diff options
Diffstat (limited to 'numpy/lib/arraysetops.py')
| -rw-r--r-- | numpy/lib/arraysetops.py | 52 |
1 files changed, 28 insertions, 24 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index bc5877c4b..e697aa07a 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -517,12 +517,12 @@ def setxor1d(ar1, ar2, assume_unique=False): def _in1d_dispatcher(ar1, ar2, assume_unique=None, invert=None, - method=None): + kind=None): return (ar1, ar2) @array_function_dispatch(_in1d_dispatcher) -def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): +def in1d(ar1, ar2, assume_unique=False, invert=False, kind=None): """ Test whether each element of a 1-D array is also present in a second array. @@ -545,9 +545,10 @@ def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): False where an element of `ar1` is in `ar2` and True otherwise). Default is False. ``np.in1d(a, b, invert=True)`` is equivalent to (but is faster than) ``np.invert(in1d(a, b))``. - method : {'auto', 'sort', 'dictionary'}, optional + kind : {None, 'sort', 'dictionary'}, optional The algorithm to use. This will not affect the final result, - but will affect the speed. Default is 'auto'. + but will affect the speed. Default will select automatically + based on memory considerations. - If 'sort', will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of @@ -559,12 +560,12 @@ def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): to be the faster method if the following formula is true: `log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927`, but may use greater memory. - - If 'auto', will automatically choose 'dictionary' if + - If `None`, will automatically choose 'dictionary' if the required memory allocation is less than or equal to 6 times the sum of the sizes of `ar1` and `ar2`, otherwise will use 'sort'. This is done to not use a large amount of memory by default, even though - 'dictionary' may be faster in most cases. + 'dictionary' may be faster in most cases. .. versionadded:: 1.8.0 @@ -624,12 +625,12 @@ def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): integer_arrays = (np.issubdtype(ar1.dtype, np.integer) and np.issubdtype(ar2.dtype, np.integer)) - if method not in {'auto', 'sort', 'dictionary'}: + if kind not in {None, 'sort', 'dictionary'}: raise ValueError( - "Invalid method: {0}. ".format(method) - + "Please use 'auto', 'sort' or 'dictionary'.") + "Invalid kind: {0}. ".format(kind) + + "Please use None, 'sort' or 'dictionary'.") - if integer_arrays and method in {'auto', 'dictionary'}: + if integer_arrays and kind in {None, 'dictionary'}: ar2_min = np.min(ar2) ar2_max = np.max(ar2) ar1_size = ar1.size @@ -655,7 +656,7 @@ def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): below_memory_constraint = False # Use the fast integer algorithm - if below_memory_constraint or method == 'dictionary': + if below_memory_constraint or kind == 'dictionary': if invert: outgoing_array = np.ones_like(ar1, dtype=bool) @@ -676,11 +677,11 @@ def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): ar2_min] return outgoing_array - elif method == 'dictionary': + elif kind == 'dictionary': raise ValueError( - "'dictionary' method is only " + "The 'dictionary' method is only " "supported for boolean or integer arrays. " - "Please select 'sort' or 'auto' for the method." + "Please select 'sort' or None for kind." ) @@ -728,13 +729,13 @@ def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): def _isin_dispatcher(element, test_elements, assume_unique=None, invert=None, - method=None): + kind=None): return (element, test_elements) @array_function_dispatch(_isin_dispatcher) def isin(element, test_elements, assume_unique=False, invert=False, - method='auto'): + kind=None): """ Calculates ``element in test_elements``, broadcasting over `element` only. Returns a boolean array of the same shape as `element` that is True @@ -756,9 +757,10 @@ def isin(element, test_elements, assume_unique=False, invert=False, calculating `element not in test_elements`. Default is False. ``np.isin(a, b, invert=True)`` is equivalent to (but faster than) ``np.invert(np.isin(a, b))``. - method : {'auto', 'sort', 'dictionary'}, optional + kind : {None, 'sort', 'dictionary'}, optional The algorithm to use. This will not affect the final result, - but will affect the speed. Default is 'auto'. + but will affect the speed. Default will select automatically + based on memory considerations. - If 'sort', will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of @@ -770,11 +772,13 @@ def isin(element, test_elements, assume_unique=False, invert=False, to be the faster method if the following formula is true: `log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927`, but may use greater memory. - - If 'auto', will automatically choose the method which is - expected to perform the fastest, using the above - formula. For larger sizes or smaller range, - 'dictionary' is chosen. For larger range or smaller - sizes, 'sort' is chosen. + - If `None`, will automatically choose 'dictionary' if + the required memory allocation is less than or equal to + 6 times the sum of the sizes of `ar1` and `ar2`, + otherwise will use 'sort'. This is done to not use + a large amount of memory by default, even though + 'dictionary' may be faster in most cases. + Returns ------- @@ -848,7 +852,7 @@ def isin(element, test_elements, assume_unique=False, invert=False, """ element = np.asarray(element) return in1d(element, test_elements, assume_unique=assume_unique, - invert=invert, method=method).reshape(element.shape) + invert=invert, kind=kind).reshape(element.shape) def _union1d_dispatcher(ar1, ar2): |