pearson/pearson.py at main · nipraxis/pearson · GitHub

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"""
This will be your new module with the Pearson correlation function

First try to fill in the "pearson_1d" function.

When you are done with "pearson_1d", you should be able to run the following
command (from the day5 directory) and see no errors or failures::

    python3 -m pytest test_pearson_1d.py

Then, if you are feeling brave, fill in the "pearson_2d" function.  Test with::

    python3 -m pytest test_pearson_2d.py
"""

import numpy as np


def pearson_1d(x, y):
    """ Pearson product-moment correlation of vectors `x` and `y`

    Parameters
    ----------
    x : array shape (N,)
        One-dimensional array to correlate with `y`
    y : array shape (N,)
        One dimensional array to correlate with `x`

    Returns
    -------
    r_xy : scalar
        Pearson product-moment correlation of vectors `x` and `y`.
    """
    # Mean-center x -> mc_x
    # Mean-center y -> mc_y
    # a : Get sum of products of mc_x, mc_y
    # b : Get sum of products of mc_x on mc_x
    # c : Get sum of products of mc_y on mc_y
    # return a / (sqrt(b) * sqrt(c))
    # +++your code here+++
    # return


def pearson_2d(x, Y):
    """ Pearson product-moment correlation of vectors `x` and array `Y`

    Parameters
    ----------
    x : array shape (N,)
        One-dimensional array to correlate with every column of `Y`
    Y : array shape (N, P)
        2D array where we correlate each column of `Y` with `x`.

    Returns
    -------
    r_xy : array shape (P,)
        Pearson product-moment correlation of vectors `x` and the columns of
        `Y`, with one correlation value for every column of `Y`.
    """
    # Mean-center x -> mc_x
    # Mean-center every column of Y -> mc_Y
    # a : Get sum of products of mc_x and every column of mc_Y
    # b : Get sum of products of mc_x on mc_x
    # c : Get sum of products of every column of mc_Y[:, i] on itself
    # return a / (sqrt(b) * sqrt(c))
    # +++your code here+++
    # return