def _get_tick_locator(self, map_name):
min_val, max_val = self._data_info.maps[map_name].min(
), self._data_info.maps[map_name].max()
scale = self._get_map_attr(map_name, 'scale', Scale())
if scale.use_max:
max_val = scale.vmax
if scale.use_min:
min_val = scale.vmin
return MyColourBarTickLocator(
min_val, max_val, numticks=self._plot_config.colorbar_nmr_ticks)
def _get_tick_locator(self, map_name):
min_val, max_val = self._data_info.get_single_map_info(
map_name).min_max()
scale = self._get_map_attr(map_name, 'scale', Scale())
if scale.use_max:
max_val = scale.vmax
if scale.use_min:
min_val = scale.vmin
return MyColourBarTickLocator(
min_val,
max_val,
numticks=self._get_colorbar_setting(map_name, 'nmr_ticks'),
round_precision=self._get_colorbar_setting(map_name,
'round_precision'))
def _get_map_plot_options(self, map_name):
output_dict = {
'vmin':
self._data_info.get_single_map_info(map_name).min(),
'vmax':
self._data_info.get_single_map_info(map_name).max(),
'cmap':
self._get_map_attr(map_name, 'colormap',
self._plot_config.colormap)
}
scale = self._get_map_attr(map_name, 'scale', Scale())
if scale.use_max:
output_dict['vmax'] = scale.vmax
if scale.use_min:
output_dict['vmin'] = scale.vmin
return output_dict
def _get_map_plot_options(self, map_name):
cmap = get_cmap(
self._get_map_attr(map_name, 'colormap',
self._plot_config.colormap))
masked_color = self._get_map_attr(
map_name, 'colormap_masked_color',
self._plot_config.colormap_masked_color)
if masked_color is not None:
cmap.set_bad(color=masked_color)
output_dict = {
'vmin': self._data_info.get_single_map_info(map_name).min(),
'vmax': self._data_info.get_single_map_info(map_name).max(),
'cmap': cmap
}
scale = self._get_map_attr(map_name, 'scale', Scale())
if scale.use_max:
output_dict['vmax'] = scale.vmax
if scale.use_min:
output_dict['vmin'] = scale.vmin
return output_dict
def _get_image(self, map_name):
"""Get the 2d image to display for the given data."""
def get_slice(data):
"""Get the slice of the data in the desired dimension."""
dimension = self._plot_config.dimension
slice_index = self._plot_config.slice_index
volume_index = self._plot_config.volume_index
data_slice = get_slice_in_dimension(data, dimension, slice_index)
if len(data_slice.shape) > 2:
if data_slice.shape[2] == 3 and self._get_map_attr(
map_name, 'interpret_as_colormap'):
data_slice = np.abs(data_slice[:, :, :])
if self._get_map_attr(map_name, 'colormap_order'):
order = self._get_map_attr(map_name, 'colormap_order')
data_slice[..., :] = data_slice[
..., [order.index(color) for color in 'rbg']]
elif volume_index < data_slice.shape[2]:
data_slice = np.squeeze(data_slice[:, :, volume_index])
else:
data_slice = np.squeeze(
data_slice[:, :, data_slice.shape[2] - 1])
if len(data_slice.shape) == 1:
data_slice = data_slice[:, None]
return data_slice
def apply_modifications(data_slice):
"""Modify the data using (possible) value clipping and masking"""
data_slice = self._get_map_attr(map_name, 'clipping',
Clipping()).apply(data_slice)
mask_name = self._get_map_attr(map_name, 'mask_name',
self._plot_config.mask_name)
if mask_name:
mask = (get_slice(self._data_info.get_map_data(mask_name)) > 0)
if len(data_slice.shape) != len(mask.shape):
mask = mask[..., None]
data_slice = data_slice * mask
return data_slice
data = self._data_info.get_map_data(map_name)
if len(data.shape) == 2:
data_slice = data
else:
data_slice = get_slice(data)
data_slice = apply_modifications(data_slice)
data_slice = _apply_transformations(self._plot_config, data_slice)
if len(data_slice.shape) == 3:
multiplication_map = self._get_map_attr(map_name,
'colormap_weight_map',
None)
if multiplication_map is not None and map_name != multiplication_map:
multiplication_map = self._get_image(multiplication_map)
if len(multiplication_map.shape) != 3:
multiplication_map = multiplication_map[..., None]
scale = self._get_map_attr(map_name, 'scale', Scale())
new_vmax = np.min(((scale.vmax if scale.use_max else 1), 1))
new_vmin = np.max(((scale.vmin if scale.use_min else 0), 0))
multiplication_map = multiplication_map + (
1 - (new_vmax - new_vmin))
data_slice = data_slice * multiplication_map
if np.max(data_slice) > 1:
data_slice /= np.max(data_slice)
return data_slice