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 test_get_md_data2d_bin_centers_transpose(self): """ Same as the test above but should be the transpose """ x, y, data = funcs.get_md_data2d_bin_centers(self.ws_MD_2d, mantid.api.MDNormalization.NumEventsNormalization, transpose=True) np.testing.assert_allclose(x, np.array([-8, -4, 0, 4, 8]), atol=1e-10) np.testing.assert_allclose(y, np.array([-2.4, -1.2, 0, 1.2, 2.4]), atol=1e-10) np.testing.assert_allclose(data, np.arange(25).reshape(5, 5).T * 0.1, atol=1e-10)
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_md_data2d_bin_centers(self): x, y, data = funcs.get_md_data2d_bin_centers( self.ws_MD_2d, mantid.api.MDNormalization.NumEventsNormalization) np.testing.assert_allclose(x, np.array([-2.4, -1.2, 0, 1.2, 2.4]), atol=1e-10) np.testing.assert_allclose(y, np.array([-8, -4, 0, 4, 8]), atol=1e-10) np.testing.assert_allclose(data, np.arange(25).reshape(5, 5) * 0.1, atol=1e-10)
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 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 test_get_md_data2d_bin_centers(self): x, y, data = funcs.get_md_data2d_bin_centers(self.ws_MD_2d, mantid.api.MDNormalization.NumEventsNormalization) np.testing.assert_allclose(x, np.array([-2.4, -1.2, 0, 1.2, 2.4]), atol=1e-10) np.testing.assert_allclose(y, np.array([-8, -4, 0, 4, 8]), atol=1e-10) np.testing.assert_allclose(data, np.arange(25).reshape(5, 5) * 0.1, atol=1e-10)