Protein Case 1-9 Gaussian Distribution

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
import math
import ptitprince as pt

# Replace 'B5 segmentSummary (1).csv' with the actual CSV file path
file_path = 'B5 segmentSummary (1).csv'

# Read the CSV file into a Pandas DataFrame
df = pd.read_csv(file_path)

# Check the available column names
print(df.columns)

# Example: Replace 'categorical_column' with the correct column name containing categorical data
categorical_column = df.columns[0]  # Change this to the correct column name

# Define the range of column positions you want to analyze (columns 6 through 10)
start_column_position = 6  # Corresponds to column 6
end_column_position = 9  # Corresponds to column 10

# Create a list of unique categories in the categorical column
categories = df[categorical_column].unique()

# Define a custom color palette for the categories
colors = sns.color_palette('husl', n_colors=len(categories))

# Iterate through each category and create separate figures with six Raincloud plots per page
for idx, category in enumerate(categories):
    category_data = df[df[categorical_column] == category]
    num_plots = end_column_position - start_column_position + 1
    num_pages = math.ceil(num_plots / 6)  # Determine the number of pages needed

    for page in range(num_pages):
        plt.figure(figsize=(18, 12))

        for i in range(6):
            plot_num = page * 6 + i
            if plot_num >= num_plots:
                break

            col = df.columns[start_column_position + plot_num]

            # Raincloud plot with different colors for each category
            plt.subplot(2, 6, i + 1)
            pt.RainCloud(data=category_data, x=col, orient='h', width_viol=0.6, width_box=0.4, palette=[colors[idx]])
            plt.title(f'Raincloud Plot\n{category} - {col}')

        plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Adjust the layout to avoid title overlap
        plt.suptitle(f'{category} Raincloud Plots - Page {page + 1}', fontsize=16)
        plt.show()