group_by

mdtools.numpy_helper_functions.group_by(keys, values, assume_sorted=False, return_keys=False, debug=False)[source]

Group the elements of values by their keys.

Parameters:

keys (array_like) – 1-dimensional array of keys used to group the elements of values.
values (array_like) – The values (elements) to be grouped by keys. values must be convertible to a numpy.ndarray and it must have the same length as keys.
assume_sorted (bool, optional) – If True, the key-value pairs are assumed to be sorted with respect to the keys, which can speed up the calculation. If False, keys and values are sorted by keys using numpy.argsort() with kind='stable'.
return_keys (bool, optional) – If True, return the unique, sorted keys.
debug (bool, optional) – If True, check the input arguments.

Deprecated since version 0.0.6.0: This argument is without functionality and will be removed in a future release.

Returns:

unique_keys (numpy.ndarray) – The unique, sorted keys. Only returned, if return_keys is True.
grouped_values (list of numpy.ndarrays) – List of numpy.ndarrays, one array for all elements of values belonging to the same key. The arrays in the list are sorted according to the unique, sorted keys.

Examples

>>> keys = ["a", "b", "c", "c", "b", "c"]
>>> values = [1, 2, 3, 3, 2, 3]
>>> k, v = mdt.nph.group_by(keys, values, return_keys=True)
>>> k
array(['a', 'b', 'c'], dtype='<U1')
>>> v
[array([1]), array([2, 2]), array([3, 3, 3])]
>>> {k: v for k, v in zip(k, v)}
{'a': array([1]), 'b': array([2, 2]), 'c': array([3, 3, 3])}

>>> keys = [2, 1, 2]
>>> values = [np.array([0, 0]), np.array([1, 1]), np.array([2, 2])]
>>> k, v = mdt.nph.group_by(keys, values, return_keys=True)
>>> k
array([1, 2])
>>> v
[array([[1, 1]]), array([[0, 0],
       [2, 2]])]
>>> {k: v for k, v in zip(k, v)}
{1: array([[1, 1]]), 2: array([[0, 0],
       [2, 2]])}