import numpy as np

def pearsonr_score(
    y_true: np.ndarray, y_pred: np.ndarray, eps: float = 1e-7
) -> np.ndarray:
    assert y_true.ndim == y_pred.ndim == 2

    y_true = y_true - y_true.mean(axis=0)
    y_true = y_true / (np.linalg.norm(y_true, axis=0) + eps)

    y_pred = y_pred - y_pred.mean(axis=0)
    y_pred = y_pred / (np.linalg.norm(y_pred, axis=0) + eps)

    score = (y_true * y_pred).sum(axis=0)
    return score

def functional_matrix():
    pass # TODO:

def psd_error():
    """Power Spectral Density error metric for comparing temporal dynamics of predicted vs true fmri signals.
    TODO:
    """
    pass