def buriedness_grid(self):
closed_g = self._multiscale_closing(self.protein_grid)
out_g = self._open_grid(closed_g, 2) * closed_g
out_array = out_g.get_array()
scaled_g = Grid.initalise_grid(self.out_grid.bounding_box,
padding=0,
spacing=0.5)
scaled_array = resize(out_array, scaled_g.nsteps, anti_aliasing=False)
# Future tweaking here
final_array = scaled_array
return Grid.array_to_grid(final_array.astype(int), scaled_g)
def normalize_to_percentile(self):
for probe, g in self.super_grids.items():
g_array = g.get_array()
out_array = np.vectorize(self._percentile_grid)(g_array)
self.super_grids[probe] = Grid.array_to_grid(out_array, g)
def _open_grid(self, g, probe):
g_array = g.get_array().astype(int)
opened_array = ndimage.binary_opening(g_array, structure=self.probe_selem_dict[probe])
return Grid.array_to_grid(opened_array.astype(int), g)
def _close_grid(self, g, probe):
g_array = g.get_array().astype(int)
closed_array = ndimage.binary_erosion(g_array, structure=self.probe_selem_dict[probe])
return Grid.array_to_grid(closed_array.astype(int), g)
