def set_figure_zoom_to_display_all(self):
axes = self.canvas.figure.get_axes()
if axes:
for ax in axes:
# We check for axes type below as a pseudo check for an axes being
# a colorbar. this is based on the same check in
# FigureManagerADSObserver.deleteHandle.
if type(ax) is not Axes:
if ax.lines: # Relim causes issues with colour plots, which have no lines.
ax.relim()
elif isinstance(ax, Axes3D):
if hasattr(ax, 'original_data_surface'):
ax.collections[0]._vec = copy.deepcopy(
ax.original_data_surface)
elif hasattr(ax, 'original_data_wireframe'):
ax.collections[0].set_segments(
copy.deepcopy(ax.original_data_wireframe))
else:
ax.view_init()
elif ax.images:
axesfunctions.update_colorplot_datalimits(
ax, ax.images)
continue
ax.autoscale()
self.canvas.draw()
def _toggle_normalization(self, selected_ax):
if figure_type(self.canvas.figure) == FigureType.Image and len(self.canvas.figure.get_axes()) > 1:
axes = datafunctions.get_axes_from_figure(self.canvas.figure)
else:
axes = [selected_ax]
for ax in axes:
waterfall = isinstance(ax, MantidAxes) and ax.is_waterfall()
if waterfall:
x, y = ax.waterfall_x_offset, ax.waterfall_y_offset
has_fill = ax.waterfall_has_fill()
if has_fill:
line_colour_fill = datafunctions.waterfall_fill_is_line_colour(ax)
if line_colour_fill:
fill_colour = None
else:
fill_colour = datafunctions.get_waterfall_fills(ax)[0].get_facecolor()
ax.update_waterfall(0, 0)
# The colorbar can get screwed up with ragged workspaces and log scales as they go
# through the normalisation toggle.
# Set it to Linear and change it back after if necessary, since there's no reason
# to duplicate the handling.
colorbar_log = False
if ax.images:
colorbar_log = isinstance(ax.images[-1].norm, LogNorm)
if colorbar_log:
self._change_colorbar_axes(Normalize)
self._change_plot_normalization(ax)
if ax.lines: # Relim causes issues with colour plots, which have no lines.
ax.relim()
ax.autoscale()
if ax.images: # Colour bar limits are wrong if workspace is ragged. Set them manually.
colorbar_min = np.nanmin(ax.images[-1].get_array())
colorbar_max = np.nanmax(ax.images[-1].get_array())
for image in ax.images:
image.set_clim(colorbar_min, colorbar_max)
# Update the colorbar label
cb = image.colorbar
if cb:
datafunctions.add_colorbar_label(cb, ax.get_figure().axes)
if colorbar_log: # If it had a log scaled colorbar before, put it back.
self._change_colorbar_axes(LogNorm)
axesfunctions.update_colorplot_datalimits(ax, ax.images)
datafunctions.set_initial_dimensions(ax)
if waterfall:
ax.update_waterfall(x, y)
if has_fill:
ax.set_waterfall_fill(True, fill_colour)
self.canvas.draw()
def _do_update_colorplot_datalimits(self, color_func):
fig, ax = plt.subplots()
mesh = color_func(ax, self.ws2d_histo)
ax.set_xlim(0.01, 0.05)
ax.set_ylim(-0.05, 0.05)
funcs.update_colorplot_datalimits(ax, mesh)
self.assertAlmostEqual(10.0, ax.get_xlim()[0])
from distutils.version import LooseVersion
# different results with 1.5.3 and 2.1.1
if color_func.__name__ != 'imshow' and LooseVersion(matplotlib.__version__) < LooseVersion("2"):
self.assertAlmostEqual(100.0, ax.get_xlim()[1])
else:
self.assertAlmostEqual(30.0, ax.get_xlim()[1])
self.assertAlmostEqual(4.0, ax.get_ylim()[0])
self.assertAlmostEqual(8.0, ax.get_ylim()[1])
def test_update_colorplot_datalimits_for_both_axis(self):
"""Check that the function is only operating on y-axis"""
ax_mock = mock.MagicMock()
funcs.update_colorplot_datalimits(ax_mock, ax_mock.images, 'both')
ax_mock.update_datalim.assert_called_once_with(mock.ANY, True, True)
ax_mock.autoscale.assert_called_once_with(axis='both')
def _apply_properties_to_axes(self, ax):
"""Apply current properties to given set of axes"""
self.set_ax_canvas_color(ax, self.current_view_props['canvas_color'])
if not isinstance(ax, Axes3D):
if self.current_view_props['minor_ticks']:
ax.minorticks_on()
else:
ax.minorticks_off()
ax.show_minor_gridlines = self.current_view_props[
'minor_gridlines']
# If the grid is enabled update it
if ax.show_minor_gridlines:
if ax.xaxis._major_tick_kw[
'gridOn'] and ax.yaxis._major_tick_kw['gridOn']:
ax.grid(True, which='minor')
elif ax.xaxis._major_tick_kw['gridOn']:
ax.grid(True, axis='x', which='minor')
elif ax.yaxis._major_tick_kw['gridOn']:
ax.grid(True, axis='y', which='minor')
else:
ax.grid(False, which='minor')
if "xlabel" in self.current_view_props:
ax.set_xlabel(self.current_view_props['xlabel'])
ax.set_xscale(self.current_view_props['xscale'])
if self.current_view_props['xautoscale']:
if ax.images:
axesfunctions.update_colorplot_datalimits(ax,
ax.images,
axis='x')
else:
ax.autoscale(True, axis="x")
else:
if isinstance(ax, Axes3D):
ax.set_xlim3d(self.current_view_props['xlim'])
else:
ax.set_xlim(self.current_view_props['xlim'])
if "ylabel" in self.current_view_props:
ax.set_ylabel(self.current_view_props['ylabel'])
ax.set_yscale(self.current_view_props['yscale'])
if self.current_view_props['yautoscale']:
if ax.images:
axesfunctions.update_colorplot_datalimits(ax,
ax.images,
axis='y')
else:
ax.autoscale(True, axis="y")
else:
if isinstance(ax, Axes3D):
ax.set_ylim3d(self.current_view_props['ylim'])
else:
ax.set_ylim(self.current_view_props['ylim'])
if isinstance(ax, Axes3D) and "zlabel" in self.current_view_props:
ax.set_zlabel(self.current_view_props['zlabel'])
ax.set_zscale(self.current_view_props['zscale'])
if self.current_view_props['zautoscale']:
ax.autoscale(True, axis="z")
else:
ax.set_zlim3d(self.current_view_props['zlim'])
