From 41d8ab03b5cc81b83ee807c9cfacff3553eb583f Mon Sep 17 00:00:00 2001 From: Lynne Jones Date: Tue, 26 Mar 2024 14:25:50 -0700 Subject: [PATCH 1/6] Remove plot_bundle -- unused --- rubin_sim/maf/plots/plot_bundle.py | 86 ------------------------------ 1 file changed, 86 deletions(-) delete mode 100644 rubin_sim/maf/plots/plot_bundle.py diff --git a/rubin_sim/maf/plots/plot_bundle.py b/rubin_sim/maf/plots/plot_bundle.py deleted file mode 100644 index e4114a9d2..000000000 --- a/rubin_sim/maf/plots/plot_bundle.py +++ /dev/null @@ -1,86 +0,0 @@ -__all__ = ("PlotBundle",) - - -import matplotlib.pylab as plt - -from .plot_handler import PlotHandler - - -class PlotBundle: - """ - Object designed to help organize multiple MetricBundles that will be plotted - together using the PlotHandler. - """ - - def __init__(self, bundle_list=None, plot_dicts=None, plot_func=None): - """ - Init object and set things if desired. - bundle_list: A list of bundleDict objects - plot_dicts: A list of dictionaries with plotting kwargs - plot_func: A single MAF plotting function - """ - if bundle_list is None: - self.bundle_list = [] - else: - self.bundle_list = bundle_list - - if plot_dicts is None: - if len(self.bundle_list) > 0: - self.plot_dicts = [{}] - else: - self.plot_dicts = [] - else: - self.plot_dicts = plot_dicts - - self.plot_func = plot_func - - def add_bundle(self, bundle, plot_dict=None, plot_func=None): - """ - Add bundle to the object. - Optionally add a plot_dict and/or replace the plot_func - """ - self.bundle_list.append(bundle) - if plot_dict is not None: - self.plot_dicts.append(plot_dict) - else: - self.plot_dicts.append({}) - if plot_func is not None: - self.plot_func = plot_func - - def increment_plot_order(self): - """ - Find the maximium order number in the display dicts, and set them to +1 that - """ - max_order = 0 - for m_b in self.bundle_list: - if "order" in list(m_b.displayDict.keys()): - max_order = max([max_order, m_b.displayDict["order"]]) - - for m_b in self.bundle_list: - m_b.displayDict["order"] = max_order + 1 - - def percentile_legend(self): - """ - Go through the bundles and change the lables if there are the correct summary stats - """ - for i, mB in enumerate(self.bundle_list): - if mB.summary_values is not None: - keys = list(mB.summary_values.keys()) - if ("25th%ile" in keys) & ("75th%ile" in keys) & ("Median" in keys): - if "label" not in list(self.plot_dicts[i].keys()): - self.plot_dicts[i]["label"] = "" - newstr = "%0.1f/%0.1f/%0.1f " % ( - mB.summary_values["25th%ile"], - mB.summary_values["Median"], - mB.summary_values["75th%ile"], - ) - self.plot_dicts[i]["label"] = newstr + self.plot_dicts[i]["label"] - - def plot(self, out_dir="Out", results_db=None, closefigs=True): - ph = PlotHandler(out_dir=out_dir, results_db=results_db) - ph.set_metric_bundles(self.bundle_list) - # Auto-generate labels and things - ph.set_plot_dicts(plot_dicts=self.plot_dicts, plot_func=self.plot_func) - ph.plot(self.plot_func, plot_dicts=self.plot_dicts) - if closefigs: - plt.close("all") From 3ed84617a942c98a6090a0df41d6ce8190776361 Mon Sep 17 00:00:00 2001 From: Lynne Jones Date: Tue, 26 Mar 2024 16:06:13 -0700 Subject: [PATCH 2/6] Change return of Plotters to figure --- rubin_sim/maf/batches/moving_objects_batch.py | 2 +- rubin_sim/maf/metric_bundles/metric_bundle.py | 12 +- rubin_sim/maf/plots/__init__.py | 1 - rubin_sim/maf/plots/hg_plotters.py | 37 ++-- rubin_sim/maf/plots/hourglass_plotters.py | 18 +- rubin_sim/maf/plots/mo_plotters.py | 43 ++-- rubin_sim/maf/plots/nd_plotters.py | 65 ++++--- rubin_sim/maf/plots/neo_distance_plotter.py | 54 +++--- rubin_sim/maf/plots/night_pointing_plotter.py | 11 +- rubin_sim/maf/plots/oned_plotters.py | 13 +- rubin_sim/maf/plots/plot_handler.py | 183 +++++++++++++----- rubin_sim/maf/plots/spatial_plotters.py | 144 ++++++++------ rubin_sim/maf/plots/special_plotters.py | 45 +++-- rubin_sim/maf/plots/two_d_plotters.py | 76 ++++---- rubin_sim/maf/plots/xyplotter.py | 13 +- 15 files changed, 449 insertions(+), 268 deletions(-) diff --git a/rubin_sim/maf/batches/moving_objects_batch.py b/rubin_sim/maf/batches/moving_objects_batch.py index 5769b16fc..9ff4b6fc2 100644 --- a/rubin_sim/maf/batches/moving_objects_batch.py +++ b/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", diff --git a/rubin_sim/maf/metric_bundles/metric_bundle.py b/rubin_sim/maf/metric_bundles/metric_bundle.py index 860e0a2ea..f87abe5b0 100644 --- a/rubin_sim/maf/metric_bundles/metric_bundle.py +++ b/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 diff --git a/rubin_sim/maf/plots/__init__.py b/rubin_sim/maf/plots/__init__.py index 7394531e0..42dee1afa 100644 --- a/rubin_sim/maf/plots/__init__.py +++ b/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 * diff --git a/rubin_sim/maf/plots/hg_plotters.py b/rubin_sim/maf/plots/hg_plotters.py index cb8477407..3df11d15e 100644 --- a/rubin_sim/maf/plots/hg_plotters.py +++ b/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. diff --git a/rubin_sim/maf/plots/hourglass_plotters.py b/rubin_sim/maf/plots/hourglass_plotters.py index 50fbb81a5..048ab4efe 100644 --- a/rubin_sim/maf/plots/hourglass_plotters.py +++ b/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 +25,21 @@ 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 +59,11 @@ 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 +118,4 @@ 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 diff --git a/rubin_sim/maf/plots/mo_plotters.py b/rubin_sim/maf/plots/mo_plotters.py index 519f6768a..0e8b99b8d 100644 --- a/rubin_sim/maf/plots/mo_plotters.py +++ b/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 diff --git a/rubin_sim/maf/plots/nd_plotters.py b/rubin_sim/maf/plots/nd_plotters.py index 0b19d057e..885e24342 100644 --- a/rubin_sim/maf/plots/nd_plotters.py +++ b/rubin_sim/maf/plots/nd_plotters.py @@ -30,28 +30,30 @@ def __init__(self): "clims": None, "cmap": perceptual_rainbow, "cbar_format": None, + "figsize": None, } - def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_values, slicer, user_plot_dict, fig=None): """ Parameters ---------- - metricValue : numpy.ma.MaskedArray - slicer : rubin_sim.maf.slicers.NDSlicer - user_plot_dict: dict - Dictionary of plot parameters set by user (overrides default values). - 'xaxis' and 'yaxis' values define which axes of the nd data to plot along the x/y axes. - fignum : int - Matplotlib figure number to use (default = None, starts new figure). + 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 ------- - int - Matplotlib figure number used to create the plot. + fig : `matplotlib.figure.Figure` + Figure with the plot. """ if slicer.slicer_name != "NDSlicer": raise ValueError("TwoDSubsetData plots ndSlicer metric values") - fig = plt.figure(fignum) plot_dict = {} plot_dict.update(self.default_plot_dict) plot_dict.update(user_plot_dict) @@ -59,6 +61,9 @@ def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): raise ValueError("xaxis and yaxis must be specified in plot_dict") xaxis = plot_dict["xaxis"] yaxis = plot_dict["yaxis"] + + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) # Reshape the metric data so we can isolate the values to plot # (just new view of data, not copy). newshape = [] @@ -66,7 +71,8 @@ def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): newshape.append(len(b) - 1) newshape.reverse() md = metric_values.reshape(newshape) - # Sum over other dimensions. Note that masked values are not included in sum. + # Sum over other dimensions. + # Note that masked values are not included in sum. sumaxes = list(range(slicer.nD)) sumaxes.remove(xaxis) sumaxes.remove(yaxis) @@ -110,7 +116,7 @@ def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): ) cb.set_label(plot_dict["units"]) plt.title(plot_dict["title"]) - return fig.number + return fig class OneDSubsetData(BasePlotter): @@ -134,33 +140,39 @@ def __init__(self): "alpha": 0.5, "cmap": perceptual_rainbow, "cbar_format": None, + "figsize": None, } - def plot_binned_data1_d(self, metric_values, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_values, slicer, user_plot_dict, fig=None): """ Parameters ---------- - metricValue : numpy.ma.MaskedArray - slicer : rubin_sim.maf.slicers.NDSlicer - user_plot_dict: dict - Dictionary of plot parameters set by user (overrides default values). - 'axis' keyword identifies which axis to show in the plot (along xaxis of plot). - fignum : int - Matplotlib figure number to use (default = None, starts new figure). + 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 ------- - int - Matplotlib figure number used to create the plot. + fig : `matplotlib.figure.Figure` + Figure with the plot. """ if slicer.slicer_name != "NDSlicer": raise ValueError("TwoDSubsetData plots ndSlicer metric values") - fig = plt.figure(fignum) + plot_dict = {} plot_dict.update(self.default_plot_dict) plot_dict.update(user_plot_dict) if "axis" not in plot_dict: raise ValueError("axis for 1-d plot must be specified in plot_dict") + + if fig is None: + fig = plt.Figure(figsize=plot_dict["figsize"]) # Reshape the metric data so we can isolate the values to plot # (just new view of data, not copy). axis = plot_dict["axis"] @@ -169,7 +181,8 @@ def plot_binned_data1_d(self, metric_values, slicer, user_plot_dict, fignum=None newshape.append(len(b) - 1) newshape.reverse() md = metric_values.reshape(newshape) - # Sum over other dimensions. Note that masked values are not included in sum. + # Sum over other dimensions. + # Note that masked values are not included in sum. sumaxes = list(range(slicer.nD)) sumaxes.remove(axis) sumaxes = tuple(sumaxes) @@ -204,4 +217,4 @@ def plot_binned_data1_d(self, metric_values, slicer, user_plot_dict, fignum=None if plot_dict["histRange"] is not None: plt.xlim(plot_dict["histRange"]) plt.title(plot_dict["title"]) - return fig.number + return fig diff --git a/rubin_sim/maf/plots/neo_distance_plotter.py b/rubin_sim/maf/plots/neo_distance_plotter.py index edcba62bb..7fba2e265 100644 --- a/rubin_sim/maf/plots/neo_distance_plotter.py +++ b/rubin_sim/maf/plots/neo_distance_plotter.py @@ -11,17 +11,21 @@ class NeoDistancePlotter(BasePlotter): """ - Special plotter to calculate and plot the maximum distance an H=22 NEO could be observable to, - in any particular particular opsim observation. + Special plotter to calculate and plot the maximum distance an H=22 NEO + could be observable to, in any particular opsim observation. + + Parameters + ---------- + step : `float`, optional + Step size to use for radial bins. Default is 0.01 AU. + eclip_max, eclip_min : `float`, `float`, optional + Range of ecliptic latitude values to include when creating the plot. """ def __init__(self, step=0.01, eclip_max=10.0, eclip_min=-10.0): - """ - eclip_min/Max: only plot observations within X degrees of the ecliptic plane - step: Step size to use for radial bins. Default is 0.01 AU. - """ self.plot_type = "neoxyPlotter" self.object_plotter = True + self.default_plot_dict = { "title": None, "xlabel": "X (AU)", @@ -31,6 +35,7 @@ def __init__(self, step=0.01, eclip_max=10.0, eclip_min=-10.0): "y_min": -0.25, "y_max": 2.5, "units": "Count", + "figsize": None, } self.filter2color = { "u": "purple", @@ -45,26 +50,25 @@ def __init__(self, step=0.01, eclip_max=10.0, eclip_min=-10.0): self.eclip_max = np.radians(eclip_max) self.eclip_min = np.radians(eclip_min) - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): """ Parameters ---------- - metric_value : numpy.ma.MaskedArray - Metric values calculated by rubin_sim.maf.metrics.PassMetric - slicer : rubin_sim.maf.slicers.UniSlicer - user_plot_dict: dict - Dictionary of plot parameters set by user (overrides default values). - fignum : int - Matplotlib figure number to use (default = None, starts new figure). + 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 ------- - int - Matplotlib figure number used to create the plot. + fig : `matplotlib.figure.Figure` + Figure with the plot. """ - fig = plt.figure(fignum) - ax = fig.add_subplot(111) - in_plane = np.where( (metric_value[0]["eclipLat"] >= self.eclip_min) & (metric_value[0]["eclipLat"] <= self.eclip_max) ) @@ -73,6 +77,10 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): plot_dict.update(self.default_plot_dict) plot_dict.update(user_plot_dict) + if fig is None: + fig = plt.figure(plot_dict["figsize"]) + ax = fig.add_subplot(111) + planet_props = {"Earth": 1.0, "Venus": 0.72, "Mars": 1.52, "Mercury": 0.39} planets = [] @@ -82,7 +90,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): for planet in planets: ax.add_artist(planet) - # Let's make a 2-d histogram in polar coords, then convert and display in cartisian + # Let's make a 2-d histogram in polar coords, + # then convert and display in cartisian r_step = self.step rvec = np.arange(0, plot_dict["x_max"] + r_step, r_step) @@ -105,7 +114,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): theta = np.arctan2(y - 1.0, x) diff = np.abs(thetavec - theta) theta_to_use = thetavec[np.where(diff == diff.min())] - # This is a slow where-clause, should be possible to speed it up using + # This is a slow where-clause, should be possible to speed it up + # using # np.searchsorted+clever slicing or hist2d to build up the map. good = np.where((thetagrid == theta_to_use) & (rgrid <= dist)) H[good] += 1 @@ -126,4 +136,4 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): ax.plot([0], [1], marker="o", color="b") ax.plot([0], [0], marker="o", color="y") - return fig.number + return fig diff --git a/rubin_sim/maf/plots/night_pointing_plotter.py b/rubin_sim/maf/plots/night_pointing_plotter.py index c35b0f563..956e7e4bb 100644 --- a/rubin_sim/maf/plots/night_pointing_plotter.py +++ b/rubin_sim/maf/plots/night_pointing_plotter.py @@ -1,5 +1,7 @@ __all__ = ("NightPointingPlotter",) +import warnings + import matplotlib.pyplot as plt import numpy as np @@ -18,6 +20,7 @@ def __init__(self, mjd_col="observationStartMJD", alt_col="alt", az_col="az"): "title": None, "xlabel": "MJD", "ylabels": ["Alt", "Az"], + "figsize": None, } self.filter2color = { "u": "purple", @@ -28,8 +31,12 @@ def __init__(self, mjd_col="observationStartMJD", alt_col="alt", az_col="az"): "y": "red", } - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): + if fig is not None: + warnings.warn("Always expect to create a new plot in " + "NightPointingPlotter.") fig, (ax1, ax2) = plt.subplots(2, sharex=True) + mv = metric_value[0] u_filters = np.unique(mv["data_slice"]["filter"]) @@ -87,4 +94,4 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): transform=ax1.transAxes, ) - return fig.number + return fig diff --git a/rubin_sim/maf/plots/oned_plotters.py b/rubin_sim/maf/plots/oned_plotters.py index a3b341258..bc252e5c6 100644 --- a/rubin_sim/maf/plots/oned_plotters.py +++ b/rubin_sim/maf/plots/oned_plotters.py @@ -10,6 +10,7 @@ class OneDBinnedData(BasePlotter): + """Plot data from a OneDSlicer. """ def __init__(self): self.plot_type = "BinnedData" self.object_plotter = False @@ -33,7 +34,7 @@ def __init__(self): "grid": True, } - def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_values, slicer, user_plot_dict, fig=None): """ Plot a set of oneD binned metric data. """ @@ -46,7 +47,8 @@ def __call__(self, metric_values, slicer, user_plot_dict, fignum=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"]) # Plot the histogrammed data. leftedge = slicer.slice_points["bins"][:-1] width = np.diff(slicer.slice_points["bins"]) @@ -62,7 +64,7 @@ def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): color=plot_dict["color"], ) else: - good = np.where(metric_values.mask == False) + good = np.where(~metric_values.mask) x = np.ravel(list(zip(leftedge[good], leftedge[good] + width[good]))) y = np.ravel(list(zip(metric_values[good], metric_values[good]))) if plot_dict["log_scale"]: @@ -89,7 +91,8 @@ def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): plt.ylabel(plot_dict["ylabel"], fontsize=plot_dict["fontsize"]) if "xlabel" in plot_dict: plt.xlabel(plot_dict["xlabel"], fontsize=plot_dict["fontsize"]) - # Set y limits (either from values in args, percentile_clipping or compressed data values). + # Set y limits (either from values in args, + # percentile_clipping or compressed data values). if plot_dict["percentile_clip"] is not None: y_min, y_max = percentile_clipping( metric_values.compressed(), percentile=plot_dict["percentile_clip"] @@ -115,4 +118,4 @@ def __call__(self, metric_values, slicer, user_plot_dict, fignum=None): if plot_dict["x_max"] is not None: plt.xlim(right=plot_dict["x_max"]) plt.title(plot_dict["title"]) - return fig.number + return fig diff --git a/rubin_sim/maf/plots/plot_handler.py b/rubin_sim/maf/plots/plot_handler.py index 45cb94bbc..7c010eab4 100644 --- a/rubin_sim/maf/plots/plot_handler.py +++ b/rubin_sim/maf/plots/plot_handler.py @@ -26,7 +26,8 @@ class BasePlotter: def __init__(self): self.plot_type = None - # This should be included in every subsequent default_plot_dict (assumed to be present). + # This should be included in every subsequent default_plot_dict + # (assumed to be present). self.default_plot_dict = { "title": None, "xlabel": None, @@ -36,11 +37,50 @@ def __init__(self): "figsize": None, } - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): + """ + Parameters + ---------- + 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. + """ pass class PlotHandler: + """Create plots from a single or series of metric bundles. + + Parameters + ---------- + out_dir : `str`, optional + Directory to save output plots. + results_db : `rubin_sim.maf.ResultsDb`, optional + ResultsDb into which to record plot location and information. + savefig : `bool`, optional + Flag for saving images to disk (versus create and return + to caller). + fig_format : `str`, optional + Figure format to use to save full-size output. Default PDF. + dpi : `int`, optional + DPI to save output figures to disk at. (for matplotlib figures). + thumbnail : `bool`, optional + Flag for saving thumbnails (reduced size pngs) to disk. + trim_whitespace : `bool`, optional + Flag for trimming whitespace option to matplotlib output figures. + Default True, usually doesn't need to be changed. + """ def __init__( self, out_dir=".", @@ -110,9 +150,9 @@ def set_metric_bundles(self, m_bundles): def set_plot_dicts(self, plot_dicts=None, plot_func=None, reset=False): """ - Set or update (or 'reset') the plot_dict for the (possibly joint) plots. + Set or update the plot_dict for the (possibly joint) plots. - Resolution is: + Resolution is: (from lowest to higher) auto-generated items (colors/labels/titles) < anything previously set in the plot_handler < defaults set by the plotter @@ -120,7 +160,8 @@ def set_plot_dicts(self, plot_dicts=None, plot_func=None, reset=False): < explicitly set items in the plot_dicts list passed to this method. """ if reset: - # Have to explicitly set each dictionary to a (separate) blank dictionary. + # Have to explicitly set each dictionary to a (separate) + # blank dictionary. self.plot_dicts = [{} for b in self.m_bundles] if isinstance(plot_dicts, dict): @@ -142,9 +183,10 @@ def set_plot_dicts(self, plot_dicts=None, plot_func=None, reset=False): tmp_plot_dict["label"] = auto_label_list[i] tmp_plot_dict["color"] = auto_color_list[i] tmp_plot_dict["cbar_format"] = auto_cbar - # Then update that with anything previously set in the plot_handler. + # Update that with anything previously set in the plot_handler. tmp_plot_dict.update(self.plot_dicts[i]) - # Then override with plot_dict items set explicitly based on the plot type. + # Then override with plot_dict items set explicitly + # based on the plot type. if plot_func is not None: tmp_plot_dict["xlabel"] = auto_xlabel tmp_plot_dict["ylabel"] = auto_ylabel @@ -154,9 +196,10 @@ def set_plot_dicts(self, plot_dicts=None, plot_func=None, reset=False): for k, v in plotter_defaults.items(): if v is not None: tmp_plot_dict[k] = v - # Then add/override based on the bundle plot_dict parameters if they are set. + # Then add/override based on the bundle plot_dict parameters + # if they are set. tmp_plot_dict.update(bundle.plot_dict) - # Finally, override with anything set explicitly by the user right now. + # Finally, override with anything set explicitly by the user. if plot_dicts is not None: tmp_plot_dict.update(plot_dicts[i]) # And save this new dictionary back in the class. @@ -178,15 +221,18 @@ def _combine_metric_names(self): if len(self.metric_names) == 1: joint_name = " ".join(self.metric_names) else: - # Split each unique name into a list to see if we can merge the names. + # Split each unique name into a list to see + # if we can merge the names. name_lengths = [len(x.split()) for x in self.metric_names] name_lists = [x.split() for x in self.metric_names] - # If the metric names are all the same length, see if we can combine any parts. + # If the metric names are all the same length, see + # if we can combine any parts. if len(set(name_lengths)) == 1: joint_name = [] for i in range(name_lengths[0]): tmp = set([x[i] for x in name_lists]) - # Try to catch special case of filters and put them in order. + # Try to catch special case of filters and + # put them in order. if tmp.intersection(order) == tmp: filterlist = "" for f in order: @@ -197,7 +243,8 @@ def _combine_metric_names(self): # Otherwise, just join and put into joint_name. joint_name.append("".join(tmp)) joint_name = " ".join(joint_name) - # If the metric names are not the same length, just join everything. + # If the metric names are not the same length, + # just join everything. else: joint_name = " ".join(self.metric_names) self.joint_metric_names = joint_name @@ -225,10 +272,12 @@ def _combine_metadata(self): else: order = ["u", "g", "r", "i", "z", "y"] # See if there are any subcomponents we can combine, - # splitting on some values we expect to separate info_label clauses. + # splitting on some values we expect to separate + # info_label clauses. splitmetas = [] for m in self.info_label: - # Try to split info_label into separate phrases (filter / proposal / constraint..). + # Try to split info_label into separate phrases + # (filter / proposal / constraint..). if " and " in m: m = m.split(" and ") elif ", " in m: @@ -240,10 +289,12 @@ def _combine_metadata(self): # Strip white spaces from individual elements. m = set([im.strip() for im in m]) splitmetas.append(m) - # Look for common elements and separate from the general info_label. + # Look for common elements and + # separate from the general info_label. common = set.intersection(*splitmetas) diff = [x.difference(common) for x in splitmetas] - # Now look within the 'diff' elements and see if there are any common words to split off. + # Now look within the 'diff' elements and + # see if there are any common words to split off. diffsplit = [] for d in diff: if len(d) > 0: @@ -253,10 +304,12 @@ def _combine_metadata(self): diffsplit.append(m) diffcommon = set.intersection(*diffsplit) diffdiff = [x.difference(diffcommon) for x in diffsplit] - # If the length of any of the 'differences' is 0, then we should stop and not try to subdivide. + # If the length of any of the 'differences' is 0, + # then we should stop and not try to subdivide. lengths = [len(x) for x in diffdiff] if min(lengths) == 0: - # Sort them in order of length (so it goes 'g', 'g dithered', etc.) + # Sort them in order of length + # (so it goes 'g', 'g dithered', etc.) tmp = [] for d in diff: tmp.append(list(d)[0]) @@ -266,7 +319,8 @@ def _combine_metadata(self): diffdiff = [diff[i] for i in idx] diffcommon = [] else: - # diffdiff is the part where we might expect our filter values to appear; + # diffdiff is the part where we might expect + # our filter values to appear; # try to put this in order. diffdiff_ordered = [] diffdiff_end = [] @@ -304,7 +358,8 @@ def _build_title(self): """ Build a plot title from the metric names, runNames and info_label. """ - # Create a plot title from the unique parts of the metric/run_name/info_label. + # Create a plot title from the unique parts of + # the metric/run_name/info_label. plot_title = "" if len(self.run_names) == 1: plot_title += list(self.run_names)[0] @@ -313,7 +368,8 @@ def _build_title(self): if len(self.metric_names) == 1: plot_title += ": " + list(self.metric_names)[0] if plot_title == "": - # If there were more than one of everything above, use joint info_label and metricNames. + # If there were more than one of everything above, + # use joint info_label and metricNames. plot_title = self.joint_metadata + " " + self.joint_metric_names return plot_title @@ -323,7 +379,7 @@ def _build_x_ylabels(self, plot_func, len_max=25): Parameters ---------- - len_max : `int` (30) + len_max : `int`, optional If the xlabel starts longer than this, add the units as a newline. """ if plot_func.plot_type == "BinnedData": @@ -372,7 +428,8 @@ def _build_x_ylabels(self, plot_func, len_max=25): def _build_legend_labels(self): """ - Build a set of legend labels, using parts of the run_name/info_label/metricNames that change. + Build a set of legend labels, + using the parts of the run_name/info_label/metricNames that change. """ if len(self.m_bundles) == 1: return [None] @@ -449,11 +506,15 @@ def _build_cbar_format(self): def _build_file_root(self, outfile_suffix=None): """ Build a root filename for plot outputs. - If there is only one metricBundle, this is equal to the metricBundle fileRoot + outfile_suffix. - For multiple metricBundles, this is created from the runNames, info_label and metric names. - - If you do not wish to use the automatic filenames, then you could set 'savefig' to False and - save the file manually to disk, using the plot figure numbers returned by 'plot'. + If there is only one metricBundle, + this is equal to the metricBundle fileRoot + outfile_suffix. + For multiple metricBundles, + this is created from the runNames, info_label and metric names. + + If you do not wish to use the automatic filenames, + then you could set 'savefig' to False and + save the file manually to disk, + using the plot figure numbers returned by 'plot'. """ if len(self.m_bundles) == 1: outfile = self.m_bundles[0].file_root @@ -468,7 +529,8 @@ def _build_file_root(self, outfile_suffix=None): def _build_display_dict(self): """ Generate a display dictionary. - This is most useful for when there are many metricBundles being combined into a single plot. + This is most useful for when there are many metricBundles + being combined into a single plot. """ if len(self.m_bundles) == 1: return self.m_bundles[0].display_dict @@ -506,7 +568,8 @@ def _build_display_dict(self): def _check_plot_dicts(self): """ - Check to make sure there are no conflicts in the plot_dicts that are being used in the same subplot. + Check to make sure there are no conflicts in the plot_dicts + that are being used in the same subplot. """ # Check that the length is OK if len(self.plot_dicts) != len(self.m_bundles): @@ -518,8 +581,10 @@ def _check_plot_dicts(self): # These are the keys that need to match (or be None) keys2_check = ["xlim", "ylim", "color_min", "color_max", "title"] - # Identify how many subplots there are. If there are more than one, just don't change anything. - # This assumes that if there are more than one, the plot_dicts are actually all compatible. + # Identify how many subplots there are. + # If there are more than one, just don't change anything. + # This assumes that if there are more than one, + # the plot_dicts are actually all compatible. subplots = set() for pd in self.plot_dicts: if "subplot" in pd: @@ -531,7 +596,8 @@ def _check_plot_dicts(self): for key in keys2_check: values = [pd[key] for pd in self.plot_dicts if key in pd] if len(np.unique(values)) > 1: - # We will reset some of the keys to the default, but for some we should do better. + # We will reset some of the keys to the default, + # but for some we should do better. if key.endswith("Max"): for pd in self.plot_dicts: pd[key] = np.max(values) @@ -548,7 +614,8 @@ def _check_plot_dicts(self): + " Will reset to default value. (found values %s)" % values ) reset_keys.append(key) - # Reset the most of the keys to defaults; this can generally be done safely. + # Reset the most of the keys to defaults; + # this can generally be done safely. for key in reset_keys: for pd in self.plot_dicts: pd[key] = None @@ -562,13 +629,34 @@ def plot( outfile_suffix=None, ): """ - Create plot for mBundles, using plot_func. + Create a plot for the active metric bundles (self.set_metric_bundles). - plot_dicts: List of plot_dicts if one wants to use a _new_ plot_dict per MetricBundle. + Parameters + ---------- + plot_func : `rubin_sim.plots.BasePlotter` + The plotter to use to make the figure. + plot_dicts : `list` of [`dict`], optional + List of plot_dicts for each metric bundle. + Can use these to override individual metric bundle colors, etc. + display_dict : `dict`, optional + Information to save to resultsDb to accompany the figure on the + show_maf pages. Generally set automatically. Includes a caption. + outfile_root : `str`, optional + Output filename. Generally set automatically, but can be + overriden (such as when output filenames get too long). + outfile_suffix : `str`, optional + A suffix to add to the end of the default output filename. + Useful when creating a series of plots, such as for a movie. + + Returns + ------- + fig : `matplotlib.figure.Figure` + The plot. """ if not plot_func.object_plotter: - # Check that metric_values type and plotter are compatible (most are float/float, but - # some plotters expect object data .. and some only do sometimes). + # Check that metric_values type and plotter are compatible + # (most are float/float, but some plotters expect object data .. + # and some only do sometimes). for m_b in self.m_bundles: if m_b.metric.metric_dtype == "object": metric_is_color = m_b.plot_dict.get("metric_is_color", False) @@ -587,7 +675,7 @@ def plot( if len(self.m_bundles) > 1: plot_type = "Combo" + plot_type # Make plot. - fignum = None + fig = None for m_b, plot_dict in zip(self.m_bundles, self.plot_dicts): if m_b.metric_values is None: # Skip this metricBundle. @@ -598,8 +686,9 @@ def plot( msg = "MetricBundle (%s) has no unmasked metric_values, skipping plots." % (m_b.file_root) warnings.warn(msg) else: - fignum = plot_func(m_b.metric_values, m_b.slicer, plot_dict, fignum=fignum) - # Add a legend if more than one metricValue is being plotted or if legendloc is specified. + fig = plot_func(m_b.metric_values, m_b.slicer, plot_dict, fig=fig) + # Add a legend if more than one metricValue is being plotted + # or if legendloc is specified. legend_loc = None if "legend_loc" in self.plot_dicts[0]: legend_loc = self.plot_dicts[0]["legend_loc"] @@ -609,7 +698,8 @@ def plot( except KeyError: legend_loc = "upper right" if legend_loc is not None: - plt.figure(fignum) + # Activate expected figure and write legend. + plt.figure(fig) plt.legend(loc=legend_loc, fancybox=True, fontsize="smaller") # Add the super title if provided. if "suptitle" in self.plot_dicts[0]: @@ -619,7 +709,7 @@ def plot( if display_dict is None: display_dict = self._build_display_dict() self.save_fig( - fignum, + fig, outfile, plot_type, self.joint_metric_names, @@ -629,11 +719,11 @@ def plot( self.joint_metadata, display_dict, ) - return fignum + return fig def save_fig( self, - fignum, + fig, outfile_root, plot_type, metric_name, @@ -643,7 +733,6 @@ def save_fig( info_label, display_dict=None, ): - fig = plt.figure(fignum) plot_file = outfile_root + "_" + plot_type + "." + self.fig_format if self.trim_whitespace: fig.savefig( diff --git a/rubin_sim/maf/plots/spatial_plotters.py b/rubin_sim/maf/plots/spatial_plotters.py index 504b47473..12ddcae87 100644 --- a/rubin_sim/maf/plots/spatial_plotters.py +++ b/rubin_sim/maf/plots/spatial_plotters.py @@ -68,7 +68,7 @@ def set_color_lims(metric_value, plot_dict, key_min="color_min", key_max="color_ # Use plotdict values if available color_min = plot_dict[key_min] color_max = plot_dict[key_max] - # If either is not set and we have data .. + # If either is not set and we have data: if color_min is None or color_max is None: if np.size(metric_value.compressed()) > 0: # is percentile clipping set? @@ -102,7 +102,7 @@ def set_color_map(plot_dict): cmap = plot_dict["cmap"] if cmap is None: cmap = "perceptual_rainbow" - if type(cmap) == str: + if isinstance(cmap, 'str'): cmap = getattr(cm, cmap) # Set background and masked pixel colors default healpy white and gray. cmap = copy.copy(cmap) @@ -141,23 +141,24 @@ def __init__(self): self.ax = None self.im = None - def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None): """ Parameters ---------- metric_value : `numpy.ma.MaskedArray` - slicer : `rubin_sim.maf.slicers.HealpixSlicer` + 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). - fignum : `int` - Matplotlib figure number to use - (default = None, starts new figure). + fig : `matplotlib.figure.Figure` + Matplotlib figure number to use. Default = None, starts new figure. Returns ------- - fignum : `int` - Matplotlib figure number used to create the plot. + fig : `matplotlib.figure.Figure` + Figure with the plot. """ # Override the default plotting parameters with user specified values. plot_dict = {} @@ -194,7 +195,8 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): badval = slicer.badval # Generate a full-sky plot. - fig = plt.figure(fignum, figsize=plot_dict["figsize"]) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) # Set up color bar limits. clims = set_color_lims(metric_value, plot_dict) cmap = set_color_map(plot_dict) @@ -306,7 +308,7 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): # If outputing to PDF, this fixes the colorbar white stripes if plot_dict["cbar_edge"]: cb.solids.set_edgecolor("face") - return fig.number + return fig class HealpixPowerSpectrum(BasePlotter): @@ -317,7 +319,7 @@ def __init__(self): self.default_plot_dict.update(base_default_plot_dict) self.default_plot_dict.update({"maxl": None, "removeDipole": True, "linestyle": "-"}) - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): """Generate and plot the power spectrum of metric_values (for metrics calculated on a healpix grid). """ @@ -327,7 +329,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): 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"]) if plot_dict["subplot"] != "111": fig.add_subplot(plot_dict["subplot"]) # If the mask is True everywhere (no data), just plot zeros @@ -366,7 +369,7 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): plt.title(plot_dict["title"]) # Return figure number # (so we can reuse/add onto/save this figure if desired). - return fig.number + return fig class HealpixHistogram(BasePlotter): @@ -387,7 +390,7 @@ def __init__(self): ) self.base_hist = BaseHistogram() - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): """Histogram metric_value for all healpix points.""" if "Healpix" not in slicer.slicer_name: raise ValueError("HealpixHistogram is for use with healpix slicer.") @@ -396,7 +399,7 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): plot_dict.update(user_plot_dict) if plot_dict["scale"] is None: plot_dict["scale"] = hp.nside2pixarea(slicer.nside, degrees=True) / 1000.0 - fignum = self.base_hist(metric_value, slicer, plot_dict, fignum=fignum) + fignum = self.base_hist(metric_value, slicer, plot_dict, fig=fig) return fignum @@ -418,11 +421,13 @@ def __init__(self): } ) - def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None): """ - Plot a histogram of metric_values (such as would come from a spatial slicer). + Plot a histogram of metric_values + (such as would come from a spatial slicer). """ - # Adjust metric values by zeropoint or norm_val, and use 'compressed' version of masked array. + # Adjust metric values by zeropoint or norm_val, + # and use 'compressed' version of masked array. plot_dict = {} plot_dict.update(self.default_plot_dict) plot_dict.update(user_plot_dict) @@ -431,20 +436,25 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): metric_value = metric_value[np.isfinite(metric_value)] x_min, x_max = set_color_lims(metric_value, plot_dict, key_min="x_min", key_max="x_max") metric_value = metric_value.compressed() - # Set up the bins for the histogram. User specified 'bins' overrides 'bin_size'. - # Note that 'bins' could be a single number or an array, simply passed to plt.histogram. + # Set up the bins for the histogram. + # User specified 'bins' overrides 'bin_size'. + # Note that 'bins' could be a single number or an array, + # simply passed to plt.histogram. if plot_dict["bins"] is not None: bins = plot_dict["bins"] elif plot_dict["bin_size"] is not None: - # If generating a cumulative histogram, want to use full range of data (but with given bin_size). - # .. but if user set histRange to be wider than full range of data, then - # extend bins to cover this range, so we can make prettier plots. + # If generating a cumulative histogram, + # want to use full range of data (but with given bin_size). + # but if user set histRange to be wider than full range of data, + # then extend bins to cover this range, + # so we can make prettier plots. if plot_dict["cumulative"]: # Potentially, expand the range for the cumulative histogram. bmin = np.min([metric_value.min(), x_min]) bmax = np.max([metric_value.max(), x_max]) bins = np.arange(bmin, bmax + plot_dict["bin_size"] / 2.0, plot_dict["bin_size"]) - # Otherwise, not cumulative so just use metric values, without potential expansion. + # Otherwise, not cumulative so just use metric values, + # without potential expansion. else: bins = np.arange( x_min, @@ -459,10 +469,12 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): plot_dict["bin_size"], ) else: - # If user did not specify bins or bin_size, then we try to figure out a good number of bins. + # If user did not specify bins or bin_size, + # then we try to figure out a good number of bins. bins = optimal_bins(metric_value) # Generate plots. - fig = plt.figure(fignum, figsize=plot_dict["figsize"]) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) if ( plot_dict["subplot"] != 111 and plot_dict["subplot"] != (1, 1, 1) @@ -471,14 +483,16 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): ax = fig.add_subplot(plot_dict["subplot"]) else: ax = plt.gca() - # Check if any data falls within histRange, because otherwise histogram generation will fail. + # Check if any data falls within histRange, + # because otherwise histogram generation will fail. if isinstance(bins, np.ndarray): condition = (metric_value >= bins.min()) & (metric_value <= bins.max()) else: condition = (metric_value >= x_min) & (metric_value <= x_max) plot_value = metric_value[condition] if len(plot_value) == 0: - # No data is within histRange/bins. So let's just make a simple histogram anyway. + # No data is within histRange/bins. + # So let's just make a simple histogram anyway. n, b, p = plt.hist( metric_value, bins=50, @@ -489,7 +503,8 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): color=plot_dict["color"], ) else: - # There is data to plot, and we've already ensured x_min/x_max/bins are more than single value. + # There is data to plot, and we've already ensured + # x_min/x_max/bins are more than single value. n, b, p = plt.hist( metric_value, bins=bins, @@ -530,8 +545,8 @@ def mjr_formatter(y, pos): if plot_dict["labelsize"] is not None: plt.tick_params(axis="x", labelsize=plot_dict["labelsize"]) plt.tick_params(axis="y", labelsize=plot_dict["labelsize"]) - # Return figure number - return fig.number + # Return figure + return fig class BaseSkyMap(BasePlotter): @@ -554,7 +569,7 @@ def __init__(self): } ) - def _plot_tissot_ellipse(self, lon, lat, radius, ax=None, **kwargs): + def _plot_tissot_ellipse(self, lon, lat, radius, **kwargs): """Plot Tissot Ellipse/Tissot Indicatrix Parameters @@ -565,7 +580,6 @@ def _plot_tissot_ellipse(self, lon, lat, radius, ax=None, **kwargs): latitude-like of ellipse centers (radians) radius : float or array_like radius of ellipses (radians) - ax : Axes object (optional) matplotlib axes instance on which to draw ellipses. Other Parameters @@ -578,10 +592,8 @@ def _plot_tissot_ellipse(self, lon, lat, radius, ax=None, **kwargs): # Code adapted from astroML, which is BSD-licensed. # See http: //github.com/astroML/astroML for details. ellipses = [] - if ax is None: - ax = plt.gca() - for l, b, diam in np.broadcast(lon, lat, radius * 2.0): - el = Ellipse((l, b), diam / np.cos(b), diam, **kwargs) + for lon, b, diam in np.broadcast(lon, lat, radius * 2.0): + el = Ellipse((lon, b), diam / np.cos(b), diam, **kwargs) ellipses.append(el) return ellipses @@ -628,7 +640,7 @@ def _plot_mw_zone( lon = -(ra - ra_cen - np.pi) % (np.pi * 2) - np.pi ax.plot(lon, dec, "b.", markersize=1.8, alpha=0.4) - def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None): """ Plot the sky map of metric_value for a generic spatial slicer. """ @@ -641,7 +653,8 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): plot_dict.update(user_plot_dict) metric_value = apply_zp_norm(metric_value_in, plot_dict) - fig = plt.figure(fignum, figsize=plot_dict["figsize"]) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) # other projections available include # ['aitoff', 'hammer', 'lambert', 'mollweide', 'polar', 'rectilinear'] ax = fig.add_subplot(plot_dict["subplot"], projection=plot_dict["projection"]) @@ -650,7 +663,8 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): # Plot all data points. good = np.ones(len(metric_value), dtype="bool") else: - # Only plot points which are not masked. Flip numpy ma mask where 'False' == 'good'. + # Only plot points which are not masked. + # Flip numpy ma mask where 'False' == 'good'. good = ~metric_value.mask # Add ellipses at RA/Dec locations - but don't add colors yet. @@ -677,7 +691,8 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): if current: ax.add_patch(current) else: - # Determine color min/max values. metricValue.compressed = non-masked points. + # Determine color min/max values. + # metricValue.compressed = non-masked points. clims = set_color_lims(metric_value, plot_dict) # Determine whether or not to use auto-log scale. if plot_dict["log_scale"] == "auto": @@ -689,7 +704,8 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): else: plot_dict["log_scale"] = False if plot_dict["log_scale"]: - # Move min/max values to things that can be marked on the colorbar. + # Move min/max values to things that can be marked + # on the colorbar. # clims[0] = 10 ** (int(np.log10(clims[0]))) # clims[1] = 10 ** (int(np.log10(clims[1]))) norml = colors.LogNorm() @@ -725,7 +741,7 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): format=plot_dict["cbar_format"], ) else: - # Most of the time we just want a standard horizontal colorbar + # Usually we just want a standard horizontal colorbar cb = plt.colorbar( p, shrink=0.75, @@ -763,7 +779,7 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): transform=ax.transAxes, fontsize=plot_dict["fontsize"], ) - return fig.number + return fig class HealpixSDSSSkyMap(BasePlotter): @@ -783,11 +799,13 @@ def __init__(self): } ) - def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None): """ - Plot the sky map of metric_value using healpy cartview plots in thin strips. + Plot the sky map of metric_value using healpy cartview plots + in thin strips. raMin: Minimum RA to plot (deg) - raMax: Max RA to plot (deg). Note raMin/raMax define the centers that will be plotted. + raMax: Max RA to plot (deg). + Note raMin/raMax define the centers that will be plotted. raLen: Length of the plotted strips in degrees decMin: minimum dec value to plot decMax: max dec value to plot @@ -804,8 +822,10 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): cmap = set_color_map(plot_dict) racenters = np.arange(plot_dict["raMin"], plot_dict["raMax"], plot_dict["raLen"]) nframes = racenters.size - fig = plt.figure(fignum) - # Do not specify or use plot_dict['subplot'] because this is done in each call to hp.cartview. + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) + # Do not specify or use plot_dict['subplot'] + # because this is done in each call to hp.cartview. for i, racenter in enumerate(racenters): if i == 0: use_title = ( @@ -834,8 +854,8 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): fig=fig, ) hp.graticule(dpar=20, dmer=20, verbose=False) - # Add colorbar (not using healpy default colorbar because want more tickmarks). - ax = fig.add_axes([0.1, 0.15, 0.8, 0.075]) # left, bottom, width, height + # Add colorbar (not using healpy default colorbar) + ax = fig.add_axes([0.1, 0.15, 0.8, 0.075]) # Add label. if plot_dict["label"] is not None: plt.figtext(0.8, 0.9, "%s" % plot_dict["label"]) @@ -865,8 +885,7 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): # If outputing to PDF, this fixes the colorbar white stripes if plot_dict["cbar_edge"]: cb.solids.set_edgecolor("face") - fig = plt.gcf() - return fig.number + return fig def project_lambert(longitude, latitude): @@ -935,7 +954,7 @@ def __init__(self): } ) - def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None): if "ra" not in slicer.slice_points or "dec" not in slicer.slice_points: err_message = 'SpatialSlicer must contain "ra" and "dec" in slice_points metadata.' err_message += " SlicePoints only contains keys %s." % (slicer.slice_points.keys()) @@ -954,16 +973,19 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): step = (clims[1] - clims[0]) / plot_dict["levels"] levels = np.arange(clims[0], clims[1] + step, step) - fig = plt.figure(fignum, figsize=plot_dict["figsize"]) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) ax = fig.add_subplot(plot_dict["subplot"]) x, y = project_lambert(slicer.slice_points["ra"], slicer.slice_points["dec"]) - # Contour the plot first to remove any anti-aliasing artifacts. Doesn't seem to work though. See: - # http: //stackoverflow.com/questions/15822159/aliasing-when-saving-matplotlib\ - # -filled-contour-plot-to-pdf-or-eps + # Contour the plot first to remove any anti-aliasing artifacts. + # Doesn't seem to work though. See: + # http://stackoverflow.com/questions/15822159/ + # aliasing-when-saving-matplotlib-filled-contour-plot-to-pdf-or-eps # tmpContour = m.contour(np.degrees(slicer.slice_points['ra']), # np.degrees(slicer.slice_points['dec']), - # metric_value.filled(np.min(clims)-1), levels, tri=True, + # metric_value.filled(np.min(clims)-1), levels, + # tri=True, # cmap=plot_dict['cmap'], ax=ax, latlon=True, # lw=1) @@ -1005,4 +1027,4 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): # If outputing to PDF, this fixes the colorbar white stripes if plot_dict["cbar_edge"]: cb.solids.set_edgecolor("face") - return fig.number + return fig diff --git a/rubin_sim/maf/plots/special_plotters.py b/rubin_sim/maf/plots/special_plotters.py index 18e11d6d9..c9e579cfe 100644 --- a/rubin_sim/maf/plots/special_plotters.py +++ b/rubin_sim/maf/plots/special_plotters.py @@ -31,32 +31,31 @@ def __init__(self): "y_max": None, "linewidth": 2, "reflinewidth": 2, + "figsize": None, } - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): """ Parameters ---------- metric_value : `numpy.ma.MaskedArray` - The metric values calculated with `rubin_sim.maf.Count` and - a healpix slicer. - slicer : `rubin_sim.maf.slicers.HealpixSlicer` + 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 to override defaults. - Note that asky and n_visits values set here and in the slicer - should be consistent, for plot labels and summary statistic - values to be consistent. - fignum : `int` - Matplotlib figure number to use. Default starts new figure. + 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 ------- - fignum : `int` - Matplotlib figure number used to create the plot. + fig : `matplotlib.figure.Figure` + Figure with the plot. """ if not hasattr(slicer, "nside"): raise ValueError("FOPlot to be used with healpix or healpix derived slicers.") - fig = plt.figure(fignum) + plot_dict = {} plot_dict.update(self.default_plot_dict) plot_dict.update(user_plot_dict) @@ -64,6 +63,9 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): if plot_dict["scale"] is None: plot_dict["scale"] = hp.nside2pixarea(slicer.nside, degrees=True) / 1000.0 + if fig is None: + fig = plt.Figure(figsize=plot_dict["figsize"]) + # Expect metric_value to be something like number of visits cumulative_area = np.arange(1, metric_value.compressed().size + 1)[::-1] * plot_dict["scale"] plt.plot( @@ -117,7 +119,7 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): plt.xlim([x_min, x_max]) if (y_min is not None) or (y_max is not None): plt.ylim([y_min, y_max]) - return fig.number + return fig class SummaryHistogram(BasePlotter): @@ -154,7 +156,7 @@ def __init__(self): "figsize": None, } - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): """ Parameters ---------- @@ -171,13 +173,13 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): results as a step histogram (True) or as a series of values (False) 'bins' (np.ndarray) sets the x values for the resulting plot and should generally match the bins used with the metric. - fignum : `int` - Matplotlib figure number to use. Default starts a new figure. + fig : `matplotlib.figure.Figure` + Matplotlib figure to use. Default starts a new figure. Returns ------- - fignum: `int` - Matplotlib figure number used to create the plot. + fig: `matplotlib.figure.Figure` + Matplotlib figure used to create the plot. """ plot_dict = {} plot_dict.update(self.default_plot_dict) @@ -203,7 +205,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): if plot_dict["cumulative"]: final_hist = final_hist.cumsum() - fig = plt.figure(fignum, figsize=plot_dict["figsize"]) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) bins = plot_dict["bins"] if plot_dict["histStyle"]: leftedge = bins[:-1] @@ -248,4 +251,4 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): if plot_dict["xscale"] is not None: plt.xscale(plot_dict["xscale"]) - return fig.number + return fig diff --git a/rubin_sim/maf/plots/two_d_plotters.py b/rubin_sim/maf/plots/two_d_plotters.py index 96473afd1..b65124d25 100644 --- a/rubin_sim/maf/plots/two_d_plotters.py +++ b/rubin_sim/maf/plots/two_d_plotters.py @@ -33,24 +33,27 @@ def __init__(self): "aspect": "auto", "xextent": None, "origin": None, + "figsize": None, } - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): """ Parameters ---------- - metric_value : numpy.ma.MaskedArray - slicer : rubin_sim.maf.slicers.BaseSpatialSlicer - (any spatial slicer) - user_plot_dict: dict - Dictionary of plot parameters set by user (overrides default values). - fignum : int - Matplotlib figure number to use (default = None, starts new figure). + 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 ------- - int - Matplotlib figure number used to create the plot. + fig : `matplotlib.figure.Figure + Figure with the plot. """ if "Healpix" in slicer.slicer_name: self.default_plot_dict["ylabel"] = "Healpix ID" @@ -77,8 +80,9 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): low_vals = np.where(metric_value.data < plot_dict["color_min"]) metric_value.mask[low_vals] = True - figure = plt.figure(fignum) - ax = figure.add_subplot(111) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) + ax = fig.add_subplot(111) yextent = [0, slicer.nslice - 1] xextent = plot_dict["xextent"] extent = [] @@ -105,12 +109,31 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): # Fix white space on pdf's if plot_dict["cbar_edge"]: cb.solids.set_edgecolor("face") - return figure.number + return fig class VisitPairsHist(BasePlotter): """ - Given an opsim2dSlicer, figure out what fraction of observations are in singles, pairs, triples, etc. + Given an TwoDSlicer, figure out what fraction of observations + are in singles, pairs, triples, etc. + + + Parameters + ---------- + 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. """ def __init__(self): @@ -124,24 +147,10 @@ def __init__(self): "color": "b", "xlim": [0, 20], "ylim": None, + "figsize": None, } - def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): - """ - Parameters - ---------- - metric_value : numpy.ma.MaskedArray - slicer : rubin_sim.maf.slicers.TwoDSlicer - user_plot_dict: dict - Dictionary of plot parameters set by user (overrides default values). - fignum : int - Matplotlib figure number to use (default = None, starts new figure). - - Returns - ------- - int - Matplotlib figure number used to create the plot. - """ + def __call__(self, metric_value, slicer, user_plot_dict, fig=None): plot_dict = {} plot_dict.update(self.default_plot_dict) # Don't clobber with None @@ -155,8 +164,9 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): vals, bins = np.histogram(metric_value, bins) xvals = (bins[:-1] + bins[1:]) / 2.0 - figure = plt.figure(fignum) - ax = figure.add_subplot(111) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) + ax = fig.add_subplot(111) ax.bar(xvals, vals * xvals, color=plot_dict["color"], label=plot_dict["label"]) ax.set_xlabel(plot_dict["xlabel"]) ax.set_ylabel(plot_dict["ylabel"]) @@ -164,4 +174,4 @@ def __call__(self, metric_value, slicer, user_plot_dict, fignum=None): ax.set_xlim(plot_dict["xlim"]) ax.set_ylim(plot_dict["ylim"]) - return figure.number + return fig diff --git a/rubin_sim/maf/plots/xyplotter.py b/rubin_sim/maf/plots/xyplotter.py index f5cbdd987..513c5f83f 100644 --- a/rubin_sim/maf/plots/xyplotter.py +++ b/rubin_sim/maf/plots/xyplotter.py @@ -12,15 +12,20 @@ class XyPlotter(BasePlotter): def __init__(self): self.object_plotter = True self.plot_type = "simple" - self.default_plot_dict = {"title": None, "xlabel": "", "ylabel": ""} + self.default_plot_dict = {"title": None, + "xlabel": "", + "ylabel": "", + "figsize": None, + } - def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): + def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None): plot_dict = {} plot_dict.update(self.default_plot_dict) plot_dict.update(user_plot_dict) plot_dict.update(metric_value_in[0]["plot_dict"]) - fig = plt.figure(fignum) + if fig is None: + fig = plt.figure(figsize=plot_dict["figsize"]) ax = fig.add_subplot(111) x = metric_value_in[0]["x"] y = metric_value_in[0]["y"] @@ -28,4 +33,4 @@ def __call__(self, metric_value_in, slicer, user_plot_dict, fignum=None): ax.set_title(plot_dict["title"]) ax.set_xlabel(plot_dict["xlabel"]) ax.set_ylabel(plot_dict["ylabel"]) - return fig.number + return fig From 95b50e1588a6de724f1364aa6f0d81f565a03bd9 Mon Sep 17 00:00:00 2001 From: Lynne Jones Date: Tue, 26 Mar 2024 16:06:27 -0700 Subject: [PATCH 3/6] update pyproject.toml for new ruff --- pyproject.toml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9ff2789c9..0d87849fa 100644 --- a/pyproject.toml +++ b/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" From ea0b6af5bef377824c054677e0e5974ddbeb3962 Mon Sep 17 00:00:00 2001 From: Lynne Jones Date: Thu, 28 Mar 2024 12:21:56 -0700 Subject: [PATCH 4/6] Black --- rubin_sim/maf/plots/hourglass_plotters.py | 3 +-- rubin_sim/maf/plots/night_pointing_plotter.py | 3 +-- rubin_sim/maf/plots/oned_plotters.py | 3 ++- rubin_sim/maf/plots/plot_handler.py | 1 + rubin_sim/maf/plots/spatial_plotters.py | 2 +- rubin_sim/maf/plots/xyplotter.py | 11 ++++++----- 6 files changed, 12 insertions(+), 11 deletions(-) diff --git a/rubin_sim/maf/plots/hourglass_plotters.py b/rubin_sim/maf/plots/hourglass_plotters.py index 048ab4efe..81b4bdbd7 100644 --- a/rubin_sim/maf/plots/hourglass_plotters.py +++ b/rubin_sim/maf/plots/hourglass_plotters.py @@ -38,8 +38,7 @@ def __call__(self, metric_value, slicer, user_plot_dict, fig=None): if fig is None: fig = plt.figure(figsize=plot_dict["figsize"]) - ax = fig.add_subplot(111 - ) + ax = fig.add_subplot(111) pernight = metric_value[0]["pernight"] perfilter = metric_value[0]["perfilter"] diff --git a/rubin_sim/maf/plots/night_pointing_plotter.py b/rubin_sim/maf/plots/night_pointing_plotter.py index 956e7e4bb..cac33728b 100644 --- a/rubin_sim/maf/plots/night_pointing_plotter.py +++ b/rubin_sim/maf/plots/night_pointing_plotter.py @@ -33,8 +33,7 @@ def __init__(self, mjd_col="observationStartMJD", alt_col="alt", az_col="az"): def __call__(self, metric_value, slicer, user_plot_dict, fig=None): if fig is not None: - warnings.warn("Always expect to create a new plot in " - "NightPointingPlotter.") + warnings.warn("Always expect to create a new plot in " "NightPointingPlotter.") fig, (ax1, ax2) = plt.subplots(2, sharex=True) mv = metric_value[0] diff --git a/rubin_sim/maf/plots/oned_plotters.py b/rubin_sim/maf/plots/oned_plotters.py index bc252e5c6..ff7cf0044 100644 --- a/rubin_sim/maf/plots/oned_plotters.py +++ b/rubin_sim/maf/plots/oned_plotters.py @@ -10,7 +10,8 @@ class OneDBinnedData(BasePlotter): - """Plot data from a OneDSlicer. """ + """Plot data from a OneDSlicer.""" + def __init__(self): self.plot_type = "BinnedData" self.object_plotter = False diff --git a/rubin_sim/maf/plots/plot_handler.py b/rubin_sim/maf/plots/plot_handler.py index 7c010eab4..927c0e798 100644 --- a/rubin_sim/maf/plots/plot_handler.py +++ b/rubin_sim/maf/plots/plot_handler.py @@ -81,6 +81,7 @@ class PlotHandler: Flag for trimming whitespace option to matplotlib output figures. Default True, usually doesn't need to be changed. """ + def __init__( self, out_dir=".", diff --git a/rubin_sim/maf/plots/spatial_plotters.py b/rubin_sim/maf/plots/spatial_plotters.py index 12ddcae87..6accf79f1 100644 --- a/rubin_sim/maf/plots/spatial_plotters.py +++ b/rubin_sim/maf/plots/spatial_plotters.py @@ -102,7 +102,7 @@ def set_color_map(plot_dict): cmap = plot_dict["cmap"] if cmap is None: cmap = "perceptual_rainbow" - if isinstance(cmap, 'str'): + if isinstance(cmap, "str"): cmap = getattr(cm, cmap) # Set background and masked pixel colors default healpy white and gray. cmap = copy.copy(cmap) diff --git a/rubin_sim/maf/plots/xyplotter.py b/rubin_sim/maf/plots/xyplotter.py index 513c5f83f..8837a9a69 100644 --- a/rubin_sim/maf/plots/xyplotter.py +++ b/rubin_sim/maf/plots/xyplotter.py @@ -12,11 +12,12 @@ class XyPlotter(BasePlotter): def __init__(self): self.object_plotter = True self.plot_type = "simple" - self.default_plot_dict = {"title": None, - "xlabel": "", - "ylabel": "", - "figsize": None, - } + self.default_plot_dict = { + "title": None, + "xlabel": "", + "ylabel": "", + "figsize": None, + } def __call__(self, metric_value_in, slicer, user_plot_dict, fig=None): plot_dict = {} From 5fd25f759216f4a753669653243860321fa1cc25 Mon Sep 17 00:00:00 2001 From: Lynne Jones Date: Fri, 29 Mar 2024 01:38:50 -0700 Subject: [PATCH 5/6] Update save to only attempt if fig not None --- rubin_sim/maf/plots/plot_handler.py | 5 ++++- rubin_sim/maf/plots/spatial_plotters.py | 2 +- rubin_sim/moving_objects/ooephemerides.py | 2 +- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/rubin_sim/maf/plots/plot_handler.py b/rubin_sim/maf/plots/plot_handler.py index 927c0e798..0fb605bf0 100644 --- a/rubin_sim/maf/plots/plot_handler.py +++ b/rubin_sim/maf/plots/plot_handler.py @@ -735,6 +735,9 @@ def save_fig( display_dict=None, ): plot_file = outfile_root + "_" + plot_type + "." + self.fig_format + if fig is None: + warnings.warn(f"Trying to save figure to {plot_file} but" "figure is None. Skipping.") + return if self.trim_whitespace: fig.savefig( os.path.join(self.out_dir, plot_file), @@ -751,7 +754,7 @@ def save_fig( # Generate a png thumbnail. if self.thumbnail: thumb_file = "thumb." + outfile_root + "_" + plot_type + ".png" - plt.savefig(os.path.join(self.out_dir, thumb_file), dpi=72, bbox_inches="tight") + fig.savefig(os.path.join(self.out_dir, thumb_file), dpi=72, bbox_inches="tight") # Save information about the file to results_db. if self.results_db: if display_dict is None: diff --git a/rubin_sim/maf/plots/spatial_plotters.py b/rubin_sim/maf/plots/spatial_plotters.py index 6accf79f1..a996b0faf 100644 --- a/rubin_sim/maf/plots/spatial_plotters.py +++ b/rubin_sim/maf/plots/spatial_plotters.py @@ -102,7 +102,7 @@ def set_color_map(plot_dict): cmap = plot_dict["cmap"] if cmap is None: cmap = "perceptual_rainbow" - if isinstance(cmap, "str"): + if isinstance(cmap, str): cmap = getattr(cm, cmap) # Set background and masked pixel colors default healpy white and gray. cmap = copy.copy(cmap) diff --git a/rubin_sim/moving_objects/ooephemerides.py b/rubin_sim/moving_objects/ooephemerides.py index 177471ec3..9c61de20b 100644 --- a/rubin_sim/moving_objects/ooephemerides.py +++ b/rubin_sim/moving_objects/ooephemerides.py @@ -51,7 +51,7 @@ class PyOrbEphemerides: Typical usage: >>> pyephs = PyOrbEphemerides() - >>> pyephs.setOrbits(orbits) + >>> pyephs.set_orbits(orbits) >>> ephs = pyephs.generateEphemerides(times, timeScale, obscode) """ From a12cb6ebf25f3b9c007bfc0bb233dbd70fd542aa Mon Sep 17 00:00:00 2001 From: Lynne Jones Date: Fri, 29 Mar 2024 02:22:19 -0700 Subject: [PATCH 6/6] rename another fignum --- rubin_sim/maf/plots/spatial_plotters.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rubin_sim/maf/plots/spatial_plotters.py b/rubin_sim/maf/plots/spatial_plotters.py index a996b0faf..f71fc67ff 100644 --- a/rubin_sim/maf/plots/spatial_plotters.py +++ b/rubin_sim/maf/plots/spatial_plotters.py @@ -399,8 +399,8 @@ def __call__(self, metric_value, slicer, user_plot_dict, fig=None): plot_dict.update(user_plot_dict) if plot_dict["scale"] is None: plot_dict["scale"] = hp.nside2pixarea(slicer.nside, degrees=True) / 1000.0 - fignum = self.base_hist(metric_value, slicer, plot_dict, fig=fig) - return fignum + fig = self.base_hist(metric_value, slicer, plot_dict, fig=fig) + return fig class BaseHistogram(BasePlotter):