def test_get_uneven_data(self):
# even points
x, y, z = funcs.get_uneven_data(self.ws2d_point_rag, True)
np.testing.assert_allclose(x[0], np.array([0.5, 1.5, 2.5, 3.5, 4.5]))
np.testing.assert_allclose(x[1], np.array([1, 3, 5, 7, 9]))
np.testing.assert_allclose(y[0], np.array([0.5, 1.5]))
np.testing.assert_allclose(y[1], np.array([1.5, 2.5]))
np.testing.assert_allclose(z[0], np.array([2, 2, 2, 2]))
np.testing.assert_allclose(z[1], np.array([2, 2, 2, 2]))
# even histo
x, y, z = funcs.get_uneven_data(self.ws2d_histo_rag, True)
np.testing.assert_allclose(x[0], np.array([1, 2, 3, 4, 5]))
np.testing.assert_allclose(x[1], np.array([2, 4, 6, 8, 10]))
np.testing.assert_allclose(y[0], np.array([5, 7]))
np.testing.assert_allclose(y[1], np.array([7, 9]))
np.testing.assert_allclose(z[0], np.array([2, 2, 2, 2]))
np.testing.assert_allclose(z[1], np.array([2, 2, 2, 2]))
# uneven points
x, y, z = funcs.get_uneven_data(self.ws2d_point_uneven, True)
np.testing.assert_allclose(x[0], np.array([5, 15, 25, 35]))
np.testing.assert_allclose(x[1], np.array([10, 20, 30, 40, 50]))
np.testing.assert_allclose(y[0], np.array([0.5, 1.5]))
np.testing.assert_allclose(y[1], np.array([1.5, 2.5]))
np.testing.assert_allclose(z[0], np.array([1, 2, 3]))
np.testing.assert_allclose(z[1], np.array([1, 2, 3, 4]))
# uneven histo
x, y, z = funcs.get_uneven_data(self.ws2d_histo_uneven, True)
np.testing.assert_allclose(x[0], np.array([10, 20, 30, 40]))
np.testing.assert_allclose(x[1], np.array([15, 25, 35, 45, 55]))
np.testing.assert_allclose(y[0], np.array([10, 15]))
np.testing.assert_allclose(y[1], np.array([15, 25]))
np.testing.assert_allclose(z[0], np.array([1, 2, 3]))
np.testing.assert_allclose(z[1], np.array([1, 2, 3, 4]))
def test_get_uneven_data(self):
# even points
x, y, z = funcs.get_uneven_data(self.ws2d_point_rag, True)
np.testing.assert_allclose(x[0], np.array([0.5, 1.5, 2.5, 3.5, 4.5]))
np.testing.assert_allclose(x[1], np.array([1, 3, 5, 7, 9]))
np.testing.assert_allclose(y[0], np.array([0.5, 1.5]))
np.testing.assert_allclose(y[1], np.array([1.5, 2.5]))
np.testing.assert_allclose(z[0], np.array([2, 2, 2, 2]))
np.testing.assert_allclose(z[1], np.array([2, 2, 2, 2]))
# even histo
x, y, z = funcs.get_uneven_data(self.ws2d_histo_rag, True)
np.testing.assert_allclose(x[0], np.array([1, 2, 3, 4, 5]))
np.testing.assert_allclose(x[1], np.array([2, 4, 6, 8, 10]))
np.testing.assert_allclose(y[0], np.array([5, 7]))
np.testing.assert_allclose(y[1], np.array([7, 9]))
np.testing.assert_allclose(z[0], np.array([2, 2, 2, 2]))
np.testing.assert_allclose(z[1], np.array([2, 2, 2, 2]))
# uneven points
x, y, z = funcs.get_uneven_data(self.ws2d_point_uneven, True)
np.testing.assert_allclose(x[0], np.array([5, 15, 25, 35]))
np.testing.assert_allclose(x[1], np.array([10, 20, 30, 40, 50]))
np.testing.assert_allclose(y[0], np.array([0.5, 1.5]))
np.testing.assert_allclose(y[1], np.array([1.5, 2.5]))
np.testing.assert_allclose(z[0], np.array([1, 2, 3]))
np.testing.assert_allclose(z[1], np.array([1, 2, 3, 4]))
# uneven histo
x, y, z = funcs.get_uneven_data(self.ws2d_histo_uneven, True)
np.testing.assert_allclose(x[0], np.array([10, 20, 30, 40]))
np.testing.assert_allclose(x[1], np.array([15, 25, 35, 45, 55]))
np.testing.assert_allclose(y[0], np.array([10, 15]))
np.testing.assert_allclose(y[1], np.array([15, 25]))
np.testing.assert_allclose(z[0], np.array([1, 2, 3]))
np.testing.assert_allclose(z[1], np.array([1, 2, 3, 4]))
def _pcolorpieces(axes, workspace, distribution, *args, **kwargs):
'''
Helper function for pcolor, pcolorfast, and pcolormesh that will
plot a 2d representation of each spectra. The polycollections or meshes
will be normalized to the same intensity limits.
:param axes: :class:`matplotlib.axes.Axes` object that will do the plotting
:param workspace: :class:`mantid.api.MatrixWorkspace` 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 pcolortype: this keyword allows the plotting to be one of pcolormesh or
pcolorfast if there is "mesh" or "fast" in the value of the keyword, or
pcolor by default
:return: A list of the pcolor pieces created
'''
(x, y, z) = get_uneven_data(workspace, distribution)
mini = numpy.min([numpy.min(i) for i in z])
maxi = numpy.max([numpy.max(i) for i in z])
if 'vmin' in kwargs:
mini = kwargs['vmin']
if 'vmax' in kwargs:
maxi = kwargs['vmax']
if 'norm' not in kwargs:
kwargs['norm'] = matplotlib.colors.Normalize(vmin=mini, vmax=maxi)
else:
if kwargs['norm'].vmin is None:
kwargs['norm'].vmin = mini
if kwargs['norm'].vmax is None:
kwargs['norm'].vmax = maxi
# setup the particular pcolor to use
pcolortype = kwargs.pop('pcolortype', '').lower()
if 'mesh' in pcolortype:
pcolor = axes.pcolormesh
elif 'fast' in pcolortype:
pcolor = axes.pcolorfast
else:
pcolor = axes.pcolor
pieces = []
for xi, yi, zi in zip(x, y, z):
XX, YY = numpy.meshgrid(xi, yi, indexing='ij')
pieces.append(pcolor(XX, YY, zi.reshape(-1, 1), **kwargs))
return pieces
def _pcolorpieces(axes, workspace, distribution, *args, **kwargs):
'''
Helper function for pcolor, pcolorfast, and pcolormesh that will
plot a 2d representation of each spectra. The polycollections or meshes
will be normalized to the same intensity limits.
:param axes: :class:`matplotlib.axes.Axes` object that will do the plotting
:param workspace: :class:`mantid.api.MatrixWorkspace` 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 pcolortype: this keyword allows the plotting to be one of pcolormesh or
pcolorfast if there is "mesh" or "fast" in the value of the keyword, or
pcolor by default
:return: A list of the pcolor pieces created
'''
(x, y, z) = get_uneven_data(workspace, distribution)
mini = numpy.min([numpy.min(i) for i in z])
maxi = numpy.max([numpy.max(i) for i in z])
if 'vmin' in kwargs:
mini = kwargs['vmin']
if 'vmax' in kwargs:
maxi = kwargs['vmax']
if 'norm' not in kwargs:
kwargs['norm'] = matplotlib.colors.Normalize(vmin=mini, vmax=maxi)
else:
if kwargs['norm'].vmin is None:
kwargs['norm'].vmin = mini
if kwargs['norm'].vmax is None:
kwargs['norm'].vmax = maxi
# setup the particular pcolor to use
pcolortype = kwargs.pop('pcolortype', '').lower()
if 'mesh' in pcolortype:
pcolor = axes.pcolormesh
elif 'fast' in pcolortype:
pcolor = axes.pcolorfast
else:
pcolor = axes.pcolor
pieces = []
for xi, yi, zi in zip(x, y, z):
XX, YY = numpy.meshgrid(xi, yi, indexing='ij')
pieces.append(pcolor(XX, YY, zi.reshape(-1, 1), **kwargs))
return pieces