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8fc241a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | 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()
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