVcxI3cpHdb26P7GYF30vd3iZHjayZH33QhQ4VCIrSuWHLBcb6bgJCIicpxJ9FTd9KE5CUrGqmjpw5QldbjbQ4RsBv2LVzCgj0nK6H8yaMIrZGXtoL5xA+1L5uH75CUAUtOHM2HweVzW74cUJ4044GdYErSGHUuO7dqLiIhInFMHxj/x9vVJfQ/YXVdjpuL2hRm/tjFm/8leH3nJzQwfsZDdfTL4+9SBvF8Yoh0/Xr+TwcGxkXI5F8Wc5/DErl8H0NbYcJB307soOImIiBxnrp/Wn2Rn5/Nf/33xSFI9dv7wtfGkeexdnreHyMBtty9Mvx3xg7sBMjLKCVut7MrN4BXnUmz22DCWZEtlYMpYrNZCTMvEuPO3LPuYBU/9mY7WFt7+0wNsWBh5+u5YWQFOa9UdgNaqExGRY9Guujb+saacgdlJnDcqL7rfNE1eWV3GD59dE3eOEz+/tT/CxdZ/YwIdTgv2QJiPJ6bjc0UCUlXVAEq3TSAtrZKammLyg1nMDIzvvIjDAv4w69qCbGmvwd/0VML6Jadn0FIfGS918mWzWPvuW3zzf+7Dm5V9+P4I3aS16kRERE5wRRke5swYFBOaAAzDwONIPBuRDwf/GfgBozoexSDS8mQLw0krGxjwSWTm79zcbZx8ygsMG/4heXkbKbPWxV7EHwYgx27B2G/A+PCp02OK7QtNAB+/9CxtjQ189NxfDvFujx4FJxERkROMw/rFP/8teGK2T2qZx9bqoYQ+10eVkxOZVTxEmOC+CTL3SrMaZNrdFHgG47C48GbnciDtzU3dqH3PUnDqwty5cxkxYgSTJiWekl5ERORYZT9AcAJ4a9DPCFpcXOO/nTq8NOJleVPseSneWuz2Dj50b+ZJ1/uUWqqix5wWg2kpbqbmXsbU3MvxZh/4qb6O1taDv5mjTMGpC3PmzGHDhg0sW7asp6siIiJyWCVay+7zthRcwjuXreaDcOSJuUYzmQ018eX6Fq9hsxlZTuU9+ycJr5XtKsTuOPA8T77WlgOW6WkKTiIiIhLHZjHwujufwGswk7GG4WdlbnzhznI5OaV4kuoZOfI9XJ4GfAQSXA0GTjyJ/uNKEh7bx9emFicRERHpZQKfH6yUgM1qIXW/4NRIEqV2O01hg9vLPFT4Iq1WHR3JjBv3BhmZZQwbvog3LZ8mvJ4VO2fdMIfiMeMTHgcIBRKHrt5EwUlEROQE4wuGDljGbjViglODmcRlzZGutKlt7eRsiczz5La1YLVGrud2N7HHsYc11u1x19vy+5V4s3I46bwrmFlwPRMyzmLWL/43psyxMD9S4ucRRURE5Lg1JDflgGXsVgtpns5xSY0kc2NDE0P8Ac5oa+cTj4MGwOrqbCUKBiPll9m3khtOo4+ZFj2W1OwHILksCcORideRSeHwUQw79XQ+W/xBpNAxMLWkWpxEREROMEUZHi4Y3Tm/05SBmXFlbBaDJIc1uu5dg5mMyzQ5v7WNd4In8WHrSXHnOJ3t2Gw+AF63r6DcUs9OS0103FP1vDW0flQRLR/2h2LWrjPNML2dWpxEREROQA98fTxfm1yEy27Fbbdy4QMfxhy3Wy0YRqS7rq7VT/V+rUdPBs+hPuQixfcZxc7YsNO3eA3btk4mbIHXHSsByA+lc35gAv4dsfM0+Xc1Y1g7l2wxwwpOIiIi0gtZLAbTBkeWN9lU1Rx3fF9L077gVE4W/y9wPTVmKsvNYaSaLfytyoZpt9AWNvjqngBjxgVJSalhpOXflHv6Ut9SAEC5tZ5ED9vV/HkdSfmdS5wE/f4jcKeHl7rqRERETnBfNJP4/gPEnwmdwTvhyJQCjSSR4Q9THbTQEjbYvXd4ktdbS8bULYya8B7pGbuBL25F6l8+mAHJYwAIh0KEgsEvdzNHmIKTiIjICc5hi48D+4Zp7x+cYhncXN8AwJS2dpqd8ZNqjhq1gKnT/kZ+/me04cPE5LngVv663wzjAJOyZ+K1ZwHQsbsRM9R7u+wUnERERE5wiZZgMfc+4dZ1cILHGufwl93VTKscjIEdEsxyYBgmAwct42XnUt6zr6cpeTvtjvVx5WYWzqYoaTj1D2+g8V+lh34zR5iCk4iIyAkuUYtTuBvB6d1wCVc3/5E7fd/DHbLzcY2NUBjebbJBTWwLVLvhp9RaHd02E8zaNCXnYgBaPio/pPs4GhScRERETnDORMFpb29Zmqfr4ASR+Z1MLNiDLp7xO/ivMg//bHQQjhtvHtv9dj8NtNLxJWrdMxScRERETnCJBocfeIxTrIwOb8x26HNDnvLyN8Vsex2f8HfXYiqNhoTX81e0YoZ734SYCk4iIiInOIslfmB3v0wPACXF6fHl44uT0p7Dd+obcYfDDPX5CZTHRozCwk/Yv9XJtEQmyvzEtithnar/sJLmhbu7ewtHjYKTiIiI8PXJRdH3I/K80cA0riiN8X3TYsqeOigr7vwGM5kfNDSyZMduJpaVkPKehZR/WMn+ZWTQuMvVRv8BK6Mzi++zzey6Ravpze2HfkNHiIKTiIiIcNelo/ntFWP42YUjePY/TsYwIs1KhmHwx6+NZ2B2EjaLwbdP7ceZw3Lizl8aHkab6aTRTOI1/5k0GQYpb1qxVxqkvBaZHbyw8FNOmfIcScm10fM+I0B4b8dgvb+KqvYdMdete3bjkbrlQ2KY5jGwol4PampqIjU1lcbGRrxe74FPEBEROU4FQmHsVgtvrKvgu39dGXfciZ8wFgLYGO/9JZd/1MzzUy38+IUgTDFpuTQyuaVpGjQ35vDZxlPx+ZLoF8rmrMAYao1Kmpr20D9ldMx1C/932hG9r4P5rVeLk4iIiHTLvvmeclNdCY/7cBDYu5rb5rwAv7nSyrY8g49GGnjftpD2f5GWJ8Mw8aZVMXDQUgC2W/cA0JHcQXOw/kjfxpei4CQiIiIHpY83PjidNTyHV+acSkaSAwBf9XnRY2+NiQQm93ILRnNn9PB6q3G7m7BaAzxOB6tdlSzPaqPGHzuzuBnqPZ1jCk4iIiJyULJTnHH7JvXLYFxRGgtvm8GD3xhPoP5k2nZ8h+J2J2WZkTJG0CBzbmf0sNv9TJz0KuPGv85blmoqmlsJ2+280foal/s7xzqZ/gRTkvcQBacuzJ07lxEjRjBp0qSeroqIiEivYrdaGJKbHLNv5qg8AJKdNi4ck0+6x0WobQD21iIwDBZOi4Qfx04L2XfZCLd0Pk3n8TRxtmNz58UMA0u4g8DeQeNhn4JTrzdnzhw2bNjAsmXLeroqIiIivc5t5w4j2RkZz3TDtP703Tvv0z7pe7vsylonAvDgVCfvjo08qWcvt+D54PPTEOw/s7iBM+yjbW9wMn3Bw38Dh8jW0xUQERGRY89ZI3JZ8uMz8QfD0ZC0vwyPg220Ut0+koywScBi0LRftkr9MEDHBZ3bAwcuZ+vWiYAF02rDFeqgHUgl0uLUsbUBa7IdW7YHI9EMnEeJgpOIiIgckiSnjaT44U5AZ4sTpp2+Pjtb3UGaPAb7FnOxNMaWzy/YSENjLrU1xZg2Gy6/j7AJGLDnoTXRcgV3nZp46vKjRF11IiIicthleDpbobLb0gAIWDuPGxjgjz3H5WqJlEvPYbKxjTQjEHPccNkwEixIfDQpOImIiMhht3/3XVJLMQD2/cZ4P3+qQc6vY8c55eVtwuNpAMCfU8jzjsX46AxPhrXnWpr2UXASERGRw27/KQvK2kfzeEUVH44waHHBB6MMGpINbHsMcn9iJ1AeWcLF7W6hZOI/cbsj/XgBi8kWa2X0OuH2nh8kruAkIiIih13/rM6R4MvModzdeAdDQ0nc8J9W5l5ooTEpcsxabxD6eFjMuWPGzCc//7PIuf4NnQfCPT8RpoKTiIiIHHYDsvaf58lgpTmEhkAfQlYDDIOqtM5uN2NzKnWbzopuO5ztDBy0DJvNh8/e82FpfwpOIiIictgVprvj9q1vjoQjW8iO1xjK3AssfDjCwB5op3r1LDa/em9Mebu9A6vDzsqRVTzr/IhVo2pob28/KvXvioKTiIiIHHY2q4XzRvaJ2efrGEDb9htp2PZf7Ank88EYCxsLDGzBNgBCPi91m86MlneGGwBYuXU9zUY7a7Z/gsMRP2fU0aTgJCIiIkfE/1w2mmun9IvZF2rvhxlMY0t7ZEmzhaMNklvLo8cbl5xDsCMFgNEnLYyOdQLIy8vDarXSkxScRERE5IjISHLwi4tHkur+/PIqYAYyyaouod1p8PwplUxeehdj1zxI/9J/EQ64ouX69V+JYUTmMSgoKDhqde+KgpOIiIgcUc4uJq006k4GwG+H5LYKMus/Jat2PTZ357TiVmuIjIwyrFY/eXl5R6W+X0TBSURERI4ojyNx91qT6eXbDU18OMJg2eDIU3aOQAsVy67BDBv4WzMBGDhoGVNOfZaa2msIBpuPWr0TUXASERGRI8rjSLw0biNJOE0Tn8Pgt1dYuXV2JGDlvbuNjS/OY9f7twDgdLbtPaOZjuZAwmsdLQpOIiIickR11eIUwMaspmZSQmEAduZEWp0Kyhdx8sd3QX38CsIWDQ4XERGR45m7i+AEkBUOs3jnbtaV7mSoz89/f92CAXjaqxm6+Xlaq/rFlDeM+PmhjiYFJxERETmiumpx2mffHOLrd91GWVbnjOKetirKl3yfcCjS1Ve+4gZcyfFP6B1NCk4iIiJyRI3MT+3y2NpwfwA2hIvpCPRhcHsOgb05y+FvItSRyqYX57HxhYdYuaOEoT99k6qmjqNR7YQUnEREROSIumZKP1z2xJHjO/5buCcwi9sCNwBQ234dG4sjx+yB1mg5M2ynzBbGHwp/YdffkZZ4mLuIiIjIYZLqtvPid6fw9L93UJDmJhA2+eO7mwGoJJOHQpdw16WjqG3xc+XEQn5ku4dR2wIYmFhDzYSskZnEy2yRQeRJXTyldzSoxUlERESOuJH5qfzv5WP4wZmD8brig8+3Ti7mprMGk5/mJpSUwW+uiESUPpUrMU0TV/N2sus34bJbsFqMuPOPFgUnEREROaouGVcQE35OH5Idc7xfXT9s1kjr0tDNz3HmB99nyorfcvfih0l29mxnmYKTiIiIHFXZKU5+c/kY+mV6mNQvnTsvHBFzPODP5fpQQ8Jzu5pM82jRGCcRERE56q4oKeSKksKEx86eOIz8DX5uvsrKnc+EcO03WXiSWpxEREREOp0xuh99g0H+y7aHOd+LfYIuxRLqoVpFKDiJiIhIr+Lw5gJwansH+ZbO5qYnzrKQay3vqWoB6qoTERGR3iZ3BEy7lde2BZlRvoRf/McO6k0rrTU/YGzq0B6tmoKTiIiI9D5n3snKtg08vnUyE33rKTDaWBAuZIoGh4uIiIjEs9siUxYsD4+K7uvJWcPhBBrj1NbWRnFxMbfeemtPV0VERES6wWGNjymJ9h1NJ0xw+vWvf81JJ53U09UQERGRbkoYnGwKTkfc5s2b+eyzzzj//PN7uioiIiLSTfYEIemED04LFy7koosuIj8/H8MweOWVV+LKPPTQQ/Tv3x+Xy0VJSQmLFi06qM+49dZbufvuuw9TjUVERORoUItTAq2trYwdO5YHH3ww4fFnn32Wm2++mZ/85CesWrWKadOmMXPmTHbu3BktU1JSwqhRo+Je5eXlvPrqqwwZMoQhQ4YcrVsSERGRw8Bljx8I3tNjnHr8qbqZM2cyc+bMLo///ve/Z/bs2Vx//fUA3H///bz11lvMmzcv2oq0YsWKLs//+OOPeeaZZ3j++edpaWkhEAjg9Xr52c9+lrC8z+fD5/NFt5uamg7ltkRERORLcjviQ5LzRG9x+iJ+v58VK1ZwzjnnxOw/55xz+Oijj7p1jbvvvptdu3axfft2fve733HDDTd0GZr2lU9NTY2+ioqKvtQ9iIiIyKFx2RK0OCk4da2mpoZQKERubm7M/tzcXCorK4/IZ95xxx00NjZGX7t27ToinyMiIiJfLGFXXQ8Hpx7vqusOwzBitk3TjNvXHddee+0ByzidTpxO50FfW0RERA4vpz3RPE6aALNLWVlZWK3WuNal6urquFYoEREROb70xhanXh2cHA4HJSUlzJ8/P2b//PnzmTJlSg/VSkRERI6GRGOc7NaD73E6nHq8q66lpYUtW7ZEt0tLS1m9ejUZGRn07duXW265hauuuoqJEydyyimn8Kc//YmdO3dy44039mCtRURE5EhzJeiqO5ShOodTjwen5cuXM2PGjOj2LbfcAsA111zDk08+yaxZs6itreWXv/wlFRUVjBo1itdff53i4uIjWq+5c+cyd+5cQqHQEf0cERERSSzRgr5h0+yBmnQyTLOHa9DLNTU1kZqaSmNjI16vt6erIyIicsKob/Uz/lexw3Uev3YiZww7vOOcD+a3vlePcRIREZETV6Kn6sLhHqjIfhScREREpFdy2614XbGjiib1z+ih2kQoOImIiEivZBgGv7h4JA6bBcOAH507lFS3vUfr1OODw0VERES6ctmEQs4akUs4bJLmcfR0ddTi1JW5c+cyYsQIJk2a1NNVEREROaF5XfZeEZpAT9UdkJ6qExEROb7pqToRERGRI0DBSURERKSbFJxEREREuknBSURERKSbFJxEREREuknBqQuajkBEREQ+T9MRHICmIxARETm+aToCERERkSNAwUlERESkmxScRERERLpJwUlERESkmxScRERERLpJwUlERESkmxScRERERLpJwakLmgBTREREPk8TYB6AJsAUERE5vh3Mb73tKNXpmLUvVzY1NfVwTURERORI2Pcb3522JAWnA2hubgagqKioh2siIiIiR1JzczOpqalfWEZddQcQDocpLy8nJSUFwzCYNGkSy5Yt6+lqRR2N+hzOz/iy1zqU8w/2nO6WP1C5pqYmioqK2LVr1wnVzdubviPH2vfjy17vUM89mPMOZ9kT8Ttyon0/DvfnHKnfENM0aW5uJj8/H4vli4d/q8XpACwWC4WFhdFtq9Xaq77gR6M+h/Mzvuy1DuX8gz2nu+W7W87r9faqf2eOtN70HTnWvh9f9nqHeu7BnHckyp5I35ET7ftxuD/nSP6GHKilaR89VXeQ5syZ09NViHE06nM4P+PLXutQzj/Yc7pbvrf9u9Bb9Ka/y7H2/fiy1zvUcw/mvCNV9kTRm/4mR6sux/pvyOepq07kCNETmSJfTN8RORapxUnkCHE6nfz85z/H6XT2dFVEeiV9R+RYpBYnERERkW5Si5OIiIhINyk4iYiIiHSTgpOIiIhINyk4iYiIiHSTgpNID3nttdcYOnQogwcP5tFHH+3p6oj0Kl/5yldIT0/niiuu6OmqiMTQU3UiPSAYDDJixAgWLFiA1+tlwoQJLFmyhIyMjJ6umkivsGDBAlpaWnjqqad44YUXero6IlFqcRLpAUuXLmXkyJEUFBSQkpLC+eefz1tvvdXT1RLpNWbMmEFKSkpPV0MkjoKTyCFYuHAhF110Efn5+RiGwSuvvBJX5qGHHqJ///64XC5KSkpYtGhR9Fh5eTkFBQXR7cLCQsrKyo5G1UWOuC/7/RDpzRScRA5Ba2srY8eO5cEHH0x4/Nlnn+Xmm2/mJz/5CatWrWLatGnMnDmTnTt3ApGVuD/PMIwjWmeRo+XLfj9EejMFJ5FDMHPmTO666y4uu+yyhMd///vfM3v2bK6//nqGDx/O/fffT1FREfPmzQOgoKAgpoVp9+7d5OXlHZW6ixxpX/b7IdKbKTiJHGZ+v58VK1ZwzjnnxOw/55xz+OijjwCYPHky69evp6ysjObmZl5//XXOPffcnqiuyFHVne+HSG9m6+kKiBxvampqCIVC5ObmxuzPzc2lsrISAJvNxr333suMGTMIh8PcdtttZGZm9kR1RY6q7nw/AM4991xWrlxJa2srhYWFvPzyy0yaNOloV1ckjoKTyBHy+TFLpmnG7Lv44ou5+OKLj3a1RHqFA30/9JSp9FbqqhM5zLKysrBarTH/9wxQXV0d93/ZIicafT/kWKfgJHKYORwOSkpKmD9/fsz++fPnM2XKlB6qlUjvoO+HHOvUVSdyCFpaWtiyZUt0u7S0lNWrV5ORkUHfvn255ZZbuOqqq5g4cSKnnHIKf/rTn9i5cyc33nhjD9Za5OjQ90OOZ1pyReQQvP/++8yYMSNu/zXXXMOTTz4JRCb4u+eee6ioqGDUqFHcd999nHbaaUe5piJHn74fcjxTcBIRERHpJo1xEhEREekmBScRERGRblJwEhEREekmBScRERGRblJwEhEREekmBScRERGRblJwEhEREekmBScRERGRblJwEhEREekmBScRkR7i9/sZNGgQixcvPqzXfe211xg/fjzhcPiwXldEFJxE5DC59tprMQwj7rX/Yq8S609/+hPFxcWceuqp0X2GYfDKK6/Elb322mu59NJLu3XdCy+8EMMw+Nvf/naYaioi+yg4ichhc95551FRURHz6t+/f1w5v9/fA7XrfR544AGuv/76I3Ltb3/72zzwwANH5NoiJzIFJxE5bJxOJ3369Il5Wa1Wpk+fzve//31uueUWsrKyOPvsswHYsGED559/PsnJyeTm5nLVVVdRU1MTvV5raytXX301ycnJ5OXlce+99zJ9+nRuvvnmaJlELTRpaWk8+eST0e2ysjJmzZpFeno6mZmZXHLJJWzfvj16fF9rzu9+9zvy8vLIzMxkzpw5BAKBaBmfz8dtt91GUVERTqeTwYMH89hjj2GaJoMGDeJ3v/tdTB3Wr1+PxWJh69atCf9WK1euZMuWLVxwwQUH+VeG7du3J2zdmz59erTMxRdfzNKlS9m2bdtBX19EuqbgJCJHxVNPPYXNZmPx4sU88sgjVFRUcPrppzNu3DiWL1/Om2++SVVVFV/96lej5/zoRz9iwYIFvPzyy7z99tu8//77rFix4qA+t62tjRkzZpCcnMzChQv58MMPSU5O5rzzzotp+VqwYAFbt25lwYIFPPXUUzz55JMx4evqq6/mmWee4Y9//COffvopDz/8MMnJyRiGwXXXXccTTzwR87mPP/4406ZNY+DAgQnrtXDhQoYMGYLX6z2o+wEoKiqKadVbtWoVmZmZnHbaadEyxcXF5OTksGjRooO+voh8AVNE5DC45pprTKvVaiYlJUVfV1xxhWmapnn66aeb48aNiyl/5513muecc07Mvl27dpmAuXHjRrO5udl0OBzmM888Ez1eW1trut1u86abboruA8yXX3455jqpqanmE088YZqmaT722GPm0KFDzXA4HD3u8/lMt9ttvvXWW9G6FxcXm8FgMFrmyiuvNGfNmmWapmlu3LjRBMz58+cnvPfy8nLTarWaS5YsMU3TNP1+v5mdnW0++eSTXf69brrpJvOMM86I2w+YLpcr5u+YlJRk2mw285JLLokr397ebp500knmhRdeaIZCoZhj48ePN3/xi190WQcROXi2no1tInI8mTFjBvPmzYtuJyUlRd9PnDgxpuyKFStYsGABycnJcdfZunUr7e3t+P1+TjnllOj+jIwMhg4delB1WrFiBVu2bCElJSVmf0dHR0w32siRI7FardHtvLw81q1bB8Dq1auxWq2cfvrpCT8jLy+PCy64gMcff5zJkyfz2muv0dHRwZVXXtllvdrb23G5XAmP3XfffZx11lkx+26//XZCoVBc2dmzZ9Pc3Mz8+fOxWGI7EdxuN21tbV3WQUQOnoKTiBw2SUlJDBo0qMtj+wuHw1x00UX85je/iSubl5fH5s2bu/WZhmFgmmbMvv3HJoXDYUpKSvjrX/8ad252dnb0vd1uj7vuvsf53W73Aetx/fXXc9VVV3HffffxxBNPMGvWLDweT5fls7KyosHs8/r06RP3d0xJSaGhoSFm31133cWbb77J0qVL44IhQF1dXcw9isiXp+AkIj1iwoQJvPjii/Tr1w+bLf4/RYMGDcJut/Pxxx/Tt29fAOrr69m0aVNMy092djYVFRXR7c2bN8e0skyYMIFnn32WnJycQxpPBDB69GjC4TAffPBBXEvQPueffz5JSUnMmzePN954g4ULF37hNcePH8+8efMwTRPDMA66Ti+++CK//OUveeONNxKOo9rXojZ+/PiDvraIdE2Dw0WkR8yZM4e6ujq+/vWvR5/+evvtt7nuuusIhUIkJycze/ZsfvSjH/Huu++yfv16rr322rjuqDPOOIMHH3yQlStXsnz5cm688caY1qNvfvObZGVlcckll7Bo0SJKS0v54IMPuOmmm9i9e3e36tqvXz+uueYarrvuOl555RVKS0t5//33ee6556JlrFYr1157LXfccQeDBg2K6WJMZMaMGbS2tvLJJ58cxF8tYv369Vx99dXcfvvtjBw5ksrKSiorK6mrq4uW+fjjj3E6nQesh4gcHAUnEekR+fn5LF68mFAoxLnnnsuoUaO46aabSE1NjYaj3/72t5x22mlcfPHFnHXWWUydOpWSkpKY69x7770UFRVx2mmn8Y1vfINbb701povM4/GwcOFC+vbty2WXXcbw4cO57rrraG9vP6gWqHnz5nHFFVfwve99j2HDhnHDDTfQ2toaU2b27Nn4/X6uu+66A14vMzOTyy67LGEX4oEsX76ctrY27rrrLvLy8qKvyy67LFrm73//O9/85je/sLtQRA6eYX5+cICISC82ffp0xo0bx/3339/TVYmzePFipk+fzu7du8nNzT1g+XXr1nHWWWclHLz+ZezZs4dhw4axfPnyhBOQisihU4uTiMiX5PP52LJlC3feeSdf/epXuxWaIDJ26p577omZjPNwKC0t5aGHHlJoEjkCNDhcRORL+vvf/87s2bMZN24cTz/99EGde8011xz2+kyePJnJkycf9uuKiLrqRERERLpNXXUiIiIi3aTgJCIiItJNCk4iIiIi3aTgJCIiItJNCk4iIiIi3aTgJCIiItJNCk4iIiIi3aTgJCIiItJNCk4iIiIi3fT/AVSPrPq0NvLfAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(figsize=(6,6))\n",
+    "ax.loglog(irasas[0].freqs, irasas[0].raw_spectrum[0,:], label='original', linewidth=3)\n",
+    "for ix, cur_irasa in zip(hmax_list, irasas):\n",
+    "    ax.loglog(cur_irasa.freqs, cur_irasa.aperiodic[0,:], label=f'hmax_{ix}')\n",
+    "\n",
+    "ax.set_ylabel('Power (a.u.)')\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "ax.set_title('Original + \\n Aperiodic Spectrum')\n",
+    "plt.legend()\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hmax', ylabel='error'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "peak_params = {'min_peak_height': 0.1,\n",
+    "               'peak_threshold': 1}\n",
+    "\n",
+    "data2plot = pd.DataFrame(dict(zip(hmax_list, [get_aperiodic_error(cur_irasa, peak_params) for cur_irasa in irasas])))\n",
+    "\n",
+    "sns.pointplot(data2plot.melt(value_name='error', var_name='hmax'), x='hmax', y='error', errorbar='se')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualizing the errors shows the best hmax for our specific dataset as the one with the lowest error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/examples/improving_irasa_models.ipynb b/doc/source/examples/improving_irasa_models.ipynb
new file mode 100644
index 0000000..ece00ed
--- /dev/null
+++ b/doc/source/examples/improving_irasa_models.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Improving your IRASA model fits\n",
+    "\n",
+    "When fitting IRASA we sometimes face the situation that residual periodic activity remains in our aperiodic spectrum.\n",
+    "In this notebook I will illustrate two possible ways that could lead to this situation and how to avoid it.\n",
+    "First we start again by simulating a fairly simple signal consisting of a 10Hz oscillation and an aperiodic exponent of 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neurodsp.sim import sim_combined\n",
+    "from neurodsp.utils import create_times\n",
+    "import numpy as np\n",
+    "import scipy.signal as dsp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from pyrasa.irasa import irasa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fs = 500\n",
+    "n_seconds = 60\n",
+    "\n",
+    "sim_components = {'sim_powerlaw': {'exponent' : -1}, \n",
+    "                  'sim_oscillation': {'freq' : 10}}\n",
+    "\n",
+    "\n",
+    "sig = sim_combined(n_seconds=n_seconds, fs=fs, components=sim_components)\n",
+    "times = create_times(n_seconds=n_seconds, fs=fs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case #1: Increase your frequency resolution\n",
+    "\n",
+    "When the frequency resolution for your psd is \"low\" up/-downsampling is not enough to reliably get rid of peaks in the signal.\n",
+    "As can be seen below where a duration of 1s was not enough to obtain a \"peak-free\" spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.168134</td>\n",
+       "      <td>1.037697</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Offset  Exponent fit_type ch_name\n",
+       "0 -1.168134  1.037697    fixed       0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#low segment length\n",
+    "duration=1\n",
+    "overlap=0.5\n",
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(1, 100), \n",
+    "                    psd_kwargs={'nperseg': int(duration*fs), \n",
+    "                                'noverlap': int(duration*fs*overlap)\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)\n",
+    "\n",
+    "irasa_out.fit_aperiodic_model().aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Increasing the length of each segment (i.e. the \"duration\") should help you out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.25566</td>\n",
+       "      <td>0.99467</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Offset  Exponent fit_type ch_name\n",
+       "0 -1.25566   0.99467    fixed       0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Increased segment length\n",
+    "duration=6\n",
+    "overlap=0.5\n",
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(1, 100), \n",
+    "                    psd_kwargs={'nperseg': int(duration*fs), \n",
+    "                                'noverlap': int(duration*fs*overlap),\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)\n",
+    "\n",
+    "irasa_out.fit_aperiodic_model().aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case #2: Increase your hmax\n",
+    "\n",
+    "Sometimes we either can't or don't want to increase our frequency resolution. In this case we can increase our maximal up/-downsampling factors to get rid of peaks in the signal.\n",
+    "However, be aware that this can - depending on our filter settings and sampling rate - limit the frequency range that we can sensibly evaluate (see irasa_pitfalls.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.252254</td>\n",
+       "      <td>0.991107</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Offset  Exponent fit_type ch_name\n",
+       "0 -1.252254  0.991107    fixed       0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#low segment length, but high hset\n",
+    "duration=1\n",
+    "overlap=0.5\n",
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(4, 40), \n",
+    "                    psd_kwargs={'nperseg': int(duration*fs), \n",
+    "                                'noverlap': int(duration*fs*overlap)\n",
+    "                            },\n",
+    "                    hset_info=(1.1, 6, 0.05))\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)\n",
+    "\n",
+    "irasa_out.fit_aperiodic_model().aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/examples/irasa_mne.ipynb b/doc/source/examples/irasa_mne.ipynb
new file mode 100644
index 0000000..d758928
--- /dev/null
+++ b/doc/source/examples/irasa_mne.ipynb
@@ -0,0 +1,5913 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using IRASA together with MNE Python\n",
+    "\n",
+    "MNE Python offers a great beginner level interface to start analysing electrophysiological data.\n",
+    "If you are already familiar with MNE Python it is very easy to integrate PyRASA in your existing workflow.\n",
+    "This example notebook shows how to use PyRASA on your raw and epoched data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from neurodsp.sim import sim_combined\n",
+    "import numpy as np\n",
+    "import scipy.signal as dsp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "sys.path.append('../')\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "new_rc_params = {'text.usetex': False,\n",
+    "                 \"svg.fonttype\": 'none'\n",
+    "}\n",
+    "mpl.rcParams.update(new_rc_params)\n",
+    "\n",
+    "import mne\n",
+    "from mne.datasets import sample\n",
+    "\n",
+    "from pyrasa.irasa_mne import irasa_raw, irasa_epochs\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by loading some MNE example data. More information about the dataset can be obtained from here TODO: insert link"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Opening raw data file /Users/fabian.schmidt/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...\n",
+      "    Read a total of 3 projection items:\n",
+      "        PCA-v1 (1 x 102)  idle\n",
+      "        PCA-v2 (1 x 102)  idle\n",
+      "        PCA-v3 (1 x 102)  idle\n",
+      "    Range : 25800 ... 192599 =     42.956 ...   320.670 secs\n",
+      "Ready.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<details open>\n",
+       "    <summary><strong>General</strong></summary>\n",
+       "    <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
+       "        <tr>\n",
+       "            <th>Measurement date</th>\n",
+       "            \n",
+       "            <td>December 03, 2002  19:01:10 GMT</td>\n",
+       "            \n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <th>Experimenter</th>\n",
+       "            \n",
+       "            <td>MEG</td>\n",
+       "            \n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <th>Participant</th>\n",
+       "            \n",
+       "            <td>Unknown</td>\n",
+       "            \n",
+       "        </tr>\n",
+       "    </table>\n",
+       "    </details>\n",
+       "    <details open>\n",
+       "        <summary><strong>Channels</strong></summary>\n",
+       "        <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
+       "            <tr>\n",
+       "                <th>Digitized points</th>\n",
+       "                \n",
+       "                <td>146 points</td>\n",
+       "                \n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>Good channels</th>\n",
+       "                <td>102 Magnetometers</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>Bad channels</th>\n",
+       "                <td>None</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>EOG channels</th>\n",
+       "                <td>Not available</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>ECG channels</th>\n",
+       "                <td>Not available</td>\n",
+       "            </tr>\n",
+       "        </table>\n",
+       "        </details>\n",
+       "        <details open>\n",
+       "            <summary><strong>Data</strong></summary>\n",
+       "            <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Sampling frequency</th>\n",
+       "                    <td>600.61 Hz</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Highpass</th>\n",
+       "                    <td>0.10 Hz</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Lowpass</th>\n",
+       "                    <td>172.18 Hz</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Projections</th>\n",
+       "                    <td>PCA-v1 : off<br />PCA-v2 : off<br />PCA-v3 : off</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Filenames</th>\n",
+       "                    <td>sample_audvis_raw.fif</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Duration</th>\n",
+       "                    <td>00:04:38 (HH:MM:SS)</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "            </table>\n",
+       "            </details>"
+      ],
+      "text/plain": [
+       "<Raw | sample_audvis_raw.fif, 102 x 166800 (277.7 s), ~3.0 MB, data not loaded>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "data_path = sample.data_path()\n",
+    "subjects_dir = data_path / \"subjects\"\n",
+    "subject = \"sample\"\n",
+    "\n",
+    "meg_path = data_path / \"MEG\" / \"sample\"\n",
+    "raw_fname = meg_path / \"sample_audvis_raw.fif\"\n",
+    "fwd_fname = meg_path / \"sample_audvis-meg-eeg-oct-6-fwd.fif\"\n",
+    "\n",
+    "\n",
+    "raw = mne.io.read_raw_fif(raw_fname)\n",
+    "picks = mne.pick_types(raw.info, meg='mag', eeg=False, \n",
+    "                       stim=False, eog=False, exclude=\"bads\")\n",
+    "raw.pick(picks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by using the irasa_raw function. This function can be directly applied to your mne raw data object and will return a `AperiodicSpectrumArray` and a `PeriodicSpectrumArray` class. Both of the returned data classes are children from the MNE SpectrumArray class and allow you to use both the internal mne based plotting and analysis methods, as well as some specific methods to further investigate your aperiodic and periodic data.\n",
+    "\n",
+    "A strength of IRASA is that the user needs to specify relatively few parameters to seperate periodic from aperiodic activity. Actually its really only one parameter that is the hset, which specifies the up- and downsampling factors IRASA uses.\n",
+    "\n",
+    "However, misspecifications of hset can have can have severe consequences (see introduction to IRASA) for specifics regarding the algorithm and how things can go wrong. But dont worry the IRASA MNE functions are defined in a way that will throw an error in case you accidently fit models that are error prone and will tell you how you should specify your parameters such that everything should run smoothely.\n",
+    "\n",
+    "Try changing the input to \"band=(0.25, 100)\" and see what happens :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "irasa_results = irasa_raw(raw, \n",
+    "                          band=(.25, 50), \n",
+    "                          duration=2, \n",
+    "                          hset_info=(1.,2.,.05))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can start investigating the returned `AperiodicSpectrumArray` and `PeriodicSpectrumArray` classes. \n",
+    "\n",
+    "You can use the basic plotting functions to compare the irasa'ed results to the classic power spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Effective window size : 3.410 (s)\n",
+      "Plotting power spectral density (dB=True).\n",
+      "Plotting power spectral density (dB=True).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_52084/1044382114.py:4: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n",
+      "  raw.compute_psd(method='welch', n_per_seg=nperseg, n_overlap=nperseg//2, fmin=0.25, fmax=50).plot(axes=axes[0])\n",
+      "/Users/fabian.schmidt/git/pyrasa/.pixi/envs/default/lib/python3.12/site-packages/mne/viz/utils.py:167: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "  (fig or plt).show(**kwargs)\n",
+      "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_52084/1044382114.py:5: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n",
+      "  irasa_results.aperiodic.plot(axes=axes[1])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting power spectral density (dB=False).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/irasa_mne/mne_objs.py:61: UserWarning: Zero value in PSD for channels MEG 0111, MEG 0121, MEG 0131, MEG 0141, MEG 0211, MEG 0221, MEG 0231, MEG 0241, MEG 0311, MEG 0321, MEG 0331, MEG 0341, MEG 0411, MEG 0421, MEG 0431, MEG 0441, MEG 0511, MEG 0521, MEG 0531, MEG 0541, MEG 0611, MEG 0621, MEG 0631, MEG 0641, MEG 0711, MEG 0721, MEG 0731, MEG 0741, MEG 0811, MEG 0821, MEG 0911, MEG 0921, MEG 0931, MEG 0941, MEG 1011, MEG 1021, MEG 1031, MEG 1041, MEG 1111, MEG 1121, MEG 1131, MEG 1141, MEG 1211, MEG 1221, MEG 1231, MEG 1241, MEG 1311, MEG 1321, MEG 1331, MEG 1341, MEG 1411, MEG 1421, MEG 1431, MEG 1441, MEG 1511, MEG 1521, MEG 1531, MEG 1541, MEG 1611, MEG 1621, MEG 1631, MEG 1641, MEG 1711, MEG 1721, MEG 1731, MEG 1741, MEG 1811, MEG 1821, MEG 1831, MEG 1841, MEG 1911, MEG 1921, MEG 1931, MEG 1941, MEG 2011, MEG 2021, MEG 2031, MEG 2041, MEG 2111, MEG 2121, MEG 2131, MEG 2141, MEG 2211, MEG 2221, MEG 2231, MEG 2241, MEG 2311, MEG 2321, MEG 2331, MEG 2341, MEG 2411, MEG 2421, MEG 2431, MEG 2441, MEG 2511, MEG 2521, MEG 2531, MEG 2541, MEG 2611, MEG 2621, MEG 2631, MEG 2641.\n",
+      "These channels might be dead.\n",
+      "  super().plot(\n",
+      "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_52084/1044382114.py:12: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  f.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, axes = plt.subplots(nrows=3, figsize=(8, 4))\n",
+    "\n",
+    "nperseg = int(raw.info['sfreq']*2)\n",
+    "raw.compute_psd(method='welch', n_per_seg=nperseg, n_overlap=nperseg//2, fmin=0.25, fmax=50).plot(axes=axes[0])\n",
+    "irasa_results.aperiodic.plot(axes=axes[1])\n",
+    "irasa_results.periodic.plot(axes=axes[2])\n",
+    "\n",
+    "axes[0].set_title('PSD')\n",
+    "axes[1].set_title('Aperiodic Spectrum')\n",
+    "axes[2].set_title('Periodic Spectrum')\n",
+    "\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now lets further investigate the returned spectra, by doing some slope fitting on the `AperiodicSpectrumArray` or trying to detect some putative oscillations in the `PeriodicSpectrumArray`. Generally speaking this is not necessary as you could just stop at this point and simply compare either periodic or aperiodic spectra directly across different subjects or experimental conditions, but sometimes we have specific expectations/assumptions about certain parameters of interest in a power spectrum and its therefore a good idea to parametrize our spectra early on.\n",
+    "\n",
+    "Lets start with the `AperiodicSpectrumArray`: \n",
+    "Here we can use the `get_slopes` method to extract two pandas dataframes. One containing the aperiodic parameters extracted from the data and another containing information about how well these parameters explained variance in the data.\n",
+    "\n",
+    "The `get_slopes` method allows you to specify different methods to model your aperiodic spectrum. You can choose between a `fixed` and a `knee` mode similarly to specparam. However, it is worth noting that the `knee` fitting in PyRASA is slightly different as it allows you to fit two slopes and a knee.\n",
+    "\n",
+    "You can also limit the range of your slope fit to a specific frequency range of interest (e.g. 30-40Hz). However, be aware that large freqeuency ranges are usually preferable, as decreasing your frequency range of interest too much usually results in a worse model fit (see Ameen et al. 2024). However, in some cases this decision might be viable e.g. understanding differences in E/I ratios (see Gao et al. 2017).\n",
+    "\n",
+    "Lets start by comparing a `fixed` to a `knee` model fit. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'R2')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fixed_model = irasa_results.aperiodic.fit_aperiodic_model(fit_func='fixed', scale=True, fit_bounds=[.5, 45])\n",
+    "knee_model = irasa_results.aperiodic.fit_aperiodic_model(fit_func='knee', scale=True, fit_bounds=[.5, 45]);\n",
+    "\n",
+    "\n",
+    "f, ax = plt.subplots(ncols=2, figsize=(8,4))\n",
+    "ax[0].hist(fixed_model.gof['r_squared']);\n",
+    "ax[0].set_xlabel('R2')\n",
+    "ax[1].hist(knee_model.gof['r_squared']);#\n",
+    "ax[1].set_xlabel('R2')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<details open>\n",
+       "    <summary><strong>General</strong></summary>\n",
+       "    <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
+       "        <tr>\n",
+       "            <th>Measurement date</th>\n",
+       "            \n",
+       "            <td>December 03, 2002  19:01:10 GMT</td>\n",
+       "            \n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <th>Experimenter</th>\n",
+       "            \n",
+       "            <td>MEG</td>\n",
+       "            \n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <th>Participant</th>\n",
+       "            \n",
+       "            <td>Unknown</td>\n",
+       "            \n",
+       "        </tr>\n",
+       "    </table>\n",
+       "    </details>\n",
+       "    <details open>\n",
+       "        <summary><strong>Channels</strong></summary>\n",
+       "        <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
+       "            <tr>\n",
+       "                <th>Digitized points</th>\n",
+       "                \n",
+       "                <td>146 points</td>\n",
+       "                \n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>Good channels</th>\n",
+       "                <td>102 Magnetometers</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>Bad channels</th>\n",
+       "                <td>None</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>EOG channels</th>\n",
+       "                <td>Not available</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <th>ECG channels</th>\n",
+       "                <td>Not available</td>\n",
+       "            </tr>\n",
+       "        </table>\n",
+       "        </details>\n",
+       "        <details open>\n",
+       "            <summary><strong>Data</strong></summary>\n",
+       "            <table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Sampling frequency</th>\n",
+       "                    <td>600.61 Hz</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Highpass</th>\n",
+       "                    <td>0.10 Hz</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Lowpass</th>\n",
+       "                    <td>172.18 Hz</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                <tr>\n",
+       "                    <th>Projections</th>\n",
+       "                    <td>PCA-v1 : off<br />PCA-v2 : off<br />PCA-v3 : off</td>\n",
+       "                </tr>\n",
+       "                \n",
+       "                \n",
+       "                \n",
+       "            </table>\n",
+       "            </details>"
+      ],
+      "text/plain": [
+       "<Info | 20 non-empty values\n",
+       " acq_pars: ACQch001 110113 ACQch002 110112 ACQch003 110111 ACQch004 110122 ...\n",
+       " bads: []\n",
+       " ch_names: MEG 0111, MEG 0121, MEG 0131, MEG 0141, MEG 0211, MEG 0221, MEG ...\n",
+       " chs: 102 Magnetometers\n",
+       " custom_ref_applied: False\n",
+       " description: acquisition (megacq) VectorView system at NMR-MGH\n",
+       " dev_head_t: MEG device -> head transform\n",
+       " dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra)\n",
+       " events: 1 item (list)\n",
+       " experimenter: MEG\n",
+       " file_id: 4 items (dict)\n",
+       " highpass: 0.1 Hz\n",
+       " hpi_meas: 1 item (list)\n",
+       " hpi_results: 1 item (list)\n",
+       " lowpass: 172.2 Hz\n",
+       " meas_date: 2002-12-03 19:01:10 UTC\n",
+       " meas_id: 4 items (dict)\n",
+       " nchan: 102\n",
+       " proj_id: 1 item (ndarray)\n",
+       " proj_name: test\n",
+       " projs: PCA-v1: off, PCA-v2: off, PCA-v3: off\n",
+       " sfreq: 600.6 Hz\n",
+       ">"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(knee_model.aperiodic_params['Knee Frequency (Hz)'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the `knee` model fit is explaining more variance of our data, but thats not enough to see whether the `knee` model is better than `fixed` model. You will soon be able to do this with IRASA by using information criteria :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now lets look at epoched data and investigate event related changes in periodic and aperiodic activity.\n",
+    "Lets start by loading the events."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%% now lets check-out the events\n",
+    "event_id = {\n",
+    "    \"Auditory/Left\": 1,\n",
+    "    \"Auditory/Right\": 2,\n",
+    "    \"Visual/Left\": 3,\n",
+    "    \"Visual/Right\": 4,\n",
+    "}\n",
+    "tmin = -0.2\n",
+    "tmax = 0.5\n",
+    "\n",
+    "# Load real data as the template\n",
+    "event_fname = meg_path / \"sample_audvis_filt-0-40_raw-eve.fif\"\n",
+    "events = mne.read_events(event_fname)\n",
+    "\n",
+    "\n",
+    "epochs = mne.Epochs(\n",
+    "    raw,\n",
+    "    events,\n",
+    "    event_id,\n",
+    "    tmin,\n",
+    "    tmax,\n",
+    "    baseline=None,\n",
+    "    preload=True,\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can just use the `irasa_epochs` function to get periodic and aperiodic data per epoch.\n",
+    "This is of course super noisy at the single epoch level and should be averaged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "irasa_epoched = irasa_epochs(epochs, \n",
+    "                                   band=(.5, 50), \n",
+    "                                   hset_info=(1.,2.,.05))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now again get the slopes. Other than in `raw` we also get the `event_id` as a column in our pandas dataframe which we can use to run e.g. statistics or to plot a change in exponents by condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in power\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n",
+      "/Users/fabian.schmidt/git/pyrasa/pyrasa/utils/fit_funcs.py:146: RuntimeWarning: overflow encountered in multiply\n",
+      "  y_hat = Offset - np.log10(x**Exponent_1 * (Knee + x**Exponent_2))\n"
+     ]
+    }
+   ],
+   "source": [
+    "knee_epoched = irasa_epoched.aperiodic.fit_aperiodic_model(fit_func='knee', scale=True, fit_bounds=[1, 45])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='event_id', ylabel='Knee Frequency (Hz)'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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GwdvbG8nJyZg9ezbc3d0xZMgQqW54eDimT58ONzc3uLq6YsaMGWjVqpU0OouIiIhqL4P12amKNWvWALg3outBkZGRCAsLg6WlJU6fPo3169cjKysL3t7e6NatG7Zs2QInJyep/rJly2BlZYXhw4cjLy8PPXr0QFRUFJevICIiIuMmO4+aiNDOzg4///zzI69ja2uLFStWYMWKFYYKjYiIiMyE3vPsEBEREdUklW7ZeXD0EgAUFBRg0aJFUCgUOuUff/yxYSIjIiIiMoBKJzsnTpzQ2Q8NDcXVq1d1yvRdCJSIiIioulU62dm/f391xkFERERULdhnh4iIiMxapZKdJUuWIDc3t1IXPHLkCHbt2vVYQREREREZSqWSnXPnzsHf3x8TJkzATz/9hFu3bknHioqKcOrUKaxevRqhoaF4/vnn4ezsXG0BExEREemjUn121q9fj1OnTmHVqlV44YUXoFarYWlpCblcjrt37wIA2rRpg1deeQVjx47lUgxERERkMirdQbl169b47LPP8Omnn+LUqVNITk5GXl4e3N3dERwcDHd39+qMk4iIiKhK9J5BWSaTISgoCEFBQdURDxEREZFBcTQWERERmTUmO0RERGTWmOwQERGRWWOyQ0RERGZN72QnKipKGm5OREREZOr0TnZmzZoFpVKJ8PBwxMfHV0dMRERERAajd7Jz7do1fP3118jMzES3bt3QvHlzfPDBB0hLS6uO+IiIiIgei97JjqWlJQYOHIjt27cjNTUVr7zyCjZu3Ih69eph4MCB+P7771FSUlIdsRIRERHp7bE6KHt6eqJTp04ICQmBhYUFTp8+jbCwMDRq1AixsbEGCpGIiIio6qqU7Ny8eRMffvghWrZsia5du0Kj0eDHH39EUlISbty4gaFDh2Ls2LGGjpWIiIhIb3ovFzFgwAD8/PPPaNq0KcaPH48xY8bA1dVVOm5nZ4fp06dj2bJlBg2UiIiIqCr0TnY8PT0RFxeHkJCQcut4e3sjKSnpsQIjIiIiMgS9k521a9c+so5MJoO/v3+VAiIiIiIyJL377EydOhX/+9//SpWvXLkSERERhoiJiIiIyGD0Tna2bduGTp06lSoPDQ3F1q1bDRIUERERkaHonexkZGRAoVCUKnd2dsbt27cNEhQRERGRoeid7DRu3Bh79uwpVf7TTz+hYcOGBgmKiIiIyFD07qA8bdo0TJ48Gbdu3UL37t0BAPv27cNHH32ETz75xNDxERERET0WvZOdcePGQavVYtGiRXjvvfcAAPXr18eaNWswZswYgwdIRERE9Dj0TnYAYMKECZgwYQJu3boFOzs7ODo6GjouIiIiIoOoUrJzn4eHh6HiICIiIqoWendQvnnzJl588UX4+PjAysoKlpaWOhsRERGRKdG7ZScsLAwpKSmYO3cuvL29IZPJqiMuIiIiIoPQO9k5cOAAfv/9dwQHB1dDOERERESGpfdjLD8/PwghqiMWIiIiIoPTO9n55JNPMHPmTCQnJ1dDOERERESGpfdjrBEjRuDu3bto1KgR7O3tYW1trXP8zp07BguOiIiI6HHpnewYcpbkxYsXY/v27fjzzz9hZ2eH0NBQfPDBB2jWrJlURwiBd999F59//jkyMzPRoUMHrFq1Ci1btpTqaLVazJgxA9988w3y8vLQo0cPrF69Gr6+vgaLlYiIiGomvZOdsWPHGuzF4+LiMGnSJDz11FMoKirC22+/jd69e+PcuXNwcHAAACxduhQff/wxoqKi0LRpUyxcuBC9evXChQsX4OTkBACIiIjADz/8gM2bN8PNzQ3Tp09H//79kZCQwOHwREREtZxMVKG38ZUrVxAZGYkrV65g+fLl8PT0xJ49e+Dn56fT4qKvW7duwdPTE3FxcXjmmWcghICPjw8iIiLw1ltvAbjXiuPl5YUPPvgAr776KtRqNTw8PLBhwwaMGDECAHDjxg34+flh9+7d6NOnzyNfV6PRQKFQQK1Ww9nZucrx1ySdlvyK61l5AIC6LnY4OLO7kSMiIiLST2W/v/XuoBwXF4dWrVrhyJEj2L59O3JycgAAp06dwrx586oeMQC1Wg0AcHV1BQAkJSUhLS0NvXv3lurI5XJ06dIF8fHxAICEhAQUFhbq1PHx8UFgYKBU52FarRYajUZnIyIiIvOkd7Izc+ZMLFy4EDExMbCxsZHKu3XrhkOHDlU5ECEEpk2bhs6dOyMwMBAAkJaWBgDw8vLSqevl5SUdS0tLg42NDerUqVNunYctXrwYCoVC2vz8/KocNxEREZk2vZOd06dPY8iQIaXKPTw8kJGRUeVAJk+ejFOnTuGbb74pdezhWZqFEI+cubmiOrNmzYJarZa21NTUKsdNREREpk3vZMfFxQUqlapU+YkTJ1C3bt0qBTFlyhTs3LkT+/fv1xlBpVQqAaBUC016errU2qNUKlFQUIDMzMxy6zxMLpfD2dlZZyMiIiLzpHeyM2rUKLz11ltIS0uDTCZDSUkJDh48iBkzZmDMmDF6XUsIgcmTJ2P79u349ddf0aBBA53jDRo0gFKpRExMjFRWUFCAuLg4hIaGAgDatWsHa2trnToqlQpnzpyR6hAREVHtpffQ80WLFiEsLAx169aFEAIBAQEoLi7GqFGjMGfOHL2uNWnSJGzatAnff/89nJycpBYchUIBOzs7yGQyRERE4P3330eTJk3QpEkTvP/++7C3t8eoUaOkuuHh4Zg+fTrc3Nzg6uqKGTNmoFWrVujZs6e+t0dERERmRu9kx9raGhs3bsSCBQtw4sQJlJSUoE2bNmjSpIneL75mzRoAQNeuXXXKIyMjERYWBgB48803kZeXh4kTJ0qTCu7du1eaYwcAli1bBisrKwwfPlyaVDAqKopz7BAREVHV5tkxN5xnh/PsEBFRzVPZ72+9W3bGjRtX4fGvvvpK30sSERERVRu9k52HRz0VFhbizJkzyMrKQvfubB0gIiIi06J3shMdHV2qrKSkBBMnTkTDhg0NEhQRERGRoeg99LzMi1hY4I033sCyZcsMcTkiIiIigzFIsgPcWxy0qKjIUJcjIiIiMgi9H2NNmzZNZ18IAZVKhV27dmHs2LEGC4yIiIjIEPROdk6cOKGzb2FhAQ8PD3z00UePHKlFRERE9KTpnezs37+/OuIgIiIiqhYG67NDREREZIr0btlp06YNZDJZpeoeP35c74CIiIiIDEnvZKdv375YvXo1AgICEBISAgA4fPgwzp49iwkTJsDOzs7gQRIRERFVld7Jzq1btzB16lS89957OuXz5s1Damoql4sgIiIik6J3n53vvvsOY8aMKVU+evRobNu2zSBBERERERmK3smOnZ0dDhw4UKr8wIEDsLW1NUhQRERERIai92OsiIgITJgwAQkJCejYsSOAe312vvrqK7zzzjsGD5CIiIjoceid7MycORMNGzbE8uXLsWnTJgBAixYtEBUVheHDhxs8QCIiIqLHoXeyAwDDhw9nYkNEREQ1QpUmFczKysKXX36J2bNn486dOwDuzalz/fp1gwZHRERE9Lj0btk5deoUevbsCYVCgeTkZLz88stwdXVFdHQ0/vrrL6xfv7464iQiIiKqEr1bdqZNm4awsDBcunRJZ/RVv3798Ntvvxk0OCIiIqLHpXeyc/ToUbz66qulyuvWrYu0tDSDBEVERERkKHonO7a2ttBoNKXKL1y4AA8PD4MERURERGQoeic7gwYNwoIFC1BYWAgAkMlkSElJwcyZMzFs2DCDB0hERET0OPROdj788EPcunULnp6eyMvLQ5cuXdC4cWM4OTlh0aJF1REjERERUZXpPRrL2dkZBw4cwK+//orjx4+jpKQEbdu2Rc+ePasjPiIiIqLHoleyU1RUBFtbWyQmJqJ79+7o3r17dcVFREREZBB6PcaysrKCv78/iouLqyseIiIiIoPSu8/OnDlzMGvWLGnmZCIiIiJTpnefnf/973+4fPkyfHx84O/vDwcHB53jx48fN1hwRERERI9L72Rn8ODB1RAGERERUfWodLLz1Vdf4YUXXsC8efOqMx4iIiIig6p0n53x48dDrVZL+z4+PkhOTq6OmIiIiIgMptLJjhBCZz87OxslJSUGD4iIiIjIkPQejUVERERUk1Q62ZHJZJDJZOXuExEREZmiSndQFkKgadOmUoKTk5ODNm3awMJCN1/i/DtERERkSiqd7ERGRhr8xX/77Tf897//RUJCAlQqFaKjo3WGtoeFhWHdunU653To0AGHDx+W9rVaLWbMmIFvvvkGeXl56NGjB1avXg1fX1+Dx0tEREQ1T6WTnbFjxxr8xXNzcxEUFISXXnoJw4YNK7NO3759dRItGxsbneMRERH44YcfsHnzZri5uWH69Ono378/EhISYGlpafCYiYiIqGbRe1JBQ+rXrx/69etXYR25XA6lUlnmMbVajbVr12LDhg3Squtff/01/Pz88Msvv6BPnz4Gj5mIiIhqFpMfjRUbGwtPT080bdoU48ePR3p6unQsISEBhYWF6N27t1Tm4+ODwMBAxMfHGyNcIiIiMjFGbdl5lH79+uG5556Dv78/kpKSMHfuXHTv3h0JCQmQy+VIS0uDjY0N6tSpo3Oel5cX0tLSyr2uVquFVquV9jUaTbXdAxERERmXSSc7I0aMkP4/MDAQ7du3h7+/P3bt2oWhQ4eWe54QosJh8YsXL8a7775r0FiJiIjINFX5MVZBQQEuXLiAoqIiQ8ZTIW9vb/j7++PSpUsAAKVSiYKCAmRmZurUS09Ph5eXV7nXmTVrFtRqtbSlpqZWa9xERERkPHonO3fv3kV4eDjs7e3RsmVLpKSkAACmTp2KJUuWGDzAB2VkZCA1NRXe3t4AgHbt2sHa2hoxMTFSHZVKhTNnziA0NLTc68jlcjg7O+tsREREZJ70TnZmzZqFkydPIjY2Fra2tlJ5z549sWXLFr2ulZOTg8TERCQmJgIAkpKSkJiYiJSUFOTk5GDGjBk4dOgQkpOTERsbiwEDBsDd3R1DhgwBACgUCoSHh2P69OnYt28fTpw4gdGjR6NVq1bS6CwiIiKq3fTus7Njxw5s2bIFHTt21OkXExAQgCtXruh1rWPHjqFbt27S/rRp0wDcm9NnzZo1OH36NNavX4+srCx4e3ujW7du2LJlC5ycnKRzli1bBisrKwwfPlyaVDAqKopz7BARERGAKiQ7t27dgqenZ6ny3NxcvdfK6tq1a6nV1B/0888/P/Iatra2WLFiBVasWKHXaxMREVHtoPdjrKeeegq7du2S9u8nOF988QVCQkIMFxkRERGRAejdsrN48WL07dsX586dQ1FREZYvX46zZ8/i0KFDiIuLq44YiYiIiKpM75ad0NBQHDx4EHfv3kWjRo2wd+9eeHl54dChQ2jXrl11xEhERERUZVWaVLBVq1alViMnIiIiMkVVmlTwypUrmDNnDkaNGiWtVbVnzx6cPXvWoMERERERPS69k524uDi0atUKR44cwbZt25CTkwMAOHXqFObNm2fwAImIiIgeh97JzsyZM7Fw4ULExMTAxsZGKu/WrRsOHTpk0OCoegghUFBcIu1r8gpxPCWzwmkAiIiIaiq9k53Tp09LMxg/yMPDAxkZGQYJiqrPxZvZGLTqIG5l/7Pqe7a2CENXx2PQqoO4eDPbiNEREREZnt7JjouLC1QqVanyEydOoG7dugYJiqrHxZvZeHZNPE5dU5d5/NQ1NZ5dE8+Eh4iIzIreyc6oUaPw1ltvIS0tDTKZDCUlJTh48CBmzJiBMWPGVEeMZABCCMz47iQ0+RWvUq/JL8J/vjvJR1pERGQ29E52Fi1ahHr16qFu3brIyclBQEAAnnnmGYSGhmLOnDnVESMZwInUrHJbdB528poaialZ1RsQERHRE6L3PDvW1tbYuHEjFixYgBMnTqCkpARt2rRBkyZNqiM+MpCYczf1qr/33E20qVenmqIhIiJ6cqo0qSAANGrUCI0aNTJkLFSN1HmF1VqfiIjIVOmd7BQXFyMqKgr79u1Deno6SkpKdI7/+uuvBguODEdhZ12t9YmIiEyV3snO66+/jqioKPzf//0fAgMDpVXPybT1CvDCmtgrla5fz9WuGqMhIiJ6cvROdjZv3oxvv/0W//73v6sjHqombfxc0NpXUelOynN3nIEMMjz/dL1qjoyIiKh66T0ay8bGBo0bN66OWKgayWQyfPhcEJxtK5ffFpUAM7efxjvfn0FhccmjTyAiIjJReic706dPx/LlyzkPSw3U1MsJWyeEorWvoszjjT0dUcdet6/O+kN/YfSXR5CRoy3zHCIiIlMnE3pmLUOGDMH+/fvh6uqKli1bwtpa98tx+/btBg3wSdBoNFAoFFCr1XB2djZ2ONVOCIGn398nLRnhJLfC+vCnEezngpsaLV7dcAwnH3rcVdfFDp+PaYeWPmUnSkRERE9aZb+/q7RcxJAhQ9ClSxe4u7tDoVDobGT6ZDIZbCz/eeud7azRpl4dyGQyKBW22PJqCIa21V3643pWHoaticcPJ2886XCJiIgei94dlCMjI8s9ptFoHisYMg221pb46LkgtPRRYNGucyj5u+0vv7AEU745gXMqDWb0bgZLC47EIyIi01fplp0PP/ywwuMajQa9e/d+7IDINMhkMoR3boD14zqUmnNnTewVhK87yokHiYioRqh0sjN37txyW3Wys7PRp08ftuyYoc5N3LFzcic083LSKY+9cAtDVh3E5fQcI0VGRERUOZVOdjZs2ICJEydix44dOuU5OTno06cP7ty5g/379xs6PjIB/m4O2D4xFH1bKnXKr97OxZBVB7HvvH7rbhERET1JlU52nn32WaxYsQKjRo2SkpqcnBz07dsXt2/fRmxsLLy8vKotUDIuB7kVVr/QFtN6NdUpz9YW4eX1x7Bq/2VOR0BERCZJr9FYL7/8MubPn4/BgwcjNjYW/fr1Q1paGvbv3w9vb+/qipFMhIWFDFN7NMHnL7aDg42lVC4E8N+fL2DyphO4W1BkxAiJiIhK03vo+ZtvvomJEyeiR48euHHjBmJjY1G3bt1Hn0hmo3dLJaIndUJ9N3ud8l2nVRi6Oh6pd+4aKTIiIqLSKj30fOjQoTr71tbWcHd3x9SpU3XKa+KkgqS/pl5O+H5SZ0z+5jh+v3RbKv8zLRsDVx7AqhfaIrSRuxEjJCIiuqfSLTsPTx44cuRIBAQEcFLBWkxhb42ol57Gq8801CnPvFuIF9f+gaiDSezHQ0RERlfplp2KJhOk2svSQoZZ/26BFt7OeGvbKWiL7i0aWlwiMP+Hczh7Q4OFQwIht7J8xJWIiIiqh959dojKMrhNXWx9LRTeClud8u8SrmHEZ4dxU5NvpMiIiKi2Y7JDBtPKV4Gdkzvjqfp1dMoTU7MwYMUBnEjJNFJkRERUmzHZIYPycJJj48sd8UKHejrl6dlajPjsML49lmqkyIiIqLZiskMGZ2NlgUVDWmHRkEBYPbBYaEFxCd7cegrv/nAWRcUlRoyQiIhqEyY7VG1e6OCPb17pCHdHG53yyIPJGPPVH8jMLTBSZEREVJsw2aFq9VR9V+yc3BmBdZ11yuOvZGDgqgM4r+LisUREVL2Y7FC183Gxw9bXQjE42EenPPVOHoaujsfu0yojRUZERLWBUZOd3377DQMGDICPjw9kMlmpFdWFEJg/fz58fHxgZ2eHrl274uzZszp1tFotpkyZAnd3dzg4OGDgwIG4du3aE7wLqgxba0ssGxGM2f9ujge68SCvsBgTNx7HR3svoKSEExASEZHhGTXZyc3NRVBQEFauXFnm8aVLl+Ljjz/GypUrcfToUSiVSvTq1QvZ2dlSnYiICERHR2Pz5s04cOAAcnJy0L9/fxQXFz+p26BKkslkeOWZRoh86Wk42+rOZ7ni18t4ZcMxZOcXGik6IiIyVzJhIvP5y2QyREdHY/DgwQDuter4+PggIiICb731FoB7rTheXl744IMP8Oqrr0KtVsPDwwMbNmzAiBEjAAA3btyAn58fdu/ejT59+lTqtTUaDRQKBdRqNZydnR99ghnotORXXM/KAwDUdbHDwZndn+jrJ93Oxfj1x3A5PUenvLGnIz5/sR0aejg+0XiIiKjmqez3t8n22UlKSkJaWhp69+4tlcnlcnTp0gXx8fEAgISEBBQWFurU8fHxQWBgoFSnLFqtFhqNRmejJ6uBuwOiJ4aiZwsvnfLL6TkYtOogYi+kGykyIiIyNyab7KSlpQEAvLx0vwy9vLykY2lpabCxsUGdOnXKrVOWxYsX6yxe6ufnZ+DoqTKcbK3x+YvtMLVHE53y7PwivBR1FJ/GXeFCokRE9NhMNtm5TyaT6ewLIUqVPexRdWbNmgW1Wi1tqamc1ddYLCxkmNarKT4d3Rb2Nv8sFioEsOSnP/H65kTkFbD/FRERVZ3JJjtKpRIASrXQpKenS609SqUSBQUFyMzMLLdOWeRyOZydnXU2Mq6+gd7YPjEUfq52OuU7T97As5/GS/2LiIiI9GWyyU6DBg2gVCoRExMjlRUUFCAuLg6hoaEAgHbt2sHa2lqnjkqlwpkzZ6Q6VHM0Vzpj56TO6NTYTaf87A0NBq44gCNXM4wUGRER1WRGTXZycnKQmJiIxMREAPc6JScmJiIlJQUymQwRERF4//33ER0djTNnziAsLAz29vYYNWoUAEChUCA8PBzTp0/Hvn37cOLECYwePRqtWrVCz549jXhnVFV1HGyw7qWnEd65gU55Rm4BXvjyCDYc/ov9eIiISC9Wj65SfY4dO4Zu3bpJ+9OmTQMAjB07FlFRUXjzzTeRl5eHiRMnIjMzEx06dMDevXvh5OQknbNs2TJYWVlh+PDhyMvLQ48ePRAVFQVLS8tSr0c1g5WlBeb2D0CAtzNmRZ9GQdG9RUOLSgTm7jiDczfUeHdgIGysTLZhkoiITIjJzLNjTJxn58nPs1NZialZeHXDMdzUaHXK2/nXwZrRbeHpZGukyIiIyNhq/Dw7RAAQ7OeCHyZ3Rtt6LjrlCX9lYuCKgzh1LcsocRERUc3BZIdMnqezLb55pSNGtNedDylNk49nPz2E7ce5FhoREZWPyQ7VCHIrSywZ1goLBrWE1QMriRYUlWDatyex8MdzKCouMWKERERkqpjsUI0hk8kwJqQ+NoR3gKuDjc6xLw8k4aWoo8i6W2Ck6IiIyFQx2aEaJ6SRG3ZO7oQAb93OaL9fuo2BKw/iQlq2kSIjIiJTxGSHaiTfOvbYNiEU/Vt765Sn3LmLIasPYs+Z8tdGIyKi2oXJDtVYdjaWWDGyDd7s2wwPLoV2t6AYr32dgE9+uYiSklo/swIRUa3HZIdqNJlMholdG+OrsU/ByVZ3jsxPfrmE175OQI62yEjRERGRKWCyQ2ahW3NP7JjUCQ09HHTK9567iaGrD+KvjFwjRUZERMbGZIfMRiMPR+yY1Andm3vqlF+8mYOBKw/i90u3jBQZEREZE5MdMivOttb4Ykx7TOrWSKdcnVeIsV/9gS9/v8qFRImIahkmO2R2LC1k+E+f5lg5qg3srP9ZELZEAAt3nce0b08iv7DYiBESEdGTxGSHzFb/1j7YOiEEdV3sdMqjT1zH8M8OQaXOM1JkRET0JDHZIbPW0keBnZM7oWNDV53yU9fUGLDiII4l3zFSZERE9KQw2SGz5+Yox4bwDggLra9TfjtHi5FfHMY3f6QYJzAiInoimOxQrWBtaYH5A1ti6bDWsLH85599YbHArO2nMWfHaRQUcSFRIiJzZPXoKkTmY/hTfmjk6YjXvk7ArWytVP714RRcvJmD1S+0hbuj3IgREhHp59k18VCp8wEA3gpbbJ0QauSITA9bdqjWaedfBz9M7owgPxed8j+S7mDgigM4c11tnMCIiKpApc7H9aw8XM/Kk5Ie0sVkh2olpcIWW17piGFtfXXKb6jz8eyn8fg+8bqRIiMiIkNjskO1lq21JT58rjXe6R8AS4t/VhLNLyzB65sTsfin8yjmQqJERDUekx2q1WQyGcZ1boD1456Gi721zrHP4q5iXNRRqO8WGik6IiIyBCY7RAA6NXbHzkmd0VzppFMed/EWBq8+iMvp2UaKjIiIHheTHaK/1XOzx7YJoegXqNQpT7qdi8Gr4vHLuZtGioyIiB4Hkx2iBzjIrbD6hbaY3qupTnmOtgjjNxzDin2XuJAoEVENw2SH6CEymQxTejTBF2Paw1H+z1RUQgAfxVzExI3HkastMmKERESkDyY7ROXoFeCFHZNC0cDdQaf8pzNpGLYmHql37hopMiIi0geTHaIKNPZ0wo5JndClqYdO+Z9p2Riw8gAOXr5tpMiIiKiymOwQPYLCzhpfhT2FV7s01CnPuluIMV/9gciDSezHQ0RkwpjsEFWCpYUMs/q1wPLngyG3+ufXprhE4N0fzuE/W08hv7DYiBESEVF5mOwQ6WFQcF1smxAKH4WtTvnWhGt4/vPDuKnhujRERKaGyQ6RngLrKrBzSmc8Xd9VpzwxNQsDVhzA8ZRMI0VGRERlYbJDVAXujnJ8/XIHjO5YT6c8PVuL5z87jG+PpRopMiIiehiTHaIqsrGywMLBrfD+kFawtvxnIdGC4hK8ufUU5u88i8LiEiNGSEREAJMdosc2qkM9bBrfEe6ONjrlUfHJGLP2D9zJLTBSZEREBDDZITKIp+q7YufkzmhVV6FTfuhqBgauPIBzNzRGioyIiJjsEBmIj4sdvnstBEPa1NUpv5aZh2Fr4rHrlMpIkRER1W5MdogMyNbaEh8PD8Lb/24Bi3+68SCvsBiTNh3Hf3/+EyUlnICQiOhJMulkZ/78+ZDJZDqbUqmUjgshMH/+fPj4+MDOzg5du3bF2bNnjRgx0b2FRMc/0xBRLz0NZ1srnWOr9l/B+PXHoMkvNFJ0RES1j0knOwDQsmVLqFQqaTt9+rR0bOnSpfj444+xcuVKHD16FEqlEr169UJ2drYRIya655mmHtg5uTOaeDrqlO/7Mx2DVx3ElVs5TyyWZ9fEo9OSX9Fpya94dk38E3tdIiJTYPLJjpWVFZRKpbR5eNxbkFEIgU8++QRvv/02hg4disDAQKxbtw53797Fpk2bjBw10T313R0QPakTegd46ZRfvZWLwSsPYv+f6U8kDpU6H9ez8nA9Kw8qNWd5JqLaxeSTnUuXLsHHxwcNGjTA888/j6tXrwIAkpKSkJaWht69e0t15XI5unTpgvj4iv9y1Wq10Gg0OhtRdXGUW+HT0e3weo8mOuXZ2iKMW3cUq2MvcyFRIqJqZNLJTocOHbB+/Xr8/PPP+OKLL5CWlobQ0FBkZGQgLS0NAODlpfsXs5eXl3SsPIsXL4ZCoZA2Pz+/arsHIgCwsJDhjV5N8enodrC3sZTKhQCW7rmAKd+cQF4BFxIlIqoOJp3s9OvXD8OGDUOrVq3Qs2dP7Nq1CwCwbt06qY5MJtM5RwhRquxhs2bNglqtlrbUVE7tT09G30Aloid2Qj1Xe53yH0+pMGxNPK5l3jVSZERE5sukk52HOTg4oFWrVrh06ZI0KuvhVpz09PRSrT0Pk8vlcHZ21tmInpRmSifsnNwJ/2rirlN+TqXBwJUHcfhqhpEiIyIyTzUq2dFqtTh//jy8vb3RoEEDKJVKxMTESMcLCgoQFxeH0NBQI0ZJ9Ggu9jaIDHsKL3duoFN+J7cAo788gvWHktmPh4geSQiBggfW4NPkFeJ4SiY/Px5i0snOjBkzEBcXh6SkJBw5cgTPPvssNBoNxo4dC5lMhoiICLz//vuIjo7GmTNnEBYWBnt7e4waNcrYoRM9kpWlBeb0D8DHw4NgY/XPr2JRicA735/FzG2noS1iPx4iKtvFm9kYtOogbmVrpbJsbRGGro7HoFUHcfEmp2G5z+rRVYzn2rVrGDlyJG7fvg0PDw907NgRhw8fhr+/PwDgzTffRF5eHiZOnIjMzEx06NABe/fuhZOTk5EjJ6q8oW190cjDEa9uSECa5p9h4VuOpeJSejY+Hd0Ons62RoyQiEzNxZvZeHZNPDT5RWUeP3VNjWfXxGPrhFA09eJ3okywrQsajQYKhQJqtbrW9N/ptORXXM/KAwDUdbHDwZndjRwRpWfnY8LXx5HwV6ZOuZezHJ+92B7Bfi5VvjbfbyLzIYTAoFUHceqa+pF1g3wV2DGp0yMH7tRUlf3+NunHWES1iaeTLTaN74CRT+tOhXBTo8Xwzw5hW8I1I0VGRKbkaPKdSiU6AHDymhqJqVnVG1ANYNKPsYhqG7mVJd4f0goBPgq8u/Msiv5eNLSgqATTvzuJcyoNZvVrDitL/p1CVBsIIfBXxl2cvJaFxNR720k9k5e9526iTb061RNgDcFkh8jEyGQyvNjRH008HTFx43HcyS2Qjq09kIQLadlYOaoNXOxtjBglEVWHjBwtTl1T48TfSc3Ja1nIuvt4Cwer87jwMJMdIhPVsaEbdk7uhFfWJ+Cc6p8lTQ5cvo2BKw/iizHt0UzJjodENVV+YTHOXL/3mOne46ZMpN7JM/jrKOysDX7NmobJDpEJ861jj20TQvGfrSfx4ymVVJ5y5y6GrD6Ij4cHoW+gtxEjJKLKKC4RuHIrB4kpWUi8dq/V5s+0bBSX6D9GyEdhixt6LOj78ELEtRGTHSITZ2djiRUj26CljwJLf/4T98dP3i0oxmtfH8fUHk0Q0aMJLCzMc7QFUU2kUufhZGoWElPvtdicvqZGbhXWv3N3tEGwnwuCfF0QXM8Freu6wNnOSq/RWI8zktNcMNkhqgFkMhkmdG2E5konTN18AtkPzK3xv32XcF6lwbIRwXCU81ea6EnLzi/E6Yf62dzUaB994kPsrC3Rqq4CwfXuJTdBfgrUdbErc9j4h88FVTjPDgA421rhv88Fme2wc33wk5GoBunW3BPfT+qE8euP4cqtXKk85txNDFl1rx9PfXcHI0ZIZN4KikpwIS0bideykJhyL7G5cisH+s5YZyEDmno53Wu18XNBsJ8Lmng6VnqkZVMvJ2ydEIoZ350ss4UnyFeB/z4XxAkF/8Zkh6iGaejhiOhJnfDG5kTs+zNdKr+UnoOBKw9gxai26NLUw4gREpkHIQRS7tzVGfJ95oYGBUUljz75IXVd7P5ObBQI8nVBK18F7G0e7yu4qZcTvp/UCU+/v09aMsJJboX14U8j2M+FLToPYLJDVAM521rjizHt8XHMRazcf1kq1+QX4aXIPzCzX3OM/1dDftgR6eH+sG8puanisG8nW6t/+tn4uaC1nwKeTtWz5ItMJoPNA61BznbWtX5OnbIw2SGqoSwsZJjRpxlaeDtjxncnkVd4r/NjiQDe3/0nzt3QYMmw1pBbWZS5KnIb/uVHtdjDw75PpmYh5c5dva9jbSlDgLez9DgqyM8FDdwcOGDAxDDZIarh/q+1Nxq4O+CVDcdwLfOfOTp2JN7A2RsaWFrIylwVubWvAh/ymT7VAtKw7wceR1V12HdDd4d7SY2vAsH16qCFtxPkVpbVEDUZEpMdIjMQ4OOMnZM7Y9LG4zh0NUMqv5SeU+45XBWZzFWaOh+JqZlITL3XYnP6uho52vJHLZXnwWHfQX4uaO2r4MzlNRSTHSIz4epgg/XhT2PRrvOIik+u1Dma/CL857uTZr0qMpm3+8O+Hxwd9TjDvoP8FAj2q1PhsG+qeZjsEJkRa0sLzB/YEg5yS6zaf6VS59xfFZmdGsnUFRaX4E9VtjQDcWKqYYZ9B/m6oKlX5Yd9U83DZIfIDOnbFWHkF4fh7+oADyc5PJzk8Pz7vx5Ocng4yuHpLIeHoy2c7az4ly49EYYe9n1/yHewnwsC6yrgwAk4axW+20RmSN9VjvMLS3DhZjYu3MyusJ6NpcU/SVCpZOjvRMnZFu6ONuy0SXq5k1sgtdac/LvlJrOKw77vJzX3OxJ7OlfPsG+qOZjsEJmh6lrluKC4BNez8nA969ErMyvsrHVbiaRk6F4r0f1kqY69NVuLapn8wmKcvaHGiRTDDPu+PwMxh31TeZjsEJmhXgFeWBNbuT47wL1VlLPzi5BdhREr5VHnFUKdV4jLFYwIA+59Ybk7lt1K5OFkq5Mw2VqztaimeXDY9/2Wmwtp2SiqwrDvBu4Of4+OUiDIzwUBPs5sQaRKYbJDZIba/D1MtrKrIt8fjXW3oAi3swtwKycft7K1SM/W4tYD2/392znaKn1ZlaWwWEClzodKnf/Iuk62VrqtRE62Oo/U7idFrvY2/OveSO4N+/6nn01Vh327OdjorBvFYd/0OJjsEJkhmUxWpVWR7W2sUM/NCvXc7Cu8fkmJQFZeIdKz80slQv/s3ztW0evrKzu/CNn5Rbj6wCKoZbG0kMHNwUa3L1E5idHjrk9Umxlq2LettcW91b4fGB3lW4fDvslw+FtOZKaqc1VkCwsZXB1s4Opgg+bKiuvmFxbjdk75rUS3crS4pcnHrRwtCosN01pUXCKQ/vdrPIqDjWWZyZBOB2wnOdwc5bCsxa1FhcX3Vvs+8XeLzcnULFx+jGHf9yfqC/bjsG+qfkx2iMyYKayKbGttCd869vCtU3FrkRAC6rzC0q1EOVqk/50M3S+ryiid8uQWFCM34y6SMyruIGshA1wdKhqa/0+Zo9x4Q/SFEDiRmoWYczehziuEws4avQK89FoL7cFh3ydT1UhMzcTZGxpoqzDs20dhi+B6/8xC3IrDvskI+C+OyMzVlFWRZTIZXOxt4GJvgyaPaG3SFhUjI6egjMQoH+karZQYpWdrqzQvS1lKBHA7515/pfOqiuvaWVtWmAzdb0Vyc7SBtQFbNC7ezC6zJW9N7JUK10Iz2LBvudXfi2H+PQsxh32TiWCyQ0Q1jtzKEj4udvBxsauwnhACmvyiCluJ7m8ZuQUGiy+vsBgpd+4+cji1TAa42tvoJEYe5SRGzrYVtxZdvJldYR+t+2uhbXy5AwqKS5CYqpY6EVd12HeL+6t9/91q09Cdw77JNDHZISKzJZPJoLCzhsLOGo09HSusW1hcIrUWSaPRHmglut9SlJ6dj/xCw7QWCQFk5BYgI7cAf6Y9YkJHK4vSEzj+nQi5O9pg6c8XHtkZXJNfhAErD1Yp1gbuDtKQ72A/F7TwduZUAFRjMNkhIsK9dcWUClsoFbYAFOXWE0Igt6D4XgtRtu4js4c7YGfkavXuwFuegqLKT+j4uNwcbHQm6gvisG+q4ZjsEBHpQSaTwVFuBUcPRzT0qLi1qKi4BHfultG3qIwO2LkFxU/oDnTdH/Yd5OsidSTmsG8yN0x2iIiqiZWlBTydbOHpZIuWj6ibqy3C7XJbif7pZ3Q7pwDFjzmhYyMPB4R3boggPwWaeTlx2HcN562wLfP/6R9Mdmop/nIQmRYHuRUc5Fbwd3OosF5xiUDm361F9xOjrcdScTjpTqVfq3dLJUZ1qPe4IZOJ2Doh1NghmDwmO7UUfzlqFya35sPS4t5aYu6OcrTwvlfW0MMBQ1fHV/oavQO8qik6ItPEZIeoFmBya970XQst2M+l+oMiMiF8UEtEVMPdXwvN2bbiv18fXguNqLZgskNEZAbur4XW2rfsYfNBvgpsnRBapbXQiGo6PsYiIjIT99dCS0zNwt4H1sbqHeD1xNZCIzJFTHaIiMyITCZDm3p1THL9MyJjMZvHWKtXr0aDBg1ga2uLdu3a4ffffzd2SERERGQCzCLZ2bJlCyIiIvD222/jxIkT+Ne//oV+/fohJSXF2KERERGRkcmEMNTKLcbToUMHtG3bFmvWrJHKWrRogcGDB2Px4sWPPF+j0UChUECtVsPZ2bk6QyUiIiIDqez3d41v2SkoKEBCQgJ69+6tU967d2/Ex1d+ki0iIiIyTzW+g/Lt27dRXFwMLy/dGUG9vLyQlpZW5jlarRZarVba12g01RojERERGU+Nb9m57+EhlUKIcodZLl68GAqFQtr8/PyeRIhERERkBDU+2XF3d4elpWWpVpz09PRSrT33zZo1C2q1WtpSU1OfRKhERERkBDU+2bGxsUG7du0QExOjUx4TE4PQ0LLXA5LL5XB2dtbZiIiIyDzV+D47ADBt2jS8+OKLaN++PUJCQvD5558jJSUFr732mrFDIyIiIiMzi2RnxIgRyMjIwIIFC6BSqRAYGIjdu3fD39+/UuffH33PjspEREQ1x/3v7UfNomMW8+w8rmvXrrGTMhERUQ2VmpoKX1/fco8z2QFQUlKCGzduwMnJqVYtlKfRaODn54fU1FT2W6oF+H7XLny/a5fa+n4LIZCdnQ0fHx9YWJTfDdksHmM9LgsLiwozQnPHTtq1C9/v2oXvd+1SG99vhULxyDo1fjQWERERUUWY7BAREZFZY7JTi8nlcsybNw9yudzYodATwPe7duH7Xbvw/a4YOygTERGRWWPLDhEREZk1JjtERERk1pjs1BDz589HcHCwtB8WFobBgwcbLZ7q8Oeff6Jjx46wtbXVuVdzZy7vbVRUFFxcXPQ6p6beK1FZHv5drm7JycmQyWRITEx87GvVr18fn3zyiVFe+0lgslNN4uPjYWlpib59+1bL9ZcvX46oqChpv2vXroiIiKiW16pIWFgYZs6cCQCQyWTYsWNHla81b948ODg44MKFC9i3b1+VvjyfhNr63t7fHB0dERQUpBMjcG/ZlosXLxo8Dn0/hE1dTf5CrMj8+fPh5eX12J8DpmrAgAHo2bNnmccOHToEmUyG7t27Y9++fU84stL++usvyOVyaDQazJ8/X/rdtbCwgI+PD1544QWkpqbqnHP06FG88sorBo3DlD7DmexUk6+++gpTpkzBgQMHkJKSYvDrKxSKavlHVFBQUOm6JSUl2LVrFwYNGmSQ175y5Qo6d+4Mf39/uLm5GeSa1aG2vreRkZFQqVQ4efIkRowYgZdeegk///yzdNzOzg6enp4GjbmmqclfiI+TgJ0/fx7vvvsuPvvsM6hUKvTr18/sktTw8HD8+uuv+Ouvv0od++qrrxAcHIxnnnnGJD67vv/+e3Tt2lWaXLBly5ZQqVS4du0atmzZgtOnT2P48OE653h4eMDe3t4Y4T4RTHaqQW5uLr799ltMmDAB/fv31/kLuKxMd8eOHaWWqViyZAm8vLzg5OSE8PBw5Ofn6xx/sPk/LCwMcXFxWL58uZTBJycnAwDi4uLw9NNPQy6Xw9vbGzNnzkRRUZF0na5du2Ly5MmYNm0a3N3d0atXL4wbNw79+/fXeb2ioiIolUp89dVXUtnBgwdhYWGBDh06VOrnEhkZiRYtWsDW1hbNmzfH6tWrpWMymQwJCQlYsGABZDIZunbtipdeeglqtVq6p/nz51fqdapTbX5vXVxcoFQq0ahRI8yePRuurq7Yu3dvhfe/cOFCeHp6wsnJCS+//DJmzpxZ5pfqhx9+CG9vb7i5uWHSpEkoLCyU7uGvv/7CG2+8Id2/KavJX4iP48qVKwCAQYMGQalUmuXw5/79+8PT07NUi+bdu3exZcsWhIeHl0oaY2Nj8fTTT8PBwQEuLi7o1KmT9G+jrEe4ERER6Nq1q7S/Z88edO7cGS4uLnBzc0P//v2ln3VFvv/+ewwcOFDat7KyglKphI+PD/71r39h/PjxOHz4sM7i1w8np3/++Sc6d+4MW1tbBAQE4Jdffimz1e7q1avo1q0b7O3tERQUhEOHDkn3bkqf4Ux2qsGWLVvQrFkzNGvWDKNHj0ZkZOQjV2R90Lfffot58+Zh0aJFOHbsGLy9vXUSg4ctX74cISEhGD9+PFQqFVQqFfz8/HD9+nX8+9//xlNPPYWTJ09izZo1WLt2LRYuXKhz/rp162BlZYWDBw/is88+w8svv4w9e/ZApVJJdXbv3o2cnBydvwZ27tyJAQMGVLgeyX1ffPEF3n77bSxatAjnz5/H+++/j7lz52LdunUAAJVKhZYtW2L69OlQqVTYuXMnPvnkEzg7O0v3NGPGjEr/DKsL31uguLgY3377Le7cuQNra+tyY9+4cSMWLVqEDz74AAkJCahXrx7WrFlTqt7+/ftx5coV7N+/H+vWrUNUVJT0hbJ9+3b4+vpiwYIF0v2bspr8hViR69evY8SIEahTpw7c3NwwaNAgKemeP38+BgwYAODe0jv3/1ipSUlqZVhZWWHMmDGIiorS+Z3/7rvvUFBQgBdeeEGnflFREQYPHowuXbrg1KlTOHToEF555RW9fha5ubmYNm0ajh49in379sHCwgJDhgxBSUlJuedkZWXh999/L/e9TUtLw/bt22FpaQlLS8sy65SUlGDw4MGwt7fHkSNH8Pnnn+Ptt98us+7bb7+NGTNmIDExEU2bNsXIkSNRVFSE0NBQ0/oMF2RwoaGh4pNPPhFCCFFYWCjc3d1FTEyMEEKIyMhIoVAodOpHR0eLB9+KkJAQ8dprr+nU6dChgwgKCpL2x44dKwYNGiTtd+nSRbz++us658yePVs0a9ZMlJSUSGWrVq0Sjo6Oori4WDovODi41D0EBASIDz74QNofPHiwCAsL06nTtGlTsXPnTmkfgIiOji51LSGE8PPzE5s2bdIpe++990RISIi0HxQUJObNmyftl/WzMrba/N7a2toKBwcHYWlpKQAIV1dXcenSJanOw/ffoUMHMWnSJJ3rdurUqdS9+vv7i6KiIqnsueeeEyNGjJD2/f39xbJly0rdh6n6z3/+I+rXr6/z3kRFRQm5XC7u3Lkj5s2bJ/0MCgsLhUKhEDNmzBCXL18W586dE1FRUeKvv/4SQpT+tyCEEK+//rro0qWLtL9161axbds2cfHiRXHixAkxYMAA0apVK+nfQVJSkgAgTpw4IZ2TmZkprK2tRUpKihBC6MT0sNzcXNGkSRMxbtw4cerUKXHu3DkxatQo0axZM6HVakV2draIjIwUAIRKpRIqlUpkZGQIX19fsWDBAqnMHJw/f14AEL/++qtU9swzz4iRI0cKIXR/jhkZGQKAiI2NLfNalXlvH5aeni4AiNOnTwshyn5vN27cKNq2bSvtz5s3T1hYWAgHBwdhZ2cnAAgAYurUqTrXfvD37KeffhJWVlY671tMTIzOZ/z91/7yyy+lOmfPnhUAxPnz54UQpvUZzpYdA7tw4QL++OMPPP/88wDu/TUwYsQInUcEj3L+/HmEhITolD28r891HvxLolOnTsjJycG1a9eksvbt25c69+WXX0ZkZCQAID09Hbt27cK4ceN0rn3t2rVy+yc86NatW0hNTUV4eDgcHR2lbeHChZX6C9RU1Pb3dtmyZUhMTERMTAyCg4OxbNkyNG7cuNwYL1y4gKefflqn7OF94F5/ggf/wvT29kZ6enq51zV148aNQ3JyMmJjY6Wyr776CkOHDkWdOnV06mo0GqjVavTv3x+NGjVCixYtMHbsWNSrV6/Srzds2DAMHToUTZo0QXBwMNauXYvTp0/j3Llz5Z6ze/dutGrVCn5+fo+8/ubNm2FhYYEvv/wSrVq1QosWLRAZGYmUlBTExsbC0dFRenypVCqhVCrh6uoKS0tLODk5SWXmoHnz5ggNDZV+569cuYLff/9d5/fnPldXV4SFhaFPnz4YMGAAli9frnfL5JUrVzBq1Cg0bNgQzs7OaNCgAQBU2FewrBa7Zs2aITExEUePHsWiRYsQHByMRYsWlXuNCxcuwM/PT+d9K+t3FwBat24t/b+3tzcAmOTvL1c9N7C1a9eiqKgIdevWlcqEELC2tkZmZiYsLCxKPfa43z/B0IQQpZpM77/2g+UODg6lzh0zZgxmzpyJQ4cO4dChQ6hfvz7+9a9/Scd37tyJXr16wc7O7pFx3G9y/eKLL0r17ymvGdUU1fb3VqlUonHjxmjcuDG+++47tGnTBu3bt0dAQEC5cZYX44MefhQmk8kqbKY3dQ9+IXbr1k36Qnywf9N9D34h9urVCz179sTw4cOlL43KuHLlCubOnYvDhw/j9u3b0s8uJSUFgYGBZZ6jzyOshIQEXL58GU5OTjrl+fn5NeqPFUMJDw/H5MmTsWrVKkRGRsLf3x89evQos25kZCSmTp2KPXv2YMuWLZgzZw5iYmLQsWPHSn1eDBgwAH5+fvjiiy/g4+ODkpISBAYGljvYoLCwEHv27MGsWbN0ym1sbKQ/TFq2bIlLly5hwoQJ2LBhQ5nXKevzpTwP/v7eP8cUf3/ZsmNARUVFWL9+PT766CMkJiZK28mTJ+Hv74+NGzfCw8MD2dnZyM3Nlc57eEhoixYtcPjwYZ2yh/cfZmNjg+LiYp2ygIAAxMfH6/xCxcfHw8nJSecLuyxubm4YPHgwIiMjERkZiZdeeknnuD4fll5eXqhbty6uXr0qfVne3+7/pVLZezIWvre6GjdujGHDhpX6UH1Qs2bN8Mcff+iUHTt2rMLrlsWU/h1UVnh4OLZt2waNRlOpL8RDhw4hNDQUW7ZsQdOmTaV/E5X9QszIyMAXX3yBI0eO4MiRIwDKH313/wuxsqMoS0pK0K5dO51/94mJibh48SJGjRpVqWuYk+HDh8PS0hKbNm3CunXr8NJLL1WYGLRp0wazZs1CfHw8AgMDsWnTJgD3Rj893NLz4OdFRkYGzp8/jzlz5qBHjx5o0aIFMjMzK4xt//79cHFxeeTIurlz5+Kbb77B8ePHyzzevHlzpKSk4ObNm1LZ0aNHK7xmWUzpd5ctOwb0448/IjMzE+Hh4VAoFDrHnn32Waxduxb79u2Dvb09Zs+ejSlTpuCPP/4o1Znx9ddfx9ixY9G+fXt07twZGzduxNmzZ9GwYcNyX7t+/fo4cuQIkpOT4ejoCFdXV0ycOBGffPIJpkyZgsmTJ+PChQuYN28epk2bVqlOxS+//DL69++P4uJijB07VipPT0/H0aNHy5xLIykpqdQXfOPGjTF//nxMnToVzs7O6NevH7RaLY4dO4bMzExMmzat3HvKycnBvn37EBQUBHt7e6MNjeR7W9r06dMRFBSEY8eOlfm4bMqUKRg/fjzat28vfZGfOnWqwnst7/5/++03PP/885DL5XB3d9frfGMYPnw4Xn/9dekLcfz48Y/8Qrz/pRgSEoJNmzahY8eO8PDwwJkzZ3TqJiYmSn9N3/9C/Oyzz6TWuQMHDlQYW2W/EO9r27YttmzZAk9PT71GbpnSF50hOTo6YsSIEZg9ezbUajXCwsLKrJeUlITPP/8cAwcOhI+PDy5cuICLFy9izJgxAIDu3bvjv//9L9avX4+QkBB8/fXXOHPmDNq0aQMAUmfwzz//HN7e3khJSZHmvSrPzp07K/VHaMOGDTFo0CC88847+PHHH0sd79WrFxo1aoSxY8di6dKlyM7Oljoo69PB2pQ+w9lB2YD69+8v/v3vf5d5LCEhQQAQCQkJIjo6WjRu3FjY2tqK/v37i88//1w8/FYsWrRIuLu7C0dHRzF27Fjx5ptvVtiJ9cKFC6Jjx45SB7SkpCQhhBCxsbHiqaeeEjY2NkKpVIq33npLFBYWSueV1fn1vpKSEuHv71/qnr788kvRqVOnUvXxd8e3h7f9+/cLIe51nAsODhY2NjaiTp064plnnhHbt2+Xzn+4g7IQQrz22mvCzc1NACh17Enie1t25/NevXqJfv36CSHK7oy4YMEC6V7HjRsnpk6dKjp27FjuvQpRupPmoUOHROvWrYVcLi/1szRl4eHhok6dOsLCwkLqcCyEbifWq1evipkzZ4r4+HiRnJwsfv75Z+Hq6ipWr14thBBiz549QiaTiXXr1omLFy+Kd955Rzg7O0s/n+LiYuHm5iZGjx4tLl26JPbt2yeeeuqpMjuS3u/EOmnSJDF58mSdWOfNmyeaNm0qTpw4obNdunRJ6qDctWtX8dtvv4mrV6+K2NhYMXXqVJGamiqEKN0RX4h7/zYGDhworl27Jm7dumXgn65xxcfHCwCid+/eOuUPvrdpaWli8ODBwtvbW9jY2Ah/f3/xzjvvSB3HhRDinXfeEV5eXkKhUIg33nhDTJ48WefffkxMjGjRooWQy+WidevWIjY2tsL31s/PTxowUVZMDzp48KAAIA4fPiyEKD0Q4Pz586JTp07CxsZGNG/eXPzwww8CgNizZ0+Zry3EvY7vD37mC2E6n+E155ODnrjc3FyhUCjEtm3bdMoHDBigM5qHah5jvbc9e/YUo0ePrrbrm5Ka9oVY1h8q919HpVKJMWPGCHd3dyGXy0XDhg3F+PHjhVqtFkKUnezU1CS1pkpISBAKhUIUFBRUy/UPHDggAIjLly9Xy/Wrm0wIPSYJoVqhpKQEaWlp+Oijj7B161ZcuXIFVlb/PPFcunQpRo4cWamRHGRanuR7e/fuXXz66afo06cPLC0t8c0332DBggWIiYmp1Cg+Mrzjx4+je/fuuHXrVoVzJFHN88cff+DChQt48cUXDXK96OhoODo6okmTJrh8+TJef/111KlT55GPSU0Vkx0qJTk5GQ0aNICvry+ioqLK7VhJNc+TfG/z8vIwYMAAHD9+HFqtFs2aNcOcOXMwdOjQantNqpihvxDJfK1fvx7vvfceUlNT4e7ujp49e+Kjjz4yidm/q4LJDhEREZk1Dj0nIiIis8Zkh4iIiMwakx0iIiIya0x2iIiIyKwx2SEiIiKzxmSHiKgSYmNjIZPJkJWVVW6dqKgoaQVwIjIdTHaIqFbq2rUrIiIiKl0/NDQUKpWq1NpoRGT6uBAoEVEl2NjYQKlUGjsMIqoCtuwQ0RMnhMDSpUvRsGFD2NnZISgoCFu3bkVJSQl8fX3x6aef6tQ/fvw4ZDIZrl69CgBQq9V45ZVXpJW4u3fvjpMnT0r158+fj+DgYGzYsAH169eHQqHA888/j+zsbABAWFgY4uLisHz5cshkMshkMiQnJ1cYc1mPsaKiolCvXj3Y29tjyJAhyMjIMMwPiIgMiskOET1xc+bMQWRkJNasWYOzZ8/ijTfewOjRo/H777/j+eefx8aNG3Xqb9q0CSEhIWjYsCGEEPi///s/pKWlYffu3UhISEDbtm3Ro0cP3LlzRzrnypUr2LFjB3788Uf8+OOPiIuLw5IlSwAAy5cvR0hICMaPHw+VSgWVSqX3emBHjhzBuHHjMHHiRCQmJqJbt25YuHDh4/9wiMjwjLcGKRHVRjk5OcLW1lbEx8frlIeHh4uRI0eK48ePC5lMJpKTk4UQQhQXF4u6deuKVatWCSGE2Ldvn3B2dhb5+fk65zdq1Eh89tlnQoh7q3jb29sLjUYjHf/Pf/4jOnToIO136dJFvP7665WOe//+/QKAyMzMFEIIMXLkSNG3b1+dOiNGjBAKhaLS1ySiJ4MtO0T0RJ07dw75+fno1asXHB0dpW39+vW4cuUK2rRpg+bNm+Obb74BAMTFxSE9PR3Dhw8HACQkJCAnJwdubm465yclJeHKlSvS69SvXx9OTk7Svre3N9LT0w12H+fPn0dISIhO2cP7RGQa2EGZiJ6okpISAMCuXbtQt25dnWNyuRwA8MILL2DTpk2YOXMmNm3ahD59+sDd3V0639vbG7GxsaWu/eCwb2tra51jMplMem1DEFxDmajGYLJDRE9UQEAA5HI5UlJS0KVLlzLrjBo1CnPmzEFCQgK2bt2KNWvWSMfatm2LtLQ0WFlZoX79+lWOw8bGBsXFxVU+PyAgAIcPH9Ype3ifiEwDkx0ieqKcnJwwY8YMvPHGGygpKUHnzp2h0WgQHx8PR0dHjB07Fg0aNEBoaCjCw8NRVFSEQYMGSef37NkTISEhGDx4MD744AM0a9YMN27cwO7duzF48GC0b9++UnHUr18fR44cQXJyMhwdHeHq6goLi8o/2Z86dSpCQ0OxdOlSDB48GHv37sWePXv0/nkQUfVjnx0ieuLee+89vPPOO1i8eDFatGiBPn364IcffkCDBg2kOi+88AJOnjyJoUOHws7OTiqXyWTYvXs3nnnmGYwbNw5NmzbF888/j+TkZHh5eVU6hhkzZsDS0hIBAQHw8PBASkqKXvfQsWNHfPnll1ixYgWCg4Oxd+9ezJkzR69rENGTIRN88ExERERmjC07REREZNaY7BARAXjttdd0hrI/uL322mvGDo+IHgMfYxERAUhPT4dGoynzmLOzMzw9PZ9wRERkKEx2iIiIyKzxMRYRERGZNSY7REREZNaY7BAREZFZY7JDREREZo3JDhEREZk1JjtERERk1pjsEBERkVljskNERERm7f8Bh2IwZ6fnljQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ap_data = knee_epoched.aperiodic_params.groupby(['event_id', 'ch_name'])['Knee Frequency (Hz)'].mean().reset_index()\n",
+    "sns.pointplot(ap_data, x='event_id', y='Knee Frequency (Hz)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly we can also analyse the periodic activity, by condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_periodic = irasa_epoched.periodic.get_peaks(smoothing_window=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyrasa.utils.peak_utils import get_band_info\n",
+    "df_alpha = get_band_info(df_periodic, freq_range=(8,14), ch_names=df_periodic['ch_name'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='event_id', ylabel='cf'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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zW6mdkVuA+b9flLEiIiJSAgYgkpVKpcLcQW1gqF88IXr3mZs4du1fGasiIqL6jgGIZNe8kTkmPd1Uq2/2zvPIK+CEaCIiqhkMQKQT3urVHE5WJlI78nYmVh29LmNFRERUnzEAkU4wNTLAbD/tCdH/O3gNN5KzZKqIiKjuEUIgr8TlRNKy83EqNhlC8J6LD2MAIp3R19MOz7o3lNrZ+YWYu/uCjBUREdUdV26lY9B3x3A7vfjWQum5BRjyfRAGfXcMV26ly1id7mEAIp2hUqngP7A1jAyKfywPXLiFQ5eSZKyKiEj3XbmVjqHLg3D2RmqZz5+9kYqhy4MYgkpgACKd4mprhik9m2n1zdkVgZz8QpkqIiLSbUIIvL/lDNJyCipcLi2nAB9sOcPhsPsYgEjnTO7RDK62plI79m4Wlh+OlLEiIiLddToupdwjPw87cyMV4XEpNVtQHcEARDrH2FAf/gNba/UtPxKJmDuZMlVERKS7Ai/cqtLyB6q4fH3FAEQ66Vn3Rni+tb3UzisowpxdETx0S0T0kNTs/Bpdvr5iACKdNdvPEyaG+lL78OXb2B/Bby5ERCVpTAxrdPn6igGIdJajlQmmPtdCq2/u7ghk5VU80Y+ISEnaOFpWafm+nnY1VEndwgBEAAAHjTGcrEzgZGUCB42x3OVIJnRvguaNzKX2zdQcfHvwmowVERHpjoibqZi983yll/dy1qCdi1XNFVSHqAQnVZSSlpYGjUaD1NRUWFpWLVlT9QuK/BejfjwhtQ30VNg37Wk0b2QhY1VERPI6GXUXEwJCkJ5buaPilsYG2PqGL1ra1d+/nVX5/OYRINJ5vs0aYKCXo9QuKBKYtYMToolIuf66dAuvrj6hFX4M9FValxApyctZU+/DT1UxAFGdMPOFVjBXG0jt4Ot3sOvMTRkrIiKSx47T8XhtXRhyC4rv+WWuNsCGCV1w+P2eaGihlvot1AbYPsUXO97sxvDzEAYgqhMaWRrjvT4ttfo++/0i0nN4OicRKcdPQdGYtjkcBUXFR8BtzYzwy2td0bWpLVQqFYz0iz/aLU0M4d3YGiqVSo5ydRoDENUZo31c0cqheEw3KT0X3wRelbEiIqLaIYTA0j+vYs6uCK1+JysT/DrZB22cNDJVVncxAFGdYaCvh/mDta8Q/VNwNC7cTJOpIiKimldUJPDp7gv45s8rWv3NGpphy2QfNGtoXs6aVBEGIKpTOrja4KUOzlK7sEhg1s7zKCrihGgiqn/yC4vw3y1nEBAUrdXf1lmDLZN94WhlIk9h9QADENU50/t7aF3JNCwmGVtP3ZCxIiKi6peTX4g3NoRh++l4rX6fprbYNKkrbMyMZKqsfmAAojrH1lyND/q5a/V9/sclpGTlyVQREVH1SsvJx+g1J/HnxSSt/r6edlg7rpPWWbH0eGQNQH///Tf8/Pzg6OgIlUqFHTt2aD0vhIC/vz8cHR1hYmKCnj17IiIiouyNlbBt2zZ4enpCrVbD09MT27dvr6E9ILmM7NwYbZ2LJ/3dzczDF/svy1gREVH1+DcjFyN/OI6TUXe1+l/q4IzvX24P4xL3SKTHJ2sAyszMhJeXF5YtW1bm84sXL8bXX3+NZcuWISQkBPb29ujTpw/S09PL3WZwcDCGDx+OV199FWfOnMGrr76KYcOG4cSJE+WuQ3WPvp4K8we3QckzOzedjMWZuBTZaiIielI3krPw0opgRDx0csekp5tg8dC2MNDnwE110ZlbYahUKmzfvh2DBw8GcO/oj6OjI6ZNm4aPPvoIAJCbmws7OzssWrQIr7/+epnbGT58ONLS0vDHH39Ifc8//zysra3x888/V6oW3gqj7pi54xw2HI+V2m2dNdg+pRv09XjNCyKqW67eSserq08iMS1Hq/+Dfu6Y0rNZpa/l0+3zvxCfkg3g3mnyx6b3qvZadVW9uBVGVFQUEhMT0bdvX6lPrVajR48eCAoKKne94OBgrXUAoF+/fhWuQ3XXB309YFtiIuDZG6n4+WRsBWsQEeme8LgUDFsZrBV+VCrgs/+0wZvPNueFDGuAzgagxMREAICdnZ1Wv52dnfRceetVdZ3c3FykpaVpPahu0JgaYnp/D62+L/Zfxr8ZuTJVRERUNceu/YtRPx5Hclbxle0N9VX4doQ3Xu7iKmNl9ZvOBqAHHk69QohHJuGqrrNw4UJoNBrp4eLi8vgFU617sb0zOrpaS+3U7Hx8/sclGSsiIqqcfecTMW5tCLLyCqU+E0N9rBrTCX4lbgJN1U9nA5C9vT0AlDpyk5SUVOoIz8PrVXWdGTNmIDU1VXrExcU9QeVU2/T0VJg3uI3WvJ+tYTcQGn23grWIiOT1a0gcpmwMQ15h8U1NLY0NsGFiZ/Ro2VDGypRBZwNQkyZNYG9vj8DAQKkvLy8PR44cga+vb7nr+fj4aK0DAAcOHKhwHbVaDUtLS60H1S2tHCwxxsdNq2/mjvMoKPGHhYhIV/zwdyQ+3HYWJS9i39BCjV8n+6CDq418hSmIrFdSysjIwLVr16R2VFQUwsPDYWNjg8aNG2PatGlYsGABWrRogRYtWmDBggUwNTXFqFGjpHVGjx4NJycnLFy4EADwzjvv4JlnnsGiRYswaNAg7Ny5E3/++SeOHj1a6/tHtevdPi2w5+xNJKXfm/9zKTEdPwXHYEL3JjJXRkR0jxACX+y/jO8PR2r1N7YxxYYJXdDY1lSmypRH1iNAoaGh8Pb2hre3NwDgvffeg7e3N2bPng0A+PDDDzFt2jRMmTIFHTt2RHx8PA4cOAALCwtpG7GxsUhISJDavr6++OWXX7B27Vq0bdsWAQEB2Lx5M7p06VK7O0e1zsLYEDMHeGr1fRN4BbceOqWUiEgOhUUCH28/Xyr8eNhbYOtkH4afWqYz1wHSJbwOUN0lhMDLq04gKPKO1DfQyxHfjvSWsSoiUrq8giK8uzkcv59L0Opv39gKa8d2hsbUsJw1q47XAarj1wEiehwqlQpzB7WBoX7xhOhdZ24i6Nq/MlZFREqWmVuACT+FlAo/PVo2xIaJXao1/FDlMQBRvdO8kTkmPt1Uq2/WzvPIK+CEaCKqXSlZeXhl9Qn8c1X7S9iAtg74cXRHmBrxpqZyYQCieuntXs3hZGUitSNvZ2LV0esyVkRESnMrLQfDVx7H6dgUrf6XuzTG0hHeMDLgR7Cc+L9P9ZKpkQFmPTQh+n8Hr0nj4kRENSn630wMXRGEy7e0b9791rPNMf+h65aRPCodgNq3b4/k5GQAwNy5c5GVlVVjRRFVh36t7dDTvfhiYtn5hZi7O0LGiohICS4mpGHoimDE3dX+wjXzhVZ4v5877+ulIyodgC5evIjMzEwAwKeffoqMjIwaK4qoOqhUKnw6sLXWYeb9Ebdw6HKSjFURUX0WGn0Xw1YGa92PUF9PhS+Gti01N5HkVenZV+3atcO4cePQvXt3CCHw5ZdfwtzcvMxlH1zHh0hurrZmmNKzGZb8eVXq898VAZ9ptjA21JexMiKqbw5dTsIbG8KQk198woWRgR6WjfRG39b2MlZGZal0AAoICMCcOXOwZ88eqFQq/PHHHzAwKL26SqViACKdMrlHM2w/HY+YO/eGbWPuZGHFkUhM691S5sqIqL7YGR6P//56BgUl7m1hZqSPH8d0hG+zBjJWRuWpdAByd3fHL7/8AgDQ09PDwYMH0ahRoxorjKi6GBvqw39ga4xbGyL1fX84Ev/xdoKrrZmMlRFRfbD+eAxm7zyPkpcVtjEzQsC4TmjrbCVbXVSxxzoLbP78+dizZ0+p/jVr1mDRokVPXBRRdXvWvRH6tbaT2nkFRZizKwK8EDoRPS4hBP538Cpm7dAOPw4aY/z6ug/Dj457rAD0ww8/wMPDo1R/69atsWLFiicuiqgmzPZrDZMS834OX76N/RG3ZKyIiOqqoiKBeXsu4qvAK1r9TRuaYesbvmjeqOw5sqQ7HisAJSYmwsHBoVR/w4YNtW5MSqRLnKxM8PZzzbX65u6OQFZegUwVEVFdVFBYhA+2nsWaY1Fa/W2cLLHldR+ti7CS7nqsAOTi4oJjx46V6j927BgcHR2fuCiimjKxe1M0a1g87+dmag7+99c1GSsiorokJ78QkzecwrZTN7T6uzSxwc+TusLWXC1TZVRVjxWAJk6ciGnTpmHt2rWIiYlBTEwM1qxZg3fffReTJk2q7hqJqo2RgR7mDWqj1bfqn+u4lsTrWhFRxdJz8jF27Un8eVF76Lx3Kzv8NL4zLIx5U9O65LHuwvbhhx/i7t27mDJlCvLy8gAAxsbG+OijjzBjxoxqLZCouvk2b4CBXo7YdeYmACC/UGD2zvPYOLELr9BKRGW6k5GLsWtDcC4+Vav/xfbOWPTiUzDQ552l6prHesdUKhUWLVqE27dv4/jx4zhz5gzu3r3L6/9QnTHzhVYwVxfn/6DIO9h9lvPXiKi0+JRsvLQyuFT4Gd+tCb4Y2pbhp456onfN3NwcnTp1Qps2baBWc9yT6o5GlsZ4t4/2hRDn77mA9Jx8mSoiIl10LSkDQ5cH4frtTK3+//ZpiVkDWkGPNzWtsxhbSbHG+LjCw95Caiel5+KbwKsVrEFESnL2RgqGrQxGQmqO1KdSAfMGt8Hbz7XgkHkdxwBEimWgr4f5g7UnRP8UHI2LCWkyVUREuiIo8l+M/OE47mbmSX0GeiosGd4Or3Z1lbEyqi4MQKRoHd1sMLSDs9QuLBKYteM8iop4hWgipdofkYixa0OQmVco9Rkb6uHHMR0xqJ2TjJVRdWIAIsWb3t8DlsbFE6JDY5JLXeODiJRhS2gc3tgQhryC4ju6WxgbYMOELnjWnfe/rE8YgEjxGpir8eHz2rd2+fyPS0jN4oRoIiVZ9c91fLD1LEoeAG5grsavr/ugo5uNfIVRjWAAIgIwsnNjtHXWSO07mXn44sAlGSsiotoihMCX+y9j/u8XtfpdbEyw7Q0ftHKwlKkyqkkMQEQA9PVUmD+4DUqe1LHxRCzO3kiRrSYiqnmFRQIzd5zHskPat8Rxt7PA1sm+cLU1K2dNqusYgIjua+tshVGdG0ttIYCZO86jkBOiieqlvIIivPPLaWw8EavV793YCptf7wo7S2OZKqPawABEVMIH/dxhY2Yktc/eSMXPJ2MrWIOI6qKsvAJMWheKPQ9dAf7pFg2wcWIXWJkalbMm1RcMQEQlWJkaYXp/7QnRX+y/jDsZuTJVRETVLTUrH6+uPokjV25r9b/wlANWjekIU6PHuk0m1TEMQEQPGdreGR1craV2anY+Pv+DE6KJ6oOktBwM/yEYYTHJWv0jOzfGtyO9oTbQl6kyqm0MQEQP0dNTYd6gNih5i58tYTcQGn1XvqKI6InF3snC0BXBuJSYrtU/pWczLPhPG+jzvl6KwgBEVAZPR0uM9W2i1Tdzx3kUFBaVswYR6bJLiWkYuiIIsXeztPo//j8PfPi8B+/rpUAMQETleLdPCzSyUEvtS4npWBccI2NFRPQ4wmKSMWxFMJLSi+fy6amAxS+2xWvPNJOxMpITAxBROSyMDfHJC620+r4OvIJbaTnlrEFEuubIldt4ZdUJpOUUSH1G+nr4/uUOGNbJRcbKSG4MQEQVGOjlCJ+mtlI7I7cAnz10tVgi0k27z9zExJ9CkJ1ffFNTMyN9rB3XCc+3sZexsprloDGGk5UJnKxM4KDhtYzKoxJC8CpvD0lLS4NGo0FqaiosLXkJdKW7lpSO/kv/QX5h8a/Kpold4Nu8gYxVEVFFNp6Iwcwd51HyE87a1BAB4zrDy8VKtrqoZlXl85tHgIgeoXkjC0zo3lSrb9bO81p3iybSZUOXB6Hb53+h2+d/YejyILnLqVFCCHx36Bo+2a4dfuwtjbFlsg/DD0kYgIgqYepzzeFY4lBy5O1MrD4aJWNFRJWXkJqD+JRsxKdkIyG1/s5hE0Jgwd6L+GL/Za3+Jg3MsPUNHzRvZCFTZaSLGICIKsHUyACz/Vpr9X178CriU7JlqoiISiooLMKHW8/ix3+0v5h4Olhiy2QfOFubylQZ6SoGIKJK6tfaDj3dG0rt7PxCzNt9QcaKiAgAcvILMWXjKWwJu6HV37mJDX55vSsamKvLWZOUjAGIqJJUKhU+HdgaRgbFvzb7IhJx6HKSjFURKVtGbgHGB4TgwIVbWv3PeTTCuvGdYWlsKFNlpOsYgIiqwNXWDG/00L5wmv+uCOSUOM2WiGrH3cw8jPrxOIIi72j1/8fbCSte7QBjQ97Xi8rHAERURW/0bIbGNsXzCWLuZGHFkUgZKyJSnpsp2XhpRRDO3kjV6h/r64avXvKCoT4/3qhi/AkhqiJjQ318OlB7QvT3hyMRcydTpoqIlCXydgZeWhGMyNvav3Pv9m6JOX6e0ONNTakSGICIHsOzHo3Q19NOaucVFMF/VwR4XVGimnU+PhXDVgSXOgPz04Gt8U7vFrypKVUaAxDRY5rt5wljw+JfoUOXb5eaiElE1ef49TsY8cNx3MnMk/oM9FRYOqIdxvi6yVcY1UkMQESPydnaFFOfa6HVN3f3BWTlFZSzBhE9rsALtzB6zUlk5Bb/fqkN9PDD6A4Y1M5JxsqormIAInoCE7s3RbOGZlI7PiUby/66JmNFRPXPb6duYPKGMK3bz1gYG2DDxC7o5WFXwZpE5WMAInoCRgZ6mDeojVbfj/9cx7WkDJkqIqpf1hyNwnu/nkFhUfH8ugbmRvjlta7o5GYjY2VU1zEAET0h3+YN4OflKLXzCwVm7zzPCdFET0AIga8PXMbcPdpXW3eyMsGWyb5o7aiRqTKqLxiAiKrBzBdawVxtILWDIu9g99kEGSsiqruKigTm7IrAtw8NJ7doZI5tb/iiSQOzctYkqjwGIKJqYGdpjGm9tSdEz99zAek5+TJVRFQ35RcWYdrmcKwLjtHq93Kxwq+v+8BeYyxTZVTfMAARVZOxvm7wsLeQ2knpuVjy51UZKyKqW7LzCvHaulDsOnNTq7978wbYNLELrM2MZKqM6iMGIKJqYqCvh3mDtSdEBwRF41JimkwVEdUdqdn5GL3mBA5dvq3V37+NPVaP7QizEkPMRNWBAYioGnVys8HQDs5Su7BIYNYOTogmqkhSeg5G/HAcIdHJWv0jOrlg2aj2UBvwpqZU/RiAiKrZ9P4esDQu/rYaEp2MbafiZayISHfF3c3CSyuCcTFB+0jp5B7NsHDIU9Dnfb2ohuh8AEpPT8e0adPg6uoKExMT+Pr6IiQkpMJ1vvvuO7Rq1QomJiZwd3fHunXraqlaIqCBuRofPO+h1bdw70WkZnFCNFFJlxPTMXRFEGLuZGn1T+/vgen9PXhfL6pROh+AJk6ciMDAQKxfvx7nzp1D37590bt3b8THl/2Nevny5ZgxYwb8/f0RERGBTz/9FG+++SZ2795dy5WTko3q3BhtnYuvU3InMw9fHLgkY0VEuuVUbDKGrQzGrbRcqU9PBXw+5ClM7tFMxspIKXQ6AGVnZ2Pbtm1YvHgxnnnmGTRv3hz+/v5o0qQJli9fXuY669evx+uvv47hw4ejadOmGDFiBCZMmIBFixbVcvWkZPp6Kswb1AYlv8BuPBGLszdSZKuJSFf8c/U2Xll1AqnZxUdFjfT18N2o9hjRubGMlZGS6HQAKigoQGFhIYyNta/7YGJigqNHj5a5Tm5ubpnLnzx5Evn5HIKg2uPlYoWRJf6YCwHM2nFe65L+REqz91wCxgeEICuvUOozNdLHmrGd0P8pBxkrI6XR6QBkYWEBHx8fzJs3Dzdv3kRhYSE2bNiAEydOICGh7Kvs9uvXD6tWrUJYWBiEEAgNDcWaNWuQn5+Pf//9t8x1cnNzkZaWpvUgqg4f9nOHTYlrl5y5kYpfQmJlrOieocuD0O3zv9Dt878wdHmQ3OWQQvx8MhZvbTqF/MLiLwFWpobYOLELurdoIGNlpEQ6HYCAe0NaQgg4OTlBrVbj22+/xahRo6CvX/ZpkbNmzUL//v3RtWtXGBoaYtCgQRg7diwAlLvOwoULodFopIeLi0tN7Q4pjJWpEab3154QvXjfZdzJyC1njdqRkJqD+JRsxKdkIyE1R9ZaSBmWH47EjN/OoeQBUDtLNX593Qfeja3lK4wUS+cDULNmzXDkyBFkZGQgLi5OGspq0qRJmcubmJhgzZo1yMrKQnR0NGJjY+Hm5gYLCws0aFD2N4wZM2YgNTVVesTFxdXkLpHCDG3vjA6uxX/gU7PzsWgfJ0STMgghsHDvxVI/8262ptg62Rct7SzKWZOoZul8AHrAzMwMDg4OSE5Oxv79+zFo0KAKlzc0NISzszP09fXxyy+/YMCAAdDTK3t31Wo1LC0ttR5E1UXv/oTokpcz+TX0BsJi7spXFFEtKCwSmL7tHFb+fV2r39PBElsm+8LFxlSmyojqQADav38/9u3bh6ioKAQGBuLZZ5+Fu7s7xo0bB+De0ZvRo0dLy1+5cgUbNmzA1atXcfLkSYwYMQLnz5/HggUL5NoFIng6WmKMr5tW3yfbz6OgsEiegohqWG5BId7ceAqbQ7WPqHdys8bPr3VFQwu1TJUR3aPzASg1NRVvvvkmPDw8MHr0aHTv3h0HDhyAoaEhACAhIQGxscWTSgsLC/HVV1/By8sLffr0QU5ODoKCguDm5ibTHhDd826fllp/9C8lppe64zVRfZCRW4DxASHYF5Go1d/LoxHWje8CjYmhTJURFdP5u8sNGzYMw4YNK/f5gIAArXarVq1w+vTpGq6KqOosjQ0x84VWeOeXcKnv68ArGNDWAY0sjctfkagOSc7Mw9iAEJyJS9HqH9TOEV++5AVDfZ3/3k0KwZ9Eolo00MsRPk1tpXZGbgE+23tRxoqIqk9iag6GrQwuFX7G+Ljim2HtGH5Ip/CnkagWqVQqzBvcGgYlZkTvDL+JoMiyr1FFVFdE/ZuJF5cH4WpShlb/O8+1gP/A1tDjTU1JxzAAEdWy5o0sMPHpplp9s3dGIK+AE6Kpbjofn4qXVgQhPiVbq3+Onyfe7dOSNzUlncQARCSDqc81h6OmeN7PtaQMrDkWJWNFRI/nZNRdjPzhOP7NyJP69PVU+Ga4F8Z1K/t6bUS6gAGISAamRgaY7eep1bf0z6ulvkET6bK/Lt3Cq6tPID23QOpTG+hh5Ssd8B9vZxkrI3o0BiAimfRrbY8eLRtK7ez8QszbfUHGiogqb8fpeExaF4bcEkO3FmoDrBvfGb097WSsjKhyGICIZKJSqfDpwNYwMij+NdwXkYjDl5NkrIro0QKORWHa5nAUlrixl62ZEX5+rSu6lDjLkUiXMQARycitgRkm92im1TdnVwRy8gtlqoiofEIILPnzCvwfOlLpZGWCLZN90MZJI1NlRFXHAEQksyk9m8HFxkRqx9zJwsoj1ytYg6j2FRUJfLr7Apb8eVWrv3kjc2x9wwdNG5rLVBnR42EAIpKZsaE+5g5so9X3/eFriL2TJVNFRNryC4vw3y1nEBAUrdXv5azBr6/7wEFjUvaKRDqMAYhIBzzr0Qh9S0wczS0ogv/uCAghKliLqObl5Bdi8vowbD8dr9Xv28wWGyd1hY2ZkUyVET0ZBiAiHTHbzxPGhsW/kn9dSkLghVsyVkRKl5aTj9GrT+LgJe2J+f1a22HN2E4wV+v87SSJysUARKQjnK1N8XavFlp9n+6+gKy8gnLWIKo5t9NzMWLlcZyMvqvVP6yjM74b1R7GhvoyVUZUPRiAiHTIpKebomlDM6kdn5KNZX9dk7EiUqK4u1kYtjIYFxLStPpfe6YpFr3YFga8qSnVA/wpJtIhRgZ6mDdIe0L0j/9cx7WHbjBJVFOu3krHSyuCEfVvplb/R8974OP/a8X7elG9wQBEpGO6NW+AAW0dpHZ+ocCcXec5IZpqXHhcCl5aGYzEtBypT6UCFvznKbzRs1kFaxLVPQxARDpo5gueMDMqnmNx7Nod7DmbIGNFVN8du/YvRv14HClZ+VKfob4Ky0a2x6gujWWsjKhmMAAR6SB7jTHe7dNSq2/+7xeQkcsJ0VT99p1PwLi1IcjKK74CuYmhPlaP6YQXShyNJKpPGICIdNRYXzd42FtI7VtpuVgSeEXGiqg+2hwSiykbTyGvsPimphoTQ2yY2AXPlLhZL1F9wwBEpKMM9PUwb7D2hOi1QdG4lJhWzhpEVbPySCQ+2nYOJe5pikYWavz6ug86uFrLVxhRLWAAItJhndxs8GJ7Z6ldWCQwawcnRFPlCSG0ju6kZefjVMxdfP7HRSz845LWsq62ptj2hi/cSxx5JKqvGICIdNyM//OApXHxFXdDopOx7VR8BWsQ3XPlVjoGfXcMt9Nzpb703AIMWR6MFQ/dcNfD3gJbJvvAxca0tsskkgUDEJGOa2Cuxgf93LX6Fu69iNQSZ+sQPezKrXQMXR6EszdSH7lsR1drbH7dB40sjGuhMiLdwABEVAeM6uKKp5w0UvtOZh6+PHBZxopIlwkh8P6WM0jLefRZgxbGBlg3vjM0Joa1UBmR7mAAIqoD9PVUmDe4DUpehHfDiRicq8S3e1Ke03EplTryAwDpOQW4fCu9hisi0j0MQER1RDsXK4zsXHxBOiGAmTvPo6iIE6LpnviUbOwMj8fsHeertN6BC7dqqCIi3WXw6EWISFd82M8d+84n4m5mHgDgTFwKfgmJ45V6FaiwSOBiQhrCYpIRGpOMsOi7uJma8+gVy5CazflkpDwMQER1iJWpEaY/74EPt52V+hbtu4R+re1ga66WsTKqaRm5BQiPTUFozF2ERifjdGwyMktcuflJcP4PKREDEFEdM7SDM34JicWp2BQA9769L9p3CYuHeslbGFWrhNRshETfO7ITGpOMiwlpqKnRzr6edjWzYSIdxgBEVMfo3Z8Q7fe/o9IH4q+hNzC8kws6uNrIWxw9lsIigUuJ94ezopMRFpOM+JTsKm3D1Egf7Vys0NHVGu1drfHF/suIuPnoq4Z7OWvQzsXqMSsnqrsYgIjqoNaOGoz2cUNAULTUN3NHBHa/1Q0G+jy3Qddl5hYgPC4FodHJCI25i9OxKVW+0a2dpRodXW3Q0c0aHV1t0MrBQuu9d7QywdDlQRWeCm9pbIAvXvKCquTphUQKwQBEVEe917clfj+XIF3l92JCGtYfj8G4bk1krowelpiaI83dCY25i4sJ6SiswniWSgW421lIYaeDqzWcrU0qDC4t7Syw9Q1fvL/lTJmnxHs5a/DFS15oacfbXpAyMQAR1VGWxoaY+UIrvPNLuNT39YEreOEpBzSy5BV95VJYJHA5MR1hMffm7oRGV304y8Tw/nCWmzU63B/SsjSu+kTllnYW2PlmN3RecFAKyhZqA6yb0BntXKx45IcUjQGIqA4b6OWIX07GIfj6HQD37vO0YO9FLBnhLXNlypGV9+DsrHuno5+OSUZ6FYezGlmo74cdG3Rys0YrB0sYVtNQpkqlglGJbVmaGMK7Me/0TsQARFSHqVQqzB3UGv2X/oOC+0MqO8JvYninxvBpZitzdfXTrbQchEYnIyT6LsJiknEhIe2xhrM6uFpLQ1qPGs4iourHAERUx7Wws8CEp5tgZYm7e8/aeR57pz4NIwNOiH4ShUUCV26lSxcaDI1Jxo3kqg1nGRvq3T87ywYd3KzRvrE1r7tDpAMYgIjqgam9WmBX+E0k3L8S8LWkDKw5FoXJPZrJXFndkpV37+yssOh7w1mnYpORXokbipbU0EKNjq735u50crOBp2P1DWcRUfVhACKqB8zUBpg9wBNvbDwl9S398yoGejnC0cpExsp0W1JajjRROTTmLi7cTJOGEiurpZ05OrrZoKPrveEsFxsOZxHVBQxARPXE823s8UzLhvj7ym0AQHZ+IebtuYDlr3SQuTLdUFQkcCUpXbrQYGjMXcTdrfpwlpezlTR3p31ja2hMOZxFVBcxABHVEyqVCnMHtkbfJX8jr6AIAPDH+UQcuXIbPVo2lLm62pedV3hvOOv+6einYpIrvChgWRqY3xvO6uhmjY5uNvB0sOS8KqJ6ggGIqB5xa2CGyT2a4duDV6W+OTvPY9+0Z2BsqC9jZTUvKT1HmrsTGpOMiPjUKg9ntWhUYjjLzRqNbUw5nEVUTzEAEdUzU3o2w/bTN6Thneg7Wfjh7+uY+lwLmSurPkVFAtduZ9w7Ff1+6Im9m1WlbagN9OB1/95ZHe+fnWVlalRDFRORrmEAIqpnjA318enA1hgfECr1fXfoGga3c0JjW1MZK3t82XmFOHMj5f7NQu9df6fqw1lG9669c/909DaOGg5nESkYAxBRPdTLww59PO0QeOEWACC3oAj+uyOwekzHOjGkczs9997cnftHd84/xnBW80bm6HT/6sodXa3hasvhLCIqxgBEVE/N8fPEP1dvIyf/3oTovy4lIfDCLfRtbS9zZdqKigQib2cgNKb46soxd6o2nGVkoId2zlbo4GaNjq73hrOszTicRUTlYwAiqqecrU3xdq8W+GL/Zanv090X8HQLec8Iy8kvxJm4e/fOCrv/SM3Or9I2bM2MpFtJdHC1QRsnS6gN6vckbyKqXgxARPXYxKebYFvYDVz/NxMAEJ+SjWWHrj5irer1b0bu/Wvv3JWGs/ILqzac1ayhmTR3p5ObDdw4nEVET4gBiKgeUxvoY+6gNnhl9Qmp74e/r8OmhoaHhLg/nBWdjJD7oSf6MYazvJw10tydDq4cziKi6scARFTPdW/RAAPaOmDP2QQAQH6hQEpW1YacypOTX4izN1IRGnPvdPSw2OQqb9vmwXDW/SGtNk4aDmcRUY1jACJSgJkveOLQpSRk5hUCuHdW2ANp2fk4FZsMbxerRw4r3cnIlebuhEbfxfn4NOQVFlW4zsOaNjST7pvVwc0aTRuYcTiLiGodAxCRAthrjPFun5aY//vFUs+l5xZgyPdBaOuswZcveaGlnQWAB8NZmVqno0fdn0tUWUb6enjKWSPdO6uDq3WNDb8REVUFAxCRQvg2s4WeCijvcjpnb6TiP98dw/BOjRF7NxNhMclIruJwlrWp4b25O/dPR2/jpKn3t+AgorqJAYhIAYQQmP7buXLDzwOZeYVYcyyq0ttt2sBMOh29o5sNh7OIqM5gACJSgNNxKTh7I/WJtmGkr4c2TpbSzUI7uFrD1lxdTRUSEdUunQ9A6enpmDVrFrZv346kpCR4e3tj6dKl6NSpU7nrbNy4EYsXL8bVq1eh0Wjw/PPP48svv4StrW0tVk6kOx7cEqMqrEwN7wede0NaT3E4i4jqEZ0PQBMnTsT58+exfv16ODo6YsOGDejduzcuXLgAJyenUssfPXoUo0ePxjfffAM/Pz/Ex8dj8uTJmDhxIrZv3y7DHhDJr6pXWh7Q1gHfjvCGnh6Hs4ioftLpWyFnZ2dj27ZtWLx4MZ555hk0b94c/v7+aNKkCZYvX17mOsePH4ebmxumTp2KJk2aoHv37nj99dcRGhpa5vJESqAxMazS8i42pgw/RFSv6XQAKigoQGFhIYyNjbX6TUxMcPTo0TLX8fX1xY0bN7B3714IIXDr1i1s3boVL7zwQrmvk5ubi7S0NK0HUX3Sx9OuSsv3reLyRER1jU4HIAsLC/j4+GDevHm4efMmCgsLsWHDBpw4cQIJCQllruPr64uNGzdi+PDhMDIygr29PaysrPC///2v3NdZuHAhNBqN9HBxcampXSKShbeLFdo6ayq1rJezBu1crGq2ICIimel0AAKA9evXQwgBJycnqNVqfPvttxg1ahT09cuejHnhwgVMnToVs2fPRlhYGPbt24eoqChMnjy53NeYMWMGUlNTpUdcXFxN7Q6RLFQqFb58yQuWxhVP+7M0NsAXL3nxVHYiqvdUQoiq3ZZZJpmZmUhLS4ODgwOGDx+OjIwM/P7776WWe/XVV5GTk4MtW7ZIfUePHsXTTz+NmzdvwsHB4ZGvlZaWBo1Gg9TUVFhaWlbrfhDJ6cqtdLy/5UyZp8R7OWvwRYkrQVP90e3zvxCfkg0AcLIywbHpvWSuiKhmVOXzW+ePAD1gZmYGBwcHJCcnY//+/Rg0aFCZy2VlZUFPT3u3HhwtqiNZj6jGtLSzwM43u6GhRfH1eyzUBtg+xRc73uzG8ENEiqHzAWj//v3SMFZgYCCeffZZuLu7Y9y4cQDuDV+NHj1aWt7Pzw+//fYbli9fjuvXr+PYsWOYOnUqOnfuDEdHR7l2g0hnqFQqGOkX/+pbmhjCu7E1h72ISFF0/jpAqampmDFjBm7cuAEbGxu8+OKL+Oyzz2BoeO+03oSEBMTGxkrLjx07Funp6Vi2bBn++9//wsrKCr169cKiRYvk2gUiIiLSMTofgIYNG4Zhw4aV+3xAQECpvrfffhtvv/12DVZFREREdZnOD4ERERERVTcGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcA7kLICKimuWgMS7z30RKxgBERFTPbX3DV+4SiHQOh8CIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEM5C6AiGqfg8a4zH8TESkFAxCRAm19w1fuEoiIZMUhMCIiIlIcBiAiIiJSHAYgIiIiUhydD0Dp6emYNm0aXF1dYWJiAl9fX4SEhJS7/NixY6FSqUo9WrduXYtVExERkS7T+QA0ceJEBAYGYv369Th37hz69u2L3r17Iz4+vszlly5dioSEBOkRFxcHGxsbvPTSS7VcOREREekqlRBCyF1EebKzs2FhYYGdO3fihRdekPrbtWuHAQMGYP78+Y/cxo4dOzBkyBBERUXB1dW1Uq+blpYGjUaD1NRUWFpaPnb9REREVHuq8vmt00eACgoKUFhYCGNj7euUmJiY4OjRo5XaxurVq9G7d+9Khx8iIiKq/3T6OkAWFhbw8fHBvHnz0KpVK9jZ2eHnn3/GiRMn0KJFi0eun5CQgD/++AObNm2qcLnc3Fzk5uZK7bS0tCeunYiIiHSXTh8BAoD169dDCAEnJyeo1Wp8++23GDVqFPT19R+5bkBAAKysrDB48OAKl1u4cCE0Go30cHFxqabqiYiISBfp9BygkjIzM5GWlgYHBwcMHz4cGRkZ+P3338tdXgiBli1bYsCAAfjmm28q3HZZR4BcXFw4B4iIiKgOqcocIJ0eAivJzMwMZmZmSE5Oxv79+7F48eIKlz9y5AiuXbuGCRMmPHLbarUaarW6ukolIiIiHafzAWj//v0QQsDd3R3Xrl3DBx98AHd3d4wbNw4AMGPGDMTHx2PdunVa661evRpdunRBmzZt5CibiIiIdJjOzwFKTU3Fm2++CQ8PD4wePRrdu3fHgQMHYGhoCODeROfY2NhS62zbtq1SR3+IiIhIeerMHKDalJqaCisrK8TFxXEOEBERUR3xYA5vSkoKNBpNhcvq/BCYHNLT0wGAZ4MRERHVQenp6Y8MQDwCVIaioiLcvHkTFhYWUKlUcpdTax4kZx75Uga+38rC91tZlPp+CyGQnp4OR0dH6OlVPMuHR4DKoKenB2dnZ7nLkI2lpaWifmGUju+3svD9VhYlvt+POvLzgM5PgiYiIiKqbgxAREREpDgMQCRRq9WYM2cOLwqpEHy/lYXvt7Lw/X40ToImIiIixeERICIiIlIcBiAiIiJSHAagOsrf3x/t2rWT2mPHjsXgwYNlq6cmXLp0CV27doWxsbHWvtZ39eW9DQgIgJWVVZXWqav7SlSWh3+Xa1p0dDRUKhXCw8OfeFtubm5YsmSJLK9dWxiAaklQUBD09fXx/PPP18j2ly5dioCAAKnds2dPTJs2rUZeqyJjx47F9OnTAQAqlQo7dux47G3NmTMHZmZmuHz5Mg4ePPhYH6i1Qanv7YOHubk5vLy8tGoEgOHDh+PKlSvVXkdV/zDrurr8IVkRf39/2NnZPfHfAV3l5+eH3r17l/lccHAwVCoVevXqhYMHD9ZyZaXFxMRArVYjLS0N/v7+0u+unp4eHB0d8fLLLyMuLk5rnZCQELz22mvVWoeu/Q1nAKola9aswdtvv42jR4+WunlrddBoNDXyg5WXl1fpZYuKivD7779j0KBB1fLakZGR6N69O1xdXWFra1st26wJSn1v165di4SEBJw5cwbDhw/HuHHjsH//ful5ExMTNGrUqFprrmvq8ofkk4Syixcv4tNPP8XKlSuRkJCA/v3717vgOmHCBPz111+IiYkp9dyaNWvQrl07PPPMMzrxt2vnzp3o2bOndEHE1q1bIyEhATdu3MDmzZtx7tw5DBs2TGudhg0bwtTUVI5yaw0DUC3IzMzEr7/+ijfeeAMDBgzQ+qZcViLesWNHqVtwfP7557Czs4OFhQUmTJiAnJwcredLDh2MHTsWR44cwdKlS6WkHx0dDQA4cuQIOnfuDLVaDQcHB0yfPh0FBQXSdnr27Im33noL7733Hho0aIA+ffpg/PjxGDBggNbrFRQUwN7eHmvWrJH6jh07Bj09PXTp0qVS/y9r165Fq1atYGxsDA8PD3z//ffScyqVCmFhYZg7dy5UKhV69uyJcePGITU1Vdonf3//Sr1OTVLye2tlZQV7e3s0a9YMH3/8MWxsbHDgwIEK93/+/Plo1KgRLCwsMHHiREyfPr3MD9ovv/wSDg4OsLW1xZtvvon8/HxpH2JiYvDuu+9K+6/L6vKH5JOIjIwEAAwaNAj29vb18lTsAQMGoFGjRqWOfGZlZWHz5s2YMGFCqSB5+PBhdO7cGWZmZrCyskK3bt2kn42yhn+nTZuGnj17Su19+/ahe/fusLKygq2tLQYMGCD9X1dk586dGDhwoNQ2MDCAvb09HB0d8fTTT2PSpEk4fvw40tLSpGUeDqyXLl1C9+7dYWxsDE9PT/z5559lHt27fv06nn32WZiamsLLywvBwcHSvuva33AGoFqwefNmuLu7w93dHa+88grWrl2Lqlx94Ndff8WcOXPw2WefITQ0FA4ODlph4WFLly6Fj48PJk2ahISEBCQkJMDFxQXx8fH4v//7P3Tq1AlnzpzB8uXLsXr1asyfP19r/Z9++gkGBgY4duwYVq5ciYkTJ2Lfvn1ISEiQltm7dy8yMjK0vjXs2rULfn5+j7z/CgD8+OOP+OSTT/DZZ5/h4sWLWLBgAWbNmoWffvoJAJCQkIDWrVvjv//9LxISErBr1y4sWbIElpaW0j69//77lf4/rCl8b4HCwkL8+uuvuHv3LgwNDcutfePGjfjss8+waNEihIWFoXHjxli+fHmp5Q4dOoTIyEgcOnQIP/30EwICAqQPmd9++w3Ozs6YO3eutP+6rC5/SFYkPj4ew4cPh7W1NWxtbTFo0CApiPv7+8PPzw/AvdsKPfgCU5eCa2UYGBhg9OjRCAgI0Pqd37JlC/Ly8vDyyy9rLV9QUIDBgwejR48eOHv2LIKDg/Haa69V6f8iMzMT7733HkJCQnDw4EHo6enhP//5D4qKispdJyUlBf/880+5721iYiJ+++036OvrQ19fv8xlioqKMHjwYJiamuLEiRP44Ycf8Mknn5S57CeffIL3338f4eHhaNmyJUaOHImCggL4+vrq3t9wQTXO19dXLFmyRAghRH5+vmjQoIEIDAwUQgixdu1aodFotJbfvn27KPnW+Pj4iMmTJ2st06VLF+Hl5SW1x4wZIwYNGiS1e/ToId555x2tdT7++GPh7u4uioqKpL7vvvtOmJubi8LCQmm9du3aldoHT09PsWjRIqk9ePBgMXbsWK1lWrZsKXbt2iW1AYjt27eX2pYQQri4uIhNmzZp9c2bN0/4+PhIbS8vLzFnzhypXdb/ldyU/N4aGxsLMzMzoa+vLwAIGxsbcfXqVWmZh/e/S5cu4s0339Tabrdu3Urtq6urqygoKJD6XnrpJTF8+HCp7erqKr755ptS+6GrPvjgA+Hm5qb13gQEBAi1Wi3u3r0r5syZI/0f5OfnC41GI95//31x7do1ceHCBREQECBiYmKEEKV/FoQQ4p133hE9evSQ2lu3bhXbtm0TV65cEadPnxZ+fn7iqaeekn4OoqKiBABx+vRpaZ3k5GRhaGgoYmNjhRBCq6aHZWZmihYtWojx48eLs2fPigsXLohRo0YJd3d3kZubK9LT08XatWsFAJGQkCASEhLEnTt3hLOzs5g7d67UVx9cvHhRABB//fWX1PfMM8+IkSNHCiG0/x/v3LkjAIjDhw+Xua3KvLcPS0pKEgDEuXPnhBBlv7cbN24U7du3l9pz5swRenp6wszMTJiYmAgAAoCYOnWq1rZL/p798ccfwsDAQOt9CwwM1Pob/+C1V61aJS0TEREhAIiLFy8KIXTvbziPANWwy5cv4+TJkxgxYgSAe98ahg8frjW88CgXL16Ej4+PVt/D7apsp+Q3jm7duiEjIwM3btyQ+jp27Fhq3YkTJ2Lt2rUAgKSkJPz+++8YP3681rZv3LhR7nyHkm7fvo24uDhMmDAB5ubm0mP+/PmV+qaqK5T+3n7zzTcIDw9HYGAg2rVrh2+++QbNmzcvt8bLly+jc+fOWn0Pt4F78xNKfhN1cHBAUlJSudvVdePHj0d0dDQOHz4s9a1ZswZDhgyBtbW11rJpaWlITU3FgAED0KxZM7Rq1QpjxoxB48aNK/16L774IoYMGYIWLVqgXbt2WL16Nc6dO4cLFy6Uu87evXvx1FNPwcXF5ZHb/+WXX6Cnp4dVq1bhqaeeQqtWrbB27VrExsbi8OHDMDc3l4Y+7e3tYW9vDxsbG+jr68PCwkLqqw88PDzg6+sr/c5HRkbin3/+0fr9ecDGxgZjx45Fv3794Ofnh6VLl1b5CGZkZCRGjRqFpk2bwtLSEk2aNAGACucelnVkz93dHeHh4QgJCcFnn32Gdu3a4bPPPit3G5cvX4aLi4vW+1bW7y4AtG3bVvq3g4MDAOjs7y/vBl/DVq9ejYKCAjg5OUl9QggYGhoiOTkZenp6pYZMHsx3qG5CiFKHWx+8dsl+MzOzUuuOHj0a06dPR3BwMIKDg+Hm5oann35aen7Xrl3o06cPTExMHlnHg8O1P/74Y6n5QuUdgtVFSn9v7e3t0bx5czRv3hxbtmyBt7c3OnbsCE9Pz3LrLK/Gkh4eRlOpVBUe4td1JT8kn332WelDsuR8qQdKfkj26dMHvXv3xrBhw6QPksqIjIzErFmzcPz4cfz777/S/11sbCzatGlT5jpVGf4KCwvDtWvXYGFhodWfk5NTp77AVJcJEybgrbfewnfffYe1a9fC1dUVzz33XJnLrl27FlOnTsW+ffuwefNmzJw5E4GBgejatWul/l74+fnBxcUFP/74IxwdHVFUVIQ2bdqUe0JDfn4+9u3bhxkzZmj1GxkZSV9WWrdujatXr+KNN97A+vXry9xOWX9fylPy9/fBOrr6+8sjQDWooKAA69atw1dffYXw8HDpcebMGbi6umLjxo1o2LAh0tPTkZmZKa338OmprVq1wvHjx7X6Hm4/zMjICIWFhVp9np6eCAoK0volCwoKgoWFhdaHeFlsbW0xePBgrF27FmvXrsW4ceO0nq/KH1A7Ozs4OTnh+vXr0gfog8eDbzSV3Se58L3V1rx5c7z44oul/tCW5O7ujpMnT2r1hYaGVrjdsujSz0FlTZgwAdu2bUNaWlqlPiSDg4Ph6+uLzZs3o2XLltLPRGU/JO/cuYMff/wRJ06cwIkTJwCUf9bfgw/Jyp69WVRUhA4dOmj93IeHh+PKlSsYNWpUpbZRnwwbNgz6+vrYtGkTfvrpJ4wbN67CsODt7Y0ZM2YgKCgIbdq0waZNmwDcO+vq4SNCJf9e3LlzBxcvXsTMmTPx3HPPoVWrVkhOTq6wtkOHDsHKyuqRZ/TNmjULP//8M06dOlXm8x4eHoiNjcWtW7ekvpCQkAq3WRZd+93lEaAatGfPHiQnJ2PChAnQaDRazw0dOhSrV6/GwYMHYWpqio8//hhvv/02Tp48WWrC5DvvvIMxY8agY8eO6N69OzZu3IiIiAg0bdq03Nd2c3PDiRMnEB0dDXNzc9jY2GDKlClYsmQJ3n77bbz11lu4fPky5syZg/fee69SE5cnTpyIAQMGoLCwEGPGjJH6k5KSEBISUua1PqKiokp96Ddv3hz+/v6YOnUqLC0t0b9/f+Tm5iI0NBTJycl47733yt2njIwMHDx4EF5eXjA1NZXtNE2+t6X997//hZeXF0JDQ8scanv77bcxadIkdOzYUfpwP3v2bIX7Wt7+//333xgxYgTUajUaNGhQpfXlMGzYMLzzzjvSh+SkSZMe+SH54IPSx8cHmzZtQteuXdGwYUOcP39ea9nw8HDpW/eDD8mVK1dKR/GOHj1aYW2V/ZB8oH379ti8eTMaNWpUpTPGdO3Dr7qYm5tj+PDh+Pjjj5GamoqxY8eWuVxUVBR++OEHDBw4EI6Ojrh8+TKuXLmC0aNHAwB69eqFL774AuvWrYOPjw82bNiA8+fPw9vbGwCkCec//PADHBwcEBsbK12Xqzy7du2q1BfTpk2bYtCgQZg9ezb27NlT6vk+ffqgWbNmGDNmDBYvXoz09HRpEnRVJnHr0t9wAJwEXZMGDBgg/u///q/M58LCwgQAERYWJrZv3y6aN28ujI2NxYABA8QPP/wgHn5rPvvsM9GgQQNhbm4uxowZIz788MMKJ8pevnxZdO3aVZrkFhUVJYQQ4vDhw6JTp07CyMhI2Nvbi48++kjk5+dL65U1wfaBoqIi4erqWmqfVq1aJbp161ZqedyfXPfw49ChQ0KIe5Pz2rVrJ4yMjIS1tbV45plnxG+//Sat//AkaCGEmDx5srC1tRUASj1Xm/jelj3BvU+fPqJ///5CiLInPM6dO1fa1/Hjx4upU6eKrl27lruvQpSeCBocHCzatm0r1Gp1qf9LXTZhwgRhbW0t9PT0pEnNQmhPlL1+/bqYPn26CAoKEtHR0WL//v3CxsZGfP/990IIIfbt2ydUKpX46aefxJUrV8Ts2bOFpaWl9P9TWFgobG1txSuvvCKuXr0qDh48KDp16lTmZNUHE2XffPNN8dZbb2nVOmfOHNGyZUtx+vRprcfVq1elSdA9e/YUf//9t7h+/bo4fPiwmDp1qoiLixNClJ7sL8S9n42BAweKGzduiNu3b1fz/668goKCBADRt29frf6S721iYqIYPHiwcHBwEEZGRsLV1VXMnj1bmpwuhBCzZ88WdnZ2QqPRiHfffVe89dZbWj/7gYGBolWrVkKtVou2bduKw4cPV/jeuri4SCdllFVTSceOHRMAxPHjx4UQpU82uHjxoujWrZswMjISHh4eYvfu3QKA2LdvX5mvLcS9yfUl/+YLoTt/w4UQou789SDZZWZmCo1GI7Zt26bV7+fnp3UWEdU9cr23vXv3Fq+88kqNbV+X1LUPybK+vDx4nYSEBDF69GjRoEEDoVarRdOmTcWkSZNEamqqEKLsAFRXg2tdFRYWJjQajcjLy6uR7R89elQAENeuXauR7dcGlRBVuGgJKVJRURESExPx1VdfYevWrYiMjISBQfHo6eLFizFy5MhKnUFCuqU239usrCysWLEC/fr1g76+Pn7++WfMnTsXgYGBlTp7kKrfqVOn0KtXL9y+fbvCazhR3XPy5ElcvnwZr776arVsb/v27TA3N0eLFi1w7do1vPPOO7C2tn7kEKsuYwCiR4qOjkaTJk3g7OyMgICAcidvUt1Tm+9tdnY2/Pz8cOrUKeTm5sLd3R0zZ87EkCFDauw1qWLV/SFJ9de6deswb948xMXFoUGDBujduze++uornbiK+eNiACIiIiLF4WnwREREpDgMQERERKQ4DEBERESkOAxAREREpDgMQERERKQ4DEBERI/h8OHDUKlUSElJKXeZgIAA6c7oRKRbGICIiAD07NkT06ZNq/Tyvr6+SEhIKHUvOCKqG3gzVCKix2BkZAR7e3u5yyCix8QjQEQkOyEEFi9ejKZNm8LExAReXl7YunUrioqK4OzsjBUrVmgtf+rUKahUKly/fh0AkJqaitdee026Q3mvXr1w5swZaXl/f3+0a9cO69evh5ubGzQaDUaMGIH09HQAwNixY3HkyBEsXboUKpUKKpUK0dHRFdZc1hBYQEAAGjduDFNTU/znP//BnTt3quc/iIiqHQMQEclu5syZWLt2LZYvX46IiAi8++67eOWVV/DPP/9gxIgR2Lhxo9bymzZtgo+PD5o2bQohBF544QUkJiZi7969CAsLQ/v27fHcc8/h7t270jqRkZHYsWMH9uzZgz179uDIkSP4/PPPAQBLly6Fj48PJk2ahISEBCQkJFT5/mcnTpzA+PHjMWXKFISHh+PZZ5/F/Pnzn/w/h4hqhnz3YSUiEiIjI0MYGxuLoKAgrf4JEyaIkSNHilOnTgmVSiWio6OFEEIUFhYKJycn8d133wkhhDh48KCwtLQUOTk5Wus3a9ZMrFy5Ughx7+7mpqamIi0tTXr+gw8+EF26dJHaPXr0EO+8806l6z506JAAIJKTk4UQQowcOVI8//zzWssMHz5caDSaSm+TiGoPjwARkawuXLiAnJwc9OnTB+bm5tJj3bp1iIyMhLe3Nzw8PPDzzz8DAI4cOYKkpCQMGzYMABAWFoaMjAzY2tpqrR8VFYXIyEjpddzc3GBhYSG1HRwckJSUVG37cfHiRfj4+Gj1PdwmIt3BSdBEJKuioiIAwO+//w4nJyet59RqNQDg5ZdfxqZNmzB9+nRs2rQJ/fr1Q4MGDaT1HRwccPjw4VLbLnkKuqGhodZzKpVKeu3qIHhfaaI6hQGIiGTl6ekJtVqN2NhY9OjRo8xlRo0ahZkzZyIsLAxbt27F8uXLpefat2+PxMREGBgYwM3N7bHrMDIyQmFh4WOv7+npiePHj2v1PdwmIt3BAEREsrKwsMD777+Pd999F0VFRejevTvS0tIQFBQEc3NzjBkzBk2aNIGvry8mTJiAgoICDBo0SFq/d+/e8PHxweDBg7Fo0SK4u7vj5s2b2Lt3LwYPHoyOHTtWqg43NzecOHEC0dHRMDc3h42NDfT0Kj9LYOrUqfD19cXixYsxePBgHDhwAPv27avy/wcR1Q7OASIi2c2bNw+zZ8/GwoUL0apVK/Tr1w+7d+9GkyZNpGVefvllnDlzBkOGDIGJiYnUr1KpsHfvXjzzzDMYP348WrZsiREjRiA6Ohp2dnaVruH999+Hvr4+PD090bBhQ8TGxlZpH7p27YpVq1bhf//7H9q1a4cDBw5g5syZVdoGEdUeleDANRERESkMjwARERGR4jAAERGVYfLkyVqn1Zd8TJ48We7yiOgJcQiMiKgMSUlJSEtLK/M5S0tLNGrUqJYrIqLqxABEREREisMhMCIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSnP8H9Z85gHTNcE0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p_data = df_alpha.groupby(['event_id', 'ch_name'])['cf'].mean().reset_index()\n",
+    "\n",
+    "sns.pointplot(p_data, x='event_id', y='cf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/examples/irasa_pitfalls.ipynb b/doc/source/examples/irasa_pitfalls.ipynb
new file mode 100644
index 0000000..b347a4b
--- /dev/null
+++ b/doc/source/examples/irasa_pitfalls.ipynb
@@ -0,0 +1,647 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pitfalls in IRASA and how to avoid them\n",
+    "\n",
+    "This notebook discusses common issues you may face when applying IRASA to your data and how to avoid them.\n",
+    "IRASA - as an algoritm - is very appealing, because its so simplistic. In the end we are just up/downsampling signals, computing psd's and averaging them.\n",
+    "This means that we only have a single hyperparameter to set when running a model, the set of up- and downsampling factors.\n",
+    "Setting this parameter not correctly can have severe consequences for the validity of our model so care should be taken, when setting this parameter.\n",
+    "In this notebook I show what happens when your model specifications are off and how to set things up correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neurodsp.sim import sim_combined\n",
+    "from neurodsp.utils import create_times\n",
+    "import numpy as np\n",
+    "import scipy.signal as dsp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from pyrasa.irasa import irasa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by simulating a fairly simple signal consisting of a 10Hz oscillation and an aperiodic exponent of 1. We will then add some preprocessing steps that are common in M/EEG research and see how some of those decisions can impact our IRASA model fits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fs = 500\n",
+    "n_seconds = 60\n",
+    "duration=6\n",
+    "overlap=0.5\n",
+    "\n",
+    "sim_components = {'sim_powerlaw': {'exponent' : -1}, \n",
+    "                  'sim_oscillation': {'freq' : 10}}\n",
+    "\n",
+    "\n",
+    "sig = sim_combined(n_seconds=n_seconds, fs=fs, components=sim_components)\n",
+    "times = create_times(n_seconds=n_seconds, fs=fs)\n",
+    "\n",
+    "max_times = times < 1\n",
+    "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
+    "axes[0].plot(times[max_times], sig[max_times])\n",
+    "axes[0].set_ylabel('Amplitude (a.u.)')\n",
+    "axes[0].set_xlabel('Time (s)')\n",
+    "freq, psd = dsp.welch(sig, fs=fs, nperseg=duration*fs, noverlap=duration*fs*overlap)\n",
+    "axes[1].loglog(freq, psd)\n",
+    "axes[1].set_ylabel('Power (a.u.)')\n",
+    "axes[1].set_xlabel('Frequency (Hz)')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case #1: Up-/Downsampling meets nyquist\n",
+    "\n",
+    "The signal we simulated previously has a sampling rate of 500Hz. When we start evaluating data that touches or exceeds the nyquist frequency i.e. fs/2. \n",
+    "We get an Error. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "The evaluated frequency range goes up to 300.0Hz which is higher than the Nyquist frequency for your data of 250.0Hz, \ntry to either lower the upper bound for the hset or decrease the upper band limit, when running IRASA.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m irasa_out \u001b[38;5;241m=\u001b[39m \u001b[43mirasa\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mband\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m150\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mpsd_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnperseg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                                \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnoverlap\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moverlap\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m                            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mhset_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/git/pyrasa/pyrasa/irasa.py:123\u001b[0m, in \u001b[0;36mirasa\u001b[0;34m(data, fs, band, psd_kwargs, ch_names, win_func, win_func_kwargs, dpss_settings_time_bandwidth, dpss_settings_low_bias, dpss_eigenvalue_weighting, filter_settings, hset_info, hset_accuracy)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m data\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData shape needs to be either of shape (Channels, Samples) or (Samples, ).\u001b[39m\u001b[38;5;124m'\u001b[39m  \u001b[38;5;66;03m# noqa PLR2004\u001b[39;00m\n\u001b[1;32m    115\u001b[0m irasa_params \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m: data,\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfs\u001b[39m\u001b[38;5;124m'\u001b[39m: fs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m: hset_accuracy,\n\u001b[1;32m    121\u001b[0m }\n\u001b[0;32m--> 123\u001b[0m \u001b[43m_check_irasa_settings\u001b[49m\u001b[43m(\u001b[49m\u001b[43mirasa_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mirasa_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhset_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhset_info\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m hset \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mround(np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m*\u001b[39mhset_info), hset_accuracy)\n\u001b[1;32m    126\u001b[0m hset \u001b[38;5;241m=\u001b[39m [h \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m hset \u001b[38;5;28;01mif\u001b[39;00m h \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# filter integers\u001b[39;00m\n",
+      "File \u001b[0;32m~/git/pyrasa/pyrasa/utils/irasa_utils.py:159\u001b[0m, in \u001b[0;36m_check_irasa_settings\u001b[0;34m(irasa_params, hset_info)\u001b[0m\n\u001b[1;32m    157\u001b[0m band_evaluated: \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m=\u001b[39m (irasa_params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mband\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m/\u001b[39m hmax, irasa_params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mband\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m*\u001b[39m hmax)\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m band_evaluated[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe evaluated frequency range is 0 or lower this makes no sense\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m--> 159\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m band_evaluated[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m<\u001b[39m nyquist, (\n\u001b[1;32m    160\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe evaluated frequency range goes up to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(band_evaluated[\u001b[38;5;241m1\u001b[39m],\u001b[38;5;250m \u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mHz \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    161\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwhich is higher than the Nyquist frequency for your data of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnyquist\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mHz, \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtry to either lower the upper bound for the hset or decrease the upper band limit, when running IRASA.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    163\u001b[0m )\n\u001b[1;32m    165\u001b[0m filter_settings: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(irasa_params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfilter_settings\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m    166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filter_settings[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[0;31mAssertionError\u001b[0m: The evaluated frequency range goes up to 300.0Hz which is higher than the Nyquist frequency for your data of 250.0Hz, \ntry to either lower the upper bound for the hset or decrease the upper band limit, when running IRASA."
+     ]
+    }
+   ],
+   "source": [
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(1, 150), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can get rid of this error in 2 ways. Either we adjust the hmax and decrease it (in this example anything below 1.66 should work) or we decrease the maximally evaluated frequency (in this example any value below 125Hz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x116dcbbf0>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#hset below 2\n",
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(1, 150), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 1.66, 0.05))\n",
+    "\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x117173cb0>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#band below 150\n",
+    "irasa_out = irasa(sig, \n",
+    "                    fs=fs, \n",
+    "                    band=(1, 124), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case #2: Fitting IRASA inside your high-pass and lowpass filters\n",
+    "\n",
+    "In the case that our model evaluations includes the nyquist frequency, pyrasa actually throws an error as long as you specify your sampling frequency correctly (labeled as fs in pyrasa.irasa).\n",
+    "However, another Issue is generated by fitting your model inside your high-pass or lowpass filters. As how their data is handled is up to every researcher before applying IRASA, \n",
+    "exact filter specifications are not always available, hence pyrasa does not per default recognize whether your data has been filtered or not. If you use the `pyrasa.irasa_mne` this is taken into account as mne stores filter information in the raw or epoched object. In order to illustrate what happens when you accidently fit in your filters see below.. what happens to our aperiodic spectrum in high and low pass filtered signals.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-3.555643</td>\n",
+       "      <td>-0.291665</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Offset  Exponent fit_type ch_name\n",
+       "0 -3.555643 -0.291665    fixed       0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from neurodsp.filt import filter_signal\n",
+    "\n",
+    "sig_hp = filter_signal(sig, fs=fs, pass_type='highpass', f_range=(4, None))\n",
+    "sig_hp = sig_hp[~np.isnan(sig_hp)]\n",
+    "\n",
+    "irasa_out = irasa(sig_hp, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 100), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)\n",
+    "irasa_out.fit_aperiodic_model().aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks clearly wrong and illustrates why our lower evaluated frequency range should never go below the high-pass filter.\n",
+    "In this case our hmax is 2 so taking 2 * the highpass filter edge is giving us the lowest point that we can sensibly evaluate (i.e. 8Hz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.385029</td>\n",
+       "      <td>0.990062</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Offset  Exponent fit_type ch_name\n",
+       "0 -1.385029  0.990062    fixed       0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "irasa_out = irasa(sig_hp, \n",
+    "                    fs=fs, \n",
+    "                    band=(8, 100), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)\n",
+    "\n",
+    "irasa_out.fit_aperiodic_model().aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same applies to lowpass filters. Here the upper-most evaluated frequency also depends on the lowpass edge. I.e. we want to stay below lowpass / hmax. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.655552</td>\n",
+       "      <td>4.197068</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Offset  Exponent fit_type ch_name\n",
+       "0  1.655552  4.197068    fixed       0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sig_lp = filter_signal(sig, fs=fs, pass_type='lowpass', f_range=(None, 50))\n",
+    "sig_lp = sig_lp[~np.isnan(sig_lp)]\n",
+    "\n",
+    "irasa_out = irasa(sig_lp, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 100), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)\n",
+    "irasa_out.fit_aperiodic_model().aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Exponent</th>\n",
+       "      <th>fit_type</th>\n",
+       "      <th>ch_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.359771</td>\n",
+       "      <td>1.018573</td>\n",
+       "      <td>fixed</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Offset  Exponent fit_type ch_name\n",
+       "0 -1.359771  1.018573    fixed       0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "irasa_out = irasa(sig_lp, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 25), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    hset_info=(1, 2, 0.05))\n",
+    "\n",
+    "plt.loglog(irasa_out.freqs, irasa_out.aperiodic.T)\n",
+    "irasa_out.fit_aperiodic_model().aperiodic_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A good way of accidently misspecifying your model is to pass the filter bounds to `pyrasa.irasa`.\n",
+    "Model misspecifications will result in informative error messages. \n",
+    "That will tell you how to improve your irasa fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "You run IRASA in a frequency range from 0.05 - 200.0Hz. \nYour settings specified in \"filter_settings\" indicate that you have a pass band from 0.05 - 50Hz. \nThis means that your evaluated range likely contains filter artifacts. \nEither change your filter settings, adjust hset or the parameter \"band\" accordingly. \nYou want to make sure that the lower band limit divided by the upper bound of the hset > 0.05 \nand that upper band limit times the upper bound of the hset < 50",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m irasa_out \u001b[38;5;241m=\u001b[39m \u001b[43mirasa\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig_lp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mband\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mpsd_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnperseg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                                \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnoverlap\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moverlap\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m                            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mfilter_settings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mhset_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/git/pyrasa/pyrasa/irasa.py:123\u001b[0m, in \u001b[0;36mirasa\u001b[0;34m(data, fs, band, psd_kwargs, ch_names, win_func, win_func_kwargs, dpss_settings_time_bandwidth, dpss_settings_low_bias, dpss_eigenvalue_weighting, filter_settings, hset_info, hset_accuracy)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m data\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData shape needs to be either of shape (Channels, Samples) or (Samples, ).\u001b[39m\u001b[38;5;124m'\u001b[39m  \u001b[38;5;66;03m# noqa PLR2004\u001b[39;00m\n\u001b[1;32m    115\u001b[0m irasa_params \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m: data,\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfs\u001b[39m\u001b[38;5;124m'\u001b[39m: fs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m: hset_accuracy,\n\u001b[1;32m    121\u001b[0m }\n\u001b[0;32m--> 123\u001b[0m \u001b[43m_check_irasa_settings\u001b[49m\u001b[43m(\u001b[49m\u001b[43mirasa_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mirasa_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhset_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhset_info\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m hset \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mround(np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m*\u001b[39mhset_info), hset_accuracy)\n\u001b[1;32m    126\u001b[0m hset \u001b[38;5;241m=\u001b[39m [h \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m hset \u001b[38;5;28;01mif\u001b[39;00m h \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# filter integers\u001b[39;00m\n",
+      "File \u001b[0;32m~/git/pyrasa/pyrasa/utils/irasa_utils.py:171\u001b[0m, in \u001b[0;36m_check_irasa_settings\u001b[0;34m(irasa_params, hset_info)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filter_settings[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    169\u001b[0m     filter_settings[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m band_evaluated[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 171\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mlogical_and(band_evaluated[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m filter_settings[\u001b[38;5;241m0\u001b[39m], band_evaluated[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m filter_settings[\u001b[38;5;241m1\u001b[39m]), (\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYou run IRASA in a frequency range from \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(band_evaluated[\u001b[38;5;241m0\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    174\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(band_evaluated[\u001b[38;5;241m1\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mHz. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    175\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYour settings specified in \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilter_settings\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m indicate that you have a pass band from \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    176\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m0\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    177\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m1\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mHz. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    178\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThis means that your evaluated range likely contains filter artifacts. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    179\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEither change your filter settings, adjust hset or the parameter \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mband\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m accordingly. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    180\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYou want to make sure that the lower band limit divided by the upper bound of the hset \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m> \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m0\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mand that upper band limit times the upper bound of the hset < \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    183\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m1\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    184\u001b[0m )\n",
+      "\u001b[0;31mAssertionError\u001b[0m: You run IRASA in a frequency range from 0.05 - 200.0Hz. \nYour settings specified in \"filter_settings\" indicate that you have a pass band from 0.05 - 50Hz. \nThis means that your evaluated range likely contains filter artifacts. \nEither change your filter settings, adjust hset or the parameter \"band\" accordingly. \nYou want to make sure that the lower band limit divided by the upper bound of the hset > 0.05 \nand that upper band limit times the upper bound of the hset < 50"
+     ]
+    }
+   ],
+   "source": [
+    "irasa_out = irasa(sig_lp, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 100), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    filter_settings=(None, 50),\n",
+    "                    hset_info=(1, 2, 0.05))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "You run IRASA in a frequency range from 0.05 - 200.0Hz. \nYour settings specified in \"filter_settings\" indicate that you have a pass band from 4 - 200.0Hz. \nThis means that your evaluated range likely contains filter artifacts. \nEither change your filter settings, adjust hset or the parameter \"band\" accordingly. \nYou want to make sure that the lower band limit divided by the upper bound of the hset > 4 \nand that upper band limit times the upper bound of the hset < 200.0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m irasa_out \u001b[38;5;241m=\u001b[39m \u001b[43mirasa\u001b[49m\u001b[43m(\u001b[49m\u001b[43msig_hp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mband\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mpsd_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnperseg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                                \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnoverlap\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moverlap\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m                            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mfilter_settings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mhset_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/git/pyrasa/pyrasa/irasa.py:123\u001b[0m, in \u001b[0;36mirasa\u001b[0;34m(data, fs, band, psd_kwargs, ch_names, win_func, win_func_kwargs, dpss_settings_time_bandwidth, dpss_settings_low_bias, dpss_eigenvalue_weighting, filter_settings, hset_info, hset_accuracy)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m data\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData shape needs to be either of shape (Channels, Samples) or (Samples, ).\u001b[39m\u001b[38;5;124m'\u001b[39m  \u001b[38;5;66;03m# noqa PLR2004\u001b[39;00m\n\u001b[1;32m    115\u001b[0m irasa_params \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m: data,\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfs\u001b[39m\u001b[38;5;124m'\u001b[39m: fs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m: hset_accuracy,\n\u001b[1;32m    121\u001b[0m }\n\u001b[0;32m--> 123\u001b[0m \u001b[43m_check_irasa_settings\u001b[49m\u001b[43m(\u001b[49m\u001b[43mirasa_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mirasa_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhset_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhset_info\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m hset \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mround(np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m*\u001b[39mhset_info), hset_accuracy)\n\u001b[1;32m    126\u001b[0m hset \u001b[38;5;241m=\u001b[39m [h \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m hset \u001b[38;5;28;01mif\u001b[39;00m h \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# filter integers\u001b[39;00m\n",
+      "File \u001b[0;32m~/git/pyrasa/pyrasa/utils/irasa_utils.py:171\u001b[0m, in \u001b[0;36m_check_irasa_settings\u001b[0;34m(irasa_params, hset_info)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filter_settings[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    169\u001b[0m     filter_settings[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m band_evaluated[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 171\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mlogical_and(band_evaluated[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m filter_settings[\u001b[38;5;241m0\u001b[39m], band_evaluated[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m filter_settings[\u001b[38;5;241m1\u001b[39m]), (\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYou run IRASA in a frequency range from \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(band_evaluated[\u001b[38;5;241m0\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    174\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(band_evaluated[\u001b[38;5;241m1\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mHz. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    175\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYour settings specified in \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilter_settings\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m indicate that you have a pass band from \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    176\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m0\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    177\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m1\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mHz. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    178\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThis means that your evaluated range likely contains filter artifacts. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    179\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEither change your filter settings, adjust hset or the parameter \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mband\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m accordingly. \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    180\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYou want to make sure that the lower band limit divided by the upper bound of the hset \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m> \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m0\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mand that upper band limit times the upper bound of the hset < \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    183\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mround(filter_settings[\u001b[38;5;241m1\u001b[39m],\u001b[38;5;250m \u001b[39mirasa_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhset_accuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    184\u001b[0m )\n",
+      "\u001b[0;31mAssertionError\u001b[0m: You run IRASA in a frequency range from 0.05 - 200.0Hz. \nYour settings specified in \"filter_settings\" indicate that you have a pass band from 4 - 200.0Hz. \nThis means that your evaluated range likely contains filter artifacts. \nEither change your filter settings, adjust hset or the parameter \"band\" accordingly. \nYou want to make sure that the lower band limit divided by the upper bound of the hset > 4 \nand that upper band limit times the upper bound of the hset < 200.0"
+     ]
+    }
+   ],
+   "source": [
+    "irasa_out = irasa(sig_hp, \n",
+    "                    fs=fs, \n",
+    "                    band=(.1, 100), \n",
+    "                    psd_kwargs={'nperseg': duration*fs, \n",
+    "                                'noverlap': duration*fs*overlap\n",
+    "                            },\n",
+    "                    filter_settings=(4, None),\n",
+    "                    hset_info=(1, 2, 0.05))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/examples/irasa_sprint.ipynb b/doc/source/examples/irasa_sprint.ipynb
new file mode 100644
index 0000000..cb82aeb
--- /dev/null
+++ b/doc/source/examples/irasa_sprint.ipynb
@@ -0,0 +1,366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run IRASA timeresolved\n",
+    "\n",
+    "One of the original features of IRASA was its applicability in the time-frequency domain (Wen & Liu 2016).\n",
+    "The authors used this to investigate changes periodic and aperiodic activity over time and even computed broadband correlations of aperiodic activity over channels across time (see  [Wen & Liu, 2016](https://doi.org/10.1523/JNEUROSCI.0187-16.2016)). To make this form of analysis more accessible and track aperiodic and periodic changes over time we implemented the irasa_sprint function, that similarly to the SPRiNT package ([Wilson, da Silva Castanheira & Baillet, 2022](https://doi.org/10.7554/eLife.77348)), enables you to compute periodic and aperiodic spectrograms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from neurodsp.sim import set_random_seed\n",
+    "from neurodsp.sim import sim_powerlaw, sim_oscillation\n",
+    "from neurodsp.utils import create_times\n",
+    "from neurodsp.plts import plot_timefrequency#\n",
+    "\n",
+    "from neurodsp.timefrequency import compute_wavelet_transform\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "#import seaborn as sns\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "new_rc_params = {'text.usetex': False,\n",
+    "                 \"svg.fonttype\": 'none'\n",
+    "}\n",
+    "mpl.rcParams.update(new_rc_params)\n",
+    "\n",
+    "set_random_seed(84)\n",
+    "\n",
+    "from pyrasa.irasa import irasa_sprint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets firs generate a signal with some alpha and beta bursts alongside a change in the spectral exponent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set some general settings, to be used across all simulations\n",
+    "fs = 500\n",
+    "n_seconds = 15\n",
+    "duration=4\n",
+    "overlap=0.5\n",
+    "\n",
+    "# Create a times vector for the simulations\n",
+    "times = create_times(n_seconds, fs)\n",
+    "\n",
+    "\n",
+    "alpha = sim_oscillation(n_seconds=.5, fs=fs, freq=10)\n",
+    "no_alpha = np.zeros(len(alpha))\n",
+    "beta = sim_oscillation(n_seconds=.5, fs=fs, freq=25)\n",
+    "no_beta = np.zeros(len(beta))\n",
+    "\n",
+    "exp_1 = sim_powerlaw(n_seconds=2.5, fs=fs, exponent=-1)\n",
+    "exp_2 = sim_powerlaw(n_seconds=2.5, fs=fs, exponent=-2)\n",
+    "\n",
+    "\n",
+    "alphas = np.concatenate([no_alpha, alpha, no_alpha, alpha, no_alpha])\n",
+    "betas = np.concatenate([beta, no_beta, beta, no_beta, beta])\n",
+    "\n",
+    "sim_ts = np.concatenate([exp_1 + alphas, \n",
+    "                         exp_1 + alphas + betas, \n",
+    "                         exp_1 + betas, \n",
+    "                         exp_2 + alphas, \n",
+    "                         exp_2 + alphas + betas, \n",
+    "                         exp_2 + betas, ])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we compute a time frequency spectrum using morlet wavelets and additionally decompose the data in the time frequency domain using pyrasa's `irasa_sprint` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freqs = np.arange(1, 50, 0.5)\n",
+    "import scipy.signal as dsp\n",
+    "\n",
+    "mwt = compute_wavelet_transform(sim_ts, fs=fs, \n",
+    "                                freqs=freqs, n_cycles=11,\n",
+    "                                )\n",
+    "\n",
+    "irasa_sprint_spectrum = irasa_sprint(sim_ts, fs=fs,\n",
+    "                                        band=(1, 50),\n",
+    "                                        overlap_fraction=.95,\n",
+    "                                        win_duration=.5,\n",
+    "                                        hset_info=(1.05, 4., 0.05),\n",
+    "                                        win_func=dsp.windows.hann)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we now plot the data we can see that we have nicely seperated our prediodic and aperiodic spectra in time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#%%\n",
+    "f, axes = plt.subplots(figsize=(14, 4), ncols=3)\n",
+    "mwt = np.abs(mwt)\n",
+    "plot_timefrequency(times, freqs, mwt, ax=axes[0], vmin=0, vmax=0.5)\n",
+    "plot_timefrequency(irasa_sprint_spectrum.time, irasa_sprint_spectrum.freqs, np.squeeze(irasa_sprint_spectrum.aperiodic), vmin=0, vmax=0.02, ax=axes[1])\n",
+    "plot_timefrequency(irasa_sprint_spectrum.time, irasa_sprint_spectrum.freqs, np.squeeze(irasa_sprint_spectrum.periodic), vmin=0, vmax=0.1, ax=axes[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can specify an aperiodic model and fit it to our aperiodic spectrogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ap_spec = irasa_sprint_spectrum.fit_aperiodic_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualize the aperiodic changes alongside the goodness of fit, to see whether its  matching our expectations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x700 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(nrows=3, figsize=(8, 7))\n",
+    "\n",
+    "ax[0].plot(ap_spec.aperiodic_params['time'], ap_spec.aperiodic_params['Offset'])\n",
+    "ax[0].set_ylabel('Offset')\n",
+    "ax[0].set_xlabel('time (s)')\n",
+    "ax[1].plot(ap_spec.aperiodic_params['time'], ap_spec.aperiodic_params['Exponent'])\n",
+    "ax[1].set_ylabel('Exponent')\n",
+    "ax[1].set_xlabel('time (s)')\n",
+    "ax[2].plot(ap_spec.aperiodic_params['time'], ap_spec.gof['R2'])\n",
+    "ax[2].set_ylabel('R2')\n",
+    "ax[2].set_xlabel('time (s)')\n",
+    "\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly we can extract our peaks or putatively oscillatory activity in time, by using the `get_peaks` method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_spec = irasa_sprint_spectrum.get_peaks(cut_spectrum=(1, 40),\n",
+    "                                  smooth=True,\n",
+    "                                  smoothing_window=1,\n",
+    "                                  peak_threshold=2,\n",
+    "                                  min_peak_height=0.01,\n",
+    "                                  peak_width_limits=(0.5, 12))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x700 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(nrows=3, figsize=(8, 7))\n",
+    "\n",
+    "for ix, cur_key in enumerate(['cf', 'pw', 'bw']):\n",
+    "\n",
+    "    ax[ix].plot(peaks_spec['time'], peaks_spec[cur_key])\n",
+    "    ax[ix].set_ylabel(cur_key)\n",
+    "    ax[ix].set_xlabel('time (s)')\n",
+    "    ax[ix].set_xlim(0, 15)\n",
+    "\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can further check whether the number of extracted peaks matches our expectations. Looks pretty good to me :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyrasa.utils.peak_utils import get_band_info\n",
+    "\n",
+    "df_alpha = get_band_info(peaks_spec, freq_range=(8,12), ch_names=[])\n",
+    "alpha_peaks = df_alpha.query('pw > 0.10')\n",
+    "\n",
+    "beta_ts = alpha_peaks['time'].to_numpy()\n",
+    "t1 = beta_ts[0]\n",
+    "n_peaks = 0\n",
+    "for ix, i in enumerate(beta_ts):\n",
+    "    try: \n",
+    "        diff = beta_ts[ix + 1] - i\n",
+    "        if diff > 0.025:\n",
+    "            n_peaks += 1\n",
+    "    except IndexError:\n",
+    "        pass\n",
+    "n_peaks\n",
+    "\n",
+    "#%%\n",
+    "df_beta = get_band_info(peaks_spec, freq_range=(20, 30), ch_names=[])\n",
+    "beta_peaks = df_beta.query('pw > 0.10')\n",
+    "\n",
+    "beta_ts = beta_peaks['time'].to_numpy()\n",
+    "t1 = beta_ts[0]\n",
+    "n_peaks = 0\n",
+    "for ix, i in enumerate(beta_ts):\n",
+    "    try: \n",
+    "        diff = beta_ts[ix + 1] - i\n",
+    "        if diff > 0.025:\n",
+    "            n_peaks += 1\n",
+    "    except IndexError:\n",
+    "        pass\n",
+    "n_peaks\n",
+    "\n",
+    "\n",
+    "# %%\n",
+    "f, ax = plt.subplots(figsize=(12, 4), ncols=2)\n",
+    "\n",
+    "ax[0].plot(df_alpha['time'], df_alpha['pw'])\n",
+    "ax[1].plot(df_beta['time'], df_beta['pw'])\n",
+    "\n",
+    "yax = ['Alpha Power (8-12Hz)', 'Beta Power (20-30Hz)']\n",
+    "for ix, c_ax in enumerate(ax):\n",
+    "    c_ax.axhline(0.1, color='r', linestyle='--')\n",
+    "    c_ax.set_xlabel('Time (s)')\n",
+    "    c_ax.set_ylabel(yax[ix])\n",
+    "\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/source/generated/pyrasa.irasa.examples b/doc/source/generated/pyrasa.irasa.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.irasa.irasa.examples b/doc/source/generated/pyrasa.irasa.irasa.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.irasa.irasa_sprint.examples b/doc/source/generated/pyrasa.irasa.irasa_sprint.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.irasa_mne.examples b/doc/source/generated/pyrasa.irasa_mne.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.irasa_mne.irasa_epochs.examples b/doc/source/generated/pyrasa.irasa_mne.irasa_epochs.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.irasa_mne.irasa_raw.examples b/doc/source/generated/pyrasa.irasa_mne.irasa_raw.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.aperiodic_utils.compute_aperiodic_model.examples b/doc/source/generated/pyrasa.utils.aperiodic_utils.compute_aperiodic_model.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.aperiodic_utils.compute_aperiodic_model_sprint.examples b/doc/source/generated/pyrasa.utils.aperiodic_utils.compute_aperiodic_model_sprint.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.aperiodic_utils.examples b/doc/source/generated/pyrasa.utils.aperiodic_utils.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.fit_funcs.AbstractFitFun.examples b/doc/source/generated/pyrasa.utils.fit_funcs.AbstractFitFun.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.fit_funcs.FixedFitFun.examples b/doc/source/generated/pyrasa.utils.fit_funcs.FixedFitFun.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.fit_funcs.FixedFitFun.func.examples b/doc/source/generated/pyrasa.utils.fit_funcs.FixedFitFun.func.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.fit_funcs.KneeFitFun.examples b/doc/source/generated/pyrasa.utils.fit_funcs.KneeFitFun.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.fit_funcs.KneeFitFun.func.examples b/doc/source/generated/pyrasa.utils.fit_funcs.KneeFitFun.func.examples
new file mode 100644
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diff --git a/doc/source/generated/pyrasa.utils.fit_funcs.examples b/doc/source/generated/pyrasa.utils.fit_funcs.examples
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diff --git a/doc/source/generated/pyrasa.utils.peak_utils.examples b/doc/source/generated/pyrasa.utils.peak_utils.examples
new file mode 100644
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diff --git a/doc/source/generated/pyrasa.utils.peak_utils.get_band_info.examples b/doc/source/generated/pyrasa.utils.peak_utils.get_band_info.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.peak_utils.get_peak_params.examples b/doc/source/generated/pyrasa.utils.peak_utils.get_peak_params.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/generated/pyrasa.utils.peak_utils.get_peak_params_sprint.examples b/doc/source/generated/pyrasa.utils.peak_utils.get_peak_params_sprint.examples
new file mode 100644
index 0000000..e69de29
diff --git a/doc/source/index.rst b/doc/source/index.rst
new file mode 100644
index 0000000..3eaea22
--- /dev/null
+++ b/doc/source/index.rst
@@ -0,0 +1,109 @@
+PyRASA - Spectral parameterization in python based on IRASA
+===========================================================
+
+.. image:: https://www.repostatus.org/badges/latest/wip.svg
+   :target: https://www.repostatus.org/#wip
+   :alt: Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.
+
+.. image:: https://img.shields.io/badge/License-BSD_2--Clause-orange.svg
+   :target: https://opensource.org/licenses/BSD-2-Clause
+   :alt: License
+
+.. image:: http://www.mypy-lang.org/static/mypy_badge.svg
+   :target: http://mypy-lang.org/
+   :alt: Checked with mypy
+
+.. image:: https://coveralls.io/repos/github/schmidtfa/pyrasa/badge.svg?branch=main
+   :target: https://coveralls.io/github/schmidtfa/pyrasa?branch=main
+   :alt: Coverage Status
+
+
+PyRASA is a Python library designed to separate and parametrize aperiodic (fractal) and periodic (oscillatory) components in time series data based on the IRASA algorithm (Wen & Liu, 2016).
+
+Features
+--------
+
+- **Aperiodic and Periodic Decomposition:** Utilize the IRASA algorithm to decompose power spectra into aperiodic and periodic components, enabling better interpretation of neurophysiological signals.
+- **Time Resolved Spectral Parametrization:** Perform time resolved spectral parametrization, allowing you to track changes in spectral components over time.
+- **Support for Raw and Epoched MNE Objects:** PyRASA provides functions designed for both continuous (Raw) and event-related (Epochs) data, making it versatile for various types of EEG/MEG analyses.
+- **Consistent Ontology:** PyRASA uses the same jargon to label parameters as specparam, the most commonly used tool to parametrize power spectra, to allow users to easily switch between tools depending on their needs, while keeping the labeling of features consistent.
+- **Custom Aperiodic Fit Models:** In addition to the built-in "fixed" and "knee" models for aperiodic fitting, users can specify their custom aperiodic fit functions, offering flexibility in how aperiodic components are modeled.
+
+Example Usage
+-------------
+
+Decompose spectra into periodic and aperiodic components::
+
+   from pyrasa.irasa import irasa
+
+   irasa_out = irasa(sig, 
+                     fs=fs, 
+                     band=(.1, 200), 
+                     psd_kwargs={'nperseg': duration*fs, 
+                                 'noverlap': duration*fs*overlap
+                                },
+                     hset_info=(1, 2, 0.05))
+
+.. image:: https://raw.githubusercontent.com/schmidtfa/pyrasa/main/simulations/example_knee.png
+   :alt: Example knee image
+
+
+Extract periodic parameters::
+
+   irasa_out.get_peaks()
+
++-----------+-----+--------+--------+
+|  ch_name  |  cf |   bw   |   pw   |
++===========+=====+========+========+
+|     0     | 9.5 | 1.4426 | 0.4178 |
++-----------+-----+--------+--------+
+
+Extract aperiodic parameters::
+
+   irasa_out.fit_aperiodic_model(fit_func='knee').aperiodic_params
+
++-----------+--------+--------------+--------------+-----------+--------------------+--------+-----------+
+|  Offset   |  Knee  | Exponent_1   | Exponent_2   | fit_type  | Knee Frequency (Hz)|   tau  |  ch_name  |
++===========+========+==============+==============+===========+====================+========+===========+
+| 1.737e-16 | 60.94  | 0.0396       | 1.4727       | knee      | 14.131             | 0.0113 |     0     |
++-----------+--------+--------------+--------------+-----------+--------------------+--------+-----------+
+
+And the goodness of fit::
+
+   irasa_out.fit_aperiodic_model(fit_func='knee').gof
+
++------------+------------+------------+------------+-----------+-----------+
+|     mse    | r_squared  |     BIC    |     AIC    | fit_type  |  ch_name  |
++============+============+============+============+===========+===========+
+|  0.000051  | 0.999751   | -3931.840  | -3947.806  | knee      |     0     |
++------------+------------+------------+------------+-----------+-----------+
+
+How to Contribute
+-----------------
+
+Contributions to PyRASA are welcome! Whether it's raising issues, improving documentation, fixing bugs, or adding new features, your help is appreciated. Please refer to the `CONTRIBUTING.md <CONTRIBUTING.md>`_ file for more information on how to get involved.
+
+Reference
+---------
+
+If you are using IRASA, please cite the smart people who came up with the algorithm:
+
+Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. *Brain Topography*, 29, 13-26. https://doi.org/10.1007/s10548-015-0448-0
+
+If you are using PyRASA, it would be nice if you could additionally cite us (whenever the paper is finally ready):
+
+Schmidt F., Hartmann T., & Weisz, N. (2049). PyRASA - Spectral parameterization in python based on IRASA. *SOME JOURNAL THAT LIKES US*
+
+
+
+
+.. toctree::
+   :hidden:
+
+   Installation Guide<install>
+   Tutorials<tutorials>
+   API<api>
+
+
+
+
diff --git a/doc/source/install.rst b/doc/source/install.rst
new file mode 100644
index 0000000..b903d4d
--- /dev/null
+++ b/doc/source/install.rst
@@ -0,0 +1,73 @@
+
+==================
+Installation Guide
+==================
+This section provides detailed information about installing PyRASA. 
+Most of PyRASA's functionality is available with the basic requirements, 
+but PyRASA also has an optional dependency to integrate PyRASA in your workflow, when using MNE Python.
+This guide will cover both basic and fully-fledged PyRASA installs and several installation methods.
+
+Stable
+PyRASA can be installed either using pip or conda-forge.
+
+Using pip
+---------
+.. code:: bash
+    
+    pip install pyrasa
+
+Using conda-forge
+-----------------
+.. code:: bash
+    
+    conda install -c conda-forge pyrasa
+
+
+Development
+-----------
+
+If you want to install the latest development version of PyRASA, use the following command:
+
+.. code:: bash
+    
+    pip install git+https://github.com/schmidtfa/pyrasa
+
+
+Dependencies
+------------
+
+
+Required dependencies
+=====================
+The required dependencies for installing PyRASA are:
+
+.. code::
+
+ numpy 
+ pandas 
+ scipy>=1.12
+ attrs 
+ 
+and
+
+.. code::
+
+    python>=3.11
+
+
+Optional dependencies
+=====================
+
+Optionally you can combine PyRASA with MNE Python to better integrate spectral parametrization in your
+M/EEG analysis workflow.
+
+.. code::
+
+    mne
+
+
+
+
+
+
+
diff --git a/doc/source/sg_execution_times.rst b/doc/source/sg_execution_times.rst
new file mode 100644
index 0000000..78433a9
--- /dev/null
+++ b/doc/source/sg_execution_times.rst
@@ -0,0 +1,37 @@
+
+:orphan:
+
+.. _sphx_glr_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.000** total execution time for 0 files **from all galleries**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - N/A
+     - N/A
+     - N/A
diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst
new file mode 100644
index 0000000..bb0eb38
--- /dev/null
+++ b/doc/source/tutorials.rst
@@ -0,0 +1,77 @@
+:orphan:
+=========
+Tutorials
+=========
+
+This section contains a number of tutorials, to get you started with PyRASA.
+
+Introductory
+------------
+
+******************
+1. Getting Started
+******************
+
+This notebook gets you familiar with the IRASA algorithm and shows you the basic functionality
+of PyRASA.
+
+  :doc:`Getting Started <../examples/basic_functionality>`
+
+******************************
+2. Improving your IRASA models
+******************************
+
+This notebook shows you how to improve your IRASA model fits.
+
+  :doc:`Improve your IRASA <../examples/improving_irasa_models>`
+
+****************************
+3. Pitfalls when using IRASA
+****************************
+
+This notebook outlines common pitfalls when fitting IRASA models.
+
+  :doc:`Pitfalls <../../examples/irasa_pitfalls>`
+
+
+*********************
+3. hset Optimization
+*********************
+
+IRASA comes only with a single hyperparameter - the set of up-/downsampling factors.
+Here we introduce a method to optimize this hset to get the most out of your model.
+
+  :doc:`Optimization <../../examples/hset_optimization>`
+
+************
+4. IRASA MNE
+************
+
+Are you analysing M/EEG data using MNE Python? You might be happy to hear that you can directly
+apply IRASA to your raw or epoched data objects. Open the notebook to see how its done.
+
+  :doc:`IRASA in MNE <../../examples/irasa_mne>`
+
+
+***********************
+4. Time Frequency IRASA
+***********************
+
+Did you know that IRASA can be used in the timefrequency domain for a time resolved spectral parametrization?
+Open this notebook to see how its done.  
+
+  :doc:`Time-Frequency IRASA <../../examples/irasa_sprint>`
+
+
+
+Advanced
+--------
+
+*********************************
+1. Custom Aperiodic Fit Functions
+*********************************
+
+PyRASA allows you to define your own functions to model aperiodic activity.
+This notebook shows you how its done.
+
+  :doc:`Custom Aperiodic models <../../examples/custom_fit_functions>`
diff --git a/examples/README.md b/examples/README.md
new file mode 100644
index 0000000..4742abc
--- /dev/null
+++ b/examples/README.md
@@ -0,0 +1 @@
+## Hello PyRasa Gallery
\ No newline at end of file
diff --git a/pixi.lock b/pixi.lock
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--- a/pixi.lock
+++ b/pixi.lock
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-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/ffmpeg-6.1.1-gpl_h97ca3ef_116.conda
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-  license: GPL-2.0-or-later
-  license_family: GPL
-  purls: []
-  size: 9663759
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+  size: 125005
+  timestamp: 1725568799108
 - kind: conda
   name: ffmpeg
-  version: 6.1.1
-  build: gpl_h9be9148_116
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+  version: 6.1.2
+  build: gpl_h3e00e3c_103
+  build_number: 103
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/ffmpeg-6.1.1-gpl_h9be9148_116.conda
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-  md5: b89791728e73df731d1560f936147a01
+  url: https://conda.anaconda.org/conda-forge/linux-64/ffmpeg-6.1.2-gpl_h3e00e3c_103.conda
+  sha256: cf3534125be68639751152f33c4e726d632e27e3fac2ef2dc7bcac429f278336
+  md5: b0b33d1ce2617d338dd3e005314b4908
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   - lame >=3.100,<3.101.0a0
-  - libass >=0.17.1,<0.17.2.0a0
-  - libgcc-ng >=12
+  - libass >=0.17.3,<0.17.4.0a0
+  - libgcc >=13
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-  - libopenvino-tensorflow-lite-frontend >=2024.2.0,<2024.2.1.0a0
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+  - libopenvino-tensorflow-frontend >=2024.3.0,<2024.3.1.0a0
+  - libopenvino-tensorflow-lite-frontend >=2024.3.0,<2024.3.1.0a0
   - libopus >=1.3.1,<2.0a0
-  - libstdcxx-ng >=12
+  - libstdcxx >=13
   - libva >=2.22.0,<3.0a0
   - libvpx >=1.14.1,<1.15.0a0
   - libxcb >=1.16,<1.17.0a0
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   - openh264 >=2.4.1,<2.4.2.0a0
-  - svt-av1 >=2.1.2,<2.1.3.0a0
+  - openssl >=3.3.2,<4.0a0
+  - svt-av1 >=2.2.1,<2.2.2.0a0
   - x264 >=1!164.3095,<1!165
   - x265 >=3.5,<3.6.0a0
   - xorg-libx11 >=1.8.9,<2.0a0
@@ -12152,17 +13712,17 @@ packages:
   license: GPL-2.0-or-later
   license_family: GPL
   purls: []
-  size: 9775216
-  timestamp: 1719927263827
+  size: 9897550
+  timestamp: 1726582038240
 - kind: conda
   name: ffmpeg
-  version: 6.1.1
-  build: gpl_hb3e10e8_116
-  build_number: 116
+  version: 6.1.2
+  build: gpl_h57e6346_103
+  build_number: 103
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/ffmpeg-6.1.1-gpl_hb3e10e8_116.conda
-  sha256: 0bb97abd76e62746d2edeef20f9bfc90124ed1fbe3a9d91dac2ecc548a9b2e78
-  md5: 6949db0c085ea23e5e87f5c48a40a92c
+  url: https://conda.anaconda.org/conda-forge/osx-64/ffmpeg-6.1.2-gpl_h57e6346_103.conda
+  sha256: 3766335c63a94b0eb3a8f1563bdfa4d2a0d22191b442c0bda934ed8891f4c829
+  md5: e28871c3a506f87377ce43dd9ea06096
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-  license: GPL-2.0-or-later
-  license_family: GPL
-  purls: []
-  size: 9722612
-  timestamp: 1719927382429
-- kind: conda
-  name: ffmpeg
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-  build: gpl_h3589c0a_102
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-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/ffmpeg-7.0.1-gpl_h3589c0a_102.conda
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+  - libopenvino-tensorflow-lite-frontend >=2024.3.0,<2024.3.1.0a0
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   - x265 >=3.5,<3.6.0a0
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   license: GPL-2.0-or-later
   license_family: GPL
   purls: []
-  size: 10092882
-  timestamp: 1718893210578
+  size: 9725269
+  timestamp: 1726582320252
 - kind: conda
   name: ffmpeg
-  version: 7.0.1
-  build: gpl_h5b99759_104
-  build_number: 104
+  version: 6.1.2
+  build: gpl_h5fc9397_103
+  build_number: 103
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/ffmpeg-7.0.1-gpl_h5b99759_104.conda
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-  md5: e426adfab50d3ec624e4a59f4f8df011
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/ffmpeg-6.1.2-gpl_h5fc9397_103.conda
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+  md5: 1d4bd3eb115ec469609beb7fa164bf6b
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   - lame >=3.100,<3.101.0a0
-  - libass >=0.17.1,<0.17.2.0a0
-  - libcxx >=16
+  - libass >=0.17.3,<0.17.4.0a0
+  - libcxx >=17
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-  - libopenvino >=2024.2.0,<2024.2.1.0a0
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   license: GPL-2.0-or-later
   license_family: GPL
   purls: []
-  size: 8971243
-  timestamp: 1719927064086
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 - kind: conda
   name: ffmpeg
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-  build: gpl_hb3e10e8_104
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-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/ffmpeg-7.0.1-gpl_hb3e10e8_104.conda
-  sha256: 76e9f20fe02283843ba418cb199270dfbc76c457496473029882916addda9e0d
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+  build: gpl_h7be0009_103
+  build_number: 103
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/ffmpeg-7.0.2-gpl_h7be0009_103.conda
+  sha256: a682880ce4a19168df2b83c1d3e4a18a4d30fba0e8506636166d41b63791cdbb
+  md5: d0ecd25d0e81322226d6098eb82e3076
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   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
-  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
@@ -14699,49 +15970,51 @@ packages:
   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
-  - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/imagecodecs?source=conda-forge-mapping
-  size: 1772323
-  timestamp: 1719237069581
+  - pkg:pypi/imagecodecs?source=hash-mapping
+  size: 1609562
+  timestamp: 1722900215383
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py311h8ebd238_2
-  build_number: 2
+  build: py311h5db3a85_3
+  build_number: 3
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py311h8ebd238_2.conda
-  sha256: f1666ba967a7c4e796d049e925a6962c1e8403ff8e277ab19d25dd78a5be4720
-  md5: 7ae7d17cf2cfe98f21d552103effb1f6
+  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py311h5db3a85_3.conda
+  sha256: a67ff3bab6cd30f958127599151af0b60230fd7481a0f6f17172047b5c56eccf
+  md5: 5f2febdd293a0797e0525dadb39421b4
   depends:
   - __osx >=10.13
-  - blosc >=1.21.5,<2.0a0
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
   - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
@@ -14750,47 +16023,48 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/imagecodecs?source=conda-forge-mapping
-  size: 1676438
-  timestamp: 1719236909244
+  - pkg:pypi/imagecodecs?source=hash-mapping
+  size: 1684473
+  timestamp: 1722900074005
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py311hb8791aa_2
-  build_number: 2
+  build: py311h732c098_3
+  build_number: 3
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py311hb8791aa_2.conda
-  sha256: e3f598e8b17868c12a2f49e9d2163e9732e96e99cca675e0ab1fbbee826ebf43
-  md5: 32be55fe61a4656fb476556b7acdb384
+  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py311h732c098_3.conda
+  sha256: e64844785a22e007c7e8dce66517c7bc80a1df3b5df80e9e8d5c83da1bbafb11
+  md5: 34ee94453a09c0abc94b194207917b50
   depends:
-  - blosc >=1.21.5,<2.0a0
+  - __glibc >=2.17,<3.0.a0
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libgcc-ng >=12
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
   - libstdcxx-ng >=12
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
@@ -14799,43 +16073,47 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/imagecodecs?source=conda-forge-mapping
-  size: 2056781
-  timestamp: 1719236700395
+  - pkg:pypi/imagecodecs?source=hash-mapping
+  size: 2054419
+  timestamp: 1722900038733
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py311hd929db6_2
-  build_number: 2
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py311hd929db6_2.conda
-  sha256: ef3ad88549d442c9a4894a75fc70547eb083bc2330b51d220791d1c89e10429a
-  md5: 85cbd41aafac750a3c4d0c6fbfd3cbbc
+  build: py311h9541020_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py311h9541020_3.conda
+  sha256: 690d324b87e813cf5a4306aeb41c347414c0038b21e76c13ae35f58be0c7c375
+  md5: 2965ba7c6dcadb4323681e6e63d685bf
   depends:
-  - blosc >=1.21.5,<2.0a0
+  - __osx >=11.0
+  - blosc >=1.21.6,<2.0a0
+  - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
+  - libcxx >=16
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
+  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
@@ -14843,51 +16121,50 @@ packages:
   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
-  - snappy >=1.2.0,<1.3.0a0
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/imagecodecs?source=conda-forge-mapping
-  size: 1609531
-  timestamp: 1719237001536
+  - pkg:pypi/imagecodecs?source=hash-mapping
+  size: 1770725
+  timestamp: 1722899976293
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312h5755a8a_2
-  build_number: 2
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py312h5755a8a_2.conda
-  sha256: 8d3a3cc94cff910b705ae9d0290f5d137f08d5c3f7df8d95e7e9f5386620cb90
-  md5: 1935447d7fa18910e95ce3e96a8b53e9
+  build: py312h36edd9b_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py312h36edd9b_3.conda
+  sha256: b8cb061810628487fb02f9f8d7cb110e94b99cf6dd6bb14b85c5cb601770d62d
+  md5: c309596a0ff59342f52e59046bbe3a8c
   depends:
-  - __osx >=10.13
-  - blosc >=1.21.5,<2.0a0
+  - __glibc >=2.17,<3.0.a0
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
+  - libgcc-ng >=12
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libtiff >=4.6.0,<4.7.0a0
+  - libstdcxx-ng >=12
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
@@ -14896,47 +16173,47 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/imagecodecs?source=conda-forge-mapping
-  size: 1656676
-  timestamp: 1719236780404
+  - pkg:pypi/imagecodecs?source=hash-mapping
+  size: 2073034
+  timestamp: 1722899938174
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312h6b2d42b_2
-  build_number: 2
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py312h6b2d42b_2.conda
-  sha256: 494058c2ab1ede38a22a31ff278ba899eb862e128f3344ea94e0b7cff6818ed1
-  md5: f71c56fe03000e3e2c4e7fa72069eac4
+  build: py312h8902707_3
+  build_number: 3
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/imagecodecs-2024.6.1-py312h8902707_3.conda
+  sha256: 463e775d9eedeb272b31bdf6e300acf2c91a80e3e796ce47fde2e961b421ed66
+  md5: a33ab62781c28ab70e53ed38fc4702e7
   depends:
-  - blosc >=1.21.5,<2.0a0
+  - __osx >=10.13
+  - blosc >=1.21.6,<2.0a0
   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
-  - libgcc-ng >=12
+  - libcxx >=16
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libstdcxx-ng >=12
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
@@ -14945,47 +16222,43 @@ packages:
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/imagecodecs?source=conda-forge-mapping
-  size: 2069273
-  timestamp: 1719236704595
+  - pkg:pypi/imagecodecs?source=hash-mapping
+  size: 1656876
+  timestamp: 1722900095184
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312h7304456_2
-  build_number: 2
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py312h7304456_2.conda
-  sha256: d1d1725c3286b3722b9c633bf2e4fa8daf74ca633f725e9f6505c948572ecb63
-  md5: 1b74007f7eea2706b527c6574d2ddb5b
+  build: py312hd2fe4a3_3
+  build_number: 3
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py312hd2fe4a3_3.conda
+  sha256: cfb62c7846f2a464cfcbcceb167897a0d0fb10700220b6f256a49d9ef382019a
+  md5: 67c2d384e46c46d64d4fd6e708071479
   depends:
-  - __osx >=11.0
-  - blosc >=1.21.5,<2.0a0
-  - brunsli >=0.1,<1.0a0
+  - blosc >=1.21.6,<2.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
   - charls >=2.4.2,<2.5.0a0
   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
   - lcms2 >=2.16,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=16
-  - libdeflate >=1.20,<1.21.0a0
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
-  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
@@ -14993,45 +16266,51 @@ packages:
   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
+  - snappy >=1.2.1,<1.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
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   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
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-  size: 1746996
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+  size: 1586933
+  timestamp: 1722900214784
 - kind: conda
   name: imagecodecs
   version: 2024.6.1
-  build: py312hecf518a_2
-  build_number: 2
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.6.1-py312hecf518a_2.conda
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-  md5: 267b973926fb052ec57f03921c2d885a
+  build: py312hd96b8b9_3
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/imagecodecs-2024.6.1-py312hd96b8b9_3.conda
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+  - __osx >=11.0
+  - blosc >=1.21.6,<2.0a0
+  - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - c-blosc2 >=2.15.0,<2.16.0a0
+  - c-blosc2 >=2.15.1,<2.16.0a0
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   - giflib >=5.2.2,<5.3.0a0
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   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.0.4,<2.0a0
+  - libavif16 >=1.1.1,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libdeflate >=1.20,<1.21.0a0
+  - libcxx >=16
+  - libdeflate >=1.21,<1.22.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
+  - libjxl >=0.10,<0.11.0a0
   - libpng >=1.6.43,<1.7.0a0
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - libzopfli >=1.0.3,<1.1.0a0
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   - numpy >=1.19,<3
   - openjpeg >=2.5.2,<3.0a0
   - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
-  - snappy >=1.2.0,<1.3.0a0
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - snappy >=1.2.1,<1.3.0a0
   - xz >=5.2.6,<6.0a0
   - zfp >=1.0.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
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 - kind: conda
   name: imageio
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+  version: 2.35.1
   build: pyh12aca89_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.2-pyh12aca89_0.conda
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-  md5: 97ad994fae55dce96bd397054b32e41a
+  url: https://conda.anaconda.org/conda-forge/noarch/imageio-2.35.1-pyh12aca89_0.conda
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+  md5: b03ff3631329c8ef17bae35d2bb216f7
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   license_family: BSD
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-  timestamp: 1719235104890
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+  size: 292678
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 - kind: conda
   name: imageio-ffmpeg
   version: 0.5.1
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   - setuptools
   license: BSD 2-Clause
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 - kind: conda
-  name: importlib-metadata
-  version: 8.0.0
-  build: pyha770c72_0
+  name: imagesize
+  version: 1.4.1
+  build: pyhd8ed1ab_0
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   noarch: python
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-  md5: 3286556cdd99048d198f72c3f6f69103
+  url: https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2
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+  md5: 7de5386c8fea29e76b303f37dde4c352
   depends:
-  - python >=3.8
-  - zipp >=0.5
-  license: Apache-2.0
-  license_family: APACHE
+  - python >=3.4
+  license: MIT
+  license_family: MIT
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 - kind: conda
   name: importlib_resources
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   noarch: python
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+  md5: c808991d29b9838fb4d96ce8267ec9ec
   depends:
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   constrains:
-  - importlib-resources >=6.4.0,<6.4.1.0a0
+  - importlib-resources >=6.4.5,<6.4.6.0a0
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 - kind: conda
   name: iniconfig
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+  - pkg:pypi/ipython?source=hash-mapping
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 - kind: conda
   name: ipywidgets
-  version: 8.1.3
+  version: 8.1.5
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.3-pyhd8ed1ab_0.conda
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-  md5: a1323654e9d87b16642ef02a03b98b32
+  url: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.5-pyhd8ed1ab_0.conda
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+  md5: a022d34163147d16b27de86dc53e93fc
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   - ipython >=6.1.0
-  - jupyterlab_widgets >=3.0.11,<3.1.0
+  - jupyterlab_widgets >=3.0.13,<3.1.0
   - python >=3.7
   - traitlets >=4.3.1
-  - widgetsnbextension >=4.0.11,<4.1.0
+  - widgetsnbextension >=4.0.13,<4.1.0
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   license_family: BSD
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 - kind: conda
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@@ -15595,28 +16853,30 @@ packages:
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   name: jsonpointer
   version: 3.0.0
-  build: py311h1ea47a8_0
+  build: py311h1ea47a8_1
+  build_number: 1
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/jsonpointer-3.0.0-py311h1ea47a8_0.conda
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-  md5: fb9c38edbc17bbaa549a8fdc933ada68
+  url: https://conda.anaconda.org/conda-forge/win-64/jsonpointer-3.0.0-py311h1ea47a8_1.conda
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+  md5: 943f7fab631e12750641efd7279a268c
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   name: jsonpointer
   version: 3.0.0
-  build: py311h267d04e_0
+  build: py311h267d04e_1
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-  md5: d962e72d8c1d0efb22f0e54e8e97cd71
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   - freetype >=2.12.1,<3.0a0
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   license_family: OTHER
   purls: []
-  size: 126461
-  timestamp: 1719631378391
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   name: libass
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   name: libblas
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   subdir: linux-64
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   subdir: osx-arm64
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+  - libgcc >=13
+  - libstdcxx >=13
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+  - ucrt >=10.0.20348.0
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   - pyparsing >=2.3.1
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+  version: 3.9.2
+  build: py312h30cc4df_1
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+  - libcxx >=17
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+  - ucrt >=10.0.20348.0
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+  - python >=3.12,<3.13.0a0 *_cpython
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+  - libgcc >=13
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+  - pkg:pypi/matplotlib-inline?source=hash-mapping
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-- kind: conda
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-  - xorg-xf86vidmodeproto
-  - zstd >=1.5.6,<1.6.0a0
-  track_features:
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   - python_abi 3.11.* *_cp311
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   name: pillow
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   - libxcb >=1.16,<1.17.0a0
   - libzlib >=1.3.1,<2.0a0
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   - python_abi 3.12.* *_cp312
   - tk >=8.6.13,<8.7.0a0
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+  - python >=3.12,<3.13.0a0 *_cpython
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   noarch: python
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 - kind: conda
   name: pixman
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   license: MIT
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   license: BSD-3-Clause
   license_family: BSD
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 - kind: conda
   name: qdarkstyle
   version: 3.2.3
@@ -30021,7 +30414,7 @@ packages:
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   license_family: MIT
   purls:
-  - pkg:pypi/qdarkstyle?source=conda-forge-mapping
+  - pkg:pypi/qdarkstyle?source=hash-mapping
   size: 629146
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   - vc14_runtime >=14.29.30139
   license: LicenseRef-Qhull
   purls: []
-  size: 1377020
-  timestamp: 1720814433486
+  size: 1377020
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+  - icu >=75.1,<76.0a0
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+  - nss >=3.104,<4.0a0
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+  license: LGPL-3.0-only
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+  size: 50504904
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 - kind: conda
   name: qt-main
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.8-h06adc49_22.conda
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-  - krb5 >=1.21.2,<1.22.0a0
+  url: https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.8-h264fbc2_26.conda
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   - libclang13 >=15.0.7
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   - libjpeg-turbo >=3.0.0,<4.0a0
-  - libpng >=1.6.43,<1.7.0a0
-  - libsqlite >=3.46.0,<4.0a0
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+  - libsqlite >=3.46.1,<4.0a0
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.3.1,<4.0a0
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   - ucrt >=10.0.20348.0
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   - vc14_runtime >=14.29.30139
@@ -30118,19 +30546,18 @@ packages:
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   - qt 5.15.8
   license: LGPL-3.0-only
-  license_family: LGPL
   purls: []
-  size: 59570135
-  timestamp: 1719037973436
+  size: 60409685
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 - kind: conda
   name: qt-main
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-  build: h320f8da_24
-  build_number: 24
+  build: h3155989_26
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   subdir: linux-64
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-  md5: bec111b67cb8dc63277c6af65d214044
+  url: https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-h3155989_26.conda
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   - alsa-lib >=1.2.12,<1.3.0a0
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+  - gstreamer >=1.24.7,<1.25.0a0
   - harfbuzz >=9.0.0,<10.0a0
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   - libclang-cpp15 >=15.0.7,<15.1.0a0
   - libclang13 >=15.0.7
   - libcups >=2.3.3,<2.4.0a0
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   - libevent >=2.1.12,<2.1.13.0a0
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   - libxkbcommon >=1.7.0,<2.0a0
   - libxml2 >=2.12.7,<3.0a0
   - libzlib >=1.3.1,<2.0a0
-  - mysql-libs >=8.3.0,<8.4.0a0
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   - nspr >=4.35,<5.0a0
-  - nss >=3.102,<4.0a0
-  - openssl >=3.3.1,<4.0a0
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   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxext >=1.3.4,<2.0a0
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   - xorg-xf86vidmodeproto
   - zstd >=1.5.6,<1.6.0a0
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   license: LGPL-3.0-only
-  license_family: LGPL
   purls: []
-  size: 60403438
-  timestamp: 1721277287096
+  size: 61436099
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-  url: https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-ha2b5568_22.conda
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-  md5: 15de976572f24032540236006d6d0e9f
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   license: LGPL-3.0-only
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   subdir: linux-64
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-  - libpq >=16.3,<17.0a0
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   - libsqlite >=3.46.0,<4.0a0
   - libstdcxx-ng >=12
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libxcb >=1.16,<1.17.0a0
   - libxkbcommon >=1.7.0,<2.0a0
   - libxml2 >=2.12.7,<3.0a0
   - libzlib >=1.3.1,<2.0a0
-  - mysql-libs >=8.3.0,<8.4.0a0
+  - mysql-libs >=9.0.1,<9.1.0a0
   - openssl >=3.3.1,<4.0a0
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   - xcb-util >=0.4.1,<0.5.0a0
   - xcb-util-cursor >=0.1.4,<0.2.0a0
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   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxext >=1.3.4,<2.0a0
+  - xorg-libxtst >=1.2.5,<2.0a0
+  - xorg-libxxf86vm >=1.1.5,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
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   license: LGPL-3.0-only
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-  size: 47018187
-  timestamp: 1719321171243
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-  license: LGPL-3.0-only
-  license_family: LGPL
-  purls: []
-  size: 41609177
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+  size: 46904534
+  timestamp: 1724536870579
 - kind: conda
   name: qt6-main
   version: 6.7.2
-  build: h913a85e_3
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+  build: hbb46ec1_5
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   subdir: win-64
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-  - icu >=73.2,<74.0a0
+  - icu >=75.1,<76.0a0
   - krb5 >=1.21.3,<1.22.0a0
   - libclang13 >=18.1.8
-  - libglib >=2.80.2,<3.0a0
+  - libglib >=2.80.3,<3.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libpng >=1.6.43,<1.7.0a0
   - libsqlite >=3.46.0,<4.0a0
-  - libtiff >=4.6.0,<4.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
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   - libzlib >=1.3.1,<2.0a0
   - openssl >=3.3.1,<4.0a0
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+  - pcre2 >=10.44,<10.45.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
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   license: LGPL-3.0-only
   license_family: LGPL
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-  size: 87269907
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-- kind: conda
-  name: qt6-main
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-  license: LGPL-3.0-only
-  license_family: LGPL
-  purls: []
-  size: 37268601
-  timestamp: 1719646602766
+  size: 85902078
+  timestamp: 1724537977958
 - kind: conda
   name: qtconsole-base
-  version: 5.5.2
+  version: 5.6.0
   build: pyha770c72_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/qtconsole-base-5.5.2-pyha770c72_0.conda
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-  md5: 0f63ec743defb9de6728a98150a80839
+  url: https://conda.anaconda.org/conda-forge/noarch/qtconsole-base-5.6.0-pyha770c72_0.conda
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+  md5: 98495833b9b9bc9c3a2711d8cc252684
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   - jupyter_client >=4.1
@@ -30508,9 +30844,9 @@ packages:
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   license_family: BSD
   purls:
-  - pkg:pypi/qtconsole?source=conda-forge-mapping
-  size: 100292
-  timestamp: 1714943047487
+  - pkg:pypi/qtconsole?source=hash-mapping
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+  timestamp: 1724884249491
 - kind: conda
   name: qtpy
   version: 2.4.1
@@ -30526,7 +30862,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/qtpy?source=conda-forge-mapping
+  - pkg:pypi/qtpy?source=hash-mapping
   size: 61808
   timestamp: 1698112171285
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@@ -30656,7 +30992,7 @@ packages:
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   license_family: MIT
   purls:
-  - pkg:pypi/referencing?source=conda-forge-mapping
+  - pkg:pypi/referencing?source=hash-mapping
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@@ -30679,7 +31015,7 @@ packages:
   license: Apache-2.0
   license_family: APACHE
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-  - pkg:pypi/requests?source=conda-forge-mapping
+  - pkg:pypi/requests?source=hash-mapping
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@@ -30697,7 +31033,7 @@ packages:
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   license_family: MIT
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+  - pkg:pypi/rfc3339-validator?source=hash-mapping
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@@ -30714,159 +31050,38 @@ packages:
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   license_family: MIT
   purls:
-  - pkg:pypi/rfc3986-validator?source=conda-forge-mapping
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-  license_family: MIT
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-- kind: conda
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-  - vc14_runtime >=14.29.30139
+  - markdown-it-py >=2.2.0
+  - pygments >=2.13.0,<3.0.0
+  - python >=3.7
+  - typing_extensions >=4.0.0,<5.0.0
   license: MIT
   license_family: MIT
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-  timestamp: 1721861952771
+  - pkg:pypi/rich?source=hash-mapping
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 - kind: conda
   name: rpds-py
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-  build: py311h98c6a39_0
+  version: 0.20.0
+  build: py311h481aa64_1
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.19.1-py311h98c6a39_0.conda
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-  md5: 7f4204fff90f47d35f8d3a55516b1574
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   license_family: MIT
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+  url: https://conda.anaconda.org/conda-forge/win-64/rpds-py-0.20.0-py311h533ab2d_1.conda
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.20.0-py312he431725_1.conda
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+  - python >=3.12,<3.13.0a0 *_cpython
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@@ -31262,29 +31440,30 @@ packages:
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-  - scikit-learn >=1.0
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   - dask-core >=2021.1.0
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+  - numpy >=1.23
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+  build: py311h4b4568b_2
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+  - numpy >=1.23
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   version: 0.24.0
-  build: py311hcf9f919_1
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+  build: py311hcf9f919_2
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/scikit-image-0.24.0-py311hcf9f919_1.conda
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-  md5: 953272a568dcec359dd4dc940ddf3d35
+  url: https://conda.anaconda.org/conda-forge/win-64/scikit-image-0.24.0-py311hcf9f919_2.conda
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+  md5: 84f3a41eb789b28e01079fa06c8e1cec
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@@ -31340,29 +31520,30 @@ packages:
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-  - cloudpickle >=0.2.1
-  - toolz >=0.10.0
   - dask-core >=2021.1.0
   - cytoolz >=0.11.0
+  - toolz >=0.10.0
+  - numpy >=1.23
+  - cloudpickle >=0.2.1
+  - matplotlib-base >=3.5
+  - pooch >=1.6.0
+  - scikit-learn >=1.0
+  - astropy >=5.0
   license: BSD-3-Clause
   license_family: BSD
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+  size: 10442607
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   name: scikit-image
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-  build: py311hfdcbad3_1
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+  build: py311hfdcbad3_2
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   subdir: osx-64
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-  md5: f3b6a06e19e229af17f4b5f9d2ca8837
+  url: https://conda.anaconda.org/conda-forge/osx-64/scikit-image-0.24.0-py311hfdcbad3_2.conda
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+  md5: e25466bcfdf3478c8e98566cfda66116
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-  - pooch >=1.6.0
   - matplotlib-base >=3.5
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   - scikit-learn >=1.0
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+  - toolz >=0.10.0
+  - numpy >=1.23
   - cloudpickle >=0.2.1
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   license_family: BSD
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-  build: py312h1171441_1
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+  build: py312h1171441_2
+  build_number: 2
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/scikit-image-0.24.0-py312h1171441_1.conda
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-  md5: 4998523a73aeacaa180b330d41c7d011
+  url: https://conda.anaconda.org/conda-forge/osx-64/scikit-image-0.24.0-py312h1171441_2.conda
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+  md5: 05548969a33d06aa403881e238ae8291
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@@ -31416,30 +31598,32 @@ packages:
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   - cloudpickle >=0.2.1
   - astropy >=5.0
   - toolz >=0.10.0
-  - matplotlib-base >=3.5
+  - cytoolz >=0.11.0
   - scikit-learn >=1.0
   - pooch >=1.6.0
+  - numpy >=1.23
+  - matplotlib-base >=3.5
+  - dask-core >=2021.1.0
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   license_family: BSD
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   name: scikit-image
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-  build: py312h1d6d2e6_1
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+  build: py312h1df14c2_2
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   subdir: linux-64
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-  md5: 2b31dbae9cfaa205a23a39e3ce1d638b
+  url: https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.24.0-py312h1df14c2_2.conda
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+  md5: 104fecd2263afe390810307ad0bfe563
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   - imageio >=2.27
   - lazy_loader >=0.2
   - libgcc-ng >=12
@@ -31454,29 +31638,30 @@ packages:
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   - tifffile >=2022.8.12
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-  - astropy >=5.0
+  - scikit-learn >=1.0
   - cytoolz >=0.11.0
+  - astropy >=5.0
+  - toolz >=0.10.0
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   - pooch >=1.6.0
-  - scikit-learn >=1.0
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   license: BSD-3-Clause
   license_family: BSD
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-  build: py312h72972c8_1
-  build_number: 1
+  build: py312h72972c8_2
+  build_number: 2
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/scikit-image-0.24.0-py312h72972c8_1.conda
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-  md5: 39eba53881aac11b55e21b6d63278b09
+  url: https://conda.anaconda.org/conda-forge/win-64/scikit-image-0.24.0-py312h72972c8_2.conda
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+  md5: b98c60c4eb24ed978ba85b80042021e5
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@@ -31494,28 +31679,29 @@ packages:
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   constrains:
   - cloudpickle >=0.2.1
+  - astropy >=5.0
+  - dask-core >=2021.1.0
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-  - scikit-learn >=1.0
-  - dask-core >=2021.1.0
-  - astropy >=5.0
   - pooch >=1.6.0
+  - scikit-learn >=1.0
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+  timestamp: 1723843270450
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-  build: py312h8ae5369_1
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+  build: py312h8ae5369_2
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   subdir: osx-arm64
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-  md5: 77de8832e2a103c5cb92c6066b3ec615
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-image-0.24.0-py312h8ae5369_2.conda
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   - imageio >=2.27
@@ -31532,33 +31718,36 @@ packages:
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   - tifffile >=2022.8.12
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+  - scikit-learn >=1.0
+  - astropy >=5.0
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   - cloudpickle >=0.2.1
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-  - astropy >=5.0
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+  - numpy >=1.23
   - toolz >=0.10.0
+  - dask-core >=2021.1.0
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   name: scikit-learn
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-  md5: 209cc9f21aced058dfe783848c7a13d9
+  version: 1.5.2
+  build: py311h57cc02b_1
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+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.2-py311h57cc02b_1.conda
+  sha256: b6489f65911847d1f9807e254e9af0815548454b911df4d0b5019f9ab16fe530
+  md5: d1b6d7a73364d9fe20d2863bd2c43e3a
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-  - llvm-openmp >=16.0.6
+  - libgcc >=13
+  - libstdcxx >=13
   - numpy >=1.19,<3
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
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   name: scikit-learn
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-  build: py311hbfb48bc_0
+  version: 1.5.2
+  build: py311h9e23f0f_1
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   subdir: osx-arm64
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.5.2-py311h9e23f0f_1.conda
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   - python >=3.11,<3.12.0a0 *_cpython
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+  build: py311ha1d5734_1
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+  url: https://conda.anaconda.org/conda-forge/osx-64/scikit-learn-1.5.2-py311ha1d5734_1.conda
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-  - libstdcxx-ng >=12
+  - libcxx >=17
+  - llvm-openmp >=17.0.6
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   - python_abi 3.11.* *_cp311
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+  version: 1.5.2
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-  md5: b31a7361a5b0560c48f7451e666cb11d
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+  md5: c3e550b20baa56f911022f6304c8f547
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-  - vc14_runtime >=14.29.30139
-  - wslink
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+  - vtk-base 9.3.1 qt_py312h8e383e5_206
+  - vtk-io-ffmpeg 9.3.1 qt_py312h98fac4b_206
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 33509511
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+  size: 20552
+  timestamp: 1726630178911
 - kind: conda
   name: vtk-base
   version: 9.3.1
-  build: qt_py311ha51819d_202
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+  build: qt_py311h4e688a3_206
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   subdir: osx-64
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+  url: https://conda.anaconda.org/conda-forge/osx-64/vtk-base-9.3.1-qt_py311h4e688a3_206.conda
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-- kind: conda
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   - libtheora >=1.1.1,<1.2.0a0
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   - loguru
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   - nlohmann_json
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-  - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
   - qt6-main >=6.7.2,<6.8.0a0
   - sqlite
-  - tbb >=2021.12.0
+  - tbb >=2021.13.0
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@@ -34784,128 +34924,140 @@ packages:
   - zlib
   constrains:
   - paraview ==9999999999
+  - libboost_headers
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 34471217
-  timestamp: 1721476201431
+  size: 36609584
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-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/vtk-base-9.3.1-qt_py312h0ad9dcb_201.conda
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-  md5: 53c02c445327a5f129f3ac522f8fa118
+  build: qt_py311ha4f9328_206
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+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/vtk-base-9.3.1-qt_py311ha4f9328_206.conda
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+  md5: a904c9318fb50d6fd11508d467a3e25d
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-  - libexpat >=2.6.2,<3.0a0
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   - pugixml >=1.14,<1.15.0a0
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
   - qt6-main >=6.7.2,<6.8.0a0
   - sqlite
-  - tbb >=2021.12.0
+  - tbb >=2021.13.0
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-  - vc14_runtime >=14.29.30139
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   - zlib
   constrains:
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+  - libboost_headers
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 33526353
-  timestamp: 1721102120794
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 - kind: conda
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+  build: qt_py311hf8658a1_206
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+  url: https://conda.anaconda.org/conda-forge/win-64/vtk-base-9.3.1-qt_py311hf8658a1_206.conda
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+  md5: 5561efcb612a8a2e6c69b6e8a0902a18
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   - jsoncpp >=1.9.5,<1.9.6.0a0
-  - libcxx >=16
-  - libexpat >=2.6.2,<3.0a0
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-  - libtiff >=4.6.0,<4.7.0a0
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   - loguru
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   - nlohmann_json
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+  - proj >=9.5.0,<9.6.0a0
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-  - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
   - qt6-main >=6.7.2,<6.8.0a0
   - sqlite
-  - tbb >=2021.12.0
+  - tbb >=2021.13.0
   - tbb-devel
-  - tk >=8.6.13,<8.7.0a0
+  - ucrt >=10.0.20348.0
   - utfcpp
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   - wslink
   - zlib
   constrains:
+  - libboost_headers
   - paraview ==9999999999
   license: BSD-3-Clause
   license_family: BSD
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-  - python >=3.12,<3.13.0a0 *_cpython
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-  - tbb >=2021.12.0
+  - tbb >=2021.13.0
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   - utfcpp
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+  - libboost_headers
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   license_family: BSD
   purls: []
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   - python_abi 3.12.* *_cp312
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   - python_abi 3.12.* *_cp312
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   - sqlite
-  - tbb >=2021.12.0
+  - tbb >=2021.13.0
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   - ucrt >=10.0.20348.0
   - utfcpp
@@ -35114,23 +35261,24 @@ packages:
   - wslink
   - zlib
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   - paraview ==9999999999
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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   license: BSD-3-Clause
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   purls: []
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   name: vtk-io-ffmpeg
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+  url: https://conda.anaconda.org/conda-forge/osx-64/vtk-io-ffmpeg-9.3.1-qt_py311h98fac4b_206.conda
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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   name: vtk-io-ffmpeg
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   name: vtk-io-ffmpeg
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   license_family: BSD
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-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
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   license: MIT
   license_family: MIT
   purls: []
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 - kind: conda
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   build: hd8ed1ab_0
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   noarch: generic
-  url: https://conda.anaconda.org/conda-forge/noarch/wayland-protocols-1.36-hd8ed1ab_0.conda
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   license: MIT
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 - kind: conda
   name: wcwidth
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@@ -35374,26 +35573,26 @@ packages:
   license: MIT
   license_family: MIT
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-  - pkg:pypi/wcwidth?source=conda-forge-mapping
+  - pkg:pypi/wcwidth?source=hash-mapping
   size: 32709
   timestamp: 1704731373922
 - kind: conda
   name: webcolors
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   noarch: python
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 - kind: conda
   name: webencodings
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   license_family: BSD
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+  - pkg:pypi/webencodings?source=hash-mapping
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   license_family: APACHE
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+  - pkg:pypi/websocket-client?source=hash-mapping
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   timestamp: 1713923494501
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   noarch: python
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   license_family: BSD
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-  - ucrt >=10.0.20348.0
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   license_family: BSD
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+  - python >=3.12,<3.13.0a0 *_cpython
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   license_family: BSD
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-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
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+  url: https://conda.anaconda.org/conda-forge/osx-64/wrapt-1.16.0-py312hb553811_1.conda
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   - python_abi 3.12.* *_cp312
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
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   license_family: BSD
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 - kind: conda
   name: wslink
-  version: 2.1.1
+  version: 2.2.1
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/wslink-2.1.1-pyhd8ed1ab_0.conda
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-  md5: 6b1aef9c878c58baa7bb9f0d44457174
+  url: https://conda.anaconda.org/conda-forge/noarch/wslink-2.2.1-pyhd8ed1ab_0.conda
+  sha256: f8a6490e0c1e059a7fa422c4b38d77754ccf53d56ca90fff3f9642eeec44fc3e
+  md5: 74674b93806167c26da4eca7613bc225
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   - aiohttp <4
   - msgpack-python >=1,<2
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   license_family: BSD
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-  size: 34232
-  timestamp: 1718909748365
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+  timestamp: 1726544126301
 - kind: conda
   name: x264
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   timestamp: 1718843348208
 - kind: conda
   name: xcb-util-cursor
-  version: 0.1.4
-  build: h4ab18f5_2
-  build_number: 2
+  version: 0.1.5
+  build: hb9d3cd8_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-cursor-0.1.4-h4ab18f5_2.conda
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-  md5: 79e46d4a6ccecb7ee1912042958a8758
+  url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-cursor-0.1.5-hb9d3cd8_0.conda
+  sha256: c7b35db96f6e32a9e5346f97adc968ef2f33948e3d7084295baebc0e33abdd5b
+  md5: eb44b3b6deb1cab08d72cb61686fe64c
   depends:
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+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
   - libxcb >=1.13
   - libxcb >=1.16,<1.17.0a0
   - xcb-util-image >=0.4.0,<0.5.0a0
@@ -35841,8 +36133,8 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 20397
-  timestamp: 1718899451268
+  size: 20296
+  timestamp: 1726125844850
 - kind: conda
   name: xcb-util-image
   version: 0.4.0
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   license_family: BSD
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+  - pkg:pypi/xlrd?source=hash-mapping
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   timestamp: 1610224499738
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   license_family: MIT
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-  - pkg:pypi/xmltodict?source=conda-forge-mapping
+  - pkg:pypi/xmltodict?source=hash-mapping
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   timestamp: 1652020928232
-- kind: conda
-  name: xorg-damageproto
-  version: 1.2.1
-  build: h7f98852_1002
-  build_number: 1002
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-damageproto-1.2.1-h7f98852_1002.tar.bz2
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-  size: 25425
-  timestamp: 1620067841828
 - kind: conda
   name: xorg-fixesproto
   version: '5.0'
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   timestamp: 1617479697350
 - kind: conda
-  name: xorg-glproto
-  version: 1.4.17
-  build: h7f98852_1002
-  build_number: 1002
+  name: xorg-inputproto
+  version: 2.3.2
+  build: hb9d3cd8_1003
+  build_number: 1003
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-glproto-1.4.17-h7f98852_1002.tar.bz2
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-  md5: e41bf01f80d46be87dcae2333a766e75
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-hb9d3cd8_1003.conda
+  sha256: 77eea289f9d3fa753a290f988533c842694b826fe1900abd6d7b142c528512ba
+  md5: 32623b33f2047dbc9ae2f2e8fd3880e9
   depends:
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+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
   license: MIT
   license_family: MIT
   purls: []
-  size: 21299
-  timestamp: 1620611690111
+  size: 22320
+  timestamp: 1726802558171
 - kind: conda
   name: xorg-kbproto
   version: 1.0.7
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   size: 14468
   timestamp: 1684637984591
-- kind: conda
-  name: xorg-libxdamage
-  version: 1.1.5
-  build: h7f98852_1
-  build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.5-h7f98852_1.tar.bz2
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-  md5: bebd3814ec2355fab6a474b07ed73093
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-  - xorg-libx11 >=1.7.2,<2.0a0
-  - xorg-libxext 1.3.*
-  - xorg-libxfixes
-  - xorg-util-macros
-  - xorg-xproto
-  license: MIT
-  license_family: MIT
-  purls: []
-  size: 11964
-  timestamp: 1641830754492
 - kind: conda
   name: xorg-libxdmcp
   version: 1.1.3
@@ -36249,27 +36504,28 @@ packages:
   size: 18145
   timestamp: 1617717802636
 - kind: conda
-  name: xorg-libxrandr
-  version: 1.5.2
-  build: h7f98852_1
+  name: xorg-libxi
+  version: 1.7.10
+  build: h4bc722e_1
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   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrandr-1.5.2-h7f98852_1.tar.bz2
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-  md5: 5b0f7da25a4556c9619c3e4b4a98ab07
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.7.10-h4bc722e_1.conda
+  sha256: e1416eb435e3d903bc658e3c637f0e87efd2dca290fe70daf29738b3a3d1f8ff
+  md5: 749baebe7e2ff3360630e069175e528b
   depends:
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-  - xorg-libx11 >=1.7.1,<2.0a0
-  - xorg-libxext
-  - xorg-libxrender
-  - xorg-randrproto
-  - xorg-renderproto
-  - xorg-xextproto
+  - __glibc >=2.17,<3.0.a0
+  - libgcc-ng >=12
+  - xorg-inputproto
+  - xorg-libx11 >=1.8.9,<2.0a0
+  - xorg-libxext 1.3.*
+  - xorg-libxext >=1.3.4,<2.0a0
+  - xorg-libxfixes 5.0.*
+  - xorg-xextproto >=7.3.0,<8.0a0
   license: MIT
   license_family: MIT
   purls: []
-  size: 29688
-  timestamp: 1621515728586
+  size: 46794
+  timestamp: 1722108216651
 - kind: conda
   name: xorg-libxrender
   version: 0.9.11
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   timestamp: 1690288540492
 - kind: conda
-  name: xorg-libxxf86vm
-  version: 1.1.5
-  build: h4bc722e_1
-  build_number: 1
+  name: xorg-libxtst
+  version: 1.2.5
+  build: h4bc722e_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-h4bc722e_1.conda
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-  md5: 0c90ad87101001080484b91bd9d2cdef
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-h4bc722e_0.conda
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+  md5: 185159d666308204eca00295599b0a5c
   depends:
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   - libgcc-ng >=12
+  - xorg-inputproto
   - xorg-libx11 >=1.8.9,<2.0a0
+  - xorg-libxext 1.3.*
   - xorg-libxext >=1.3.4,<2.0a0
-  - xorg-xextproto >=7.3.0,<8.0a0
+  - xorg-libxi 1.7.*
+  - xorg-libxi >=1.7.10,<2.0a0
+  - xorg-recordproto
   license: MIT
   license_family: MIT
   purls: []
-  size: 18443
-  timestamp: 1722110433983
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+  timestamp: 1722575571554
 - kind: conda
-  name: xorg-randrproto
-  version: 1.5.0
-  build: h7f98852_1001
-  build_number: 1001
+  name: xorg-libxxf86vm
+  version: 1.1.5
+  build: hb9d3cd8_2
+  build_number: 2
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-randrproto-1.5.0-h7f98852_1001.tar.bz2
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-  md5: 68cce654461713977dac6f9ac1bce89a
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-hb9d3cd8_2.conda
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+  - xorg-libxext >=1.3.4,<2.0a0
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   license: MIT
   license_family: MIT
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-  name: xorg-renderproto
-  version: 0.11.1
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   build: h7f98852_1002
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   subdir: linux-64
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@@ -36937,45 +37218,45 @@ packages:
   timestamp: 1716874280334
 - kind: conda
   name: zlib-ng
-  version: 2.2.1
-  build: h00cdb27_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.2.1-h00cdb27_0.conda
-  sha256: f6092fbcc88dc46ae0781f41dc8901c84dd8974ba0efdb39f1c31654078c621a
-  md5: 2cd592101665c8cec43a35b069740c2b
+  version: 2.2.2
+  build: h5888daf_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.2-h5888daf_0.conda
+  sha256: 9288b88a2448a6ef9824ff4a9f9384f45f6444b009b9fa3e5f335d0c52e86e4b
+  md5: 135fd3c66bccad3d2254f50f9809e86a
   depends:
-  - __osx >=11.0
-  - libcxx >=16
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
   license: Zlib
   license_family: Other
   purls: []
-  size: 89871
-  timestamp: 1719947606865
+  size: 105350
+  timestamp: 1726590588913
 - kind: conda
   name: zlib-ng
-  version: 2.2.1
-  build: he02047a_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.1-he02047a_0.conda
-  sha256: f555ee579fc1cd5ccf1ef760970c4bc34db8783d3ba5c42c9d50541c924c5b66
-  md5: 8fd1654184917db2cb74fc84cb4fff79
+  version: 2.2.2
+  build: hac325c4_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/zlib-ng-2.2.2-hac325c4_0.conda
+  sha256: 6720165ad8062aa37936ec873adb6c73d85e2c4690c7ae9954a8d06b25d33791
+  md5: 7bf4702f5d8f9c75e4892c34e61c2f49
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
+  - __osx >=10.13
+  - libcxx >=17
   license: Zlib
   license_family: Other
   purls: []
-  size: 104865
-  timestamp: 1719947458940
+  size: 103862
+  timestamp: 1726590657892
 - kind: conda
   name: zlib-ng
-  version: 2.2.1
+  version: 2.2.2
   build: he0c23c2_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.2.1-he0c23c2_0.conda
-  sha256: f9a17a353395d2fd02034106817dd8c82c2ef817a7e1745b86f1885fc698cc65
-  md5: 455223e3b69e05d99e239276f42d3b6e
+  url: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.2.2-he0c23c2_0.conda
+  sha256: 90c6e86dc30a1f768fd5b131464644bedd4ebd52e80e75595066cd37facf6b01
+  md5: ba337fbdf9bdfe84ed0030329069d405
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
@@ -36983,102 +37264,106 @@ packages:
   license: Zlib
   license_family: Other
   purls: []
-  size: 108602
-  timestamp: 1719947895843
+  size: 108763
+  timestamp: 1726591296481
 - kind: conda
   name: zlib-ng
-  version: 2.2.1
-  build: hf036a51_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zlib-ng-2.2.1-hf036a51_0.conda
-  sha256: 0c4c8a95532c9db56c3f08d0ad50b622e25898d833b286272bc1f9a557e44492
-  md5: f3fc1f57c309c1c6836fe90584d3ac6f
+  version: 2.2.2
+  build: hf9b8971_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.2.2-hf9b8971_0.conda
+  sha256: 5fb88d017d9e35f7c155db180824f265b92538ce8d95fc58d1ec983698e2c52b
+  md5: 652e10e21567dbd1dd6662faf85e3220
   depends:
-  - __osx >=10.13
-  - libcxx >=16
+  - __osx >=11.0
+  - libcxx >=17
   license: Zlib
   license_family: Other
   purls: []
-  size: 103568
-  timestamp: 1719947664333
+  size: 87371
+  timestamp: 1726590686716
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py311h4a6b76e_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py311h4a6b76e_0.conda
-  sha256: c372898778c58816cf8ad0031504ffb9a451d92c8547e2e524e6e61c1df5d9a3
-  md5: 0571c2ddfd8f08fbf08f3333ac826b2d
+  build: py311h53056dc_1
+  build_number: 1
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py311h53056dc_1.conda
+  sha256: a93584e6167c3598854a47f3bf8276fa646a3bb4d12fcfc23a54e37d5879f35c
+  md5: 7d4c123cbb5e6293dd4dd2f8d30f0de4
   depends:
-  - __osx >=11.0
   - cffi >=1.11
   - python >=3.11,<3.12.0a0
-  - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 332378
-  timestamp: 1721044254516
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 321357
+  timestamp: 1725305930669
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py311h51fa951_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py311h51fa951_0.conda
-  sha256: 5cbac17776b5c8bd27f08f6db4b05a7dc886966370626132654e1418a828931f
-  md5: 9e5d830263cca953b20bb760ca4b6a0d
+  build: py311ha60cc69_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py311ha60cc69_1.conda
+  sha256: d2f2f1a408e2353fc61d2bf064313270be2260ee212fe827dcf3cfd3754f1354
+  md5: 29d320d6450b2948740a9be3761b2e9d
   depends:
-  - __osx >=10.13
+  - __osx >=11.0
   - cffi >=1.11
   - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 412173
-  timestamp: 1721044344005
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 332271
+  timestamp: 1725305847224
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py311h53056dc_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py311h53056dc_0.conda
-  sha256: 3bd0e287215152d6e3a17090c015368b7632f907cecfb363e7a3391f8ee04f8e
-  md5: a2663051856bfd6ac673a93a0baa7aba
+  build: py311hbc35293_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py311hbc35293_1.conda
+  sha256: a5cf0eef1ffce0d710eb3dffcb07d9d5922d4f7a141abc96f6476b98600f718f
+  md5: aec590674ba365e50ae83aa2d6e1efae
   depends:
+  - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
+  - libgcc >=13
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 322403
-  timestamp: 1721044645946
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 417923
+  timestamp: 1725305669690
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py311h5cd10c7_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py311h5cd10c7_0.conda
-  sha256: ee4e7202ed6d6027eabb9669252b4dfd8144d4fde644435ebe39ab608086e7af
-  md5: 8efe4fe2396281627b3450af8357b190
+  build: py311hdf6fcd6_1
+  build_number: 1
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py311hdf6fcd6_1.conda
+  sha256: d9bf977b620750049eb60fffca299a701342a2df59bcc2586a79b2f7c5783fa1
+  md5: 4fc42d6f85a21b09ee6477f456554df3
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=10.13
   - cffi >=1.11
-  - libgcc-ng >=12
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
   - zstd >=1.5.6,<1.5.7.0a0
@@ -37086,42 +37371,44 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 416323
-  timestamp: 1721044178290
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 411350
+  timestamp: 1725305723486
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h331e495_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py312h331e495_0.conda
-  sha256: c1d379d1062f23e3fbd3dd8548fc6cf61b23d6f96b11e78c4e01f4761580cb02
-  md5: fb62d40e45f51f7d6a7df47c9a12caf4
+  build: py312h15fbf35_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h15fbf35_1.conda
+  sha256: d00ca25c1e28fd31199b26a94f8c96574475704a825d244d7a6351ad3745eeeb
+  md5: a4cde595509a7ad9c13b1a3809bcfe51
   depends:
-  - __osx >=10.13
+  - __osx >=11.0
   - cffi >=1.11
   - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 411066
-  timestamp: 1721044218542
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 330788
+  timestamp: 1725305806565
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h3483029_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312h3483029_0.conda
-  sha256: 7e1e105ea7eab2af591faebf743ff2493f53c313079e316419577925e4492b03
-  md5: eab52e88c858d87cf5a069f79d10bb50
+  build: py312h7122b0e_1
+  build_number: 1
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py312h7122b0e_1.conda
+  sha256: 2685dde42478fae0780fba5d1f8a06896a676ae105f215d32c9f9e76f3c6d8fd
+  md5: bd132ba98f3fc0a6067f355f8efe4cb6
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=10.13
   - cffi >=1.11
-  - libgcc-ng >=12
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
   - zstd >=1.5.6,<1.5.7.0a0
@@ -37129,54 +37416,56 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 416708
-  timestamp: 1721044154409
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 410873
+  timestamp: 1725305688706
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h721a963_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h721a963_0.conda
-  sha256: 6fc0d2f7a0a49a7c1453bb9eacd5456214b6cf000760067d72f0cce464975fa1
-  md5: caf7f5b85615a132c0fa586b82bd59e6
+  build: py312h7606c53_1
+  build_number: 1
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py312h7606c53_1.conda
+  sha256: 3e0c718aa18dcac7f080844dbe0aea41a9cea75083019ce02e8a784926239826
+  md5: a92cc3435b2fd6f51463f5a4db5c50b1
   depends:
-  - __osx >=11.0
   - cffi >=1.11
   - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 332489
-  timestamp: 1721044244889
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 320624
+  timestamp: 1725305934189
 - kind: conda
   name: zstandard
   version: 0.23.0
-  build: py312h7606c53_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py312h7606c53_0.conda
-  sha256: 907edf473419a5aff6151900d09bb3f2b2c2ede8964f20ae87cb6fae04d0cbb7
-  md5: c405924e081cb476495ffe72c88e92c2
+  build: py312hef9b889_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
+  sha256: b97015e146437283f2213ff0e95abdc8e2480150634d81fbae6b96ee09f5e50b
+  md5: 8b7069e9792ee4e5b4919a7a306d2e67
   depends:
+  - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
+  - libgcc >=13
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=conda-forge-mapping
-  size: 320649
-  timestamp: 1721044547910
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 419552
+  timestamp: 1725305670210
 - kind: conda
   name: zstd
   version: 1.5.6
diff --git a/pyproject.toml b/pyproject.toml
index beea3d7..d43580d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,11 +46,24 @@ ipykernel = ">=6.29.5,<6.30"
 seaborn = ">=0.13.2,<0.14"
 matplotlib = ">=3.9.1,<3.10"
 
+[tool.pixi.feature.doc.dependencies]
+sphinx = "*"
+pydata-sphinx-theme = "*"
+sphinx-autobuild = "*"
+numpydoc = "*"
+nbsphinx = "*"
+
+[tool.pixi.feature.doc.pypi-dependencies]
+sphinx_gallery = "*"
+sphinxcontrib-serializinghtml = "*"
+
 [tool.pixi.tasks]
 test = "pytest --cov=pyrasa --cov-report=lcov"
 lint = "ruff format && ruff check --fix"
 lint_ci = "ruff check"
 check_types = "mypy pyrasa tests"
+doc_dev = "sphinx-autobuild --ignore 'doc/source/auto_examples/*' doc/source doc/build"
+build_docs = "cd doc; make clean; make html"
 
 [tool.pixi.dependencies]
 hatch = ">=1.12.0,<1.13"
@@ -74,6 +87,7 @@ py312 = {dependencies = {python="3.12.*"}}
 default = {features = [], solve-group = "default"}
 mne = {features = ["mne"], solve-group = "default"}
 jupyter = {features = ["jupyter"], solve-group = "default"}
+doc = {features = ["doc"], solve-group = "default"}
 testpy311 = ['py311']
 testpy312 = ['py312']
 
diff --git a/pyrasa/__init__.py b/pyrasa/__init__.py
index f1ae2b0..fd1f83d 100644
--- a/pyrasa/__init__.py
+++ b/pyrasa/__init__.py
@@ -1,5 +1,6 @@
 """IRASA Core Functions."""
 
+from pyrasa.__version__ import __version__
 from pyrasa.irasa import irasa, irasa_sprint
 
-__all__ = ['irasa', 'irasa_sprint']
+__all__ = ['irasa', 'irasa_sprint', '__version__']