neurosynth/analysis/decode.py
def _pearson_correlation(self, imgs_to_decode):
        """ Decode images using Pearson's r.

        Computes the correlation between each input image and each feature
        image across voxels.

        Args:
            imgs_to_decode: An ndarray of images to decode, with voxels in rows
                and images in columns.

        Returns:
            An n_features x n_images 2D array, with each cell representing the
            pearson correlation between the i'th feature and the j'th image
            across all voxels.
        """
        x, y = imgs_to_decode.astype(float), self.feature_images.astype(float)
        return self._xy_corr(x, y)
def _pearson_correlation(self, imgs_to_decode):
        x, y = imgs_to_decode.astype(float), self.feature_images.astype(float)
        return self._xy_corr(x, y)
def _pearson_correlation ( self , imgs_to_decode ) : x , y = imgs_to_decode . astype ( float ) , self . feature_images . astype ( float ) return self . _xy_corr ( x , y )
Decode images using Pearson's r.

        Computes the correlation between each input image and each feature
        image across voxels.

        Args:
            imgs_to_decode: An ndarray of images to decode, with voxels in rows
                and images in columns.

        Returns:
            An n_features x n_images 2D array, with each cell representing the
            pearson correlation between the i'th feature and the j'th image
            across all voxels.
Decode images using Pearson s r .