Fix formatting issues

Author: Sophie Reed

doc/manifest.yaml

@@ -8,5 +8,5 @@ modules:
 # Name of the static content directories (subdirectories of `_static`).
 # Static content directories are usually named after the package.
 # Most packages do not need a static content directory (leave commented out).
- statics:
-   - "_static/analysis_tools"
+statics:
+  - "_static/analysis_tools"

python/lsst/analysis/tools/actions/plot/barPlots.py

@@ -54,7 +54,7 @@ class BarPanel(Config):
 
 
 class BarPlot(PlotAction):
-    """A plotting tool which can take multiple keyed data inputs 
+    """A plotting tool which can take multiple keyed data inputs
     and can create one or more bar graphs.
     """
 

python/lsst/analysis/tools/actions/plot/colorColorFitPlot.py

@@ -40,10 +40,10 @@
 
 
 class ColorColorFitPlot(PlotAction):
-    """Makes a color-color plot and overplots a 
+    """Makes a color-color plot and overplots a
     prefited line to the specified area of the plot.
-    This is mostly used for the stellar locus plots 
-    and also includes panels that illustrate the 
+    This is mostly used for the stellar locus plots
+    and also includes panels that illustrate the
     goodness of the given fit.
     """
 
@@ -113,96 +113,100 @@ def makePlot(
     ) -> Figure:
         """Make stellar locus plots using pre fitted values.
 
-        Parameters
-        ----------
-        data : `KeyedData`
-            The data to plot the points from, for more information
-            please see the notes section.
-        plotInfo : `dict`
-            A dictionary of information about the data being plotted with keys:
-
-            * ``"run"``
-                  The output run for the plots (`str`).
-            * ``"skymap"``
-                  The type of skymap used for the data (`str`).
-            * ``"filter"``
-                  The filter used for this data (`str`).
-            * ``"tract"``
-                  The tract that the data comes from (`str`).
-
-       Returns
-        -------
-        fig : `matplotlib.figure.Figure`
-            The resulting figure.
-
-        Notes
-        -----
-        The axis labels are given by `self.config.xLabel` and 
-        `self.config.yLabel`. The perpendicular distance of the points to
-        the fit line is given in a histogram in the second panel.
-
-        For the code to work it expects various quantities to be
-        present in the 'data' that it is given.
-
-        The quantities that are expected to be present are: 
-
-         * Statistics that are shown on the plot or used by the plotting code:
-            * ``approxMagDepth``
-                  The approximate magnitude corresponding to the SN cut used.
-            * ``f"{self.plotName}_sigmaMAD"``
-                  The sigma mad of the distances to the line fit.
-            * ``f"{self.identity or ''}_median"``
-                  The median of the distances to the line fit.
-            * ``f"{self.identity or ''}_hardwired_sigmaMAD"``
-                  The sigma mad of the distances to the initial fit.
-            * ``f"{self.identity or ''}_hardwired_median"``
-                  The median of the distances to the initial fit.
-
-
-         * Parameters from the fitting code that are illustrated on the plot:
-            * ``"bHW"``
-                  The hardwired intercept to fall back on.
-            * ``"bODR"``
-                  The intercept calculated by the orthogonal distance
-                  regression fitting.
-            * ``"bODR2"``
-                  The intercept calculated by the second iteration of
-                  orthogonal distance regression fitting.
-            * ``"mHW"``
-                  The hardwired gradient to fall back on.
-            * ``"mODR"``
-                  The gradient calculated by the orthogonal distance
-                  regression fitting.
-            * ``"mODR2"``
-                  The gradient calculated by the second iteration of 
-                  orthogonal distance regression fitting.
-            * ``"xMin`"``
-                  The x minimum of the box used in the fit.
-            * ``"xMax"``
-                  The x maximum of the box used in the fit.
-            * ``"yMin"``
-                  The y minimum of the box used in the fit.
-            * ``"yMax"``
-                  The y maximum of the box used in the fit.
-            * ``"mPerp"``
-                  The gradient of the line perpendicular to the line from 
-                  the second ODR fit.
-            * ``"bPerpMin"``
-                  The intercept of the perpendicular line that goes through xMin.
-            * ``"bPerpMax"``
-                  The intercept of the perpendicular line that goes through xMax.
- 
-         * The main inputs to plot:
-               x, y, mag
-
-        Examples
-        --------
-        An example of the plot produced from this code is here: 
-
-        .. image:: /_static/analysis_tools/stellarLocusExample.png
-
-        For a detailed example of how to make a plot from the command line 
-        please see the :ref:`getting started guide<analysis-tools-getting-started>`.
+         Parameters
+         ----------
+         data : `KeyedData`
+             The data to plot the points from, for more information
+             please see the notes section.
+         plotInfo : `dict`
+             A dictionary of information about the data being plotted
+             with keys:
+
+             * ``"run"``
+                   The output run for the plots (`str`).
+             * ``"skymap"``
+                   The type of skymap used for the data (`str`).
+             * ``"filter"``
+                   The filter used for this data (`str`).
+             * ``"tract"``
+                   The tract that the data comes from (`str`).
+
+        Returns
+         -------
+         fig : `matplotlib.figure.Figure`
+             The resulting figure.
+
+         Notes
+         -----
+         The axis labels are given by `self.config.xLabel` and
+         `self.config.yLabel`. The perpendicular distance of the points to
+         the fit line is given in a histogram in the second panel.
+
+         For the code to work it expects various quantities to be
+         present in the 'data' that it is given.
+
+         The quantities that are expected to be present are:
+
+          * Statistics that are shown on the plot or used by the plotting code:
+             * ``approxMagDepth``
+                   The approximate magnitude corresponding to the SN cut used.
+             * ``f"{self.plotName}_sigmaMAD"``
+                   The sigma mad of the distances to the line fit.
+             * ``f"{self.identity or ''}_median"``
+                   The median of the distances to the line fit.
+             * ``f"{self.identity or ''}_hardwired_sigmaMAD"``
+                   The sigma mad of the distances to the initial fit.
+             * ``f"{self.identity or ''}_hardwired_median"``
+                   The median of the distances to the initial fit.
+
+
+          * Parameters from the fitting code that are illustrated on the plot:
+             * ``"bHW"``
+                   The hardwired intercept to fall back on.
+             * ``"bODR"``
+                   The intercept calculated by the orthogonal distance
+                   regression fitting.
+             * ``"bODR2"``
+                   The intercept calculated by the second iteration of
+                   orthogonal distance regression fitting.
+             * ``"mHW"``
+                   The hardwired gradient to fall back on.
+             * ``"mODR"``
+                   The gradient calculated by the orthogonal distance
+                   regression fitting.
+             * ``"mODR2"``
+                   The gradient calculated by the second iteration of
+                   orthogonal distance regression fitting.
+             * ``"xMin`"``
+                   The x minimum of the box used in the fit.
+             * ``"xMax"``
+                   The x maximum of the box used in the fit.
+             * ``"yMin"``
+                   The y minimum of the box used in the fit.
+             * ``"yMax"``
+                   The y maximum of the box used in the fit.
+             * ``"mPerp"``
+                   The gradient of the line perpendicular to the line from
+                   the second ODR fit.
+             * ``"bPerpMin"``
+                   The intercept of the perpendicular line that goes through
+                   xMin.
+             * ``"bPerpMax"``
+                   The intercept of the perpendicular line that goes through
+                   xMax.
+
+          * The main inputs to plot:
+                x, y, mag
+
+         Examples
+         --------
+         An example of the plot produced from this code is here:
+
+         .. image:: /_static/analysis_tools/stellarLocusExample.png
+
+         For a detailed example of how to make a plot from the command line
+         please see the
+         :ref:`getting started guide<analysis-tools-getting-started>`.
         """
 
         # Define a new colormap

python/lsst/analysis/tools/actions/plot/scatterplotWithTwoHists.py

@@ -52,7 +52,7 @@
 
 
 class ScatterPlotStatsAction(KeyedDataAction):
-    """Calculates the statistics needed for the 
+    """Calculates the statistics needed for the
     scatter plot with two hists.
     """
 
@@ -138,7 +138,7 @@ class _StatsContainer(NamedTuple):
 
 
 class ScatterPlotWithTwoHists(PlotAction):
-    """Makes a scatter plot of the data with a marginal 
+    """Makes a scatter plot of the data with a marginal
     histogram for each axis.
     """
 
@@ -287,26 +287,26 @@ def makePlot(
         which points to plot and the statisticSelector actions to determine
         which points to use for the printed statistics.
 
-        If this function is being used within the pipetask framework 
-        that takes care of making sure that data has all the required 
-        elements but if you are runnign this as a standalone function 
-        then you will need to provide the following things in the 
+        If this function is being used within the pipetask framework
+        that takes care of making sure that data has all the required
+        elements but if you are runnign this as a standalone function
+        then you will need to provide the following things in the
         input data.
 
         * If stars is in self.plotTypes:
-            xStars, yStars, starsHighSNMask, starsLowSNMask and 
-            {band}_highSNStars_{name}, {band}_lowSNStars_{name} 
+            xStars, yStars, starsHighSNMask, starsLowSNMask and
+            {band}_highSNStars_{name}, {band}_lowSNStars_{name}
             where name is median, sigma_Mad, count and approxMag.
 
-        * If it is for galaxies/unknowns then replace stars in the above 
+        * If it is for galaxies/unknowns then replace stars in the above
           names with galaxies/unknowns.
 
-        * If it is for any (which covers all the points) then it 
-          becomes, x, y, and any instead of stars for the other 
+        * If it is for any (which covers all the points) then it
+          becomes, x, y, and any instead of stars for the other
           parameters given above.
 
         * In every case it is expected that data contains:
-            lowSnThreshold, highSnThreshold and patch 
+            lowSnThreshold, highSnThreshold and patch
             (if the summary plot is being plotted).
 
         Examples
@@ -316,7 +316,8 @@ def makePlot(
         .. image:: /_static/analysis_tools/scatterPlotExample.png
 
         For a detailed example of how to make a plot from the command line
-        please see the :ref:`getting started guide<analysis-tools-getting-started>`.
+        please see the
+        :ref:`getting started guide<analysis-tools-getting-started>`.
         """
         if not self.plotTypes:
             noDataFig = Figure()

python/lsst/analysis/tools/actions/plot/skyPlot.py

@@ -42,9 +42,9 @@
 class SkyPlot(PlotAction):
     """Plots the on sky distribution of a parameter.
 
-    Plots the values of the parameter given for the z axis 
-    according to the positions given for x and y. Optimised 
-    for use with RA and Dec. Also calculates some basic 
+    Plots the values of the parameter given for the z axis
+    according to the positions given for x and y. Optimised
+    for use with RA and Dec. Also calculates some basic
     statistics and includes those on the plot.
     """
 
@@ -170,19 +170,19 @@ def makePlot(
 
         Notes
         -----
-        Expects the data to contain slightly different things 
-        depending on the types specified in plotTypes. This 
-        is handled automatically if you go through the pipetask 
-        framework but if you call this method separately then you 
+        Expects the data to contain slightly different things
+        depending on the types specified in plotTypes. This
+        is handled automatically if you go through the pipetask
+        framework but if you call this method separately then you
         need to make sure that data contains what the code is expecting.
 
-        If stars is in the plot types given then it is expected that 
+        If stars is in the plot types given then it is expected that
         data contains: xStars, yStars, zStars and starStatMask.
 
-        If galaxies is present: xGalaxies, yGalaxies, zGalaxies and 
+        If galaxies is present: xGalaxies, yGalaxies, zGalaxies and
         galaxyStatsMask.
 
-        If unknown is present: xUnknowns, yUnknowns, zUnknowns and 
+        If unknown is present: xUnknowns, yUnknowns, zUnknowns and
         unknownStatMask.
 
         If any is specified: x, y, z, statMask.