nutrimorph.py

# 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.

# Time profiling:
#     python -m cProfile -o runtime/profiling.log -s name main.py
# Memory profiling:
#     python -m memory_profiler main.py
#     or
#     mprof run main.py
#     mprof plot

import os as os_
import pathlib
import sys as sy_
import time as tm_

import imageio as io_
import matplotlib.pyplot as pl_
import networkx as nx_
import numpy as np_
import pandas as pd_
import skimage.exposure as ex_
import skimage.measure as ms_
import skimage.morphology as mp_

import brick.component.connection as cn_
import brick.component.extension as xt_
import brick.component.soma as sm_
import brick.feature.graph as ge_
import brick.image.input as in_
import brick.image.unit
import brick.io.console.feedback
import brick.io.screen.feedback as fb_
import brick.io.screen.plot as po_
import brick.io.screen.soma_validation as smvl
import brick.processing.dijkstra_1_to_n as dk_
from brick.io.storage.parameters import NutrimorphArguments
from skl_graph.graph import skl_graph_t
from skl_graph.map import SKLMapFromObjectMap


def NutriMorph(
    data_path: str,
    save_images,
    save_csv,
    channel=None,
    dilatation_erosion=None,
    size_voxel_in_micron=None,
    soma_low_c=None,
    soma_high_c=None,
    soma_selem_micron_c=None,
    soma_min_area_c=None,
    soma_max_area_c=None,
    adapt_hist_equalization=None,
    ext_low_c=None,
    ext_high_c=None,
    ext_selem_micron_c=None,
    ext_min_area_c=None,
    max_straight_sq_dist_c=None,
    max_weighted_length_c=None,
    scale_range=None,
    scale_step=None,
    alpha=None,
    beta=None,
    frangi_c=None,
    diff_mode=None,
    bright_on_dark=None,
    method=None,
    hist_min_length=None,
    hist_step_length=None,
    number_of_bins_length=None,
    hist_bins_borders_length=None,
    hist_min_curvature=None,
    hist_step_curvature=None,
    number_of_bins_curvature=None,
    hist_bins_borders_curvature=None,
    with_plot=None,
    interactive=None,
    in_parallel=None,
) -> pd_.DataFrame():
    """"""
    soma_t = sm_.soma_t
    extension_t = xt_.extension_t

    print(f"STARTED: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
    start_time = tm_.time()

    # TODO add proper warning with warn module
    # --- Images

    # Find the name of the file, eq. to the biological tested condition number x
    name_file = os_.path.basename(data_path)
    print("FILE: ", name_file)
    name_file = name_file.replace(".tif", "")

    name_dir = os_.path.dirname(data_path)
    print("DIR: ", name_dir)

    # Find the dimension of the image voxels in micron
    # TODO do something more general, here too specific to one microscope metadata
    size_voxel_in_micron = brick.image.unit.FindVoxelDimensionInMicron(
        data_path, size_voxel_in_micron=size_voxel_in_micron
    )

    # Read the image
    image = io_.volread(data_path)

    # Image size verification - simple version without user interface
    image = in_.ImageVerification(image, channel)

    # iv_.image_verification(image, channel)  # -> PySide2 user interface  # TODO: must return the modified image!
    # /!\ conflicts between some versions of PySide2 and Python3

    # image = image[:, 800:, 300:]
    image = image[:, 800:, 500:]
    # image = image[:, 600:, 500:]
    img_shape = image.shape
    img_shape_as_str = str(img_shape)[1:-1].replace(", ", "x")
    #
    print(
        f"IMAGE: s.{img_shape_as_str} t.{image.dtype} m.{np_.amin(image)} M.{np_.amax(image)}"
    )

    # Intensity relative normalization (between 0 and 1).
    # Image for soma normalized for hysteresis
    image_for_soma = in_.IntensityNormalizedImage(image)
    # Image for extensions not normalized for frangi enhancement
    image_for_ext = image.copy()

    print(
        f"NRM-IMG: t.{image_for_soma.dtype} m.{np_.amin(image_for_soma):.2f} M.{np_.amax(image_for_soma):.2f}"
    )

    # --- Initialization
    som_nfo = {}
    ext_nfo = {}
    axes = {}

    #
    # --- Somas
    print("\n--- Soma Detection")

    # Change the soma parameters from micron to pixel dimensions, using the previously determined voxel size
    soma_min_area_c = brick.image.unit.ToPixel(
        soma_min_area_c, size_voxel_in_micron, dimension=(0, 1, 2)
    )
    soma_max_area_c = brick.image.unit.ToPixel(
        soma_max_area_c, size_voxel_in_micron, dimension=(0, 1, 2)
    )
    soma_selem_c = mp_.disk(
        brick.image.unit.ToPixel(soma_selem_micron_c, size_voxel_in_micron)
    )

    # - Create the maps for enhancing and detecting the soma
    # Hysteresis thresholding and closing/opening
    print("Hysteresis thresholding: ", end="")
    som_nfo["map_small"] = soma_t.Map(
        image_for_soma, soma_low_c, soma_high_c, soma_selem_c
    )
    print("Done")
    # Deletion of too small elements
    print("Morphological cleaning: ", end="")
    som_nfo["map_small"], som_lmp_small = soma_t.DeleteSmallSomas(
        som_nfo["map_small"], soma_min_area_c
    )
    # Deletion of too big elements (probably aggregation of cells)
    # Separated from the deletion of small elements to avoid extensions to be detected where too big somas were just deleted
    som_nfo["map"], som_lmp = soma_t.DeleteBigSomas(
        som_nfo["map_small"], som_lmp_small, soma_max_area_c
    )
    print("Done")

    # Labelling of somas, find the number of somas
    print("Somas labelling: ", end="")
    som_nfo["lmp"], n_somas = ms_.label(som_nfo["map"], return_num=True)
    print("Done")

    # Use relabel instead of label to optimize the algorithm. /!\ But BUG.
    # som_nfo["lmp"] = relabel_sequential(som_lmp)[0]
    # n_somas = som_nfo["lmp"].max()

    # Create the distance and influence maps used next for connexions between somas-extensions and extensions-extensions
    print("Distance and Influence Map creation: ", end="")
    som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(
        som_nfo["lmp"]
    )
    print("Done")

    # Create the tuple somas, containing all the objects 'soma'
    print("Somas creation: ", end="")
    somas = tuple(
        soma_t().FromMap(som_nfo["lmp"], uid) for uid in range(1, n_somas + 1)
    )
    print("Done")

    print(f"    n = {n_somas}")

    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")

    if with_plot:
        fb_.PlotSomas(somas, som_nfo, axes)
    elif interactive:
        window = smvl.soma_validation_window_t(
            image_for_soma,
            som_nfo["lmp"],
            mip_axis=0,
        )
        n_somas = window.LaunchValidation()
        print(f"Validated Somas: {n_somas}")

    #
    # -- Extensions
    print("\n--- Extension Detection")

    # Set to zeros the pixels belonging to the somas.
    # Take into account the aggregation of cells detected as one big soma to avoid extension creation there
    del_soma = (som_nfo["map_small"] > 0).nonzero()
    image_for_ext[del_soma] = 0

    # Change the extensions parameters from micron to pixel dimensions
    ext_min_area_c = brick.image.unit.ToPixel(
        ext_min_area_c, size_voxel_in_micron, dimension=(0, 1, 2)
    )

    if ext_selem_micron_c == 0:
        ext_selem_pixel_c = None
    else:
        ext_selem_pixel_c = mp_.disk(
            brick.image.unit.ToPixel(ext_selem_micron_c, size_voxel_in_micron)
        )

    # - Perform frangi feature enhancement (via python or c - faster - implementation)
    enhanced_ext, ext_scales = extension_t.EnhancedForDetection(
        image_for_ext,
        scale_range,
        scale_step,
        alpha,
        beta,
        frangi_c,
        bright_on_dark,
        method,
        diff_mode=diff_mode,
        in_parallel=in_parallel,
    )

    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"Elapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}\n")

    # - Creation of the enhanced maps
    # Enhanced the contrast in frangi output
    print("Enhancing Contrast: ", end="")
    if adapt_hist_equalization:
        # necessary but not sufficient, histogram equalisation (adaptative)
        enhanced_ext = ex_.equalize_adapthist(enhanced_ext)
    else:
        # necessary but not sufficient, histogram equalisation (global)
        enhanced_ext = ex_.equalize_hist(enhanced_ext)

    # Rescale the image by stretching the intensities between 0 and 255, necessary but not sufficient
    enhanced_ext = ex_.rescale_intensity(enhanced_ext, out_range=(0, 255))
    print("Done")
    # Hysteresis thersholding
    print("Hysteresis Thresholding: ", end="")
    ext_nfo["coarse_map"] = extension_t.CoarseMap(
        enhanced_ext, ext_low_c, ext_high_c, ext_selem_pixel_c
    )
    print("Done")
    # Deletion of too small elements
    print("Morphological cleaning: ", end="")
    ext_nfo["coarse_map"], ext_lmp = extension_t.FilteredCoarseMap(
        ext_nfo["coarse_map"], ext_min_area_c
    )
    print("Done")
    # Creation of the extensions skeleton
    print("Skeletonization and Thinning: ", end="")
    ext_nfo["map"] = extension_t.FineMapFromCoarseMap(ext_nfo["coarse_map"])
    print("Done")
    # Deletion of extensions found within the somas
    print("Map Labelling: ", end="")
    ext_nfo["map"][som_nfo["map"] > 0] = 0
    # Labelling of extensions and number of extensions determined
    ext_nfo["lmp"], n_extensions = ms_.label(ext_nfo["map"], return_num=True)
    print("Done")

    # Use relabel instead of label to optimize the algorithm. BUT PROBLEM WITH THE NUMBER OF EXTENSIONS DETECTED !
    # ext_nfo["lmp"] = relabel_sequential(ext_lmp)[0]
    # n_extensions = ext_nfo["lmp"].max()

    # Create the tuple extensions, containing all the objects 'extension'
    print("Extensions creation: ", end="")
    extensions = tuple(
        extension_t().FromMap(ext_nfo["lmp"], ext_scales, uid)
        for uid in range(1, n_extensions + 1)
    )
    print("Done")
    print(f"    n = {n_extensions}")

    # Create global end point map for extensions
    glob_ext_ep_map = xt_.EndPointMap(ext_nfo["lmp"] > 0)
    all_end_points = list(
        zip(*glob_ext_ep_map.nonzero())
    )  # updated in ValidateConnexion

    #
    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")

    if with_plot:
        fb_.PlotExtensions(extensions, ext_nfo, img_shape)

    # -- Preparation for Connections
    # Calculate the COSTS of each pixel on the image and DILATE the existing extensions to
    # avoid tangency of connexions to extensions
    dijkstra_costs = dk_.DijkstraCosts(image, som_nfo["map"], ext_nfo["map"])

    if dilatation_erosion:
        # Dilate all extensions
        for extension in extensions:
            cn_.Dilate(extension.sites, dijkstra_costs)

    # -- Soma-Extention
    print("\n--- Soma <-> Extension")

    # Change the extensions parameters from micron to pixel dimensions
    max_straight_sq_dist_c = brick.image.unit.ToPixel(
        max_straight_sq_dist_c, size_voxel_in_micron
    )
    max_weighted_length_c = brick.image.unit.ToPixel(
        max_weighted_length_c, size_voxel_in_micron
    )

    # Find the candidate extensions, with their endpoints, for connexion to the somas
    candidate_conn_nfo = cn_.CandidateConnections(
        somas,
        som_nfo["influence_map"],
        som_nfo["dist_to_closest"],
        extensions,
        max_straight_sq_dist_c,
    )

    # For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
    for ep_idx, soma, extension, end_point in candidate_conn_nfo:
        # Only try to connect if the extension is not already connected
        if extension.is_unconnected:
            print(f"    Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
            if dilatation_erosion:
                # Erode the end_point of the extension in the costs map
                cn_.Erode(
                    (end_point,),
                    dijkstra_costs,
                    extension,
                    extensions,
                    image,
                    all_end_points=all_end_points,
                )

            # Use of Dijkstra shortest weighted path
            path, length = cn_.ShortestPathFromToN(
                image=image,
                point=end_point,
                extension=extension,
                costs=dijkstra_costs,
                candidate_points_fct=soma.ContourPointsCloseTo,
                max_straight_sq_dist=max_straight_sq_dist_c,
            )

            # Keep the connexion only if inferior to the allowed max weighted distance
            if path.__len__() > 0:
                # length in pixel and max_weighted_length_c too
                if length <= max_weighted_length_c:
                    # Validate and update all the fields + dilate again the whole extension
                    cn_.ValidateConnection(
                        soma,
                        extension,
                        end_point,
                        path,
                        dijkstra_costs,
                        all_ep=all_end_points,
                    )
                    if dilatation_erosion:
                        # Dilate extensions
                        cn_.Dilate(extension.sites, dijkstra_costs)
                    print(": Made")
                else:
                    if dilatation_erosion:
                        cn_.Dilate(extension.sites, dijkstra_costs)
                    print("")
            else:
                if dilatation_erosion:
                    cn_.Dilate(extension.sites, dijkstra_costs)
                print("")

    brick.io.console.feedback.PrintConnectedExtensions(extensions)

    #
    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")

    if with_plot:
        fb_.PlotSomasWithExtensions(somas, som_nfo, "all")

    # -- Extention-Extention
    print("\n--- Extension <-> Extension")

    should_look_for_connections = True

    # Update the soma maps with next Ext-Ext connexions
    while should_look_for_connections:

        if dilatation_erosion:
            # Update the costs by dilating everything again plus the contour points of the soma
            for soma in somas:
                cn_.Dilate(soma.contour_points, dijkstra_costs)
                # Below probably not necessary but security
                for extension in soma.extensions:
                    cn_.Dilate(extension.sites, dijkstra_costs)

        # Update the final somas + ext map by adding the new extensions and their connexion paths
        som_nfo["soma_w_ext_lmp"] = soma_t.SomasLMap(somas)

        # Update the influence map
        som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(
            som_nfo["soma_w_ext_lmp"]
        )

        # Find the candidate extensions for connexion to the primary extensions
        candidate_conn_nfo = cn_.CandidateConnections(
            somas,
            som_nfo["influence_map"],
            som_nfo["dist_to_closest"],
            extensions,
            max_straight_sq_dist_c,
        )

        should_look_for_connections = False

        # For each candidate, verify if valid based on the shortest path and the maximum allowed straight distance
        for ep_idx, soma, extension, end_point in candidate_conn_nfo:
            # Only try to connect if the extension is not already connected
            if extension.is_unconnected:
                print(
                    f"    Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end=""
                )
                if dilatation_erosion:
                    # Erode the end_point of the extension in the costs map
                    cn_.Erode(
                        (end_point,),
                        dijkstra_costs,
                        extension,
                        extensions,
                        image,
                        all_end_points=all_end_points,
                    )
                # Use of Dijkstra shortest weighted path
                path, length = cn_.ShortestPathFromToN(
                    image=image,
                    point=end_point,
                    extension=extension,
                    costs=dijkstra_costs,
                    candidate_points_fct=soma.ExtensionPointsCloseTo,
                    extensions=extensions,
                    all_end_points=all_end_points,
                    max_straight_sq_dist=max_straight_sq_dist_c,
                    erode_path=dilatation_erosion,
                )

                # Keep the connexion only if inferior to the allowed max weighted distance
                if path.__len__() > 0:
                    if length <= max_weighted_length_c:
                        # Verify that the last element of the connexion path is contained in the extension to be connected
                        # If so, returns the extensions containing the path last site.
                        tgt_extenstion = extension_t.ExtensionContainingSite(
                            extensions, path[-1]
                        )
                        # Validate connexion: update the glial component objects and store more info in their fields
                        cn_.ValidateConnection(
                            tgt_extenstion,
                            extension,
                            end_point,
                            path,
                            dijkstra_costs,
                            all_ep=all_end_points,
                        )
                        if dilatation_erosion:
                            # Dilate extensions
                            cn_.Dilate(extension.sites, dijkstra_costs)

                        should_look_for_connections = True

                        print(": Made")
                    else:
                        if dilatation_erosion:
                            cn_.Dilate(extension.sites, dijkstra_costs)
                        print("")
                else:
                    if dilatation_erosion:
                        cn_.Dilate(extension.sites, dijkstra_costs)
                    print("")

    # -- Create an extension map containing all the ext + connexions, without the somas.
    # Used for the graphs extraction
    ext_nfo["lmp_soma"] = som_nfo["soma_w_ext_lmp"].copy()
    ext_nfo["lmp_soma"][som_nfo["map"] > 0] = 0
    #

    brick.io.console.feedback.PrintConnectedExtensions(extensions)

    #
    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")

    if with_plot:
        fb_.PlotSomasWithExtensions(somas, som_nfo, "with_ext_of_ext")

    # -- Summary
    print("\n")
    for soma in somas:
        print(soma)

    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")

    if with_plot:
        pl_.show()

    if save_images is not None:
        po_.MaximumIntensityProjectionZ(
            som_nfo["soma_w_ext_lmp"],
            block=False,
            output_image_file_name=str(pathlib.Path(save_images) / f"MIP_{name_file}.png"),
        )

    # --- Extract all the extensions of all somas as a graph
    print("\n--- Graph extraction")

    # Create the graphs with SKLGraph module (available on Eric Debreuve Gitlab)
    for soma in somas:
        print(f" Soma {soma.uid}", end="")
        # Create SKLGraph skeletonized map
        ext_map = SKLMapFromObjectMap(
            ext_nfo["lmp_soma"] == soma.uid,
            with_width=True,
        )

        # Create the graph from the SKLGaph skeletonized map
        soma.skl_graph = skl_graph_t.FromSKLMap(
            ext_map, width_map=size_voxel_in_micron
        )

        # --- Find the root of the {ext+conn} graphs.
        # Roots are the nodes of degree 1 that are to be linked to the soma
        soma.graph_roots = ge_.FindGraphsRootWithNodes(soma)

        # Add a node "soma" and link it to the root nodes
        soma_node = (
            f"S-{int(soma.centroid[0])}-{int(soma.centroid[1])}-{int(soma.centroid[2])}"
        )
        soma.skl_graph.add_node(soma_node, soma=True, soma_nfo=soma)

        for node in soma.graph_roots.values():
            soma.skl_graph.add_edge(node, soma_node, root=True)

        if save_images is not None:
            nx_.draw_networkx(soma.skl_graph)
            pl_.savefig(str(pathlib.Path(save_images) / f"graph_{name_file}_soma{soma.uid}.png"))
            # pl_.show(block=True)
            pl_.close()

        print(": Done")

    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")

    # --- Extract features
    print("\n--- Features Extraction\n")

    # Parameters
    if hist_bins_borders_length is None:
        number_of_bins_length = int(number_of_bins_length)
        bins_length = np_.linspace(
            hist_min_length,
            hist_min_length + hist_step_length * number_of_bins_length,
            num=number_of_bins_length,
        )
        bins_length[-1] = np_.inf
    else:
        bins_length = np_.array(hist_bins_borders_length)
        bins_length[-1] = np_.inf

    if hist_bins_borders_curvature is None:
        number_of_bins_curvature = int(number_of_bins_curvature)
        bins_curvature = np_.linspace(
            hist_min_curvature,
            hist_min_curvature + hist_step_curvature * number_of_bins_curvature,
            num=number_of_bins_curvature,
        )
        bins_curvature[-1] = np_.inf
    else:
        bins_curvature = np_.array(hist_bins_borders_curvature)
        bins_curvature[-1] = np_.inf

    # Pandas dataframe creation with all the measured features
    features_df = ge_.ExtractFeaturesInDF(
        name_file,
        somas,
        size_voxel_in_micron,
        bins_length,
        bins_curvature,
        ext_scales,
    )

    # Save the pandas df into .csv
    if save_csv is not None:
        features_df.to_csv(f"{save_csv}/{name_file}.csv")
    else:
        features_df.to_csv(f"{name_dir}/{name_file}.csv")

    elapsed_time = tm_.gmtime(tm_.time() - start_time)
    print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
    print(f"DONE: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}\n")

    return features_df


def Main():
    """"""
    if sy_.argv.__len__() < 2:
        print("Missing parameter file argument")
        sy_.exit(0)
    argument = sy_.argv[1]
    if not (
        os_.path.isfile(argument)
        and os_.access(argument, os_.R_OK)
        and (os_.path.splitext(argument)[1].lower() == ".ini")
    ):
        print("Wrong parameter file path or type, or parameter file unreadable")
        sy_.exit(0)

    (
        data_path,
        save_images,
        save_csv,
        save_features_analysis,
        statistical_analysis,
        arguments,
    ) = NutrimorphArguments(argument)

    # Differentiate between path to a tiff file or to a repository
    if pathlib.Path(data_path).is_file():
        # Perform NutriMorph algorithm on the file entered in parameters
        print(
            "WARNING: Will not perform features analysis on a single image.\n For features analysis, "
            "give a directory path.\n"
        )
        _ = NutriMorph(data_path, save_images, save_csv, **arguments)

    elif pathlib.Path(data_path).is_dir():
        # Keep the directory to the repository
        # Initialize the future concatenated features
        concatenated_features_df = pd_.DataFrame()
        # Find all the tiff files in the parent and child repositories
        for path in pathlib.Path(data_path).glob("**/*.tif"):
            if path.is_file():
                # Perform NutriMorph algorithm
                features_df = NutriMorph(str(path), save_images, save_csv, **arguments)
                # Concatenate all the dataframes
                concatenated_features_df = concatenated_features_df.append(features_df)
                # If some rows (ie. somas0) have NaN, drop them
                # -- due to best fitting ellipsoid algo (JTJ is singular due to convex hull being flat)
                concatenated_features_df = concatenated_features_df.dropna()

        # Save to .csv in the parent repository
        if save_csv is None:
            concatenated_features_df.to_csv(f"{data_path}/features.csv")
        else:
            concatenated_features_df.to_csv(f"{save_csv}/features.csv")

        # Clustering with this df and module features_analysis.py
        if statistical_analysis:
            os_.system(
                f"feature_analysis.py {save_csv}/features.csv {save_features_analysis}"
            )
    else:
        raise ImportError(f"{data_path}: Not a valid data path!")


print("\n\n--- NOT IMPORTING MEMORY PROFILING ---\n\n")
# import brick.auto_decorator as dctr
# dctr.MemoryProfileCallables(__name__)


if __name__ == "__main__":
    #
    Main()

    # data_path = None
    # channel = None
    # save_images = None
    # save_csv = None
    # save_features_analysis = None
    # dilatation_erosion = None
    # size_voxel_in_micron = None
    # statistical_analysis = None
    # soma_low_c = None
    # soma_high_c = None
    # soma_selem_micron_c = None
    # soma_min_area_c = None
    # soma_max_area_c = None
    # adapt_hist_equalization = None
    # ext_low_c = None
    # ext_high_c = None
    # ext_selem_micron_c = None
    # ext_min_area_c = None
    # ext_max_length_c = None
    # max_straight_sq_dist_c = None
    # max_weighted_length_c = None
    # scale_range = None
    # scale_step = None
    # alpha = None
    # beta = None
    # frangi_c = None
    # diff_mode = None
    # bright_on_dark = None
    # method = None
    # hist_min_length = None
    # hist_step_length = None
    # number_of_bins_length = None
    # hist_bins_borders_length = None
    # hist_min_curvature = None
    # hist_step_curvature = None
    # number_of_bins_curvature = None
    # hist_bins_borders_curvature = None
    # with_plot = None
    # interactive = None
    # in_parallel = None