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0ab0788 | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import streamlit as st
import polars as pl
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error
def compute_statistic_per_point(df: pl.DataFrame, stat_key: str) -> pl.DataFrame:
cols = df.columns
if stat_key == "mean":
has_h = "mean_mm_h" in cols
if has_h:
df = df.with_columns(
(pl.col("mean_mm_h") * 24).alias("mean_mm_j")
)
return df.group_by("NUM_POSTE").agg([
*( [pl.col("mean_mm_h").mean().alias("mean_all_mm_h")] if has_h else [] ),
*( [pl.col("mean_mm_j").mean().alias("mean_all_mm_j")] if has_h else [] ),
])
elif stat_key == "max":
return df.group_by("NUM_POSTE").agg([
*( [pl.col("max_mm_h").max().alias("max_all_mm_h")] if "max_mm_h" in cols else [] ),
*( [pl.col("max_mm_j").max().alias("max_all_mm_j")] if "max_mm_j" in cols else [] ),
])
elif stat_key == "mean-max":
return df.group_by("NUM_POSTE").agg([
*( [pl.col("max_mm_h").mean().alias("max_mean_mm_h")] if "max_mm_h" in cols else [] ),
*( [pl.col("max_mm_j").mean().alias("max_mean_mm_j")] if "max_mm_j" in cols else [] ),
])
elif stat_key == "date":
res = []
if "max_mm_h" in cols and "max_date_mm_h" in cols:
df_h = (
df.sort("max_mm_h", descending=True)
.group_by("NUM_POSTE")
.agg(pl.col("max_date_mm_h").first().alias("date_max_h"))
)
res.append(df_h)
if "max_mm_j" in cols and "max_date_mm_j" in cols:
df_j = (
df.sort("max_mm_j", descending=True)
.group_by("NUM_POSTE")
.agg(pl.col("max_date_mm_j").first().alias("date_max_j"))
)
res.append(df_j)
if not res:
raise ValueError("Aucune date de maximum disponible.")
elif len(res) == 1:
return res[0]
else:
return res[0].join(res[1], on="NUM_POSTE", how="outer")
elif stat_key == "month":
exprs = []
if "max_date_mm_h" in cols:
exprs.append(
pl.col("max_date_mm_h")
.str.strptime(pl.Datetime, format="%Y-%m-%d %H:%M:%S%.f", strict=False)
.dt.month()
.alias("mois_max_h")
)
if "max_date_mm_j" in cols:
exprs.append(
pl.col("max_date_mm_j")
.str.strptime(pl.Datetime, format="%Y-%m-%d %H:%M:%S%.f", strict=False)
.dt.month()
.alias("mois_max_j")
)
if not exprs:
raise ValueError("Aucune date de maximum pour extraire les mois.")
df = df.with_columns(exprs)
mois_h = mois_j = None
if "mois_max_h" in df.columns:
mois_h = (
df.drop_nulls("mois_max_h")
.group_by(["NUM_POSTE", "mois_max_h"])
.len()
.sort(["NUM_POSTE", "len"], descending=[False, True])
.unique(subset=["NUM_POSTE"])
.select(["NUM_POSTE", "mois_max_h"])
.rename({"mois_max_h": "mois_pluvieux_h"})
)
if "mois_max_j" in df.columns:
mois_j = (
df.drop_nulls("mois_max_j")
.group_by(["NUM_POSTE", "mois_max_j"])
.len()
.sort(["NUM_POSTE", "len"], descending=[False, True])
.unique(subset=["NUM_POSTE"])
.select(["NUM_POSTE", "mois_max_j"])
.rename({"mois_max_j": "mois_pluvieux_j"})
)
if mois_h is None and mois_j is None:
return pl.DataFrame(schema={"NUM_POSTE": pl.Int64, "mois_pluvieux_h": pl.Int32, "mois_pluvieux_j": pl.Int32})
elif mois_h is None:
return mois_j.with_columns([pl.lit(None, dtype=pl.Int32).alias("mois_pluvieux_h")])
elif mois_j is None:
return mois_h.with_columns([pl.lit(None, dtype=pl.Int32).alias("mois_pluvieux_j")])
else:
return mois_h.join(mois_j, on="NUM_POSTE", how="outer")
elif stat_key == "numday":
if "n_days_gt1mm" not in df.columns:
raise ValueError("Colonne `n_days_gt1mm` manquante.")
return (
df.group_by("NUM_POSTE")
.agg(pl.col("n_days_gt1mm").mean().alias("jours_pluie_moyen"))
)
else:
raise ValueError(f"Statistique inconnue : {stat_key}")
def generate_metrics(df: pl.DataFrame, x_label: str = "AROME", y_label: str = "Station"):
x = df[x_label].to_numpy()
y = df[y_label].to_numpy()
if len(x) != len(y):
st.error("Longueur x et y différente")
return np.nan, np.nan, np.nan, np.nan
# Filtrage des NaNs sur les deux colonnes
mask = ~np.isnan(x) & ~np.isnan(y)
x_valid = x[mask]
y_valid = y[mask]
if len(x_valid) == 0:
st.warning("Aucune donnée valide après suppression des NaN.")
return np.nan, np.nan, np.nan, np.nan
rmse = np.sqrt(mean_squared_error(y_valid, x_valid))
mae = mean_absolute_error(y_valid, x_valid)
me = np.mean(x_valid - y_valid)
corr = np.corrcoef(x_valid, y_valid)[0, 1] if len(x_valid) > 1 else np.nan
r2_corr = corr**2 if not np.isnan(corr) else np.nan
return me, mae, rmse, r2_corr |