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