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analysis/generators/task_breakdown_by_occupation.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mtick
+import matplotlib.colors as mcolors
+from pathlib import Path
+import tempfile
+import logging
+
+# This mapping helps translate the O*NET 2-digit major group codes
+# into human-readable labels for the plot's y-axis.
+OCCUPATION_MAJOR_CODES = {
+    '11': 'Management',
+    '13': 'Business & Financial',
+    '15': 'Computer & Mathematical',
+    '17': 'Architecture & Engineering',
+    '19': 'Life, Physical, & Social Science',
+    '21': 'Community & Social Service',
+    '23': 'Legal',
+    '25': 'Education, Training, & Library',
+    '27': 'Arts, Design, & Media',
+    '29': 'Healthcare Practitioners',
+    '31': 'Healthcare Support',
+    '33': 'Protective Service',
+    '35': 'Food Preparation & Serving',
+    '37': 'Building & Grounds Maintenance',
+    '39': 'Personal Care & Service',
+    '41': 'Sales & Related',
+    '43': 'Office & Admin Support',
+    '45': 'Farming, Fishing, & Forestry',
+    '47': 'Construction & Extraction',
+    '49': 'Installation, Maintenance, & Repair',
+    '51': 'Production',
+    '53': 'Transportation & Material Moving',
+    '55': 'Military Specific',
+}
+
+# Define colors to match the original notebook's palette.
+# These are standard hex codes for gray and lime shades.
+BAR_COLORS = [
+    '#D1D5DB', # gray-300
+    '#84CC16', # lime-500
+    '#D9F99D', # lime-200
+]
+
+
+def _get_contrasting_text_color(bg_color_hex):
+    """
+    Determines if black or white text provides better contrast against a given background color.
+    """
+    try:
+        rgba = mcolors.to_rgba(bg_color_hex)
+        # Calculate luminance (Y) using the sRGB formula
+        luminance = 0.2126 * rgba[0] + 0.7152 * rgba[1] + 0.0722 * rgba[2]
+        return 'black' if luminance > 0.55 else 'white'
+    except ValueError:
+        return 'black' # Default to black if color is invalid
+
+
+def generate(processed_df: pd.DataFrame):
+    """
+    Generates a stacked bar chart breaking down tasks by remote status and estimability.
+
+    This corresponds to 'cell10' from the original analysis notebook. It shows,
+    for each occupation, the percentage of tasks that are not remote, remote and
+    estimable, or remote and not estimable.
+
+    Args:
+        processed_df (pd.DataFrame): The preprocessed data. Expected columns:
+                                     'onetsoc_major', 'remote_status', 'estimateable'.
+
+    Returns:
+        Path: The path to the generated temporary image file, or None on failure.
+    """
+    logging.info("Generating task breakdown by occupation plot...")
+
+    # --- Data Validation ---
+    required_cols = ['onetsoc_major', 'remote_status', 'estimateable']
+    if not all(col in processed_df.columns for col in required_cols):
+        logging.error(f"Missing one or more required columns: {required_cols}. Cannot generate plot.")
+        return None
+
+    df = processed_df.copy()
+
+    # --- Data Summarization ---
+    summary_data = []
+    for code, label in OCCUPATION_MAJOR_CODES.items():
+        occ_df = df[df['onetsoc_major'] == code]
+        total_tasks = len(occ_df)
+        if total_tasks == 0:
+            continue
+
+        not_remote_count = len(occ_df[occ_df['remote_status'] != 'remote'])
+        remote_df = occ_df[occ_df['remote_status'] == 'remote']
+        remote_atomic_count = len(remote_df[remote_df['estimateable'] == 'ATOMIC'])
+        remote_ongoing_count = len(remote_df[remote_df['estimateable'] == 'ONGOING-CONSTRAINT'])
+
+        summary_data.append({
+            'occupation_label': label,
+            'count_not_remote': not_remote_count,
+            'count_remote_atomic': remote_atomic_count,
+            'count_remote_ongoing': remote_ongoing_count,
+            'total_tasks': total_tasks
+        })
+
+    if not summary_data:
+        logging.warning("No data available to generate the task breakdown plot.")
+        return None
+
+    summary_df = pd.DataFrame(summary_data)
+
+    # --- Percentage Calculation ---
+    summary_df['pct_not_remote'] = (summary_df['count_not_remote'] / summary_df['total_tasks']) * 100
+    summary_df['pct_remote_atomic'] = (summary_df['count_remote_atomic'] / summary_df['total_tasks']) * 100
+    summary_df['pct_remote_ongoing'] = (summary_df['count_remote_ongoing'] / summary_df['total_tasks']) * 100
+
+    plot_df = summary_df.set_index('occupation_label')[
+        ['pct_not_remote', 'pct_remote_atomic', 'pct_remote_ongoing']
+    ]
+    plot_df.columns = ['Not Remote', 'Remote & Estimable', 'Remote & Not Estimable']
+    plot_df = plot_df.sort_values(by='Not Remote', ascending=False)
+
+
+    # --- Plotting ---
+    try:
+        fig, ax = plt.subplots(figsize=(14, 10))
+        plot_df.plot(kind='barh', stacked=True, ax=ax, color=BAR_COLORS, width=0.8)
+
+        ax.set_xlabel("Percentage of Tasks", fontsize=12)
+        ax.set_ylabel("Occupation Major Group", fontsize=12)
+        ax.set_title("Task Breakdown by Occupation, Remote Status, and Estimability", fontsize=16, pad=20)
+        ax.xaxis.set_major_formatter(mtick.PercentFormatter())
+        ax.set_xlim(0, 100)
+        ax.spines['right'].set_visible(False)
+        ax.spines['top'].set_visible(False)
+
+        # Add percentage labels inside each bar segment
+        for i, container in enumerate(ax.containers):
+            text_color = _get_contrasting_text_color(BAR_COLORS[i])
+            for patch in container.patches:
+                width = patch.get_width()
+                if width > 3:  # Only label segments wider than 3%
+                    x = patch.get_x() + width / 2
+                    y = patch.get_y() + patch.get_height() / 2
+                    ax.text(x, y, f"{width:.1f}%", ha='center', va='center',
+                            fontsize=8, color=text_color, fontweight='medium')
+
+        ax.legend(title="Task Category", bbox_to_anchor=(1.02, 1), loc='upper left', frameon=False)
+
+        # --- File Saving ---
+        temp_dir = tempfile.gettempdir()
+        temp_path = Path(temp_dir) / "task_breakdown_by_occupation.png"
+        plt.savefig(temp_path, dpi=300, bbox_inches='tight')
+        logging.info(f"Successfully saved plot to temporary file: {temp_path}")
+
+        return temp_path
+
+    except Exception as e:
+        logging.error(f"An error occurred while generating the plot: {e}", exc_info=True)
+        return None
+    finally:
+        plt.close()