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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from datetime import datetime
from data import extract_model_data
COLORS = {
'passed': '#4CAF50',
'failed': '#E53E3E',
'skipped': '#FFD54F',
'error': '#8B0000',
'amd': '#ED1C24',
'nvidia': '#76B900'
}
FIGURE_WIDTH = 20
FIGURE_HEIGHT = 12
BLACK = '#000000'
LABEL_COLOR = '#CCCCCC'
TITLE_COLOR = '#FFFFFF'
GRID_COLOR = '#333333'
TITLE_FONT_SIZE = 24
LABEL_FONT_SIZE = 14
LEGEND_FONT_SIZE = 12
def create_time_series_summary(historical_df: pd.DataFrame) -> plt.Figure:
if historical_df.empty or 'date' not in historical_df.columns:
fig, ax = plt.subplots(figsize=(FIGURE_WIDTH, FIGURE_HEIGHT), facecolor=BLACK)
ax.set_facecolor(BLACK)
ax.text(0.5, 0.5, 'No historical data available',
horizontalalignment='center', verticalalignment='center',
transform=ax.transAxes, fontsize=20, color='#888888',
fontfamily='monospace', weight='normal')
ax.axis('off')
return fig
historical_df['date_dt'] = pd.to_datetime(historical_df['date'])
historical_df = historical_df.sort_values('date_dt')
daily_stats = []
dates = []
for date in historical_df['date_dt'].unique():
date_data = historical_df[historical_df['date_dt'] == date]
total_amd_passed = total_amd_failed = total_amd_skipped = 0
total_nvidia_passed = total_nvidia_failed = total_nvidia_skipped = 0
for _, row in date_data.iterrows():
amd_stats, nvidia_stats = extract_model_data(row)[:2]
total_amd_passed += amd_stats['passed']
total_amd_failed += amd_stats['failed']
total_amd_skipped += amd_stats['skipped']
total_nvidia_passed += nvidia_stats['passed']
total_nvidia_failed += nvidia_stats['failed']
total_nvidia_skipped += nvidia_stats['skipped']
amd_total = total_amd_passed + total_amd_failed
nvidia_total = total_nvidia_passed + total_nvidia_failed
amd_failure_rate = (total_amd_failed / amd_total * 100) if amd_total > 0 else 0
nvidia_failure_rate = (total_nvidia_failed / nvidia_total * 100) if nvidia_total > 0 else 0
daily_stats.append({
'amd_failure_rate': amd_failure_rate,
'nvidia_failure_rate': nvidia_failure_rate,
'amd_passed': total_amd_passed,
'amd_failed': total_amd_failed,
'amd_skipped': total_amd_skipped,
'nvidia_passed': total_nvidia_passed,
'nvidia_failed': total_nvidia_failed,
'nvidia_skipped': total_nvidia_skipped
})
dates.append(date)
fig = plt.figure(figsize=(FIGURE_WIDTH, FIGURE_HEIGHT + 4), facecolor=BLACK)
gs = fig.add_gridspec(3, 2, height_ratios=[1.2, 1, 1], width_ratios=[2, 1],
hspace=0.3, wspace=0.25)
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[2, 0])
ax4 = fig.add_subplot(gs[1:, 1])
for ax in [ax1, ax2, ax3, ax4]:
ax.set_facecolor(BLACK)
dates_array = np.array(dates)
amd_rates = [stat['amd_failure_rate'] for stat in daily_stats]
nvidia_rates = [stat['nvidia_failure_rate'] for stat in daily_stats]
ax1.fill_between(dates_array, 0, amd_rates, color=COLORS['amd'], alpha=0.15)
ax1.fill_between(dates_array, 0, nvidia_rates, color=COLORS['nvidia'], alpha=0.15)
ax1.plot(dates_array, amd_rates, color=COLORS['amd'], linewidth=3,
label='AMD', marker='o', markersize=7, markeredgewidth=2, markeredgecolor=BLACK)
ax1.plot(dates_array, nvidia_rates, color=COLORS['nvidia'], linewidth=3,
label='NVIDIA', marker='s', markersize=7, markeredgewidth=2, markeredgecolor=BLACK)
if len(amd_rates) > 2:
z_amd = np.polyfit(range(len(amd_rates)), amd_rates, 1)
p_amd = np.poly1d(z_amd)
ax1.plot(dates_array, p_amd(range(len(amd_rates))),
color=COLORS['amd'], linestyle='--', alpha=0.5, linewidth=2)
z_nvidia = np.polyfit(range(len(nvidia_rates)), nvidia_rates, 1)
p_nvidia = np.poly1d(z_nvidia)
ax1.plot(dates_array, p_nvidia(range(len(nvidia_rates))),
color=COLORS['nvidia'], linestyle='--', alpha=0.5, linewidth=2)
ax1.set_title('Overall Failure Rates Over Time', fontsize=TITLE_FONT_SIZE,
color=TITLE_COLOR, fontfamily='monospace', fontweight='bold', pad=20)
ax1.set_ylabel('Failure Rate (%)', fontsize=LABEL_FONT_SIZE, color=LABEL_COLOR, fontfamily='monospace')
ax1.grid(True, color=GRID_COLOR, alpha=0.3, linestyle='-', linewidth=0.5)
ax1.legend(fontsize=LEGEND_FONT_SIZE, loc='upper right', frameon=False,
labelcolor=LABEL_COLOR, prop={'family': 'monospace'})
ax1.tick_params(colors=LABEL_COLOR, labelsize=LABEL_FONT_SIZE, axis='x', rotation=45)
amd_passed = [stat['amd_passed'] for stat in daily_stats]
amd_failed = [stat['amd_failed'] for stat in daily_stats]
amd_skipped = [stat['amd_skipped'] for stat in daily_stats]
ax2.stackplot(dates_array, amd_passed, amd_failed, amd_skipped,
colors=[COLORS['passed'], COLORS['failed'], COLORS['skipped']],
alpha=0.8, labels=['Passed', 'Failed', 'Skipped'])
ax2.set_title('AMD Test Results', fontsize=TITLE_FONT_SIZE - 2,
color=TITLE_COLOR, fontfamily='monospace', fontweight='bold', pad=15)
ax2.set_ylabel('Tests', fontsize=LABEL_FONT_SIZE, color=LABEL_COLOR, fontfamily='monospace')
ax2.grid(True, color=GRID_COLOR, alpha=0.3, linestyle='-', linewidth=0.5)
ax2.tick_params(colors=LABEL_COLOR, labelsize=LABEL_FONT_SIZE - 1, axis='x', rotation=45)
nvidia_passed = [stat['nvidia_passed'] for stat in daily_stats]
nvidia_failed = [stat['nvidia_failed'] for stat in daily_stats]
nvidia_skipped = [stat['nvidia_skipped'] for stat in daily_stats]
ax3.stackplot(dates_array, nvidia_passed, nvidia_failed, nvidia_skipped,
colors=[COLORS['passed'], COLORS['failed'], COLORS['skipped']],
alpha=0.8, labels=['Passed', 'Failed', 'Skipped'])
ax3.set_title('NVIDIA Test Results', fontsize=TITLE_FONT_SIZE - 2,
color=TITLE_COLOR, fontfamily='monospace', fontweight='bold', pad=15)
ax3.set_ylabel('Tests', fontsize=LABEL_FONT_SIZE, color=LABEL_COLOR, fontfamily='monospace')
ax3.set_xlabel('Date', fontsize=LABEL_FONT_SIZE, color=LABEL_COLOR, fontfamily='monospace')
ax3.grid(True, color=GRID_COLOR, alpha=0.3, linestyle='-', linewidth=0.5)
ax3.tick_params(colors=LABEL_COLOR, labelsize=LABEL_FONT_SIZE - 1, axis='x', rotation=45)
latest = daily_stats[-1]
metrics = [
('Latest AMD Failure Rate', f"{latest['amd_failure_rate']:.1f}%", COLORS['amd']),
('Latest NVIDIA Failure Rate', f"{latest['nvidia_failure_rate']:.1f}%", COLORS['nvidia']),
('', '', None),
('Total AMD Tests', str(latest['amd_passed'] + latest['amd_failed'] + latest['amd_skipped']), '#888888'),
('Total NVIDIA Tests', str(latest['nvidia_passed'] + latest['nvidia_failed'] + latest['nvidia_skipped']), '#888888'),
]
ax4.axis('off')
y_pos = 0.9
ax4.text(0.5, 0.95, 'SUMMARY', ha='center', va='top', fontsize=TITLE_FONT_SIZE - 2,
color=TITLE_COLOR, fontfamily='monospace', fontweight='bold',
transform=ax4.transAxes)
for label, value, color in metrics:
if label:
ax4.text(0.1, y_pos, label, ha='left', va='center', fontsize=LABEL_FONT_SIZE,
color=LABEL_COLOR, fontfamily='monospace', transform=ax4.transAxes)
ax4.text(0.9, y_pos, value, ha='right', va='center', fontsize=LABEL_FONT_SIZE + 2,
color=color or LABEL_COLOR, fontfamily='monospace', fontweight='bold',
transform=ax4.transAxes)
y_pos -= 0.15
handles = [plt.Rectangle((0,0),1,1, fc=COLORS['passed'], alpha=0.8),
plt.Rectangle((0,0),1,1, fc=COLORS['failed'], alpha=0.8),
plt.Rectangle((0,0),1,1, fc=COLORS['skipped'], alpha=0.8)]
ax4.legend(handles, ['Passed', 'Failed', 'Skipped'],
loc='lower center', fontsize=LEGEND_FONT_SIZE,
frameon=False, labelcolor=LABEL_COLOR, prop={'family': 'monospace'})
plt.close('all')
return fig
def create_model_time_series(historical_df: pd.DataFrame, model_name: str) -> plt.Figure:
if historical_df.empty or 'date' not in historical_df.columns:
fig, ax = plt.subplots(figsize=(FIGURE_WIDTH, FIGURE_HEIGHT), facecolor=BLACK)
ax.set_facecolor(BLACK)
ax.text(0.5, 0.5, f'No historical data available for {model_name}',
horizontalalignment='center', verticalalignment='center',
transform=ax.transAxes, fontsize=20, color='#888888',
fontfamily='monospace', weight='normal')
ax.axis('off')
return fig
model_data = historical_df[historical_df.index.str.lower() == model_name.lower()]
if model_data.empty:
fig, ax = plt.subplots(figsize=(FIGURE_WIDTH, FIGURE_HEIGHT), facecolor=BLACK)
ax.set_facecolor(BLACK)
ax.text(0.5, 0.5, f'No data found for model: {model_name}',
horizontalalignment='center', verticalalignment='center',
transform=ax.transAxes, fontsize=20, color='#888888',
fontfamily='monospace', weight='normal')
ax.axis('off')
return fig
model_data = model_data.copy()
model_data['date_dt'] = pd.to_datetime(model_data['date'])
model_data = model_data.sort_values('date_dt')
dates = model_data['date_dt'].values
amd_stats_list = []
nvidia_stats_list = []
for _, row in model_data.iterrows():
amd_stats, nvidia_stats = extract_model_data(row)[:2]
amd_stats_list.append(amd_stats)
nvidia_stats_list.append(nvidia_stats)
fig = plt.figure(figsize=(FIGURE_WIDTH, FIGURE_HEIGHT), facecolor=BLACK)
gs = fig.add_gridspec(2, 2, height_ratios=[1, 1], width_ratios=[3, 1],
hspace=0.3, wspace=0.2)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[:, 1])
for ax in [ax1, ax2, ax3]:
ax.set_facecolor(BLACK)
amd_passed = [stats['passed'] for stats in amd_stats_list]
amd_failed = [stats['failed'] for stats in amd_stats_list]
amd_skipped = [stats['skipped'] for stats in amd_stats_list]
ax1.stackplot(dates, amd_passed, amd_failed, amd_skipped,
colors=[COLORS['passed'], COLORS['failed'], COLORS['skipped']],
alpha=0.7, labels=['Passed', 'Failed', 'Skipped'])
ax1.plot(dates, amd_failed, color=COLORS['failed'], linewidth=2.5,
marker='o', markersize=7, markeredgewidth=2, markeredgecolor=BLACK,
linestyle='-', label='_nolegend_')
ax1.set_title(f'{model_name.upper()} - AMD Results', fontsize=TITLE_FONT_SIZE,
color=TITLE_COLOR, fontfamily='monospace', fontweight='bold', pad=20)
ax1.set_ylabel('Number of Tests', fontsize=LABEL_FONT_SIZE, color=LABEL_COLOR, fontfamily='monospace')
ax1.grid(True, color=GRID_COLOR, alpha=0.3, linestyle='-', linewidth=0.5)
ax1.legend(fontsize=LEGEND_FONT_SIZE, loc='upper left', frameon=False,
labelcolor=LABEL_COLOR, prop={'family': 'monospace'})
ax1.tick_params(colors=LABEL_COLOR, labelsize=LABEL_FONT_SIZE, axis='x', rotation=45)
nvidia_passed = [stats['passed'] for stats in nvidia_stats_list]
nvidia_failed = [stats['failed'] for stats in nvidia_stats_list]
nvidia_skipped = [stats['skipped'] for stats in nvidia_stats_list]
ax2.stackplot(dates, nvidia_passed, nvidia_failed, nvidia_skipped,
colors=[COLORS['passed'], COLORS['failed'], COLORS['skipped']],
alpha=0.7, labels=['Passed', 'Failed', 'Skipped'])
ax2.plot(dates, nvidia_failed, color=COLORS['failed'], linewidth=2.5,
marker='s', markersize=7, markeredgewidth=2, markeredgecolor=BLACK,
linestyle='-', label='_nolegend_')
ax2.set_title(f'{model_name.upper()} - NVIDIA Results', fontsize=TITLE_FONT_SIZE,
color=TITLE_COLOR, fontfamily='monospace', fontweight='bold', pad=20)
ax2.set_ylabel('Number of Tests', fontsize=LABEL_FONT_SIZE, color=LABEL_COLOR, fontfamily='monospace')
ax2.set_xlabel('Date', fontsize=LABEL_FONT_SIZE, color=LABEL_COLOR, fontfamily='monospace')
ax2.grid(True, color=GRID_COLOR, alpha=0.3, linestyle='-', linewidth=0.5)
ax2.tick_params(colors=LABEL_COLOR, labelsize=LABEL_FONT_SIZE, axis='x', rotation=45)
ax3.axis('off')
latest_amd = amd_stats_list[-1]
latest_nvidia = nvidia_stats_list[-1]
amd_total = latest_amd['passed'] + latest_amd['failed']
nvidia_total = latest_nvidia['passed'] + latest_nvidia['failed']
amd_fail_rate = (latest_amd['failed'] / amd_total * 100) if amd_total > 0 else 0
nvidia_fail_rate = (latest_nvidia['failed'] / nvidia_total * 100) if nvidia_total > 0 else 0
ax3.text(0.5, 0.95, 'LATEST RESULTS', ha='center', va='top',
fontsize=TITLE_FONT_SIZE - 4, color=TITLE_COLOR, fontfamily='monospace',
fontweight='bold', transform=ax3.transAxes)
y = 0.80
sections = [
('AMD', [
('Pass Rate', f"{(latest_amd['passed']/amd_total*100) if amd_total > 0 else 0:.1f}%", COLORS['passed']),
('Fail Rate', f"{amd_fail_rate:.1f}%", COLORS['failed']),
('Total', str(latest_amd['passed'] + latest_amd['failed'] + latest_amd['skipped']), '#888888'),
]),
('NVIDIA', [
('Pass Rate', f"{(latest_nvidia['passed']/nvidia_total*100) if nvidia_total > 0 else 0:.1f}%", COLORS['passed']),
('Fail Rate', f"{nvidia_fail_rate:.1f}%", COLORS['failed']),
('Total', str(latest_nvidia['passed'] + latest_nvidia['failed'] + latest_nvidia['skipped']), '#888888'),
])
]
for section_name, metrics in sections:
ax3.text(0.5, y, section_name, ha='center', va='center',
fontsize=LABEL_FONT_SIZE + 2, color=TITLE_COLOR,
fontfamily='monospace', fontweight='bold', transform=ax3.transAxes)
y -= 0.08
for label, value, color in metrics:
ax3.text(0.15, y, label, ha='left', va='center',
fontsize=LABEL_FONT_SIZE - 1, color=LABEL_COLOR,
fontfamily='monospace', transform=ax3.transAxes)
ax3.text(0.85, y, value, ha='right', va='center',
fontsize=LABEL_FONT_SIZE, color=color,
fontfamily='monospace', fontweight='bold', transform=ax3.transAxes)
y -= 0.07
y -= 0.05
plt.close('all')
return fig |