def annotate_frame(i):
axes.set_title("{s.name}".format(s=self[i]))
axes.set_xlabel(
axis_labels_from_ctype(self[i].coordinate_system[0],
self[i].spatial_units[0]))
axes.set_ylabel(
axis_labels_from_ctype(self[i].coordinate_system[1],
self[i].spatial_units[1]))
def _annotate_plot(self, ind):
"""
Annotate the image.
This may overwrite some stuff in `GenericMap.plot()`
"""
# Normal plot
self.axes.set_title("{s.name}".format(s=self.data[ind]))
self.axes.set_xlabel(axis_labels_from_ctype(self.data[ind].coordinate_system[0],
self.data[ind].spatial_units[0]))
self.axes.set_ylabel(axis_labels_from_ctype(self.data[ind].coordinate_system[1],
self.data[ind].spatial_units[1]))
def _annotate_plot(self, ind):
"""
Annotate the image.
This may overwrite some stuff in `sunpy.map.GenericMap.plot`
"""
# Normal plot
self.axes.set_title("{s.name}".format(s=self.data[ind]))
self.axes.set_xlabel(axis_labels_from_ctype(self.data[ind].coordinate_system[0],
self.data[ind].spatial_units[0]))
self.axes.set_ylabel(axis_labels_from_ctype(self.data[ind].coordinate_system[1],
self.data[ind].spatial_units[1]))
def plots(smaps):
"""
Plot an image of the Sun in multiple wavelengths, up to nine per figure.
Inputs:
smaps: list of SunPy Map objects (up to nine)
Outputs:
None.
"""
nmap = len(smaps)
if (nmap < 1 or nmap > 9):
raise Exception('Please input a valid number of smaps.')
nc = int(np.ceil(np.sqrt(nmap)))
nr = nc - 1 * (nmap <= (nc**2 - nc))
fig, ax = plt.subplots(nr,
nc,
sharex=True,
sharey=True,
figsize=(2 * nc + 2, 2 * nr + 1))
if nmap == 1:
ax = np.array([ax])
fig.set_size_inches(5, 4.5) # prevent text overlap
ax = ax.reshape(-1)
# hide extra axes
for i in range(nmap, nr * nc):
ax[i].axis('off')
add_plots(smaps, fig, ax)
# share axis labels over the whole figure
ylab = axis_labels_from_ctype(smaps[0].coordinate_system[1],
smaps[0].spatial_units[1])
xlab = axis_labels_from_ctype(smaps[0].coordinate_system[0],
smaps[0].spatial_units[0])
fig.text(0.5, 0.04, xlab, ha='center')
fig.text(0.01, 0.5, ylab, va='center', rotation='vertical')
title = 'AIA ' + smaps[0].meta.get('date').replace('T', ' ')[:-3]
plt.suptitle(title)
plt.show()
return
def plot(
self,
axes=None,
annotate=True, # pylint: disable=W0613
title="SunPy Composite Plot",
**matplot_args):
"""Plots the composite map object using matplotlib
Parameters
----------
axes: `~matplotlib.axes.Axes` or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
annotate : `bool`
If true, the data is plotted at it's natural scale; with
title and axis labels.
title : `str`
Title of the composite map.
**matplot_args : `dict`
Matplotlib Any additional imshow arguments that should be used
when plotting.
Returns
-------
ret : `list`
List of axes image or quad contour sets that have been plotted.
"""
# Get current axes
if not axes:
axes = plt.gca()
if annotate:
axes.set_xlabel(
axis_labels_from_ctype(self._maps[0].coordinate_system[0],
self._maps[0].spatial_units[0]))
axes.set_ylabel(
axis_labels_from_ctype(self._maps[0].coordinate_system[1],
self._maps[0].spatial_units[1]))
axes.set_title(title)
# Define a list of plotted objects
ret = []
# Plot layers of composite map
for m in self._maps:
# Parameters for plotting
params = {
"origin": "lower",
"extent": list(m.xrange.value) + list(m.yrange.value),
"cmap": m.plot_settings['cmap'],
"norm": m.plot_settings['norm'],
"alpha": m.alpha,
"zorder": m.zorder,
}
params.update(matplot_args)
if m.levels is False:
ret.append(axes.imshow(m.data, **params))
# Use contour for contour data, and imshow otherwise
if m.levels is not False:
# Set data with values <= 0 to transparent
# contour_data = np.ma.masked_array(m, mask=(m <= 0))
ret.append(axes.contour(m.data, m.levels, **params))
# Set the label of the first line so a legend can be created
ret[-1].collections[0].set_label(m.name)
# Adjust axes extents to include all data
axes.axis('image')
# Set current image (makes colorbar work)
plt.sci(ret[0])
return ret
def plot(
self,
axes=None,
annotate=True, # pylint: disable=W0613
title="SunPy Composite Plot",
**matplot_args):
"""Plots the composite map object using matplotlib
Parameters
----------
axes: `~matplotlib.axes.Axes` or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
annotate : `bool`
If true, the data is plotted at it's natural scale; with
title and axis labels.
title : `str`
Title of the composite map.
**matplot_args : `dict`
Matplotlib Any additional imshow arguments that should be used
when plotting.
Returns
-------
ret : `list`
List of axes image or quad contour sets that have been plotted.
"""
# Get current axes
if not axes:
axes = plt.gca()
if annotate:
axes.set_xlabel(
axis_labels_from_ctype(self._maps[0].coordinate_system[0],
self._maps[0].spatial_units[0]))
axes.set_ylabel(
axis_labels_from_ctype(self._maps[0].coordinate_system[1],
self._maps[0].spatial_units[1]))
axes.set_title(title)
# Define a list of plotted objects
ret = []
# Plot layers of composite map
for m in self._maps:
# Parameters for plotting
bl = m._get_lon_lat(m.bottom_left_coord)
tr = m._get_lon_lat(m.top_right_coord)
x_range = list(
u.Quantity([bl[0], tr[0]]).to(m.spatial_units[0]).value)
y_range = list(
u.Quantity([bl[1], tr[1]]).to(m.spatial_units[1]).value)
params = {
"origin": "lower",
"extent": x_range + y_range,
"cmap": m.plot_settings['cmap'],
"norm": m.plot_settings['norm'],
"alpha": m.alpha,
"zorder": m.zorder,
}
params.update(matplot_args)
# The request to show a map layer rendered as a contour is indicated by a
# non False levels property. If levels is False, then the layer is
# rendered using imshow.
if m.levels is False:
# Check for the presence of masked map data
if m.mask is None:
ret.append(axes.imshow(m.data, **params))
else:
ret.append(
axes.imshow(
np.ma.array(np.asarray(m.data), mask=m.mask),
**params))
else:
# Check for the presence of masked map data
if m.mask is None:
ret.append(axes.contour(m.data, m.levels, **params))
else:
ret.append(
axes.contour(
np.ma.array(np.asarray(m.data), mask=m.mask),
m.levels, **params))
# Set the label of the first line so a legend can be created
ret[-1].collections[0].set_label(m.name)
# Adjust axes extents to include all data
axes.axis('image')
# Set current image (makes colorbar work)
plt.sci(ret[0])
return ret
def plot(self, axes=None, annotate=True, # pylint: disable=W0613
title="SunPy Composite Plot", **matplot_args):
"""Plots the composite map object using matplotlib
Parameters
----------
axes: `~matplotlib.axes.Axes` or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
annotate : `bool`
If true, the data is plotted at it's natural scale; with
title and axis labels.
title : `str`
Title of the composite map.
**matplot_args : `dict`
Matplotlib Any additional imshow arguments that should be used
when plotting.
Returns
-------
ret : `list`
List of axes image or quad contour sets that have been plotted.
"""
# Get current axes
if not axes:
axes = plt.gca()
if annotate:
axes.set_xlabel(axis_labels_from_ctype(self._maps[0].coordinate_system[0],
self._maps[0].spatial_units[0]))
axes.set_ylabel(axis_labels_from_ctype(self._maps[0].coordinate_system[1],
self._maps[0].spatial_units[1]))
axes.set_title(title)
# Define a list of plotted objects
ret = []
# Plot layers of composite map
for m in self._maps:
# Parameters for plotting
bl = m._get_lon_lat(m.bottom_left_coord)
tr = m._get_lon_lat(m.top_right_coord)
x_range = list(u.Quantity([bl[0], tr[0]]).to(m.spatial_units[0]).value)
y_range = list(u.Quantity([bl[1], tr[1]]).to(m.spatial_units[1]).value)
params = {
"origin": "lower",
"extent": x_range + y_range,
"cmap": m.plot_settings['cmap'],
"norm": m.plot_settings['norm'],
"alpha": m.alpha,
"zorder": m.zorder,
}
params.update(matplot_args)
# The request to show a map layer rendered as a contour is indicated by a
# non False levels property. If levels is False, then the layer is
# rendered using imshow.
if m.levels is False:
# Check for the presence of masked map data
if m.mask is None:
ret.append(axes.imshow(m.data, **params))
else:
ret.append(axes.imshow(np.ma.array(np.asarray(m.data), mask=m.mask), **params))
else:
# Check for the presence of masked map data
if m.mask is None:
ret.append(axes.contour(m.data, m.levels, **params))
else:
ret.append(axes.contour(np.ma.array(np.asarray(m.data), mask=m.mask), m.levels, **params))
# Set the label of the first line so a legend can be created
ret[-1].collections[0].set_label(m.name)
# Adjust axes extents to include all data
axes.axis('image')
# Set current image (makes colorbar work)
plt.sci(ret[0])
return ret
def annotate_frame(i):
axes.set_title("{s.name}".format(s=self[i]))
axes.set_xlabel(axis_labels_from_ctype(self[i].coordinate_system[0],
self[i].spatial_units[0]))
axes.set_ylabel(axis_labels_from_ctype(self[i].coordinate_system[1],
self[i].spatial_units[1]))
def plot(self, axes=None, annotate=True,
title="SunPy Composite Plot", **matplot_args):
"""Plots the composite map object by calling :meth:`~sunpy.map.GenericMap.plot`
or :meth:`~sunpy.map.GenericMap.draw_contours`.
By default, each map is plotted as an image. If a given map has levels
defined (via :meth:`~sunpy.map.CompositeMap.set_levels`), that map will instead
be plotted as contours.
Parameters
----------
axes: `~matplotlib.axes.Axes` or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
annotate : `bool`
If true, the data is plotted at it's natural scale; with
title and axis labels.
title : `str`
Title of the composite map.
**matplot_args : `dict`
Any additional Matplotlib arguments that should be used
when plotting.
Returns
-------
ret : `list`
List of axes image or quad contour sets that have been plotted.
Notes
-----
If a line-width Matplotlib argument (``linewidth``, ``linewidths``, or ``lw``)
is provided, it will apply only to those maps that are plotted as contours.
If a transformation is required to overlay the maps with the correct
alignment, the plot limits may need to be manually set because
Matplotlib autoscaling may not work as intended.
"""
# If axes are not provided, create a WCSAxes based on the first map
if not axes:
axes = wcsaxes_compat.gca_wcs(self._maps[0].wcs)
if annotate:
axes.set_xlabel(axis_labels_from_ctype(self._maps[0].coordinate_system[0],
self._maps[0].spatial_units[0]))
axes.set_ylabel(axis_labels_from_ctype(self._maps[0].coordinate_system[1],
self._maps[0].spatial_units[1]))
axes.set_title(title)
# Define a list of plotted objects
ret = []
# Plot layers of composite map
for m in self._maps:
# Parameters for plotting
params = {
"alpha": m.alpha,
"zorder": m.zorder,
}
params.update(matplot_args)
# The request to show a map layer rendered as a contour is indicated by a
# non False levels property.
if m.levels is False:
# Check if any linewidth argument is provided, if so, then delete it from params.
for item in ['linewidth', 'linewidths', 'lw']:
if item in matplot_args:
del params[item]
# We tell GenericMap.plot() that we need to autoalign the map
if wcsaxes_compat.is_wcsaxes(axes):
params['autoalign'] = True
params['annotate'] = False
ret.append(m.plot(**params))
else:
ret.append(m.draw_contours(m.levels, **params))
# Set the label of the first line so a legend can be created
ret[-1].collections[0].set_label(m.name)
# Adjust axes extents to include all data
axes.axis('image')
# Set current image (makes colorbar work)
plt.sci(ret[0])
return ret
def plot(self, axes=None, annotate=True, # pylint: disable=W0613
title="SunPy Composite Plot", **matplot_args):
"""Plots the composite map object using matplotlib
Parameters
----------
axes: `~matplotlib.axes.Axes` or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
annotate : `bool`
If true, the data is plotted at it's natural scale; with
title and axis labels.
title : `str`
Title of the composite map.
**matplot_args : `dict`
Matplotlib Any additional imshow arguments that should be used
when plotting.
Returns
-------
ret : `list`
List of axes image or quad contour sets that have been plotted.
"""
# Get current axes
if not axes:
axes = plt.gca()
if annotate:
axes.set_xlabel(axis_labels_from_ctype(self._maps[0].coordinate_system[0],
self._maps[0].spatial_units[0]))
axes.set_ylabel(axis_labels_from_ctype(self._maps[0].coordinate_system[1],
self._maps[0].spatial_units[1]))
axes.set_title(title)
# Define a list of plotted objects
ret = []
# Plot layers of composite map
for m in self._maps:
# Parameters for plotting
params = {
"origin": "lower",
"extent": list(m.xrange.value) + list(m.yrange.value),
"cmap": m.plot_settings['cmap'],
"norm": m.plot_settings['norm'],
"alpha": m.alpha,
"zorder": m.zorder,
}
params.update(matplot_args)
if m.levels is False:
ret.append(axes.imshow(m.data, **params))
# Use contour for contour data, and imshow otherwise
if m.levels is not False:
# Set data with values <= 0 to transparent
# contour_data = np.ma.masked_array(m, mask=(m <= 0))
ret.append(axes.contour(m.data, m.levels, **params))
# Set the label of the first line so a legend can be created
ret[-1].collections[0].set_label(m.name)
# Adjust axes extents to include all data
axes.axis('image')
# Set current image (makes colorbar work)
plt.sci(ret[0])
return ret
def plot(self, axes=None, annotate=True,
title="SunPy Composite Plot", **matplot_args):
"""Plots the composite map object by calling :meth:`~sunpy.map.GenericMap.plot`
or :meth:`~sunpy.map.GenericMap.draw_contours`.
By default, each map is plotted as an image. If a given map has levels
defined (via :meth:`~sunpy.map.CompositeMap.set_levels`), that map will instead
be plotted as contours.
Parameters
----------
axes: `~matplotlib.axes.Axes` or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
annotate : `bool`
If true, the data is plotted at it's natural scale; with
title and axis labels.
title : `str`
Title of the composite map.
**matplot_args : `dict`
Any additional Matplotlib arguments that should be used
when plotting.
Returns
-------
ret : `list`
List of axes image or quad contour sets that have been plotted.
Notes
-----
Images are plotted using either `~matplotlib.axes.Axes.imshow` or
`~matplotlib.axes.Axes.pcolormesh`, and contours are plotted using
`~matplotlib.axes.Axes.contour`.
The Matplotlib arguments accepted by the plotting method are passed to it.
(For compatability reasons, we enforce a more restrictive set of
accepted `~matplotlib.axes.Axes.pcolormesh` arguments.)
If any Matplotlib arguments are not used by any plotting method,
a ``TypeError`` will be raised.
The ``sunpy.map.compositemap`` module includes variables which list the
full set of arguments passed to each plotting method. These are:
>>> import sunpy.map.compositemap
>>> sorted(sunpy.map.compositemap.ACCEPTED_IMSHOW_KWARGS)
{ACCEPTED_IMSHOW_KWARGS}
>>> sorted(sunpy.map.compositemap.ACCEPTED_PCOLORMESH_KWARGS)
{ACCEPTED_PCOLORMESH_KWARGS}
>>> sorted(sunpy.map.compositemap.ACCEPTED_CONTOUR_KWARGS)
{ACCEPTED_CONTOUR_KWARGS}
If a transformation is required to overlay the maps with the correct
alignment, the plot limits may need to be manually set because
Matplotlib autoscaling may not work as intended.
"""
# If axes are not provided, create a WCSAxes based on the first map
if not axes:
axes = wcsaxes_compat.gca_wcs(self._maps[0].wcs)
if annotate:
axes.set_xlabel(axis_labels_from_ctype(self._maps[0].coordinate_system[0],
self._maps[0].spatial_units[0]))
axes.set_ylabel(axis_labels_from_ctype(self._maps[0].coordinate_system[1],
self._maps[0].spatial_units[1]))
axes.set_title(title)
# Checklist to determine unused keywords in `matplot_args`
unused_kwargs = set(matplot_args.keys())
# Define a list of plotted objects
ret = []
# Plot layers of composite map
for m in self._maps:
# Parameters for plotting
params = {
"alpha": m.alpha,
"zorder": m.zorder,
}
params.update(matplot_args)
# The request to show a map layer rendered as a contour is indicated by a
# non False levels property.
if m.levels is False:
# We tell GenericMap.plot() that we need to autoalign the map
if wcsaxes_compat.is_wcsaxes(axes):
params['autoalign'] = True
# Filter `matplot_args`
if params.get('autoalign', None) in (True, 'pcolormesh'):
accepted_kwargs = ACCEPTED_PCOLORMESH_KWARGS
else:
accepted_kwargs = ACCEPTED_IMSHOW_KWARGS
for item in matplot_args.keys():
if item not in accepted_kwargs:
del params[item]
else: # mark as used
unused_kwargs -= {item}
params['annotate'] = False
ret.append(m.plot(**params))
else:
# Filter `matplot_args`
for item in matplot_args.keys():
if item not in ACCEPTED_CONTOUR_KWARGS:
del params[item]
else: # mark as used
unused_kwargs -= {item}
ret.append(m.draw_contours(m.levels, **params))
# Set the label of the first line so a legend can be created
ret[-1].collections[0].set_label(m.name)
if len(unused_kwargs) > 0:
raise TypeError(f'plot() got unexpected keyword arguments {unused_kwargs}')
# Adjust axes extents to include all data
axes.axis('image')
# Set current image (makes colorbar work)
plt.sci(ret[0])
return ret
