index_regression

spy4cast.spy4cast.index_regression(data: LandArray | ndarray[Any, dtype[float32]], index: ndarray[Any, dtype[float32]], alpha: float, sig: str, montecarlo_iterations: int | None = None) → Tuple[ndarray[Any, dtype[float32]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float32]]]

Create correlation (pearson correlation) and regression

Parameters:

• data (npt.NDArray[np.float32]) – Data to perform the methodology in (space x time)

• index (npt.NDArray[np.float32]) – Unidimensional array: temporal series

• alpha (float) – Significance level

• sig ('monte-carlo' or 'test-t') – Type of sygnificance

• montecarlo_iterations (optional, int) – Number of iterations for monte-carlo sig

Returns:

• Cor (npt.NDArray[np.float32] (space)) – Correlation map

• Pvalue (npt.NDArray[np.float32] (space)) – Map with p values

• Cor_sig (npt.NDArray[np.float32] (space)) – Significative corrrelation map

• reg (npt.NDArray[np.float32] (space)) – Regression map

• reg_sig (npt.NDArray[np.float32] (space)) – Significative regression map