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distance_calculations_and_histograms.py
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from brainrender.scene import Scene
from brainrender.actors import Points
from bg_atlasapi.bg_atlas import BrainGlobeAtlas
from brainrender import settings
from brainrender.actors import Cylinder
import bg_space as bg
import numpy as np
from rich import print
from myterial import orange
from pathlib import Path
import pandas as pd
import json
from bg_atlasapi import show_atlases
from bg_atlasapi.bg_atlas import BrainGlobeAtlas
import UpdateME
from UpdateME import cellfinder_output_path, mouse_id, brain_regions_to_evalutate, allen_mouse_10um, estim_tip_coordinates, extra_brain_region_acryonm, estim_shank_radius_um, estim_tip_radius_um, estim_propigation_radius_um
import os
import brainrender
from vedo import Spheres, Sphere
from vedo import Points as vPoints
import matplotlib.pyplot as plt
from scipy.spatial import distance
import seaborn as sns
brainrender.SHADER_STYLE = "cartoon"
import cellfinder_backend
from cellfinder_backend import analyze_data_cellfinder
import pickle
# from brainrender_backend import brainrender_folder_path
def distance_calculations_histograms(brainrender_folder_path,mouse_id,estim_tip_coordinates,cellfinder_output_path):
# Create new folder in your cellfinder output folder
mouseid_estim_tip_coordinates_folder_path = brainrender_folder_path + '/' + str(mouse_id) + '_' + str(estim_tip_coordinates) # create the path for the new folder
histogram_folder_path = mouseid_estim_tip_coordinates_folder_path + '/' + str(mouse_id) + '_' + str(estim_tip_coordinates) + '_histograms/gfp_cells'
npy_folder_path = histogram_folder_path + '/' + "distance_arrays"
if not os.path.exists(histogram_folder_path):
os.makedirs(histogram_folder_path)
os.makedirs(npy_folder_path)
print('Histogram folder has been created at')
print(f'{ histogram_folder_path}')
print('')
print('Making histograms...')
print('')
histogram_save_path = histogram_folder_path
# npy_save_path = histogram_save_path + '/' + 'distance_arrays'
# Calaculate 3d-space distances for cells relative to estim_tip_coordinates. and save out histograms of those distances
# Path to cellfinder_output points.npy file
print('cellfinder Output Path:' + str(cellfinder_output_path))
gfp_cells_path = cellfinder_output_path + 'points/gfp_points.npy'
gfp_cells_path
points = np.load(gfp_cells_path)
hist_save_path = histogram_save_path +'/'+ str(mouse_id)+ '_' + str(estim_tip_coordinates) + '/gfp_cells'
# Subtract the reference point from each cell coordinate
displacement = points - estim_tip_coordinates
# Calculate the magnitude of the displacement vectors
euclidean_distances = np.linalg.norm(displacement, axis=1)
plt.hist(euclidean_distances, bins=100)
# Add labels and a title
plt.xlabel('Euclidean Distance (um)')
plt.ylabel('number of cells')
plt.title(str(mouse_id) + ' gfp, Histogram of cell Euclidean distances from Estim tip')
# Display & save the histogram
hist_save_path = histogram_save_path +'/'+ str(mouse_id)+ '_' + str(estim_tip_coordinates)
plt.savefig(hist_save_path + '_euclidean_distances.png')
np.save(npy_folder_path +'/'+ 'euclidean_distances.npy', euclidean_distances)
plt.close()
sns.histplot(euclidean_distances, kde = True)
plt.xlabel('Euclidean Distance (um)')
plt.ylabel('number of cells')
plt.title(str(mouse_id) + ' gfp, Histogram of cell Euclidean distances from Estim tip')
plt.savefig(hist_save_path + '_sns_euclidean_distances.png')
plt.close()
# manhattan distances calculations: sum of the absolute differences of their coordinates
manhattan_distances = np.sum(np.abs(points - estim_tip_coordinates), axis=1)
manhattan_distances = distance.cdist(points, [estim_tip_coordinates], metric='cityblock')
# Plot manhattan distances histogram
plt.hist(manhattan_distances, bins = 100)
# Add labels
plt.title(str(mouse_id) + ' gfp, Manhattan distances from Estim tip')
plt.xlabel('Manhattan distance (um)')
plt.ylabel('number of cells')
# Save the histogram
plt.savefig(hist_save_path + '_manhattan_distances.png')
np.save(npy_folder_path+'/' + 'manhattan_distances.npy', manhattan_distances)
plt.close()
# create subplot with smooth line overlay
sns.histplot(manhattan_distances, kde = True)
plt.title(str(mouse_id) + ' gfp, Manhattan distances from Estim tip')
plt.xlabel('Manhattan distance (um)')
plt.ylabel('number of cells')
plt.savefig(hist_save_path + '_sns_manhattan_distances.png')
plt.close()
p = 3
minkowski_distances = distance.cdist(points, [estim_tip_coordinates], 'minkowski', p=p)
plt.hist(minkowski_distances, bins=100)
plt.xlabel('Minkowski Distance (um) (p={})'.format(p))
plt.ylabel('Cell Count')
plt.title(str(mouse_id) + ' gfp, Minkowski Distances from estim tip')
plt.savefig(hist_save_path + '_minkowski_distances.png')
np.save(npy_folder_path +'/'+ 'minkowski_distances.npy', minkowski_distances)
plt.close()
sns.histplot(minkowski_distances, kde = True)
plt.xlabel('Minkowski Distance (um) (p={})'.format(p))
plt.ylabel('Cell Count')
plt.title(str(mouse_id) + ' gfp, Minkowski Distances from estim tip')
plt.savefig(hist_save_path + '_sns_minkowski_distances.png')
plt.close()
chebyshev_distances = distance.cdist(points, [estim_tip_coordinates], 'chebyshev')
plt.hist(chebyshev_distances, bins=100)
plt.xlabel('gfp, Chebyshev Distance (um)')
plt.ylabel('Cell count')
plt.title(str(mouse_id) + ' gfp Chebyshev Distances from estim tip')
plt.savefig(hist_save_path + '_chebyshev_distances.png')
np.save(npy_folder_path +'/'+ 'chebyshev_distances.npy', chebyshev_distances)
plt.close()
sns.histplot(minkowski_distances, kde = True)
plt.xlabel('gfp, Chebyshev Distance (um)')
plt.ylabel('Cell count')
plt.title(str(mouse_id) + ' gfp Chebyshev Distances from estim tip')
plt.savefig(hist_save_path + '_sns_chebyshev_distances.png')
plt.close()
else:
print('Skipping histogram creation, because ' + 'histograms for ' + str(mouse_id) + " with estim_tip_coordinates " + str(estim_tip_coordinates) + ' have already been created')
print('')