boyle/nifti/mask.py
def matrix_to_4dvolume(arr, mask, order='C'):
    """Transform a given vector to a volume. This is a reshape function for
    4D flattened masked matrices where the second dimension of the matrix
    corresponds to the original 4th dimension.

    Parameters
    ----------
    arr: numpy.array
        2D numpy.array

    mask: numpy.ndarray
        Mask image. Must have 3 dimensions, bool dtype.

    dtype: return type
        If None, will get the type from vector

    Returns
    -------
    data: numpy.ndarray
        Unmasked data.
        Shape: (mask.shape[0], mask.shape[1], mask.shape[2], X.shape[1])
    """
    if mask.dtype != np.bool:
        raise ValueError("mask must be a boolean array")

    if arr.ndim != 2:
        raise ValueError("X must be a 2-dimensional array")

    if mask.sum() != arr.shape[0]:
        # raise an error if the shape of arr is not what expected
        raise ValueError('Expected arr of shape ({}, samples). Got {}.'.format(mask.sum(), arr.shape))

    data = np.zeros(mask.shape + (arr.shape[1],), dtype=arr.dtype,
                    order=order)
    data[mask, :] = arr
    return data
def matrix_to_4dvolume(arr, mask, order='C'):
    """Transform a given vector to a volume. This is a reshape function for
    4D flattened masked matrices where the second dimension of the matrix
    corresponds to the original 4th dimension.

    Parameters
    ----------
    arr: numpy.array
        2D numpy.array

    mask: numpy.ndarray
        Mask image. Must have 3 dimensions, bool dtype.

    dtype: return type
        If None, will get the type from vector

    Returns
    -------
    data: numpy.ndarray
        Unmasked data.
        Shape: (mask.shape[0], mask.shape[1], mask.shape[2], X.shape[1])
    """
    if mask.dtype != np.bool:
        raise ValueError("mask must be a boolean array")

    if arr.ndim != 2:
        raise ValueError("X must be a 2-dimensional array")

    if mask.sum() != arr.shape[0]:
        # raise an error if the shape of arr is not what expected
        raise ValueError('Expected arr of shape ({}, samples). Got {}.'.format(mask.sum(), arr.shape))

    data = np.zeros(mask.shape + (arr.shape[1],), dtype=arr.dtype,
                    order=order)
    data[mask, :] = arr
    return data
def matrix_to_4dvolume ( arr , mask , order = 'C' ) : if mask . dtype != np . bool : raise ValueError ( "mask must be a boolean array" ) if arr . ndim != 2 : raise ValueError ( "X must be a 2-dimensional array" ) if mask . sum ( ) != arr . shape [ 0 ] : # raise an error if the shape of arr is not what expected raise ValueError ( 'Expected arr of shape ({}, samples). Got {}.' . format ( mask . sum ( ) , arr . shape ) ) data = np . zeros ( mask . shape + ( arr . shape [ 1 ] , ) , dtype = arr . dtype , order = order ) data [ mask , : ] = arr return data
Transform a given vector to a volume. This is a reshape function for
    4D flattened masked matrices where the second dimension of the matrix
    corresponds to the original 4th dimension.

    Parameters
    ----------
    arr: numpy.array
        2D numpy.array

    mask: numpy.ndarray
        Mask image. Must have 3 dimensions, bool dtype.

    dtype: return type
        If None, will get the type from vector

    Returns
    -------
    data: numpy.ndarray
        Unmasked data.
        Shape: (mask.shape[0], mask.shape[1], mask.shape[2], X.shape[1])
Transform a given vector to a volume . This is a reshape function for 4D flattened masked matrices where the second dimension of the matrix corresponds to the original 4th dimension .