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| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import os | |
| # Set style | |
| sns.set_style("white") # Clean white background as requested | |
| plt.rcParams['font.family'] = 'sans-serif' | |
| plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica', 'Roboto', 'DejaVu Sans'] | |
| plt.rcParams['font.size'] = 12 | |
| def generate_chart(): | |
| # Load validation data | |
| data_path = './data/nasa_model_validation.csv' | |
| if not os.path.exists(data_path): | |
| print(f"Error: {data_path} not found.") | |
| return | |
| df = pd.read_csv(data_path) | |
| # Filter for B0007 validation cycles | |
| b0007_df = df[(df['battery_id'] == 'B0007') & (df['dataset'] == 'validation')].copy() | |
| b0007_df = b0007_df.sort_values('cycle_index') | |
| # User requested cycle index 0 to 168 | |
| # The actual data has 168 rows, we'll map them to a 0-indexed range for the plot if needed | |
| # but the actual cycle_index values are better for physical context. | |
| # However, the user explicitly said "Cycle index (0 to 168 for B0007)". | |
| # B0007 has 168 cycles total. | |
| cycle_index = np.arange(len(b0007_df)) | |
| actual_soh = b0007_df['actual_soh'].values | |
| predicted_soh = b0007_df['predicted_soh'].values | |
| lower_95 = b0007_df['pred_lower_95'].values | |
| upper_95 = b0007_df['pred_upper_95'].values | |
| # Create figure - 10x6 as requested for LinkedIn | |
| fig, ax = plt.subplots(figsize=(10, 6), dpi=300) | |
| fig.patch.set_facecolor('white') | |
| # Plot 1: 95% prediction interval (shaded region) - Shaded region in light orange/peach with alpha=0.3 | |
| ax.fill_between(cycle_index, | |
| lower_95, upper_95, | |
| color='#ff7f0e', alpha=0.3, label='95% Confidence Interval', zorder=1) | |
| # Plot 2: Actual SOH (Blue line) - rgb: 31, 119, 180 | |
| ax.plot(cycle_index, actual_soh, | |
| color='#1f77b4', linewidth=2.5, label='Actual SOH (B0007)', zorder=3) | |
| # Plot 3: Random Forest predicted SOH (Orange line) - rgb: 255, 127, 14 | |
| ax.plot(cycle_index, predicted_soh, | |
| color='#ff7f0e', linewidth=2, linestyle='-', label='Predicted SOH (Random Forest)', zorder=2) | |
| # Plot 4: EOL threshold line (Red dashed) | |
| ax.axhline(y=0.80, color='red', linestyle='--', linewidth=1.5, | |
| label='EOL Threshold (80% SOH)', zorder=0) | |
| # Find and annotate EOL crossing point | |
| crossing_idx = np.where(actual_soh < 0.80)[0] | |
| if len(crossing_idx) > 0: | |
| eol_cycle_idx = crossing_idx[0] | |
| ax.axvline(x=eol_cycle_idx, color='red', linestyle=':', linewidth=1, alpha=0.5, zorder=0) | |
| ax.text(eol_cycle_idx + 3, 0.75, f'EOL Crossing: Cycle {eol_cycle_idx}', | |
| fontsize=11, color='red', fontweight='bold', verticalalignment='bottom') | |
| # Add metrics text box | |
| # Using confirmed metrics from summary | |
| metrics_text = ( | |
| 'Random Forest Validation\n' | |
| 'R² = 0.9996\n' | |
| 'MAPE = 0.14%\n' | |
| 'MAE = 0.0012' | |
| ) | |
| ax.text(0.97, 0.95, metrics_text, | |
| transform=ax.transAxes, fontsize=11, | |
| verticalalignment='top', horizontalalignment='right', | |
| bbox=dict(boxstyle='round,pad=0.5', facecolor='white', alpha=0.9, edgecolor='#e0e0e0')) | |
| # Labels and title | |
| ax.set_xlabel('Cycle Index', fontsize=12, fontweight='bold', labelpad=10, color='#333333') | |
| ax.set_ylabel('State of Health (SOH)', fontsize=12, fontweight='bold', labelpad=10, color='#333333') | |
| # Title - 14pt as requested | |
| ax.set_title('Battery SOH Prediction: NASA B0007 (Held-Out Validation)\n' | |
| 'Random Forest with 95% Prediction Intervals', | |
| fontsize=14, fontweight='bold', pad=25, color='#1a1a1a') | |
| # Legend - Lower left as requested | |
| ax.legend(loc='lower left', fontsize=10, framealpha=1, edgecolor='#e0e0e0', facecolor='white') | |
| # Grid - Light gray horizontal only | |
| ax.grid(True, alpha=0.2, axis='y', color='#cccccc', linestyle='-') | |
| ax.set_axisbelow(True) | |
| # Clean spines - no borders as requested | |
| sns.despine(left=True, bottom=False) | |
| # Axis limits | |
| ax.set_ylim([0.70, 1.05]) | |
| ax.set_xlim([0, len(cycle_index)]) | |
| # Formatting ticks | |
| plt.xticks(fontsize=10, color='#666666') | |
| plt.yticks(fontsize=10, color='#666666') | |
| # Tight layout | |
| plt.tight_layout() | |
| # Save | |
| output_filename = 'zenvolt_rf_validation_B0007.png' | |
| plt.savefig(output_filename, dpi=300, bbox_inches='tight', facecolor='white') | |
| print(f"✓ High-resolution chart saved as: {output_filename}") | |
| # Also save to plots directory for app use | |
| plots_dir = './plots' | |
| os.makedirs(plots_dir, exist_ok=True) | |
| plt.savefig(os.path.join(plots_dir, 'linkedin_validation_B0007.png'), dpi=300, bbox_inches='tight', facecolor='white') | |
| if __name__ == "__main__": | |
| generate_chart() | |