# 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".
#
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# 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
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#
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# 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
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# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
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# 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 re as re_
import numpy as np_
import math as mt_
import scipy.stats as st_
import pandas as pd_
from brick.component.soma import soma_t
from brick.general.type import array_t
import brick.processing.best_fit_ellipsoid as bf_
import brick.processing.input as in_
from typing import Tuple, Dict, Union, Any
def FindGraphsRootWithEdges(soma: soma_t, ext_nfo: Dict[str, Union[array_t, Any]]) -> dict:
"""
Finds the soma roots of the graph extension.
"""
# For a given soma, find the roots of the graphs
root_nodes = {}
# Finds the primary extensions
primary_extension_uids = tuple(extension.uid for extension in soma.extensions)
print(primary_extension_uids, '\nn = ', len(primary_extension_uids))
# List of the degree 1 nodes of the graph
for node1_id, node2_id, edge_nfo in soma.skl_graph.edges.data('as_edge_t'):
if (soma.skl_graph.degree[node1_id] == 1) or (soma.skl_graph.degree[node2_id] == 1):
# Find the pixels of the terminal extension
sites = ext_nfo['lmp'][edge_nfo.sites]
ext_uid = np_.unique(sites)[-1]
# sites > 0 because ext_nfo['lmp'] do not contain the connexions
# Save the root node candidates (one-degree nodes)
if ext_uid in primary_extension_uids:
if soma.skl_graph.degree[node1_id] == 1:
root_node = node1_id
else:
root_node = node2_id
# Get the node coordinates and extend them to the 26 neighboring voxels
root_node_coor = GetNodesCoordinates((root_node,))[0] # tuple('x-y-z') -> list[(x,y,z)]
root_sites = set(
(root_node_coor[0] + i, root_node_coor[1] + j, root_node_coor[2] + k)
for i in (-1, 0, 1)
for j in (-1, 0, 1)
for k in (-1, 0, 1)
if i != 0 or j != 0 or k != 0)
# Find the intersection between the extended root node candidate and the soma contour points
intersections = set(soma.contour_points).intersection(root_sites)
# if the graph root sites are included in the soma extensions sites (non-nul intersection):
if len(intersections) > 0:
# Keep the info of the root node. Key = ext uid, Value = root node
root_nodes[ext_uid] = root_node
## By construction, only one root node possible for an ext
return root_nodes # TODO: find out why there are less root points than extensions !!
def FindGraphsRootWithNodes(soma: soma_t) -> dict:
"""
Find the roots of the {extension+connexion} graphs to be lined to the soma.
Add a key "root" (bool) in the dict of nodes attributes.
"""
node_degree_bool = tuple(degree == 1 for _, degree in soma.skl_graph.degree)
node_coord = tuple(xyz for xyz, _ in soma.skl_graph.degree)
root_nodes = {}
# get the coordinates of the nodes (x,y,z)
coordinates = GetNodesCoordinates(node_coord)
# get a list with elements = (soma_uid, extension_uid, root coordinates)
roots = GetListRoots(soma)
# for each node in the graph, search among the degree 1 nodes the nodes that are roots (linked to soma)
for node in range(len(coordinates)):
if node_degree_bool[node]:
# compare the coor with end points
for ext_root in roots:
if ext_root[1] == coordinates[node]:
root_nodes[ext_root[0]] = node_coord[node]
return root_nodes
def GetListRoots(soma: soma_t) -> list:
"""
Gives a list containing the following information for all somas: [soma id: int, extension id: int, root = (x,y,z): tuple]
"""
roots = []
for ext_id, ext_root in enumerate(soma.ext_roots):
roots.append((soma.extensions[ext_id].uid, ext_root))
return roots
def GetNodesCoordinates(node_coord: Tuple[str, ...]) -> list:
"""
Input: nodes attributes -> Tuple('x1-y1-z1', 'x2-y2-z2', ...) .
Output: coordinates -> List[Tuple(x1,y1,z1), Tuple(x2,y2,z2), ...]
"""
coord = []
for c in node_coord:
coord.append(c)
for node in range(len(node_coord)):
coord_node = coord[node]
pattern = '\d+'
coord_node = re_.findall(pattern, coord_node)
coor = []
for i in range(3):
coor.append(int(coord_node[i]))
coor = tuple(coor)
coord[node] = coor
return coord
def ExtractFeaturesInDF(somas, size_voxel_in_micron: list, number_of_bins: int, max_range: float, hist_min_length: float, scale_map: array_t, decimals: int = 4):
"""
Extract the features from somas and graphs.
Returns a pandas dataframe.
"""
somas_features_dict = {} # Dict{soma 1: [features], soma 2: [features], ...}
columns = [
"Coef_V_soma__V_convex_hull",
# "theta_a",
# "phi_a",
# "theta_b",
# "phi_b",
"Coef_axes_ellips_y__x",
"Coef_axes_ellips_z__x",
#
"N_nodes",
"N_ext",
"N_primary_ext",
"N_sec_ext",
"min_degree",
"mean_degree",
"median_degree",
"max_degree",
"std_degree",
#
"total_ext_length",
"min_length",
"mean_length",
"median_length",
"max_length",
"std_lengths",
"entropy_lengths",
"hist_lengths",
"min_thickness",
"mean_thickness",
"median_thickness",
"max_thickness",
"std_thickness",
"entropy_thickness",
"min_volume",
"mean_volume",
"median_volume",
"max_volume",
"std_volume",
"entropy_volume",
#
"total_ext_length_P",
"min_length_P",
"mean_length_P",
"median_length_P",
"max_length_P",
"std_lengths_P",
"entropy_lengths_P",
"hist_lengths_P",
"min_thickness_P",
"mean_thickness_P",
"median_thickness_P",
"max_thickness_P",
"std_thickness_P",
"entropy_thickness_P",
"min_volume_P",
"mean_volume_P",
"median_volume_P",
"max_volume_P",
"std_volume_P",
"entropy_volume_P",
#
"total_ext_length_S",
"min_length_S",
"mean_length_S",
"median_length_S",
"max_length_S",
"std_lengths_S",
"entropy_lengths_S",
"hist_lengths_S",
"min_thickness_S",
"mean_thickness_S",
"median_thickness_S",
"max_thickness_S",
"std_thickness_S",
"entropy_thickness_S",
"min_volume_S",
"mean_volume_S",
"median_volume_S",
"max_volume_S",
"std_volume_S",
"entropy_volume_S",
]
for soma in somas:
# Soma features
# print('***Soma***')
# # Volume of the soma
volume_pixel_micron = round(np_.prod(size_voxel_in_micron), 4)
soma.volume_soma_micron = volume_pixel_micron * len(soma.sites[0])
volume_convex_hull = volume_pixel_micron * bf_.GetConvexHull3D(soma.sites)[1]
Coef_V_soma__V_convex_hull = soma.volume_soma_micron / volume_convex_hull
# print(
# f"Volume soma = {soma.volume_soma_micron}\n"
# f"Volume soma / Volume Convex Hull = {Coef_V_soma__V_convex_hull}"
# )
# # Axes of the best fitting ellipsoid
soma.axes_ellipsoid = bf_.FindBestFittingEllipsoid3D(soma)[2]
Coef_axes_ellips_y__x = soma.axes_ellipsoid[1] / soma.axes_ellipsoid[0]
Coef_axes_ellips_z__x = soma.axes_ellipsoid[2] / soma.axes_ellipsoid[0]
# -- Extension features
# # Graph features
N_nodes = soma.skl_graph.n_nodes # number of nodes
N_ext = soma.skl_graph.n_edges - len(
soma.graph_roots) # number of edges except the constructed ones from node soma to the roots
N_primary_ext = len(
soma.graph_roots) # number of primary edges = linked to the soma except the constructed ones from node soma to the roots
N_sec_ext = N_ext - N_primary_ext # number of secondary edges = not linked to the soma.
print(
# f"\n***Extension***\n"
f"\n Soma {soma.uid}\n"
f"N nodes = {N_nodes}\n"
f"N edges = {N_ext}\n"
f"N primary extensions = {N_primary_ext}\n"
f"N secondary extensions = {N_sec_ext}\n"
)
if N_primary_ext > 0:
# Calculate the extensions lengths
ext_lengths = list(soma.skl_graph.edge_lengths)
for idx, length in enumerate(ext_lengths):
ext_lengths[idx] = in_.ToMicron(length, size_voxel_in_micron, decimals=decimals)
total_ext_length = in_.ToMicron(soma.skl_graph.length, size_voxel_in_micron, decimals=decimals)
#
# Lengths histogram
hist_lengths = np_.histogram(ext_lengths, bins=number_of_bins, range=(hist_min_length, max_range))[0]
#
# min, mean, median, max and standard deviation of the ALL extensions
min_length = in_.ToMicron(soma.skl_graph.min_length, size_voxel_in_micron, decimals=decimals)
mean_length = in_.ToMicron(soma.skl_graph.mean_length, size_voxel_in_micron, decimals=decimals)
median_length = in_.ToMicron(soma.skl_graph.median_length, size_voxel_in_micron, decimals=decimals)
max_length = in_.ToMicron(soma.skl_graph.max_length, size_voxel_in_micron, decimals=decimals)
std_lengths = np_.std(ext_lengths)
entropy_lengths = st_.entropy(ext_lengths)
#
# Curvature
for _, _, edge in soma.skl_graph.edges.data("as_edge_t"):
if edge is not None:
edge.SetEndPointDirections(size_voxel_in_micron)
for point in
# Find the thickness of the extensions
for ___, ___, edge in soma.skl_graph.edges.data("as_edge_t"):
if edge is not None:
edge.widths = scale_map[edge.sites] * size_voxel_in_micron[1]
mean_widths = soma.skl_graph.edge_reduced_widths()
ext_thickness = np_.array(mean_widths) ** 2
min_thickness = min(ext_thickness)
mean_thickness = np_.mean(ext_thickness)
median_thickness = np_.median(ext_thickness)
max_thickness = max(ext_thickness)
std_thickness = np_.std(ext_thickness)
entropy_thickness = st_.entropy(ext_thickness)
#
ext_volume = np_.array(ext_lengths) * ext_thickness
min_volume = min(ext_volume)
mean_volume = np_.mean(ext_volume)
median_volume = np_.median(ext_volume)
max_volume = max(ext_volume)
std_volume = np_.std(ext_volume)
entropy_volume = st_.entropy(ext_volume)
# print(
# f"ALL EXTENSIONS\n Total Length = {total_ext_length} <- {ext_lengths}\n"
# f" Min/Mean/Median/Max Length = {min_length} / {mean_length} / {median_length} / {max_length}\n"
# f" Standard Deviation = {std_lengths} / Entropy = {entropy_lengths}")
# pl_.plot(hist_lengths[1][:-1], hist_lengths[0])
# PRIMARY extensions
ext_lengths_P = list(soma.skl_graph.primary_edge_lengths(soma))
for idx, length in enumerate(ext_lengths_P):
ext_lengths_P[idx] = in_.ToMicron(length, size_voxel_in_micron, decimals=decimals)
total_ext_length_P = sum(ext_lengths_P)
#
# Lengths histogram
hist_lengths_P = np_.histogram(ext_lengths_P, bins=number_of_bins, range=(hist_min_length, max_range))[0]
#
# min, mean, median, max and standard deviation of the PRIMARY extensions
min_length_P = min(ext_lengths_P)
mean_length_P = np_.mean(ext_lengths_P)
median_length_P = np_.median(ext_lengths_P)
max_length_P = max(ext_lengths_P)
std_lengths_P = np_.std(ext_lengths_P)
entropy_lengths_P = st_.entropy(ext_lengths_P)
#
mean_widths_P = soma.skl_graph.P_edge_reduced_widths(soma)
ext_thickness_P = np_.array(mean_widths_P) ** 2
min_thickness_P = min(ext_thickness_P)
mean_thickness_P = np_.mean(ext_thickness_P)
median_thickness_P = np_.median(ext_thickness_P)
max_thickness_P = max(ext_thickness_P)
std_thickness_P = np_.std(ext_thickness_P)
entropy_thickness_P = st_.entropy(ext_thickness_P)
#
#
ext_volume_P = np_.array(ext_lengths_P) * ext_thickness_P
min_volume_P = min(ext_volume_P)
mean_volume_P = np_.mean(ext_volume_P)
median_volume_P = np_.median(ext_volume_P)
max_volume_P = max(ext_volume_P)
std_volume_P = np_.std(ext_volume_P)
entropy_volume_P = st_.entropy(ext_volume_P)
# print(
# f"PRIMARY EXTENSIONS\n Total Length = {total_ext_length_P}\n"
# f" Min/Mean/Median/Max Length = {min_length_P} / {mean_length_P} / {median_length_P} / {max_length_P}\n"
# f" Standard Deviation = {std_lengths_P} / Entropy = {entropy_lengths_P}")
# pl_.plot(hist_lengths_P[1][:-1], hist_lengths_P[0])
if N_sec_ext > 0:
# min, mean, median, max and standard deviation of the degrees of non-leaves nodes
min_degree = soma.skl_graph.min_degree_except_leaves_and_roots
mean_degree = soma.skl_graph.mean_degree_except_leaves_and_roots
median_degree = soma.skl_graph.median_degree_except_leaves_and_roots
max_degree = soma.skl_graph.max_degree_except_leaves_an_roots
std_degree = soma.skl_graph.std_degree_except_leaves_and_roots
# SECONDARY extensions length
ext_lengths_S = list(soma.skl_graph.secondary_edge_lengths(soma))
for idx, length in enumerate(ext_lengths_S):
ext_lengths_S[idx] = in_.ToMicron(length, size_voxel_in_micron, decimals=decimals)
total_ext_length_S = sum(ext_lengths_S)
#
# Lengths histogram
hist_lengths_S = np_.histogram(ext_lengths_S, bins=number_of_bins, range=(hist_min_length, max_range))[0]
#
# min, mean, median, max and standard deviation of the PRIMARY extensions
min_length_S = min(ext_lengths_S)
mean_length_S = np_.mean(ext_lengths_S)
median_length_S = np_.median(ext_lengths_S)
max_length_S = max(ext_lengths_S)
std_lengths_S = np_.std(ext_lengths_S)
entropy_lengths_S = st_.entropy(ext_lengths_S)
#
mean_widths_S = soma.skl_graph.S_edge_reduced_widths(soma)
ext_thickness_S = np_.array(mean_widths_S) ** 2
min_thickness_S = min(ext_thickness_S)
mean_thickness_S = np_.mean(ext_thickness_S)
median_thickness_S = np_.median(ext_thickness_S)
max_thickness_S = max(ext_thickness_S)
std_thickness_S = np_.std(ext_thickness_S)
entropy_thickness_S = st_.entropy(ext_thickness_S)
#
ext_volume_S = np_.array(ext_lengths_S) * ext_thickness_S
min_volume_S = min(ext_volume_S)
mean_volume_S = np_.mean(ext_volume_S)
median_volume_S = np_.median(ext_volume_S)
max_volume_S = max(ext_volume_S)
std_volume_S = np_.std(ext_volume_S)
entropy_volume_S = st_.entropy(ext_volume_S)
# print(
# f"SECONDARY EXTENSIONS\n Total Length = {total_ext_length_S}\n"
# f" Min/Mean/Median/Max Length = {min_length_S} / {mean_length_S} / {median_length_S} / {max_length_S}\n"
# f" Standard Deviation = {std_lengths_S} / Entropy = {entropy_lengths_S}"
# )
# pl_.plot(hist_lengths_S[1][:-1], hist_lengths_S[0])
if N_sec_ext == 0:
# min, mean, median, max and standard deviation of the degrees of non-leaves nodes
min_degree = 1
mean_degree = 1
median_degree = 1
max_degree = 1
std_degree = 0
total_ext_length_S = 0
min_length_S = 0
mean_length_S = 0
median_length_S = 0
max_length_S = 0
std_lengths_S = 0
entropy_lengths_S = 0
hist_lengths_S = 0
#
min_thickness_S = 0
mean_thickness_S = 0
median_thickness_S = 0
max_thickness_S = 0
std_thickness_S = 0
entropy_thickness_S = 0
#
min_volume_S = 0
mean_volume_S = 0
median_volume_S = 0
max_volume_S = 0
std_volume_S = 0
entropy_volume_S = 0
else:
min_degree = 0
mean_degree = 0
median_degree = 0
max_degree = 0
std_degree = 0
#
total_ext_length = 0
min_length = 0
mean_length = 0
median_length = 0
max_length = 0
std_lengths = 0
entropy_lengths = 0
hist_lengths = 0
min_thickness = 0
mean_thickness = 0
median_thickness = 0
max_thickness = 0
std_thickness = 0
entropy_thickness = 0
min_volume = 0
mean_volume = 0
median_volume = 0
max_volume = 0
std_volume = 0
entropy_volume = 0
#
total_ext_length_P = 0
min_length_P = 0
mean_length_P = 0
median_length_P = 0
max_length_P = 0
std_lengths_P = 0
entropy_lengths_P = 0
hist_lengths_P = 0
min_thickness_P = 0
mean_thickness_P = 0
median_thickness_P = 0
max_thickness_P = 0
std_thickness_P = 0
entropy_thickness_P = 0
min_volume_P = 0
mean_volume_P = 0
median_volume_P = 0
max_volume_P = 0
std_volume_P = 0
entropy_volume_P = 0
#
total_ext_length_S = 0
min_length_S = 0
mean_length_S = 0
median_length_S = 0
max_length_S = 0
std_lengths_S = 0
entropy_lengths_S = 0
hist_lengths_S = 0
min_thickness_S = 0
mean_thickness_S = 0
median_thickness_S = 0
max_thickness_S = 0
std_thickness_S = 0
entropy_thickness_S = 0
min_volume_S = 0
mean_volume_S = 0
median_volume_S = 0
max_volume_S = 0
std_volume_S = 0
entropy_volume_S = 0
#
# print(
# f"NODES DEGREES\n"
# f"Min/Mean/Median/Max degree (except soma & leaves) = {min_degree} / {mean_degree} / {median_degree} / {max_degree}\n"
# f"Standard deviation (except soma & leaves) = {std_degree}\n\n"
# )
somas_features_dict[f"soma {soma.uid}"] = [
Coef_V_soma__V_convex_hull,
Coef_axes_ellips_y__x,
Coef_axes_ellips_z__x,
N_nodes,
N_ext,
N_primary_ext,
N_sec_ext,
min_degree,
mean_degree,
median_degree,
max_degree,
std_degree,
#
total_ext_length,
min_length,
mean_length,
median_length,
max_length,
std_lengths,
entropy_lengths,
hist_lengths,
min_thickness,
mean_thickness,
median_thickness,
max_thickness,
std_thickness,
entropy_thickness,
min_volume,
mean_volume,
median_volume,
max_volume,
std_volume,
entropy_volume,
#
total_ext_length_P,
min_length_P,
mean_length_P,
median_length_P,
max_length_P,
std_lengths_P,
entropy_lengths_P,
hist_lengths_P,
min_thickness_P,
mean_thickness_P,
median_thickness_P,
max_thickness_P,
std_thickness_P,
entropy_thickness_P,
min_volume_P,
mean_volume_P,
median_volume_P,
max_volume_P,
std_volume_P,
entropy_volume_P,
#
total_ext_length_S,
min_length_S,
mean_length_S,
median_length_S,
max_length_S,
std_lengths_S,
entropy_lengths_S,
hist_lengths_S,
min_thickness_S,
mean_thickness_S,
median_thickness_S,
max_thickness_S,
std_thickness_S,
entropy_thickness_S,
min_volume_S,
mean_volume_S,
median_volume_S,
max_volume_S,
std_volume_S,
entropy_volume_S,
]
features_df = pd_.DataFrame.from_dict(somas_features_dict, orient="index", columns=columns)
return features_df