import matplotlib.pyplot as plt import pandas as pd from utils import generate_underlined_line def extract_failure_info(failures_obj, device: str, multi_count: int, single_count: int) -> str: """Extract failure information from failures object.""" if (not failures_obj or pd.isna(failures_obj)) and multi_count == 0 and single_count == 0: return f"No failures on {device}" info_lines = [] # Add counts summary if multi_count > 0 or single_count > 0: info_lines.append(generate_underlined_line(f"Failure Summary for {device}:")) if multi_count > 0: info_lines.append(f"Multi GPU failures: {multi_count}") if single_count > 0: info_lines.append(f"Single GPU failures: {single_count}") info_lines.append("") # Try to extract detailed failure information try: if isinstance(failures_obj, dict): # Check for multi and single failure categories if 'multi' in failures_obj and failures_obj['multi']: info_lines.append(generate_underlined_line(f"Multi GPU failure details:")) if isinstance(failures_obj['multi'], list): # Handle list of failures (could be strings or dicts) for i, failure in enumerate(failures_obj['multi'][:10]): # Limit to first 10 if isinstance(failure, dict): # Extract meaningful info from dict (e.g., test name, line, etc.) failure_str = failure.get('line', failure.get('test', failure.get('name', str(failure)))) info_lines.append(f" {i+1}. {failure_str}") else: info_lines.append(f" {i+1}. {str(failure)}") if len(failures_obj['multi']) > 10: info_lines.append(f"... and {len(failures_obj['multi']) - 10} more") else: info_lines.append(str(failures_obj['multi'])) info_lines.append("") if 'single' in failures_obj and failures_obj['single']: info_lines.append(generate_underlined_line(f"Single GPU failure details:")) if isinstance(failures_obj['single'], list): # Handle list of failures (could be strings or dicts) for i, failure in enumerate(failures_obj['single'][:10]): # Limit to first 10 if isinstance(failure, dict): # Extract meaningful info from dict (e.g., test name, line, etc.) failure_str = failure.get('line', failure.get('test', failure.get('name', str(failure)))) info_lines.append(f" {i+1}. {failure_str}") else: info_lines.append(f" {i+1}. {str(failure)}") if len(failures_obj['single']) > 10: info_lines.append(f"... and {len(failures_obj['single']) - 10} more") else: info_lines.append(str(failures_obj['single'])) return "\n".join(info_lines) if info_lines else f"No detailed failure info for {device}" except Exception as e: if multi_count > 0 or single_count > 0: return f"Failures detected on {device} (Multi: {multi_count}, Single: {single_count})\nDetails unavailable: {str(e)}" return f"Error processing failure info for {device}: {str(e)}" def plot_model_stats( df: pd.DataFrame, model_name: str, ) -> tuple[plt.Figure, str, str]: """Draws a pie chart of model's passed, failed, skipped, and error stats.""" if df.empty or model_name not in df.index: # Handle case where model data is not available fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000') ax.set_facecolor('#000000') ax.text(0.5, 0.5, f'No data available for {model_name}', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, color='#888888', fontfamily='monospace', weight='normal') ax.set_xlim(0, 1) ax.set_ylim(0, 1) ax.axis('off') return fig, "No data available", "No data available" row = df.loc[model_name] # Handle missing values and get counts directly from dataframe success_amd = int(row.get('success_amd', 0)) if pd.notna(row.get('success_amd', 0)) else 0 success_nvidia = int(row.get('success_nvidia', 0)) if pd.notna(row.get('success_nvidia', 0)) else 0 failed_multi_amd = int(row.get('failed_multi_no_amd', 0)) if pd.notna(row.get('failed_multi_no_amd', 0)) else 0 failed_multi_nvidia = int(row.get('failed_multi_no_nvidia', 0)) if pd.notna(row.get('failed_multi_no_nvidia', 0)) else 0 failed_single_amd = int(row.get('failed_single_no_amd', 0)) if pd.notna(row.get('failed_single_no_amd', 0)) else 0 failed_single_nvidia = int(row.get('failed_single_no_nvidia', 0)) if pd.notna(row.get('failed_single_no_nvidia', 0)) else 0 # Calculate total failures total_failed_amd = failed_multi_amd + failed_single_amd total_failed_nvidia = failed_multi_nvidia + failed_single_nvidia # Softer color palette - less pastel, more vibrant colors = { 'passed': '#4CAF50', # Medium green 'failed': '#E53E3E', # More red 'skipped': '#FFD54F', # Medium yellow 'error': '#8B0000' # Dark red } # Create stats dictionaries directly from dataframe values amd_stats = { 'passed': success_amd, 'failed': total_failed_amd, 'skipped': 0, # Not available in this dataset 'error': 0 # Not available in this dataset } nvidia_stats = { 'passed': success_nvidia, 'failed': total_failed_nvidia, 'skipped': 0, # Not available in this dataset 'error': 0 # Not available in this dataset } # Filter out categories with 0 values for cleaner visualization amd_filtered = {k: v for k, v in amd_stats.items() if v > 0} nvidia_filtered = {k: v for k, v in nvidia_stats.items() if v > 0} if not amd_filtered and not nvidia_filtered: # Handle case where all values are 0 - minimal empty state fig, ax = plt.subplots(figsize=(10, 8), facecolor='#000000') ax.set_facecolor('#000000') ax.text(0.5, 0.5, 'No test results available', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, color='#888888', fontfamily='monospace', weight='normal') ax.set_xlim(0, 1) ax.set_ylim(0, 1) ax.axis('off') return fig, "", "" # Create figure with two subplots side by side with padding fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9), facecolor='#000000') ax1.set_facecolor('#000000') ax2.set_facecolor('#000000') def create_pie_chart(ax, device_label, filtered_stats): if not filtered_stats: ax.text(0.5, 0.5, 'No test results', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=14, color='#888888', fontfamily='monospace', weight='normal') ax.set_title(device_label, fontsize=28, weight='bold', pad=2, color='#FFFFFF', fontfamily='monospace') ax.axis('off') return chart_colors = [colors[category] for category in filtered_stats.keys()] # Create minimal pie chart - full pie, no donut effect wedges, texts, autotexts = ax.pie( filtered_stats.values(), labels=[label.lower() for label in filtered_stats.keys()], # Lowercase for minimal look colors=chart_colors, autopct=lambda pct: f'{int(pct/100*sum(filtered_stats.values()))}', startangle=90, explode=None, # No separation shadow=False, wedgeprops=dict(edgecolor='#1a1a1a', linewidth=0.5), # Minimal borders textprops={'fontsize': 12, 'weight': 'normal', 'color': '#CCCCCC', 'fontfamily': 'monospace'} ) # Enhanced percentage text styling for better readability for autotext in autotexts: autotext.set_color('#000000') # Black text for better contrast autotext.set_weight('bold') autotext.set_fontsize(14) autotext.set_fontfamily('monospace') # Minimal category labels for text in texts: text.set_color('#AAAAAA') text.set_weight('normal') text.set_fontsize(13) text.set_fontfamily('monospace') # Device label closer to chart and bigger ax.set_title(device_label, fontsize=28, weight='normal', pad=2, color='#FFFFFF', fontfamily='monospace') # Create both pie charts with device labels create_pie_chart(ax1, "amd", amd_filtered) create_pie_chart(ax2, "nvidia", nvidia_filtered) # Add subtle separation line between charts - stops at device labels level line_x = 0.5 fig.add_artist(plt.Line2D([line_x, line_x], [0.0, 0.85], color='#333333', linewidth=1, alpha=0.5, transform=fig.transFigure)) # Add central shared title for model name fig.suptitle(f'{model_name.lower()}', fontsize=32, weight='bold', color='#CCCCCC', fontfamily='monospace', y=1) # Clean layout with padding and space for central title plt.tight_layout() plt.subplots_adjust(top=0.85, wspace=0.4) # Added wspace for padding between charts # Generate failure info directly from dataframe failures_amd = row.get('failures_amd', {}) failures_nvidia = row.get('failures_nvidia', {}) amd_failed_info = extract_failure_info(failures_amd, 'AMD', failed_multi_amd, failed_single_amd) nvidia_failed_info = extract_failure_info(failures_nvidia, 'NVIDIA', failed_multi_nvidia, failed_single_nvidia) return fig, amd_failed_info, nvidia_failed_info