1-3minutes.py

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

# Load the CSV files into a list of DataFrames
stimuli_files = ['Ensure.csv', 'Saline.csv', 'IP Dex.csv', 'CCK.csv', '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]


def filter_relative_time(data, start1=0, end1=1.75, start2=2.25, end2=5):
    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])

filtered_data = [filter_relative_time(data) for data in stimuli_data]

filtered_data = [filter_relative_time(data) for data in stimuli_data]

# Calculate the correlation matrices for each stimulus
correlation_matrices = [data.corr() for data in filtered_data]

# Set up the figure and axes for the grid of heatmaps
fig, axs = plt.subplots(nrows=3, ncols=4, figsize=(20, 10))

# Create a list of axes for each heatmap
ax_list = axs.ravel()

# Draw a heatmap for each stimulus
for i in range(len(stimuli_files)):
    sns.heatmap(correlation_matrices[i], cmap="coolwarm", vmin=-1, vmax=1, ax=ax_list[i])
    ax_list[i].set_title(stimuli_files[i][:-4])  # Use the filename without the .csv extension as the title

# Remove extra axes that aren't used for a heatmap
for ax in ax_list[len(stimuli_files):]:
    ax.remove()

# Add a title
fig.suptitle('(data, start1=0.25*60, end1=1.75*60, start2=2.25*60, end2=3*60)')

plt.show()