learning pathways birthing

import json
import os

# Function to load data from a file
def load_data(file_path):
    with open(file_path, 'r') as file:
        data = json.load(file)
    return data

# Function to evaluate data against a set of rules
def evaluate_data(data, rules):
    results = {}
    for key, value in rules.items():
        results[key] = []
        for entry in data:
            if entry[key] == value:  # Simple equality check for demonstration
                results[key].append(entry)
    return results

# Function to create chain codes based on evaluation results
def create_chain_codes(results):
git add .
git commit -m "Initial project structure and setup"
git push origin main
    chain_codes = {}
    for key, value in results.items():
        chain_codes[key] = len(value)  # Count the number of matching entries as chain code
    return chain_codes

# Main function to tie everything together
def main(data_file, rules_file):
    # Load data and rules
    data = load_data(data_file)
    with open(rules_file, 'r') as file:
        rules = json.load(file)

    # Evaluate data
    results = evaluate_data(data, rules)

    # Create chain codes
    chain_codes = create_chain_codes(results)

    # Output the chain codes
    print("Chain Codes:", chain_codes)

if __name__ == "__main__":
    # Define paths to your data file and rules file
    DATA_FILE = 'path_to_your_data_file.json'
    RULES_FILE = 'path_to_your_rules_file.json'

    main(DATA_FILE, RULES_FILE)import pandas as pd
import hashlib
import os

# Function to read data from CSV or other data source
def load_data(file_path):
    data = pd.read_csv(file_path)
    return data

# Function to perform data analysis
def analyze_data(data):
    # Example analysis: Calculate basic statistics
    analysis_results = {
        'mean': data.mean(),
        'std_dev': data.std(),
        'min': data.min(),
        'max': data.max()
    }
    return analysis_results

# Function to create chain codes
def create_chain_codes(data):
    # Example: Generate hash codes (e.g., SHA-256) for each row
    data['chain_code'] = data.apply(lambda row: hashlib.sha256(str(row).encode()).hexdigest(), axis=1)
    return data

# Path to the data file
file_path = 'path_to_your_data_file.csv'

# Load data
data = load_data(file_path)

# Analyze data
analysis_results = analyze_data(data)
print("Data Analysis Results:", analysis_results)

# Create chain codes
data_with_chain_codes = create_chain_codes(data)
print("Data with Chain Codes:", data_with_chain_codes)

# Optionally, save the result with chain codes back to a new file
data_with_chain_codes.to_csv('data_with_chain_codes.csv', index=False)