# 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.
from typing import Any, Dict, Sequence, Union
import numpy as nmpy
import pandas as pd_
import scipy.stats as st_
from logger_36 import LOGGER
import brick.task.ellipsoid_fit as bf_
import brick.task.unit
from brick.type.base import array_t
from brick.type.soma import soma_t
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("details"):
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_soma"][edge_nfo.sites]
ext_uid = nmpy.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
# tuple('x-y-z') -> list[(x,y,z)]
root_node_coor = soma.skl_graph.nodes[root_node][
"details"
].position.tolist() # _GetNodesCoordinates((root_node,))[0]
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
# TODO: find out why there are less root points than extensions !!
return root_nodes
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.
"""
is_end_point = []
node_label = []
coordinates = []
for label, degree in soma.skl_graph.degree:
is_end_point.append(degree == 1)
node_label.append(label)
coordinates.append(
tuple(soma.skl_graph.nodes[label]["details"].position.tolist())
)
# Find the nodes of degree == 1, and the str coordinates of the nodes
# is_end_point = tuple(degree == 1 for _, degree in soma.skl_graph.degree)
# node_label = tuple(xyz for xyz, _ in soma.skl_graph.degree)
root_nodes = {}
# get the coordinates of the nodes (x,y,z)
# coordinates = _GetNodesCoordinates(node_label)
# get a list with elements = (extension_uid, root coordinates), which length is the number of primary extensions
roots = soma.ext_roots
# 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 is_end_point[node]:
# compare the coor with end points
for ext_root in roots:
if ext_root[1] == coordinates[node]:
root_nodes[ext_root[0]] = node_label[node]
if root_nodes.__len__() != roots.__len__():
# raise ValueError("Number of extensions roots not equal to number of graph roots.")
LOGGER.warning(
f"Number of extensions roots: {root_nodes.__len__()} "
f"not equal to number of graph roots: {roots.__len__()}."
)
return root_nodes
# 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 = r"\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
FEATURE_SHEET_COLUMNS = [
"condition",
"duration",
"soma uid",
"coef_V_soma__V_convex_hull",
"coef_axes_ellips_b__a",
"coef_axes_ellips_c__a",
"spherical_angles_eva",
"spherical_angles_evb",
#
"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",
"min_curvature",
"max_curvature",
"mean_curvature",
"median_curvature",
"std_curvature",
"entropy_curvature",
"hist_curvature",
#
"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",
"min_curvature_P",
"max_curvature_P",
"mean_curvature_P",
"median_curvature_P",
"std_curvature_P",
"entropy_curvature_P",
"hist_curvature_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",
"min_curvature_S",
"max_curvature_S",
"mean_curvature_S",
"median_curvature_S",
"std_curvature_S",
"entropy_curvature_S",
"hist_curvature_S",
]
def ExtractFeaturesInDF(
name_file,
Condition,
Duration,
somas,
voxel_size_in_micron: array_t,
bins_length: array_t,
bins_curvature: array_t,
scale_map: array_t,
decimals: int = 4,
_=None, # condition
__=None, # duration
):
"""
Extract the features from somas and graphs.
Store the condition and duration of the image
if (condition is None) or (duration is None):
Condition = re_.findall(r"[A-Z]{3}", name_file)[0]
Duration = re_.findall(r"\dH", name_file)
Find our whether the duration is in hours or in weeks
if Duration.__len__() == 0:
Duration = re_.findall(r"\dW", name_file)[0]
else:
Duration = Duration[0]
Returns a pandas dataframe, without NaN.
"""
somas_features_dict = {} # Dict{soma 1: [features], soma 2: [features], ...}
(
min_degree,
mean_degree,
median_degree,
max_degree,
std_degree,
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,
min_curvature_S,
max_curvature_S,
mean_curvature_S,
median_curvature_S,
std_curvature_S,
entropy_curvature_S,
hist_curvature_S,
) = 32 * (None,)
for soma in somas:
# -- Soma features
# Axes of the best fitting ellipsoid: a > b > c
(
_,
_,
soma.axes_ellipsoid,
_,
spherical_coor,
_,
volume_convex_hull,
) = bf_.FindBestFittingEllipsoid3D(soma)
# These ratios give info about the shape of the soma. ex: rather flat, rather patatoide, rather spherical...
if type(soma.axes_ellipsoid[0]) is str:
(
Coef_axes_ellips_b__a,
Coef_axes_ellips_c__a,
spherical_angles_eva,
spherical_angles_evb,
soma.volume_soma_micron,
Coef_V_soma__V_convex_hull,
) = 6 * (None,)
else:
Coef_axes_ellips_b__a = soma.axes_ellipsoid[0] / soma.axes_ellipsoid[2]
Coef_axes_ellips_c__a = soma.axes_ellipsoid[1] / soma.axes_ellipsoid[2]
# Spherical angles give the orientation of the somas in the 3D volume
spherical_angles_eva = (spherical_coor[0][1], spherical_coor[0][2])
spherical_angles_evb = (spherical_coor[1][1], spherical_coor[1][2])
# Volume of the in micron**3
soma.volume_soma_micron = brick.task.unit.ToMicron(
len(soma.sites[0]),
voxel_size_in_micron,
dimension=(0, 1, 2),
decimals=2,
)
# Calculates volume of soma's convex hull in voxel volume
# Take into account anisotropy of the 3D space ( volume = x * y * z with z > x=y)
volume_convex_hull = (
volume_convex_hull * voxel_size_in_micron[2] / voxel_size_in_micron[0]
)
# Volume of the soma / Volume of its convex hull gives the info about the convexity of the soma
# If close to 0, the soma has a lot of invaginations, if close to 1, it is smooth and convex
Coef_V_soma__V_convex_hull = len(soma.sites[0]) / round(
volume_convex_hull + 0.5
)
# -- Extension features
# Graph features
# number of nodes except the constructed ones from node soma to the roots
N_nodes = soma.skl_graph.n_nodes - len(soma.graph_roots)
# number of edges except the constructed ones from node soma to the roots
N_ext = soma.skl_graph.n_edges - len(soma.graph_roots)
# number of primary edges = linked to the soma except the constructed ones from node soma to the roots
N_primary_ext = len(soma.graph_roots)
# number of secondary edges = not linked to the soma.
N_sec_ext = N_ext - N_primary_ext
LOGGER.info(
f"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:
ext_lengths = [_elm[-1] for _elm in soma.skl_graph.lengths]
for idx, length in enumerate(ext_lengths):
ext_lengths[idx] = brick.task.unit.ToMicron(
length, voxel_size_in_micron, decimals=decimals
)
total_ext_length = brick.task.unit.ToMicron(
soma.skl_graph.length, voxel_size_in_micron, decimals=decimals
)
(
min_length,
max_length,
median_length,
mean_length,
std_lengths,
hist_lengths,
entropy_lengths,
) = _Statistics_mMdashe(ext_lengths, bins=bins_length)
# hist_lengths = nmpy.histogram(ext_lengths, bins_length)[0]
min_length = brick.task.unit.ToMicron(
min_length, voxel_size_in_micron, decimals=decimals
)
mean_length = brick.task.unit.ToMicron(
mean_length, voxel_size_in_micron, decimals=decimals
)
median_length = brick.task.unit.ToMicron(
median_length, voxel_size_in_micron, decimals=decimals
)
max_length = brick.task.unit.ToMicron(
max_length, voxel_size_in_micron, decimals=decimals
)
# std_lengths = nmpy.std(ext_lengths)
# entropy_lengths = st_.entropy(hist_lengths)
# Find the thickness of the extensions
for *_, edge in soma.skl_graph.edges.data("details"):
if edge is not None:
edge.widths = scale_map[edge.sites] * voxel_size_in_micron[1]
mean_widths = soma.skl_graph.edge_reduced_widths()
ext_thickness = nmpy.array(mean_widths) ** 2
(
min_thickness,
max_thickness,
median_thickness,
mean_thickness,
std_thickness,
_,
entropy_thickness,
) = _Statistics_mMdashe(ext_thickness)
# min_thickness = min(ext_thickness)
# mean_thickness = nmpy.mean(ext_thickness)
# median_thickness = nmpy.median(ext_thickness)
# max_thickness = max(ext_thickness)
# std_thickness = nmpy.std(ext_thickness)
# entropy_thickness = st_.entropy(nmpy.histogram(ext_thickness)[0])
# Find the volume of the extensions
ext_volume = nmpy.array(ext_lengths) * ext_thickness
(
min_volume,
max_volume,
median_volume,
mean_volume,
std_volume,
_,
entropy_volume,
) = _Statistics_mMdashe(ext_volume)
# min_volume = min(ext_volume)
# mean_volume = nmpy.mean(ext_volume)
# median_volume = nmpy.median(ext_volume)
# max_volume = max(ext_volume)
# std_volume = nmpy.std(ext_volume)
# entropy_volume = st_.entropy(nmpy.histogram(ext_volume)[0])
curvatures = soma.skl_graph.curvature_and_torsion()
(
min_curvature,
max_curvature,
median_curvature,
mean_curvature,
std_curvature,
hist_curvature,
entropy_curvature,
) = _Statistics_mMdashe(curvatures, bins=bins_curvature)
# hist_curvature = nmpy.histogram(curvatures, bins_curvature)[0]
# min_curvature = min(curvatures)
# max_curvature = max(curvatures)
# mean_curvature = nmpy.mean(curvatures)
# median_curvature = nmpy.median(curvatures)
# std_curvature = nmpy.std(curvatures)
# entropy_curvature = st_.entropy(hist_curvature)
# 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] = brick.task.unit.ToMicron(
length, voxel_size_in_micron, decimals=decimals
)
total_ext_length_P = sum(ext_lengths_P)
(
min_length_P,
max_length_P,
median_length_P,
mean_length_P,
std_lengths_P,
hist_lengths_P,
entropy_lengths_P,
) = _Statistics_mMdashe(ext_lengths_P, bins=bins_length)
# hist_lengths_P = nmpy.histogram(ext_lengths_P, bins_length)[0]
# min_length_P = min(ext_lengths_P)
# mean_length_P = nmpy.mean(ext_lengths_P)
# median_length_P = nmpy.median(ext_lengths_P)
# max_length_P = max(ext_lengths_P)
# std_lengths_P = nmpy.std(ext_lengths_P)
# entropy_lengths_P = st_.entropy(hist_lengths_P)
mean_widths_P = soma.skl_graph.P_edge_reduced_widths(soma)
ext_thickness_P = nmpy.array(mean_widths_P) ** 2
(
min_thickness_P,
max_thickness_P,
median_thickness_P,
mean_thickness_P,
std_thickness_P,
_,
entropy_thickness_P,
) = _Statistics_mMdashe(ext_thickness_P)
# min_thickness_P = min(ext_thickness_P)
# mean_thickness_P = nmpy.mean(ext_thickness_P)
# median_thickness_P = nmpy.median(ext_thickness_P)
# max_thickness_P = max(ext_thickness_P)
# std_thickness_P = nmpy.std(ext_thickness_P)
# entropy_thickness_P = st_.entropy(nmpy.histogram(ext_thickness_P)[0])
ext_volume_P = nmpy.array(ext_lengths_P) * ext_thickness_P
(
min_volume_P,
max_volume_P,
median_volume_P,
mean_volume_P,
std_volume_P,
_,
entropy_volume_P,
) = _Statistics_mMdashe(ext_volume_P)
# min_volume_P = min(ext_volume_P)
# mean_volume_P = nmpy.mean(ext_volume_P)
# median_volume_P = nmpy.median(ext_volume_P)
# max_volume_P = max(ext_volume_P)
# std_volume_P = nmpy.std(ext_volume_P)
# entropy_volume_P = st_.entropy(nmpy.histogram(ext_volume_P)[0])
curvatures_P = soma.skl_graph.P_curvature_and_torsion(soma)
(
min_curvature_P,
max_curvature_P,
median_curvature_P,
mean_curvature_P,
std_curvature_P,
hist_curvature_P,
entropy_curvature_P,
) = _Statistics_mMdashe(curvatures_P, bins=bins_curvature)
# hist_curvature_P = nmpy.histogram(curvatures_P, bins_curvature)[0]
# min_curvature_P = min(curvatures_P)
# max_curvature_P = max(curvatures_P)
# mean_curvature_P = nmpy.mean(curvatures_P)
# median_curvature_P = nmpy.median(curvatures_P)
# std_curvature_P = nmpy.std(curvatures_P)
# entropy_curvature_P = st_.entropy(hist_curvature_P)
# Secondary extensions
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] = brick.task.unit.ToMicron(
length, voxel_size_in_micron, decimals=decimals
)
total_ext_length_S = sum(ext_lengths_S)
(
min_length_S,
max_length_S,
median_length_S,
mean_length_S,
std_lengths_S,
hist_lengths_S,
entropy_lengths_S,
) = _Statistics_mMdashe(ext_lengths_S, bins=bins_length)
# hist_lengths_S = nmpy.histogram(ext_lengths_S, bins_length)[0]
# min_length_S = min(ext_lengths_S)
# mean_length_S = nmpy.mean(ext_lengths_S)
# median_length_S = nmpy.median(ext_lengths_S)
# max_length_S = max(ext_lengths_S)
# std_lengths_S = nmpy.std(ext_lengths_S)
# entropy_lengths_S = st_.entropy(hist_lengths_S)
mean_widths_S = soma.skl_graph.S_edge_reduced_widths(soma)
ext_thickness_S = nmpy.array(mean_widths_S) ** 2
(
min_thickness_S,
max_thickness_S,
median_thickness_S,
mean_thickness_S,
std_thickness_S,
_,
entropy_thickness_S,
) = _Statistics_mMdashe(ext_thickness_S)
# min_thickness_S = min(ext_thickness_S)
# mean_thickness_S = nmpy.mean(ext_thickness_S)
# median_thickness_S = nmpy.median(ext_thickness_S)
# max_thickness_S = max(ext_thickness_S)
# std_thickness_S = nmpy.std(ext_thickness_S)
# entropy_thickness_S = st_.entropy(nmpy.histogram(ext_thickness_S)[0])
ext_volume_S = nmpy.array(ext_lengths_S) * ext_thickness_S
(
min_volume_S,
max_volume_S,
median_volume_S,
mean_volume_S,
std_volume_S,
_,
entropy_volume_S,
) = _Statistics_mMdashe(ext_volume_S)
# min_volume_S = min(ext_volume_S)
# mean_volume_S = nmpy.mean(ext_volume_S)
# median_volume_S = nmpy.median(ext_volume_S)
# max_volume_S = max(ext_volume_S)
# std_volume_S = nmpy.std(ext_volume_S)
# entropy_volume_S = st_.entropy(nmpy.histogram(ext_volume_S)[0])
curvatures_S = soma.skl_graph.S_curvature_and_torsion(soma)
(
min_curvature_S,
max_curvature_S,
median_curvature_S,
mean_curvature_S,
std_curvature_S,
hist_curvature_S,
entropy_curvature_S,
) = _Statistics_mMdashe(curvatures_S, bins=bins_curvature)
# hist_curvature_S = nmpy.histogram(curvatures_S, bins_curvature)[0]
# min_curvature_S = min(curvatures_S)
# max_curvature_S = max(curvatures_S)
# mean_curvature_S = nmpy.mean(curvatures_S)
# median_curvature_S = nmpy.median(curvatures_S)
# std_curvature_S = nmpy.std(curvatures_S)
# entropy_curvature_S = st_.entropy(hist_curvature_S)
# If no secondary extensions, give certain value to parameters
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
min_curvature_S = -1
max_curvature_S = -1
mean_curvature_S = -1
median_curvature_S = -1
std_curvature_S = 0
entropy_curvature_S = 0
hist_curvature_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
min_curvature = -1
max_curvature = -1
mean_curvature = -1
median_curvature = -1
std_curvature = 0
entropy_curvature = 0
hist_curvature = 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
min_curvature_P = -1
max_curvature_P = -1
mean_curvature_P = -1
median_curvature_P = -1
std_curvature_P = 0
entropy_curvature_P = 0
hist_curvature_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
min_curvature_S = -1
max_curvature_S = -1
mean_curvature_S = -1
median_curvature_S = -1
std_curvature_S = 0
entropy_curvature_S = 0
hist_curvature_S = 0
# Create a dictionary with all the features for every somas
somas_features_dict[name_file + f" soma {soma.uid}"] = [
Condition,
Duration,
soma.uid,
Coef_V_soma__V_convex_hull,
Coef_axes_ellips_b__a,
Coef_axes_ellips_c__a,
spherical_angles_eva,
spherical_angles_evb,
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,
min_curvature,
max_curvature,
mean_curvature,
median_curvature,
std_curvature,
entropy_curvature,
hist_curvature,
#
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,
min_curvature_P,
max_curvature_P,
mean_curvature_P,
median_curvature_P,
std_curvature_P,
entropy_curvature_P,
hist_curvature_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,
min_curvature_S,
max_curvature_S,
mean_curvature_S,
median_curvature_S,
std_curvature_S,
entropy_curvature_S,
hist_curvature_S,
]
features_df = pd_.DataFrame.from_dict(
somas_features_dict, orient="index", columns=FEATURE_SHEET_COLUMNS
)
return features_df
_Median = lambda _msr: nmpy.median(_msr).item()
_Mean = lambda _msr: nmpy.mean(_msr).item()
_Std = lambda _msr: nmpy.std(_msr).item()
def _Statistics_mMdashe(measures: Sequence, /, *, bins = None) -> tuple:
""""""
if bins is None:
bins = {}
else:
bins = {"bins": bins}
histogram = nmpy.histogram(measures, **bins)[0]
histogram = tuple(histogram.tolist())
entropy = st_.entropy(histogram).item()
output = [
_StatisticOf(measures) for _StatisticOf in (min, max, _Median, _Mean, _Std)
]
output.extend((histogram, entropy))
return tuple(output)