# Licensed under a 3-clause BSD style license - see LICENSE.rst
import matplotlib.pyplot as plt
__all__ = [
"plot_spectrum_datasets_off_regions",
"plot_npred_signal",
]
[docs]def plot_spectrum_datasets_off_regions(
datasets, ax=None, legend=None, legend_kwargs=None, **kwargs
):
"""Plot the off regions of spectrum datasets.
Parameters
----------
datasets : `~gammapy.datasets.Datasets` or list of `~gammapy.datasets.SpectrumDatasetOnOff`
List of spectrum on-off datasets.
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
Axes object to plot on. Default is None.
legend : bool, optional
Whether to add/display the labels of the off regions in a legend. By default True if
``len(datasets) <= 10``. Default is None.
legend_kwargs : dict, optional
Keyword arguments used in `matplotlib.axes.Axes.legend`. The ``handler_map`` cannot be
overridden. Default is None.
**kwargs : dict
Keyword arguments used in `gammapy.maps.RegionNDMap.plot_region`. Can contain a
`~cycler.Cycler` in a ``prop_cycle`` argument.
Notes
-----
Properties from the ``prop_cycle`` have maximum priority, except ``color``,
``edgecolor``/``color`` is selected from the sources below in this order:
``kwargs["edgecolor"]``, ``kwargs["prop_cycle"]``, ``matplotlib.rcParams["axes.prop_cycle"]``
``matplotlib.rcParams["patch.edgecolor"]``, ``matplotlib.rcParams["patch.facecolor"]``
is never used.
Examples
--------
Plot without legend and with thick circles::
plot_spectrum_datasets_off_regions(datasets, ax, legend=False, linewidth=2.5)
Plot to visualise the overlap of off regions::
plot_spectrum_datasets_off_regions(datasets, ax, alpha=0.3, facecolor='black')
Plot that cycles through colors (``edgecolor``) and line styles together::
plot_spectrum_datasets_off_regions(datasets, ax, prop_cycle=plt.cycler(color=list('rgb'), ls=['--', '-', ':'])) # noqa: E501
Plot that uses a modified `~matplotlib.rcParams`, has two legend columns, static and
dynamic colors, but only shows labels for ``datasets1`` and ``datasets2``. Note that
``legend_kwargs`` only applies if it's given in the last function call with ``legend=True``::
plt.rc('legend', columnspacing=1, fontsize=9)
plot_spectrum_datasets_off_regions(datasets1, ax, legend=True, edgecolor='cyan')
plot_spectrum_datasets_off_regions(datasets2, ax, legend=True, legend_kwargs=dict(ncol=2))
plot_spectrum_datasets_off_regions(datasets3, ax, legend=False, edgecolor='magenta')
"""
from matplotlib.legend_handler import HandlerPatch, HandlerTuple
from matplotlib.patches import CirclePolygon, Patch
if ax is None:
ax = plt.subplot(projection=datasets[0].counts_off.geom.wcs)
legend = legend or legend is None and len(datasets) <= 10
legend_kwargs = legend_kwargs or {}
handles, labels = [], []
prop_cycle = kwargs.pop("prop_cycle", plt.rcParams["axes.prop_cycle"])
for props, dataset in zip(prop_cycle(), datasets):
plot_kwargs = kwargs.copy()
plot_kwargs["facecolor"] = "None"
plot_kwargs.setdefault("edgecolor")
plot_kwargs.update(props)
dataset.counts_off.plot_region(ax, **plot_kwargs)
# create proxy artist for the custom legend
if legend:
handle = Patch(**plot_kwargs)
handles.append(handle)
labels.append(dataset.name)
if legend:
legend = ax.get_legend()
if legend:
handles = legend.legendHandles + handles
labels = [text.get_text() for text in legend.texts] + labels
handles = [(handle, handle) for handle in handles]
tuple_handler = HandlerTuple(ndivide=None, pad=0)
def patch_func(
legend, orig_handle, xdescent, ydescent, width, height, fontsize
):
radius = width / 2
return CirclePolygon((radius - xdescent, height / 2 - ydescent), radius)
patch_handler = HandlerPatch(patch_func)
legend_kwargs.setdefault("handletextpad", 0.5)
legend_kwargs["handler_map"] = {Patch: patch_handler, tuple: tuple_handler}
ax.legend(handles, labels, **legend_kwargs)
return ax
[docs]def plot_npred_signal(
dataset,
ax=None,
model_names=None,
region=None,
**kwargs,
):
"""
Plot the energy distribution of predicted counts of a selection of models assigned to a dataset.
The background and the sum af all the considered models predicted counts are plotted on top of individual
contributions of the considered models.
Parameters
----------
dataset : an instance of `~gammapy.datasets.MapDataset`
The dataset from which to plot the npred_signal.
ax : `~matplotlib.axes.Axes`, optional
Axis object to plot on. Default is None.
model_names : list of str, optional
The list of models for which the npred_signal is plotted. Default is None.
If None, all models are considered.
region : `~regions.Region` or `~astropy.coordinates.SkyCoord`, optional
Region used to reproject predicted counts. Default is None.
If None, use the full dataset geometry.
**kwargs : dict
Keyword arguments to pass to `~gammapy.maps.RegionNDMap.plot`.
Returns
-------
axes : `~matplotlib.axes.Axes`
Axis object.
"""
npred_not_stack = dataset.npred_signal(
model_names=model_names, stack=False
).to_region_nd_map(region)
npred_background = dataset.npred_background().to_region_nd_map(region)
if ax is None:
ax = plt.gca()
npred_not_stack.plot(ax=ax, axis_name="energy", **kwargs)
if npred_not_stack.geom.axes["models"].nbin > 1:
npred_stack = npred_not_stack.sum_over_axes(["models"])
npred_stack.plot(ax=ax, label="stacked models")
npred_background.plot(ax=ax, label="background", **kwargs)
ax.set_ylabel("Predicted counts")
ax.legend()
return ax