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 test_get_matrix_2d_data_ragged(self):
# contour from ragged point data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_point_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([1., 2., 3., 4., 5., 6., 7., 8.]))
np.testing.assert_allclose(y, np.array([0.5, 1.5, 2.5]))
# contour from ragged histo data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([1.5, 2.4375, 3.375, 4.3125, 5.25, 6.1875, 7.125, 8.0625, 9.]))
np.testing.assert_allclose(y, np.array([4., 6., 8., 10.]))
# mesh from ragged point data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_point_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5]))
np.testing.assert_allclose(y, np.array([0.5, 1.5, 2.5]))
# mesh from ragged histo data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([1.03125, 1.96875, 2.90625, 3.84375, 4.78125, 5.71875, 6.65625, 7.59375, 8.53125, 9.46875]))
np.testing.assert_allclose(y, np.array([4., 6, 8., 10.]))
# 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_ragged(self):
# contour from ragged point data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_point_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([1., 2., 3., 4., 5., 6., 7., 8.]))
np.testing.assert_allclose(y, np.array([0.5, 1.5, 2.5]))
# contour from ragged histo data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag, True, histogram2D=False)
np.testing.assert_allclose(x, np.array([1.5, 2.4375, 3.375, 4.3125, 5.25, 6.1875, 7.125, 8.0625, 9.]))
np.testing.assert_allclose(y, np.array([4., 6., 8., 10.]))
# mesh from ragged point data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_point_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5]))
np.testing.assert_allclose(y, np.array([0.5, 1.5, 2.5]))
# mesh from ragged histo data
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag, True, histogram2D=True)
np.testing.assert_allclose(x, np.array(
[1.03125, 1.96875, 2.90625, 3.84375, 4.78125, 5.71875, 6.65625, 7.59375, 8.53125, 9.46875]))
np.testing.assert_allclose(y, np.array([4., 6, 8., 10.]))
# 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 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)