Various minor changes to clean up doc build log of all warnings

Fixes #29

docs/api_reference.rst

@@ -112,4 +112,5 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 

docs/classes.rst

@@ -20,7 +20,7 @@ disturbances.
 
 .. |copy|   unicode:: U+000A9 .. COPYRIGHT SIGN
 
-.. figure:: images/concept.png
+.. figure:: _static/concept.png
 
    |copy| Copyright European Union, 2022; Jonas Viehweger
 

docs/conf.py

@@ -58,7 +58,7 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
+exclude_patterns = ['gallery/README.rst']
 
 
 # -- Options for HTML output -------------------------------------------------
@@ -71,8 +71,8 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['images']
-html_logo = "images/logo.png"
+html_static_path = ['_static']
+html_logo = "_static/logo.png"
 html_theme_options = {
     'logo_only': True,
     'display_version': False,

docs/gallery/dontplot_bolivia_stac.py

@@ -27,7 +27,7 @@
 -  Generate a simple rule-based forest mask
 -  Simulate a near real-time monitoring scenario
 """
-# sphinx_gallery_thumbnail_path = 'images/iqr_results_bolivia.png'
+# sphinx_gallery_thumbnail_path = '_static/iqr_results_bolivia.png'
 
 #############################################
 # Define the study area
@@ -262,7 +262,7 @@
 ax.set_yticks([])
 
 ###############################################################################
-# .. image:: ../../_static/forest_mask_bolivia.png
+# .. image:: ../_static/forest_mask_bolivia.png
 
 ###############################################################################
 # Monitoring simulation
@@ -316,7 +316,7 @@
 ax.set_yticks([])
 
 ###############################################################################
-# .. image:: ../../_static/iqr_results_bolivia.png
+# .. image:: ../_static/iqr_results_bolivia.png
 # 
 # The resulting mask reveals clear signs of agricultural expansion in the
 # east and south-west of the study area, as well as more subtle traces of

nrt/monitor/__init__.py

@@ -44,12 +44,14 @@ class and must implement the abstract methods ``fit()``, ``monitor()``
         mask (numpy.ndarray): A 2D numpy array containing pixels that should
             be monitored (1) and not (0). The mask may be updated following
             historing period stability check, and after a call to monitor
-            following a confirmed break. Values are as follow.
-            ``{0: 'Not monitored',
-               1: 'monitored',
-               2: 'Unstable history',
-               3: 'Confirmed break - no longer monitored',
-               4: 'Not enough observations - not monitored'}``
+            following a confirmed break. Values are as follow::
+
+                {0: 'Not monitored',
+                 1: 'monitored',
+                 2: 'Unstable history',
+                 3: 'Confirmed break - no longer monitored',
+                 4: 'Not enough observations - not monitored'}
+
         trend (bool): Indicate whether stable period fit is performed with
             trend or not
         harmonic_order (int): The harmonic order of the time-series regression
@@ -66,7 +68,7 @@ class and must implement the abstract methods ``fit()``, ``monitor()``
             when using stable fitting algorithms
         update_mask (bool): Specifies whether to update the mask and halt
             further monitoring when values exceed boundary limits during a
-            `.monitor()` call. A ``True`` value indicates that crossing the
+            ``.monitor()`` call. A ``True`` value indicates that crossing the
             boundary limits will trigger a mask update and stop successive
             observation monitoring.
 
@@ -81,7 +83,7 @@ class and must implement the abstract methods ``fit()``, ``monitor()``
         harmonic_order (int): The harmonic order of the time-series regression
         save_fit_start (bool): If start of the fit should be reported in the
             model. Only applicable to stable fits (e.g. 'ROC', 'CCDC-stable').
-            If true, the data will be saved in the attribute `fit_start`
+            If true, the data will be saved in the attribute ``fit_start``
         x_coords (numpy.ndarray): x coordinates
         y_coords (numpy.ndarray): y coordinates
         process (numpy.ndarray): 2D numpy array containing the
@@ -95,7 +97,7 @@ class and must implement the abstract methods ``fit()``, ``monitor()``
             when using stable fitting algorithms
         update_mask (bool): Specifies whether to update the mask and halt
             further monitoring when values exceed boundary limits during a
-            `.monitor()` call. A ``True`` value (default) indicates that
+            ``.monitor()`` call. A ``True`` value (default) indicates that
             crossing the boundary limits will trigger a mask update and stop
             successive observation monitoring.
     """
@@ -329,9 +331,9 @@ def _report(self, layers, dtype):
                 the report. Valid options are ``'mask'`` (the main output layer
                 containing confirmed breaks, non-monitored pixels, etc), ``'detection_date'``
                 (an integer value matching each confirmed break and indicating the date
-                 the break was confirmed in days since epoch), ``'process'`` (the process
-                 value). The process value has a different meaning and interpretation
-                 for each monitoring method.
+                the break was confirmed in days since epoch), ``'process'`` (the process
+                value). The process value has a different meaning and interpretation
+                for each monitoring method.
             dtype (type): The datatype of the stacked layers. Note that when returning
                 process value for MoSum, CuSum or EWMA the ``dtype`` should be set
                 to a float type to retain values
@@ -356,9 +358,9 @@ def report(self, filename, layers=['mask', 'detection_date'],
                 the report. Valid options are ``'mask'`` (the main output layer
                 containing confirmed breaks, non-monitored pixels, etc), ``'detection_date'``
                 (an integer value matching each confirmed break and indicating the date
-                 the break was confirmed in days since epoch), ``'process'`` (the process
-                 value). The process value has a different meaning and interpretation
-                 for each monitoring method.
+                the break was confirmed in days since epoch), ``'process'`` (the process
+                value). The process value has a different meaning and interpretation
+                for each monitoring method.
             dtype (type): The datatype of the stacked layers. Note that when returning
                 process value for MoSum, CuSum or EWMA the ``dtype`` should be set
                 to a float type to retain values

nrt/utils_efp.py

@@ -81,11 +81,11 @@ def history_roc(X, y, alpha=0.05, crit=0.9478982340418134):
         crit (float): Critical value corresponding to the chosen alpha. Can be
             calculated with ``_cusum_rec_test_crit``.
             Default is the value for alpha=0.05
-    Returns:
-        (int) Index of structural change in y.
-             0: y completely stable
-            >0: y stable after this index
 
+    Returns:
+        int: Index of structural change in y.
+        ``0`` - y completely stable
+        ``>0`` -  y stable after this index
     """
     # Index, where instability in time-series is detected
     #  0: time-series completely stable