def test_get_matrix_2d_data_ragged_with_extent(self):
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_point_rag,
False,
histogram2D=True,
extent=[2, 4, 1, 2],
xbins=8,
ybins=5)
np.testing.assert_allclose(
x, np.array([2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4]))
np.testing.assert_allclose(y, np.array([1, 1.25, 1.5, 1.75, 2.0]))
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag,
False,
histogram2D=True,
extent=[2, 6, 5, 9],
xbins=8,
ybins=5)
np.testing.assert_allclose(
x, np.array([2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6]))
np.testing.assert_allclose(y, np.array([5, 6, 7, 8, 9]))
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag,
False,
histogram2D=True,
extent=[2, 6, -5, 9],
xbins=8,
ybins=5)
np.testing.assert_allclose(
x, np.array([2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6]))
np.testing.assert_allclose(y, np.array([-5, -1.5, 2, 5.5, 9]))
# first z array is off the spectrum axis scale so should be masked
np.testing.assert_array_equal(z[0].data, [np.nan] * 8)
np.testing.assert_array_equal(z[0].mask, [1] * 8)
np.testing.assert_array_equal(z[4].data, [2.0] * 8)
np.testing.assert_array_equal(z[4].mask, [0] * 8)
def _resample_image(self, xbins=None, ybins=None):
if self._resample_required:
extent = self.get_extent()
if xbins is None or ybins is None:
xbins, ybins = self._calculate_bins_from_extent()
x, y, data = get_matrix_2d_ragged(self.ws,
self.normalize_by_bin_width,
histogram2D=True,
transpose=self.transpose,
extent=extent,
xbins=xbins,
ybins=ybins,
spec_info=self.spectrum_info,
maxpooling=self._maxpooling)
# Data is an MxN matrix.
# If origin = upper extent is set as [xmin, xmax, ymax, ymin].
# Data[M,0] is the data at [xmin, ymin], which should be drawn at the top left corner,
# whereas Data[0,0] is the data at [xmin, ymax], which should be drawn at the bottom left corner.
# Origin upper starts drawing the data from top-left, which means we need to horizontally flip the matrix
if self.origin == "upper":
data = np.flip(data, 0)
self.set_data(data)
self._xbins = xbins
self._ybins = ybins
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:
(aligned, kwargs) = check_resample_to_regular_grid(workspace, **kwargs)
(normalize_by_bin_width,
kwargs) = get_normalize_by_bin_width(workspace, axes, **kwargs)
(distribution, kwargs) = get_distribution(workspace, **kwargs)
if aligned:
(x, y, z) = get_matrix_2d_ragged(workspace,
normalize_by_bin_width,
histogram2D=True,
transpose=transpose)
else:
(x, y, z) = get_matrix_2d_data(workspace,
distribution=distribution,
histogram2D=True,
transpose=transpose)
_setLabels2D(axes,
workspace,
transpose=transpose,
normalize_by_bin_width=normalize_by_bin_width)
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_with_extent(self):
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_point_rag,
False,
histogram2D=True,
extent=[2, 4, 1, 2],
xbins=8,
ybins=5)
np.testing.assert_allclose(
x, np.array([2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4]))
np.testing.assert_allclose(y, np.array([1, 1.25, 1.5, 1.75, 2.0]))
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag,
False,
histogram2D=True,
extent=[2, 6, 5, 9],
xbins=8,
ybins=5)
np.testing.assert_allclose(
x, np.array([2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6]))
np.testing.assert_allclose(y, np.array([5, 6, 7, 8, 9]))
def test_get_matrix2d_ragged_without_maxpooling(self):
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_high_counting_detector,
False,
histogram2D=True,
extent=[1, 4, 1, 1000],
xbins=4,
ybins=20,
maxpooling=False)
# 12th spectra is high counting but will skipped if we don't use maxpooling
np.testing.assert_allclose(z[0],
self.ws2d_high_counting_detector.readY(0))
def test_get_matrix2d_ragged_with_maxpooling(self):
x, y, z = funcs.get_matrix_2d_ragged(self.ws2d_high_counting_detector,
False,
histogram2D=True,
extent=[1, 4, 1, 1000],
xbins=4,
ybins=20,
maxpooling=True)
# 12th spectra is high counting and will be the first entry in the data when we are using maxpooling
np.testing.assert_allclose(z[0],
self.ws2d_high_counting_detector.readY(12))
def test_get_matrix_2d_ragged_when_transpose_is_true(self):
x, y, z_transposed = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag,
False,
histogram2D=True,
extent=[5, 9, 2, 6],
xbins=8,
ybins=5,
transpose=True)
np.testing.assert_allclose(x, np.array([5, 6, 7, 8, 9]))
np.testing.assert_allclose(
y, np.array([2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6]))
_, _, z = funcs.get_matrix_2d_ragged(self.ws2d_histo_rag,
False,
histogram2D=True,
extent=[2, 6, 5, 9],
xbins=8,
ybins=5,
transpose=False)
np.testing.assert_allclose(z_transposed, z.T)
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)
