Ajout des matrices de corrélation + Refactoring

meteo/analysis/__init__.py

@@ -6,9 +6,11 @@ from .core import BinnedStatistics, DiurnalCycleStats, MONTH_ORDER
 from .correlations import (
     compute_correlation_matrix,
     compute_correlation_matrix_for_variables,
+    compute_correlation_matrices_for_methods,
     compute_lagged_correlation,
     compute_rolling_correlation_series,
     compute_rolling_correlations_for_pairs,
+    transform_correlation_matrix,
 )
 from .events import build_event_aligned_segments, detect_threshold_events
 from .filters import filter_by_condition
@@ -28,9 +30,11 @@ __all__ = [
     "MONTH_ORDER",
     "compute_correlation_matrix",
     "compute_correlation_matrix_for_variables",
+    "compute_correlation_matrices_for_methods",
     "compute_lagged_correlation",
     "compute_rolling_correlation_series",
     "compute_rolling_correlations_for_pairs",
+    "transform_correlation_matrix",
     "build_event_aligned_segments",
     "detect_threshold_events",
     "filter_by_condition",

meteo/analysis/correlations.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Literal, Sequence
+from typing import Callable, Literal, Sequence
 
 import numpy as np
 import pandas as pd
@@ -11,13 +11,16 @@ from meteo.variables import Variable
 
 from .core import _ensure_datetime_index
 
-__all__ = ['compute_correlation_matrix', 'compute_correlation_matrix_for_variables', 'compute_lagged_correlation', 'compute_rolling_correlation_series', 'compute_rolling_correlations_for_pairs']
+__all__ = ['compute_correlation_matrix', 'compute_correlation_matrix_for_variables', 'compute_correlation_matrices_for_methods', 'compute_lagged_correlation', 'compute_rolling_correlation_series', 'compute_rolling_correlations_for_pairs', 'transform_correlation_matrix']
+
+CorrelationMethod = Literal["pearson", "spearman", "kendall"]
+CorrelationTransform = Literal["identity", "absolute", "square"]
 
 
 def compute_correlation_matrix(
     df: pd.DataFrame,
     *,
-    method: Literal["pearson", "spearman"] = "pearson",
+    method: CorrelationMethod = "pearson",
 ) -> pd.DataFrame:
     """
     Calcule la matrice de corrélation entre toutes les colonnes numériques
@@ -36,7 +39,7 @@ def compute_correlation_matrix_for_variables(
     df: pd.DataFrame,
     variables: Sequence[Variable],
     *,
-    method: Literal["pearson", "spearman"] = "pearson",
+    method: CorrelationMethod = "pearson",
 ) -> pd.DataFrame:
     """
     Calcule la matrice de corrélation pour un sous-ensemble de variables,
@@ -70,6 +73,46 @@ def compute_correlation_matrix_for_variables(
     corr = corr.loc[columns, columns]
     return corr
 
+
+def transform_correlation_matrix(
+    corr: pd.DataFrame,
+    *,
+    transform: CorrelationTransform | Callable[[pd.DataFrame], pd.DataFrame] = "identity",
+) -> pd.DataFrame:
+    """Applique une transformation générique sur une matrice de corrélation."""
+
+    if callable(transform):
+        return transform(corr)
+
+    if transform == "identity":
+        return corr
+    if transform == "absolute":
+        return corr.abs()
+    if transform == "square":
+        return corr.pow(2)
+
+    raise ValueError(f"Transformation de corrélation inconnue : {transform!r}")
+
+
+def compute_correlation_matrices_for_methods(
+    df: pd.DataFrame,
+    variables: Sequence[Variable],
+    *,
+    methods: Sequence[CorrelationMethod],
+    transform: CorrelationTransform | Callable[[pd.DataFrame], pd.DataFrame] = "identity",
+) -> dict[str, pd.DataFrame]:
+    """Calcule plusieurs matrices de corrélation en une seule passe."""
+
+    if not methods:
+        raise ValueError("La liste des méthodes de corrélation est vide.")
+
+    matrices: dict[str, pd.DataFrame] = {}
+    for method in methods:
+        corr = compute_correlation_matrix_for_variables(df, variables, method=method)
+        matrices[method] = transform_correlation_matrix(corr, transform=transform)
+
+    return matrices
+
 def compute_lagged_correlation(
     df: pd.DataFrame,
     var_x: Variable,