Exemple for my class on the importance of clearability

exemple.py

"""Good example"""
def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0,
                 fill_mode='nearest', cval=0., tx=None, ty=None):
    """Performs a random spatial shift of a Numpy image tensor.
    # Arguments
        x: Input tensor. Must be 3D.
        wrg: Width shift range, as a float fraction of the width.
        hrg: Height shift range, as a float fraction of the height.
        row_axis: Index of axis for rows in the input tensor.
        col_axis: Index of axis for columns in the input tensor.
        channel_axis: Index of axis for channels in the input tensor.
        fill_mode: Points outside the boundaries of the input
            are filled according to the given mode
            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
        cval: Value used for points outside the boundaries
            of the input if `mode='constant'`.
        tx : Value to disable randomness in X or None.
        ty : Value to disable randomness in Y or None.
    # Returns
        Shifted Numpy image tensor.
    """
    h, w = x.shape[row_axis], x.shape[col_axis]
    tx = np.random.uniform(-hrg, hrg) * h if tx is None else tx
    ty = np.random.uniform(-wrg, wrg) * w if ty is None else ty

    tx *= h
    ty *= w

    translation_matrix = np.array([[1, 0, tx],
                                   [0, 1, ty],
                                   [0, 0, 1]])

    transform_matrix = translation_matrix  # no need to do offset
    x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)
    return x
  
  
 """BAD FORMAT, hard to read"""
def random_shift(x,wrg,hrg,row_axis=1,col_axis=2,channel_axis=0,fill_mode='nearest',cval=0.,tx=None,ty=None):
    h, w = x.shape[row_axis],x.shape[col_axis]
    tx=np.random.uniform(-hrg,hrg)*h if tx is None else tx
    ty=np.random.uniform(-wrg,wrg)*w if ty is None else ty
    tx*=h
    ty*=w
    translation_matrix=np.array([[1,0,tx],[0,1,ty],[0,0,1]])
    transform_matrix=translation_matrix
    x=apply_transform(x,transform_matrix,channel_axis,fill_mode,cval)
    return x