diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 319 |
1 files changed, 152 insertions, 167 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index b9f3bbb16..4ebca6360 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 @@ -605,14 +604,13 @@ def piecewise(x, condlist, funclist, *args, **kw): ) y = zeros(x.shape, x.dtype) - for k in range(n): - item = funclist[k] - if not isinstance(item, collections.abc.Callable): - y[condlist[k]] = item + for cond, func in zip(condlist, funclist): + if not isinstance(func, collections.abc.Callable): + y[cond] = func else: - vals = x[condlist[k]] + vals = x[cond] if vals.size > 0: - y[condlist[k]] = item(vals, *args, **kw) + y[cond] = func(vals, *args, **kw) return y @@ -683,8 +681,7 @@ def select(condlist, choicelist, default=0): choicelist = np.broadcast_arrays(*choicelist) # If cond array is not an ndarray in boolean format or scalar bool, abort. - for i in range(len(condlist)): - cond = condlist[i] + for i, cond in enumerate(condlist): if cond.dtype.type is not np.bool_: raise TypeError( 'invalid entry {} in condlist: should be boolean ndarray'.format(i)) @@ -765,6 +762,30 @@ def copy(a, order='K', subok=False): >>> x[0] == z[0] False + Note that np.copy is a shallow copy and will not copy object + elements within arrays. This is mainly important for arrays + containing Python objects. The new array will contain the + same object which may lead to surprises if that object can + be modified (is mutable): + + >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object) + >>> b = np.copy(a) + >>> b[2][0] = 10 + >>> a + array([1, 'm', list([10, 3, 4])], dtype=object) + + To ensure all elements within an ``object`` array are copied, + use `copy.deepcopy`: + + >>> import copy + >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object) + >>> c = copy.deepcopy(a) + >>> c[2][0] = 10 + >>> c + array([1, 'm', list([10, 3, 4])], dtype=object) + >>> a + array([1, 'm', list([2, 3, 4])], dtype=object) + """ return array(a, order=order, subok=subok, copy=True) @@ -1309,6 +1330,10 @@ def interp(x, xp, fp, left=None, right=None, period=None): If `xp` or `fp` are not 1-D sequences If `period == 0` + See Also + -------- + scipy.interpolate + Notes ----- The x-coordinate sequence is expected to be increasing, but this is not @@ -1430,6 +1455,11 @@ 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 @@ -2022,7 +2052,7 @@ class vectorize: self.pyfunc = pyfunc self.cache = cache self.signature = signature - self._ufunc = None # Caching to improve default performance + self._ufunc = {} # Caching to improve default performance if doc is None: self.__doc__ = pyfunc.__doc__ @@ -2087,14 +2117,22 @@ class vectorize: if self.otypes is not None: otypes = self.otypes - nout = len(otypes) - # Note logic here: We only *use* self._ufunc if func is self.pyfunc - # even though we set self._ufunc regardless. - if func is self.pyfunc and self._ufunc is not None: - ufunc = self._ufunc + # self._ufunc is a dictionary whose keys are the number of + # arguments (i.e. len(args)) and whose values are ufuncs created + # by frompyfunc. len(args) can be different for different calls if + # self.pyfunc has parameters with default values. We only use the + # cache when func is self.pyfunc, which occurs when the call uses + # only positional arguments and no arguments are excluded. + + nin = len(args) + nout = len(self.otypes) + if func is not self.pyfunc or nin not in self._ufunc: + ufunc = frompyfunc(func, nin, nout) else: - ufunc = self._ufunc = frompyfunc(func, len(args), nout) + ufunc = None # We'll get it from self._ufunc + if func is self.pyfunc: + ufunc = self._ufunc.setdefault(nin, ufunc) else: # Get number of outputs and output types by calling the function on # the first entries of args. We also cache the result to prevent @@ -3220,7 +3258,6 @@ def kaiser(M, beta): >>> plt.show() """ - from numpy.dual import i0 if M == 1: return np.array([1.]) n = arange(0, M) @@ -3234,10 +3271,17 @@ def _sinc_dispatcher(x): @array_function_dispatch(_sinc_dispatcher) def sinc(x): - """ - Return the sinc function. + r""" + Return the normalized sinc function. + + The sinc function is :math:`\sin(\pi x)/(\pi x)`. + + .. note:: - The sinc function is :math:`\\sin(\\pi x)/(\\pi x)`. + Note the normalization factor of ``pi`` used in the definition. + This is the most commonly used definition in signal processing. + Use ``sinc(x / np.pi)`` to obtain the unnormalized sinc function + :math:`\sin(x)/(x)` that is more common in mathematics. Parameters ---------- @@ -3834,15 +3878,20 @@ def _quantile_is_valid(q): return True +def _lerp(a, b, t, out=None): + """ Linearly interpolate from a to b by a factor of t """ + return add(a*(1 - t), b*t, out=out) + + def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): a = asarray(a) - if q.ndim == 0: - # Do not allow 0-d arrays because following code fails for scalar - zerod = True - q = q[None] - else: - zerod = False + + # ufuncs cause 0d array results to decay to scalars (see gh-13105), which + # makes them problematic for __setitem__ and attribute access. As a + # workaround, we call this on the result of every ufunc on a possibly-0d + # array. + not_scalar = np.asanyarray # prepare a for partitioning if overwrite_input: @@ -3859,9 +3908,14 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, if axis is None: axis = 0 - Nx = ap.shape[axis] - indices = q * (Nx - 1) + if q.ndim > 2: + # The code below works fine for nd, but it might not have useful + # semantics. For now, keep the supported dimensions the same as it was + # before. + raise ValueError("q must be a scalar or 1d") + Nx = ap.shape[axis] + indices = not_scalar(q * (Nx - 1)) # round fractional indices according to interpolation method if interpolation == 'lower': indices = floor(indices).astype(intp) @@ -3878,87 +3932,60 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, "interpolation can only be 'linear', 'lower' 'higher', " "'midpoint', or 'nearest'") - n = np.array(False, dtype=bool) # check for nan's flag - 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])) - - ap.partition(indices, axis=axis) - # ensure axis with q-th is first - ap = np.moveaxis(ap, axis, 0) - axis = 0 - - # Check if the array contains any nan's - if np.issubdtype(a.dtype, np.inexact): - indices = indices[:-1] - n = np.isnan(ap[-1:, ...]) + # The dimensions of `q` are prepended to the output shape, so we need the + # axis being sampled from `ap` to be first. + ap = np.moveaxis(ap, axis, 0) + del axis - if zerod: - indices = indices[0] - r = take(ap, indices, axis=axis, out=out) + if np.issubdtype(indices.dtype, np.integer): + # take the points along axis - else: # weight the points above and below the indices - indices_below = floor(indices).astype(intp) - indices_above = indices_below + 1 - indices_above[indices_above > Nx - 1] = Nx - 1 - - # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact): - indices_above = concatenate((indices_above, [-1])) - - weights_above = indices - indices_below - weights_below = 1 - weights_above + # may contain nan, which would sort to the end + ap.partition(concatenate((indices.ravel(), [-1])), axis=0) + n = np.isnan(ap[-1]) + else: + # cannot contain nan + ap.partition(indices.ravel(), axis=0) + n = np.array(False, dtype=bool) - weights_shape = [1, ] * ap.ndim - weights_shape[axis] = len(indices) - weights_below.shape = weights_shape - weights_above.shape = weights_shape + r = take(ap, indices, axis=0, out=out) - ap.partition(concatenate((indices_below, indices_above)), axis=axis) + else: + # weight the points above and below the indices - # ensure axis with q-th is first - ap = np.moveaxis(ap, axis, 0) - weights_below = np.moveaxis(weights_below, axis, 0) - weights_above = np.moveaxis(weights_above, axis, 0) - axis = 0 + indices_below = not_scalar(floor(indices)).astype(intp) + indices_above = not_scalar(indices_below + 1) + indices_above[indices_above > Nx - 1] = Nx - 1 - # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact): - indices_above = indices_above[:-1] - n = np.isnan(ap[-1:, ...]) - - x1 = take(ap, indices_below, axis=axis) * weights_below - x2 = take(ap, indices_above, axis=axis) * weights_above + # may contain nan, which would sort to the end + ap.partition(concatenate(( + indices_below.ravel(), indices_above.ravel(), [-1] + )), axis=0) + n = np.isnan(ap[-1]) + else: + # cannot contain nan + ap.partition(concatenate(( + indices_below.ravel(), indices_above.ravel() + )), axis=0) + n = np.array(False, dtype=bool) - # ensure axis with q-th is first - x1 = np.moveaxis(x1, axis, 0) - x2 = np.moveaxis(x2, axis, 0) + weights_shape = indices.shape + (1,) * (ap.ndim - 1) + weights_above = not_scalar(indices - indices_below).reshape(weights_shape) - if zerod: - x1 = x1.squeeze(0) - x2 = x2.squeeze(0) + x_below = take(ap, indices_below, axis=0) + x_above = take(ap, indices_above, axis=0) - if out is not None: - r = add(x1, x2, out=out) - else: - r = add(x1, x2) + r = _lerp(x_below, x_above, weights_above, out=out) + # if any slice contained a nan, then all results on that slice are also nan if np.any(n): - if zerod: - if ap.ndim == 1: - if out is not None: - out[...] = a.dtype.type(np.nan) - r = out - else: - r = a.dtype.type(np.nan) - else: - r[..., n.squeeze(0)] = a.dtype.type(np.nan) + if r.ndim == 0 and out is None: + # can't write to a scalar + r = a.dtype.type(np.nan) else: - if r.ndim == 1: - r[:] = a.dtype.type(np.nan) - else: - r[..., n.repeat(q.size, 0)] = a.dtype.type(np.nan) + r[..., n] = a.dtype.type(np.nan) return r @@ -4207,12 +4234,17 @@ 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 ------- @@ -4270,20 +4302,11 @@ 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] @@ -4339,18 +4362,8 @@ 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 " @@ -4366,35 +4379,23 @@ 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)] @@ -4510,19 +4511,9 @@ 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 @@ -4531,12 +4522,13 @@ 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 " @@ -4586,13 +4578,6 @@ 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) |