clustermaprelative.py

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

def filter_relative_time(data, start1=0, end1=1.75*60, start2=2.25*60, end2=4*60):
    relative_time = data[' Time'] - data[' Time'].iloc[0]
    period1 = data[(relative_time >= start1) & (relative_time <= end1)]
    period2 = data[(relative_time >= start2) & (relative_time <= end2)]
    return pd.concat([period1, period2])

stimuli_files = ['Ensure.csv', 'Saline.csv', 'IP Dex.csv', 'CCK.dcsv', 'WSS.csv', 'Oral Dex.csv', 'FACHOW.csv', 'FEDCHOW.csv', 'FAHF.csv', 'EX4.csv', 'LEP.csv']
stimuli_data = [pd.read_csv(file) for file in stimuli_files]
filtered_data = [filter_relative_time(data) for data in stimuli_data]

correlation_matrices = [data.corr() for data in filtered_data]

# Function to draw a clustered heatmap for each stimulus
def draw_clustered_heatmap(correlation_matrix, title):
    g = sns.clustermap(correlation_matrix, cmap="coolwarm", vmin=-1, vmax=1, row_cluster=True, col_cluster=True, figsize=(4, 4), dendrogram_ratio=0.15, cbar_pos=None)
    g.ax_heatmap.set_title(title, pad=20)

# Iterate through each stimulus file and draw a clustered heatmap
for i in range(len(stimuli_files)):
    draw_clustered_heatmap(correlation_matrices[i], stimuli_files[i][:-4])  # Use the filename without the .csv extension as the title

plt.show()