def _plot_last_data(self, data):
if len(data)==0:
plottable_data = np.zeros((16, 16, 3), dtype = np.uint8)
clims = (0, 1)
else:
if isinstance(data, list):
data = put_list_of_images_in_array(data, fill_colour=(np.nan, np.nan, np.nan))
elif data.ndim == 1:
data = data[None]
clims = ((np.nanmin(data), np.nanmax(data)) if data.size != 0 else (0, 1)) if self._clims is None else self._clims
if self.only_grow_clims:
clims = min(self._old_clims[0], clims[0]), max(self._old_clims[1], clims[1])
self._old_clims = clims
if self._is_colour_data is None:
self._is_colour_data = data.shape[-1]==3
plottable_data = put_data_in_grid(data, clims = clims, cmap = self._cmap, is_color_data = self._is_colour_data, fill_colour = np.array((0, 0, 128)), nan_colour = np.array((0, 0, 128))) \
if not (self._is_colour_data and data.ndim==3 or data.ndim==2) else \
data_to_image(data, clims = clims, cmap = self._cmap, nan_colour = np.array((0, 0, 128)))
if self._plot is None:
self._plot = plt.imshow(plottable_data, interpolation = self._interpolation, aspect = self._aspect, cmap = self._cmap)
if not self._show_axes:
self._plot.axes.tick_params(labelbottom = 'off')
self._plot.axes.get_yaxis().set_visible(False)
else:
self._plot.set_array(plottable_data)
if self.show_clims:
self._plot.axes.set_xlabel('%.2f - %.2f' % clims)
def _plot_last_data(self, data):
if self.channel_first:
if data.ndim == 3:
assert data.shape[0] in (1, 3)
data = np.rollaxis(data, 0, 3)
elif data.ndim == 4:
assert data.shape[1] in (1, 3)
data = np.rollaxis(data, 1, 4)
else:
print(
'Image should be 3D (3, size_y, size_x) or 4D (n_images, 3, size_y, size_x) for channel-first-mode.'
)
if len(data) == 0:
plottable_data = np.zeros((16, 16, 3), dtype=np.uint8)
clims = (0, 1)
else:
if isinstance(data, list):
data = put_list_of_images_in_array(data,
fill_colour=(np.nan, np.nan,
np.nan))
elif data.ndim == 1:
data = data[None]
clims = ((np.nanmin(data), np.nanmax(data)) if data.size != 0 else
(0, 1)) if self._clims is None else self._clims
if self.only_grow_clims:
clims = min(self._old_clims[0],
clims[0]), max(self._old_clims[1], clims[1])
self._old_clims = clims
if self._is_colour_data is None:
self._is_colour_data = data.shape[-1] == 3
plottable_data = put_data_in_grid(data, clims = clims, cmap = self._cmap, is_color_data = self._is_colour_data, fill_colour = np.array((0, 0, 128)), nan_colour = np.array((0, 0, 128))) \
if not (self._is_colour_data and data.ndim==3 or data.ndim==2) else \
data_to_image(data, clims = clims, cmap = self._cmap, nan_colour = np.array((0, 0, 128)))
if self._plot is None:
self._plot = plt.imshow(plottable_data,
interpolation=self._interpolation,
aspect=self._aspect,
cmap=self._cmap)
if not self._show_axes:
self._plot.axes.tick_params(labelbottom='off')
self._plot.axes.get_yaxis().set_visible(False)
else:
self._plot.set_array(plottable_data)
if self.show_clims:
self._plot.axes.set_xlabel('{:.3g} <> {:.3g}'.format(*clims))
def _plot_last_data(self, data):
if self.channel_first:
if data.ndim==3:
assert data.shape[0] in (1, 3)
data = np.rollaxis(data, 0, 3)
elif data.ndim==4:
assert data.shape[1] in (1, 3)
data = np.rollaxis(data, 1, 4)
else:
print('Image should be 3D (3, size_y, size_x) or 4D (n_images, 3, size_y, size_x) for channel-first-mode.')
if len(data)==0:
plottable_data = np.zeros((16, 16, 3), dtype = np.uint8)
clims = (0, 1)
else:
if isinstance(data, list):
data = put_list_of_images_in_array(data, fill_colour=(np.nan, np.nan, np.nan))
elif data.ndim == 1:
data = data[None]
clims = ((np.nanmin(data), np.nanmax(data)) if data.size != 0 else (0, 1)) if self._clims is None else self._clims
if self.only_grow_clims:
clims = min(self._old_clims[0], clims[0]), max(self._old_clims[1], clims[1])
self._old_clims = clims
if self._is_colour_data is None:
self._is_colour_data = data.shape[-1]==3
plottable_data = put_data_in_grid(data, clims = clims, cmap = self._cmap, is_color_data = self._is_colour_data, fill_colour = np.array((0, 0, 128)), nan_colour = np.array((0, 0, 128))) \
if not (self._is_colour_data and data.ndim==3 or data.ndim==2) else \
data_to_image(data, clims = clims, cmap = self._cmap, nan_colour = np.array((0, 0, 128)))
if self._plot is None:
self._plot = plt.imshow(plottable_data, interpolation = self._interpolation, aspect = self._aspect, cmap = self._cmap)
if not self._show_axes:
self._plot.axes.tick_params(labelbottom = 'off')
self._plot.axes.get_yaxis().set_visible(False)
else:
self._plot.set_array(plottable_data)
if self.show_clims:
self._plot.axes.set_xlabel('{:.3g} <> {:.3g}'.format(*clims))