Python script for generating pie charts for the CLI.EXE

[TheBarret]

I was playing around with the new feature CLI.EXE , to see how if one can make or play with those numbers to make it visually appealing and perhaps useful to share online with other ALIEN users.

I let GPT analyze the output and I asked it to make sure we can control some min/max and also differentiate between the colors and sum them up into a hash table and we can display a pie chart.
Its very basic, not finished at all however GPT managed to produce a working python script that can read the output of the cli and visualize it.

This python script assumes the CSV file is called stats.csv , you can adjust the MIN_THRESHOLD to discard the small portions since these would clutter the chart quite negatively, other then that its just a test script so expect bugs and mistakes.

import csv
import matplotlib.pyplot as plt

# Define constants for columns in the CSV file
TIME_STEP_COL = 0
COLOR_COLUMNS_START = 1
MIN_THRESHOLD = 100000  # Adjust this threshold as needed

# Read data from the 'stats.csv' file
data = {}
colors = []

with open('stats.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    headers = next(reader)  # Read the header row
    
    # Extract color names from the headers and remove leading spaces
    colors = [color.strip() for color in headers[COLOR_COLUMNS_START:]]
    
    # Initialize data dictionary with zeros for each color
    for color in colors:
        data[color] = [0] * len(headers[COLOR_COLUMNS_START:])

    # Accumulate data for each time step
    for row in reader:
        for i, color in enumerate(colors):
            data[color] = [sum(x) for x in zip(data[color], [float(val) for val in row[COLOR_COLUMNS_START:]])]

# Define attributes
attributes = headers[COLOR_COLUMNS_START:]

# Create pie charts for each color genome
for color in colors:
    # Calculate attribute values for the current color genome
    color_data = data[color]
    
    # Filter attributes based on the threshold
    filtered_attributes = [attr for attr, value in zip(attributes, color_data) if value >= MIN_THRESHOLD]
    filtered_values = [value for value in color_data if value >= MIN_THRESHOLD]
    
    # Create labels for slices
    labels = filtered_attributes
    
    # Create values for slices
    values = filtered_values
    
    # Create a pie chart only if there are significant values
    if values:
        plt.figure(figsize=(10, 6))  # Adjust figure size as needed
        plt.pie(values, labels=labels, autopct='%1.1f%%', startangle=140)
        plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
        
        # Set title
        plt.title(f'Genomes')
        
        # Place legend outside the pie chart
        plt.legend(labels, title="Attributes", bbox_to_anchor=(1.05, 1), loc='upper left')
        
        plt.show()