diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 129 |
1 files changed, 51 insertions, 78 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 2721b04dd..7eeed7825 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -29,7 +29,6 @@ from numpy.core.multiarray import ( interp as compiled_interp, interp_complex as compiled_interp_complex ) from numpy.core.umath import _add_newdoc_ufunc as add_newdoc_ufunc -from numpy.compat import long import builtins @@ -708,12 +707,12 @@ def select(condlist, choicelist, default=0): return result -def _copy_dispatcher(a, order=None): +def _copy_dispatcher(a, order=None, subok=None): return (a,) @array_function_dispatch(_copy_dispatcher) -def copy(a, order='K'): +def copy(a, order='K', subok=False): """ Return an array copy of the given object. @@ -728,12 +727,21 @@ def copy(a, order='K'): as possible. (Note that this function and :meth:`ndarray.copy` are very similar, but have different default values for their order= arguments.) + subok : bool, optional + If True, then sub-classes will be passed-through, otherwise the + returned array will be forced to be a base-class array (defaults to False). + + .. versionadded:: 1.19.0 Returns ------- arr : ndarray Array interpretation of `a`. + See Also + -------- + ndarray.copy : Preferred method for creating an array copy + Notes ----- This is equivalent to: @@ -757,7 +765,7 @@ def copy(a, order='K'): False """ - return array(a, order=order, copy=True) + return array(a, order=order, subok=subok, copy=True) # Basic operations @@ -1421,6 +1429,11 @@ def angle(z, deg=False): arctan2 absolute + Notes + ----- + Although the angle of the complex number 0 is undefined, ``numpy.angle(0)`` + returns the value 0. + Examples -------- >>> np.angle([1.0, 1.0j, 1+1j]) # in radians @@ -3870,7 +3883,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, "'midpoint', or 'nearest'") n = np.array(False, dtype=bool) # check for nan's flag - if indices.dtype == intp: # take the points along axis + if np.issubdtype(indices.dtype, np.integer): # take the points along axis # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact): indices = concatenate((indices, [-1])) @@ -4198,12 +4211,17 @@ def delete(arr, obj, axis=None): Parameters ---------- arr : array_like - Input array. + Input array. obj : slice, int or array of ints - Indicate indices of sub-arrays to remove along the specified axis. + Indicate indices of sub-arrays to remove along the specified axis. + + .. versionchanged:: 1.19.0 + Boolean indices are now treated as a mask of elements to remove, + rather than being cast to the integers 0 and 1. + axis : int, optional - The axis along which to delete the subarray defined by `obj`. - If `axis` is None, `obj` is applied to the flattened array. + The axis along which to delete the subarray defined by `obj`. + If `axis` is None, `obj` is applied to the flattened array. Returns ------- @@ -4261,20 +4279,11 @@ def delete(arr, obj, axis=None): if axis is None: if ndim != 1: arr = arr.ravel() + # needed for np.matrix, which is still not 1d after being ravelled ndim = arr.ndim - axis = -1 - - if ndim == 0: - # 2013-09-24, 1.9 - warnings.warn( - "in the future the special handling of scalars will be removed " - "from delete and raise an error", DeprecationWarning, stacklevel=3) - if wrap: - return wrap(arr) - else: - return arr.copy(order=arrorder) - - axis = normalize_axis_index(axis, ndim) + axis = ndim - 1 + else: + axis = normalize_axis_index(axis, ndim) slobj = [slice(None)]*ndim N = arr.shape[axis] @@ -4330,18 +4339,8 @@ def delete(arr, obj, axis=None): else: return new - _obj = obj - obj = np.asarray(obj) - # After removing the special handling of booleans and out of - # bounds values, the conversion to the array can be removed. - if obj.dtype == bool: - warnings.warn("in the future insert will treat boolean arrays and " - "array-likes as boolean index instead of casting it " - "to integer", FutureWarning, stacklevel=3) - obj = obj.astype(intp) - if isinstance(_obj, (int, long, integer)): + if isinstance(obj, (int, integer)) and not isinstance(obj, bool): # optimization for a single value - obj = obj.item() if (obj < -N or obj >= N): raise IndexError( "index %i is out of bounds for axis %i with " @@ -4357,35 +4356,23 @@ def delete(arr, obj, axis=None): slobj2[axis] = slice(obj+1, None) new[tuple(slobj)] = arr[tuple(slobj2)] else: + _obj = obj + obj = np.asarray(obj) if obj.size == 0 and not isinstance(_obj, np.ndarray): obj = obj.astype(intp) - if not np.can_cast(obj, intp, 'same_kind'): - # obj.size = 1 special case always failed and would just - # give superfluous warnings. - # 2013-09-24, 1.9 - warnings.warn( - "using a non-integer array as obj in delete will result in an " - "error in the future", DeprecationWarning, stacklevel=3) - obj = obj.astype(intp) - keep = ones(N, dtype=bool) - # Test if there are out of bound indices, this is deprecated - inside_bounds = (obj < N) & (obj >= -N) - if not inside_bounds.all(): - # 2013-09-24, 1.9 - warnings.warn( - "in the future out of bounds indices will raise an error " - "instead of being ignored by `numpy.delete`.", - DeprecationWarning, stacklevel=3) - obj = obj[inside_bounds] - positive_indices = obj >= 0 - if not positive_indices.all(): - warnings.warn( - "in the future negative indices will not be ignored by " - "`numpy.delete`.", FutureWarning, stacklevel=3) - obj = obj[positive_indices] + if obj.dtype == bool: + if obj.shape != (N,): + raise ValueError('boolean array argument obj to delete ' + 'must be one dimensional and match the axis ' + 'length of {}'.format(N)) + + # optimization, the other branch is slower + keep = ~obj + else: + keep = ones(N, dtype=bool) + keep[obj,] = False - keep[obj, ] = False slobj[axis] = keep new = arr[tuple(slobj)] @@ -4501,19 +4488,9 @@ def insert(arr, obj, values, axis=None): if axis is None: if ndim != 1: arr = arr.ravel() + # needed for np.matrix, which is still not 1d after being ravelled ndim = arr.ndim axis = ndim - 1 - elif ndim == 0: - # 2013-09-24, 1.9 - warnings.warn( - "in the future the special handling of scalars will be removed " - "from insert and raise an error", DeprecationWarning, stacklevel=3) - arr = arr.copy(order=arrorder) - arr[...] = values - if wrap: - return wrap(arr) - else: - return arr else: axis = normalize_axis_index(axis, ndim) slobj = [slice(None)]*ndim @@ -4522,12 +4499,13 @@ def insert(arr, obj, values, axis=None): if isinstance(obj, slice): # turn it into a range object - indices = arange(*obj.indices(N), **{'dtype': intp}) + indices = arange(*obj.indices(N), dtype=intp) else: # need to copy obj, because indices will be changed in-place indices = np.array(obj) if indices.dtype == bool: # See also delete + # 2012-10-11, NumPy 1.8 warnings.warn( "in the future insert will treat boolean arrays and " "array-likes as a boolean index instead of casting it to " @@ -4577,13 +4555,6 @@ def insert(arr, obj, values, axis=None): # Can safely cast the empty list to intp indices = indices.astype(intp) - if not np.can_cast(indices, intp, 'same_kind'): - # 2013-09-24, 1.9 - warnings.warn( - "using a non-integer array as obj in insert will result in an " - "error in the future", DeprecationWarning, stacklevel=3) - indices = indices.astype(intp) - indices[indices < 0] += N numnew = len(indices) @@ -4654,7 +4625,9 @@ def append(arr, values, axis=None): >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0) Traceback (most recent call last): ... - ValueError: all the input arrays must have same number of dimensions + ValueError: all the input arrays must have same number of dimensions, but + the array at index 0 has 2 dimension(s) and the array at index 1 has 1 + dimension(s) """ arr = asanyarray(arr) |