def METHOD_NAME(hypo_maps):
    """
    Get binwise (linear) derivatives of counts w.r.t. a parameter, whose
    variations and associated templates are stored in `hypo_maps`.
    Parameters
    -----------
    hypo_maps : dict
        dictionary of the form
        {
        'test_point1': {
            'params': {},
            {'map': [[],[],...],
             'ebins': [],
             'czbins': []
            },
        }
        'test_point2': ...
        }
    Returns
    -------
    derivative_map : sequence
        Flat array of derivatives (of length = no. of bins)
    """
    test_points = sorted(hypo_maps.keys())
    # flatten data map (for use with polyfit - not employed currently)
    hypo_maps_flat = [hypo_maps[pvalue].flatten() for pvalue in test_points]
    assert len(test_points) == 2
    # we only have 2 test points
    del_x = test_points[1] - test_points[0]
    del_counts = np.subtract(hypo_maps_flat[1], hypo_maps_flat[0])
    derivative_map = np.divide(del_counts, del_x.magnitude)
    # keep flat
    return derivative_map