def pcolormesh(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.pcolormesh`.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 axisaligned: ``False`` (default). If ``True``, or if the workspace has a variable
number of bins, the polygons will be aligned with the axes
'''
_setLabels2D(axes, workspace)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
x, y, z = get_md_data2d_bin_bounds(workspace, normalization)
else:
(aligned, kwargs) = get_data_uneven_flag(workspace, **kwargs)
(distribution, kwargs) = get_distribution(workspace, **kwargs)
if aligned:
kwargs['pcolortype'] = 'mesh'
return _pcolorpieces(axes, workspace, distribution, *args,
**kwargs)
else:
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=True)
return axes.pcolormesh(x, y, z, *args, **kwargs)
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 scatter(axes, workspace, *args, **kwargs):
'''
Unpack mantid workspace and render it with matplotlib. ``args`` and
``kwargs`` are passed to :py:meth:`matplotlib.axes.Axes.scatter` 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 workspace. ``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 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
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
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
(x, y, _) = get_md_data1d(workspace, normalization)
else:
(wkspIndex, distribution,
kwargs) = get_wksp_index_dist_and_label(workspace, **kwargs)
(x, y, _, _) = get_spectrum(workspace, wkspIndex, distribution)
_setLabels1D(axes, workspace)
return axes.scatter(x, y, *args, **kwargs)
def contourf(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.contourf`
but calculates the countour levels. Currently this only works with
workspaces that have a constant number of bins between spectra.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
x, y, z = get_md_data2d_bin_centers(workspace, normalization)
else:
(distribution, kwargs) = get_distribution(workspace, **kwargs)
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=False)
_setLabels2D(axes, workspace)
return axes.contourf(x, y, z, *args, **kwargs)
def tripcolor(axes, workspace, *args, **kwargs):
'''
To be used with non-uniform grids. Currently this only works with workspaces
that have a constant number of bins between spectra or with
MDHistoWorkspaces.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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
See :meth:`matplotlib.axes.Axes.tripcolor` for more information.
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
x_temp, y_temp, z = get_md_data2d_bin_centers(workspace, normalization)
x, y = numpy.meshgrid(x_temp, y_temp)
else:
(distribution, kwargs) = get_distribution(workspace, **kwargs)
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=False)
_setLabels2D(axes, workspace)
return axes.tripcolor(x.ravel(), y.ravel(), z.ravel(), *args, **kwargs)
def plot(axes, workspace, *args, **kwargs):
'''
3D plots - line plots
:param axes: class:`matplotlib.axes.Axes3D` object that will do the plotting
:param workspace: :class:`mantid.api.MatrixWorkspace` or
:class:`mantid.api.IMDHistoWorkspace` to extract the data from
:param zdir: Which direction to use as z ('x', 'y' or 'z') when plotting a 2D set.
: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.
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
(x, y, z) = get_md_data1d(workspace, normalization, indices)
else:
(wksp_index, distribution, kwargs) = get_wksp_index_dist_and_label(workspace, **kwargs)
(x, z, _, _) = get_spectrum(workspace, wksp_index, distribution, withDy=False, withDx=False)
y_val = workspace.getAxis(1).extractValues()[wksp_index]
y = [y_val for _ in range(len(x))] # fill x size array with y value
_set_labels_3d(axes, workspace)
return axes.plot(x, y, z, *args, **kwargs)
def imshow(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.imshow`.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 axisaligned: ``False`` (default). If ``True``, or if the workspace has a variable
number of bins, the polygons will be aligned with the axes
'''
_setLabels2D(axes, workspace)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
x, y, z = get_md_data2d_bin_bounds(workspace, normalization)
else:
(uneven_bins, kwargs) = get_data_uneven_flag(workspace, **kwargs)
(distribution, kwargs) = get_distribution(workspace, **kwargs)
if check_resample_to_regular_grid(workspace):
(x, y, z) = get_matrix_2d_ragged(workspace, distribution, histogram2D=True)
else:
(x, y, z) = get_matrix_2d_data(workspace, distribution, histogram2D=True)
if 'extent' not in kwargs:
if x.ndim == 2 and y.ndim == 2:
kwargs['extent'] = [x[0, 0], x[0, -1], y[0, 0], y[-1, 0]]
else:
kwargs['extent'] = [x[0], x[-1], y[0], y[-1]]
return mantid.plots.modest_image.imshow(axes, z, *args, **kwargs)
def plot(axes, workspace, *args, **kwargs):
'''
3D plots - line plots
:param axes: class:`matplotlib.axes.Axes3D` object that will do the plotting
:param workspace: :class:`mantid.api.MatrixWorkspace` or
:class:`mantid.api.IMDHistoWorkspace` to extract the data from
:param zdir: Which direction to use as z ('x', 'y' or 'z') when plotting a 2D set.
: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.
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
(x, y, z) = get_md_data1d(workspace, normalization, indices)
else:
(wksp_index, distribution,
kwargs) = get_wksp_index_dist_and_label(workspace, **kwargs)
(x, z, _, _) = get_spectrum(workspace,
wksp_index,
distribution,
withDy=False,
withDx=False)
y_val = workspace.getAxis(1).extractValues()[wksp_index]
y = [y_val for _ in range(len(x))] # fill x size array with y value
_set_labels_3d(axes, workspace)
return axes.plot(x, y, z, *args, **kwargs)
def _extract_3d_data(workspace, **kwargs):
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
normalization, kwargs = get_normalization(workspace, **kwargs)
x_temp, y_temp, z = get_md_data2d_bin_centers(workspace, normalization)
x, y = numpy.meshgrid(x_temp, y_temp)
else:
distribution, kwargs = get_distribution(workspace, **kwargs)
x, y, z = get_matrix_2d_data(workspace, distribution, histogram2D=False)
return x, y, z
def tricontourf(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`mantid.plots.contourf`, but works
for non-uniform grids. Currently this only works with workspaces
that have a constant number of bins between spectra or with
MDHistoWorkspaces.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 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 dimensions to plot. *e.g.* to select the last two axes to plot from a
3D volume use ``indices=(5, slice(None), slice(None))`` where the 5 is the bin selected
for the first axis.
: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 last
two axes to plot from a 3D volume use ``slicepoint=(1.0, None, None)`` where the 1.0 is
the value of the dimension selected for the first axis.
:param transpose: ``bool`` to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace
See :meth:`matplotlib.axes.Axes.tricontourf` for more information.
'''
transpose = kwargs.pop('transpose', False)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
x_temp, y_temp, z = get_md_data2d_bin_centers(workspace, normalization,
indices, transpose)
x, y = numpy.meshgrid(x_temp, y_temp)
_setLabels2D(axes, workspace, indices, transpose)
else:
(distribution, kwargs) = get_distribution(workspace, **kwargs)
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=False,
transpose=transpose)
_setLabels2D(axes, workspace, transpose=transpose)
# tricontourf segfaults if many z values are not finite
# https://github.com/matplotlib/matplotlib/issues/10167
x = x.ravel()
y = y.ravel()
z = z.ravel()
condition = numpy.isfinite(z)
x = x[condition]
y = y[condition]
z = z[condition]
return axes.tricontourf(x, y, z, *args, **kwargs)
def _extract_3d_data(workspace, **kwargs):
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
normalization, kwargs = get_normalization(workspace, **kwargs)
x_temp, y_temp, z = get_md_data2d_bin_centers(workspace, normalization)
x, y = numpy.meshgrid(x_temp, y_temp)
else:
distribution, kwargs = get_distribution(workspace, **kwargs)
x, y, z = get_matrix_2d_data(workspace,
distribution,
histogram2D=False)
return x, y, z
def imshow(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.imshow`.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 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 dimensions to plot. *e.g.* to select the last two axes to plot from a
3D volume use ``indices=(5, slice(None), slice(None))`` where the 5 is the bin selected
for the first axis.
: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 last
two axes to plot from a 3D volume use ``slicepoint=(1.0, None, None)`` where the 1.0 is
the value of the dimension selected for the first axis.
:param axisaligned: ``False`` (default). If ``True``, or if the workspace has a variable
number of bins, the polygons will be aligned with the axes
:param transpose: ``bool`` to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace
'''
transpose = kwargs.pop('transpose', False)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
x, y, z = get_md_data2d_bin_bounds(workspace, normalization, indices,
transpose)
_setLabels2D(axes, workspace, indices, transpose)
else:
(uneven_bins, kwargs) = get_data_uneven_flag(workspace, **kwargs)
(distribution, kwargs) = get_distribution(workspace, **kwargs)
if check_resample_to_regular_grid(workspace):
(x, y, z) = get_matrix_2d_ragged(workspace,
distribution,
histogram2D=True,
transpose=transpose)
else:
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=True,
transpose=transpose)
_setLabels2D(axes, workspace, transpose=transpose)
if 'extent' not in kwargs:
if x.ndim == 2 and y.ndim == 2:
kwargs['extent'] = [x[0, 0], x[0, -1], y[0, 0], y[-1, 0]]
else:
kwargs['extent'] = [x[0], x[-1], y[0], y[-1]]
return mantid.plots.modest_image.imshow(axes, z, *args, **kwargs)
def tricontourf(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`mantid.plots.contourf`, but works
for non-uniform grids. Currently this only works with workspaces
that have a constant number of bins between spectra or with
MDHistoWorkspaces.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 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 dimensions to plot. *e.g.* to select the last two axes to plot from a
3D volume use ``indices=(5, slice(None), slice(None))`` where the 5 is the bin selected
for the first axis.
: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 last
two axes to plot from a 3D volume use ``slicepoint=(1.0, None, None)`` where the 1.0 is
the value of the dimension selected for the first axis.
:param transpose: ``bool`` to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace
See :meth:`matplotlib.axes.Axes.tricontourf` for more information.
'''
transpose = kwargs.pop('transpose', False)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
x_temp, y_temp, z = get_md_data2d_bin_centers(workspace, normalization, indices, transpose)
x, y = numpy.meshgrid(x_temp, y_temp)
_setLabels2D(axes, workspace, indices, transpose)
else:
(distribution, kwargs) = get_distribution(workspace, **kwargs)
(x, y, z) = get_matrix_2d_data(workspace, distribution, histogram2D=False, transpose=transpose)
_setLabels2D(axes, workspace, transpose=transpose)
# tricontourf segfaults if many z values are not finite
# https://github.com/matplotlib/matplotlib/issues/10167
x = x.ravel()
y = y.ravel()
z = z.ravel()
condition = numpy.isfinite(z)
x = x[condition]
y = y[condition]
z = z[condition]
return axes.tricontourf(x, y, z, *args, **kwargs)
def imshow(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.imshow`.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 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 dimensions to plot. *e.g.* to select the last two axes to plot from a
3D volume use ``indices=(5, slice(None), slice(None))`` where the 5 is the bin selected
for the first axis.
: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 last
two axes to plot from a 3D volume use ``slicepoint=(1.0, None, None)`` where the 1.0 is
the value of the dimension selected for the first axis.
:param axisaligned: ``False`` (default). If ``True``, or if the workspace has a variable
number of bins, the polygons will be aligned with the axes
:param transpose: ``bool`` to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace
'''
transpose = kwargs.pop('transpose', False)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
x, y, z = get_md_data2d_bin_bounds(workspace, normalization, indices, transpose)
_setLabels2D(axes, workspace, indices, transpose)
else:
(uneven_bins, kwargs) = get_data_uneven_flag(workspace, **kwargs)
(distribution, kwargs) = get_distribution(workspace, **kwargs)
if check_resample_to_regular_grid(workspace):
(x, y, z) = get_matrix_2d_ragged(workspace, distribution, histogram2D=True, transpose=transpose)
else:
(x, y, z) = get_matrix_2d_data(workspace, distribution, histogram2D=True, transpose=transpose)
_setLabels2D(axes, workspace, transpose=transpose)
if 'extent' not in kwargs:
if x.ndim == 2 and y.ndim == 2:
kwargs['extent'] = [x[0, 0], x[0, -1], y[0, 0], y[-1, 0]]
else:
kwargs['extent'] = [x[0], x[-1], y[0], y[-1]]
return mantid.plots.modest_image.imshow(axes, z, *args, **kwargs)
def scatter(axes, workspace, *args, **kwargs):
'''
Unpack mantid workspace and render it with matplotlib. ``args`` and
``kwargs`` are passed to :py:meth:`matplotlib.axes.Axes.scatter` 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 workspace. ``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 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 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
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
(x, y, _) = get_md_data1d(workspace, normalization, indices)
_setLabels1D(axes, workspace, indices)
else:
(wkspIndex, distribution,
kwargs) = get_wksp_index_dist_and_label(workspace, **kwargs)
(x, y, _, _) = get_spectrum(workspace, wkspIndex, distribution)
_setLabels1D(axes, workspace)
return axes.scatter(x, y, *args, **kwargs)
def scatter(axes, workspace, *args, **kwargs):
'''
Unpack mantid workspace and render it with matplotlib. ``args`` and
``kwargs`` are passed to :py:meth:`matplotlib.axes.Axes.scatter` 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 workspace. ``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 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 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
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
(x, y, _) = get_md_data1d(workspace, normalization, indices)
_setLabels1D(axes, workspace, indices)
else:
(wkspIndex, distribution, kwargs) = get_wksp_index_dist_and_label(workspace, **kwargs)
(x, y, _, _) = get_spectrum(workspace, wkspIndex, distribution)
_setLabels1D(axes, workspace)
return axes.scatter(x, y, *args, **kwargs)
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
else:
axis = kwargs.pop("axis", MantidAxType.SPECTRUM)
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, distribution, with_dy, with_dx)
else:
raise ValueError("Axis {} is not a valid axis number.".format(axis))
indices = None
return x, y, dy, dx, indices, kwargs
def plot(axes, workspace, *args, **kwargs):
'''
3D plots - line plots
:param axes: class:`matplotlib.axes.Axes3D` object that will do the plotting
:param workspace: :class:`mantid.api.MatrixWorkspace` or
:class:`mantid.api.IMDHistoWorkspace` to extract the data from
:param zdir: Which direction to use as z ('x', 'y' or 'z') when plotting a 2D set.
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
(x, y, z) = get_md_data1d(workspace, normalization)
else:
(wksp_index, distribution, kwargs) = get_wksp_index_dist_and_label(workspace, **kwargs)
(x, z, _, _) = get_spectrum(workspace, wksp_index, distribution, withDy=False, withDx=False)
y_val = workspace.getAxis(1).extractValues()[wksp_index]
y = [y_val for _ in range(len(x))] # fill x size array with y value
_set_labels_3d(axes, workspace)
return axes.plot(x, y, z, *args, **kwargs)
def contourf(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.contourf`
but calculates the countour levels. Currently this only works with
workspaces that have a constant number of bins between spectra.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 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 dimensions to plot. *e.g.* to select the last two axes to plot from a
3D volume use ``indices=(5, slice(None), slice(None))`` where the 5 is the bin selected
for the first axis.
: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 last
two axes to plot from a 3D volume use ``slicepoint=(1.0, None, None)`` where the 1.0 is
the value of the dimension selected for the first axis.
:param transpose: ``bool`` to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace
'''
transpose = kwargs.pop('transpose', False)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
x, y, z = get_md_data2d_bin_centers(workspace, normalization, indices,
transpose)
_setLabels2D(axes, workspace, indices, transpose)
else:
(distribution, kwargs) = get_distribution(workspace, **kwargs)
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=False,
transpose=transpose)
_setLabels2D(axes, workspace, transpose=transpose)
return axes.contourf(x, y, z, *args, **kwargs)
def tricontourf(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`mantid.plots.contourf`, but works
for non-uniform grids. Currently this only works with workspaces
that have a constant number of bins between spectra or with
MDHistoWorkspaces.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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
See :meth:`matplotlib.axes.Axes.tricontourf` for more information.
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
(x_temp, y_temp,
z) = get_md_data2d_bin_centers(workspace, normalization)
(x, y) = numpy.meshgrid(x_temp, y_temp)
else:
(distribution, kwargs) = get_distribution(workspace, **kwargs)
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=False)
_setLabels2D(axes, workspace)
# tricontourf segfaults if many z values are not finite
# https://github.com/matplotlib/matplotlib/issues/10167
x = x.ravel()
y = y.ravel()
z = z.ravel()
condition = numpy.isfinite(z)
x = x[condition]
y = y[condition]
z = z[condition]
return axes.tricontourf(x, y, z, *args, **kwargs)
def imshow(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.imshow`.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 axisaligned: ``False`` (default). If ``True``, or if the workspace has a variable
number of bins, the polygons will be aligned with the axes
'''
_setLabels2D(axes, workspace)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
x, y, z = get_md_data2d_bin_bounds(workspace, normalization)
else:
(uneven_bins, kwargs) = get_data_uneven_flag(workspace, **kwargs)
(distribution, kwargs) = get_distribution(workspace, **kwargs)
if uneven_bins:
raise Exception(
'Variable number of bins is not supported by imshow.')
else:
(x, y, z) = get_matrix_2d_data(workspace,
distribution,
histogram2D=True)
diffs = numpy.diff(x, axis=1)
x_spacing_equal = numpy.alltrue(diffs == diffs[0])
diffs = numpy.diff(y, axis=0)
y_spacing_equal = numpy.alltrue(diffs == diffs[0])
if not x_spacing_equal or not y_spacing_equal:
raise Exception('Unevenly spaced bins are not supported by imshow')
if 'extent' not in kwargs:
kwargs['extent'] = [x[0, 0], x[0, -1], y[0, 0], y[-1, 0]]
return _imshow(axes, z, *args, **kwargs)
def contourf(axes, workspace, *args, **kwargs):
'''
Essentially the same as :meth:`matplotlib.axes.Axes.contourf`
but calculates the countour levels. Currently this only works with
workspaces that have a constant number of bins between spectra.
: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 distribution: ``None`` (default) asks the workspace. ``False`` means
divide by bin width. ``True`` means do not divide by bin width.
Applies only when the the matrix workspace is a histogram.
: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 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 dimensions to plot. *e.g.* to select the last two axes to plot from a
3D volume use ``indices=(5, slice(None), slice(None))`` where the 5 is the bin selected
for the first axis.
: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 last
two axes to plot from a 3D volume use ``slicepoint=(1.0, None, None)`` where the 1.0 is
the value of the dimension selected for the first axis.
:param transpose: ``bool`` to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace
'''
transpose = kwargs.pop('transpose', False)
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
indices, kwargs = get_indices(workspace, **kwargs)
x, y, z = get_md_data2d_bin_centers(workspace, normalization, indices, transpose)
_setLabels2D(axes, workspace, indices, transpose)
else:
(distribution, kwargs) = get_distribution(workspace, **kwargs)
(x, y, z) = get_matrix_2d_data(workspace, distribution, histogram2D=False, transpose=transpose)
_setLabels2D(axes, workspace, transpose=transpose)
return axes.contourf(x, y, z, *args, **kwargs)
def plot(axes, workspace, *args, **kwargs):
'''
3D plots - line plots
:param axes: class:`matplotlib.axes.Axes3D` object that will do the plotting
:param workspace: :class:`mantid.api.MatrixWorkspace` or
:class:`mantid.api.IMDHistoWorkspace` to extract the data from
:param zdir: Which direction to use as z ('x', 'y' or 'z') when plotting a 2D set.
'''
if isinstance(workspace, mantid.dataobjects.MDHistoWorkspace):
(normalization, kwargs) = get_normalization(workspace, **kwargs)
(x, y, z) = get_md_data1d(workspace, normalization)
else:
(wksp_index, distribution,
kwargs) = get_wksp_index_dist_and_label(workspace, **kwargs)
(x, z, _, _) = get_spectrum(workspace,
wksp_index,
distribution,
withDy=False,
withDx=False)
y_val = workspace.getAxis(1).extractValues()[wksp_index]
y = [y_val for _ in range(len(x))] # fill x size array with y value
_set_labels_3d(axes, workspace)
return axes.plot(x, y, z, *args, **kwargs)
