neurosynth/base/imageutils.py
def load_imgs(filenames, masker, nan_to_num=True):
    """ Load multiple images from file into an ndarray.

    Args:
      filenames: A single filename or list of filenames pointing to valid
        images.
      masker: A Masker instance.
      nan_to_num: Optional boolean indicating whether to convert NaNs to zero.

    Returns:
      An m x n 2D numpy array, where m = number of voxels in mask and
      n = number of images passed.
    """
    if isinstance(filenames, string_types):
        filenames = [filenames]
    data = np.zeros((masker.n_vox_in_mask, len(filenames)))
    for i, f in enumerate(filenames):
        data[:, i] = masker.mask(f, nan_to_num)
    return data
def load_imgs(filenames, masker, nan_to_num=True):
    if isinstance(filenames, string_types):
        filenames = [filenames]
    data = np.zeros((masker.n_vox_in_mask, len(filenames)))
    for i, f in enumerate(filenames):
        data[:, i] = masker.mask(f, nan_to_num)
    return data
def load_imgs ( filenames , masker , nan_to_num = True ) : if isinstance ( filenames , string_types ) : filenames = [ filenames ] data = np . zeros ( ( masker . n_vox_in_mask , len ( filenames ) ) ) for i , f in enumerate ( filenames ) : data [ : , i ] = masker . mask ( f , nan_to_num ) return data
Load multiple images from file into an ndarray.

    Args:
      filenames: A single filename or list of filenames pointing to valid
        images.
      masker: A Masker instance.
      nan_to_num: Optional boolean indicating whether to convert NaNs to zero.

    Returns:
      An m x n 2D numpy array, where m = number of voxels in mask and
      n = number of images passed.
Load multiple images from file into an ndarray .