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Initial commit: Complete Paw Burn Risk Assessment Tool with WeatherAPI integration, comprehensive risk scoring, SQLite storage, rich visualizations, configurable parameters, and full documentation with examples
This commit is contained in:
Chris Sewell
360
plotting.py
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360
plotting.py
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"""Plotting and visualization for paw burn risk assessment."""
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import matplotlib
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from datetime import datetime
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from typing import List, Optional, Tuple
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import numpy as np
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import warnings
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import os
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import shutil
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from models import WeatherHour, RiskScore
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# Suppress matplotlib UserWarnings and macOS GUI warnings
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warnings.filterwarnings('ignore', category=UserWarning, module='matplotlib')
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warnings.filterwarnings('ignore', message='.*NSSavePanel.*')
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class RiskPlotter:
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"""Handles plotting and visualization of risk data."""
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def __init__(self, figure_size: Tuple[int, int] = (12, 8)):
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self.figure_size = figure_size
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self.plots_dir = "plots"
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self._plots_dir_setup = False
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# Set up matplotlib style and suppress warnings
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plt.style.use('default')
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plt.rcParams['figure.figsize'] = figure_size
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plt.rcParams['font.size'] = 10
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def _setup_plots_directory(self):
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"""Create and clear plots directory."""
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if os.path.exists(self.plots_dir):
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# Clear existing plots
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shutil.rmtree(self.plots_dir)
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os.makedirs(self.plots_dir, exist_ok=True)
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print(f"📁 Plots directory ready: {self.plots_dir}/")
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def _safe_save_plot(self, filename: str, dpi: int = 300):
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"""Safely save plot to plots directory."""
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if filename:
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# Setup plots directory on first save
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if not self._plots_dir_setup:
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self._setup_plots_directory()
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self._plots_dir_setup = True
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# Create full path in plots directory
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save_path = os.path.join(self.plots_dir, filename)
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# Save with current backend (don't switch backends as it causes blank files)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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plt.savefig(save_path, dpi=dpi, bbox_inches='tight', facecolor='white')
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print(f"📊 Plot saved: {save_path}")
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def plot_risk_timeline(self, risk_scores: List[RiskScore],
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weather_hours: List[WeatherHour],
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threshold: float = 6.0,
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save_path: Optional[str] = None,
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show: bool = True) -> None:
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"""Plot risk scores over time with recommendation threshold."""
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if not risk_scores:
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print("No risk data to plot")
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return
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=self.figure_size,
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sharex=True, gridspec_kw={'height_ratios': [2, 1, 1]})
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# Extract data
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times = [score.datetime for score in risk_scores]
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total_scores = [score.total_score for score in risk_scores]
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temperatures = [hour.temperature_f for hour in weather_hours]
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uv_indices = [hour.uv_index or 0 for hour in weather_hours]
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# Main risk score plot
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ax1.plot(times, total_scores, 'b-', linewidth=2, label='Risk Score')
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ax1.axhline(y=threshold, color='red', linestyle='--', alpha=0.7,
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label=f'Shoe Threshold ({threshold})')
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# Highlight high-risk periods
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high_risk_times = []
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high_risk_scores = []
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for score in risk_scores:
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if score.recommend_shoes:
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high_risk_times.append(score.datetime)
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high_risk_scores.append(score.total_score)
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if high_risk_times:
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ax1.scatter(high_risk_times, high_risk_scores, color='red', s=50,
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alpha=0.7, zorder=5, label='Shoes Recommended')
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ax1.set_ylabel('Risk Score')
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ax1.set_title('Paw Burn Risk Assessment Timeline')
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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ax1.set_ylim(0, 10)
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# Temperature subplot
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ax2.plot(times, temperatures, 'orange', linewidth=2, label='Temperature (°F)')
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ax2.axhline(y=80, color='yellow', linestyle=':', alpha=0.7, label='80°F')
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ax2.axhline(y=90, color='orange', linestyle=':', alpha=0.7, label='90°F')
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ax2.axhline(y=100, color='red', linestyle=':', alpha=0.7, label='100°F')
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ax2.set_ylabel('Temperature (°F)')
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ax2.legend(loc='upper right')
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ax2.grid(True, alpha=0.3)
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# UV Index subplot
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ax3.plot(times, uv_indices, 'purple', linewidth=2, label='UV Index')
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ax3.axhline(y=6, color='yellow', linestyle=':', alpha=0.7, label='UV 6')
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ax3.axhline(y=8, color='orange', linestyle=':', alpha=0.7, label='UV 8')
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ax3.axhline(y=10, color='red', linestyle=':', alpha=0.7, label='UV 10')
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ax3.set_ylabel('UV Index')
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ax3.set_xlabel('Time')
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ax3.legend(loc='upper right')
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ax3.grid(True, alpha=0.3)
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# Format x-axis
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for ax in [ax1, ax2, ax3]:
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
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ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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try:
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plt.tight_layout()
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except:
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# If tight_layout fails, adjust manually
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plt.subplots_adjust(hspace=0.4, bottom=0.15)
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if save_path:
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self._safe_save_plot(save_path)
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if show:
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plt.show()
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def plot_risk_components(self, risk_scores: List[RiskScore],
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save_path: Optional[str] = None,
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show: bool = True) -> None:
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"""Plot breakdown of risk score components."""
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if not risk_scores:
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print("No risk data to plot")
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return
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fig, ax = plt.subplots(figsize=self.figure_size)
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# Extract data
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times = [score.datetime for score in risk_scores]
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temp_scores = [score.temperature_score for score in risk_scores]
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uv_scores = [score.uv_score for score in risk_scores]
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condition_scores = [score.condition_score for score in risk_scores]
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accumulated_scores = [score.accumulated_heat_score for score in risk_scores]
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recovery_scores = [score.surface_recovery_score for score in risk_scores]
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# Create stacked area plot
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ax.stackplot(times, temp_scores, uv_scores, condition_scores,
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accumulated_scores, recovery_scores,
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labels=['Temperature', 'UV Index', 'Condition', 'Accumulated Heat', 'Surface Recovery'],
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alpha=0.7)
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ax.set_ylabel('Risk Score Components')
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ax.set_xlabel('Time')
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ax.set_title('Risk Score Component Breakdown')
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ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
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ax.grid(True, alpha=0.3)
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# Format x-axis
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
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ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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try:
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plt.tight_layout()
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except:
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# If tight_layout fails, adjust manually
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plt.subplots_adjust(right=0.75, bottom=0.15)
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if save_path:
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self._safe_save_plot(save_path)
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print(f"Component plot saved")
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if show:
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plt.show()
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def plot_risk_heatmap(self, risk_scores: List[RiskScore],
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save_path: Optional[str] = None,
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show: bool = True) -> None:
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"""Create a heatmap visualization of risk throughout the day."""
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if not risk_scores:
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print("No risk data to plot")
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return
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fig, ax = plt.subplots(figsize=(12, 3))
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# Create time vs risk matrix
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hours = [score.datetime.hour for score in risk_scores]
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scores = [score.total_score for score in risk_scores]
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# Create a matrix for the heatmap
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hour_range = list(range(24))
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risk_matrix = np.zeros((1, 24))
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for hour, score in zip(hours, scores):
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risk_matrix[0, hour] = score
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# Create heatmap
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im = ax.imshow(risk_matrix, cmap='RdYlBu_r', aspect='auto', vmin=0, vmax=10)
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# Customize the plot
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ax.set_xticks(range(24))
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ax.set_xticklabels([f'{h:02d}:00' for h in range(24)])
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ax.set_yticks([])
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ax.set_xlabel('Hour of Day')
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ax.set_title('Paw Burn Risk Heatmap')
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# Add colorbar
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cbar = plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.1)
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cbar.set_label('Risk Score')
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# Add threshold line
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threshold_line = 6.0 / 10.0 # Normalize to colormap scale
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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try:
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plt.tight_layout()
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except:
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# If tight_layout fails, adjust manually
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plt.subplots_adjust(bottom=0.2)
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if save_path:
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self._safe_save_plot(save_path)
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print(f"Heatmap saved")
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if show:
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plt.show()
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def create_summary_dashboard(self, risk_scores: List[RiskScore],
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weather_hours: List[WeatherHour],
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recommendations: dict,
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save_path: Optional[str] = None,
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show: bool = True) -> None:
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"""Create a comprehensive dashboard with all visualizations."""
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if not risk_scores or not weather_hours:
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print("Insufficient data for dashboard")
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return
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fig = plt.figure(figsize=(16, 12))
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# Create subplots
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gs = fig.add_gridspec(3, 2, height_ratios=[2, 1, 1], hspace=0.3, wspace=0.3)
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# Main timeline plot
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ax1 = fig.add_subplot(gs[0, :])
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times = [score.datetime for score in risk_scores]
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total_scores = [score.total_score for score in risk_scores]
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ax1.plot(times, total_scores, 'b-', linewidth=3, label='Risk Score')
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ax1.axhline(y=6, color='red', linestyle='--', alpha=0.7, label='Shoe Threshold')
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# Highlight high-risk periods
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high_risk_periods = []
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current_start = None
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for i, score in enumerate(risk_scores):
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if score.recommend_shoes and current_start is None:
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current_start = i
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elif not score.recommend_shoes and current_start is not None:
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high_risk_periods.append((current_start, i))
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current_start = None
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if current_start is not None:
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high_risk_periods.append((current_start, len(risk_scores)))
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for start_idx, end_idx in high_risk_periods:
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ax1.axvspan(times[start_idx], times[end_idx-1], alpha=0.3, color='red')
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ax1.set_ylabel('Risk Score')
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ax1.set_title('Paw Burn Risk Assessment - Daily Overview')
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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ax1.set_ylim(0, 10)
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# Component breakdown
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ax2 = fig.add_subplot(gs[1, 0])
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component_names = ['Temp', 'UV', 'Condition', 'Heat Accum', 'Recovery']
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avg_components = [
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np.mean([s.temperature_score for s in risk_scores]),
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np.mean([s.uv_score for s in risk_scores]),
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np.mean([s.condition_score for s in risk_scores]),
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np.mean([s.accumulated_heat_score for s in risk_scores]),
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np.mean([s.surface_recovery_score for s in risk_scores])
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]
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bars = ax2.bar(component_names, avg_components, color=['orange', 'purple', 'lightblue', 'yellow', 'green'])
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ax2.set_ylabel('Average Score')
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ax2.set_title('Average Risk Components')
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ax2.tick_params(axis='x', rotation=45)
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# Statistics
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ax3 = fig.add_subplot(gs[1, 1])
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ax3.axis('off')
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stats_text = f"""
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DAILY SUMMARY
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Total Hours Analyzed: {recommendations['summary']['total_hours_analyzed']}
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High Risk Hours: {recommendations['summary']['high_risk_hours']}
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Max Risk Score: {recommendations['summary']['max_risk_score']}
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Average Risk Score: {recommendations['summary']['average_risk_score']}
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Peak Risk Time: {recommendations['summary']['peak_risk_time']}
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Risk Periods: {recommendations['summary']['continuous_risk_blocks']}
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"""
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ax3.text(0.1, 0.9, stats_text, transform=ax3.transAxes, fontsize=10,
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verticalalignment='top', fontfamily='monospace')
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# Weather conditions
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ax4 = fig.add_subplot(gs[2, :])
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temperatures = [hour.temperature_f for hour in weather_hours]
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uv_indices = [hour.uv_index or 0 for hour in weather_hours]
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ax4_twin = ax4.twinx()
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line1 = ax4.plot(times, temperatures, 'orange', linewidth=2, label='Temperature (°F)')
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line2 = ax4_twin.plot(times, uv_indices, 'purple', linewidth=2, label='UV Index')
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ax4.set_ylabel('Temperature (°F)', color='orange')
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ax4_twin.set_ylabel('UV Index', color='purple')
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ax4.set_xlabel('Time')
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ax4.set_title('Weather Conditions')
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# Format x-axis for all subplots
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for ax in [ax1, ax4]:
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
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ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
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# Use constrained layout or manual adjustment instead of tight_layout
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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try:
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plt.tight_layout()
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except:
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# If tight_layout fails with complex layouts, adjust manually
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plt.subplots_adjust(hspace=0.4, wspace=0.4, bottom=0.1, top=0.95)
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if save_path:
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self._safe_save_plot(save_path)
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print(f"Dashboard saved")
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if show:
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plt.show()
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def create_plotter(figure_size: Tuple[int, int] = (12, 8)) -> RiskPlotter:
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"""Create a risk plotter with specified figure size."""
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return RiskPlotter(figure_size)
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