def _get_data_for_plot(axes, workspace, kwargs, with_dy=False, with_dx=False):
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
(x, y, dy) = get_md_data1d(workspace, normalization, indices)
dx = None
axis = None
else:
axis = MantidAxType(kwargs.pop("axis", MantidAxType.SPECTRUM))
normalize_by_bin_width, kwargs = get_normalize_by_bin_width(
workspace, axes, **kwargs)
workspace_index, distribution, kwargs = get_wksp_index_dist_and_label(
workspace, axis, **kwargs)
if axis == MantidAxType.BIN:
# Overwrite any user specified xlabel
axes.set_xlabel("Spectrum")
x, y, dy, dx = get_bins(workspace, workspace_index, with_dy)
elif axis == MantidAxType.SPECTRUM:
x, y, dy, dx = get_spectrum(workspace, workspace_index,
normalize_by_bin_width, with_dy,
with_dx)
else:
raise ValueError(
"Axis {} is not a valid axis number.".format(axis))
indices = None
return x, y, dy, dx, indices, axis, kwargs
def _plot_impl(axes, workspace, args, kwargs):
"""
Compute data and labels for plot. Used by workspace
replacement handlers to recompute data. See plot for
argument details
"""
if 'LogName' in kwargs:
(x, y, FullTime, LogName, units,
kwargs) = get_sample_log(workspace, **kwargs)
axes.set_ylabel('{0} ({1})'.format(LogName, units))
axes.set_xlabel('Time (s)')
if FullTime:
axes.xaxis_date()
axes.xaxis.set_major_formatter(
mdates.DateFormatter('%H:%M:%S\n%b-%d'))
axes.set_xlabel('Time')
kwargs['linestyle'] = 'steps-post'
else:
normalize_by_bin_width, kwargs = get_normalize_by_bin_width(
workspace, axes, **kwargs)
x, y, _, _, indices, axis, kwargs = _get_data_for_plot(
axes, workspace, kwargs)
if kwargs.pop('update_axes_labels', True):
_setLabels1D(axes,
workspace,
indices,
normalize_by_bin_width=normalize_by_bin_width,
axis=axis)
return x, y, 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(axes, workspace, *args, **kwargs):
"""
Unpack mantid workspace and render it with matplotlib. ``args`` and
``kwargs`` are passed to :py:meth:`matplotlib.axes.Axes.errorbar` after special
keyword arguments are removed. This will automatically label the
line according to the spectrum number unless specified otherwise.
:param axes: :class:`matplotlib.axes.Axes` object that will do the plotting
:param workspace: :class:`mantid.api.MatrixWorkspace` or :class:`mantid.api.IMDHistoWorkspace`
to extract the data from
:param specNum: spectrum number to plot if MatrixWorkspace
:param wkspIndex: workspace index to plot if MatrixWorkspace
:param distribution: ``None`` (default) asks the global setting. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the workspace is a MatrixWorkspace histogram.
:param normalize_by_bin_width: Plot the workspace as a distribution. If None default to global
setting: config['graph1d.autodistribution']
:param normalization: ``None`` (default) ask the workspace. Applies to MDHisto workspaces. It can override
the value from displayNormalizationHisto. It checks only if
the normalization is mantid.api.MDNormalization.NumEventsNormalization
:param axis: Specify which axis will be plotted. Use axis=MantidAxType.BIN to plot a bin,
and axis=MantidAxType.SPECTRUM to plot a spectrum.
The default value is axis=1, plotting spectra by default.
:param indices: Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple
and will be interpreted as a list of indices. You need to use ``slice(None)`` to
select which dimension to plot. *e.g.* to select the second axis to plot from a
3D volume use ``indices=(5, slice(None), 10)`` where the 5/10 are the bins selected
for the other 2 axes.
:param slicepoint: Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units.
You need to use ``None`` to select which dimension to plot. *e.g.* to select the second
axis to plot from a 3D volume use ``slicepoint=(1.0, None, 2.0)`` where the 1.0/2.0 are
the dimension selected for the other 2 axes.
For matrix workspaces with more than one spectra, either ``specNum`` or ``wkspIndex``
needs to be specified. Giving both will generate a :class:`RuntimeError`. There is no similar
keyword for MDHistoWorkspaces. These type of workspaces have to have exactly one non integrated
dimension
"""
normalize_by_bin_width, kwargs = get_normalize_by_bin_width(
workspace, axes, **kwargs)
x, y, dy, dx, indices, axis, kwargs = _get_data_for_plot(axes,
workspace,
kwargs,
with_dy=True,
with_dx=False)
if kwargs.pop('update_axes_labels', True):
_setLabels1D(axes,
workspace,
indices,
normalize_by_bin_width=normalize_by_bin_width,
axis=axis)
return axes.errorbar(x, y, dy, dx, *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')
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)
