Merge pull request #400 from lsst/tickets/OPSIM-1141

OPSIM-1141: Plotters return Figure objects, not fig.number

pyproject.toml

@@ -106,6 +106,10 @@ line_length = 110
 exclude = [
     "__init__.py",
 ]
+line-length = 110
+target-version = "py311"
+
+[tool.ruff.lint]
 ignore = [
     "N802",
     "N803",
@@ -124,21 +128,19 @@ ignore = [
     "D400",
     "E712",
 ]
-line-length = 110
 select = [
     "E",  # pycodestyle
     "F",  # pyflakes
     "N",  # pep8-naming
     "W",  # pycodestyle
 ]
-target-version = "py311"
 extend-select = [
     "RUF100", # Warn about unused noqa
 ]
 
-[tool.ruff.pycodestyle]
+[tool.ruff.lint.pycodestyle]
 max-doc-length = 79
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "numpy"
 

rubin_sim/maf/batches/moving_objects_batch.py

@@ -835,7 +835,7 @@ def _codePlot(key):
     display_dict["subgroup"] = "Completeness over time"
     display_dict["caption"] = "Completeness over time, for H values indicated in legend."
     ph.save_fig(
-        fig.number,
+        fig,
         f"{figroot}_CompletenessOverTime",
         "Combo",
         "CompletenessOverTime",

rubin_sim/maf/metric_bundles/metric_bundle.py

@@ -790,9 +790,9 @@ def plot(self, plot_handler=None, plot_func=None, outfile_suffix=None, savefig=F
         Returns
         -------
         made_plots : `dict`
-            Dictionary of plot_type:figure number key/value pairs,
+            Dictionary of plot_type:figure key/value pairs,
             indicating what plots were created
-            and what matplotlib figure numbers were used.
+            and what matplotlib figures were used.
         """
         # Generate a plot_handler if none was set.
         if plot_handler is None:
@@ -808,10 +808,10 @@ def plot(self, plot_handler=None, plot_func=None, outfile_suffix=None, savefig=F
         plot_handler.set_plot_dicts(plot_dicts=[self.plot_dict], reset=True)
         made_plots = {}
         if plot_func is not None:
-            fignum = plot_handler.plot(plot_func, outfile_suffix=outfile_suffix)
-            made_plots[plot_func.plotType] = fignum
+            fig = plot_handler.plot(plot_func, outfile_suffix=outfile_suffix)
+            made_plots[plot_func.plotType] = fig
         else:
             for plot_func in self.plot_funcs:
-                fignum = plot_handler.plot(plot_func, outfile_suffix=outfile_suffix)
-                made_plots[plot_func.plot_type] = fignum
+                fig = plot_handler.plot(plot_func, outfile_suffix=outfile_suffix)
+                made_plots[plot_func.plot_type] = fig
         return made_plots

rubin_sim/maf/plots/__init__.py

@@ -6,7 +6,6 @@
 from .night_pointing_plotter import *
 from .oned_plotters import *
 from .perceptual_rainbow import *
-from .plot_bundle import *
 from .plot_handler import *
 from .spatial_plotters import *
 from .special_plotters import *

rubin_sim/maf/plots/hg_plotters.py

@@ -305,21 +305,25 @@ def _map_colors(self, values):  # pylint: disable=invalid-name, no-self-use
 
         return colors, color_mappable
 
-    def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
+    def __call__(self, metric_value, slicer, user_plot_dict, fig=None):
         """Restructure the metric data to use, and build the figure.
 
         Parameters
         ----------
-        metric_value : `numpy.ndarray`
-           Metric values
-        slicer : `rubin_sim.maf.slicers.baseSlicer.BaseSlicer`
-           must have "mjd" and "duration" slice points, in units
-           of days and seconds, respectively.
-        user_plot_dict : `dict`
-           Plotting parameters
-        fignum : `int`
-           matplotlib figure number
+        metric_value : `numpy.ma.MaskedArray`
+            The metric values from the bundle.
+        slicer : `rubin_sim.maf.slicers.TwoDSlicer`
+            The slicer.
+        user_plot_dict: `dict`
+            Dictionary of plot parameters set by user
+            (overrides default values).
+        fig : `matplotlib.figure.Figure`
+            Matplotlib figure number to use. Default = None, starts new figure.
 
+        Returns
+        -------
+        fig : `matplotlib.figure.Figure`
+           Figure with the plot.
         """
         # Highest level driver for the plotter.
         # Prepares data structures and figure-wide elements
@@ -344,7 +348,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
         self.plot_dict.update(user_plot_dict)
 
         # Generate the figure
-        fig = plt.figure(fignum, figsize=self.plot_dict["figsize"])
+        if fig is None:
+            fig = plt.figure(figsize=self.plot_dict["figsize"])
 
         # Add the plots
         color_mappable, axes = self._plot(fig, intervals)
@@ -355,7 +360,9 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
 
         # add the legend, if requested
         if self.plot_dict["legend"] and len(self.color_map) > 0:
-            self._add_figure_legend(fig, axes)
+            fig = self._add_figure_legend(fig, axes)
+
+        return fig
 
     def _add_figure_legend(self, fig, axes):
         """Creates and adds the figure legend.
@@ -370,8 +377,8 @@ def _add_figure_legend(self, fig, axes):
 
         Returns
         -------
-        figure_number : `int`
-           The matplotlib figure number
+        fig : `matplotlib.figure.Figure`
+           The matplotlib figure
         """
         # Creates and adds the figure legend.  This method follows two
         # stages: first, it adds entries for each element in the color
@@ -423,7 +430,7 @@ def _add_figure_legend(self, fig, axes):
             bbox_to_anchor=self.plot_dict["legend_bbox_to_anchor"],
         )
 
-        return fig.number
+        return fig
 
     def _add_colorbar(self, fig, color_mappable, axes):  # pylint: disable=invalid-name, no-self-use
         """Add a colorbar.

rubin_sim/maf/plots/hourglass_plotters.py

@@ -14,6 +14,7 @@ def __init__(self):
             "title": None,
             "xlabel": "Night - min(Night)",
             "ylabel": "Hours from local midnight",
+            "figsize": None,
         }
         self.filter2color = {
             "u": "purple",
@@ -24,20 +25,20 @@ def __init__(self):
             "y": "red",
         }
 
-    def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
+    def __call__(self, metric_value, slicer, user_plot_dict, fig=None):
         """
         Generate the hourglass plot
         """
         if slicer.slicer_name != "HourglassSlicer":
             raise ValueError("HourglassPlot is for use with hourglass slicers")
 
-        fig = plt.figure(fignum)
-        ax = fig.add_subplot(111)
-
         plot_dict = {}
         plot_dict.update(self.default_plot_dict)
         plot_dict.update(user_plot_dict)
 
+        if fig is None:
+            fig = plt.figure(figsize=plot_dict["figsize"])
+        ax = fig.add_subplot(111)
         pernight = metric_value[0]["pernight"]
         perfilter = metric_value[0]["perfilter"]
 
@@ -57,9 +58,11 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
                 color=self.filter2color[key],
                 transform=ax.transAxes,
             )
-        # ax.plot(pernight['mjd'] - dmin, (pernight['twi6_rise'] - pernight['midnight']) * 24.,
+        # ax.plot(pernight['mjd'] - dmin,
+        # (pernight['twi6_rise'] - pernight['midnight']) * 24.,
         #        'blue', label=r'6$^\circ$ twilight')
-        # ax.plot(pernight['mjd'] - dmin, (pernight['twi6_set'] - pernight['midnight']) * 24.,
+        # ax.plot(pernight['mjd'] - dmin,
+        # (pernight['twi6_set'] - pernight['midnight']) * 24.,
         #        'blue')
         ax.plot(
             pernight["mjd"] - dmin,
@@ -114,4 +117,4 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
             )
             ax.axhline((pernight["twi18_set"] - pernight["midnight"]) * 24.0, color="red")
 
-        return fig.number
+        return fig

rubin_sim/maf/plots/mo_plotters.py

@@ -7,9 +7,12 @@
 
 from .plot_handler import BasePlotter
 
-# mag_sun = -27.1 # apparent r band magnitude of the sun. this sets the band for the magnitude limit.
-# see http://www.ucolick.org/~cnaw/sun.html for apparent magnitudes in other bands.
-mag_sun = -26.74  # apparent V band magnitude of the Sun (our H mags translate to V band)
+# mag_sun = -27.1
+# apparent r band magnitude of the sun.
+# this sets the band for the magnitude limit.
+# see http://www.ucolick.org/~cnaw/sun.html for apparent mags in other bands.
+mag_sun = -26.74
+# apparent V band magnitude of the Sun (our H mags translate to V band)
 km_per_au = 1.496e8
 m_per_km = 1000
 
@@ -37,7 +40,7 @@ def __init__(self):
         }
         self.min_hrange = 1.0
 
-    def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
+    def __call__(self, metric_value, slicer, user_plot_dict, fig=None):
         if "linestyle" not in user_plot_dict:
             user_plot_dict["linestyle"] = "-"
         plot_dict = {}
@@ -49,11 +52,13 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
             reduce_func = np.mean
         if hvals.shape[0] == 1:
             # We have a simple set of values to plot against H.
-            # This may be due to running a summary metric, such as completeness.
+            # This may be due to running a summary metric,
+            # such as completeness.
             m_vals = metric_value[0].filled()
         elif len(hvals) == slicer.shape[1]:
             # Using cloned H distribution.
-            # Apply 'np_reduce' method directly to metric values, and plot at matching H values.
+            # Apply 'np_reduce' method directly to metric values,
+            # and plot at matching H values.
             m_vals = reduce_func(metric_value.filled(), axis=0)
         else:
             # Probably each object has its own H value.
@@ -67,7 +72,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
                 nbins = 30
             stepsize = hrange / float(nbins)
             bins = np.arange(min_h, min_h + hrange + stepsize / 2.0, stepsize)
-            # In each bin of H, calculate the 'np_reduce' value of the corresponding metric_values.
+            # In each bin of H, calculate the 'np_reduce' value of the
+            # corresponding metric_values.
             inds = np.digitize(hvals, bins)
             inds = inds - 1
             m_vals = np.zeros(len(bins), float)
@@ -79,7 +85,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
                     m_vals[i] = reduce_func(match.filled())
             hvals = bins
         # Plot the values.
-        fig = plt.figure(fignum, figsize=plot_dict["figsize"])
+        if fig is None:
+            fig = plt.figure(figsize=plot_dict["figsize"])
         ax = plt.gca()
         ax.plot(
             hvals,
@@ -130,7 +137,7 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
         plt.xlabel(plot_dict["xlabel"])
         plt.ylabel(plot_dict["ylabel"])
         plt.tight_layout()
-        return fig.number
+        return fig
 
 
 class MetricVsOrbit(BasePlotter):
@@ -159,11 +166,12 @@ def __init__(self, xaxis="q", yaxis="e"):
             "figsize": None,
         }
 
-    def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
+    def __call__(self, metric_value, slicer, user_plot_dict, fig=None):
         plot_dict = {}
         plot_dict.update(self.default_plot_dict)
         plot_dict.update(user_plot_dict)
-        fig = plt.figure(fignum, figsize=plot_dict["figsize"])
+        if fig is None:
+            fig = plt.figure(figsize=plot_dict["figsize"])
         xvals = slicer.slice_points["orbits"][plot_dict["xaxis"]]
         yvals = slicer.slice_points["orbits"][plot_dict["yaxis"]]
         # Set x/y bins.
@@ -249,13 +257,13 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
         plt.title(plot_dict["title"])
         plt.xlabel(plot_dict["xlabel"])
         plt.ylabel(plot_dict["ylabel"])
-        return fig.number
+        return fig
 
 
 class MetricVsOrbitPoints(BasePlotter):
     """
-    Plot metric values (at a particular H value) as function of orbital parameters,
-    using points for each metric value.
+    Plot metric values (at a particular H value) as function
+    of orbital parameters, using points for each metric value.
     """
 
     def __init__(self, xaxis="q", yaxis="e"):
@@ -276,11 +284,12 @@ def __init__(self, xaxis="q", yaxis="e"):
             "figsize": None,
         }
 
-    def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
+    def __call__(self, metric_value, slicer, user_plot_dict, fig=None):
         plot_dict = {}
         plot_dict.update(self.default_plot_dict)
         plot_dict.update(user_plot_dict)
-        fig = plt.figure(fignum, figsize=plot_dict["figsize"])
+        if fig is None:
+            fig = plt.figure(figsize=plot_dict["figsize"])
         xvals = slicer.slice_points["orbits"][plot_dict["xaxis"]]
         yvals = slicer.slice_points["orbits"][plot_dict["yaxis"]]
         # Identify the relevant metric_values for the Hvalue we want to plot.
@@ -346,4 +355,4 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None):
         plt.title(plot_dict["title"])
         plt.xlabel(plot_dict["xlabel"])
         plt.ylabel(plot_dict["ylabel"])
-        return fig.number
+        return fig