__all__ = ("MetricBundle", "create_empty_metric_bundle")
import os
import warnings
from copy import deepcopy
import numpy as np
import numpy.ma as ma
import rubin_sim.maf.maps as maps
import rubin_sim.maf.metrics as metrics
import rubin_sim.maf.plots as plots
import rubin_sim.maf.slicers as slicers
import rubin_sim.maf.stackers as stackers
import rubin_sim.maf.utils as utils
from rubin_sim.maf.stackers import ColInfo
[docs]
def create_empty_metric_bundle():
"""Create an empty metric bundle.
Returns
-------
MetricBundle : `MetricBundle`
An empty metric bundle,
configured with just the :class:`BaseMetric` and :class:`BaseSlicer`.
"""
return MetricBundle(metrics.BaseMetric(), slicers.BaseSlicer(), "")
[docs]
class MetricBundle:
"""Define a metric bundle combination of metric, slicer, and constraint.
Parameters
----------
metric : `~rubin_sim.maf.metric`
The Metric class to run per slice_point
slicer : `~rubin_sim.maf.slicer`
The Slicer to apply to the incoming visit data (the observations).
constraint : `str` or None, opt
A (sql-style) constraint to apply to the visit data, to apply a
broad sub-selection.
stacker_list : `list` [`~rubin_sim.maf.stacker`], opt
A list of pre-configured stackers to use to generate additional
columns per visit.
These will be generated automatically if needed, but pre-configured
versions will override these.
run_name : `str`, opt
The name of the simulation being run.
This will be added to output files and plots.
Setting it prevents file conflicts when running the same
metric on multiple simulations, and
provides a way to identify which simulation is being analyzed.
metadata : `str`, opt
A deprecated version of info_label (below).
Values set by metadata will be used for info_label.
If both are set, info_label is used.
info_label : `str` or None, opt
Information to add to the output metric data file name and plot labels.
If this is not provided, it will be auto-generated from the
constraint (if any).
Setting this provides an easy way to specify different
configurations of a metric, a slicer,
or just to rewrite your constraint into friendlier terms.
(i.e. a constraint like 'note not like "%DD%"' can become
"non-DD" in the file name and plot labels
by specifying info_label).
plot_dict : `dict` of plotting parameters, opt
Specify general plotting parameters, such as x/y/color limits.
display_dict : `dict` of display parameters, opt
Specify parameters for show_maf web pages, such as the
side bar labels and figure captions.
Keys: 'group', 'subgroup', 'caption', and 'order'
(such as to set metrics in filter order, etc)
summary_metrics : `list` of `~rubin_sim.maf.metrics`
A list of summary metrics to run to summarize the
primary metric, such as MedianMetric, etc.
maps_list : `list` of `~rubin_sim.maf.maps`
A list of pre-configured maps to use for the metric.
This will be auto-generated if specified
by the metric class, but pre-configured versions will override these.
Notes
-----
Define the "thing" you are measuring, with a combination of
* metric (calculated per data_slice)
* slicer (how to create the data_slices)
* constraint (an optional definition of a large subset of data)
Together these define a unique combination of an opsim benchmark,
or "metric bundle".
An example would be:
a CountMetric, a HealpixSlicer, and a constraint of 'filter="r"'.
After the metric is evaluated at each slice_point created by the
slicer, the resulting metric values are saved in the MetricBundle.
The MetricBundle also saves the summary metrics to be used
to generate summary statistics over those metric values,
as well as the resulting summary statistic values.
Plotting parameters and display parameters (for show_maf) are saved
in the MetricBundle, as well as additional info_label such as the
opsim run name, and relevant stackers and maps
to apply when calculating the metric values.
"""
col_info = ColInfo()
def __init__(
self,
metric,
slicer,
constraint=None,
stacker_list=None,
run_name="run name",
metadata=None,
info_label=None,
plot_dict=None,
display_dict=None,
summary_metrics=None,
maps_list=None,
file_root=None,
plot_funcs=None,
):
# Set the metric.
if not isinstance(metric, metrics.BaseMetric):
raise ValueError("metric must be an rubin_sim.maf.metrics object")
self.metric = metric
# Set the slicer.
if not isinstance(slicer, slicers.BaseSlicer):
raise ValueError("slicer must be an rubin_sim.maf.slicers object")
self.slicer = slicer
# Set the constraint.
self.constraint = constraint
if self.constraint is None:
self.constraint = ""
# Set the stacker_list if applicable.
if stacker_list is not None:
if isinstance(stacker_list, stackers.BaseStacker):
self.stacker_list = [
stacker_list,
]
else:
self.stacker_list = []
for s in stacker_list:
if s is None:
pass
else:
if not isinstance(s, stackers.BaseStacker):
raise ValueError("stacker_list must only contain rubin_sim.maf.stackers objs")
self.stacker_list.append(s)
else:
self.stacker_list = []
# Set the 'maps' to apply to the slicer, if applicable.
if maps_list is not None:
if isinstance(maps_list, maps.BaseMap):
self.known_cols = [
maps_list,
]
else:
self.maps_list = []
for m in maps_list:
if not isinstance(m, maps.BaseMap):
raise ValueError("maps_list must only contain rubin_sim.maf.maps objects")
self.maps_list.append(m)
else:
self.maps_list = []
# If the metric knows it needs a particular map, add it to the list.
map_names = [map_name.__class__.__name__ for map_name in self.maps_list]
if hasattr(self.metric, "maps"):
for map_needed in self.metric.maps:
if isinstance(map_needed, str):
if map_needed not in map_names:
temp_map = getattr(maps, map_needed)()
self.maps_list.append(temp_map)
map_names.append(map_needed)
else:
if map_needed not in self.maps_list:
self.maps_list.append(map_needed)
map_names.append(map_needed.__class__.__name__)
# Add the summary stats, if applicable.
self.set_summary_metrics(summary_metrics)
# Set the provenance/info_label.
self._build_metadata(info_label, metadata)
# Set the run name and build the output filename base (fileRoot).
self.set_run_name(run_name)
# Reset fileRoot, if provided.
if file_root is not None:
self.file_root = file_root
# Determine the columns needed from the database.
self._find_req_cols()
# Set the plotting classes/functions.
self.set_plot_funcs(plot_funcs)
# Set the plot_dict and displayDicts.
self.plot_dict = {}
self.set_plot_dict(plot_dict)
# Update/set display_dict.
self.display_dict = {}
self.set_display_dict(display_dict)
# This is where we store the metric values and summary stats.
self.metric_values = None
self.summary_values = None
def _reset_metric_bundle(self):
"""Reset all properties of MetricBundle."""
self.metric = None
self.slicer = None
self.constraint = None
self.stacker_list = []
self.summary_metrics = []
self.plot_funcs = []
self.maps_list = None
self.run_name = "run name"
self.info_label = ""
self.db_cols = None
self.file_root = None
self.plot_dict = {}
self.display_dict = {}
self.metric_values = None
self.summary_values = None
def _setup_metric_values(self):
"""Set up the numpy masked array to store the metric value data."""
dtype = self.metric.metric_dtype
# Can't store healpix slicer mask values in an int array.
if dtype == "int":
dtype = "float"
if self.metric.shape == 1:
shape = self.slicer.shape
else:
shape = (self.slicer.shape, self.metric.shape)
self.metric_values = ma.MaskedArray(
data=np.empty(shape, dtype),
mask=np.zeros(shape, "bool"),
fill_value=self.slicer.badval,
)
if hasattr(self.slicer, "mask"):
self.metric_values.mask = self.slicer.mask
def _build_metadata(self, info_label, metadata=None):
"""If no info_label is provided, process the constraint
(by removing extra spaces, quotes, the word 'filter' and equal signs)
to make a info_label version.
e.g. 'filter = "r"' becomes 'r'
"""
# Pass the deprecated version into info_label if info_label is not set
if metadata is not None and info_label is None:
warnings.warn(
'Use of "metadata" as a kwarg is deprecated, please use info_label instead.'
f" (copying {metadata} into info_label). "
)
info_label = metadata
if info_label is None:
self.info_label = self.constraint.replace("=", "").replace("filter", "").replace("'", "")
self.info_label = self.info_label.replace('"', "").replace(" ", " ")
self.info_label = self.info_label.strip(" ")
else:
self.info_label = info_label
def _build_file_root(self):
"""
Build an auto-generated output filename root
(i.e. minus the plot type or .npz ending).
"""
# Build basic version.
self.file_root = "_".join(
[
self.run_name,
self.metric.name,
self.info_label,
self.slicer.slicer_name[:4].upper(),
]
)
# Sanitize output name if needed.
self.file_root = utils.name_sanitize(self.file_root)
def _find_req_cols(self):
"""Find the columns needed by the metrics, slicers, and stackers.
If there are any additional stackers required, instatiate them
and add them to the self.stackers list.
(default stackers have to be instantiated to determine
what additional columns are needed from database).
"""
# Find all the columns needed by metric and slicer.
known_cols = self.slicer.columns_needed + list(self.metric.col_name_arr)
# For the stackers already set up, find their required columns.
for s in self.stacker_list:
known_cols += s.cols_req
known_cols = set(known_cols)
# Track sources of all of these columns.
self.db_cols = set()
new_stackers = set()
for col in known_cols:
if self.col_info.get_data_source(col) == self.col_info.default_data_source:
self.db_cols.add(col)
else:
# New default stackers could come from metric/slicer
# or stackers.
new_stackers.add(self.col_info.get_data_source(col))
# Remove already-specified stackers from default list.
for s in self.stacker_list:
if s.__class__ in new_stackers:
new_stackers.remove(s.__class__)
# Loop and check if stackers are introducing new columns or stackers.
while len(new_stackers) > 0:
# Check for the sources of the columns in any of the new stackers.
new_cols = []
for s in new_stackers:
new_stacker = s()
new_cols += new_stacker.cols_req
self.stacker_list.append(new_stacker)
new_cols = set(new_cols)
new_stackers = set()
for col in new_cols:
if self.col_info.get_data_source(col) == self.col_info.default_data_source:
self.db_cols.add(col)
else:
new_stackers.add(self.col_info.get_data_source(col))
for s in self.stacker_list:
if s.__class__ in new_stackers:
new_stackers.remove(s.__class__)
# A Bit of cleanup.
# Remove 'metricdata' from dbcols if it ended here by default.
if "metricdata" in self.db_cols:
self.db_cols.remove("metricdata")
if "None" in self.db_cols:
self.db_cols.remove("None")
[docs]
def set_summary_metrics(self, summary_metrics):
"""Set (or reset) the summary metrics for the metricbundle.
Parameters
----------
summary_metrics : `List` [`BaseMetric`]
Instantiated summary metrics to use to calculate
summary statistics for this metric.
"""
if summary_metrics is not None:
if isinstance(summary_metrics, metrics.BaseMetric):
self.summary_metrics = [summary_metrics]
else:
self.summary_metrics = []
for s in summary_metrics:
if not isinstance(s, metrics.BaseMetric):
raise ValueError("SummaryStats must only contain rubin_sim.maf.metrics objects")
self.summary_metrics.append(s)
else:
# Add identity metric to unislicer metric values
# (to get them into resultsDB).
if self.slicer.slicer_name == "UniSlicer":
self.summary_metrics = [metrics.IdentityMetric()]
else:
self.summary_metrics = []
[docs]
def set_plot_funcs(self, plot_funcs):
"""Set or reset the plotting functions.
The default is to use all the plotFuncs associated with the slicer,
which is what happens in self.plot if setPlotFuncs is not used to
override self.plotFuncs.
Parameters
----------
plot_funcs : `List` [`BasePlotter`]
The plotter or plotters to use to generate visuals for this metric.
"""
if plot_funcs is not None:
if plot_funcs is isinstance(plot_funcs, plots.BasePlotter):
self.plot_funcs = [plot_funcs]
else:
self.plot_funcs = []
for p_func in plot_funcs:
if not isinstance(p_func, plots.BasePlotter):
raise ValueError(
"plotFuncs should contain instantiated " + "rubin_sim.maf.plotter objects."
)
self.plot_funcs.append(p_func)
else:
self.plot_funcs = []
for p_func in self.slicer.plot_funcs:
if isinstance(p_func, plots.BasePlotter):
self.plot_funcs.append(p_func)
else:
self.plot_funcs.append(p_func())
[docs]
def set_plot_dict(self, plot_dict):
"""Set or update any property of plot_dict.
Parameters
----------
plot_dict : dict
A dictionary of plotting parameters.
The usable keywords vary with each rubin_sim.maf.plots Plotter.
"""
# Don't auto-generate anything here - the plotHandler does it.
if plot_dict is not None:
self.plot_dict.update(plot_dict)
# Check for bad zp or norm_val values.
if "zp" in self.plot_dict:
if self.plot_dict["zp"] is not None:
if not np.isfinite(self.plot_dict["zp"]):
warnings.warn(
"Warning! Plot zp for %s was infinite: removing zp from plot_dict" % (self.file_root)
)
del self.plot_dict["zp"]
if "norm_val" in self.plot_dict:
if self.plot_dict["norm_val"] == 0:
warnings.warn(
"Warning! Plot normalization value for %s was 0: removing norm_val from plot_dict"
% (self.file_root)
)
del self.plot_dict["norm_val"]
[docs]
def set_display_dict(self, display_dict=None, results_db=None):
"""Set or update any property of display_dict.
Parameters
----------
display_dict : `dict` of `str`
Dictionary of display parameters for show_maf.
Expected keys are: 'group', 'subgroup', 'order', 'caption'.
'group', 'subgroup', and 'order' control where the
metric results are shown on the show_maf page.
'caption' provides a caption to use with the metric results.
These values are saved in the results database.
results_db : `maf.ResultsDb`
A MAF results database, used to save the display parameters.
"""
# Set up a temporary dictionary with the default values.
tmp_display_dict = {
"group": None,
"subgroup": None,
"order": 0,
"caption": None,
}
# Update from self.display_dict (to use existing values, if present).
tmp_display_dict.update(self.display_dict)
# And then update from any values being passed now.
if display_dict is not None:
tmp_display_dict.update(display_dict)
# Reset self.display_dict to this updated dictionary.
self.display_dict = tmp_display_dict
# If we still need to auto-generate a caption, do it.
if self.display_dict["caption"] is None:
if self.metric.comment is None:
caption = self.metric.name + " calculated on a %s basis" % (self.slicer.slicer_name)
if self.constraint != "" and self.constraint is not None:
caption += " using a subset of data selected via %s." % (self.constraint)
else:
caption += "."
else:
caption = self.metric.comment
if "zp" in self.plot_dict:
caption += " Values plotted with a zeropoint of %.2f." % (self.plot_dict["zp"])
if "norm_val" in self.plot_dict:
caption += " Values plotted with a normalization value of %.2f." % (
self.plot_dict["norm_val"]
)
self.display_dict["caption"] = caption
if results_db:
# Update the display values in the results_db.
metric_id = results_db.update_metric(
self.metric.name,
self.slicer.slicer_name,
self.run_name,
self.constraint,
self.info_label,
None,
)
results_db.update_display(metric_id, self.display_dict)
[docs]
def set_run_name(self, run_name, update_file_root=True):
"""Set (or reset) the run_name.
FileRoot will be updated accordingly if desired.
Parameters
----------
run_name : `str`
Run Name, which will become part of the fileRoot.
fileRoot : `bool`, optional
Flag to update the fileRoot with the run_name.
"""
self.run_name = run_name
if update_file_root:
self._build_file_root()
[docs]
def write_db(self, results_db=None, outfile_suffix=None):
"""Write the metric information to the results database"""
if outfile_suffix is not None:
outfile = self.file_root + "_" + outfile_suffix + ".npz"
else:
outfile = self.file_root + ".npz"
if results_db is not None:
metric_id = results_db.update_metric(
self.metric.name,
self.slicer.slicer_name,
self.run_name,
self.constraint,
self.info_label,
outfile,
)
results_db.update_display(metric_id, self.display_dict)
[docs]
def write(self, comment="", out_dir=".", outfile_suffix=None, results_db=None):
"""Write metric_values (and associated info_label) to disk.
Parameters
----------
comment : `str`
Any additional comments to add to the output file
out_dir : `str`
The output directory
outfile_suffix :`str`
Additional suffix to add to the output files
(typically a numerical suffix for movies)
results_db : `maf.ResultsDb`
Results database to store information on the file output
"""
if outfile_suffix is not None:
outfile = self.file_root + "_" + outfile_suffix + ".npz"
else:
outfile = self.file_root + ".npz"
self.slicer.write_data(
os.path.join(out_dir, outfile),
self.metric_values,
metric_name=self.metric.name,
sim_data_name=self.run_name,
constraint=self.constraint,
info_label=self.info_label + comment,
display_dict=self.display_dict,
plot_dict=self.plot_dict,
summary_values=self.summary_values,
)
if results_db is not None:
self.write_db(results_db=results_db)
[docs]
def output_json(self):
"""Set up and call the baseSlicer outputJSON method,
to output to IO string.
Returns
-------
io
IO object containing JSON data representing the metric bundle data.
"""
io = self.slicer.output_json(
self.metric_values,
metric_name=self.metric.name,
simDataName=self.run_name,
info_label=self.info_label,
plot_dict=self.plot_dict,
)
return io
[docs]
def read(self, filename):
"""Read metric data from disk.
Overwrites any data currently in metricbundle.
Parameters
----------
filename : `str`
The file from which to read the metric bundle data.
"""
if not os.path.isfile(filename):
raise IOError("%s not found" % filename)
self._reset_metric_bundle()
# Set up a base slicer to read data (we don't know type yet).
baseslicer = slicers.BaseSlicer()
# Use baseslicer to read file.
metric_values, slicer, header = baseslicer.read_data(filename)
self.slicer = slicer
self.metric_values = metric_values
self.metric_values.fill_value = slicer.badval
# It's difficult to reinstantiate the metric object, as we don't
# know what it is necessarily -- the metric_name can be changed.
self.metric = metrics.BaseMetric()
# But, for plot label building, we do need to try to recreate the
# metric name and units.
self.metric.units = ""
if header is not None:
self.metric.name = header["metric_name"]
if "plot_dict" in header:
if "units" in header["plot_dict"]:
self.metric.units = header["plot_dict"]["units"]
self.run_name = header["sim_data_name"]
try:
self.constraint = header["constraint"]
except KeyError:
self.constraint = header["sqlconstraint"]
# and then use info_label if it's there instead
if "info_label" in header:
self.info_label = header["info_label"]
if "plot_dict" in header:
self.set_plot_dict(header["plot_dict"])
if "display_dict" in header:
self.set_display_dict(header["display_dict"])
if "summary_values" in header:
self.summary_values = header["summary_values"]
if self.info_label is None:
self._build_metadata()
path, head = os.path.split(filename)
self.file_root = head.replace(".npz", "")
self.set_plot_funcs(None)
[docs]
@classmethod
def load(cls, filename):
"""Create a metric bundle and load its content from disk.
Parameters
----------
filename : `str`
The file from which to read the metric bundle data.
"""
metric_bundle = cls(metrics.BaseMetric(), slicers.BaseSlicer(), "")
metric_bundle.read(filename)
return metric_bundle
[docs]
def compute_summary_stats(self, results_db=None):
"""Compute summary statistics on metric_values,
using summaryMetrics (metricbundle list).
Parameters
----------
results_db : Optional[ResultsDb]
ResultsDb object to use to store the
summary statistic values on disk.
"""
if self.summary_values is None:
self.summary_values = {}
if self.summary_metrics is not None:
# Build array of metric values, to use for summary statistics.
rarr_std = np.array(
list(zip(self.metric_values.compressed())),
dtype=[("metricdata", self.metric_values.dtype)],
)
for m in self.summary_metrics:
# The summary metric colname should already be set
# to 'metricdata', but in case it's not:
m.colname = "metricdata"
summary_name = m.name.replace(" metricdata", "").replace(" None", "")
if hasattr(m, "mask_val"):
# summary metric requests to use the mask value,
# as specified by itself,
# rather than skipping masked vals.
rarr = np.array(
list(zip(self.metric_values.filled(m.mask_val))),
dtype=[("metricdata", self.metric_values.dtype)],
)
else:
rarr = rarr_std
if np.size(rarr) == 0:
summary_val = self.slicer.badval
else:
summary_val = m.run(rarr)
self.summary_values[summary_name] = summary_val
# Add summary metric info to results database, if applicable.
if results_db:
metric_id = results_db.update_metric(
self.metric.name,
self.slicer.slicer_name,
self.run_name,
self.constraint,
self.info_label,
None,
)
results_db.update_summary_stat(
metric_id, summary_name=summary_name, summary_value=summary_val
)
[docs]
def reduce_metric(
self,
reduce_func,
reduce_func_name=None,
reduce_plot_dict=None,
reduce_display_dict=None,
):
"""Run 'reduceFunc' (any function that operates on self.metric_values).
Typically reduceFunc will be the metric reduce functions,
as they are tailored to expect the metric_values format.
reduceDisplayDict and reducePlotDicts are displayDicts
and plotDicts to be applied to the new metricBundle.
Parameters
----------
reduce_func : `Func`
Any function that will operate on self.metric_values
(typically metric.reduce* function).
reduce_plot_dict : `dict`, opt
Plot dictionary for the results of the reduce function.
reduce_display_dict : `dict`, opt
Display dictionary for the results of the reduce function.
Returns
-------
newmetric_bundle: `MetricBundle`
New metric bundle, inheriting info_label from this metric bundle,
but containing the new metric values calculated with
the 'reduceFunc'.
"""
# Generate a name for the metric values processed by the reduceFunc.
if reduce_func_name is not None:
r_name = reduce_func_name.replace("reduce_", "")
else:
r_name = reduce_func.__name__.replace("reduce_", "")
reduce_name = self.metric.name + "_" + r_name
# Set up metricBundle to store new metric values,
# and add plot_dict/display_dict.
newmetric = deepcopy(self.metric)
newmetric.name = reduce_name
newmetric.metric_dtype = "float"
if reduce_plot_dict is not None:
if "units" in reduce_plot_dict:
newmetric.units = reduce_plot_dict["units"]
newmetric_bundle = MetricBundle(
metric=newmetric,
slicer=self.slicer,
stacker_list=self.stacker_list,
constraint=self.constraint,
info_label=self.info_label,
run_name=self.run_name,
plot_dict=None,
plot_funcs=self.plot_funcs,
display_dict=None,
summary_metrics=self.summary_metrics,
maps_list=self.maps_list,
file_root="",
)
# Build a new output file root name.
newmetric_bundle._build_file_root()
# Add existing plot_dict (except for title/xlabels etc)
# into new plot_dict.
for k, v in self.plot_dict.items():
if k not in newmetric_bundle.plot_dict:
newmetric_bundle.plot_dict[k] = v
# Update newmetric_bundle's plot dictionary with
# any set explicitly by reducePlotDict.
newmetric_bundle.set_plot_dict(reduce_plot_dict)
# Copy the parent metric's display dict into the reduce display dict.
newmetric_bundle.set_display_dict(self.display_dict)
# Set the reduce function display 'order'
# (this is set in the BaseMetric by default,
# but can be overriden in a metric).
order = newmetric.reduce_order[r_name]
newmetric_bundle.display_dict["order"] = order
# And then update the newmetric_bundle's
# display dictionary with any set
# explicitly by reduceDisplayDict.
newmetric_bundle.set_display_dict(reduce_display_dict)
# Set up new metricBundle's metric_values masked arrays,
# copying metricValue's mask.
newmetric_bundle.metric_values = ma.MaskedArray(
data=np.empty(len(self.slicer), "float"),
mask=self.metric_values.mask.copy(),
fill_value=self.slicer.badval,
)
# Fill the reduced metric data using the reduce function.
for i, (mVal, mMask) in enumerate(zip(self.metric_values.data, self.metric_values.mask)):
if not mMask:
val = reduce_func(mVal)
newmetric_bundle.metric_values.data[i] = val
if val == newmetric.badval:
newmetric_bundle.metric_values.mask[i] = True
return newmetric_bundle
[docs]
def plot(self, plot_handler=None, plot_func=None, outfile_suffix=None, savefig=False):
"""
Create all plots available from the slicer.
plotHandler holds the output directory info, etc.
Parameters
----------
plot_handler : `~maf.plots.plot_handler`, opt
The plot_handler saves the output location
and results_db connection for a set of plots.
plot_func : `maf.plots.BasePlotter`, opt
Any plotter function.
If not specified, the plotters in self.plotFuncs will be used.
outfile_suffix : `str`, opt
Optional string to append to the end of the plot output files.
Useful when creating sequences of images for movies.
savefig : `bool`, opt
Flag indicating whether or not to save the figure to disk.
Default is False.
Returns
-------
made_plots : `dict`
Dictionary of plot_type:figure key/value pairs,
indicating what plots were created
and what matplotlib figures were used.
"""
# Generate a plot_handler if none was set.
if plot_handler is None:
plot_handler = plots.PlotHandler(savefig=savefig)
# Make plots.
if plot_func is not None:
if isinstance(plot_func, plots.BasePlotter):
plot_func = plot_func
else:
plot_func = plot_func()
plot_handler.set_metric_bundles([self])
plot_handler.set_plot_dicts(plot_dicts=[self.plot_dict], reset=True)
made_plots = {}
if plot_func is not None:
fig = plot_handler.plot(plot_func, outfile_suffix=outfile_suffix)
made_plots[plot_func.plotType] = fig
else:
for plot_func in self.plot_funcs:
fig = plot_handler.plot(plot_func, outfile_suffix=outfile_suffix)
made_plots[plot_func.plot_type] = fig
return made_plots