i #1 Created event_processing file with 3 methods to run analysis

api/__init__.py

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+"""
+The `process_mining.api` module provides tools for generating and manipulating event logs. These event logs can then be used for process mining.
+It uses [PM4Py](https://github.com/process-intelligence-solutions/pm4py) library for process mining models and algorithms and uses `pandas` for data handling and manipulating. 
+
+---
+
+## Overview
+
+- `csv_generator.py` : Allows fake event logs to be created to run process mining.
+- `io.py` : Provides event log loader and file naming convention.
+- `process_visaul_generation` : Contains functions to create process visualizations from PM4PY.
+
+"""
+

api/csv_generator.py

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+import random
+import pandas as pd
+import uuid
+from datetime import datetime, timedelta
+
+
+# Function to generate fake data
+def generate_fake_event_log(num_issues=1, num_events_per_issue=7, output_csv="generated_csv.csv", seed=42):
+    """
+    Generates fake event log data and saves it to a CSV file. The event log always startes with event 'assigned' and ends
+    with event 'closed'. Events generated in between are randomly picked with options {'labeled', 'assigned', 'milestoned', 'closed', 'unlabeled', 'referenced', 'mentioned', 'subscribed'}.
+
+    Args:
+    - num_issues (int): Number of unique issues to generate.
+    - num_events_per_issue (int): Number of events per issue
+    - output_csv (str): The path where the CSV will be saved.
+    - seed (int): Seed to get same results
+
+    Returns:
+    - str: The path to the saved CSV file
+    """
+    # Set seed given as parameter 
+    random.seed(seed)
+    # Set base time for all times to be based off of
+    base_time = datetime(2020, 1, 1, 12, 0, 0)
+
+    all_events = ['labeled', 'assigned', 'milestoned', 'closed', 'unlabeled', 'referenced', 'mentioned', 'subscribed']
+    event_data = []
+
+    for issue_num in range(1, num_issues + 1):
+        # Always start with 'assigned' and end with 'closed'
+        issue_events = ['assigned']
+
+        # Choose (num_events_per_issue - 2) random events from the rest, excluding 'assigned' and 'closed'
+        middle_candidates = [e for e in all_events if e not in ['assigned', 'closed']]
+        middle_events = random.choices(middle_candidates, k=num_events_per_issue - 2)
+        issue_events += middle_events
+        issue_events.append('closed')
+
+        for event_num, event in enumerate(issue_events):
+            unique_id = str(uuid.uuid4())
+            created_at = base_time + timedelta(minutes=event_num * 5 + issue_num * 60)
+            created_at_str = created_at.strftime('%Y-%m-%dT%H:%M:%SZ')
+            event_data.append([unique_id, created_at_str, event, issue_num])
+
+    event_log_df = pd.DataFrame(event_data, columns=['id', 'created_at', 'event', 'issue_number'])
+    event_log_df.to_csv(output_csv, index=False)
+    print(f"Fake event log data saved to {output_csv}")
+    return output_csv
+
+def modify_event(event_log_df, row_index, new_event):
+    """
+    Modify events in the given event log dataframe.
+
+    Args:
+    - event_log_df (pd.DataFrame): The event log data frame.
+    - row_index (int): The index of the row to modify.
+    - new_event (str): The new event value to assign.
+
+    Returns:
+    - pd.DataFrame: The modified event log dataframe.
+    """
+    # Ensure row_index is within the bounds of the dataframe
+    if row_index < 0 or row_index >= len(event_log_df):
+        raise IndexError(f"Row index {row_index} is out of bounds.")
+
+    event_log_df.loc[row_index, 'event'] = new_event
+    return event_log_df
+
+

api/io.py

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+from datetime import datetime
+import pandas
+import pm4py
+
+
+def generate_timestamped_filename(prefix, extension="png"):
+    """
+    Generates a unique file name with a given prefix and file extension.
+
+    Args:
+    - prefix (str): The prefix to be added to the file name.
+    - extension (str): The extension of the file (default is "png").
+
+    Returns:
+    - str: The generated file name with the current timestamp.
+    """
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    return f"{prefix}_{timestamp}.{extension}"
+
+def read_event_log(csv_path):
+    """
+    Loads and formats an event log CSV for use with PM4Py.
+
+    Args:
+    - csv_path (str): Path to the event log CSV file.
+
+    Returns:
+    - pd.DataFrame: A formatted DataFrame ready for process mining.
+
+    Raises:
+        FileNotFoundError: If the CSV file does not exist.
+        ValueError: If required columns are missing.
+    """
+    event_log = pandas.read_csv(csv_path)
+
+    if not {'issue_number', 'event', 'created_at'}.issubset(event_log.columns):
+        raise ValueError("CSV must contain 'issue_number', 'event', and 'created_at' columns.")
+
+    event_log['issue_number'] = event_log['issue_number'].astype(str)
+    event_log['created_at'] = pandas.to_datetime(event_log['created_at'], errors='coerce')
+
+    # PM4PY format
+    formatted_log = pm4py.format_dataframe(event_log, case_id='issue_number', activity_key='event', timestamp_key='created_at')
+
+    return formatted_log