[pypy-commit] pypy better-jit-hooks: merged default
alex_gaynor
noreply at buildbot.pypy.org
Mon Jan 9 23:38:43 CET 2012
Author: Alex Gaynor <alex.gaynor at gmail.com>
Branch: better-jit-hooks
Changeset: r51185:9e69f381ba7e
Date: 2012-01-09 16:34 -0600
http://bitbucket.org/pypy/pypy/changeset/9e69f381ba7e/
Log: merged default
diff --git a/lib_pypy/numpypy/__init__.py b/lib_pypy/numpypy/__init__.py
--- a/lib_pypy/numpypy/__init__.py
+++ b/lib_pypy/numpypy/__init__.py
@@ -1,1 +1,2 @@
from _numpypy import *
+from fromnumeric import *
diff --git a/lib_pypy/numpypy/fromnumeric.py b/lib_pypy/numpypy/fromnumeric.py
new file mode 100644
--- /dev/null
+++ b/lib_pypy/numpypy/fromnumeric.py
@@ -0,0 +1,2400 @@
+######################################################################
+# This is a copy of numpy/core/fromnumeric.py modified for numpypy
+######################################################################
+# Each name in __all__ was a function in 'numeric' that is now
+# a method in 'numpy'.
+# When the corresponding method is added to numpypy BaseArray
+# each function should be added as a module function
+# at the applevel
+# This can be as simple as doing the following
+#
+# def func(a, ...):
+# if not hasattr(a, 'func')
+# a = numpypy.array(a)
+# return a.func(...)
+#
+######################################################################
+
+import numpypy
+
+# Module containing non-deprecated functions borrowed from Numeric.
+__docformat__ = "restructuredtext en"
+
+# functions that are now methods
+__all__ = ['take', 'reshape', 'choose', 'repeat', 'put',
+ 'swapaxes', 'transpose', 'sort', 'argsort', 'argmax', 'argmin',
+ 'searchsorted', 'alen',
+ 'resize', 'diagonal', 'trace', 'ravel', 'nonzero', 'shape',
+ 'compress', 'clip', 'sum', 'product', 'prod', 'sometrue', 'alltrue',
+ 'any', 'all', 'cumsum', 'cumproduct', 'cumprod', 'ptp', 'ndim',
+ 'rank', 'size', 'around', 'round_', 'mean', 'std', 'var', 'squeeze',
+ 'amax', 'amin',
+ ]
+
+def take(a, indices, axis=None, out=None, mode='raise'):
+ """
+ Take elements from an array along an axis.
+
+ This function does the same thing as "fancy" indexing (indexing arrays
+ using arrays); however, it can be easier to use if you need elements
+ along a given axis.
+
+ Parameters
+ ----------
+ a : array_like
+ The source array.
+ indices : array_like
+ The indices of the values to extract.
+ axis : int, optional
+ The axis over which to select values. By default, the flattened
+ input array is used.
+ out : ndarray, optional
+ If provided, the result will be placed in this array. It should
+ be of the appropriate shape and dtype.
+ mode : {'raise', 'wrap', 'clip'}, optional
+ Specifies how out-of-bounds indices will behave.
+
+ * 'raise' -- raise an error (default)
+ * 'wrap' -- wrap around
+ * 'clip' -- clip to the range
+
+ 'clip' mode means that all indices that are too large are replaced
+ by the index that addresses the last element along that axis. Note
+ that this disables indexing with negative numbers.
+
+ Returns
+ -------
+ subarray : ndarray
+ The returned array has the same type as `a`.
+
+ See Also
+ --------
+ ndarray.take : equivalent method
+
+ Examples
+ --------
+ >>> a = [4, 3, 5, 7, 6, 8]
+ >>> indices = [0, 1, 4]
+ >>> np.take(a, indices)
+ array([4, 3, 6])
+
+ In this example if `a` is an ndarray, "fancy" indexing can be used.
+
+ >>> a = np.array(a)
+ >>> a[indices]
+ array([4, 3, 6])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+# not deprecated --- copy if necessary, view otherwise
+def reshape(a, newshape, order='C'):
+ """
+ Gives a new shape to an array without changing its data.
+
+ Parameters
+ ----------
+ a : array_like
+ Array to be reshaped.
+ newshape : int or tuple of ints
+ The new shape should be compatible with the original shape. If
+ an integer, then the result will be a 1-D array of that length.
+ One shape dimension can be -1. In this case, the value is inferred
+ from the length of the array and remaining dimensions.
+ order : {'C', 'F', 'A'}, optional
+ Determines whether the array data should be viewed as in C
+ (row-major) order, FORTRAN (column-major) order, or the C/FORTRAN
+ order should be preserved.
+
+ Returns
+ -------
+ reshaped_array : ndarray
+ This will be a new view object if possible; otherwise, it will
+ be a copy.
+
+
+ See Also
+ --------
+ ndarray.reshape : Equivalent method.
+
+ Notes
+ -----
+
+ It is not always possible to change the shape of an array without
+ copying the data. If you want an error to be raise if the data is copied,
+ you should assign the new shape to the shape attribute of the array::
+
+ >>> a = np.zeros((10, 2))
+ # A transpose make the array non-contiguous
+ >>> b = a.T
+ # Taking a view makes it possible to modify the shape without modiying the
+ # initial object.
+ >>> c = b.view()
+ >>> c.shape = (20)
+ AttributeError: incompatible shape for a non-contiguous array
+
+
+ Examples
+ --------
+ >>> a = np.array([[1,2,3], [4,5,6]])
+ >>> np.reshape(a, 6)
+ array([1, 2, 3, 4, 5, 6])
+ >>> np.reshape(a, 6, order='F')
+ array([1, 4, 2, 5, 3, 6])
+
+ >>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
+ array([[1, 2],
+ [3, 4],
+ [5, 6]])
+
+ """
+ if not hasattr(a, 'reshape'):
+ a = numpypy.array(a)
+ return a.reshape(newshape)
+
+
+def choose(a, choices, out=None, mode='raise'):
+ """
+ Construct an array from an index array and a set of arrays to choose from.
+
+ First of all, if confused or uncertain, definitely look at the Examples -
+ in its full generality, this function is less simple than it might
+ seem from the following code description (below ndi =
+ `numpy.lib.index_tricks`):
+
+ ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.
+
+ But this omits some subtleties. Here is a fully general summary:
+
+ Given an "index" array (`a`) of integers and a sequence of `n` arrays
+ (`choices`), `a` and each choice array are first broadcast, as necessary,
+ to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =
+ 0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
+ for each `i`. Then, a new array with shape ``Ba.shape`` is created as
+ follows:
+
+ * if ``mode=raise`` (the default), then, first of all, each element of
+ `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that
+ `i` (in that range) is the value at the `(j0, j1, ..., jm)` position
+ in `Ba` - then the value at the same position in the new array is the
+ value in `Bchoices[i]` at that same position;
+
+ * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)
+ integer; modular arithmetic is used to map integers outside the range
+ `[0, n-1]` back into that range; and then the new array is constructed
+ as above;
+
+ * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)
+ integer; negative integers are mapped to 0; values greater than `n-1`
+ are mapped to `n-1`; and then the new array is constructed as above.
+
+ Parameters
+ ----------
+ a : int array
+ This array must contain integers in `[0, n-1]`, where `n` is the number
+ of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any
+ integers are permissible.
+ choices : sequence of arrays
+ Choice arrays. `a` and all of the choices must be broadcastable to the
+ same shape. If `choices` is itself an array (not recommended), then
+ its outermost dimension (i.e., the one corresponding to
+ ``choices.shape[0]``) is taken as defining the "sequence".
+ out : array, optional
+ If provided, the result will be inserted into this array. It should
+ be of the appropriate shape and dtype.
+ mode : {'raise' (default), 'wrap', 'clip'}, optional
+ Specifies how indices outside `[0, n-1]` will be treated:
+
+ * 'raise' : an exception is raised
+ * 'wrap' : value becomes value mod `n`
+ * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1
+
+ Returns
+ -------
+ merged_array : array
+ The merged result.
+
+ Raises
+ ------
+ ValueError: shape mismatch
+ If `a` and each choice array are not all broadcastable to the same
+ shape.
+
+ See Also
+ --------
+ ndarray.choose : equivalent method
+
+ Notes
+ -----
+ To reduce the chance of misinterpretation, even though the following
+ "abuse" is nominally supported, `choices` should neither be, nor be
+ thought of as, a single array, i.e., the outermost sequence-like container
+ should be either a list or a tuple.
+
+ Examples
+ --------
+
+ >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
+ ... [20, 21, 22, 23], [30, 31, 32, 33]]
+ >>> np.choose([2, 3, 1, 0], choices
+ ... # the first element of the result will be the first element of the
+ ... # third (2+1) "array" in choices, namely, 20; the second element
+ ... # will be the second element of the fourth (3+1) choice array, i.e.,
+ ... # 31, etc.
+ ... )
+ array([20, 31, 12, 3])
+ >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
+ array([20, 31, 12, 3])
+ >>> # because there are 4 choice arrays
+ >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
+ array([20, 1, 12, 3])
+ >>> # i.e., 0
+
+ A couple examples illustrating how choose broadcasts:
+
+ >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
+ >>> choices = [-10, 10]
+ >>> np.choose(a, choices)
+ array([[ 10, -10, 10],
+ [-10, 10, -10],
+ [ 10, -10, 10]])
+
+ >>> # With thanks to Anne Archibald
+ >>> a = np.array([0, 1]).reshape((2,1,1))
+ >>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
+ >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
+ >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
+ array([[[ 1, 1, 1, 1, 1],
+ [ 2, 2, 2, 2, 2],
+ [ 3, 3, 3, 3, 3]],
+ [[-1, -2, -3, -4, -5],
+ [-1, -2, -3, -4, -5],
+ [-1, -2, -3, -4, -5]]])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def repeat(a, repeats, axis=None):
+ """
+ Repeat elements of an array.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+ repeats : {int, array of ints}
+ The number of repetitions for each element. `repeats` is broadcasted
+ to fit the shape of the given axis.
+ axis : int, optional
+ The axis along which to repeat values. By default, use the
+ flattened input array, and return a flat output array.
+
+ Returns
+ -------
+ repeated_array : ndarray
+ Output array which has the same shape as `a`, except along
+ the given axis.
+
+ See Also
+ --------
+ tile : Tile an array.
+
+ Examples
+ --------
+ >>> x = np.array([[1,2],[3,4]])
+ >>> np.repeat(x, 2)
+ array([1, 1, 2, 2, 3, 3, 4, 4])
+ >>> np.repeat(x, 3, axis=1)
+ array([[1, 1, 1, 2, 2, 2],
+ [3, 3, 3, 4, 4, 4]])
+ >>> np.repeat(x, [1, 2], axis=0)
+ array([[1, 2],
+ [3, 4],
+ [3, 4]])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def put(a, ind, v, mode='raise'):
+ """
+ Replaces specified elements of an array with given values.
+
+ The indexing works on the flattened target array. `put` is roughly
+ equivalent to:
+
+ ::
+
+ a.flat[ind] = v
+
+ Parameters
+ ----------
+ a : ndarray
+ Target array.
+ ind : array_like
+ Target indices, interpreted as integers.
+ v : array_like
+ Values to place in `a` at target indices. If `v` is shorter than
+ `ind` it will be repeated as necessary.
+ mode : {'raise', 'wrap', 'clip'}, optional
+ Specifies how out-of-bounds indices will behave.
+
+ * 'raise' -- raise an error (default)
+ * 'wrap' -- wrap around
+ * 'clip' -- clip to the range
+
+ 'clip' mode means that all indices that are too large are replaced
+ by the index that addresses the last element along that axis. Note
+ that this disables indexing with negative numbers.
+
+ See Also
+ --------
+ putmask, place
+
+ Examples
+ --------
+ >>> a = np.arange(5)
+ >>> np.put(a, [0, 2], [-44, -55])
+ >>> a
+ array([-44, 1, -55, 3, 4])
+
+ >>> a = np.arange(5)
+ >>> np.put(a, 22, -5, mode='clip')
+ >>> a
+ array([ 0, 1, 2, 3, -5])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def swapaxes(a, axis1, axis2):
+ """
+ Interchange two axes of an array.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+ axis1 : int
+ First axis.
+ axis2 : int
+ Second axis.
+
+ Returns
+ -------
+ a_swapped : ndarray
+ If `a` is an ndarray, then a view of `a` is returned; otherwise
+ a new array is created.
+
+ Examples
+ --------
+ >>> x = np.array([[1,2,3]])
+ >>> np.swapaxes(x,0,1)
+ array([[1],
+ [2],
+ [3]])
+
+ >>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
+ >>> x
+ array([[[0, 1],
+ [2, 3]],
+ [[4, 5],
+ [6, 7]]])
+
+ >>> np.swapaxes(x,0,2)
+ array([[[0, 4],
+ [2, 6]],
+ [[1, 5],
+ [3, 7]]])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def transpose(a, axes=None):
+ """
+ Permute the dimensions of an array.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+ axes : list of ints, optional
+ By default, reverse the dimensions, otherwise permute the axes
+ according to the values given.
+
+ Returns
+ -------
+ p : ndarray
+ `a` with its axes permuted. A view is returned whenever
+ possible.
+
+ See Also
+ --------
+ rollaxis
+
+ Examples
+ --------
+ >>> x = np.arange(4).reshape((2,2))
+ >>> x
+ array([[0, 1],
+ [2, 3]])
+
+ >>> np.transpose(x)
+ array([[0, 2],
+ [1, 3]])
+
+ >>> x = np.ones((1, 2, 3))
+ >>> np.transpose(x, (1, 0, 2)).shape
+ (2, 1, 3)
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def sort(a, axis=-1, kind='quicksort', order=None):
+ """
+ Return a sorted copy of an array.
+
+ Parameters
+ ----------
+ a : array_like
+ Array to be sorted.
+ axis : int or None, optional
+ Axis along which to sort. If None, the array is flattened before
+ sorting. The default is -1, which sorts along the last axis.
+ kind : {'quicksort', 'mergesort', 'heapsort'}, optional
+ Sorting algorithm. Default is 'quicksort'.
+ order : list, optional
+ When `a` is a structured array, this argument specifies which fields
+ to compare first, second, and so on. This list does not need to
+ include all of the fields.
+
+ Returns
+ -------
+ sorted_array : ndarray
+ Array of the same type and shape as `a`.
+
+ See Also
+ --------
+ ndarray.sort : Method to sort an array in-place.
+ argsort : Indirect sort.
+ lexsort : Indirect stable sort on multiple keys.
+ searchsorted : Find elements in a sorted array.
+
+ Notes
+ -----
+ The various sorting algorithms are characterized by their average speed,
+ worst case performance, work space size, and whether they are stable. A
+ stable sort keeps items with the same key in the same relative
+ order. The three available algorithms have the following
+ properties:
+
+ =========== ======= ============= ============ =======
+ kind speed worst case work space stable
+ =========== ======= ============= ============ =======
+ 'quicksort' 1 O(n^2) 0 no
+ 'mergesort' 2 O(n*log(n)) ~n/2 yes
+ 'heapsort' 3 O(n*log(n)) 0 no
+ =========== ======= ============= ============ =======
+
+ All the sort algorithms make temporary copies of the data when
+ sorting along any but the last axis. Consequently, sorting along
+ the last axis is faster and uses less space than sorting along
+ any other axis.
+
+ The sort order for complex numbers is lexicographic. If both the real
+ and imaginary parts are non-nan then the order is determined by the
+ real parts except when they are equal, in which case the order is
+ determined by the imaginary parts.
+
+ Previous to numpy 1.4.0 sorting real and complex arrays containing nan
+ values led to undefined behaviour. In numpy versions >= 1.4.0 nan
+ values are sorted to the end. The extended sort order is:
+
+ * Real: [R, nan]
+ * Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]
+
+ where R is a non-nan real value. Complex values with the same nan
+ placements are sorted according to the non-nan part if it exists.
+ Non-nan values are sorted as before.
+
+ Examples
+ --------
+ >>> a = np.array([[1,4],[3,1]])
+ >>> np.sort(a) # sort along the last axis
+ array([[1, 4],
+ [1, 3]])
+ >>> np.sort(a, axis=None) # sort the flattened array
+ array([1, 1, 3, 4])
+ >>> np.sort(a, axis=0) # sort along the first axis
+ array([[1, 1],
+ [3, 4]])
+
+ Use the `order` keyword to specify a field to use when sorting a
+ structured array:
+
+ >>> dtype = [('name', 'S10'), ('height', float), ('age', int)]
+ >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
+ ... ('Galahad', 1.7, 38)]
+ >>> a = np.array(values, dtype=dtype) # create a structured array
+ >>> np.sort(a, order='height') # doctest: +SKIP
+ array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
+ ('Lancelot', 1.8999999999999999, 38)],
+ dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
+
+ Sort by age, then height if ages are equal:
+
+ >>> np.sort(a, order=['age', 'height']) # doctest: +SKIP
+ array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),
+ ('Arthur', 1.8, 41)],
+ dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def argsort(a, axis=-1, kind='quicksort', order=None):
+ """
+ Returns the indices that would sort an array.
+
+ Perform an indirect sort along the given axis using the algorithm specified
+ by the `kind` keyword. It returns an array of indices of the same shape as
+ `a` that index data along the given axis in sorted order.
+
+ Parameters
+ ----------
+ a : array_like
+ Array to sort.
+ axis : int or None, optional
+ Axis along which to sort. The default is -1 (the last axis). If None,
+ the flattened array is used.
+ kind : {'quicksort', 'mergesort', 'heapsort'}, optional
+ Sorting algorithm.
+ order : list, optional
+ When `a` is an array with fields defined, this argument specifies
+ which fields to compare first, second, etc. Not all fields need be
+ specified.
+
+ Returns
+ -------
+ index_array : ndarray, int
+ Array of indices that sort `a` along the specified axis.
+ In other words, ``a[index_array]`` yields a sorted `a`.
+
+ See Also
+ --------
+ sort : Describes sorting algorithms used.
+ lexsort : Indirect stable sort with multiple keys.
+ ndarray.sort : Inplace sort.
+
+ Notes
+ -----
+ See `sort` for notes on the different sorting algorithms.
+
+ As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
+ nan values. The enhanced sort order is documented in `sort`.
+
+ Examples
+ --------
+ One dimensional array:
+
+ >>> x = np.array([3, 1, 2])
+ >>> np.argsort(x)
+ array([1, 2, 0])
+
+ Two-dimensional array:
+
+ >>> x = np.array([[0, 3], [2, 2]])
+ >>> x
+ array([[0, 3],
+ [2, 2]])
+
+ >>> np.argsort(x, axis=0)
+ array([[0, 1],
+ [1, 0]])
+
+ >>> np.argsort(x, axis=1)
+ array([[0, 1],
+ [0, 1]])
+
+ Sorting with keys:
+
+ >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
+ >>> x
+ array([(1, 0), (0, 1)],
+ dtype=[('x', '<i4'), ('y', '<i4')])
+
+ >>> np.argsort(x, order=('x','y'))
+ array([1, 0])
+
+ >>> np.argsort(x, order=('y','x'))
+ array([0, 1])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def argmax(a, axis=None):
+ """
+ Indices of the maximum values along an axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+ axis : int, optional
+ By default, the index is into the flattened array, otherwise
+ along the specified axis.
+
+ Returns
+ -------
+ index_array : ndarray of ints
+ Array of indices into the array. It has the same shape as `a.shape`
+ with the dimension along `axis` removed.
+
+ See Also
+ --------
+ ndarray.argmax, argmin
+ amax : The maximum value along a given axis.
+ unravel_index : Convert a flat index into an index tuple.
+
+ Notes
+ -----
+ In case of multiple occurrences of the maximum values, the indices
+ corresponding to the first occurrence are returned.
+
+ Examples
+ --------
+ >>> a = np.arange(6).reshape(2,3)
+ >>> a
+ array([[0, 1, 2],
+ [3, 4, 5]])
+ >>> np.argmax(a)
+ 5
+ >>> np.argmax(a, axis=0)
+ array([1, 1, 1])
+ >>> np.argmax(a, axis=1)
+ array([2, 2])
+
+ >>> b = np.arange(6)
+ >>> b[1] = 5
+ >>> b
+ array([0, 5, 2, 3, 4, 5])
+ >>> np.argmax(b) # Only the first occurrence is returned.
+ 1
+
+ """
+ if not hasattr(a, 'argmax'):
+ a = numpypy.array(a)
+ return a.argmax()
+
+
+def argmin(a, axis=None):
+ """
+ Return the indices of the minimum values along an axis.
+
+ See Also
+ --------
+ argmax : Similar function. Please refer to `numpy.argmax` for detailed
+ documentation.
+
+ """
+ if not hasattr(a, 'argmin'):
+ a = numpypy.array(a)
+ return a.argmin()
+
+
+def searchsorted(a, v, side='left'):
+ """
+ Find indices where elements should be inserted to maintain order.
+
+ Find the indices into a sorted array `a` such that, if the corresponding
+ elements in `v` were inserted before the indices, the order of `a` would
+ be preserved.
+
+ Parameters
+ ----------
+ a : 1-D array_like
+ Input array, sorted in ascending order.
+ v : array_like
+ Values to insert into `a`.
+ side : {'left', 'right'}, optional
+ If 'left', the index of the first suitable location found is given. If
+ 'right', return the last such index. If there is no suitable
+ index, return either 0 or N (where N is the length of `a`).
+
+ Returns
+ -------
+ indices : array of ints
+ Array of insertion points with the same shape as `v`.
+
+ See Also
+ --------
+ sort : Return a sorted copy of an array.
+ histogram : Produce histogram from 1-D data.
+
+ Notes
+ -----
+ Binary search is used to find the required insertion points.
+
+ As of Numpy 1.4.0 `searchsorted` works with real/complex arrays containing
+ `nan` values. The enhanced sort order is documented in `sort`.
+
+ Examples
+ --------
+ >>> np.searchsorted([1,2,3,4,5], 3)
+ 2
+ >>> np.searchsorted([1,2,3,4,5], 3, side='right')
+ 3
+ >>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
+ array([0, 5, 1, 2])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def resize(a, new_shape):
+ """
+ Return a new array with the specified shape.
+
+ If the new array is larger than the original array, then the new
+ array is filled with repeated copies of `a`. Note that this behavior
+ is different from a.resize(new_shape) which fills with zeros instead
+ of repeated copies of `a`.
+
+ Parameters
+ ----------
+ a : array_like
+ Array to be resized.
+
+ new_shape : int or tuple of int
+ Shape of resized array.
+
+ Returns
+ -------
+ reshaped_array : ndarray
+ The new array is formed from the data in the old array, repeated
+ if necessary to fill out the required number of elements. The
+ data are repeated in the order that they are stored in memory.
+
+ See Also
+ --------
+ ndarray.resize : resize an array in-place.
+
+ Examples
+ --------
+ >>> a=np.array([[0,1],[2,3]])
+ >>> np.resize(a,(1,4))
+ array([[0, 1, 2, 3]])
+ >>> np.resize(a,(2,4))
+ array([[0, 1, 2, 3],
+ [0, 1, 2, 3]])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def squeeze(a):
+ """
+ Remove single-dimensional entries from the shape of an array.
+
+ Parameters
+ ----------
+ a : array_like
+ Input data.
+
+ Returns
+ -------
+ squeezed : ndarray
+ The input array, but with with all dimensions of length 1
+ removed. Whenever possible, a view on `a` is returned.
+
+ Examples
+ --------
+ >>> x = np.array([[[0], [1], [2]]])
+ >>> x.shape
+ (1, 3, 1)
+ >>> np.squeeze(x).shape
+ (3,)
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def diagonal(a, offset=0, axis1=0, axis2=1):
+ """
+ Return specified diagonals.
+
+ If `a` is 2-D, returns the diagonal of `a` with the given offset,
+ i.e., the collection of elements of the form ``a[i, i+offset]``. If
+ `a` has more than two dimensions, then the axes specified by `axis1`
+ and `axis2` are used to determine the 2-D sub-array whose diagonal is
+ returned. The shape of the resulting array can be determined by
+ removing `axis1` and `axis2` and appending an index to the right equal
+ to the size of the resulting diagonals.
+
+ Parameters
+ ----------
+ a : array_like
+ Array from which the diagonals are taken.
+ offset : int, optional
+ Offset of the diagonal from the main diagonal. Can be positive or
+ negative. Defaults to main diagonal (0).
+ axis1 : int, optional
+ Axis to be used as the first axis of the 2-D sub-arrays from which
+ the diagonals should be taken. Defaults to first axis (0).
+ axis2 : int, optional
+ Axis to be used as the second axis of the 2-D sub-arrays from
+ which the diagonals should be taken. Defaults to second axis (1).
+
+ Returns
+ -------
+ array_of_diagonals : ndarray
+ If `a` is 2-D, a 1-D array containing the diagonal is returned.
+ If the dimension of `a` is larger, then an array of diagonals is
+ returned, "packed" from left-most dimension to right-most (e.g.,
+ if `a` is 3-D, then the diagonals are "packed" along rows).
+
+ Raises
+ ------
+ ValueError
+ If the dimension of `a` is less than 2.
+
+ See Also
+ --------
+ diag : MATLAB work-a-like for 1-D and 2-D arrays.
+ diagflat : Create diagonal arrays.
+ trace : Sum along diagonals.
+
+ Examples
+ --------
+ >>> a = np.arange(4).reshape(2,2)
+ >>> a
+ array([[0, 1],
+ [2, 3]])
+ >>> a.diagonal()
+ array([0, 3])
+ >>> a.diagonal(1)
+ array([1])
+
+ A 3-D example:
+
+ >>> a = np.arange(8).reshape(2,2,2); a
+ array([[[0, 1],
+ [2, 3]],
+ [[4, 5],
+ [6, 7]]])
+ >>> a.diagonal(0, # Main diagonals of two arrays created by skipping
+ ... 0, # across the outer(left)-most axis last and
+ ... 1) # the "middle" (row) axis first.
+ array([[0, 6],
+ [1, 7]])
+
+ The sub-arrays whose main diagonals we just obtained; note that each
+ corresponds to fixing the right-most (column) axis, and that the
+ diagonals are "packed" in rows.
+
+ >>> a[:,:,0] # main diagonal is [0 6]
+ array([[0, 2],
+ [4, 6]])
+ >>> a[:,:,1] # main diagonal is [1 7]
+ array([[1, 3],
+ [5, 7]])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None):
+ """
+ Return the sum along diagonals of the array.
+
+ If `a` is 2-D, the sum along its diagonal with the given offset
+ is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
+
+ If `a` has more than two dimensions, then the axes specified by axis1 and
+ axis2 are used to determine the 2-D sub-arrays whose traces are returned.
+ The shape of the resulting array is the same as that of `a` with `axis1`
+ and `axis2` removed.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array, from which the diagonals are taken.
+ offset : int, optional
+ Offset of the diagonal from the main diagonal. Can be both positive
+ and negative. Defaults to 0.
+ axis1, axis2 : int, optional
+ Axes to be used as the first and second axis of the 2-D sub-arrays
+ from which the diagonals should be taken. Defaults are the first two
+ axes of `a`.
+ dtype : dtype, optional
+ Determines the data-type of the returned array and of the accumulator
+ where the elements are summed. If dtype has the value None and `a` is
+ of integer type of precision less than the default integer
+ precision, then the default integer precision is used. Otherwise,
+ the precision is the same as that of `a`.
+ out : ndarray, optional
+ Array into which the output is placed. Its type is preserved and
+ it must be of the right shape to hold the output.
+
+ Returns
+ -------
+ sum_along_diagonals : ndarray
+ If `a` is 2-D, the sum along the diagonal is returned. If `a` has
+ larger dimensions, then an array of sums along diagonals is returned.
+
+ See Also
+ --------
+ diag, diagonal, diagflat
+
+ Examples
+ --------
+ >>> np.trace(np.eye(3))
+ 3.0
+ >>> a = np.arange(8).reshape((2,2,2))
+ >>> np.trace(a)
+ array([6, 8])
+
+ >>> a = np.arange(24).reshape((2,2,2,3))
+ >>> np.trace(a).shape
+ (2, 3)
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+def ravel(a, order='C'):
+ """
+ Return a flattened array.
+
+ A 1-D array, containing the elements of the input, is returned. A copy is
+ made only if needed.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array. The elements in ``a`` are read in the order specified by
+ `order`, and packed as a 1-D array.
+ order : {'C','F', 'A', 'K'}, optional
+ The elements of ``a`` are read in this order. 'C' means to view
+ the elements in C (row-major) order. 'F' means to view the elements
+ in Fortran (column-major) order. 'A' means to view the elements
+ in 'F' order if a is Fortran contiguous, 'C' order otherwise.
+ 'K' means to view the elements in the order they occur in memory,
+ except for reversing the data when strides are negative.
+ By default, 'C' order is used.
+
+ Returns
+ -------
+ 1d_array : ndarray
+ Output of the same dtype as `a`, and of shape ``(a.size(),)``.
+
+ See Also
+ --------
+ ndarray.flat : 1-D iterator over an array.
+ ndarray.flatten : 1-D array copy of the elements of an array
+ in row-major order.
+
+ Notes
+ -----
+ In row-major order, the row index varies the slowest, and the column
+ index the quickest. This can be generalized to multiple dimensions,
+ where row-major order implies that the index along the first axis
+ varies slowest, and the index along the last quickest. The opposite holds
+ for Fortran-, or column-major, mode.
+
+ Examples
+ --------
+ It is equivalent to ``reshape(-1, order=order)``.
+
+ >>> x = np.array([[1, 2, 3], [4, 5, 6]])
+ >>> print np.ravel(x)
+ [1 2 3 4 5 6]
+
+ >>> print x.reshape(-1)
+ [1 2 3 4 5 6]
+
+ >>> print np.ravel(x, order='F')
+ [1 4 2 5 3 6]
+
+ When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:
+
+ >>> print np.ravel(x.T)
+ [1 4 2 5 3 6]
+ >>> print np.ravel(x.T, order='A')
+ [1 2 3 4 5 6]
+
+ When ``order`` is 'K', it will preserve orderings that are neither 'C'
+ nor 'F', but won't reverse axes:
+
+ >>> a = np.arange(3)[::-1]; a
+ array([2, 1, 0])
+ >>> a.ravel(order='C')
+ array([2, 1, 0])
+ >>> a.ravel(order='K')
+ array([2, 1, 0])
+
+ >>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
+ array([[[ 0, 2, 4],
+ [ 1, 3, 5]],
+ [[ 6, 8, 10],
+ [ 7, 9, 11]]])
+ >>> a.ravel(order='C')
+ array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])
+ >>> a.ravel(order='K')
+ array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def nonzero(a):
+ """
+ Return the indices of the elements that are non-zero.
+
+ Returns a tuple of arrays, one for each dimension of `a`, containing
+ the indices of the non-zero elements in that dimension. The
+ corresponding non-zero values can be obtained with::
+
+ a[nonzero(a)]
+
+ To group the indices by element, rather than dimension, use::
+
+ transpose(nonzero(a))
+
+ The result of this is always a 2-D array, with a row for
+ each non-zero element.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+
+ Returns
+ -------
+ tuple_of_arrays : tuple
+ Indices of elements that are non-zero.
+
+ See Also
+ --------
+ flatnonzero :
+ Return indices that are non-zero in the flattened version of the input
+ array.
+ ndarray.nonzero :
+ Equivalent ndarray method.
+ count_nonzero :
+ Counts the number of non-zero elements in the input array.
+
+ Examples
+ --------
+ >>> x = np.eye(3)
+ >>> x
+ array([[ 1., 0., 0.],
+ [ 0., 1., 0.],
+ [ 0., 0., 1.]])
+ >>> np.nonzero(x)
+ (array([0, 1, 2]), array([0, 1, 2]))
+
+ >>> x[np.nonzero(x)]
+ array([ 1., 1., 1.])
+ >>> np.transpose(np.nonzero(x))
+ array([[0, 0],
+ [1, 1],
+ [2, 2]])
+
+ A common use for ``nonzero`` is to find the indices of an array, where
+ a condition is True. Given an array `a`, the condition `a` > 3 is a
+ boolean array and since False is interpreted as 0, np.nonzero(a > 3)
+ yields the indices of the `a` where the condition is true.
+
+ >>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])
+ >>> a > 3
+ array([[False, False, False],
+ [ True, True, True],
+ [ True, True, True]], dtype=bool)
+ >>> np.nonzero(a > 3)
+ (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
+
+ The ``nonzero`` method of the boolean array can also be called.
+
+ >>> (a > 3).nonzero()
+ (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def shape(a):
+ """
+ Return the shape of an array.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+
+ Returns
+ -------
+ shape : tuple of ints
+ The elements of the shape tuple give the lengths of the
+ corresponding array dimensions.
+
+ See Also
+ --------
+ alen
+ ndarray.shape : Equivalent array method.
+
+ Examples
+ --------
+ >>> np.shape(np.eye(3))
+ (3, 3)
+ >>> np.shape([[1, 2]])
+ (1, 2)
+ >>> np.shape([0])
+ (1,)
+ >>> np.shape(0)
+ ()
+
+ >>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
+ >>> np.shape(a)
+ (2,)
+ >>> a.shape
+ (2,)
+
+ """
+ if not hasattr(a, 'shape'):
+ a = numpypy.array(a)
+ return a.shape
+
+
+def compress(condition, a, axis=None, out=None):
+ """
+ Return selected slices of an array along given axis.
+
+ When working along a given axis, a slice along that axis is returned in
+ `output` for each index where `condition` evaluates to True. When
+ working on a 1-D array, `compress` is equivalent to `extract`.
+
+ Parameters
+ ----------
+ condition : 1-D array of bools
+ Array that selects which entries to return. If len(condition)
+ is less than the size of `a` along the given axis, then output is
+ truncated to the length of the condition array.
+ a : array_like
+ Array from which to extract a part.
+ axis : int, optional
+ Axis along which to take slices. If None (default), work on the
+ flattened array.
+ out : ndarray, optional
+ Output array. Its type is preserved and it must be of the right
+ shape to hold the output.
+
+ Returns
+ -------
+ compressed_array : ndarray
+ A copy of `a` without the slices along axis for which `condition`
+ is false.
+
+ See Also
+ --------
+ take, choose, diag, diagonal, select
+ ndarray.compress : Equivalent method.
+ numpy.doc.ufuncs : Section "Output arguments"
+
+ Examples
+ --------
+ >>> a = np.array([[1, 2], [3, 4], [5, 6]])
+ >>> a
+ array([[1, 2],
+ [3, 4],
+ [5, 6]])
+ >>> np.compress([0, 1], a, axis=0)
+ array([[3, 4]])
+ >>> np.compress([False, True, True], a, axis=0)
+ array([[3, 4],
+ [5, 6]])
+ >>> np.compress([False, True], a, axis=1)
+ array([[2],
+ [4],
+ [6]])
+
+ Working on the flattened array does not return slices along an axis but
+ selects elements.
+
+ >>> np.compress([False, True], a)
+ array([2])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def clip(a, a_min, a_max, out=None):
+ """
+ Clip (limit) the values in an array.
+
+ Given an interval, values outside the interval are clipped to
+ the interval edges. For example, if an interval of ``[0, 1]``
+ is specified, values smaller than 0 become 0, and values larger
+ than 1 become 1.
+
+ Parameters
+ ----------
+ a : array_like
+ Array containing elements to clip.
+ a_min : scalar or array_like
+ Minimum value.
+ a_max : scalar or array_like
+ Maximum value. If `a_min` or `a_max` are array_like, then they will
+ be broadcasted to the shape of `a`.
+ out : ndarray, optional
+ The results will be placed in this array. It may be the input
+ array for in-place clipping. `out` must be of the right shape
+ to hold the output. Its type is preserved.
+
+ Returns
+ -------
+ clipped_array : ndarray
+ An array with the elements of `a`, but where values
+ < `a_min` are replaced with `a_min`, and those > `a_max`
+ with `a_max`.
+
+ See Also
+ --------
+ numpy.doc.ufuncs : Section "Output arguments"
+
+ Examples
+ --------
+ >>> a = np.arange(10)
+ >>> np.clip(a, 1, 8)
+ array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
+ >>> a
+ array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+ >>> np.clip(a, 3, 6, out=a)
+ array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
+ >>> a = np.arange(10)
+ >>> a
+ array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+ >>> np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8)
+ array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def sum(a, axis=None, dtype=None, out=None):
+ """
+ Sum of array elements over a given axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Elements to sum.
+ axis : integer, optional
+ Axis over which the sum is taken. By default `axis` is None,
+ and all elements are summed.
+ dtype : dtype, optional
+ The type of the returned array and of the accumulator in which
+ the elements are summed. By default, the dtype of `a` is used.
+ An exception is when `a` has an integer type with less precision
+ than the default platform integer. In that case, the default
+ platform integer is used instead.
+ out : ndarray, optional
+ Array into which the output is placed. By default, a new array is
+ created. If `out` is given, it must be of the appropriate shape
+ (the shape of `a` with `axis` removed, i.e.,
+ ``numpy.delete(a.shape, axis)``). Its type is preserved. See
+ `doc.ufuncs` (Section "Output arguments") for more details.
+
+ Returns
+ -------
+ sum_along_axis : ndarray
+ An array with the same shape as `a`, with the specified
+ axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar
+ is returned. If an output array is specified, a reference to
+ `out` is returned.
+
+ See Also
+ --------
+ ndarray.sum : Equivalent method.
+
+ cumsum : Cumulative sum of array elements.
+
+ trapz : Integration of array values using the composite trapezoidal rule.
+
+ mean, average
+
+ Notes
+ -----
+ Arithmetic is modular when using integer types, and no error is
+ raised on overflow.
+
+ Examples
+ --------
+ >>> np.sum([0.5, 1.5])
+ 2.0
+ >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
+ 1
+ >>> np.sum([[0, 1], [0, 5]])
+ 6
+ >>> np.sum([[0, 1], [0, 5]], axis=0)
+ array([0, 6])
+ >>> np.sum([[0, 1], [0, 5]], axis=1)
+ array([1, 5])
+
+ If the accumulator is too small, overflow occurs:
+
+ >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
+ -128
+
+ """
+ if not hasattr(a, "sum"):
+ a = numpypy.array(a)
+ return a.sum()
+
+
+def product (a, axis=None, dtype=None, out=None):
+ """
+ Return the product of array elements over a given axis.
+
+ See Also
+ --------
+ prod : equivalent function; see for details.
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def sometrue(a, axis=None, out=None):
+ """
+ Check whether some values are true.
+
+ Refer to `any` for full documentation.
+
+ See Also
+ --------
+ any : equivalent function
+
+ """
+ if not hasattr(a, 'any'):
+ a = numpypy.array(a)
+ return a.any()
+
+
+def alltrue (a, axis=None, out=None):
+ """
+ Check if all elements of input array are true.
+
+ See Also
+ --------
+ numpy.all : Equivalent function; see for details.
+
+ """
+ if not hasattr(a, 'all'):
+ a = numpypy.array(a)
+ return a.all()
+
+def any(a,axis=None, out=None):
+ """
+ Test whether any array element along a given axis evaluates to True.
+
+ Returns single boolean unless `axis` is not ``None``
+
+ Parameters
+ ----------
+ a : array_like
+ Input array or object that can be converted to an array.
+ axis : int, optional
+ Axis along which a logical OR is performed. The default
+ (`axis` = `None`) is to perform a logical OR over a flattened
+ input array. `axis` may be negative, in which case it counts
+ from the last to the first axis.
+ out : ndarray, optional
+ Alternate output array in which to place the result. It must have
+ the same shape as the expected output and its type is preserved
+ (e.g., if it is of type float, then it will remain so, returning
+ 1.0 for True and 0.0 for False, regardless of the type of `a`).
+ See `doc.ufuncs` (Section "Output arguments") for details.
+
+ Returns
+ -------
+ any : bool or ndarray
+ A new boolean or `ndarray` is returned unless `out` is specified,
+ in which case a reference to `out` is returned.
+
+ See Also
+ --------
+ ndarray.any : equivalent method
+
+ all : Test whether all elements along a given axis evaluate to True.
+
+ Notes
+ -----
+ Not a Number (NaN), positive infinity and negative infinity evaluate
+ to `True` because these are not equal to zero.
+
+ Examples
+ --------
+ >>> np.any([[True, False], [True, True]])
+ True
+
+ >>> np.any([[True, False], [False, False]], axis=0)
+ array([ True, False], dtype=bool)
+
+ >>> np.any([-1, 0, 5])
+ True
+
+ >>> np.any(np.nan)
+ True
+
+ >>> o=np.array([False])
+ >>> z=np.any([-1, 4, 5], out=o)
+ >>> z, o
+ (array([ True], dtype=bool), array([ True], dtype=bool))
+ >>> # Check now that z is a reference to o
+ >>> z is o
+ True
+ >>> id(z), id(o) # identity of z and o # doctest: +SKIP
+ (191614240, 191614240)
+
+ """
+ if not hasattr(a, 'any'):
+ a = numpypy.array(a)
+ return a.any()
+
+
+def all(a,axis=None, out=None):
+ """
+ Test whether all array elements along a given axis evaluate to True.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array or object that can be converted to an array.
+ axis : int, optional
+ Axis along which a logical AND is performed.
+ The default (`axis` = `None`) is to perform a logical AND
+ over a flattened input array. `axis` may be negative, in which
+ case it counts from the last to the first axis.
+ out : ndarray, optional
+ Alternate output array in which to place the result.
+ It must have the same shape as the expected output and its
+ type is preserved (e.g., if ``dtype(out)`` is float, the result
+ will consist of 0.0's and 1.0's). See `doc.ufuncs` (Section
+ "Output arguments") for more details.
+
+ Returns
+ -------
+ all : ndarray, bool
+ A new boolean or array is returned unless `out` is specified,
+ in which case a reference to `out` is returned.
+
+ See Also
+ --------
+ ndarray.all : equivalent method
+
+ any : Test whether any element along a given axis evaluates to True.
+
+ Notes
+ -----
+ Not a Number (NaN), positive infinity and negative infinity
+ evaluate to `True` because these are not equal to zero.
+
+ Examples
+ --------
+ >>> np.all([[True,False],[True,True]])
+ False
+
+ >>> np.all([[True,False],[True,True]], axis=0)
+ array([ True, False], dtype=bool)
+
+ >>> np.all([-1, 4, 5])
+ True
+
+ >>> np.all([1.0, np.nan])
+ True
+
+ >>> o=np.array([False])
+ >>> z=np.all([-1, 4, 5], out=o)
+ >>> id(z), id(o), z # doctest: +SKIP
+ (28293632, 28293632, array([ True], dtype=bool))
+
+ """
+ if not hasattr(a, 'all'):
+ a = numpypy.array(a)
+ return a.all()
+
+
+def cumsum (a, axis=None, dtype=None, out=None):
+ """
+ Return the cumulative sum of the elements along a given axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+ axis : int, optional
+ Axis along which the cumulative sum is computed. The default
+ (None) is to compute the cumsum over the flattened array.
+ dtype : dtype, optional
+ Type of the returned array and of the accumulator in which the
+ elements are summed. If `dtype` is not specified, it defaults
+ to the dtype of `a`, unless `a` has an integer dtype with a
+ precision less than that of the default platform integer. In
+ that case, the default platform integer is used.
+ out : ndarray, optional
+ Alternative output array in which to place the result. It must
+ have the same shape and buffer length as the expected output
+ but the type will be cast if necessary. See `doc.ufuncs`
+ (Section "Output arguments") for more details.
+
+ Returns
+ -------
+ cumsum_along_axis : ndarray.
+ A new array holding the result is returned unless `out` is
+ specified, in which case a reference to `out` is returned. The
+ result has the same size as `a`, and the same shape as `a` if
+ `axis` is not None or `a` is a 1-d array.
+
+
+ See Also
+ --------
+ sum : Sum array elements.
+
+ trapz : Integration of array values using the composite trapezoidal rule.
+
+ Notes
+ -----
+ Arithmetic is modular when using integer types, and no error is
+ raised on overflow.
+
+ Examples
+ --------
+ >>> a = np.array([[1,2,3], [4,5,6]])
+ >>> a
+ array([[1, 2, 3],
+ [4, 5, 6]])
+ >>> np.cumsum(a)
+ array([ 1, 3, 6, 10, 15, 21])
+ >>> np.cumsum(a, dtype=float) # specifies type of output value(s)
+ array([ 1., 3., 6., 10., 15., 21.])
+
+ >>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns
+ array([[1, 2, 3],
+ [5, 7, 9]])
+ >>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows
+ array([[ 1, 3, 6],
+ [ 4, 9, 15]])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def cumproduct(a, axis=None, dtype=None, out=None):
+ """
+ Return the cumulative product over the given axis.
+
+
+ See Also
+ --------
+ cumprod : equivalent function; see for details.
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def ptp(a, axis=None, out=None):
+ """
+ Range of values (maximum - minimum) along an axis.
+
+ The name of the function comes from the acronym for 'peak to peak'.
+
+ Parameters
+ ----------
+ a : array_like
+ Input values.
+ axis : int, optional
+ Axis along which to find the peaks. By default, flatten the
+ array.
+ out : array_like
+ Alternative output array in which to place the result. It must
+ have the same shape and buffer length as the expected output,
+ but the type of the output values will be cast if necessary.
+
+ Returns
+ -------
+ ptp : ndarray
+ A new array holding the result, unless `out` was
+ specified, in which case a reference to `out` is returned.
+
+ Examples
+ --------
+ >>> x = np.arange(4).reshape((2,2))
+ >>> x
+ array([[0, 1],
+ [2, 3]])
+
+ >>> np.ptp(x, axis=0)
+ array([2, 2])
+
+ >>> np.ptp(x, axis=1)
+ array([1, 1])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def amax(a, axis=None, out=None):
+ """
+ Return the maximum of an array or maximum along an axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Input data.
+ axis : int, optional
+ Axis along which to operate. By default flattened input is used.
+ out : ndarray, optional
+ Alternate output array in which to place the result. Must be of
+ the same shape and buffer length as the expected output. See
+ `doc.ufuncs` (Section "Output arguments") for more details.
+
+ Returns
+ -------
+ amax : ndarray or scalar
+ Maximum of `a`. If `axis` is None, the result is a scalar value.
+ If `axis` is given, the result is an array of dimension
+ ``a.ndim - 1``.
+
+ See Also
+ --------
+ nanmax : NaN values are ignored instead of being propagated.
+ fmax : same behavior as the C99 fmax function.
+ argmax : indices of the maximum values.
+
+ Notes
+ -----
+ NaN values are propagated, that is if at least one item is NaN, the
+ corresponding max value will be NaN as well. To ignore NaN values
+ (MATLAB behavior), please use nanmax.
+
+ Examples
+ --------
+ >>> a = np.arange(4).reshape((2,2))
+ >>> a
+ array([[0, 1],
+ [2, 3]])
+ >>> np.amax(a)
+ 3
+ >>> np.amax(a, axis=0)
+ array([2, 3])
+ >>> np.amax(a, axis=1)
+ array([1, 3])
+
+ >>> b = np.arange(5, dtype=np.float)
+ >>> b[2] = np.NaN
+ >>> np.amax(b)
+ nan
+ >>> np.nanmax(b)
+ 4.0
+
+ """
+ if not hasattr(a, "max"):
+ a = numpypy.array(a)
+ return a.max()
+
+
+def amin(a, axis=None, out=None):
+ """
+ Return the minimum of an array or minimum along an axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Input data.
+ axis : int, optional
+ Axis along which to operate. By default a flattened input is used.
+ out : ndarray, optional
+ Alternative output array in which to place the result. Must
+ be of the same shape and buffer length as the expected output.
+ See `doc.ufuncs` (Section "Output arguments") for more details.
+
+ Returns
+ -------
+ amin : ndarray
+ A new array or a scalar array with the result.
+
+ See Also
+ --------
+ nanmin: nan values are ignored instead of being propagated
+ fmin: same behavior as the C99 fmin function
+ argmin: Return the indices of the minimum values.
+
+ amax, nanmax, fmax
+
+ Notes
+ -----
+ NaN values are propagated, that is if at least one item is nan, the
+ corresponding min value will be nan as well. To ignore NaN values (matlab
+ behavior), please use nanmin.
+
+ Examples
+ --------
+ >>> a = np.arange(4).reshape((2,2))
+ >>> a
+ array([[0, 1],
+ [2, 3]])
+ >>> np.amin(a) # Minimum of the flattened array
+ 0
+ >>> np.amin(a, axis=0) # Minima along the first axis
+ array([0, 1])
+ >>> np.amin(a, axis=1) # Minima along the second axis
+ array([0, 2])
+
+ >>> b = np.arange(5, dtype=np.float)
+ >>> b[2] = np.NaN
+ >>> np.amin(b)
+ nan
+ >>> np.nanmin(b)
+ 0.0
+
+ """
+ # amin() is equivalent to min()
+ if not hasattr(a, 'min'):
+ a = numpypy.array(a)
+ return a.min()
+
+def alen(a):
+ """
+ Return the length of the first dimension of the input array.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+
+ Returns
+ -------
+ l : int
+ Length of the first dimension of `a`.
+
+ See Also
+ --------
+ shape, size
+
+ Examples
+ --------
+ >>> a = np.zeros((7,4,5))
+ >>> a.shape[0]
+ 7
+ >>> np.alen(a)
+ 7
+
+ """
+ if not hasattr(a, 'shape'):
+ a = numpypy.array(a)
+ return a.shape[0]
+
+
+def prod(a, axis=None, dtype=None, out=None):
+ """
+ Return the product of array elements over a given axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Input data.
+ axis : int, optional
+ Axis over which the product is taken. By default, the product
+ of all elements is calculated.
+ dtype : data-type, optional
+ The data-type of the returned array, as well as of the accumulator
+ in which the elements are multiplied. By default, if `a` is of
+ integer type, `dtype` is the default platform integer. (Note: if
+ the type of `a` is unsigned, then so is `dtype`.) Otherwise,
+ the dtype is the same as that of `a`.
+ out : ndarray, optional
+ Alternative output array in which to place the result. It must have
+ the same shape as the expected output, but the type of the
+ output values will be cast if necessary.
+
+ Returns
+ -------
+ product_along_axis : ndarray, see `dtype` parameter above.
+ An array shaped as `a` but with the specified axis removed.
+ Returns a reference to `out` if specified.
+
+ See Also
+ --------
+ ndarray.prod : equivalent method
+ numpy.doc.ufuncs : Section "Output arguments"
+
+ Notes
+ -----
+ Arithmetic is modular when using integer types, and no error is
+ raised on overflow. That means that, on a 32-bit platform:
+
+ >>> x = np.array([536870910, 536870910, 536870910, 536870910])
+ >>> np.prod(x) #random
+ 16
+
+ Examples
+ --------
+ By default, calculate the product of all elements:
+
+ >>> np.prod([1.,2.])
+ 2.0
+
+ Even when the input array is two-dimensional:
+
+ >>> np.prod([[1.,2.],[3.,4.]])
+ 24.0
+
+ But we can also specify the axis over which to multiply:
+
+ >>> np.prod([[1.,2.],[3.,4.]], axis=1)
+ array([ 2., 12.])
+
+ If the type of `x` is unsigned, then the output type is
+ the unsigned platform integer:
+
+ >>> x = np.array([1, 2, 3], dtype=np.uint8)
+ >>> np.prod(x).dtype == np.uint
+ True
+
+ If `x` is of a signed integer type, then the output type
+ is the default platform integer:
+
+ >>> x = np.array([1, 2, 3], dtype=np.int8)
+ >>> np.prod(x).dtype == np.int
+ True
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def cumprod(a, axis=None, dtype=None, out=None):
+ """
+ Return the cumulative product of elements along a given axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+ axis : int, optional
+ Axis along which the cumulative product is computed. By default
+ the input is flattened.
+ dtype : dtype, optional
+ Type of the returned array, as well as of the accumulator in which
+ the elements are multiplied. If *dtype* is not specified, it
+ defaults to the dtype of `a`, unless `a` has an integer dtype with
+ a precision less than that of the default platform integer. In
+ that case, the default platform integer is used instead.
+ out : ndarray, optional
+ Alternative output array in which to place the result. It must
+ have the same shape and buffer length as the expected output
+ but the type of the resulting values will be cast if necessary.
+
+ Returns
+ -------
+ cumprod : ndarray
+ A new array holding the result is returned unless `out` is
+ specified, in which case a reference to out is returned.
+
+ See Also
+ --------
+ numpy.doc.ufuncs : Section "Output arguments"
+
+ Notes
+ -----
+ Arithmetic is modular when using integer types, and no error is
+ raised on overflow.
+
+ Examples
+ --------
+ >>> a = np.array([1,2,3])
+ >>> np.cumprod(a) # intermediate results 1, 1*2
+ ... # total product 1*2*3 = 6
+ array([1, 2, 6])
+ >>> a = np.array([[1, 2, 3], [4, 5, 6]])
+ >>> np.cumprod(a, dtype=float) # specify type of output
+ array([ 1., 2., 6., 24., 120., 720.])
+
+ The cumulative product for each column (i.e., over the rows) of `a`:
+
+ >>> np.cumprod(a, axis=0)
+ array([[ 1, 2, 3],
+ [ 4, 10, 18]])
+
+ The cumulative product for each row (i.e. over the columns) of `a`:
+
+ >>> np.cumprod(a,axis=1)
+ array([[ 1, 2, 6],
+ [ 4, 20, 120]])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def ndim(a):
+ """
+ Return the number of dimensions of an array.
+
+ Parameters
+ ----------
+ a : array_like
+ Input array. If it is not already an ndarray, a conversion is
+ attempted.
+
+ Returns
+ -------
+ number_of_dimensions : int
+ The number of dimensions in `a`. Scalars are zero-dimensional.
+
+ See Also
+ --------
+ ndarray.ndim : equivalent method
+ shape : dimensions of array
+ ndarray.shape : dimensions of array
+
+ Examples
+ --------
+ >>> np.ndim([[1,2,3],[4,5,6]])
+ 2
+ >>> np.ndim(np.array([[1,2,3],[4,5,6]]))
+ 2
+ >>> np.ndim(1)
+ 0
+
+ """
+ if not hasattr(a, 'ndim'):
+ a = numpypy.array(a)
+ return a.ndim
+
+
+def rank(a):
+ """
+ Return the number of dimensions of an array.
+
+ If `a` is not already an array, a conversion is attempted.
+ Scalars are zero dimensional.
+
+ Parameters
+ ----------
+ a : array_like
+ Array whose number of dimensions is desired. If `a` is not an array,
+ a conversion is attempted.
+
+ Returns
+ -------
+ number_of_dimensions : int
+ The number of dimensions in the array.
+
+ See Also
+ --------
+ ndim : equivalent function
+ ndarray.ndim : equivalent property
+ shape : dimensions of array
+ ndarray.shape : dimensions of array
+
+ Notes
+ -----
+ In the old Numeric package, `rank` was the term used for the number of
+ dimensions, but in Numpy `ndim` is used instead.
+
+ Examples
+ --------
+ >>> np.rank([1,2,3])
+ 1
+ >>> np.rank(np.array([[1,2,3],[4,5,6]]))
+ 2
+ >>> np.rank(1)
+ 0
+
+ """
+ if not hasattr(a, 'ndim'):
+ a = numpypy.array(a)
+ return a.ndim
+
+
+def size(a, axis=None):
+ """
+ Return the number of elements along a given axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Input data.
+ axis : int, optional
+ Axis along which the elements are counted. By default, give
+ the total number of elements.
+
+ Returns
+ -------
+ element_count : int
+ Number of elements along the specified axis.
+
+ See Also
+ --------
+ shape : dimensions of array
+ ndarray.shape : dimensions of array
+ ndarray.size : number of elements in array
+
+ Examples
+ --------
+ >>> a = np.array([[1,2,3],[4,5,6]])
+ >>> np.size(a)
+ 6
+ >>> np.size(a,1)
+ 3
+ >>> np.size(a,0)
+ 2
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def around(a, decimals=0, out=None):
+ """
+ Evenly round to the given number of decimals.
+
+ Parameters
+ ----------
+ a : array_like
+ Input data.
+ decimals : int, optional
+ Number of decimal places to round to (default: 0). If
+ decimals is negative, it specifies the number of positions to
+ the left of the decimal point.
+ out : ndarray, optional
+ Alternative output array in which to place the result. It must have
+ the same shape as the expected output, but the type of the output
+ values will be cast if necessary. See `doc.ufuncs` (Section
+ "Output arguments") for details.
+
+ Returns
+ -------
+ rounded_array : ndarray
+ An array of the same type as `a`, containing the rounded values.
+ Unless `out` was specified, a new array is created. A reference to
+ the result is returned.
+
+ The real and imaginary parts of complex numbers are rounded
+ separately. The result of rounding a float is a float.
+
+ See Also
+ --------
+ ndarray.round : equivalent method
+
+ ceil, fix, floor, rint, trunc
+
+
+ Notes
+ -----
+ For values exactly halfway between rounded decimal values, Numpy
+ rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+ -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due
+ to the inexact representation of decimal fractions in the IEEE
+ floating point standard [1]_ and errors introduced when scaling
+ by powers of ten.
+
+ References
+ ----------
+ .. [1] "Lecture Notes on the Status of IEEE 754", William Kahan,
+ http://www.cs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
+ .. [2] "How Futile are Mindless Assessments of
+ Roundoff in Floating-Point Computation?", William Kahan,
+ http://www.cs.berkeley.edu/~wkahan/Mindless.pdf
+
+ Examples
+ --------
+ >>> np.around([0.37, 1.64])
+ array([ 0., 2.])
+ >>> np.around([0.37, 1.64], decimals=1)
+ array([ 0.4, 1.6])
+ >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
+ array([ 0., 2., 2., 4., 4.])
+ >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
+ array([ 1, 2, 3, 11])
+ >>> np.around([1,2,3,11], decimals=-1)
+ array([ 0, 0, 0, 10])
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def round_(a, decimals=0, out=None):
+ """
+ Round an array to the given number of decimals.
+
+ Refer to `around` for full documentation.
+
+ See Also
+ --------
+ around : equivalent function
+
+ """
+ raise NotImplemented('Waiting on interp level method')
+
+
+def mean(a, axis=None, dtype=None, out=None):
+ """
+ Compute the arithmetic mean along the specified axis.
+
+ Returns the average of the array elements. The average is taken over
+ the flattened array by default, otherwise over the specified axis.
+ `float64` intermediate and return values are used for integer inputs.
+
+ Parameters
+ ----------
+ a : array_like
+ Array containing numbers whose mean is desired. If `a` is not an
+ array, a conversion is attempted.
+ axis : int, optional
+ Axis along which the means are computed. The default is to compute
+ the mean of the flattened array.
+ dtype : data-type, optional
+ Type to use in computing the mean. For integer inputs, the default
+ is `float64`; for floating point inputs, it is the same as the
+ input dtype.
+ out : ndarray, optional
+ Alternate output array in which to place the result. The default
+ is ``None``; if provided, it must have the same shape as the
+ expected output, but the type will be cast if necessary.
+ See `doc.ufuncs` for details.
+
+ Returns
+ -------
+ m : ndarray, see dtype parameter above
+ If `out=None`, returns a new array containing the mean values,
+ otherwise a reference to the output array is returned.
+
+ See Also
+ --------
+ average : Weighted average
+
+ Notes
+ -----
+ The arithmetic mean is the sum of the elements along the axis divided
+ by the number of elements.
+
+ Note that for floating-point input, the mean is computed using the
+ same precision the input has. Depending on the input data, this can
+ cause the results to be inaccurate, especially for `float32` (see
+ example below). Specifying a higher-precision accumulator using the
+ `dtype` keyword can alleviate this issue.
+
+ Examples
+ --------
+ >>> a = np.array([[1, 2], [3, 4]])
+ >>> np.mean(a)
+ 2.5
+ >>> np.mean(a, axis=0)
+ array([ 2., 3.])
+ >>> np.mean(a, axis=1)
+ array([ 1.5, 3.5])
+
+ In single precision, `mean` can be inaccurate:
+
+ >>> a = np.zeros((2, 512*512), dtype=np.float32)
+ >>> a[0, :] = 1.0
+ >>> a[1, :] = 0.1
+ >>> np.mean(a)
+ 0.546875
+
+ Computing the mean in float64 is more accurate:
+
+ >>> np.mean(a, dtype=np.float64)
+ 0.55000000074505806
+
+ """
+ if not hasattr(a, "mean"):
+ a = numpypy.array(a)
+ return a.mean()
+
+
+def std(a, axis=None, dtype=None, out=None, ddof=0):
+ """
+ Compute the standard deviation along the specified axis.
+
+ Returns the standard deviation, a measure of the spread of a distribution,
+ of the array elements. The standard deviation is computed for the
+ flattened array by default, otherwise over the specified axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Calculate the standard deviation of these values.
+ axis : int, optional
+ Axis along which the standard deviation is computed. The default is
+ to compute the standard deviation of the flattened array.
+ dtype : dtype, optional
+ Type to use in computing the standard deviation. For arrays of
+ integer type the default is float64, for arrays of float types it is
+ the same as the array type.
+ out : ndarray, optional
+ Alternative output array in which to place the result. It must have
+ the same shape as the expected output but the type (of the calculated
+ values) will be cast if necessary.
+ ddof : int, optional
+ Means Delta Degrees of Freedom. The divisor used in calculations
+ is ``N - ddof``, where ``N`` represents the number of elements.
+ By default `ddof` is zero.
+
+ Returns
+ -------
+ standard_deviation : ndarray, see dtype parameter above.
+ If `out` is None, return a new array containing the standard deviation,
+ otherwise return a reference to the output array.
+
+ See Also
+ --------
+ var, mean
+ numpy.doc.ufuncs : Section "Output arguments"
+
+ Notes
+ -----
+ The standard deviation is the square root of the average of the squared
+ deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.
+
+ The average squared deviation is normally calculated as ``x.sum() / N``, where
+ ``N = len(x)``. If, however, `ddof` is specified, the divisor ``N - ddof``
+ is used instead. In standard statistical practice, ``ddof=1`` provides an
+ unbiased estimator of the variance of the infinite population. ``ddof=0``
+ provides a maximum likelihood estimate of the variance for normally
+ distributed variables. The standard deviation computed in this function
+ is the square root of the estimated variance, so even with ``ddof=1``, it
+ will not be an unbiased estimate of the standard deviation per se.
+
+ Note that, for complex numbers, `std` takes the absolute
+ value before squaring, so that the result is always real and nonnegative.
+
+ For floating-point input, the *std* is computed using the same
+ precision the input has. Depending on the input data, this can cause
+ the results to be inaccurate, especially for float32 (see example below).
+ Specifying a higher-accuracy accumulator using the `dtype` keyword can
+ alleviate this issue.
+
+ Examples
+ --------
+ >>> a = np.array([[1, 2], [3, 4]])
+ >>> np.std(a)
+ 1.1180339887498949
+ >>> np.std(a, axis=0)
+ array([ 1., 1.])
+ >>> np.std(a, axis=1)
+ array([ 0.5, 0.5])
+
+ In single precision, std() can be inaccurate:
+
+ >>> a = np.zeros((2,512*512), dtype=np.float32)
+ >>> a[0,:] = 1.0
+ >>> a[1,:] = 0.1
+ >>> np.std(a)
+ 0.45172946707416706
+
+ Computing the standard deviation in float64 is more accurate:
+
+ >>> np.std(a, dtype=np.float64)
+ 0.44999999925552653
+
+ """
+ if not hasattr(a, "std"):
+ a = numpypy.array(a)
+ return a.std()
+
+
+def var(a, axis=None, dtype=None, out=None, ddof=0):
+ """
+ Compute the variance along the specified axis.
+
+ Returns the variance of the array elements, a measure of the spread of a
+ distribution. The variance is computed for the flattened array by
+ default, otherwise over the specified axis.
+
+ Parameters
+ ----------
+ a : array_like
+ Array containing numbers whose variance is desired. If `a` is not an
+ array, a conversion is attempted.
+ axis : int, optional
+ Axis along which the variance is computed. The default is to compute
+ the variance of the flattened array.
+ dtype : data-type, optional
+ Type to use in computing the variance. For arrays of integer type
+ the default is `float32`; for arrays of float types it is the same as
+ the array type.
+ out : ndarray, optional
+ Alternate output array in which to place the result. It must have
+ the same shape as the expected output, but the type is cast if
+ necessary.
+ ddof : int, optional
+ "Delta Degrees of Freedom": the divisor used in the calculation is
+ ``N - ddof``, where ``N`` represents the number of elements. By
+ default `ddof` is zero.
+
+ Returns
+ -------
+ variance : ndarray, see dtype parameter above
+ If ``out=None``, returns a new array containing the variance;
+ otherwise, a reference to the output array is returned.
+
+ See Also
+ --------
+ std : Standard deviation
+ mean : Average
+ numpy.doc.ufuncs : Section "Output arguments"
+
+ Notes
+ -----
+ The variance is the average of the squared deviations from the mean,
+ i.e., ``var = mean(abs(x - x.mean())**2)``.
+
+ The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
+ If, however, `ddof` is specified, the divisor ``N - ddof`` is used
+ instead. In standard statistical practice, ``ddof=1`` provides an
+ unbiased estimator of the variance of a hypothetical infinite population.
+ ``ddof=0`` provides a maximum likelihood estimate of the variance for
+ normally distributed variables.
+
+ Note that for complex numbers, the absolute value is taken before
+ squaring, so that the result is always real and nonnegative.
+
+ For floating-point input, the variance is computed using the same
+ precision the input has. Depending on the input data, this can cause
+ the results to be inaccurate, especially for `float32` (see example
+ below). Specifying a higher-accuracy accumulator using the ``dtype``
+ keyword can alleviate this issue.
+
+ Examples
+ --------
+ >>> a = np.array([[1,2],[3,4]])
+ >>> np.var(a)
+ 1.25
+ >>> np.var(a,0)
+ array([ 1., 1.])
+ >>> np.var(a,1)
+ array([ 0.25, 0.25])
+
+ In single precision, var() can be inaccurate:
+
+ >>> a = np.zeros((2,512*512), dtype=np.float32)
+ >>> a[0,:] = 1.0
+ >>> a[1,:] = 0.1
+ >>> np.var(a)
+ 0.20405951142311096
+
+ Computing the standard deviation in float64 is more accurate:
+
+ >>> np.var(a, dtype=np.float64)
+ 0.20249999932997387
+ >>> ((1-0.55)**2 + (0.1-0.55)**2)/2
+ 0.20250000000000001
+
+ """
+ if not hasattr(a, "var"):
+ a = numpypy.array(a)
+ return a.var()
diff --git a/lib_pypy/numpypy/test/test_fromnumeric.py b/lib_pypy/numpypy/test/test_fromnumeric.py
new file mode 100644
--- /dev/null
+++ b/lib_pypy/numpypy/test/test_fromnumeric.py
@@ -0,0 +1,109 @@
+
+from pypy.module.micronumpy.test.test_base import BaseNumpyAppTest
+
+class AppTestFromNumeric(BaseNumpyAppTest):
+ def test_argmax(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, arange, argmax
+ a = arange(6).reshape((2,3))
+ assert argmax(a) == 5
+ # assert (argmax(a, axis=0) == array([1, 1, 1])).all()
+ # assert (argmax(a, axis=1) == array([2, 2])).all()
+ b = arange(6)
+ b[1] = 5
+ assert argmax(b) == 1
+
+ def test_argmin(self):
+ # tests adapted from test_argmax
+ from numpypy import array, arange, argmin
+ a = arange(6).reshape((2,3))
+ assert argmin(a) == 0
+ # assert (argmax(a, axis=0) == array([0, 0, 0])).all()
+ # assert (argmax(a, axis=1) == array([0, 0])).all()
+ b = arange(6)
+ b[1] = 0
+ assert argmin(b) == 0
+
+ def test_shape(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, identity, shape
+ assert shape(identity(3)) == (3, 3)
+ assert shape([[1, 2]]) == (1, 2)
+ assert shape([0]) == (1,)
+ assert shape(0) == ()
+ # a = array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
+ # assert shape(a) == (2,)
+
+ def test_sum(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, sum, ones
+ assert sum([0.5, 1.5])== 2.0
+ assert sum([[0, 1], [0, 5]]) == 6
+ # assert sum([0.5, 0.7, 0.2, 1.5], dtype=int32) == 1
+ # assert (sum([[0, 1], [0, 5]], axis=0) == array([0, 6])).all()
+ # assert (sum([[0, 1], [0, 5]], axis=1) == array([1, 5])).all()
+ # If the accumulator is too small, overflow occurs:
+ # assert ones(128, dtype=int8).sum(dtype=int8) == -128
+
+ def test_amin(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, arange, amin
+ a = arange(4).reshape((2,2))
+ assert amin(a) == 0
+ # # Minima along the first axis
+ # assert (amin(a, axis=0) == array([0, 1])).all()
+ # # Minima along the second axis
+ # assert (amin(a, axis=1) == array([0, 2])).all()
+ # # NaN behaviour
+ # b = arange(5, dtype=float)
+ # b[2] = NaN
+ # assert amin(b) == nan
+ # assert nanmin(b) == 0.0
+
+ def test_amax(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, arange, amax
+ a = arange(4).reshape((2,2))
+ assert amax(a) == 3
+ # assert (amax(a, axis=0) == array([2, 3])).all()
+ # assert (amax(a, axis=1) == array([1, 3])).all()
+ # # NaN behaviour
+ # b = arange(5, dtype=float)
+ # b[2] = NaN
+ # assert amax(b) == nan
+ # assert nanmax(b) == 4.0
+
+ def test_alen(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, zeros, alen
+ a = zeros((7,4,5))
+ assert a.shape[0] == 7
+ assert alen(a) == 7
+
+ def test_ndim(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, ndim
+ assert ndim([[1,2,3],[4,5,6]]) == 2
+ assert ndim(array([[1,2,3],[4,5,6]])) == 2
+ assert ndim(1) == 0
+
+ def test_rank(self):
+ # tests taken from numpy/core/fromnumeric.py docstring
+ from numpypy import array, rank
+ assert rank([[1,2,3],[4,5,6]]) == 2
+ assert rank(array([[1,2,3],[4,5,6]])) == 2
+ assert rank(1) == 0
+
+ def test_var(self):
+ from numpypy import array, var
+ a = array([[1,2],[3,4]])
+ assert var(a) == 1.25
+ # assert (np.var(a,0) == array([ 1., 1.])).all()
+ # assert (np.var(a,1) == array([ 0.25, 0.25])).all()
+
+ def test_std(self):
+ from numpypy import array, std
+ a = array([[1, 2], [3, 4]])
+ assert std(a) == 1.1180339887498949
+ # assert (std(a, axis=0) == array([ 1., 1.])).all()
+ # assert (std(a, axis=1) == array([ 0.5, 0.5]).all()
diff --git a/pypy/annotation/description.py b/pypy/annotation/description.py
--- a/pypy/annotation/description.py
+++ b/pypy/annotation/description.py
@@ -257,7 +257,8 @@
try:
inputcells = args.match_signature(signature, defs_s)
except ArgErr, e:
- raise TypeError, "signature mismatch: %s" % e.getmsg(self.name)
+ raise TypeError("signature mismatch: %s() %s" %
+ (self.name, e.getmsg()))
return inputcells
def specialize(self, inputcells, op=None):
diff --git a/pypy/interpreter/argument.py b/pypy/interpreter/argument.py
--- a/pypy/interpreter/argument.py
+++ b/pypy/interpreter/argument.py
@@ -428,8 +428,8 @@
return self._match_signature(w_firstarg,
scope_w, signature, defaults_w, 0)
except ArgErr, e:
- raise OperationError(self.space.w_TypeError,
- self.space.wrap(e.getmsg(fnname)))
+ raise operationerrfmt(self.space.w_TypeError,
+ "%s() %s", fnname, e.getmsg())
def _parse(self, w_firstarg, signature, defaults_w, blindargs=0):
"""Parse args and kwargs according to the signature of a code object,
@@ -450,8 +450,8 @@
try:
return self._parse(w_firstarg, signature, defaults_w, blindargs)
except ArgErr, e:
- raise OperationError(self.space.w_TypeError,
- self.space.wrap(e.getmsg(fnname)))
+ raise operationerrfmt(self.space.w_TypeError,
+ "%s() %s", fnname, e.getmsg())
@staticmethod
def frompacked(space, w_args=None, w_kwds=None):
@@ -626,7 +626,7 @@
class ArgErr(Exception):
- def getmsg(self, fnname):
+ def getmsg(self):
raise NotImplementedError
class ArgErrCount(ArgErr):
@@ -642,11 +642,10 @@
self.num_args = got_nargs
self.num_kwds = nkwds
- def getmsg(self, fnname):
+ def getmsg(self):
n = self.expected_nargs
if n == 0:
- msg = "%s() takes no arguments (%d given)" % (
- fnname,
+ msg = "takes no arguments (%d given)" % (
self.num_args + self.num_kwds)
else:
defcount = self.num_defaults
@@ -672,8 +671,7 @@
msg2 = " non-keyword"
else:
msg2 = ""
- msg = "%s() takes %s %d%s argument%s (%d given)" % (
- fnname,
+ msg = "takes %s %d%s argument%s (%d given)" % (
msg1,
n,
msg2,
@@ -686,9 +684,8 @@
def __init__(self, argname):
self.argname = argname
- def getmsg(self, fnname):
- msg = "%s() got multiple values for keyword argument '%s'" % (
- fnname,
+ def getmsg(self):
+ msg = "got multiple values for keyword argument '%s'" % (
self.argname)
return msg
@@ -722,13 +719,11 @@
break
self.kwd_name = name
- def getmsg(self, fnname):
+ def getmsg(self):
if self.num_kwds == 1:
- msg = "%s() got an unexpected keyword argument '%s'" % (
- fnname,
+ msg = "got an unexpected keyword argument '%s'" % (
self.kwd_name)
else:
- msg = "%s() got %d unexpected keyword arguments" % (
- fnname,
+ msg = "got %d unexpected keyword arguments" % (
self.num_kwds)
return msg
diff --git a/pypy/interpreter/test/test_argument.py b/pypy/interpreter/test/test_argument.py
--- a/pypy/interpreter/test/test_argument.py
+++ b/pypy/interpreter/test/test_argument.py
@@ -393,8 +393,8 @@
class FakeArgErr(ArgErr):
- def getmsg(self, fname):
- return "msg "+fname
+ def getmsg(self):
+ return "msg"
def _match_signature(*args):
raise FakeArgErr()
@@ -404,7 +404,7 @@
excinfo = py.test.raises(OperationError, args.parse_obj, "obj", "foo",
Signature(["a", "b"], None, None))
assert excinfo.value.w_type is TypeError
- assert excinfo.value._w_value == "msg foo"
+ assert excinfo.value.get_w_value(space) == "foo() msg"
def test_args_parsing_into_scope(self):
@@ -448,8 +448,8 @@
class FakeArgErr(ArgErr):
- def getmsg(self, fname):
- return "msg "+fname
+ def getmsg(self):
+ return "msg"
def _match_signature(*args):
raise FakeArgErr()
@@ -460,7 +460,7 @@
"obj", [None, None], "foo",
Signature(["a", "b"], None, None))
assert excinfo.value.w_type is TypeError
- assert excinfo.value._w_value == "msg foo"
+ assert excinfo.value.get_w_value(space) == "foo() msg"
def test_topacked_frompacked(self):
space = DummySpace()
@@ -493,35 +493,35 @@
# got_nargs, nkwds, expected_nargs, has_vararg, has_kwarg,
# defaults_w, missing_args
err = ArgErrCount(1, 0, 0, False, False, None, 0)
- s = err.getmsg('foo')
- assert s == "foo() takes no arguments (1 given)"
+ s = err.getmsg()
+ assert s == "takes no arguments (1 given)"
err = ArgErrCount(0, 0, 1, False, False, [], 1)
- s = err.getmsg('foo')
- assert s == "foo() takes exactly 1 argument (0 given)"
+ s = err.getmsg()
+ assert s == "takes exactly 1 argument (0 given)"
err = ArgErrCount(3, 0, 2, False, False, [], 0)
- s = err.getmsg('foo')
- assert s == "foo() takes exactly 2 arguments (3 given)"
+ s = err.getmsg()
+ assert s == "takes exactly 2 arguments (3 given)"
err = ArgErrCount(3, 0, 2, False, False, ['a'], 0)
- s = err.getmsg('foo')
- assert s == "foo() takes at most 2 arguments (3 given)"
+ s = err.getmsg()
+ assert s == "takes at most 2 arguments (3 given)"
err = ArgErrCount(1, 0, 2, True, False, [], 1)
- s = err.getmsg('foo')
- assert s == "foo() takes at least 2 arguments (1 given)"
+ s = err.getmsg()
+ assert s == "takes at least 2 arguments (1 given)"
err = ArgErrCount(0, 1, 2, True, False, ['a'], 1)
- s = err.getmsg('foo')
- assert s == "foo() takes at least 1 non-keyword argument (0 given)"
+ s = err.getmsg()
+ assert s == "takes at least 1 non-keyword argument (0 given)"
err = ArgErrCount(2, 1, 1, False, True, [], 0)
- s = err.getmsg('foo')
- assert s == "foo() takes exactly 1 non-keyword argument (2 given)"
+ s = err.getmsg()
+ assert s == "takes exactly 1 non-keyword argument (2 given)"
err = ArgErrCount(0, 1, 1, False, True, [], 1)
- s = err.getmsg('foo')
- assert s == "foo() takes exactly 1 non-keyword argument (0 given)"
+ s = err.getmsg()
+ assert s == "takes exactly 1 non-keyword argument (0 given)"
err = ArgErrCount(0, 1, 1, True, True, [], 1)
- s = err.getmsg('foo')
- assert s == "foo() takes at least 1 non-keyword argument (0 given)"
+ s = err.getmsg()
+ assert s == "takes at least 1 non-keyword argument (0 given)"
err = ArgErrCount(2, 1, 1, False, True, ['a'], 0)
- s = err.getmsg('foo')
- assert s == "foo() takes at most 1 non-keyword argument (2 given)"
+ s = err.getmsg()
+ assert s == "takes at most 1 non-keyword argument (2 given)"
def test_bad_type_for_star(self):
space = self.space
@@ -543,12 +543,12 @@
def test_unknown_keywords(self):
space = DummySpace()
err = ArgErrUnknownKwds(space, 1, ['a', 'b'], [True, False], None)
- s = err.getmsg('foo')
- assert s == "foo() got an unexpected keyword argument 'b'"
+ s = err.getmsg()
+ assert s == "got an unexpected keyword argument 'b'"
err = ArgErrUnknownKwds(space, 2, ['a', 'b', 'c'],
[True, False, False], None)
- s = err.getmsg('foo')
- assert s == "foo() got 2 unexpected keyword arguments"
+ s = err.getmsg()
+ assert s == "got 2 unexpected keyword arguments"
def test_unknown_unicode_keyword(self):
class DummySpaceUnicode(DummySpace):
@@ -558,13 +558,13 @@
err = ArgErrUnknownKwds(space, 1, ['a', None, 'b', 'c'],
[True, False, True, True],
[unichr(0x1234), u'b', u'c'])
- s = err.getmsg('foo')
- assert s == "foo() got an unexpected keyword argument '\xe1\x88\xb4'"
+ s = err.getmsg()
+ assert s == "got an unexpected keyword argument '\xe1\x88\xb4'"
def test_multiple_values(self):
err = ArgErrMultipleValues('bla')
- s = err.getmsg('foo')
- assert s == "foo() got multiple values for keyword argument 'bla'"
+ s = err.getmsg()
+ assert s == "got multiple values for keyword argument 'bla'"
class AppTestArgument:
def test_error_message(self):
diff --git a/pypy/jit/backend/x86/regalloc.py b/pypy/jit/backend/x86/regalloc.py
--- a/pypy/jit/backend/x86/regalloc.py
+++ b/pypy/jit/backend/x86/regalloc.py
@@ -741,7 +741,7 @@
self.xrm.possibly_free_var(op.getarg(0))
def consider_cast_int_to_float(self, op):
- loc0 = self.rm.loc(op.getarg(0))
+ loc0 = self.rm.force_allocate_reg(op.getarg(0))
loc1 = self.xrm.force_allocate_reg(op.result)
self.Perform(op, [loc0], loc1)
self.rm.possibly_free_var(op.getarg(0))
diff --git a/pypy/jit/metainterp/resoperation.py b/pypy/jit/metainterp/resoperation.py
--- a/pypy/jit/metainterp/resoperation.py
+++ b/pypy/jit/metainterp/resoperation.py
@@ -16,17 +16,15 @@
# debug
name = ""
pc = 0
+ opnum = 0
_attrs_ = ('result',)
def __init__(self, result):
self.result = result
- # methods implemented by each concrete class
- # ------------------------------------------
-
def getopnum(self):
- raise NotImplementedError
+ return self.opnum
# methods implemented by the arity mixins
# ---------------------------------------
@@ -592,12 +590,9 @@
baseclass = PlainResOp
mixin = arity2mixin.get(arity, N_aryOp)
- def getopnum(self):
- return opnum
-
cls_name = '%s_OP' % name
bases = (get_base_class(mixin, baseclass),)
- dic = {'getopnum': getopnum}
+ dic = {'opnum': opnum}
return type(cls_name, bases, dic)
setup(__name__ == '__main__') # print out the table when run directly
diff --git a/pypy/jit/metainterp/test/test_resoperation.py b/pypy/jit/metainterp/test/test_resoperation.py
--- a/pypy/jit/metainterp/test/test_resoperation.py
+++ b/pypy/jit/metainterp/test/test_resoperation.py
@@ -30,17 +30,17 @@
cls = rop.opclasses[rop.rop.INT_ADD]
assert issubclass(cls, rop.PlainResOp)
assert issubclass(cls, rop.BinaryOp)
- assert cls.getopnum.im_func(None) == rop.rop.INT_ADD
+ assert cls.getopnum.im_func(cls) == rop.rop.INT_ADD
cls = rop.opclasses[rop.rop.CALL]
assert issubclass(cls, rop.ResOpWithDescr)
assert issubclass(cls, rop.N_aryOp)
- assert cls.getopnum.im_func(None) == rop.rop.CALL
+ assert cls.getopnum.im_func(cls) == rop.rop.CALL
cls = rop.opclasses[rop.rop.GUARD_TRUE]
assert issubclass(cls, rop.GuardResOp)
assert issubclass(cls, rop.UnaryOp)
- assert cls.getopnum.im_func(None) == rop.rop.GUARD_TRUE
+ assert cls.getopnum.im_func(cls) == rop.rop.GUARD_TRUE
def test_mixins_in_common_base():
INT_ADD = rop.opclasses[rop.rop.INT_ADD]
diff --git a/pypy/module/_lsprof/interp_lsprof.py b/pypy/module/_lsprof/interp_lsprof.py
--- a/pypy/module/_lsprof/interp_lsprof.py
+++ b/pypy/module/_lsprof/interp_lsprof.py
@@ -19,8 +19,9 @@
# cpu affinity settings
srcdir = py.path.local(pypydir).join('translator', 'c', 'src')
-eci = ExternalCompilationInfo(separate_module_files=
- [srcdir.join('profiling.c')])
+eci = ExternalCompilationInfo(
+ separate_module_files=[srcdir.join('profiling.c')],
+ export_symbols=['pypy_setup_profiling', 'pypy_teardown_profiling'])
c_setup_profiling = rffi.llexternal('pypy_setup_profiling',
[], lltype.Void,
diff --git a/pypy/module/micronumpy/__init__.py b/pypy/module/micronumpy/__init__.py
--- a/pypy/module/micronumpy/__init__.py
+++ b/pypy/module/micronumpy/__init__.py
@@ -48,6 +48,7 @@
'int_': 'interp_boxes.W_LongBox',
'inexact': 'interp_boxes.W_InexactBox',
'floating': 'interp_boxes.W_FloatingBox',
+ 'float_': 'interp_boxes.W_Float64Box',
'float32': 'interp_boxes.W_Float32Box',
'float64': 'interp_boxes.W_Float64Box',
}
diff --git a/pypy/module/micronumpy/interp_boxes.py b/pypy/module/micronumpy/interp_boxes.py
--- a/pypy/module/micronumpy/interp_boxes.py
+++ b/pypy/module/micronumpy/interp_boxes.py
@@ -78,6 +78,7 @@
descr_sub = _binop_impl("subtract")
descr_mul = _binop_impl("multiply")
descr_div = _binop_impl("divide")
+ descr_pow = _binop_impl("power")
descr_eq = _binop_impl("equal")
descr_ne = _binop_impl("not_equal")
descr_lt = _binop_impl("less")
@@ -103,7 +104,7 @@
_attrs_ = ()
class W_IntegerBox(W_NumberBox):
- pass
+ descr__new__, get_dtype = new_dtype_getter("long")
class W_SignedIntegerBox(W_IntegerBox):
pass
@@ -170,6 +171,7 @@
__sub__ = interp2app(W_GenericBox.descr_sub),
__mul__ = interp2app(W_GenericBox.descr_mul),
__div__ = interp2app(W_GenericBox.descr_div),
+ __pow__ = interp2app(W_GenericBox.descr_pow),
__radd__ = interp2app(W_GenericBox.descr_radd),
__rsub__ = interp2app(W_GenericBox.descr_rsub),
@@ -198,6 +200,7 @@
)
W_IntegerBox.typedef = TypeDef("integer", W_NumberBox.typedef,
+ __new__ = interp2app(W_IntegerBox.descr__new__.im_func),
__module__ = "numpypy",
)
diff --git a/pypy/module/micronumpy/interp_numarray.py b/pypy/module/micronumpy/interp_numarray.py
--- a/pypy/module/micronumpy/interp_numarray.py
+++ b/pypy/module/micronumpy/interp_numarray.py
@@ -568,6 +568,18 @@
def descr_mean(self, space):
return space.div(self.descr_sum(space), space.wrap(self.size))
+ def descr_var(self, space):
+ ''' var = mean( (values - mean(values))**2 ) '''
+ w_res = self.descr_sub(space, self.descr_mean(space))
+ assert isinstance(w_res, BaseArray)
+ w_res = w_res.descr_pow(space, space.wrap(2))
+ assert isinstance(w_res, BaseArray)
+ return w_res.descr_mean(space)
+
+ def descr_std(self, space):
+ ''' std(v) = sqrt(var(v)) '''
+ return interp_ufuncs.get(space).sqrt.call(space, [self.descr_var(space)] )
+
def descr_nonzero(self, space):
if self.size > 1:
raise OperationError(space.w_ValueError, space.wrap(
@@ -1209,6 +1221,8 @@
all = interp2app(BaseArray.descr_all),
any = interp2app(BaseArray.descr_any),
dot = interp2app(BaseArray.descr_dot),
+ var = interp2app(BaseArray.descr_var),
+ std = interp2app(BaseArray.descr_std),
copy = interp2app(BaseArray.descr_copy),
reshape = interp2app(BaseArray.descr_reshape),
diff --git a/pypy/module/micronumpy/test/test_dtypes.py b/pypy/module/micronumpy/test/test_dtypes.py
--- a/pypy/module/micronumpy/test/test_dtypes.py
+++ b/pypy/module/micronumpy/test/test_dtypes.py
@@ -166,6 +166,15 @@
# You can't subclass dtype
raises(TypeError, type, "Foo", (dtype,), {})
+ def test_new(self):
+ import _numpypy as np
+ assert np.int_(4) == 4
+ assert np.float_(3.4) == 3.4
+
+ def test_pow(self):
+ from _numpypy import int_
+ assert int_(4) ** 2 == 16
+
class AppTestTypes(BaseNumpyAppTest):
def test_abstract_types(self):
import _numpypy as numpy
diff --git a/pypy/module/micronumpy/test/test_numarray.py b/pypy/module/micronumpy/test/test_numarray.py
--- a/pypy/module/micronumpy/test/test_numarray.py
+++ b/pypy/module/micronumpy/test/test_numarray.py
@@ -978,6 +978,20 @@
assert a[:, 0].tolist() == [17.1, 40.3]
assert a[0].tolist() == [17.1, 27.2]
+ def test_var(self):
+ from _numpypy import array
+ a = array(range(10))
+ assert a.var() == 8.25
+ a = array([5.0])
+ assert a.var() == 0.0
+
+ def test_std(self):
+ from _numpypy import array
+ a = array(range(10))
+ assert a.std() == 2.8722813232690143
+ a = array([5.0])
+ assert a.std() == 0.0
+
class AppTestMultiDim(BaseNumpyAppTest):
def test_init(self):
diff --git a/pypy/rlib/jit.py b/pypy/rlib/jit.py
--- a/pypy/rlib/jit.py
+++ b/pypy/rlib/jit.py
@@ -385,6 +385,18 @@
class JitHintError(Exception):
"""Inconsistency in the JIT hints."""
+PARAMETER_DOCS = {
+ 'threshold': 'number of times a loop has to run for it to become hot',
+ 'function_threshold': 'number of times a function must run for it to become traced from start',
+ 'trace_eagerness': 'number of times a guard has to fail before we start compiling a bridge',
+ 'trace_limit': 'number of recorded operations before we abort tracing with ABORT_TRACE_TOO_LONG',
+ 'inlining': 'inline python functions or not (1/0)',
+ 'loop_longevity': 'a parameter controlling how long loops will be kept before being freed, an estimate',
+ 'retrace_limit': 'how many times we can try retracing before giving up',
+ 'max_retrace_guards': 'number of extra guards a retrace can cause',
+ 'enable_opts': 'optimizations to enabled or all, INTERNAL USE ONLY'
+ }
+
PARAMETERS = {'threshold': 1039, # just above 1024, prime
'function_threshold': 1619, # slightly more than one above, also prime
'trace_eagerness': 200,
diff --git a/pypy/translator/c/src/profiling.c b/pypy/translator/c/src/profiling.c
--- a/pypy/translator/c/src/profiling.c
+++ b/pypy/translator/c/src/profiling.c
@@ -29,6 +29,35 @@
profiling_setup = 0;
}
}
+
+#elif defined(_WIN32)
+#include <windows.h>
+
+DWORD_PTR base_affinity_mask;
+int profiling_setup = 0;
+
+void pypy_setup_profiling() {
+ if (!profiling_setup) {
+ DWORD_PTR affinity_mask, system_affinity_mask;
+ GetProcessAffinityMask(GetCurrentProcess(),
+ &base_affinity_mask, &system_affinity_mask);
+ affinity_mask = 1;
+ /* Pick one cpu allowed by the system */
+ if (system_affinity_mask)
+ while ((affinity_mask & system_affinity_mask) == 0)
+ affinity_mask <<= 1;
+ SetProcessAffinityMask(GetCurrentProcess(), affinity_mask);
+ profiling_setup = 1;
+ }
+}
+
+void pypy_teardown_profiling() {
+ if (profiling_setup) {
+ SetProcessAffinityMask(GetCurrentProcess(), base_affinity_mask);
+ profiling_setup = 0;
+ }
+}
+
#else
void pypy_setup_profiling() { }
void pypy_teardown_profiling() { }
More information about the pypy-commit
mailing list