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082c711 | 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 | from __future__ import annotations
from typing import Dict, Any, List, Tuple
import math
import numpy as np
import pandas as pd
from .utils import heat_index_c
class RiskModel:
"""Hackathon-friendly, transparent risk model.
Produces:
- daily risk (0–100)
- 12-month livability bands from climatology + warming offset
"""
def __init__(self,
weights: Dict[str, float] | None = None,
heat_threshold_c: float = 38.0,
flood_daily_mm: float = 50.0,
wind_max_kph: float = 60.0):
self.weights = weights or {
"heat": 0.45,
"humidity": 0.15,
"precip": 0.20,
"wind": 0.10,
"air": 0.10,
}
self.heat_threshold = heat_threshold_c
self.flood_daily_mm = flood_daily_mm
self.wind_max_kph = wind_max_kph
def daily_score(self, tmax_c: float, tmin_c: float, rh: float | None,
precip_mm: float, wind_kph: float, aqi: float | None) -> Tuple[float, Dict[str, float]]:
# Heat component via heat index on Tmax with RH (fallback RH=40%)
rh_eff = 40.0 if (rh is None or math.isnan(rh)) else rh
hi = heat_index_c(tmax_c, rh_eff)
heat_risk = np.clip((hi - self.heat_threshold) / 10.0, 0, 1)
# Humidity discomfort
hum_risk = np.clip((rh_eff - 70.0) / 30.0, 0, 1)
# Precip flood proxy
precip_risk = np.clip((precip_mm - self.flood_daily_mm) / 50.0, 0, 1)
# Wind storm proxy (Beaufort 8+ ≈ >62 kph)
wind_risk = np.clip((wind_kph - self.wind_max_kph) / 40.0, 0, 1)
# Air quality (AQI 0–500). If None, neutral at 0.3
if aqi is None or math.isnan(aqi):
air_risk = 0.3
else:
air_risk = np.clip((aqi - 100.0) / 200.0, 0, 1)
factors = {
"heat": float(heat_risk),
"humidity": float(hum_risk),
"precip": float(precip_risk),
"wind": float(wind_risk),
"air": float(air_risk),
}
score01 = sum(self.weights[k] * factors[k] for k in self.weights)
return float(round(score01 * 100, 1)), factors
def band_from_score(self, score: float) -> str:
if score < 25:
return "SAFE"
if score < 50:
return "CAUTION"
if score < 75:
return "DANGER"
return "EXTREME"
def monthly_projection(self, monthly_t_mean: List[float], monthly_prcp: List[float], warming_offset_c: float = 0.4) -> List[Dict[str, Any]]:
# Apply simple warming offset to means and map to bands via thresholds
out = []
for m in range(12):
t = monthly_t_mean[m] + warming_offset_c
p = monthly_prcp[m]
# Map to synthetic risk
heat_component = np.clip((t - 32.0) / 8.0, 0, 1) # hotter than 32C pushes to danger
rain_component = np.clip((p - 150.0) / 150.0, 0, 1) # very wet months push flood risk
score = (0.7 * heat_component + 0.3 * rain_component) * 100
out.append({
"month": m + 1,
"temp_c": round(t, 1),
"precip_mm": round(p, 1),
"score": round(float(score), 1),
"band": self.band_from_score(score),
})
return out
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