Add and update MEI_time_series_visualization script

scripts/MEI_time_series_visualization.py

@@ -0,0 +1,185 @@
+import datetime
+import os
+import sys
+from datetime import timedelta
+from typing import Dict, List
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+def setup_bugzilla_import():
+    # Path to the directory where `bugzilla.py` is located
+    base_dir = os.getcwd()  # Get the current working directory
+
+    bugbug_dir = os.path.join(base_dir)
+    bugzilla_dir = os.path.join(base_dir, "bugbug")
+    bugzilla_dir = os.path.abspath(bugzilla_dir)  # Convert to absolute path
+
+    # Add this directory to the system path
+    if bugbug_dir not in sys.path:
+        sys.path.insert(0, bugbug_dir)
+    if bugzilla_dir not in sys.path:
+        sys.path.insert(0, bugzilla_dir)
+
+    from bugzilla import calculate_maintenance_effectiveness_indicator
+
+    return calculate_maintenance_effectiveness_indicator
+
+
+# Call the function to set up the import
+calculate_maintenance_effectiveness_indicator = setup_bugzilla_import()
+
+file_path = "./bugbug/data/bug_sample.json"
+
+# If the JSON file is line-delimited (each line is a complete JSON object),
+# use the 'lines' parameter:
+df = pd.read_json(file_path, lines=True)
+
+# Define the team
+team = "DOM LWS"
+
+# Define the date range
+start_year = 2023
+end_year = 2023
+
+# Create a dictionary to hold the MEI and other values for each week and delta
+values: Dict[str, List] = {
+    "week": [],
+    "delta": [],
+    "MEI": [],
+    "BDTime": [],
+    "WBDTime": [],
+    "Incoming": [],
+    "Closed": [],
+}
+
+# Loop through each year in the specified range
+for year in range(start_year, end_year + 1):
+    # Loop through each week in the year
+    start_date = datetime.date(year, 1, 1)
+    end_date = datetime.date(year + 1, 1, 1)
+    week = timedelta(weeks=1)
+    current_week = start_date
+    while current_week < end_date:
+        # Calculate deltas
+        for delta in [timedelta(weeks=1), timedelta(days=90)]:
+            from_date = current_week - delta
+            to_date = current_week
+
+            # Call the calculate_maintenance_effectiveness_indicator function
+            result = calculate_maintenance_effectiveness_indicator(
+                teams=[team], from_date=from_date, to_date=to_date
+            )
+
+            # Store the values
+            values["week"].append(current_week)
+            values["delta"].append(delta.days)
+            values["MEI"].append(result["stats"]["ME"])
+            values["BDTime"].append(result["stats"]["BDTime"])
+            values["WBDTime"].append(result["stats"]["WBDTime"])
+            values["Incoming"].append(result["stats"]["Incoming vs total open"])
+            values["Closed"].append(result["stats"]["Closed vs total open"])
+
+        current_week += week
+
+# Convert the data to a DataFrame for easier plotting
+df = pd.DataFrame(values)
+
+
+# Plotting
+plt.figure(figsize=(15, 7))
+for delta in df["delta"].unique():
+    subset = df[df["delta"] == delta]
+    plt.plot(subset["week"], subset["MEI"], marker="o", label=f"Delta {delta} days")
+
+# Set the y-axis limit to start from 0 and go up to 500% maximum
+plt.ylim(0, 500)
+
+# Add a horizontal 100% line emphasized more than other grid lines
+plt.axhline(y=100, color="red", linestyle="--", linewidth=2, label="100%")
+
+plt.title("Maintenance Effectiveness Indicator (MEI) Over Weeks for team: DOM LWS")
+plt.xlabel("Week")
+plt.ylabel("MEI Value")
+plt.legend()
+plt.grid(True)
+plt.show()
+
+
+delta_90_days_df = df[df["delta"] == 90]
+delta_90_days_df.head()
+# Scale the Incoming and Closed values to represent a yearly percentage
+delta_90_days_df["Scaled_Incoming"] = (delta_90_days_df["Incoming"] / 52) * 100
+delta_90_days_df["Scaled_Closed"] = (delta_90_days_df["Closed"] / 52) * 100
+
+
+# Create a figure and the first axis (left)
+fig, ax1 = plt.subplots(figsize=(15, 7))
+
+# Plot BD Time and WBD Time on the first axis
+ax1.plot(
+    delta_90_days_df["week"],
+    delta_90_days_df["BDTime"],
+    marker="o",
+    label="BD Time",
+    color="b",
+)
+ax1.plot(
+    delta_90_days_df["week"],
+    delta_90_days_df["WBDTime"],
+    marker="o",
+    label="WBD Time",
+    color="g",
+)
+
+# Set labels and limits for the first axis
+ax1.set_xlabel("Week")
+ax1.set_ylabel("Time (in hours)", color="b")
+ax1.set_ylim(0, 15)  # Set the y-axis limit for BD Time and WBD Time
+ax1.legend(loc="upper left")
+
+# Create a second axis (right) sharing the same x-axis
+ax2 = ax1.twinx()
+
+# Plot Scaled Incoming and Scaled Closed on the second axis
+ax2.plot(
+    delta_90_days_df["week"],
+    delta_90_days_df["Scaled_Incoming"],
+    label="Scaled Incoming",
+    marker="o",
+    linestyle="-",
+    markersize=5,
+    color="r",
+)
+ax2.plot(
+    delta_90_days_df["week"],
+    delta_90_days_df["Scaled_Closed"],
+    label="Scaled Closed",
+    marker="s",
+    linestyle="-",
+    markersize=5,
+    color="purple",
+)
+
+# Set labels and limits for the second axis
+ax2.set_ylabel("Percentage of Defects Backlog", color="r")
+ax2.set_ylim(
+    0,
+    max(
+        delta_90_days_df["Scaled_Incoming"].max(),
+        delta_90_days_df["Scaled_Closed"].max(),
+    )
+    + 10,
+)  # Adjust the y-axis limit as needed
+ax2.legend(loc="upper right")
+
+# Add a title and grid
+plt.title(
+    "BD Time, WBD Time, Scaled Incoming, and Scaled Closed Over 12 Months (2023) with 3-Month Delta for team: DOM LWS"
+)
+plt.grid(True)
+
+# Show the plot
+plt.tight_layout()
+plt.show()