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301 lines
12 KiB
Python
301 lines
12 KiB
Python
"""Risk calculation engine for paw burn assessment."""
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import logging
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from datetime import datetime, timedelta
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from typing import List, Optional, Tuple
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from models import WeatherHour, RiskScore
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from config import RiskConfig, get_config
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logger = logging.getLogger(__name__)
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class RiskCalculator:
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"""Calculates paw burn risk scores based on weather conditions."""
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def __init__(self, config: Optional[RiskConfig] = None):
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self.config = config or get_config().risk_config
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def calculate_temperature_score(self, temperature_f: float) -> float:
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"""Calculate risk score based on air temperature."""
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if temperature_f >= self.config.temp_threshold_high: # ≥100°F
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return 3.0
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elif temperature_f >= self.config.temp_threshold_med: # ≥90°F
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return 2.0
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elif temperature_f >= self.config.temp_threshold_low: # ≥80°F
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return 1.0
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else:
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return 0.0
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def calculate_uv_score(self, uv_index: Optional[float]) -> float:
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"""Calculate risk score based on UV index."""
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if uv_index is None:
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return 0.0
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if uv_index >= self.config.uv_threshold_high: # ≥10
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return 3.0
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elif uv_index >= self.config.uv_threshold_med: # ≥8
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return 2.0
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elif uv_index >= self.config.uv_threshold_low: # ≥6
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return 1.0
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else:
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return 0.0
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def calculate_condition_score(self, condition: str) -> float:
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"""Calculate risk score based on weather condition."""
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condition_lower = condition.lower()
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if 'sunny' in condition_lower or 'clear' in condition_lower:
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return 1.0
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else:
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return 0.0
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def calculate_accumulated_heat_score(self, weather_hours: List[WeatherHour],
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current_index: int) -> float:
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"""Calculate risk score based on accumulated heat (rolling average)."""
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if current_index < 1:
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return 0.0
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# Get the last N hours (including current)
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window_size = min(self.config.rolling_window_hours, current_index + 1)
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start_index = current_index - window_size + 1
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window_hours = weather_hours[start_index:current_index + 1]
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# Calculate average temperature and UV
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temp_sum = sum(hour.temperature_f for hour in window_hours)
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avg_temp = temp_sum / len(window_hours)
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# Calculate average UV (handling None values)
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uv_values = [hour.uv_index for hour in window_hours if hour.uv_index is not None]
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avg_uv = sum(uv_values) / len(uv_values) if uv_values else 0
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# Score based on accumulated heat criteria
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score = 0.0
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if avg_temp > 85.0:
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score += 1.0
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if avg_uv >= 6.0:
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score += 1.0
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return min(score, 1.0) # Cap at 1.0 as per specification
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def calculate_surface_recovery_score(self, weather_hours: List[WeatherHour],
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current_index: int) -> float:
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"""Calculate surface recovery score (time since last peak temperature)."""
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if current_index < 2:
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return 0.0
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# Look back to find the last time temperature was ≥90°F
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hours_since_peak = 0
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for i in range(current_index - 1, -1, -1):
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hours_since_peak += 1
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if weather_hours[i].temperature_f >= 90.0:
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break
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else:
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# No peak found in available data
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hours_since_peak = current_index + 1
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# Give recovery credit if it's been >2 hours since last 90°F reading
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if hours_since_peak > self.config.surface_recovery_hours:
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return -1.0
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else:
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return 0.0
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def interpolate_missing_uv(self, weather_hours: List[WeatherHour]) -> List[WeatherHour]:
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"""Interpolate missing UV values using nearby hours."""
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if not weather_hours:
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return weather_hours
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# Create a copy to avoid modifying the original
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processed_hours = [WeatherHour(
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datetime=hour.datetime,
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temperature_f=hour.temperature_f,
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uv_index=hour.uv_index,
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condition=hour.condition,
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is_forecast=hour.is_forecast
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) for hour in weather_hours]
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# Find UV values that need interpolation
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for i, hour in enumerate(processed_hours):
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if hour.uv_index is None:
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# Look for nearest non-None values
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left_uv = None
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right_uv = None
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# Search left
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for j in range(i - 1, -1, -1):
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if processed_hours[j].uv_index is not None:
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left_uv = processed_hours[j].uv_index
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break
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# Search right
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for j in range(i + 1, len(processed_hours)):
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if processed_hours[j].uv_index is not None:
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right_uv = processed_hours[j].uv_index
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break
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# Interpolate or use fallback
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if left_uv is not None and right_uv is not None:
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processed_hours[i].uv_index = (left_uv + right_uv) / 2
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elif left_uv is not None:
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processed_hours[i].uv_index = left_uv
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elif right_uv is not None:
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processed_hours[i].uv_index = right_uv
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else:
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# Use temperature-based fallback (rough approximation)
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temp_f = hour.temperature_f
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if temp_f >= 95:
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processed_hours[i].uv_index = 8.0
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elif temp_f >= 85:
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processed_hours[i].uv_index = 6.0
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elif temp_f >= 75:
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processed_hours[i].uv_index = 4.0
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else:
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processed_hours[i].uv_index = 2.0
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return processed_hours
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def detect_rapid_heat_swings(self, weather_hours: List[WeatherHour]) -> List[int]:
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"""Detect hours with rapid temperature changes."""
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rapid_swing_indices = []
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for i in range(1, len(weather_hours)):
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temp_diff = abs(weather_hours[i].temperature_f - weather_hours[i-1].temperature_f)
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if temp_diff >= 15.0: # 15°F+ change in one hour
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rapid_swing_indices.append(i)
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logger.warning(f"Rapid temperature swing detected at {weather_hours[i].datetime}: "
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f"{temp_diff:.1f}°F change")
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return rapid_swing_indices
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def calculate_risk_scores(self, weather_hours: List[WeatherHour]) -> List[RiskScore]:
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"""Calculate risk scores for all weather hours."""
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if not weather_hours:
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return []
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# Preprocess data
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processed_hours = self.interpolate_missing_uv(weather_hours)
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rapid_swings = self.detect_rapid_heat_swings(processed_hours)
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risk_scores = []
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for i, hour in enumerate(processed_hours):
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# Calculate individual component scores
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temp_score = self.calculate_temperature_score(hour.temperature_f)
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uv_score = self.calculate_uv_score(hour.uv_index)
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condition_score = self.calculate_condition_score(hour.condition)
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accumulated_score = self.calculate_accumulated_heat_score(processed_hours, i)
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recovery_score = self.calculate_surface_recovery_score(processed_hours, i)
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# Apply rapid swing bonus
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rapid_swing_bonus = 0.5 if i in rapid_swings else 0.0
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# Calculate total score
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total_score = (temp_score + uv_score + condition_score +
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accumulated_score + recovery_score + rapid_swing_bonus)
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# Ensure score is within bounds
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total_score = max(0.0, min(10.0, total_score))
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# Determine if shoes are recommended
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recommend_shoes = total_score >= self.config.risk_threshold_shoes
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risk_scores.append(RiskScore(
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datetime=hour.datetime,
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temperature_score=temp_score,
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uv_score=uv_score,
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condition_score=condition_score,
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accumulated_heat_score=accumulated_score,
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surface_recovery_score=recovery_score,
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total_score=total_score,
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recommend_shoes=recommend_shoes
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))
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return risk_scores
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def identify_continuous_risk_blocks(self, risk_scores: List[RiskScore]) -> List[Tuple[datetime, datetime]]:
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"""Identify continuous time blocks where shoes are recommended."""
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if not risk_scores:
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return []
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blocks = []
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current_block_start = None
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for score in risk_scores:
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if score.recommend_shoes:
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if current_block_start is None:
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current_block_start = score.datetime
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else:
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if current_block_start is not None:
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blocks.append((current_block_start, score.datetime))
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current_block_start = None
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# Handle case where risk period extends to the end
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if current_block_start is not None:
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blocks.append((current_block_start, risk_scores[-1].datetime))
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return blocks
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def generate_recommendations(self, risk_scores: List[RiskScore]) -> dict:
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"""Generate comprehensive recommendations based on risk scores."""
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if not risk_scores:
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return {"error": "No risk data available"}
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# Calculate statistics
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total_hours = len(risk_scores)
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high_risk_hours = sum(1 for score in risk_scores if score.recommend_shoes)
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max_score = max(score.total_score for score in risk_scores)
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avg_score = sum(score.total_score for score in risk_scores) / total_hours
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# Find peak risk time
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peak_score = max(risk_scores, key=lambda x: x.total_score)
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# Identify continuous risk blocks
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risk_blocks = self.identify_continuous_risk_blocks(risk_scores)
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recommendations = {
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"summary": {
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"total_hours_analyzed": total_hours,
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"high_risk_hours": high_risk_hours,
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"max_risk_score": round(max_score, 1),
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"average_risk_score": round(avg_score, 1),
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"peak_risk_time": peak_score.datetime.strftime("%H:%M"),
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"continuous_risk_blocks": len(risk_blocks)
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},
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"risk_periods": [
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{
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"start": start.strftime("%H:%M"),
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"end": end.strftime("%H:%M"),
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"duration_hours": round((end - start).total_seconds() / 3600, 1)
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}
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for start, end in risk_blocks
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],
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"recommendations": []
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}
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# Generate specific recommendations
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if high_risk_hours == 0:
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recommendations["recommendations"].append(
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"🐾 Great news! No protective footwear needed today - paws should be safe on all surfaces."
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)
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else:
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recommendations["recommendations"].append(
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f"⚠️ Protective dog shoes recommended for {high_risk_hours} hours today."
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)
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if risk_blocks:
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recommendations["recommendations"].append(
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"🕐 Avoid walks during the identified high-risk time periods, or ensure your dog wears protective booties."
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)
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if max_score >= 8:
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recommendations["recommendations"].append(
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"🔥 EXTREME RISK: Surface temperatures may cause immediate paw burns. Keep walks very short and on grass/shaded areas only."
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)
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elif max_score >= 7:
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recommendations["recommendations"].append(
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"🌡️ HIGH RISK: Hot surfaces likely. Test pavement with your hand - if too hot for 5 seconds, it's too hot for paws."
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)
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return recommendations
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def create_risk_calculator(config: Optional[RiskConfig] = None) -> RiskCalculator:
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"""Create a risk calculator with optional custom configuration."""
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return RiskCalculator(config) |