# 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 brick.component.connection as cn_
import brick.component.extension as xt_
import brick.component.soma as sm_
import brick.general.feedback as fb_
import brick.processing.input as in_
import brick.processing.plot as po_
import brick.processing.map_labeling as ml_
# import brick.processing.image_verification as iv_
from sklgraph.skl_fgraph import skl_graph_t
from sklgraph.skl_map import skl_map_t
import brick.processing.graph_feat_extraction as ge_
import features_analysis as fa_
import os as os_
import sys as sy_
import time as tm_
import pathlib
import psutil as mu_
import matplotlib.pyplot as pl_
import numpy as np_
import imageio as io_
import skimage.morphology as mp_
import skimage.measure as ms_
import skimage.exposure as ex_
import pandas as pd_
import networkx as nx_
print(sy_.argv, sy_.argv.__len__())
if sy_.argv.__len__() < 2:
print("Missing parameter file argument")
sy_.exit(0)
if not (os_.path.isfile(sy_.argv[1]) and os_.access(sy_.argv[1], os_.R_OK)):
print("Wrong parameter file path or parameter file unreadable")
sy_.exit(0)
data_path = None
channel = None
save_images = None
save_csv = 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
in_parallel = None
exec(open(sy_.argv[1]).read()) # Only with search_parameters.py (stable version)
def Test_Tangency_ext_conn(soma):
pb = []
for extension in soma.extensions:
for site in list(zip(*extension.sites)):
close_end_pt = tuple(
(site[0] + i, site[1] + j, site[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)
for ext in tuple(zip(soma.connection_path.values())):
if ext[0] is not None:
for conn in list(zip(*np_.asarray(ext[0]))):
if (conn in close_end_pt) and (conn not in list(zip(*extension.sites))):
for extens in soma.extensions:
if conn not in zip(*extens.end_points):
if conn not in pb:
pb.append(conn)
print("\nconn: ", conn, "\next_site: ", site, "\next_uid: ", extension.uid)
#########----------- Executable version with files AND repositories ------------###########
def NutriMorph(data_path: str,
channel=channel,
dilatation_erosion=dilatation_erosion,
size_voxel_in_micron=size_voxel_in_micron,
soma_low_c=soma_low_c,
soma_high_c=soma_high_c,
soma_selem_micron_c=soma_selem_micron_c,
soma_min_area_c=soma_min_area_c,
soma_max_area_c=soma_max_area_c,
adapt_hist_equalization=adapt_hist_equalization,
ext_low_c=ext_low_c,
ext_high_c=ext_high_c,
ext_selem_micron_c=ext_selem_micron_c,
ext_min_area_c=ext_min_area_c,
max_straight_sq_dist_c=max_straight_sq_dist_c,
max_weighted_length_c=max_weighted_length_c,
scale_range=scale_range,
scale_step=scale_step,
alpha=alpha,
beta=beta,
frangi_c=frangi_c,
diff_mode=diff_mode,
bright_on_dark=bright_on_dark,
method=method,
hist_min_length=hist_min_length,
hist_step_length=hist_step_length,
number_of_bins_length=number_of_bins_length,
hist_bins_borders_length=hist_bins_borders_length,
hist_min_curvature=hist_min_curvature,
hist_step_curvature=hist_step_curvature,
number_of_bins_curvature=number_of_bins_curvature,
hist_bins_borders_curvature=hist_bins_borders_curvature,
with_plot=with_plot,
in_parallel=in_parallel) -> 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 = in_.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:]
img_shape = image.shape
#
print(f"IMAGE: s.{img_shape} t.{image.dtype} m.{image.min()} M.{image.max()}")
# 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.{image_for_soma.min():.2f} M.{image_for_soma.max():.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 = in_.ToPixel(soma_min_area_c, size_voxel_in_micron, dimension=(0, 1))
soma_max_area_c = in_.ToPixel(soma_max_area_c, size_voxel_in_micron, dimension=(0, 1))
soma_selem_c = mp_.disk(in_.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)
#
# -- Extentions
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 = in_.ToPixel(ext_min_area_c, size_voxel_in_micron, dimension=(0, 1))
if ext_selem_micron_c == 0:
ext_selem_pixel_c = None
else:
ext_selem_pixel_c = mp_.disk(in_.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 = in_.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 = in_.ToPixel(max_straight_sq_dist_c, size_voxel_in_micron)
max_weighted_length_c = in_.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,
erode_path=False,
)
# 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("")
fb_.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
#
fb_.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=f"{save_images}\\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 = skl_map_t.FromShapeMap(ext_nfo['lmp_soma'] == soma.uid,
store_widths=True,
skeletonize=True,
do_post_thinning=True)
# Create the graph from the SKLGaph skeletonized map
soma.skl_graph = skl_graph_t.FromSkeleton(ext_map, end_point, size_voxel=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(f"{save_images}\\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
if __name__ == '__main__':
# --- Extract cell graphs and features from microscope images using NutriMorph function.
#
# 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")
features_df = NutriMorph(data_path)
elif pathlib.Path(data_path).is_dir():
# Keep the directory to the repository
name_dir = os_.path.dirname(data_path)
# 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():
name_file = os_.path.basename(path)
# Perform NutriMorph algorithm
features_df = NutriMorph(path)
# 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(axis=0, how="any")
# 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")
else:
raise ImportError("Not a valid data path!")