def _pcolor_func(self, name, *args, **kwargs):
"""
Implementation of pcolor-style methods
:param name: The name of the method
:param args: The args passed from the user
:param kwargs: The kwargs passed from the use
:return: The return value of the pcolor* function
"""
plotfunctions_func = getattr(plotfunctions, name)
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
def _update_data(artists, workspace):
return self._redraw_colorplot(plotfunctions_func, artists,
workspace, **kwargs)
workspace = args[0]
# We return the last mesh so the return type is a single artist like the standard Axes
artists = self.track_workspace_artist(
workspace, plotfunctions_func(self, *args, **kwargs),
_update_data)
try:
return artists[-1]
except TypeError:
return artists
else:
return getattr(Axes, name)(self, *args, **kwargs)
def plot(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.plot` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.plot(workspace,'rs',specNum=1) #for workspaces
ax.plot(x,y,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.plot`.
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
def _data_update(artists, workspace):
# It's only possible to plot 1 line at a time from a workspace
x, y, _, __ = plotfunctions._plot_impl(self, workspace, args, kwargs)
artists[0].set_data(x, y)
self.relim()
self.autoscale()
return artists
return self.track_workspace_artist(args[0], plotfunctions.plot(self, *args, **kwargs), _data_update)
else:
return Axes.plot(self, *args, **kwargs)
def tricontourf(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.tricontourf` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.tricontourf(workspace) #for workspaces
ax.tricontourf(x,y,z) #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.tricontourf`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
workspace = args[0]
return self.track_workspace_artist(workspace,
plotfunctions.tricontourf(self, *args, **kwargs))
else:
return Axes.tricontourf(self, *args, **kwargs)
def tricontourf(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.tricontourf` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.tricontourf(workspace) #for workspaces
ax.tricontourf(x,y,z) #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.tricontourf`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
workspace = args[0]
return self.track_workspace_artist(
workspace, plotfunctions.tricontourf(self, *args, **kwargs))
else:
return Axes.tricontourf(self, *args, **kwargs)
def _pcolor_func(self, name, *args, **kwargs):
"""
Implementation of pcolor-style methods
:param name: The name of the method
:param args: The args passed from the user
:param kwargs: The kwargs passed from the use
:return: The return value of the pcolor* function
"""
plotfunctions_func = getattr(plotfunctions, name)
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
def _update_data(artists, workspace):
return self._redraw_colorplot(plotfunctions_func,
artists, workspace, **kwargs)
workspace = args[0]
# We return the last mesh so the return type is a single artist like the standard Axes
artists = self.track_workspace_artist(workspace,
plotfunctions_func(self, *args, **kwargs),
_update_data)
try:
return artists[-1]
except TypeError:
return artists
else:
return getattr(Axes, name)(self, *args, **kwargs)
def imshow(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.imshow` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.imshow(workspace) #for workspaces
ax.imshow(C) #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.imshow`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
def _update_data(artists, workspace):
return self._redraw_colorplot(plotfunctions.imshow,
artists, workspace, **kwargs)
return self.track_workspace_artist(args[0], plotfunctions.imshow(self, *args, **kwargs),
_update_data)
else:
return Axes.imshow(self, *args, **kwargs)
def errorbar(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.errorbar` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.errorbar(workspace,'rs',specNum=1) #for workspaces
ax.errorbar(x,y,yerr,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.errorbar`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
def _data_update(artists, workspace):
# errorbar with workspaces can only return a single container
container_orig = artists[0]
# It is not possible to simply reset the error bars so
# we have to plot new lines but ensure we don't reorder them on the plot!
orig_idx = self.containers.index(container_orig)
container_orig.remove()
# The container does not remove itself from the containers list
# but protect this just in case matplotlib starts doing this
try:
self.containers.remove(container_orig)
except ValueError:
pass
# this gets pushed back onto the containers list
container_new = plotfunctions.errorbar(self, workspace,
**kwargs)
self.containers.insert(orig_idx, container_new)
self.containers.pop()
# update line properties to match original
orig_flat, new_flat = cbook.flatten(
container_orig), cbook.flatten(container_new)
for artist_orig, artist_new in zip(orig_flat, new_flat):
artist_new.update_from(artist_orig)
# ax.relim does not support collections...
self._update_line_limits(container_new[0])
self.autoscale()
return container_new
workspace = args[0]
spec_num = self._get_spec_number(workspace, kwargs)
return self.track_workspace_artist(workspace,
plotfunctions.errorbar(
self, *args, **kwargs),
_data_update,
spec_num=spec_num)
else:
return Axes.errorbar(self, *args, **kwargs)
def plot(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.plot` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.plot(workspace,'rs',specNum=1) #for workspaces
ax.plot(x,y,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.plot`.
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
autoscale_on_update = kwargs.pop("autoscale_on_update", True)
def _data_update(artists, workspace, new_kwargs=None):
# It's only possible to plot 1 line at a time from a workspace
if new_kwargs:
x, y, _, __ = plotfunctions._plot_impl(self, workspace, args,
new_kwargs)
else:
x, y, _, __ = plotfunctions._plot_impl(self, workspace, args,
kwargs)
artists[0].set_data(x, y)
self.relim()
if autoscale_on_update:
self.autoscale()
return artists
workspace = args[0]
spec_num = self.get_spec_number(workspace, kwargs)
normalize_by_bin_width, kwargs = get_normalize_by_bin_width(
workspace, self, **kwargs)
is_normalized = normalize_by_bin_width or workspace.isDistribution()
# If we are making the first plot on an axes object
# i.e. self.lines is empty, axes has default ylim values.
# Therefore we need to autoscale regardless of autoscale_on_update.
if self.lines:
# Otherwise set autoscale to autoscale_on_update.
self.set_autoscaley_on(autoscale_on_update)
artist = self.track_workspace_artist(
workspace, plotfunctions.plot(self, *args, **kwargs),
_data_update, spec_num, is_normalized)
self.set_autoscaley_on(True)
return artist
else:
return Axes.plot(self, *args, **kwargs)
def errorbar(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.errorbar` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.errorbar(workspace,'rs',specNum=1) #for workspaces
ax.errorbar(x,y,yerr,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.errorbar`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
def _data_update(artists, workspace):
# errorbar with workspaces can only return a single container
container_orig = artists[0]
# It is not possible to simply reset the error bars so
# we have to plot new lines but ensure we don't reorder them on the plot!
orig_idx = self.containers.index(container_orig)
container_orig.remove()
# The container does not remove itself from the containers list
# but protect this just in case matplotlib starts doing this
try:
self.containers.remove(container_orig)
except ValueError:
pass
# this gets pushed back onto the containers list
container_new = plotfunctions.errorbar(self, workspace, **kwargs)
self.containers.insert(orig_idx, container_new)
self.containers.pop()
# update line properties to match original
orig_flat, new_flat = cbook.flatten(container_orig), cbook.flatten(container_new)
for artist_orig, artist_new in zip(orig_flat, new_flat):
artist_new.update_from(artist_orig)
# ax.relim does not support collections...
self._update_line_limits(container_new[0])
self.autoscale()
return container_new
workspace = args[0]
spec_num = self._get_spec_number(workspace, kwargs)
return self.track_workspace_artist(workspace,
plotfunctions.errorbar(self, *args, **kwargs),
_data_update, spec_num=spec_num)
else:
return Axes.errorbar(self, *args, **kwargs)
def wrapper(self, *args, **kwargs):
func_value = func(self, *args, **kwargs)
# Saves saving it on array objects
if helperfunctions.validate_args(*args, **kwargs):
# Fill out kwargs with the values of args
kwargs["workspaces"] = args[0].name()
kwargs["function"] = func.__name__
if "cmap" in kwargs and isinstance(kwargs["cmap"], Colormap):
kwargs["cmap"] = kwargs["cmap"].name
self.creation_args.append(kwargs)
return func_value
def wrapper(self, *args, **kwargs):
func_value = func(self, *args, **kwargs)
# Saves saving it on array objects
if helperfunctions.validate_args(*args, **kwargs):
# Fill out kwargs with the values of args
kwargs["workspaces"] = args[0].name()
kwargs["function"] = func.__name__
if "cmap" in kwargs and isinstance(kwargs["cmap"], Colormap):
kwargs["cmap"] = kwargs["cmap"].name
self.creation_args.append(kwargs)
return func_value
def scatter(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.scatter` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.scatter(workspace,'rs',specNum=1) #for workspaces
ax.scatter(x,y,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.scatter`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
else:
return Axes.scatter(self, *args, **kwargs)
def scatter(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.scatter` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.scatter(workspace,'rs',specNum=1) #for workspaces
ax.scatter(x,y,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.scatter`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
else:
return Axes.scatter(self, *args, **kwargs)
def plot(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.plot` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.plot(workspace,'rs',specNum=1) #for workspaces
ax.plot(x,y,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.plot`.
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
def _data_update(artists, workspace):
# It's only possible to plot 1 line at a time from a workspace
x, y, _, __ = plotfunctions._plot_impl(self, workspace, args, kwargs)
artists[0].set_data(x, y)
self.relim()
self.autoscale()
return artists
workspace = args[0]
spec_num = self._get_spec_number(workspace, kwargs)
return self.track_workspace_artist(
workspace, plotfunctions.plot(self, *args, **kwargs),
_data_update, spec_num)
else:
return Axes.plot(self, *args, **kwargs)
def test_validate_args_success(self):
ws = CreateSampleWorkspace()
result = funcs.validate_args(ws)
self.assertEqual(True, result)
def errorbar(self, *args, **kwargs):
"""
If the **mantid** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes.errorbar` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.errorbar(workspace,'rs',specNum=1) #for workspaces
ax.errorbar(x,y,yerr,'bo') #for arrays
fig.show()
For keywords related to workspaces, see :func:`plotfunctions.errorbar`
"""
if helperfunctions.validate_args(*args):
logger.debug('using plotfunctions')
autoscale_on_update = kwargs.pop("autoscale_on_update", True)
def _data_update(artists, workspace, new_kwargs=None):
if self.lines:
self.set_autoscaley_on(autoscale_on_update)
# errorbar with workspaces can only return a single container
container_orig = artists[0]
# It is not possible to simply reset the error bars so
# we have to plot new lines but ensure we don't reorder them on the plot!
orig_idx = self.containers.index(container_orig)
container_orig.remove()
# The container does not remove itself from the containers list
# but protect this just in case matplotlib starts doing this
try:
self.containers.remove(container_orig)
except ValueError:
pass
# this gets pushed back onto the containers list
if new_kwargs:
container_new = plotfunctions.errorbar(self, workspace,
**new_kwargs)
else:
container_new = plotfunctions.errorbar(self, workspace,
**kwargs)
self.containers.insert(orig_idx, container_new)
self.containers.pop()
# Update joining line
if container_new[0] and container_orig[0]:
container_new[0].update_from(container_orig[0])
# Update caps
for orig_caps, new_caps in zip(container_orig[1], container_new[1]):
new_caps.update_from(orig_caps)
# Update bars
for orig_bars, new_bars in zip(container_orig[2], container_new[2]):
new_bars.update_from(orig_bars)
# Re-plotting in the config dialog will assign this attr
if hasattr(container_orig, 'errorevery'):
setattr(container_new, 'errorevery', container_orig.errorevery)
# ax.relim does not support collections...
self._update_line_limits(container_new[0])
self.set_autoscaley_on(True)
return container_new
workspace = args[0]
spec_num = self.get_spec_number(workspace, kwargs)
is_normalized, kwargs = get_normalize_by_bin_width(workspace, self,
**kwargs)
if self.lines:
self.set_autoscaley_on(autoscale_on_update)
artist = self.track_workspace_artist(
workspace, plotfunctions.errorbar(self, *args, **kwargs),
_data_update, spec_num, is_normalized)
self.set_autoscaley_on(True)
return artist
else:
return Axes.errorbar(self, *args, **kwargs)
def test_validate_args_success(self):
ws = CreateSampleWorkspace()
result = funcs.validate_args(ws)
self.assertEqual(True, result)
