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 sys as sstm
from datetime import datetime
from pathlib import Path as path_t
from typing import Any, Sequence

import better_exceptions as xcpt
# import imageio as io_
import matplotlib.pyplot as pl_

# import networkx as nx_
import numpy as nmpy
import pandas as pd_
import skimage.draw as sidw
import skimage.exposure as ex_
import skimage.measure as ms_
import skimage.morphology as mp_
from logger_36 import LOGGER
from skl_graph import SKLMapFromObjectMap
from skl_graph.task.check.graph import Issues as GraphIssues
from skl_graph.task.plot.graph import Plot as PlotSKLGraph
from skl_graph.type.constant import SKL_GRAPH
from skl_graph.type.edge import edge_t

import brick.feature.cell as ge_
import brick.io.console.feedback
import brick.io.screen.feedback as fb_
import brick.io.screen.plot as po_
import brick.io.screen.type.soma_validation as smvl
import brick.task.check as in_
import brick.task.dijkstra as dk_
import brick.task.img_processing
import brick.type.connection as cn_
from brick.io.storage.parameters import NutrimorphArguments
from brick.task.img_processing import IntensityNormalizedImage
from brick.task.unit import FindVoxelDimensionInMicron, ToPixel
from brick.type.extension import extension_t
from brick.type.graph import skl_graph_t
from brick.type.soma import soma_t
from im_tools_36.input import ImageVolumeOrSequence

xcpt.hook()


array_t = nmpy.ndarray


_CSV_FEATURE_FILE = "features.csv"


def NutriMorph(
    connection_cfg: dict[str, Any],
    extension_cfg: dict[str, Any],
    feature_cfg: dict[str, Any],
    frangi_cfg: dict[str, Any],
    input_cfg: dict[str, Any],
    output_cfg: dict[str, Any],
    soma_cfg: dict[str, Any],
    workflow_cfg: dict[str, Any],
    /,
) -> pd_.DataFrame:
    """"""
    LOGGER.info("STARTED")

    # --- Images
    LOGGER.info(f"Image {input_cfg['data_path']}")

    image = ImageVolumeOrSequence(input_cfg["data_path"])  # io_.volread for imageio
    # [:, 1400:, 1200:]  # [:, 800:, 300:]  # [:, 600:, 500:]  # [:, 800:, 500:]
    image = image[:, 800:, 500:]
    img_shape = image.shape
    # TODO: Do something more general; Here, too specific to one microscope metadata.
    input_cfg["voxel_size_in_micron"] = FindVoxelDimensionInMicron(
        input_cfg["data_path"], voxel_size_in_micron=input_cfg["voxel_size_in_micron"]
    )

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

    img_shape_as_str = str(img_shape)[1:-1].replace(", ", "x")
    LOGGER.info(
        f"IMAGE: s.{img_shape_as_str} t.{image.dtype} m.{nmpy.amin(image)} M.{nmpy.amax(image)}"
    )

    image_for_soma = IntensityNormalizedImage(image)
    image_for_ext = image.copy()
    LOGGER.info(
        f"NRM-IMG: t.{image_for_soma.dtype} m.{nmpy.amin(image_for_soma):.2f} M.{nmpy.amax(image_for_soma):.2f}"
    )

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

    # --- Somas
    LOGGER.info("SOMA DETECTION")

    soma_cfg["soma_min_area"] = ToPixel(
        soma_cfg["soma_min_area"],
        input_cfg["voxel_size_in_micron"],
        dimension=(0, 1, 2),
    )
    soma_cfg["soma_max_area"] = ToPixel(
        soma_cfg["soma_max_area"],
        input_cfg["voxel_size_in_micron"],
        dimension=(0, 1, 2),
    )
    soma_selem_c = mp_.disk(
        ToPixel(soma_cfg["soma_selem_micron"], input_cfg["voxel_size_in_micron"])
    )

    LOGGER.info("Soma maps...")
    som_nfo["map"], som_nfo["lmp"], som_nfo["map_w_large"], n_somas = soma_t.Maps(
        image_for_soma,
        soma_cfg["soma_low"],
        soma_cfg["soma_high"],
        soma_selem_c,
        soma_cfg["soma_min_area"],
        soma_cfg["soma_max_area"],
    )

    if workflow_cfg["interactive"]:
        window = smvl.soma_validation_window_t(
            image_for_soma,
            som_nfo["lmp"],
            mip_axis=0,
        )
        som_nfo["lmp"], n_somas = window.LaunchValidation()
        # TODO: som_nfo["map"] and som_nfo["map_w_large"] become out-of-sync. Is it a problem?

    if n_somas == 0:
        if workflow_cfg["interactive"]:
            LOGGER.info(f"Validated somas: {n_somas}")
        else:
            LOGGER.info(f"Detected somas: {n_somas}")
        sstm.exit(0)

    LOGGER.info("Distance and influence map creation...")
    # For soma<->extension and extension<->extension
    som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(
        som_nfo["lmp"]
    )

    LOGGER.info(f"Somas creation ({n_somas})...")
    somas = tuple(
        soma_t.NewFromMap(som_nfo["lmp"], uid) for uid in range(1, n_somas + 1)
    )

    LOGGER.info(f"END OF SOMA DETECTION STEP. Number of somas = {n_somas}")

    if workflow_cfg["with_plot"]:
        fb_.PlotSomas(somas, som_nfo, axes)

    # --- Extensions
    LOGGER.info("EXTENSION DETECTION")

    extension_cfg["ext_min_area"] = ToPixel(
        extension_cfg["ext_min_area"],
        input_cfg["voxel_size_in_micron"],
        dimension=(0, 1, 2),
    )
    ext_selem_pixel_c = mp_.disk(
        ToPixel(extension_cfg["ext_selem_micron"], input_cfg["voxel_size_in_micron"])
    )

    # Use som_nfo["map_w_large"] instead of som_nfo["map"] to take into account the aggregation of cells detected as one
    # big soma to avoid extension creation there
    image_for_ext[som_nfo["map_w_large"]] = 0.0

    LOGGER.info("Frangi Enhancement...")
    enhanced_ext, ext_scales = extension_t.EnhancedForDetection(
        image_for_ext,
        frangi_cfg["scale_range"],
        frangi_cfg["scale_step"],
        frangi_cfg["alpha"],
        frangi_cfg["beta"],
        frangi_cfg["frangi_c"],
        frangi_cfg["bright_on_dark"],
        frangi_cfg["method"],
        frangi_cfg["diff_mode"],
        in_parallel=workflow_cfg["in_parallel"],
    )

    LOGGER.info("Enhancing contrast...")
    if extension_cfg["adapt_hist_equalization"]:
        enhanced_ext = ex_.equalize_adapthist(enhanced_ext)
    else:
        enhanced_ext = ex_.equalize_hist(enhanced_ext)
    enhanced_ext = ex_.rescale_intensity(enhanced_ext, out_range=(0, 255))

    LOGGER.info("Coarse extension map...")
    ext_nfo["coarse_map"] = extension_t.CoarseMap(
        enhanced_ext,
        extension_cfg["ext_low"],
        extension_cfg["ext_high"],
        ext_selem_pixel_c,
        extension_cfg["ext_min_area"],
    )
    del enhanced_ext

    _, n_coarse_extensions = ms_.label(ext_nfo["coarse_map"], return_num=True)
    LOGGER.info(f"Coarse extensions: {n_coarse_extensions}")

    LOGGER.info("Skeletonization...")
    ext_nfo["map"] = extension_t.FineMapFromCoarseMap(
        ext_nfo["coarse_map"], soma=som_nfo["map"]
    )

    LOGGER.info("Map Labelling...")
    ext_nfo["lmp"], n_extensions = ms_.label(ext_nfo["map"], return_num=True)

    LOGGER.info(f"Extension Creation ({n_extensions})...")
    extensions = tuple(
        extension_t.NewFromMap(ext_nfo["lmp"], ext_scales, uid)
        for uid in range(1, n_extensions + 1)
    )
    LOGGER.info(f"Number of extensions = {n_extensions}")
    if n_extensions == 0:
        sstm.exit(0)

    LOGGER.info("Global end-point map for extensions...")
    glob_ext_ep_map = extension_t.EndPointMap(ext_nfo["map"])
    all_end_points = list(zip(*glob_ext_ep_map.nonzero()))

    LOGGER.info("END OF EXTENSION DETECTION STEP")

    if workflow_cfg["with_plot"]:
        fb_.PlotExtensions(extensions, ext_nfo, img_shape)

    # -- Preparation for Connections
    LOGGER.info("DIJKSTRA COSTS")
    # Calculate the cost of each pixel and dilate the existing extensions to avoid tangency of connections to extensions
    dijkstra_costs = dk_.DijkstraCosts(image, som_nfo["map"], ext_nfo["map"])
    if connection_cfg["dilatation_erosion"]:
        for extension in extensions:
            cn_.Dilate(extension.sites, dijkstra_costs)

    # -- Soma-Extention
    LOGGER.info("SOMA <-> EXTENSION")

    connection_cfg["max_straight_sq_dist"] = ToPixel(
        connection_cfg["max_straight_sq_dist"], input_cfg["voxel_size_in_micron"]
    )
    connection_cfg["max_weighted_length"] = ToPixel(
        connection_cfg["max_weighted_length"], input_cfg["voxel_size_in_micron"]
    )

    LOGGER.info("Look for candidate connections...")
    candidate_conn_nfo = cn_.CandidateConnections(
        somas,
        som_nfo["influence_map"],
        som_nfo["dist_to_closest"],
        extensions,
        connection_cfg["max_straight_sq_dist"],
    )

    # 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:
        if extension.is_unconnected:
            LOGGER.info(f"    Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})")
            if connection_cfg["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,
                )

            path, length = cn_.ShortestPathFromToN(
                image=image,
                point=end_point,
                extension=extension,
                costs=dijkstra_costs,
                candidate_points_fct=soma.ContourPointsCloseTo,
                max_straight_sq_dist=connection_cfg["max_straight_sq_dist"],
            )

            if path.__len__() > 0:
                if length <= connection_cfg["max_weighted_length"]:
                    cn_.ValidateConnection(
                        soma,
                        extension,
                        end_point,
                        path,
                        dijkstra_costs,
                        all_ep=all_end_points,
                    )
                    if connection_cfg["dilatation_erosion"]:
                        cn_.Dilate(extension.sites, dijkstra_costs)
                    LOGGER.info("    : Made")
                else:
                    if connection_cfg["dilatation_erosion"]:
                        cn_.Dilate(extension.sites, dijkstra_costs)
                    LOGGER.info(
                        f"    : Cancelled: Too long: {length} > {connection_cfg['max_weighted_length_c']}"
                    )
            else:
                if connection_cfg["dilatation_erosion"]:
                    cn_.Dilate(extension.sites, dijkstra_costs)
                LOGGER.info("    : Cancelled: No found path")

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

    LOGGER.info("END OF SOMA <-> EXTENSION STEP")

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

    # -- Extention-Extention
    LOGGER.info("EXTENSION <-> EXTENSION")

    should_look_for_connections = True

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

        som_nfo["soma_w_ext_lmp"] = soma_t.SomasLMap(somas)
        som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(
            som_nfo["soma_w_ext_lmp"]
        )

        candidate_conn_nfo = cn_.CandidateConnections(
            somas,
            som_nfo["influence_map"],
            som_nfo["dist_to_closest"],
            extensions,
            connection_cfg["max_straight_sq_dist"],
        )

        should_look_for_connections = False

        for ep_idx, soma, extension, end_point in candidate_conn_nfo:
            if extension.is_unconnected:
                LOGGER.info(f"    Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})")
                if connection_cfg["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,
                    )

                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=connection_cfg["max_straight_sq_dist"],
                    erode_path=connection_cfg["dilatation_erosion"],
                )

                if path.__len__() > 0:
                    if length <= connection_cfg["max_weighted_length"]:
                        # 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]
                        )
                        cn_.ValidateConnection(
                            tgt_extenstion,
                            extension,
                            end_point,
                            path,
                            dijkstra_costs,
                            all_ep=all_end_points,
                        )
                        if connection_cfg["dilatation_erosion"]:
                            cn_.Dilate(extension.sites, dijkstra_costs)

                        should_look_for_connections = True

                        LOGGER.info("    : Made")
                    else:
                        if connection_cfg["dilatation_erosion"]:
                            cn_.Dilate(extension.sites, dijkstra_costs)
                        LOGGER.info(
                            f"    : Cancelled: Too long: {length} > {connection_cfg['max_weighted_length_c']}"
                        )
                else:
                    if connection_cfg["dilatation_erosion"]:
                        cn_.Dilate(extension.sites, dijkstra_costs)
                    LOGGER.info("    : Cancelled: No found path")

    # -- 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)

    LOGGER.info("END OF EXTENSION <-> EXTENSION STEP")

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

    # -- Summary
    for info, name in ((som_nfo, "som_nfo"), (ext_nfo, "ext_nfo")):
        for key, value in info.items():
            if isinstance(value, array_t):
                LOGGER.info(
                    f"{name}[{key}]: {value.dtype.name}x{value.shape}={value.nbytes}B"
                )

    LOGGER.info("\n")
    for soma in somas:
        LOGGER.info(soma)

    LOGGER.info("END OF IMAGE PROCESSING STEPS")

    if workflow_cfg["with_plot"]:
        pl_.show()

    if output_cfg["save_images"] is not None:
        po_.MaximumIntensityProjectionZ(
            som_nfo["soma_w_ext_lmp"],
            block=False,
            output_image_file_name=str(
                output_cfg["save_images"] / f"MIP_{input_cfg['data_path'].stem}.png"
            ),
        )

    # --- Extract all the extensions of all somas as a graph
    LOGGER.info("GRAPH EXTRACTION")

    for soma in somas:
        LOGGER.info(f"Soma {soma.uid}")
        ext_map, width_map = SKLMapFromObjectMap(
            ext_nfo["lmp_soma"] == soma.uid, with_width=True
        )
        soma.skl_graph = skl_graph_t.NewFromSKLMap(ext_map, width_map=width_map)

        # --- 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)

        # TODO: the update of this code to link soma nodes to extension is a WIP
        # Add a node "soma" and link it to the root nodes. /!\ Put the coordinates
        # inside an iterable (e.g., a tuple), otherwise nmpy.array creates a
        # 0-dimensional array, which is invalid for branch node sites.
        soma_uid = soma.skl_graph.AddBranchNode(
            tuple(nmpy.array((int(round(_elm)),)) for _elm in soma.centroid),
            width_map=width_map,
        )
        soma.skl_graph.nodes[soma_uid]["soma"] = True
        soma.skl_graph.nodes[soma_uid]["soma_nfo"] = soma
        for node in soma.graph_roots.values():
            sites = sidw.line_nd(
                soma.skl_graph.nodes[soma_uid][SKL_GRAPH].position,
                soma.skl_graph.nodes[node][SKL_GRAPH].position,
                endpoint=True,
            )
            adjacent_node_uids = (soma_uid, node)
            edge = edge_t.NewWithDetails(
                sites,
                True,
                adjacent_node_uids,
                width_map=width_map,
            )
            soma.skl_graph.AddEdge(edge, adjacent_node_uids)
            # The ", edge.uid" at the end of indexing is due to the Multigraph nature of soma
            soma.skl_graph.edges[
                adjacent_node_uids[0], adjacent_node_uids[1], edge.uid
            ]["root"] = True

        issues = GraphIssues(soma.skl_graph)
        if issues.__len__() > 0:
            LOGGER.info(f"Soma {soma.uid} - Graph Issues")
            LOGGER.info("\n".join(issues))
        if output_cfg["save_images"] is not None:
            output_path = str(
                output_cfg["save_images"]
                / f"graph_{input_cfg['data_path'].stem}_soma{soma.uid}.png"
            )
            from json_any.task.storage import StoreAsJSON
            from skl_graph.task.storage import DescriptionForJSON

            json_path = path_t(output_path.replace(".", "-")).with_suffix(".json.bz2")
            print(f">>> Saving Graph in {json_path}")
            history = StoreAsJSON(
                soma.skl_graph,
                json_path,
                descriptors={"": DescriptionForJSON},
                should_continue_on_error=True,
            )
            if isinstance(history, Sequence):
                LOGGER.info("\n".join(history))
            print("<<< Saving Done")

            soma.skl_graph.SetPlotStyles(label=False)
            figure, axes = PlotSKLGraph(
                soma.skl_graph,
                should_block=False,
                should_return_axes=True,
                should_return_figure=True,
            )
            axes.set_title(f"{soma.uid} - {soma_uid}")
            figure.savefig(output_path)
            if workflow_cfg["with_plot"]:
                pl_.show()
            else:
                pl_.close()

    LOGGER.info("END OF GRAPH EXTRACTION STEP")

    # --- Extract features
    LOGGER.info("FEATURE EXTRACTION")

    if feature_cfg["hist_bins_borders_length"] is None:
        bins_length = nmpy.linspace(
            feature_cfg["hist_min_length"],
            feature_cfg["hist_min_length"]
            + feature_cfg["hist_step_length"] * feature_cfg["number_of_bins_length"],
            num=feature_cfg["number_of_bins_length"],
        )
        bins_length[-1] = nmpy.inf
    else:
        bins_length = nmpy.array(feature_cfg["hist_bins_borders_length"])
        bins_length[-1] = nmpy.inf

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

    features_df = ge_.ExtractFeaturesInDF(
        input_cfg["data_path"].stem,
        input_cfg["condition"],
        input_cfg["duration"],
        somas,
        input_cfg["voxel_size_in_micron"],
        bins_length,
        bins_curvature,
        ext_scales,
    )

    if output_cfg["save_csv"] is None:
        features_df.to_csv(
            str(input_cfg["data_path"].parent / f"{input_cfg['data_path'].stem}.csv")
        )
    else:
        features_df.to_csv(
            str(output_cfg["save_csv"] / f"{input_cfg['data_path'].stem}.csv")
        )

    LOGGER.info("END OF FEATURE EXTRACTION STEP")

    return features_df


def Main():
    """"""
    if sstm.argv.__len__() < 2:
        print("Missing parameter file argument")
        sstm.exit(0)
    argument = path_t(sstm.argv[1])
    if not (argument.is_file() and (argument.suffix.lower() == ".ini")):
        print("Wrong parameter file path or type, or parameter file unreadable")
        sstm.exit(0)

    config = NutrimorphArguments(argument)
    (
        connection_cfg,
        extension_cfg,
        feature_cfg,
        frangi_cfg,
        input_cfg,
        output_cfg,
        soma_cfg,
        workflow_cfg,
    ) = config
    base_folder = (
        output_cfg["save_images"]
        / input_cfg["data_path"].stem
        / str(datetime.now()).replace(" ", "_").replace(":", "-").replace(".", "-")
    )
    output_cfg["save_images"] = base_folder / "image"
    output_cfg["save_csv"] = base_folder / "features"
    output_cfg["save_features_analysis"] = base_folder / "analysis"
    for path in (
        output_cfg["save_images"],
        output_cfg["save_csv"],
        output_cfg["save_features_analysis"],
    ):
        path.mkdir(parents=True, exist_ok=True)

    if input_cfg["data_path"].is_file():
        LOGGER.info(
            "WARNING: Will not perform features analysis on a single image.\n"
            "For features analysis, give a directory path"
        )
        _ = NutriMorph(
            connection_cfg,
            extension_cfg,
            feature_cfg,
            frangi_cfg,
            input_cfg,
            output_cfg,
            soma_cfg,
            workflow_cfg,
        )
    elif input_cfg["data_path"].is_dir():
        paths = sorted(input_cfg["data_path"].glob("**/*.tif"))
        concatenated_features_df = pd_.DataFrame()
        for path in paths:
            if path.is_file():
                input_cfg["data_path"] = path
                features_df = NutriMorph(
                    connection_cfg.copy(),
                    extension_cfg.copy(),
                    feature_cfg.copy(),
                    frangi_cfg.copy(),
                    input_cfg.copy(),
                    output_cfg.copy(),
                    soma_cfg.copy(),
                    workflow_cfg.copy(),
                )
                concatenated_features_df = pd_.concat(
                    (concatenated_features_df, features_df)
                )
                # If some rows (i.e. somas) have NaN, drop them (due to best fitting ellipsoid algo (JTJ is singular due
                # to convex hull being flat)).
                concatenated_features_df.dropna(inplace=True)
                LOGGER.info(f"Features: New={features_df.shape}, So far={concatenated_features_df.shape}.")

        concatenated_features_df.to_csv(output_cfg["save_csv"] / _CSV_FEATURE_FILE)

        # Clustering with this df and module features_analysis.py
        if output_cfg["statistical_analysis"]:
            from brick.feature.analysis import Main as Analysis

            path_1 = output_cfg["save_csv"]
            path_2 = output_cfg["save_features_analysis"]
            Analysis(str(path_1 / _CSV_FEATURE_FILE), path_2)
            # os_.system(
            #     str(path_t(__file__).parent / "brick" / "feature" / "analysis.py")
            #     + " "
            #     + str(path_1 / _CSV_FEATURE_FILE)
            #     + f" {path_2}"
            # )
    else:
        path = input_cfg["data_path"]
        raise ImportError(f"{path}: Not a valid data path!")


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