def plot_interactive(self, ax=None, fig=None, **kwargs):
"""
Plot ND array on matplotlib WCS axes with interactive widgets
to explore the non spatial axes.
Parameters
----------
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
WCS axis object to plot on.
fig : `~matplotlib.figure.Figure`
Figure object.
**kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.imshow`.
Examples
--------
You can try this out e.g. using a Fermi-LAT diffuse model cube with an energy axis::
%matplotlib inline
from gammapy.maps import Map
m = Map.read("$GAMMAPY_EXTRA/datasets/vela_region/gll_iem_v05_rev1_cutout.fits")
m.plot_interactive(cmap='gnuplot2')
"""
import matplotlib.pyplot as plt
from astropy.visualization import simple_norm
from ipywidgets.widgets.interaction import interact, fixed
import ipywidgets as widgets
kwargs.setdefault('interpolation', 'nearest')
kwargs.setdefault('origin', 'lower')
kwargs.setdefault('cmap', 'afmhot')
@interact(
index=widgets.IntSlider(min=0,
max=self.data.shape[0] - 1,
step=1,
value=1,
description=self.geom.axes_names[0] +
' slice'),
stretch=widgets.RadioButtons(options=['linear', 'sqrt', 'log'],
value='sqrt',
description='Plot stretch'),
ax=fixed(ax),
fig=fixed(fig),
)
def _plot_interactive(index, stretch, ax=None, fig=None):
if self.geom.is_image:
raise TypeError('Use .plot() for 2D Maps')
if fig is None:
fig = plt.gcf()
if ax is None:
ax = fig.add_subplot(1, 1, 1, projection=self.geom.wcs)
axes = self.geom.get_axis_by_name(self.geom.axes_names[0])
data = self.get_image_by_idx([index]).data
norm = simple_norm(data[np.isfinite(data)], stretch)
caxes = ax.imshow(data, norm=norm, **kwargs)
fig.colorbar(caxes, ax=ax)
ax.set_title('{:.2f}-{:.2f} {} '.format(
axes.edges[index],
axes.edges[index + 1],
self.geom.axes[0].unit.name,
))
try:
ax.coords['glon'].set_axislabel('Galactic Longitude')
ax.coords['glat'].set_axislabel('Galactic Latitude')
except KeyError:
ax.coords['ra'].set_axislabel('Right Ascension')
ax.coords['dec'].set_axislabel('Declination')
except AttributeError:
log.info(
"Can't set coordinate axes. No WCS information available.")
plt.show()
def plot_interactive(self, rc_params=None, **kwargs):
"""
Plot map with interactive widgets to explore the non spatial axes.
Parameters
----------
rc_params : dict
Passed to ``matplotlib.rc_context(rc=rc_params)`` to style the plot.
**kwargs : dict
Keyword arguments passed to `WcsNDMap.plot`.
Examples
--------
You can try this out e.g. using a Fermi-LAT diffuse model cube with an energy axis::
from gammapy.maps import Map
m = Map.read("$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits")
m.plot_interactive(add_cbar=True, stretch="sqrt")
If you would like to adjust the figure size you can use the ``rc_params`` argument::
rc_params = {'figure.figsize': (12, 6), 'font.size': 12}
m.plot_interactive(rc_params=rc_params)
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
from ipywidgets.widgets.interaction import interact, fixed
from ipywidgets import SelectionSlider, RadioButtons
if self.geom.is_image:
raise TypeError("Use .plot() for 2D Maps")
kwargs.setdefault("interpolation", "nearest")
kwargs.setdefault("origin", "lower")
kwargs.setdefault("cmap", "afmhot")
rc_params = rc_params or {}
stretch = kwargs.pop("stretch", "sqrt")
interact_kwargs = {}
for axis in self.geom.axes:
if axis.node_type == "edges":
options = [
"{:.2e} - {:.2e} {}".format(val_min, val_max, axis.unit)
for val_min, val_max in zip(axis.edges[:-1], axis.edges[1:])
]
else:
options = ["{:.2e} {}".format(val, axis.unit) for val in axis.center]
interact_kwargs[axis.name] = SelectionSlider(
options=options,
description="Select {}:".format(axis.name),
continuous_update=False,
style={"description_width": "initial"},
layout={"width": "50%"},
)
interact_kwargs[axis.name + "_options"] = fixed(options)
interact_kwargs["stretch"] = RadioButtons(
options=["linear", "sqrt", "log"],
value=stretch,
description="Select stretch:",
style={"description_width": "initial"},
)
@interact(**interact_kwargs)
def _plot_interactive(**ikwargs):
idx = [
ikwargs[ax.name + "_options"].index(ikwargs[ax.name])
for ax in self.geom.axes
]
img = self.get_image_by_idx(idx)
stretch = ikwargs["stretch"]
with mpl.rc_context(rc=rc_params):
fig, ax, cbar = img.plot(stretch=stretch, **kwargs)
plt.show()
def plot_interactive(self, rc_params=None, **kwargs):
"""
Plot map with interactive widgets to explore the non spatial axes.
Parameters
----------
rc_params : dict
Passed to ``matplotlib.rc_context(rc=rc_params)`` to style the plot.
**kwargs : dict
Keyword arguments passed to `WcsNDMap.plot`.
Examples
--------
You can try this out e.g. using a Fermi-LAT diffuse model cube with an energy axis::
from gammapy.maps import Map
m = Map.read("$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits")
m.plot_interactive(add_cbar=True, stretch="sqrt")
If you would like to adjust the figure size you can use the ``rc_params`` argument::
rc_params = {'figure.figsize': (12, 6), 'font.size': 12}
m.plot_interactive(rc_params=rc_params)
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
from ipywidgets.widgets.interaction import interact, fixed
from ipywidgets import SelectionSlider, RadioButtons
if self.geom.is_image:
raise TypeError("Use .plot() for 2D Maps")
kwargs.setdefault("interpolation", "nearest")
kwargs.setdefault("origin", "lower")
kwargs.setdefault("cmap", "afmhot")
rc_params = rc_params or {}
stretch = kwargs.pop("stretch", "sqrt")
interact_kwargs = {}
for axis in self.geom.axes:
if axis.node_type == "edges":
options = [
"{:.2e} - {:.2e} {}".format(val_min, val_max, axis.unit)
for val_min, val_max in zip(axis.edges[:-1], axis.edges[1:])
]
else:
options = ["{:.2e} {}".format(val, axis.unit) for val in axis.center]
interact_kwargs[axis.name] = SelectionSlider(
options=options,
description="Select {}:".format(axis.name),
continuous_update=False,
style={"description_width": "initial"},
layout={"width": "50%"},
)
interact_kwargs[axis.name + "_options"] = fixed(options)
interact_kwargs["stretch"] = RadioButtons(
options=["linear", "sqrt", "log"],
value=stretch,
description="Select stretch:",
style={"description_width": "initial"},
)
@interact(**interact_kwargs)
def _plot_interactive(**ikwargs):
idx = [
ikwargs[ax.name + "_options"].index(ikwargs[ax.name])
for ax in self.geom.axes
]
img = self.get_image_by_idx(idx)
stretch = ikwargs["stretch"]
with mpl.rc_context(rc=rc_params):
fig, ax, cbar = img.plot(stretch=stretch, **kwargs)
plt.show()
