def test_get_indices(self):
indices, kwargs = funcs.get_indices(self.ws_MD_2d)
self.assertIsNone(indices)
self.assertNotIn('label', kwargs)
self.assertRaises(AssertionError,
funcs.get_indices,
self.ws_MD_2d,
indices=(0, slice(None)))
self.assertRaises(AssertionError,
funcs.get_indices,
self.ws_MD_2d,
slicepoint=(0, None))
self.assertRaises(ValueError,
funcs.get_indices,
self.ws_MD_2d,
indices=(1, 2),
slicepoint=(3, 4))
indices, kwargs = funcs.get_indices(self.ws_MD_2d,
indices=(1, slice(None),
slice(None)))
np.testing.assert_equal(indices, (1, slice(None), slice(None)))
self.assertIn('label', kwargs)
self.assertEqual(kwargs['label'], 'ws_MD_2d: Dim1=-1.2')
indices, kwargs = funcs.get_indices(self.ws_MD_2d,
slicepoint=(-1, None, None))
np.testing.assert_equal(indices, (1, slice(None), slice(None)))
self.assertIn('label', kwargs)
self.assertEqual(kwargs['label'], 'ws_MD_2d: Dim1=-1.2')
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 _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(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 get_data_MDH(self, slicepoint, transpose=False):
indices, _ = get_indices(self.get_ws(), slicepoint=slicepoint)
if transpose:
return np.ma.masked_invalid(
self.get_ws().getSignalArray()[indices]).T
else:
return np.ma.masked_invalid(
self.get_ws().getSignalArray()[indices])
def test_get_indices(self):
indices, kwargs = funcs.get_indices(self.ws_MD_2d)
self.assertIsNone(indices)
self.assertNotIn('label', kwargs)
self.assertRaises(AssertionError, funcs.get_indices, self.ws_MD_2d, indices=(0,slice(None)))
self.assertRaises(AssertionError, funcs.get_indices, self.ws_MD_2d, slicepoint=(0,None))
self.assertRaises(ValueError, funcs.get_indices, self.ws_MD_2d, indices=(1,2), slicepoint=(3,4))
indices, kwargs = funcs.get_indices(self.ws_MD_2d,indices=(1,slice(None),slice(None)))
np.testing.assert_equal(indices, (1,slice(None),slice(None)))
self.assertIn('label', kwargs)
self.assertEqual(kwargs['label'], 'ws_MD_2d: Dim1=-1.2')
indices, kwargs = funcs.get_indices(self.ws_MD_2d,slicepoint=(-1,None,None))
np.testing.assert_equal(indices, (1,slice(None),slice(None)))
self.assertIn('label', kwargs)
self.assertEqual(kwargs['label'], 'ws_MD_2d: Dim1=-1.2')
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)
indices, kwargs = get_indices(workspace, **kwargs)
x_temp, y_temp, z = get_md_data2d_bin_centers(workspace, normalization, indices)
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)
indices = None
return x, y, z, indices
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 _extract_3d_data(workspace, **kwargs):
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)
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)
indices = None
return x, y, z, indices
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 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 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 get_data_MDH(self, slicepoint, transpose=False):
indices, _ = get_indices(self.get_ws(), slicepoint=slicepoint)
if transpose:
return np.ma.masked_invalid(self.get_ws().getSignalArray()[indices]).T
else:
return np.ma.masked_invalid(self.get_ws().getSignalArray()[indices])
