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removing old endpoints etc

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sstent
2025-10-06 12:54:15 -07:00
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commit 76d874fe60
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visualizers/chart_generator.py
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@@ -12,461 +12,652 @@ import plotly.express as px
from plotly.subplots import make_subplots
from models.workout import WorkoutData
from models.zones import ZoneCalculator
logger = logging.getLogger(__name__)
class ChartGenerator:
"""Generate various charts and visualizations for workout data."""
def __init__(self, output_dir: Path = None):
"""Initialize chart generator.
Args:
output_dir: Directory to save charts
"""
self.output_dir = output_dir or Path('charts')
self.output_dir.mkdir(exist_ok=True)
self.zone_calculator = ZoneCalculator()
# Set style
plt.style.use('seaborn-v0_8')
sns.set_palette("husl")
def _get_avg_max_values(self, analysis: Dict[str, Any], data_type: str, workout: WorkoutData) -> Tuple[float, float]:
"""Get avg and max values from analysis dict or compute from workout data.
Args:
analysis: Analysis results from WorkoutAnalyzer
data_type: 'power', 'hr', or 'speed'
workout: WorkoutData object
Returns:
Tuple of (avg_value, max_value)
"""
if analysis and 'summary' in analysis:
summary = analysis['summary']
if data_type == 'power':
avg_key, max_key = 'avg_power', 'max_power'
elif data_type == 'hr':
avg_key, max_key = 'avg_hr', 'max_hr'
elif data_type == 'speed':
avg_key, max_key = 'avg_speed_kmh', 'max_speed_kmh'
else:
raise ValueError(f"Unsupported data_type: {data_type}")
avg_val = summary.get(avg_key)
max_val = summary.get(max_key)
if avg_val is not None and max_val is not None:
return avg_val, max_val
# Fallback: compute from workout data
if data_type == 'power' and workout.power and workout.power.power_values:
return np.mean(workout.power.power_values), np.max(workout.power.power_values)
elif data_type == 'hr' and workout.heart_rate and workout.heart_rate.heart_rate_values:
return np.mean(workout.heart_rate.heart_rate_values), np.max(workout.heart_rate.heart_rate_values)
elif data_type == 'speed' and workout.speed and workout.speed.speed_values:
return np.mean(workout.speed.speed_values), np.max(workout.speed.speed_values)
# Default fallback
return 0, 0
def _get_avg_max_labels(self, data_type: str, analysis: Dict[str, Any], workout: WorkoutData) -> Tuple[str, str]:
"""Get formatted average and maximum labels for chart annotations.
Args:
data_type: 'power', 'hr', or 'speed'
analysis: Analysis results from WorkoutAnalyzer
workout: WorkoutData object
Returns:
Tuple of (avg_label, max_label)
"""
avg_val, max_val = self._get_avg_max_values(analysis, data_type, workout)
if data_type == 'power':
avg_label = f'Avg: {avg_val:.0f}W'
max_label = f'Max: {max_val:.0f}W'
elif data_type == 'hr':
avg_label = f'Avg: {avg_val:.0f} bpm'
max_label = f'Max: {max_val:.0f} bpm'
elif data_type == 'speed':
avg_label = f'Avg: {avg_val:.1f} km/h'
max_label = f'Max: {max_val:.1f} km/h'
else:
avg_label = f'Avg: {avg_val:.1f}'
max_label = f'Max: {max_val:.1f}'
return avg_label, max_label
def generate_workout_charts(self, workout: WorkoutData, analysis: Dict[str, Any]) -> Dict[str, str]:
"""Generate all workout charts.
Args:
workout: WorkoutData object
analysis: Analysis results from WorkoutAnalyzer
Returns:
Dictionary mapping chart names to file paths
"""
charts = {}
# Time series charts
charts['power_time_series'] = self._create_power_time_series(workout)
charts['heart_rate_time_series'] = self._create_heart_rate_time_series(workout)
charts['speed_time_series'] = self._create_speed_time_series(workout)
charts['power_time_series'] = self._create_power_time_series(workout, analysis, elevation_overlay=True, zone_shading=True)
charts['heart_rate_time_series'] = self._create_heart_rate_time_series(workout, analysis, elevation_overlay=True)
charts['speed_time_series'] = self._create_speed_time_series(workout, analysis, elevation_overlay=True)
charts['elevation_time_series'] = self._create_elevation_time_series(workout)
# Distribution charts
charts['power_distribution'] = self._create_power_distribution(workout, analysis)
charts['heart_rate_distribution'] = self._create_heart_rate_distribution(workout, analysis)
charts['speed_distribution'] = self._create_speed_distribution(workout, analysis)
# Zone charts
charts['power_zones'] = self._create_power_zones_chart(analysis)
charts['heart_rate_zones'] = self._create_heart_rate_zones_chart(analysis)
# Correlation charts
charts['power_vs_heart_rate'] = self._create_power_vs_heart_rate(workout)
charts['power_vs_speed'] = self._create_power_vs_speed(workout)
# Summary dashboard
charts['workout_dashboard'] = self._create_workout_dashboard(workout, analysis)
return charts
def _create_power_time_series(self, workout: WorkoutData) -> str:
def _create_power_time_series(self, workout: WorkoutData, analysis: Dict[str, Any] = None, elevation_overlay: bool = True, zone_shading: bool = True) -> str:
"""Create power vs time chart.
Args:
workout: WorkoutData object
analysis: Analysis results from WorkoutAnalyzer
elevation_overlay: Whether to add an elevation overlay
zone_shading: Whether to add power zone shading
Returns:
Path to saved chart
"""
if not workout.power or not workout.power.power_values:
return None
fig, ax = plt.subplots(figsize=(12, 6))
fig, ax1 = plt.subplots(figsize=(12, 6))
power_values = workout.power.power_values
time_minutes = np.arange(len(power_values)) / 60
ax.plot(time_minutes, power_values, linewidth=0.5, alpha=0.8)
ax.axhline(y=workout.power.avg_power, color='r', linestyle='--',
label=f'Avg: {workout.power.avg_power:.0f}W')
ax.axhline(y=workout.power.max_power, color='g', linestyle='--',
label=f'Max: {workout.power.max_power:.0f}W')
ax.set_xlabel('Time (minutes)')
ax.set_ylabel('Power (W)')
ax.set_title('Power Over Time')
ax.legend()
ax.grid(True, alpha=0.3)
# Plot power
ax1.plot(time_minutes, power_values, linewidth=0.5, alpha=0.8, color='blue')
ax1.set_xlabel('Time (minutes)')
ax1.set_ylabel('Power (W)', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
# Add avg/max annotations from analysis or fallback
avg_power_label, max_power_label = self._get_avg_max_labels('power', analysis, workout)
ax1.axhline(y=self._get_avg_max_values(analysis, 'power', workout)[0], color='red', linestyle='--',
label=avg_power_label)
ax1.axhline(y=self._get_avg_max_values(analysis, 'power', workout)[1], color='green', linestyle='--',
label=max_power_label)
# Add power zone shading
if zone_shading and analysis and 'power_analysis' in analysis:
power_zones = self.zone_calculator.get_power_zones()
# Try to get FTP from analysis, otherwise use a default or the zone calculator's default
ftp = analysis.get('power_analysis', {}).get('ftp', 250) # Fallback to 250W if not in analysis
# Recalculate zones based on FTP percentage
power_zones_percent = {
'Recovery': {'min': 0, 'max': 0.5}, # <50% FTP
'Endurance': {'min': 0.5, 'max': 0.75}, # 50-75% FTP
'Tempo': {'min': 0.75, 'max': 0.9}, # 75-90% FTP
'Threshold': {'min': 0.9, 'max': 1.05}, # 90-105% FTP
'VO2 Max': {'min': 1.05, 'max': 1.2}, # 105-120% FTP
'Anaerobic': {'min': 1.2, 'max': 10} # >120% FTP (arbitrary max for shading)
}
for zone_name, zone_def_percent in power_zones_percent.items():
min_power = ftp * zone_def_percent['min']
max_power = ftp * zone_def_percent['max']
# Find the corresponding ZoneDefinition to get the color
zone_color = next((z.color for z_name, z in power_zones.items() if z_name == zone_name), 'grey')
ax1.axhspan(min_power, max_power,
alpha=0.1, color=zone_color,
label=f'{zone_name} ({min_power:.0f}-{max_power:.0f}W)')
# Add elevation overlay if available
if elevation_overlay and workout.elevation and workout.elevation.elevation_values:
# Create twin axis for elevation
ax2 = ax1.twinx()
elevation_values = workout.elevation.elevation_values
# Apply light smoothing to elevation for visual stability
# Using a simple rolling mean, NaN-safe
elevation_smoothed = pd.Series(elevation_values).rolling(window=5, min_periods=1, center=True).mean().values
# Align lengths (assume same sampling rate)
min_len = min(len(power_values), len(elevation_smoothed))
elevation_aligned = elevation_smoothed[:min_len]
time_aligned = time_minutes[:min_len]
ax2.fill_between(time_aligned, elevation_aligned, alpha=0.2, color='brown', label='Elevation')
ax2.set_ylabel('Elevation (m)', color='brown')
ax2.tick_params(axis='y', labelcolor='brown')
# Combine legends
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')
else:
ax1.legend()
ax1.set_title('Power Over Time')
ax1.grid(True, alpha=0.3)
filepath = self.output_dir / 'power_time_series.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_heart_rate_time_series(self, workout: WorkoutData) -> str:
def _create_heart_rate_time_series(self, workout: WorkoutData, analysis: Dict[str, Any] = None, elevation_overlay: bool = True) -> str:
"""Create heart rate vs time chart.
Args:
workout: WorkoutData object
analysis: Analysis results from WorkoutAnalyzer
elevation_overlay: Whether to add an elevation overlay
Returns:
Path to saved chart
"""
if not workout.heart_rate or not workout.heart_rate.heart_rate_values:
return None
fig, ax = plt.subplots(figsize=(12, 6))
fig, ax1 = plt.subplots(figsize=(12, 6))
hr_values = workout.heart_rate.heart_rate_values
time_minutes = np.arange(len(hr_values)) / 60
ax.plot(time_minutes, hr_values, linewidth=0.5, alpha=0.8, color='red')
ax.axhline(y=workout.heart_rate.avg_hr, color='darkred', linestyle='--',
label=f'Avg: {workout.heart_rate.avg_hr:.0f} bpm')
ax.axhline(y=workout.heart_rate.max_hr, color='darkgreen', linestyle='--',
label=f'Max: {workout.heart_rate.max_hr:.0f} bpm')
ax.set_xlabel('Time (minutes)')
ax.set_ylabel('Heart Rate (bpm)')
ax.set_title('Heart Rate Over Time')
ax.legend()
ax.grid(True, alpha=0.3)
# Plot heart rate
ax1.plot(time_minutes, hr_values, linewidth=0.5, alpha=0.8, color='red')
ax1.set_xlabel('Time (minutes)')
ax1.set_ylabel('Heart Rate (bpm)', color='red')
ax1.tick_params(axis='y', labelcolor='red')
# Add avg/max annotations from analysis or fallback
avg_hr_label, max_hr_label = self._get_avg_max_labels('hr', analysis, workout)
ax1.axhline(y=self._get_avg_max_values(analysis, 'hr', workout)[0], color='darkred', linestyle='--',
label=avg_hr_label)
ax1.axhline(y=self._get_avg_max_values(analysis, 'hr', workout)[1], color='darkgreen', linestyle='--',
label=max_hr_label)
# Add elevation overlay if available
if elevation_overlay and workout.elevation and workout.elevation.elevation_values:
# Create twin axis for elevation
ax2 = ax1.twinx()
elevation_values = workout.elevation.elevation_values
# Apply light smoothing to elevation for visual stability
elevation_smoothed = pd.Series(elevation_values).rolling(window=5, min_periods=1, center=True).mean().values
# Align lengths (assume same sampling rate)
min_len = min(len(hr_values), len(elevation_smoothed))
elevation_aligned = elevation_smoothed[:min_len]
time_aligned = time_minutes[:min_len]
ax2.fill_between(time_aligned, elevation_aligned, alpha=0.2, color='brown', label='Elevation')
ax2.set_ylabel('Elevation (m)', color='brown')
ax2.tick_params(axis='y', labelcolor='brown')
# Combine legends
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')
else:
ax1.legend()
ax1.set_title('Heart Rate Over Time')
ax1.grid(True, alpha=0.3)
filepath = self.output_dir / 'heart_rate_time_series.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_speed_time_series(self, workout: WorkoutData) -> str:
def _create_speed_time_series(self, workout: WorkoutData, analysis: Dict[str, Any] = None, elevation_overlay: bool = True) -> str:
"""Create speed vs time chart.
Args:
workout: WorkoutData object
analysis: Analysis results from WorkoutAnalyzer
elevation_overlay: Whether to add an elevation overlay
Returns:
Path to saved chart
"""
if not workout.speed or not workout.speed.speed_values:
return None
fig, ax = plt.subplots(figsize=(12, 6))
fig, ax1 = plt.subplots(figsize=(12, 6))
speed_values = workout.speed.speed_values
time_minutes = np.arange(len(speed_values)) / 60
ax.plot(time_minutes, speed_values, linewidth=0.5, alpha=0.8, color='blue')
ax.axhline(y=workout.speed.avg_speed, color='darkblue', linestyle='--',
label=f'Avg: {workout.speed.avg_speed:.1f} km/h')
ax.axhline(y=workout.speed.max_speed, color='darkgreen', linestyle='--',
label=f'Max: {workout.speed.max_speed:.1f} km/h')
ax.set_xlabel('Time (minutes)')
ax.set_ylabel('Speed (km/h)')
ax.set_title('Speed Over Time')
ax.legend()
ax.grid(True, alpha=0.3)
# Plot speed
ax1.plot(time_minutes, speed_values, linewidth=0.5, alpha=0.8, color='blue')
ax1.set_xlabel('Time (minutes)')
ax1.set_ylabel('Speed (km/h)', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
# Add avg/max annotations from analysis or fallback
avg_speed_label, max_speed_label = self._get_avg_max_labels('speed', analysis, workout)
ax1.axhline(y=self._get_avg_max_values(analysis, 'speed', workout)[0], color='darkblue', linestyle='--',
label=avg_speed_label)
ax1.axhline(y=self._get_avg_max_values(analysis, 'speed', workout)[1], color='darkgreen', linestyle='--',
label=max_speed_label)
# Add elevation overlay if available
if elevation_overlay and workout.elevation and workout.elevation.elevation_values:
# Create twin axis for elevation
ax2 = ax1.twinx()
elevation_values = workout.elevation.elevation_values
# Apply light smoothing to elevation for visual stability
elevation_smoothed = pd.Series(elevation_values).rolling(window=5, min_periods=1, center=True).mean().values
# Align lengths (assume same sampling rate)
min_len = min(len(speed_values), len(elevation_smoothed))
elevation_aligned = elevation_smoothed[:min_len]
time_aligned = time_minutes[:min_len]
ax2.fill_between(time_aligned, elevation_aligned, alpha=0.2, color='brown', label='Elevation')
ax2.set_ylabel('Elevation (m)', color='brown')
ax2.tick_params(axis='y', labelcolor='brown')
# Combine legends
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')
else:
ax1.legend()
ax1.set_title('Speed Over Time')
ax1.grid(True, alpha=0.3)
filepath = self.output_dir / 'speed_time_series.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_elevation_time_series(self, workout: WorkoutData) -> str:
"""Create elevation vs time chart.
Args:
workout: WorkoutData object
Returns:
Path to saved chart
"""
if not workout.elevation or not workout.elevation.elevation_values:
return None
fig, ax = plt.subplots(figsize=(12, 6))
elevation_values = workout.elevation.elevation_values
time_minutes = np.arange(len(elevation_values)) / 60
ax.plot(time_minutes, elevation_values, linewidth=1, alpha=0.8, color='brown')
ax.fill_between(time_minutes, elevation_values, alpha=0.3, color='brown')
ax.set_xlabel('Time (minutes)')
ax.set_ylabel('Elevation (m)')
ax.set_title('Elevation Profile')
ax.grid(True, alpha=0.3)
filepath = self.output_dir / 'elevation_time_series.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_power_distribution(self, workout: WorkoutData, analysis: Dict[str, Any]) -> str:
"""Create power distribution histogram.
Args:
workout: WorkoutData object
analysis: Analysis results
Returns:
Path to saved chart
"""
if not workout.power or not workout.power.power_values:
return None
fig, ax = plt.subplots(figsize=(10, 6))
power_values = workout.power.power_values
ax.hist(power_values, bins=50, alpha=0.7, color='orange', edgecolor='black')
ax.axvline(x=workout.power.avg_power, color='red', linestyle='--',
ax.axvline(x=workout.power.avg_power, color='red', linestyle='--',
label=f'Avg: {workout.power.avg_power:.0f}W')
ax.set_xlabel('Power (W)')
ax.set_ylabel('Frequency')
ax.set_title('Power Distribution')
ax.legend()
ax.grid(True, alpha=0.3)
filepath = self.output_dir / 'power_distribution.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_heart_rate_distribution(self, workout: WorkoutData, analysis: Dict[str, Any]) -> str:
"""Create heart rate distribution histogram.
Args:
workout: WorkoutData object
analysis: Analysis results
Returns:
Path to saved chart
"""
if not workout.heart_rate or not workout.heart_rate.heart_rate_values:
return None
fig, ax = plt.subplots(figsize=(10, 6))
hr_values = workout.heart_rate.heart_rate_values
ax.hist(hr_values, bins=30, alpha=0.7, color='red', edgecolor='black')
ax.axvline(x=workout.heart_rate.avg_hr, color='darkred', linestyle='--',
ax.axvline(x=workout.heart_rate.avg_hr, color='darkred', linestyle='--',
label=f'Avg: {workout.heart_rate.avg_hr:.0f} bpm')
ax.set_xlabel('Heart Rate (bpm)')
ax.set_ylabel('Frequency')
ax.set_title('Heart Rate Distribution')
ax.legend()
ax.grid(True, alpha=0.3)
filepath = self.output_dir / 'heart_rate_distribution.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_speed_distribution(self, workout: WorkoutData, analysis: Dict[str, Any]) -> str:
"""Create speed distribution histogram.
Args:
workout: WorkoutData object
analysis: Analysis results
Returns:
Path to saved chart
"""
if not workout.speed or not workout.speed.speed_values:
return None
fig, ax = plt.subplots(figsize=(10, 6))
speed_values = workout.speed.speed_values
ax.hist(speed_values, bins=30, alpha=0.7, color='blue', edgecolor='black')
ax.axvline(x=workout.speed.avg_speed, color='darkblue', linestyle='--',
ax.axvline(x=workout.speed.avg_speed, color='darkblue', linestyle='--',
label=f'Avg: {workout.speed.avg_speed:.1f} km/h')
ax.set_xlabel('Speed (km/h)')
ax.set_ylabel('Frequency')
ax.set_title('Speed Distribution')
ax.legend()
ax.grid(True, alpha=0.3)
filepath = self.output_dir / 'speed_distribution.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_power_zones_chart(self, analysis: Dict[str, Any]) -> str:
"""Create power zones pie chart.
Args:
analysis: Analysis results
Returns:
Path to saved chart
"""
if 'power_analysis' not in analysis or 'power_zones' not in analysis['power_analysis']:
return None
power_zones = analysis['power_analysis']['power_zones']
fig, ax = plt.subplots(figsize=(8, 8))
labels = list(power_zones.keys())
sizes = list(power_zones.values())
colors = plt.cm.Set3(np.linspace(0, 1, len(labels)))
ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
ax.set_title('Time in Power Zones')
filepath = self.output_dir / 'power_zones.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_heart_rate_zones_chart(self, analysis: Dict[str, Any]) -> str:
"""Create heart rate zones pie chart.
Args:
analysis: Analysis results
Returns:
Path to saved chart
"""
if 'heart_rate_analysis' not in analysis or 'hr_zones' not in analysis['heart_rate_analysis']:
return None
hr_zones = analysis['heart_rate_analysis']['hr_zones']
fig, ax = plt.subplots(figsize=(8, 8))
labels = list(hr_zones.keys())
sizes = list(hr_zones.values())
colors = plt.cm.Set3(np.linspace(0, 1, len(labels)))
ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
ax.set_title('Time in Heart Rate Zones')
filepath = self.output_dir / 'heart_rate_zones.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_power_vs_heart_rate(self, workout: WorkoutData) -> str:
"""Create power vs heart rate scatter plot.
Args:
workout: WorkoutData object
Returns:
Path to saved chart
"""
if (not workout.power or not workout.power.power_values or
not workout.heart_rate or not workout.heart_rate.heart_rate_values):
return None
power_values = workout.power.power_values
hr_values = workout.heart_rate.heart_rate_values
# Align arrays
min_len = min(len(power_values), len(hr_values))
if min_len == 0:
return None
power_values = power_values[:min_len]
hr_values = hr_values[:min_len]
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(power_values, hr_values, alpha=0.5, s=1)
# Add trend line
z = np.polyfit(power_values, hr_values, 1)
p = np.poly1d(z)
ax.plot(power_values, p(power_values), "r--", alpha=0.8)
ax.set_xlabel('Power (W)')
ax.set_ylabel('Heart Rate (bpm)')
ax.set_title('Power vs Heart Rate')
ax.grid(True, alpha=0.3)
filepath = self.output_dir / 'power_vs_heart_rate.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_power_vs_speed(self, workout: WorkoutData) -> str:
"""Create power vs speed scatter plot.
Args:
workout: WorkoutData object
Returns:
Path to saved chart
"""
if (not workout.power or not workout.power.power_values or
not workout.speed or not workout.speed.speed_values):
return None
power_values = workout.power.power_values
speed_values = workout.speed.speed_values
# Align arrays
min_len = min(len(power_values), len(speed_values))
if min_len == 0:
return None
power_values = power_values[:min_len]
speed_values = speed_values[:min_len]
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(power_values, speed_values, alpha=0.5, s=1)
# Add trend line
z = np.polyfit(power_values, speed_values, 1)
p = np.poly1d(z)
ax.plot(power_values, p(power_values), "r--", alpha=0.8)
ax.set_xlabel('Power (W)')
ax.set_ylabel('Speed (km/h)')
ax.set_title('Power vs Speed')
ax.grid(True, alpha=0.3)
filepath = self.output_dir / 'power_vs_speed.png'
plt.tight_layout()
plt.savefig(filepath, dpi=300, bbox_inches='tight')
plt.close()
return str(filepath)
def _create_workout_dashboard(self, workout: WorkoutData, analysis: Dict[str, Any]) -> str:
"""Create comprehensive workout dashboard.
Args:
workout: WorkoutData object
analysis: Analysis results
Returns:
Path to saved chart
"""
@@ -479,7 +670,7 @@ class ChartGenerator:
[{"secondary_y": False}, {"secondary_y": False}],
[{"secondary_y": False}, {"secondary_y": False}]]
)
# Power time series
if workout.power and workout.power.power_values:
power_values = workout.power.power_values
@@ -488,7 +679,7 @@ class ChartGenerator:
go.Scatter(x=time_minutes, y=power_values, name='Power', line=dict(color='orange')),
row=1, col=1
)
# Heart rate time series
if workout.heart_rate and workout.heart_rate.heart_rate_values:
hr_values = workout.heart_rate.heart_rate_values
@@ -497,7 +688,7 @@ class ChartGenerator:
go.Scatter(x=time_minutes, y=hr_values, name='Heart Rate', line=dict(color='red')),
row=1, col=2
)
# Speed time series
if workout.speed and workout.speed.speed_values:
speed_values = workout.speed.speed_values
@@ -506,7 +697,7 @@ class ChartGenerator:
go.Scatter(x=time_minutes, y=speed_values, name='Speed', line=dict(color='blue')),
row=2, col=1
)
# Elevation profile
if workout.elevation and workout.elevation.elevation_values:
elevation_values = workout.elevation.elevation_values
@@ -515,7 +706,7 @@ class ChartGenerator:
go.Scatter(x=time_minutes, y=elevation_values, name='Elevation', line=dict(color='brown')),
row=2, col=2
)
# Power distribution
if workout.power and workout.power.power_values:
power_values = workout.power.power_values
@@ -523,7 +714,7 @@ class ChartGenerator:
go.Histogram(x=power_values, name='Power Distribution', nbinsx=50),
row=3, col=1
)
# Heart rate distribution
if workout.heart_rate and workout.heart_rate.heart_rate_values:
hr_values = workout.heart_rate.heart_rate_values
@@ -531,14 +722,14 @@ class ChartGenerator:
go.Histogram(x=hr_values, name='HR Distribution', nbinsx=30),
row=3, col=2
)
# Update layout
fig.update_layout(
height=1200,
title_text=f"Workout Dashboard - {workout.metadata.activity_name}",
showlegend=False
)
# Update axes labels
fig.update_xaxes(title_text="Time (minutes)", row=1, col=1)
fig.update_yaxes(title_text="Power (W)", row=1, col=1)
@@ -550,8 +741,8 @@ class ChartGenerator:
fig.update_yaxes(title_text="Elevation (m)", row=2, col=2)
fig.update_xaxes(title_text="Power (W)", row=3, col=1)
fig.update_xaxes(title_text="Heart Rate (bpm)", row=3, col=2)
filepath = self.output_dir / 'workout_dashboard.html'
fig.write_html(str(filepath))
return str(filepath)