main_script.py

import preprocess as pr
import connectome as connect
import networkx as nx
import pandas as pd
import matplotlib as mpl

mpl.use('TkAgg')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

ch1_p = r"ch1.csv"
ch2_p = r"ch2.csv"
y1 = pr.main(ch1_p, ch2_p)  # generate normalized mat
sns.clustermap(y1.T, col_cluster=False, cmap='jet')  # draw a signal fig
co_eff = np.corrcoef(y1.T)  # co_eff matrix
sns.clustermap(co_eff)  # heatmap of co_eff matrix
pr.histplot(co_eff, label='young')
plt.legend()
plt.show()
# elect 40 most activated neurons via std
# ele = pr.elect(fret,40)
co, pos, neg, null = pr.cor(fret, 0.3)
type_dict = {'sensory': 1.3, 'interneuron': 1.1, 'motorneuron': 0.8, 'muscle': 0.1}
transmitter_dict = {'unknown': 0.5, 'GABA': -1.4, 'Glu': 1,
                    'Ach': 1.1, 'ACh': 1.1, 'Unknown (orphan)': 0.5,
                    'unknown MA (cat-1)': 0.5, 'DA': 0.7, 'ACh (minor)': 0.3,
                    'Octopamine': 1.3, '': 1}
network = connect.csv2net('connectome_all.csv', 'celltype.csv', 'transmitter.csv', transmitter_dict, type_dict)
Graph = network.G_chem
path_dict = network.count_path(4, pos)

plt.bar(range(len(path_dict)), list(path_dict.values()), align='center')
plt.xticks(range(len(path_dict)), list(path_dict.keys()))
plt.show()

a = list(path_dict.items())
a = sorted(a, key=lambda x: x[1], reverse=True)
dict(a)