*pees diapy*

https://chat.openai.com/share/a59ae788-06a1-46d8-8170-3697d2b9cc6e

Here's an example of what the CLI output might look like, including the data board with source and destination script information:

```
Summary of Script Connections:
Script: script1.py, Complexity: Low
Connections:
    - script2.py: Low
    - script3.py: Medium

Script: script2.py, Complexity: Medium
Connections:
    - script1.py: Medium
    - script3.py: Low

Script: script3.py, Complexity: High
Connections:
    - script1.py: Medium
    - script2.py: High

Data Flow Summary:
Script: script1.py, Complexity: Low
Output Data: ['data1', 'data2'] --> script2.py

Script: script2.py, Complexity: Medium
Input Data: ['data1', 'data2'] <-- script1.py
Output Data: ['processed_data1', 'processed_data2'] --> script3.py

Script: script3.py, Complexity: High
Input Data: ['processed_data1', 'processed_data2'] <-- script2.py
Output Data: ['final_result']

Data Board:
Timestamp: 1646782561.540615, Source Script: script1.py, Destination Script: script2.py, Data Type: processed_data, Data Amount: 2, Data Speed: 0.5
Timestamp: 1646782562.7201335, Source Script: script2.py, Destination Script: script3.py, Data Type: processed_data, Data Amount: 2, Data Speed: 0.4
```

In this example:

- The summary of script connections provides information about the complexity levels and connections between scripts.
- The data flow summary details the input and output data for each script.
- The data board logs information about the processed data, including the source and destination scripts involved in the data flow, along with other relevant data information such as data amount, speed, and timestamp.

This comprehensive output provides insights into the script connections, data flow, and the performance of data processing, including source and destination script information.

import time

class DataBoard:
    def __init__(self):
        self.data_logs = []

    def log_data(self, source_script, destination_script, data_type, data_amount, data_speed, timestamp):
        self.data_logs.append({
            'source_script': source_script,
            'destination_script': destination_script,
            'data_type': data_type,
            'data_amount': data_amount,
            'data_speed': data_speed,
            'timestamp': timestamp
        })

def analyze_data_flow(summary, script_data):
    # Analyze the data flow and identify potential bottlenecks or critical points
    analysis_results = []
    for item in summary:
        script_name = item['Script']
        connections = item['Connections']
        for other_script, _ in connections.items():
            # Check if there's a significant change in data amount or speed between scripts
            if 'output_data' in script_data[script_name] and 'input_data' in script_data[other_script]:
                output_data_amount = len(script_data[script_name]['output_data'])
                input_data_amount = len(script_data[other_script]['input_data'].split('\n'))
                output_timestamp = time.time()  # Replace with actual timestamp
                input_timestamp = time.time()   # Replace with actual timestamp
                data_speed = (output_data_amount - input_data_amount) / (output_timestamp - input_timestamp)
                if data_speed > 0:  # Ensure data_speed is positive to avoid division by zero
                    analysis_results.append({
                        'source_script': script_name,
                        'destination_script': other_script,
                        'data_speed': data_speed,
                        'timestamp': output_timestamp
                    })
    return analysis_results

def main():
    # Provide the path to the folder containing the scripts
    folder_path = '/path/to/scripts/folder'

    # Summarize the connections between scripts based on stdin and stdout
    summary, script_data = summarize_connections(folder_path)

    # Analyze the data flow
    analysis_results = analyze_data_flow(summary, script_data)

    # Print the summary
    print_connections_summary(summary)
    print_data_flow_summary(script_data)

    # Generate the data board
    data_board = DataBoard()
    for result in analysis_results:
        data_board.log_data(result['source_script'], result['destination_script'], 'processed_data', result['data_speed'], len(script_data[result['source_script']]['output_data']), result['timestamp'])

    # Print the data board
    print("\nData Board:")
    for log in data_board.data_logs:
        print(f"Timestamp: {log['timestamp']}, Source Script: {log['source_script']}, Destination Script: {log['destination_script']}, Data Type: {log['data_type']}, Data Amount: {log['data_amount']}, Data Speed: {log['data_speed']}")

if __name__ == "__main__":
    main()