def test_get_matrix_2d_data_rect(self):
# contour from aligned point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point,
True,
histogram2D=False)
np.testing.assert_allclose(
x, np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]))
np.testing.assert_allclose(
y, np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]))
# mesh from aligned point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point,
True,
histogram2D=True)
np.testing.assert_allclose(
x,
np.array([[0.5, 1.5, 2.5, 3.5, 4.5], [0.5, 1.5, 2.5, 3.5, 4.5],
[0.5, 1.5, 2.5, 3.5, 4.5], [0.5, 1.5, 2.5, 3.5, 4.5]]))
np.testing.assert_allclose(
y,
np.array([[0.5, 0.5, 0.5, 0.5, 0.5], [1.5, 1.5, 1.5, 1.5, 1.5],
[2.5, 2.5, 2.5, 2.5, 2.5], [3.5, 3.5, 3.5, 3.5, 3.5]]))
# contour from aligned histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo,
True,
histogram2D=False)
np.testing.assert_allclose(x, np.array([[15, 25], [15, 25]]))
np.testing.assert_allclose(y, np.array([[5, 5], [7, 7]]))
# mesh from aligned histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo,
True,
histogram2D=True)
np.testing.assert_allclose(
x, np.array([[10, 20, 30], [10, 20, 30], [10, 20, 30]]))
np.testing.assert_allclose(y,
np.array([[4, 4, 4], [6, 6, 6], [8, 8, 8]]))
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 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 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 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 _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 test_get_matrix_2d_data_rag(self):
# contour from ragged point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([[1, 2, 3, 4], [2, 4, 6, 8]]))
np.testing.assert_allclose(y, np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
# contour from ragged histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([[1.5, 2.5, 3.5, 4.5], [3, 5, 7, 9]]))
np.testing.assert_allclose(y, np.array([[6, 6, 6, 6], [8, 8, 8, 8]]))
# mesh from ragged point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([[0.5, 1.5, 2.5, 3.5, 4.5], [1, 3, 5, 7, 9], [1, 3, 5, 7, 9]]))
np.testing.assert_allclose(y, np.array(
[[0.5, 0.5, 0.5, 0.5, 0.5], [1.5, 1.5, 1.5, 1.5, 1.5], [2.5, 2.5, 2.5, 2.5, 2.5]]))
# mesh from ragged histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([[1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [2, 4, 6, 8, 10]]))
np.testing.assert_allclose(y, np.array([[5, 5, 5, 5, 5], [7, 7, 7, 7, 7], [9, 9, 9, 9, 9]]))
# check that fails for uneven data
self.assertRaises(ValueError, funcs.get_matrix_2d_data, self.ws2d_point_uneven, True)
def test_get_matrix_2d_data_rag(self):
# contour from ragged point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([[1, 2, 3, 4], [2, 4, 6, 8]]))
np.testing.assert_allclose(y, np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
# contour from ragged histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([[1.5, 2.5, 3.5, 4.5], [3, 5, 7, 9]]))
np.testing.assert_allclose(y, np.array([[6, 6, 6, 6], [8, 8, 8, 8]]))
# mesh from ragged point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([[0.5, 1.5, 2.5, 3.5, 4.5], [1, 3, 5, 7, 9], [1, 3, 5, 7, 9]]))
np.testing.assert_allclose(y, np.array(
[[0.5, 0.5, 0.5, 0.5, 0.5], [1.5, 1.5, 1.5, 1.5, 1.5], [2.5, 2.5, 2.5, 2.5, 2.5]]))
# mesh from ragged histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([[1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [2, 4, 6, 8, 10]]))
np.testing.assert_allclose(y, np.array([[5, 5, 5, 5, 5], [7, 7, 7, 7, 7], [9, 9, 9, 9, 9]]))
# check that fails for uneven data
self.assertRaises(ValueError, funcs.get_matrix_2d_data, self.ws2d_point_uneven, True)
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 test_get_matrix_2d_data_rect(self):
# contour from aligned point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]))
np.testing.assert_allclose(y, np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]))
# mesh from aligned point data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_point, True, histogram2D=True)
np.testing.assert_allclose(x, np.array(
[[0.5, 1.5, 2.5, 3.5, 4.5], [0.5, 1.5, 2.5, 3.5, 4.5], [0.5, 1.5, 2.5, 3.5, 4.5],
[0.5, 1.5, 2.5, 3.5, 4.5]]))
np.testing.assert_allclose(y, np.array(
[[0.5, 0.5, 0.5, 0.5, 0.5], [1.5, 1.5, 1.5, 1.5, 1.5], [2.5, 2.5, 2.5, 2.5, 2.5],
[3.5, 3.5, 3.5, 3.5, 3.5]]))
# contour from aligned histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([[15, 25], [15, 25]]))
np.testing.assert_allclose(y, np.array([[5, 5], [7, 7]]))
# mesh from aligned histo data
x, y, z = funcs.get_matrix_2d_data(self.ws2d_histo, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([[10, 20, 30], [10, 20, 30], [10, 20, 30]]))
np.testing.assert_allclose(y, np.array([[4, 4, 4], [6, 6, 6], [8, 8, 8]]))
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 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)
