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# Copyright CNRS/Inria/UNS
# Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020)
#
# eric.debreuve@cnrs.fr
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
import brick.processing.dijkstra_1_to_n as dk_
from brick.component.extension import extension_t
from brick.component.soma import soma_t
from brick.general.type import array_t, py_array_picker_h
from typing import Any, Optional, Sequence, Tuple, Union
import matplotlib.pyplot as pl_
import mpl_toolkits.mplot3d as p3_
import numpy as np_
import skimage.measure as ms_
def MaximumIntensityProjectionZ(img: array_t, cmap: str ='tab20', axis: int = 0, block=True, output_image_file_name: str = None) -> None:
""" Maximum Image Projection on the Z axis. """
#
xy = np_.amax(img, axis=axis)
pl_.imshow(xy, cmap=cmap)
pl_.show(block=block)
if output_image_file_name is not None:
pl_.imsave(output_image_file_name, xy, cmap=cmap)
pl_.close()
print('Image saved in', output_image_file_name)
colors_c = ("g", "r", "b", "m", "c", "y")
mc_precision_c = 5 # mc=marching cubes
def PlotLMap(
lmp: array_t, axes=None, labels: Union[int, Tuple[int, ...]] = None
) -> Optional[Any]:
#
depth_factor, depth_limit = __DepthFactorAndLimit__(lmp.shape)
new_axes = axes is None
if new_axes:
_, axes = __FigAndAxes__(lmp.shape, depth_limit)
if isinstance(labels, int):
labels = (labels,)
elif labels is None:
labels = range(1, lmp.max() + 1)
for label in labels:
try:
vertices, faces, _, _ = ms_.marching_cubes_lewiner(
lmp == label, 0.5, step_size=mc_precision_c
)
vertices[:, 0] *= depth_factor
triangles = vertices[faces]
mesh = p3_.art3d.Poly3DCollection(triangles)
mesh.set_facecolor(colors_c[label % colors_c.__len__()])
axes.add_collection3d(mesh)
except RuntimeError as exc:
print(f"{PlotLMap.__name__}: label.{label}: {exc.args[0]}")
pl_.tight_layout()
if new_axes:
return axes
def PlotConnection(
connection: py_array_picker_h, soma_uid: int, shape: Sequence[int], axes=None
) -> Optional[Any]:
#
depth_factor, depth_limit = __DepthFactorAndLimit__(shape)
new_axes = axes is None
if new_axes:
_, axes = __FigAndAxes__(shape, depth_limit)
# TODO This test is put here but could be move outside this function
if connection is not None:
axes.plot(
depth_factor * np_.array(connection[0]),
*connection[1:],
colors_c[soma_uid % colors_c.__len__()],
)
pl_.tight_layout()
if new_axes:
return axes
def PlotExtensions(
extensions: Union[extension_t, Sequence[extension_t]],
shape: Sequence[int],
axes=None,
) -> Optional[Any]:
#
depth_factor, depth_limit = __DepthFactorAndLimit__(shape)
new_axes = axes is None
if new_axes:
_, axes = __FigAndAxes__(shape, depth_limit)
costs = np_.empty(shape, dtype=np_.float32)
if isinstance(extensions, extension_t):
extensions = (extensions,)
for extension in extensions:
# Remove voxels that can be removed w/o breaking connectivity
costs.fill(np_.inf)
costs[extension.sites] = 1
for src_ep_idx in range(extension.end_points_as_array.shape[1] - 1):
src_point = tuple(extension.end_points_as_array[:, src_ep_idx].tolist())
for tgt_ep_idx in range(
src_ep_idx + 1, extension.end_points_as_array.shape[1]
):
tgt_point = tuple(extension.end_points_as_array[:, tgt_ep_idx].tolist())
sites, _ = dk_.DijkstraShortestPath(
costs,
src_point,
tgt_point,
limit_to_sphere=False,
constrain_direction=False,
)
sites = tuple(zip(*sites))
if extension.soma_uid is None:
uid = extension.uid
else:
uid = extension.soma_uid
# /!\ Redundant plots within ep-to-ep path
axes.plot(
depth_factor * sites[0],
*sites[1:],
colors_c[uid % colors_c.__len__()],
)
pl_.tight_layout()
if new_axes:
return axes
def PlotSomaWithExtensions(soma: soma_t, soma_lmp: array_t, axes=None) -> Optional[Any]:
#
shape = soma_lmp.shape
depth_factor, depth_limit = __DepthFactorAndLimit__(shape)
new_axes = axes is None
if new_axes:
_, axes = __FigAndAxes__(shape, depth_limit)
PlotLMap(soma_lmp, labels=soma.uid, axes=axes)
for connection_path in filter(
lambda path: path is not None, soma.connection_path.values()
):
PlotConnection(connection_path, soma.uid, shape, axes=axes)
for extension in soma.Extensions():
for connection_path in filter(
lambda path: path is not None, extension.connection_path.values()
):
PlotConnection(connection_path, soma.uid, shape, axes=axes)
PlotExtensions(extension, shape, axes=axes)
pl_.tight_layout()
if new_axes:
return axes
def __DepthFactorAndLimit__(shape: Sequence[int]) -> Tuple[float, int]:
#
depth_factor = min(0.5 * (shape[1] + shape[2]) / shape[0], 1)
depth_limit = int(depth_factor * shape[0]) + 1
return depth_factor, depth_limit
def __FigAndAxes__(shape: Sequence[int], depth_limit: float) -> Tuple[Any, Any]:
#
fig = pl_.figure()
axes = fig.add_subplot(111, projection=p3_.Axes3D.name)
axes.set_xlabel(f"depth: {shape[0]}")
axes.set_ylabel(f"row: {shape[1]}")
axes.set_zlabel(f"col: {shape[2]}")
axes.set_xlim3d(0, depth_limit)
axes.set_ylim3d(0, shape[1])
axes.set_zlim3d(0, shape[2])
return fig, axes