diff --git a/PyNutil/io/propagation.py b/PyNutil/io/propagation.py
index 22a611fe44b7ee85384cc70a56dcc942154ea5cd..600ad6033c5b296b17a6037a183ac15b14d160bb 100644
--- a/PyNutil/io/propagation.py
+++ b/PyNutil/io/propagation.py
@@ -1,5 +1,6 @@
import math, re
+
def propagate(arr):
arr = arr.copy()
for slice in arr:
diff --git a/PyNutil/io/reconstruct_dzi.py b/PyNutil/io/reconstruct_dzi.py
index 862c2ec55078ed9e0ddbcdfc8f96f48a07ad476b..bbdff4038d0577d2d0fba447d657af634627801f 100644
--- a/PyNutil/io/reconstruct_dzi.py
+++ b/PyNutil/io/reconstruct_dzi.py
@@ -4,6 +4,7 @@ import os
import zipfile
import xmltodict
+
def reconstruct_dzi(zip_file_path):
"""
Reconstructs a Deep Zoom Image (DZI) from a zip file containing the tiles.
diff --git a/PyNutil/main.py b/PyNutil/main.py
index 8522cea0c6d5d2e999c3d0c19676ab719d57ee26..3642a72d5a1e98f84e6dfc1828dd4ebe65ec667d 100644
--- a/PyNutil/main.py
+++ b/PyNutil/main.py
@@ -1,10 +1,15 @@
import json
from .io.atlas_loader import load_atlas_data, load_custom_atlas
-from .processing.data_analysis import quantify_labeled_points, map_to_custom_regions, apply_custom_regions
+from .processing.data_analysis import (
+ quantify_labeled_points,
+ map_to_custom_regions,
+ apply_custom_regions,
+)
from .io.file_operations import save_analysis_output
from .io.read_and_write import open_custom_region_file
from .processing.coordinate_extraction import folder_to_atlas_space
+
class PyNutil:
"""
A class used to perform brain-wide quantification and spatial analysis of features in serial section images.
@@ -150,7 +155,7 @@ class PyNutil:
self.centroids_len,
self.segmentation_filenames,
self.per_point_undamaged,
- self.per_centroid_undamaged
+ self.per_centroid_undamaged,
) = folder_to_atlas_space(
self.segmentation_folder,
self.alignment_json,
@@ -201,7 +206,7 @@ class PyNutil:
self.centroids_labels,
self.atlas_labels,
self.per_point_undamaged,
- self.per_centroid_undamaged
+ self.per_centroid_undamaged,
)
if self.custom_regions_dict is not None:
self.custom_label_df, self.label_df = apply_custom_regions(
diff --git a/PyNutil/processing/coordinate_extraction.py b/PyNutil/processing/coordinate_extraction.py
index ddf569eb565e14624d939792c89b9122ca43d878..f6d26c2bfdd6f8aa7773f6df3f7b2519d3effd63 100644
--- a/PyNutil/processing/coordinate_extraction.py
+++ b/PyNutil/processing/coordinate_extraction.py
@@ -43,15 +43,17 @@ def get_centroids_and_area(segmentation, pixel_cut_off=0):
coords = np.array([label.coords for label in labels_info], dtype=object)
return centroids, area, coords
+
def update_spacing(anchoring, width, height, grid_spacing):
if len(anchoring) != 9:
print("Anchoring does not have 9 elements.")
- ow = np.sqrt(sum([anchoring[i+3] ** 2 for i in range(3)]))
- oh = np.sqrt(sum([anchoring[i+6] ** 2 for i in range(3)]))
+ ow = np.sqrt(sum([anchoring[i + 3] ** 2 for i in range(3)]))
+ oh = np.sqrt(sum([anchoring[i + 6] ** 2 for i in range(3)]))
xspacing = int(width * grid_spacing / ow)
yspacing = int(height * grid_spacing / oh)
return xspacing, yspacing
+
def create_damage_mask(section, grid_spacing):
width = section["width"]
height = section["height"]
@@ -65,7 +67,10 @@ def create_damage_mask(section, grid_spacing):
y_coords = np.arange(gridy, height, yspacing)
num_markers = len(grid_values)
- markers = [(x_coords[i % len(x_coords)], y_coords[i // len(x_coords)]) for i in range(num_markers)]
+ markers = [
+ (x_coords[i % len(x_coords)], y_coords[i // len(x_coords)])
+ for i in range(num_markers)
+ ]
binary_image = np.ones((len(y_coords), len(x_coords)), dtype=int)
@@ -75,6 +80,7 @@ def create_damage_mask(section, grid_spacing):
return binary_image
+
def folder_to_atlas_space(
folder,
quint_alignment,
@@ -104,9 +110,15 @@ def folder_to_atlas_space(
slices, gridspacing = load_visualign_json(quint_alignment)
segmentations = get_segmentations(folder)
flat_files, flat_file_nrs = get_flat_files(folder, use_flat)
- points_list, centroids_list, region_areas_list, centroids_labels, points_labels,per_point_undamaged_list,per_centroid_undamaged_list = (
- initialize_lists(len(segmentations))
- )
+ (
+ points_list,
+ centroids_list,
+ region_areas_list,
+ centroids_labels,
+ points_labels,
+ per_point_undamaged_list,
+ per_centroid_undamaged_list,
+ ) = initialize_lists(len(segmentations))
threads = create_threads(
segmentations,
slices,
@@ -125,11 +137,25 @@ def folder_to_atlas_space(
object_cutoff,
atlas_volume,
use_flat,
- gridspacing
+ gridspacing,
)
start_and_join_threads(threads)
- points, centroids, points_labels, centroids_labels, points_len, centroids_len, per_point_undamaged_list, per_centroid_undamaged_list = (
- process_results(points_list, centroids_list, points_labels, centroids_labels, per_point_undamaged_list, per_centroid_undamaged_list)
+ (
+ points,
+ centroids,
+ points_labels,
+ centroids_labels,
+ points_len,
+ centroids_len,
+ per_point_undamaged_list,
+ per_centroid_undamaged_list,
+ ) = process_results(
+ points_list,
+ centroids_list,
+ points_labels,
+ centroids_labels,
+ per_point_undamaged_list,
+ per_centroid_undamaged_list,
)
return (
points,
@@ -141,7 +167,7 @@ def folder_to_atlas_space(
centroids_len,
segmentations,
per_point_undamaged_list,
- per_centroid_undamaged_list
+ per_centroid_undamaged_list,
)
@@ -183,7 +209,7 @@ def initialize_lists(length):
centroids_labels,
points_labels,
per_point_undamaged_list,
- per_centroid_undamaged_list
+ per_centroid_undamaged_list,
)
@@ -205,7 +231,7 @@ def create_threads(
object_cutoff,
atlas_volume,
use_flat,
- gridspacing
+ gridspacing,
):
"""
Creates threads for processing segmentations.
@@ -261,7 +287,7 @@ def create_threads(
object_cutoff,
atlas_volume,
use_flat,
- gridspacing
+ gridspacing,
),
)
threads.append(x)
@@ -309,7 +335,7 @@ def get_region_areas(
slice_dict,
atlas_volume,
triangulation,
- damage_mask
+ damage_mask,
):
"""
Gets the region areas.
@@ -327,11 +353,16 @@ def get_region_areas(
Returns:
DataFrame: DataFrame with region areas.
"""
- atlas_map = load_image(flat_file_atlas,slice_dict["anchoring"], atlas_volume, triangulation, (seg_width, seg_height), atlas_labels)
-
- region_areas = flat_to_dataframe(
- atlas_map, damage_mask, (seg_width, seg_height)
+ atlas_map = load_image(
+ flat_file_atlas,
+ slice_dict["anchoring"],
+ atlas_volume,
+ triangulation,
+ (seg_width, seg_height),
+ atlas_labels,
)
+
+ region_areas = flat_to_dataframe(atlas_map, damage_mask, (seg_width, seg_height))
return region_areas, atlas_map
@@ -392,7 +423,7 @@ def segmentation_to_atlas_space(
slice_dict,
atlas_volume,
triangulation,
- damage_mask
+ damage_mask,
)
atlas_map = rescale_image(atlas_map, (reg_width, reg_height))
y_scale, x_scale = transform_to_registration(
@@ -411,16 +442,21 @@ def segmentation_to_atlas_space(
np.round(scaled_centroidsX).astype(int), np.round(scaled_centroidsY).astype(int)
]
if damage_mask is not None:
- damage_mask = cv2.resize(damage_mask.astype(np.uint8), (atlas_map.shape[::-1]), interpolation=cv2.INTER_NEAREST).astype(bool)
+ damage_mask = cv2.resize(
+ damage_mask.astype(np.uint8),
+ (atlas_map.shape[::-1]),
+ interpolation=cv2.INTER_NEAREST,
+ ).astype(bool)
per_point_undamaged = damage_mask[
- np.round(scaled_x).astype(int), np.round(scaled_y).astype(int)
- ]
+ np.round(scaled_x).astype(int), np.round(scaled_y).astype(int)
+ ]
per_centroid_undamaged = damage_mask[
- np.round(scaled_centroidsX).astype(int), np.round(scaled_centroidsY).astype(int)
- ]
+ np.round(scaled_centroidsX).astype(int),
+ np.round(scaled_centroidsY).astype(int),
+ ]
else:
per_point_undamaged = np.ones(scaled_x.shape, dtype=bool)
- per_centroid_undamaged = np.ones(scaled_centroidsX.shape, dtype=bool)
+ per_centroid_undamaged = np.ones(scaled_centroidsX.shape, dtype=bool)
per_point_labels = per_point_labels[per_point_undamaged]
per_centroid_labels = per_centroid_labels[per_centroid_undamaged]
new_x, new_y, centroids_new_x, centroids_new_y = get_transformed_coordinates(
@@ -451,7 +487,9 @@ def segmentation_to_atlas_space(
per_centroid_undamaged if centroids is not None else []
)
points_labels[index] = np.array(per_point_labels if points is not None else [])
- per_point_undamaged_list[index] = np.array(per_point_undamaged if points is not None else [])
+ per_point_undamaged_list[index] = np.array(
+ per_point_undamaged if points is not None else []
+ )
def get_triangulation(slice_dict, reg_width, reg_height, non_linear):
diff --git a/PyNutil/processing/counting_and_load.py b/PyNutil/processing/counting_and_load.py
index da1fce6f65091054f932022d4620b6c5ba45fe24..0b549e6a4037cdb5ebe0e0e021d969376c47b8a1 100644
--- a/PyNutil/processing/counting_and_load.py
+++ b/PyNutil/processing/counting_and_load.py
@@ -5,6 +5,7 @@ import cv2
from .generate_target_slice import generate_target_slice
from .visualign_deformations import transform_vec
+
# related to counting and load
def label_points(points, label_volume, scale_factor=1):
"""
@@ -49,7 +50,11 @@ def label_points(points, label_volume, scale_factor=1):
# related to counting_and_load
def pixel_count_per_region(
- labels_dict_points, labeled_dict_centroids, current_points_undamaged, current_centroids_undamaged, df_label_colours
+ labels_dict_points,
+ labeled_dict_centroids,
+ current_points_undamaged,
+ current_centroids_undamaged,
+ df_label_colours,
):
"""
Counts the number of pixels per region and writes to a DataFrame.
@@ -77,75 +82,59 @@ def pixel_count_per_region(
# Which regions have pixels, and how many pixels are there per region
# Create a list of unique regions and pixel counts per region
counts_per_label = {
- "idx" : [],
- "name" : [],
- "r" : [],
- "g" : [],
- "b" : [],
- "pixel_count" : [],
- "undamaged_pixel_count" : [],
- "damaged_pixel_counts" : [],
- "object_count" : [],
- "undamaged_object_count" : [],
- "damaged_object_count" : [],
- }
+ "idx": [],
+ "name": [],
+ "r": [],
+ "g": [],
+ "b": [],
+ "pixel_count": [],
+ "undamaged_pixel_count": [],
+ "damaged_pixel_counts": [],
+ "object_count": [],
+ "undamaged_object_count": [],
+ "damaged_object_count": [],
+ }
for index, row in df_label_colours.iterrows():
if row["idx"] in counted_labels_points_undamaged:
- clpu = label_counts_points_undamaged[counted_labels_points_undamaged == row["idx"]][0]
+ clpu = label_counts_points_undamaged[
+ counted_labels_points_undamaged == row["idx"]
+ ][0]
else:
clpu = 0
if row["idx"] in counted_labels_points_damaged:
- clpd = label_counts_points_damaged[counted_labels_points_damaged == row["idx"]][0]
+ clpd = label_counts_points_damaged[
+ counted_labels_points_damaged == row["idx"]
+ ][0]
else:
clpd = 0
if row["idx"] in counted_labels_centroids_undamaged:
- clcu = counted_labels_centroids_undamaged[counted_labels_centroids_undamaged == row["idx"]][0]
+ clcu = counted_labels_centroids_undamaged[
+ counted_labels_centroids_undamaged == row["idx"]
+ ][0]
else:
clcu = 0
if row["idx"] in counted_labels_centroids_damaged:
- clcd = counted_labels_centroids_damaged[counted_labels_centroids_damaged == row["idx"]][0]
+ clcd = counted_labels_centroids_damaged[
+ counted_labels_centroids_damaged == row["idx"]
+ ][0]
else:
clcd = 0
- if clcd==clcu==clpd==clpu==0:
+ if clcd == clcu == clpd == clpu == 0:
continue
- counts_per_label["idx"].append(
- row["idx"]
- )
- counts_per_label["name"].append(
- row["name"]
- )
- counts_per_label["r"].append(
- int(row["r"])
- )
- counts_per_label["g"].append(
- int(row["g"])
- )
- counts_per_label["b"].append(
- int(row["b"])
- )
- counts_per_label["pixel_count"].append(
- clpu + clpd
- )
- counts_per_label["undamaged_pixel_count"].append(
- clpu
- )
- counts_per_label["damaged_pixel_counts"].append(
- clpd
- )
- counts_per_label["object_count"].append(
- clcu + clcd
- )
- counts_per_label["undamaged_object_count"].append(
- clcu
- )
- counts_per_label["damaged_object_count"].append(
- clcd
- )
-
- df_counts_per_label = pd.DataFrame(
- counts_per_label
- )
+ counts_per_label["idx"].append(row["idx"])
+ counts_per_label["name"].append(row["name"])
+ counts_per_label["r"].append(int(row["r"]))
+ counts_per_label["g"].append(int(row["g"]))
+ counts_per_label["b"].append(int(row["b"]))
+ counts_per_label["pixel_count"].append(clpu + clpd)
+ counts_per_label["undamaged_pixel_count"].append(clpu)
+ counts_per_label["damaged_pixel_counts"].append(clpd)
+ counts_per_label["object_count"].append(clcu + clcd)
+ counts_per_label["undamaged_object_count"].append(clcu)
+ counts_per_label["damaged_object_count"].append(clcd)
+
+ df_counts_per_label = pd.DataFrame(counts_per_label)
return df_counts_per_label
@@ -311,10 +300,7 @@ def warp_image(image, triangulation, rescaleXY):
return new_image
-def flat_to_dataframe(
- image,
- damage_mask,
- rescaleXY=None):
+def flat_to_dataframe(image, damage_mask, rescaleXY=None):
"""
Converts a flat file to a DataFrame.
@@ -332,13 +318,33 @@ def flat_to_dataframe(
"""
scale_factor = calculate_scale_factor(image, rescaleXY)
if damage_mask is not None:
- damage_mask = cv2.resize(damage_mask.astype(np.uint8), (image.shape[::-1]), interpolation=cv2.INTER_NEAREST).astype(bool)
- undamaged_df_area_per_label = count_pixels_per_label(image[damage_mask], scale_factor)
- damaged_df_area_per_label = count_pixels_per_label(image[~damage_mask], scale_factor)
- undamaged_df_area_per_label = undamaged_df_area_per_label.rename(columns={"region_area": "undamaged_region_area"})
- damaged_df_area_per_label = damaged_df_area_per_label.rename(columns={"region_area": "damaged_region_area"})
- df_area_per_label = pd.merge(undamaged_df_area_per_label, damaged_df_area_per_label, on='idx', how='outer').fillna(0)
- df_area_per_label["region_area"] = df_area_per_label["undamaged_region_area"] + df_area_per_label["damaged_region_area"]
+ damage_mask = cv2.resize(
+ damage_mask.astype(np.uint8),
+ (image.shape[::-1]),
+ interpolation=cv2.INTER_NEAREST,
+ ).astype(bool)
+ undamaged_df_area_per_label = count_pixels_per_label(
+ image[damage_mask], scale_factor
+ )
+ damaged_df_area_per_label = count_pixels_per_label(
+ image[~damage_mask], scale_factor
+ )
+ undamaged_df_area_per_label = undamaged_df_area_per_label.rename(
+ columns={"region_area": "undamaged_region_area"}
+ )
+ damaged_df_area_per_label = damaged_df_area_per_label.rename(
+ columns={"region_area": "damaged_region_area"}
+ )
+ df_area_per_label = pd.merge(
+ undamaged_df_area_per_label,
+ damaged_df_area_per_label,
+ on="idx",
+ how="outer",
+ ).fillna(0)
+ df_area_per_label["region_area"] = (
+ df_area_per_label["undamaged_region_area"]
+ + df_area_per_label["damaged_region_area"]
+ )
else:
df_area_per_label = count_pixels_per_label(image, scale_factor)
df_area_per_label["undamaged_region_area"] = df_area_per_label["region_area"]
diff --git a/PyNutil/processing/data_analysis.py b/PyNutil/processing/data_analysis.py
index 64cadce6b9942512fdd0a3366b7e61719637532e..dc7816afdebc3dcc2c3972145ee9025206d8ef30 100644
--- a/PyNutil/processing/data_analysis.py
+++ b/PyNutil/processing/data_analysis.py
@@ -2,6 +2,7 @@ import pandas as pd
from .counting_and_load import pixel_count_per_region
import numpy as np
+
def map_to_custom_regions(custom_regions_dict, points_labels):
custom_points_labels = np.zeros_like(points_labels)
for i in np.unique(points_labels):
@@ -74,7 +75,9 @@ def apply_custom_regions(df, custom_regions_dict):
grouped_df = grouped_df.rename(columns={"custom_region_name": "name"})
grouped_df["area_fraction"] = grouped_df["pixel_count"] / grouped_df["region_area"]
- grouped_df["undamaged_area_fraction"] = grouped_df["undamaged_pixel_count"] / grouped_df["undamaged_region_area"]
+ grouped_df["undamaged_area_fraction"] = (
+ grouped_df["undamaged_pixel_count"] / grouped_df["undamaged_region_area"]
+ )
common_columns = [col for col in df.columns if col in grouped_df.columns]
grouped_df = grouped_df.reindex(
columns=common_columns
@@ -82,6 +85,7 @@ def apply_custom_regions(df, custom_regions_dict):
)
return grouped_df, df
+
def quantify_labeled_points(
points_len,
centroids_len,
@@ -90,7 +94,7 @@ def quantify_labeled_points(
labeled_points_centroids,
atlas_labels,
per_point_undamaged,
- per_centroid_undamaged
+ per_centroid_undamaged,
):
"""
Quantifies labeled points and returns various DataFrames.
@@ -118,7 +122,7 @@ def quantify_labeled_points(
region_areas_list,
atlas_labels,
per_point_undamaged,
- per_centroid_undamaged
+ per_centroid_undamaged,
)
label_df = _combine_slice_reports(per_section_df, atlas_labels)
@@ -133,7 +137,7 @@ def _quantify_per_section(
region_areas_list,
atlas_labels,
per_point_undamaged,
- per_centroid_undamaged
+ per_centroid_undamaged,
):
"""
Quantifies labeled points per section.
@@ -159,7 +163,11 @@ def _quantify_per_section(
current_points_undamaged = per_point_undamaged[prev_pl : prev_pl + pl]
current_centroids_undamaged = per_centroid_undamaged[prev_cl : prev_cl + cl]
current_df = pixel_count_per_region(
- current_points, current_centroids, current_points_undamaged, current_centroids_undamaged, atlas_labels
+ current_points,
+ current_centroids,
+ current_points_undamaged,
+ current_centroids_undamaged,
+ atlas_labels,
)
current_df_new = _merge_dataframes(current_df, ra, atlas_labels)
per_section_df.append(current_df_new)
@@ -211,7 +219,9 @@ def _combine_slice_reports(per_section_df, atlas_labels):
.drop(columns=["area_fraction"])
)
label_df["area_fraction"] = label_df["pixel_count"] / label_df["region_area"]
- label_df["undamaged_area_fraction"] = label_df["undamaged_pixel_count"] / label_df["undamaged_region_area"]
+ label_df["undamaged_area_fraction"] = (
+ label_df["undamaged_pixel_count"] / label_df["undamaged_region_area"]
+ )
label_df.fillna(0, inplace=True)
label_df = label_df.set_index("idx")
diff --git a/PyNutil/processing/generate_target_slice.py b/PyNutil/processing/generate_target_slice.py
index 937d4d474047f7c8484744c2e50c9d11a47c58b1..a8bc9c570d4d43b7d4db49e0f73c1cfd44bfb686 100644
--- a/PyNutil/processing/generate_target_slice.py
+++ b/PyNutil/processing/generate_target_slice.py
@@ -1,6 +1,7 @@
import numpy as np
import math
+
def generate_target_slice(ouv, atlas):
"""
Generates a target slice from the atlas using the given orientation and vectors.
diff --git a/PyNutil/processing/transformations.py b/PyNutil/processing/transformations.py
index 671ea2d5e9789bac7a6d2e5210a480e0a3096a1d..62b8ddb4ebdc8b18ce4fb05c07b45fd65114f0e4 100644
--- a/PyNutil/processing/transformations.py
+++ b/PyNutil/processing/transformations.py
@@ -1,6 +1,7 @@
import numpy as np
from .visualign_deformations import transform_vec
+
def transform_to_registration(seg_height, seg_width, reg_height, reg_width):
"""
Returns the scaling factors to transform the segmentation to the registration space.
diff --git a/PyNutil/processing/utils.py b/PyNutil/processing/utils.py
index b09bc8323834a577b6513e04af1a24e34b82fcbe..ed9b64c554e733fd9675420a69cfc66aa46ced61 100644
--- a/PyNutil/processing/utils.py
+++ b/PyNutil/processing/utils.py
@@ -3,6 +3,7 @@ import pandas as pd
import re
from glob import glob
+
def number_sections(filenames, legacy=False):
"""
Returns the section numbers of filenames.
@@ -196,7 +197,14 @@ def start_and_join_threads(threads):
[t.join() for t in threads]
-def process_results(points_list, centroids_list, points_labels, centroids_labels, points_undamaged_list, centroids_undamaged_list):
+def process_results(
+ points_list,
+ centroids_list,
+ points_labels,
+ centroids_labels,
+ points_undamaged_list,
+ centroids_undamaged_list,
+):
"""
Processes the results from the threads.
@@ -218,7 +226,9 @@ def process_results(points_list, centroids_list, points_labels, centroids_labels
points_labels = [pl for pl in points_labels if None not in pl]
centroids_labels = [cl for cl in centroids_labels if None not in cl]
points_undamaged_list = [pul for pul in points_undamaged_list if None not in pul]
- centroids_undamaged_list = [cul for cul in centroids_undamaged_list if None not in cul]
+ centroids_undamaged_list = [
+ cul for cul in centroids_undamaged_list if None not in cul
+ ]
if len(points_list) == 0:
points = np.array([])
points_labels = np.array([])
@@ -237,4 +247,13 @@ def process_results(points_list, centroids_list, points_labels, centroids_labels
centroids_labels = np.concatenate(centroids_labels)
centroids_undamaged = np.concatenate(centroids_undamaged_list)
- return points, centroids, points_labels, centroids_labels, points_len, centroids_len, points_undamaged, centroids_undamaged
+ return (
+ points,
+ centroids,
+ points_labels,
+ centroids_labels,
+ points_len,
+ centroids_len,
+ points_undamaged,
+ centroids_undamaged,
+ )
diff --git a/PyNutil/processing/visualign_deformations.py b/PyNutil/processing/visualign_deformations.py
index 3b6e12ff5c88736aca1801d1dd167c6fef1c069c..d0303452215f11f5fc399124621e02cf6cb91759 100644
--- a/PyNutil/processing/visualign_deformations.py
+++ b/PyNutil/processing/visualign_deformations.py
@@ -1,6 +1,8 @@
"""This code was written by Gergely Csucs, Harry Carey and Rembrandt Bakker"""
+
import numpy as np
+
def triangulate(w, h, markers):
"""
Triangulates a set of markers.
diff --git a/demos/basic_example_custom_atlas.py b/demos/basic_example_custom_atlas.py
index b2b9c189f5e61108bd56a1c9037d2a3cc13ea78e..d0f4978d1beb77a768ec7557abd5ddf0390a02e2 100644
--- a/demos/basic_example_custom_atlas.py
+++ b/demos/basic_example_custom_atlas.py
@@ -1,4 +1,5 @@
import sys
+
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from PyNutil import PyNutil
import os