[pypy-commit] pypy ppc-jit-backend: merge default
bivab
noreply at buildbot.pypy.org
Tue Jan 10 14:13:53 CET 2012
Author: David Schneider <david.schneider at picle.org>
Branch: ppc-jit-backend
Changeset: r51203:d094b25960ad
Date: 2012-01-10 14:13 +0100
http://bitbucket.org/pypy/pypy/changeset/d094b25960ad/
Log: merge default
diff --git a/LICENSE b/LICENSE
--- a/LICENSE
+++ b/LICENSE
@@ -27,7 +27,7 @@
DEALINGS IN THE SOFTWARE.
-PyPy Copyright holders 2003-2011
+PyPy Copyright holders 2003-2012
-----------------------------------
Except when otherwise stated (look for LICENSE files or information at
diff --git a/lib_pypy/numpypy/__init__.py b/lib_pypy/numpypy/__init__.py
new file mode 100644
--- /dev/null
+++ b/lib_pypy/numpypy/__init__.py
@@ -0,0 +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/doc/coding-guide.rst b/pypy/doc/coding-guide.rst
--- a/pypy/doc/coding-guide.rst
+++ b/pypy/doc/coding-guide.rst
@@ -175,15 +175,15 @@
RPython
=================
-RPython Definition, not
------------------------
+RPython Definition
+------------------
-The list and exact details of the "RPython" restrictions are a somewhat
-evolving topic. In particular, we have no formal language definition
-as we find it more practical to discuss and evolve the set of
-restrictions while working on the whole program analysis. If you
-have any questions about the restrictions below then please feel
-free to mail us at pypy-dev at codespeak net.
+RPython is a restricted subset of Python that is amenable to static analysis.
+Although there are additions to the language and some things might surprisingly
+work, this is a rough list of restrictions that should be considered. Note
+that there are tons of special cased restrictions that you'll encounter
+as you go. The exact definition is "RPython is everything that our translation
+toolchain can accept" :)
.. _`wrapped object`: coding-guide.html#wrapping-rules
@@ -198,7 +198,7 @@
contain both a string and a int must be avoided. It is allowed to
mix None (basically with the role of a null pointer) with many other
types: `wrapped objects`, class instances, lists, dicts, strings, etc.
- but *not* with int and floats.
+ but *not* with int, floats or tuples.
**constants**
@@ -209,9 +209,12 @@
have this restriction, so if you need mutable global state, store it
in the attributes of some prebuilt singleton instance.
+
+
**control structures**
- all allowed but yield, ``for`` loops restricted to builtin types
+ all allowed, ``for`` loops restricted to builtin types, generators
+ very restricted.
**range**
@@ -226,7 +229,8 @@
**generators**
- generators are not supported.
+ generators are supported, but their exact scope is very limited. you can't
+ merge two different generator in one control point.
**exceptions**
@@ -245,22 +249,27 @@
**strings**
- a lot of, but not all string methods are supported. Indexes can be
+ a lot of, but not all string methods are supported and those that are
+ supported, not necesarilly accept all arguments. Indexes can be
negative. In case they are not, then you get slightly more efficient
code if the translator can prove that they are non-negative. When
slicing a string it is necessary to prove that the slice start and
- stop indexes are non-negative.
+ stop indexes are non-negative. There is no implicit str-to-unicode cast
+ anywhere.
**tuples**
no variable-length tuples; use them to store or return pairs or n-tuples of
- values. Each combination of types for elements and length constitute a separate
- and not mixable type.
+ values. Each combination of types for elements and length constitute
+ a separate and not mixable type.
**lists**
lists are used as an allocated array. Lists are over-allocated, so list.append()
- is reasonably fast. Negative or out-of-bound indexes are only allowed for the
+ is reasonably fast. However, if you use a fixed-size list, the code
+ is more efficient. Annotator can figure out most of the time that your
+ list is fixed-size, even when you use list comprehension.
+ Negative or out-of-bound indexes are only allowed for the
most common operations, as follows:
- *indexing*:
@@ -287,16 +296,14 @@
**dicts**
- dicts with a unique key type only, provided it is hashable.
- String keys have been the only allowed key types for a while, but this was generalized.
- After some re-optimization,
- the implementation could safely decide that all string dict keys should be interned.
+ dicts with a unique key type only, provided it is hashable. Custom
+ hash functions and custom equality will not be honored.
+ Use ``pypy.rlib.objectmodel.r_dict`` for custom hash functions.
**list comprehensions**
- may be used to create allocated, initialized arrays.
- After list over-allocation was introduced, there is no longer any restriction.
+ May be used to create allocated, initialized arrays.
**functions**
@@ -334,9 +341,7 @@
**objects**
- in PyPy, wrapped objects are borrowed from the object space. Just like
- in CPython, code that needs e.g. a dictionary can use a wrapped dict
- and the object space operations on it.
+ Normal rules apply.
This layout makes the number of types to take care about quite limited.
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/baseobjspace.py b/pypy/interpreter/baseobjspace.py
--- a/pypy/interpreter/baseobjspace.py
+++ b/pypy/interpreter/baseobjspace.py
@@ -1591,12 +1591,15 @@
'ArithmeticError',
'AssertionError',
'AttributeError',
+ 'BaseException',
+ 'DeprecationWarning',
'EOFError',
'EnvironmentError',
'Exception',
'FloatingPointError',
'IOError',
'ImportError',
+ 'ImportWarning',
'IndentationError',
'IndexError',
'KeyError',
@@ -1617,7 +1620,10 @@
'TabError',
'TypeError',
'UnboundLocalError',
+ 'UnicodeDecodeError',
'UnicodeError',
+ 'UnicodeEncodeError',
+ 'UnicodeTranslateError',
'ValueError',
'ZeroDivisionError',
'UnicodeEncodeError',
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
@@ -683,7 +683,7 @@
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.make_sure_var_in_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/backend/x86/test/test_runner.py b/pypy/jit/backend/x86/test/test_runner.py
--- a/pypy/jit/backend/x86/test/test_runner.py
+++ b/pypy/jit/backend/x86/test/test_runner.py
@@ -420,8 +420,8 @@
debug._log = None
#
assert ops_offset is looptoken._x86_ops_offset
- # getfield_raw/int_add/setfield_raw + ops + None
- assert len(ops_offset) == 3 + len(operations) + 1
+ # 2*(getfield_raw/int_add/setfield_raw) + ops + None
+ assert len(ops_offset) == 2*3 + len(operations) + 1
assert (ops_offset[operations[0]] <=
ops_offset[operations[1]] <=
ops_offset[operations[2]] <=
diff --git a/pypy/jit/metainterp/optimizeopt/fficall.py b/pypy/jit/metainterp/optimizeopt/fficall.py
--- a/pypy/jit/metainterp/optimizeopt/fficall.py
+++ b/pypy/jit/metainterp/optimizeopt/fficall.py
@@ -234,11 +234,11 @@
# longlongs are treated as floats, see
# e.g. llsupport/descr.py:getDescrClass
is_float = True
- elif kind == 'u':
+ elif kind == 'u' or kind == 's':
# they're all False
pass
else:
- assert False, "unsupported ffitype or kind"
+ raise NotImplementedError("unsupported ffitype or kind: %s" % kind)
#
fieldsize = rffi.getintfield(ffitype, 'c_size')
return self.optimizer.cpu.interiorfielddescrof_dynamic(
diff --git a/pypy/jit/metainterp/optimizeopt/test/test_multilabel.py b/pypy/jit/metainterp/optimizeopt/test/test_multilabel.py
--- a/pypy/jit/metainterp/optimizeopt/test/test_multilabel.py
+++ b/pypy/jit/metainterp/optimizeopt/test/test_multilabel.py
@@ -442,6 +442,22 @@
"""
self.optimize_loop(ops, expected)
+ def test_optimizer_renaming_boxes_not_imported(self):
+ ops = """
+ [p1]
+ i1 = strlen(p1)
+ label(p1)
+ jump(p1)
+ """
+ expected = """
+ [p1]
+ i1 = strlen(p1)
+ label(p1, i1)
+ i11 = same_as(i1)
+ jump(p1, i11)
+ """
+ self.optimize_loop(ops, expected)
+
class TestLLtype(OptimizeoptTestMultiLabel, LLtypeMixin):
diff --git a/pypy/jit/metainterp/optimizeopt/unroll.py b/pypy/jit/metainterp/optimizeopt/unroll.py
--- a/pypy/jit/metainterp/optimizeopt/unroll.py
+++ b/pypy/jit/metainterp/optimizeopt/unroll.py
@@ -271,6 +271,10 @@
if newresult is not op.result and not newvalue.is_constant():
op = ResOperation(rop.SAME_AS, [op.result], newresult)
self.optimizer._newoperations.append(op)
+ if self.optimizer.loop.logops:
+ debug_print(' Falling back to add extra: ' +
+ self.optimizer.loop.logops.repr_of_resop(op))
+
self.optimizer.flush()
self.optimizer.emitting_dissabled = False
@@ -435,7 +439,13 @@
return
for a in op.getarglist():
if not isinstance(a, Const) and a not in seen:
- self.ensure_short_op_emitted(self.short_boxes.producer(a), optimizer, seen)
+ self.ensure_short_op_emitted(self.short_boxes.producer(a), optimizer,
+ seen)
+
+ if self.optimizer.loop.logops:
+ debug_print(' Emitting short op: ' +
+ self.optimizer.loop.logops.repr_of_resop(op))
+
optimizer.send_extra_operation(op)
seen[op.result] = True
if op.is_ovf():
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
@@ -16,15 +16,13 @@
# debug
name = ""
pc = 0
+ opnum = 0
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
# ---------------------------------------
@@ -590,12 +588,9 @@
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_fficall.py b/pypy/jit/metainterp/test/test_fficall.py
--- a/pypy/jit/metainterp/test/test_fficall.py
+++ b/pypy/jit/metainterp/test/test_fficall.py
@@ -148,28 +148,38 @@
self.check_resops({'jump': 1, 'int_lt': 2, 'setinteriorfield_raw': 4,
'getinteriorfield_raw': 8, 'int_add': 6, 'guard_true': 2})
- def test_array_getitem_uint8(self):
+ def _test_getitem_type(self, TYPE, ffitype, COMPUTE_TYPE):
+ reds = ["n", "i", "s", "data"]
+ if COMPUTE_TYPE is lltype.Float:
+ # Move the float var to the back.
+ reds.remove("s")
+ reds.append("s")
myjitdriver = JitDriver(
greens = [],
- reds = ["n", "i", "s", "data"],
+ reds = reds,
)
def f(data, n):
- i = s = 0
+ i = 0
+ s = rffi.cast(COMPUTE_TYPE, 0)
while i < n:
myjitdriver.jit_merge_point(n=n, i=i, s=s, data=data)
- s += rffi.cast(lltype.Signed, array_getitem(types.uchar, 1, data, 0, 0))
+ s += rffi.cast(COMPUTE_TYPE, array_getitem(ffitype, rffi.sizeof(TYPE), data, 0, 0))
i += 1
return s
+ def main(n):
+ with lltype.scoped_alloc(rffi.CArray(TYPE), 1) as data:
+ data[0] = rffi.cast(TYPE, 200)
+ return f(data, n)
+ assert self.meta_interp(main, [10]) == 2000
- def main(n):
- with lltype.scoped_alloc(rffi.CArray(rffi.UCHAR), 1) as data:
- data[0] = rffi.cast(rffi.UCHAR, 200)
- return f(data, n)
-
- assert self.meta_interp(main, [10]) == 2000
+ def test_array_getitem_uint8(self):
+ self._test_getitem_type(rffi.UCHAR, types.uchar, lltype.Signed)
self.check_resops({'jump': 1, 'int_lt': 2, 'getinteriorfield_raw': 2,
'guard_true': 2, 'int_add': 4})
+ def test_array_getitem_float(self):
+ self._test_getitem_type(rffi.FLOAT, types.float, lltype.Float)
+
class TestFfiCall(FfiCallTests, LLJitMixin):
supports_all = False
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/cpyext/api.py b/pypy/module/cpyext/api.py
--- a/pypy/module/cpyext/api.py
+++ b/pypy/module/cpyext/api.py
@@ -23,6 +23,7 @@
from pypy.interpreter.function import StaticMethod
from pypy.objspace.std.sliceobject import W_SliceObject
from pypy.module.__builtin__.descriptor import W_Property
+from pypy.module.__builtin__.interp_memoryview import W_MemoryView
from pypy.rlib.entrypoint import entrypoint
from pypy.rlib.unroll import unrolling_iterable
from pypy.rlib.objectmodel import specialize
@@ -387,6 +388,8 @@
"Float": "space.w_float",
"Long": "space.w_long",
"Complex": "space.w_complex",
+ "ByteArray": "space.w_bytearray",
+ "MemoryView": "space.gettypeobject(W_MemoryView.typedef)",
"BaseObject": "space.w_object",
'None': 'space.type(space.w_None)',
'NotImplemented': 'space.type(space.w_NotImplemented)',
diff --git a/pypy/module/cpyext/buffer.py b/pypy/module/cpyext/buffer.py
--- a/pypy/module/cpyext/buffer.py
+++ b/pypy/module/cpyext/buffer.py
@@ -1,6 +1,36 @@
+from pypy.interpreter.error import OperationError
from pypy.rpython.lltypesystem import rffi, lltype
from pypy.module.cpyext.api import (
cpython_api, CANNOT_FAIL, Py_buffer)
+from pypy.module.cpyext.pyobject import PyObject
+
+ at cpython_api([PyObject], rffi.INT_real, error=CANNOT_FAIL)
+def PyObject_CheckBuffer(space, w_obj):
+ """Return 1 if obj supports the buffer interface otherwise 0."""
+ return 0 # the bf_getbuffer field is never filled by cpyext
+
+ at cpython_api([PyObject, lltype.Ptr(Py_buffer), rffi.INT_real],
+ rffi.INT_real, error=-1)
+def PyObject_GetBuffer(space, w_obj, view, flags):
+ """Export obj into a Py_buffer, view. These arguments must
+ never be NULL. The flags argument is a bit field indicating what
+ kind of buffer the caller is prepared to deal with and therefore what
+ kind of buffer the exporter is allowed to return. The buffer interface
+ allows for complicated memory sharing possibilities, but some caller may
+ not be able to handle all the complexity but may want to see if the
+ exporter will let them take a simpler view to its memory.
+
+ Some exporters may not be able to share memory in every possible way and
+ may need to raise errors to signal to some consumers that something is
+ just not possible. These errors should be a BufferError unless
+ there is another error that is actually causing the problem. The
+ exporter can use flags information to simplify how much of the
+ Py_buffer structure is filled in with non-default values and/or
+ raise an error if the object can't support a simpler view of its memory.
+
+ 0 is returned on success and -1 on error."""
+ raise OperationError(space.w_TypeError, space.wrap(
+ 'PyPy does not yet implement the new buffer interface'))
@cpython_api([lltype.Ptr(Py_buffer), lltype.Char], rffi.INT_real, error=CANNOT_FAIL)
def PyBuffer_IsContiguous(space, view, fortran):
diff --git a/pypy/module/cpyext/include/object.h b/pypy/module/cpyext/include/object.h
--- a/pypy/module/cpyext/include/object.h
+++ b/pypy/module/cpyext/include/object.h
@@ -123,10 +123,6 @@
typedef Py_ssize_t (*segcountproc)(PyObject *, Py_ssize_t *);
typedef Py_ssize_t (*charbufferproc)(PyObject *, Py_ssize_t, char **);
-typedef int (*objobjproc)(PyObject *, PyObject *);
-typedef int (*visitproc)(PyObject *, void *);
-typedef int (*traverseproc)(PyObject *, visitproc, void *);
-
/* Py3k buffer interface */
typedef struct bufferinfo {
void *buf;
@@ -153,6 +149,41 @@
typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
+ /* Flags for getting buffers */
+#define PyBUF_SIMPLE 0
+#define PyBUF_WRITABLE 0x0001
+/* we used to include an E, backwards compatible alias */
+#define PyBUF_WRITEABLE PyBUF_WRITABLE
+#define PyBUF_FORMAT 0x0004
+#define PyBUF_ND 0x0008
+#define PyBUF_STRIDES (0x0010 | PyBUF_ND)
+#define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
+#define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
+#define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
+#define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
+
+#define PyBUF_CONTIG (PyBUF_ND | PyBUF_WRITABLE)
+#define PyBUF_CONTIG_RO (PyBUF_ND)
+
+#define PyBUF_STRIDED (PyBUF_STRIDES | PyBUF_WRITABLE)
+#define PyBUF_STRIDED_RO (PyBUF_STRIDES)
+
+#define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_WRITABLE | PyBUF_FORMAT)
+#define PyBUF_RECORDS_RO (PyBUF_STRIDES | PyBUF_FORMAT)
+
+#define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_WRITABLE | PyBUF_FORMAT)
+#define PyBUF_FULL_RO (PyBUF_INDIRECT | PyBUF_FORMAT)
+
+
+#define PyBUF_READ 0x100
+#define PyBUF_WRITE 0x200
+#define PyBUF_SHADOW 0x400
+/* end Py3k buffer interface */
+
+typedef int (*objobjproc)(PyObject *, PyObject *);
+typedef int (*visitproc)(PyObject *, void *);
+typedef int (*traverseproc)(PyObject *, visitproc, void *);
+
typedef struct {
/* For numbers without flag bit Py_TPFLAGS_CHECKTYPES set, all
arguments are guaranteed to be of the object's type (modulo
diff --git a/pypy/module/cpyext/include/pystate.h b/pypy/module/cpyext/include/pystate.h
--- a/pypy/module/cpyext/include/pystate.h
+++ b/pypy/module/cpyext/include/pystate.h
@@ -5,7 +5,7 @@
struct _is; /* Forward */
typedef struct _is {
- int _foo;
+ struct _is *next;
} PyInterpreterState;
typedef struct _ts {
diff --git a/pypy/module/cpyext/pystate.py b/pypy/module/cpyext/pystate.py
--- a/pypy/module/cpyext/pystate.py
+++ b/pypy/module/cpyext/pystate.py
@@ -2,7 +2,10 @@
cpython_api, generic_cpy_call, CANNOT_FAIL, CConfig, cpython_struct)
from pypy.rpython.lltypesystem import rffi, lltype
-PyInterpreterState = lltype.Ptr(cpython_struct("PyInterpreterState", ()))
+PyInterpreterStateStruct = lltype.ForwardReference()
+PyInterpreterState = lltype.Ptr(PyInterpreterStateStruct)
+cpython_struct(
+ "PyInterpreterState", [('next', PyInterpreterState)], PyInterpreterStateStruct)
PyThreadState = lltype.Ptr(cpython_struct("PyThreadState", [('interp', PyInterpreterState)]))
@cpython_api([], PyThreadState, error=CANNOT_FAIL)
@@ -54,7 +57,8 @@
class InterpreterState(object):
def __init__(self, space):
- self.interpreter_state = lltype.malloc(PyInterpreterState.TO, flavor='raw', immortal=True)
+ self.interpreter_state = lltype.malloc(
+ PyInterpreterState.TO, flavor='raw', zero=True, immortal=True)
def new_thread_state(self):
capsule = ThreadStateCapsule()
diff --git a/pypy/module/cpyext/stubs.py b/pypy/module/cpyext/stubs.py
--- a/pypy/module/cpyext/stubs.py
+++ b/pypy/module/cpyext/stubs.py
@@ -34,141 +34,6 @@
@cpython_api([PyObject], rffi.INT_real, error=CANNOT_FAIL)
def PyObject_CheckBuffer(space, obj):
- """Return 1 if obj supports the buffer interface otherwise 0."""
- raise NotImplementedError
-
- at cpython_api([PyObject, Py_buffer, rffi.INT_real], rffi.INT_real, error=-1)
-def PyObject_GetBuffer(space, obj, view, flags):
- """Export obj into a Py_buffer, view. These arguments must
- never be NULL. The flags argument is a bit field indicating what
- kind of buffer the caller is prepared to deal with and therefore what
- kind of buffer the exporter is allowed to return. The buffer interface
- allows for complicated memory sharing possibilities, but some caller may
- not be able to handle all the complexity but may want to see if the
- exporter will let them take a simpler view to its memory.
-
- Some exporters may not be able to share memory in every possible way and
- may need to raise errors to signal to some consumers that something is
- just not possible. These errors should be a BufferError unless
- there is another error that is actually causing the problem. The
- exporter can use flags information to simplify how much of the
- Py_buffer structure is filled in with non-default values and/or
- raise an error if the object can't support a simpler view of its memory.
-
- 0 is returned on success and -1 on error.
-
- The following table gives possible values to the flags arguments.
-
- Flag
-
- Description
-
- PyBUF_SIMPLE
-
- This is the default flag state. The returned
- buffer may or may not have writable memory. The
- format of the data will be assumed to be unsigned
- bytes. This is a "stand-alone" flag constant. It
- never needs to be '|'d to the others. The exporter
- will raise an error if it cannot provide such a
- contiguous buffer of bytes.
-
- PyBUF_WRITABLE
-
- The returned buffer must be writable. If it is
- not writable, then raise an error.
-
- PyBUF_STRIDES
-
- This implies PyBUF_ND. The returned
- buffer must provide strides information (i.e. the
- strides cannot be NULL). This would be used when
- the consumer can handle strided, discontiguous
- arrays. Handling strides automatically assumes
- you can handle shape. The exporter can raise an
- error if a strided representation of the data is
- not possible (i.e. without the suboffsets).
-
- PyBUF_ND
-
- The returned buffer must provide shape
- information. The memory will be assumed C-style
- contiguous (last dimension varies the
- fastest). The exporter may raise an error if it
- cannot provide this kind of contiguous buffer. If
- this is not given then shape will be NULL.
-
- PyBUF_C_CONTIGUOUS
- PyBUF_F_CONTIGUOUS
- PyBUF_ANY_CONTIGUOUS
-
- These flags indicate that the contiguity returned
- buffer must be respectively, C-contiguous (last
- dimension varies the fastest), Fortran contiguous
- (first dimension varies the fastest) or either
- one. All of these flags imply
- PyBUF_STRIDES and guarantee that the
- strides buffer info structure will be filled in
- correctly.
-
- PyBUF_INDIRECT
-
- This flag indicates the returned buffer must have
- suboffsets information (which can be NULL if no
- suboffsets are needed). This can be used when
- the consumer can handle indirect array
- referencing implied by these suboffsets. This
- implies PyBUF_STRIDES.
-
- PyBUF_FORMAT
-
- The returned buffer must have true format
- information if this flag is provided. This would
- be used when the consumer is going to be checking
- for what 'kind' of data is actually stored. An
- exporter should always be able to provide this
- information if requested. If format is not
- explicitly requested then the format must be
- returned as NULL (which means 'B', or
- unsigned bytes)
-
- PyBUF_STRIDED
-
- This is equivalent to (PyBUF_STRIDES |
- PyBUF_WRITABLE).
-
- PyBUF_STRIDED_RO
-
- This is equivalent to (PyBUF_STRIDES).
-
- PyBUF_RECORDS
-
- This is equivalent to (PyBUF_STRIDES |
- PyBUF_FORMAT | PyBUF_WRITABLE).
-
- PyBUF_RECORDS_RO
-
- This is equivalent to (PyBUF_STRIDES |
- PyBUF_FORMAT).
-
- PyBUF_FULL
-
- This is equivalent to (PyBUF_INDIRECT |
- PyBUF_FORMAT | PyBUF_WRITABLE).
-
- PyBUF_FULL_RO
-
- This is equivalent to (PyBUF_INDIRECT |
- PyBUF_FORMAT).
-
- PyBUF_CONTIG
-
- This is equivalent to (PyBUF_ND |
- PyBUF_WRITABLE).
-
- PyBUF_CONTIG_RO
-
- This is equivalent to (PyBUF_ND)."""
raise NotImplementedError
@cpython_api([rffi.CCHARP], Py_ssize_t, error=CANNOT_FAIL)
diff --git a/pypy/module/cpyext/test/test_pystate.py b/pypy/module/cpyext/test/test_pystate.py
--- a/pypy/module/cpyext/test/test_pystate.py
+++ b/pypy/module/cpyext/test/test_pystate.py
@@ -37,6 +37,7 @@
def test_thread_state_interp(self, space, api):
ts = api.PyThreadState_Get()
assert ts.c_interp == api.PyInterpreterState_Head()
+ assert ts.c_interp.c_next == nullptr(PyInterpreterState.TO)
def test_basic_threadstate_dance(self, space, api):
# Let extension modules call these functions,
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
@@ -9,7 +9,7 @@
appleveldefs = {}
class Module(MixedModule):
- applevel_name = 'numpypy'
+ applevel_name = '_numpypy'
submodules = {
'pypy': PyPyModule
@@ -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/app_numpy.py b/pypy/module/micronumpy/app_numpy.py
--- a/pypy/module/micronumpy/app_numpy.py
+++ b/pypy/module/micronumpy/app_numpy.py
@@ -1,6 +1,6 @@
import math
-import numpypy
+import _numpypy
inf = float("inf")
@@ -14,29 +14,29 @@
return mean(a)
def identity(n, dtype=None):
- a = numpypy.zeros((n,n), dtype=dtype)
+ a = _numpypy.zeros((n,n), dtype=dtype)
for i in range(n):
a[i][i] = 1
return a
def mean(a):
if not hasattr(a, "mean"):
- a = numpypy.array(a)
+ a = _numpypy.array(a)
return a.mean()
def sum(a):
if not hasattr(a, "sum"):
- a = numpypy.array(a)
+ a = _numpypy.array(a)
return a.sum()
def min(a):
if not hasattr(a, "min"):
- a = numpypy.array(a)
+ a = _numpypy.array(a)
return a.min()
def max(a):
if not hasattr(a, "max"):
- a = numpypy.array(a)
+ a = _numpypy.array(a)
return a.max()
def arange(start, stop=None, step=1, dtype=None):
@@ -47,9 +47,9 @@
stop = start
start = 0
if dtype is None:
- test = numpypy.array([start, stop, step, 0])
+ test = _numpypy.array([start, stop, step, 0])
dtype = test.dtype
- arr = numpypy.zeros(int(math.ceil((stop - start) / step)), dtype=dtype)
+ arr = _numpypy.zeros(int(math.ceil((stop - start) / step)), dtype=dtype)
i = start
for j in range(arr.size):
arr[j] = i
@@ -90,5 +90,5 @@
you should assign the new shape to the shape attribute of the array
'''
if not hasattr(a, 'reshape'):
- a = numpypy.array(a)
+ a = _numpypy.array(a)
return a.reshape(shape)
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")
@@ -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),
@@ -245,6 +247,7 @@
long_name = "int64"
W_LongBox.typedef = TypeDef(long_name, (W_SignedIntegerBox.typedef, int_typedef,),
__module__ = "numpypy",
+ __new__ = interp2app(W_LongBox.descr__new__.im_func),
)
W_ULongBox.typedef = TypeDef("u" + long_name, W_UnsignedIntegerBox.typedef,
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
@@ -380,6 +380,9 @@
def descr_get_dtype(self, space):
return space.wrap(self.find_dtype())
+ def descr_get_ndim(self, space):
+ return space.wrap(len(self.shape))
+
@jit.unroll_safe
def descr_get_shape(self, space):
return space.newtuple([space.wrap(i) for i in self.shape])
@@ -409,7 +412,7 @@
def descr_repr(self, space):
res = StringBuilder()
res.append("array(")
- concrete = self.get_concrete()
+ concrete = self.get_concrete_or_scalar()
dtype = concrete.find_dtype()
if not concrete.size:
res.append('[]')
@@ -422,8 +425,9 @@
else:
concrete.to_str(space, 1, res, indent=' ')
if (dtype is not interp_dtype.get_dtype_cache(space).w_float64dtype and
- dtype is not interp_dtype.get_dtype_cache(space).w_int64dtype) or \
- not self.size:
+ not (dtype.kind == interp_dtype.SIGNEDLTR and
+ dtype.itemtype.get_element_size() == rffi.sizeof(lltype.Signed)) or
+ not self.size):
res.append(", dtype=" + dtype.name)
res.append(")")
return space.wrap(res.build())
@@ -559,6 +563,18 @@
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(
@@ -840,80 +856,80 @@
each line will begin with indent.
'''
size = self.size
+ ccomma = ',' * comma
+ ncomma = ',' * (1 - comma)
+ dtype = self.find_dtype()
if size < 1:
builder.append('[]')
return
+ elif size == 1:
+ builder.append(dtype.itemtype.str_format(self.getitem(0)))
+ return
if size > 1000:
# Once this goes True it does not go back to False for recursive
# calls
use_ellipsis = True
- dtype = self.find_dtype()
ndims = len(self.shape)
i = 0
- start = True
builder.append('[')
if ndims > 1:
if use_ellipsis:
- for i in range(3):
- if start:
- start = False
- else:
- builder.append(',' * comma + '\n')
- if ndims == 3:
+ for i in range(min(3, self.shape[0])):
+ if i > 0:
+ builder.append(ccomma + '\n')
+ if ndims >= 3:
builder.append('\n' + indent)
else:
builder.append(indent)
- # create_slice requires len(chunks) > 1 in order to reduce
- # shape
- view = self.create_slice([(i, 0, 0, 1), (0, self.shape[1], 1, self.shape[1])]).get_concrete()
- view.to_str(space, comma, builder, indent=indent + ' ', use_ellipsis=use_ellipsis)
- builder.append('\n' + indent + '..., ')
- i = self.shape[0] - 3
+ view = self.create_slice([(i, 0, 0, 1)]).get_concrete()
+ view.to_str(space, comma, builder, indent=indent + ' ',
+ use_ellipsis=use_ellipsis)
+ if i < self.shape[0] - 1:
+ builder.append(ccomma +'\n' + indent + '...' + ncomma)
+ i = self.shape[0] - 3
+ else:
+ i += 1
while i < self.shape[0]:
- if start:
- start = False
- else:
- builder.append(',' * comma + '\n')
- if ndims == 3:
+ if i > 0:
+ builder.append(ccomma + '\n')
+ if ndims >= 3:
builder.append('\n' + indent)
else:
builder.append(indent)
# create_slice requires len(chunks) > 1 in order to reduce
# shape
- view = self.create_slice([(i, 0, 0, 1), (0, self.shape[1], 1, self.shape[1])]).get_concrete()
- view.to_str(space, comma, builder, indent=indent + ' ', use_ellipsis=use_ellipsis)
+ view = self.create_slice([(i, 0, 0, 1)]).get_concrete()
+ view.to_str(space, comma, builder, indent=indent + ' ',
+ use_ellipsis=use_ellipsis)
i += 1
elif ndims == 1:
- spacer = ',' * comma + ' '
+ spacer = ccomma + ' '
item = self.start
# An iterator would be a nicer way to walk along the 1d array, but
# how do I reset it if printing ellipsis? iterators have no
# "set_offset()"
i = 0
if use_ellipsis:
- for i in range(3):
- if start:
- start = False
- else:
+ for i in range(min(3, self.shape[0])):
+ if i > 0:
builder.append(spacer)
builder.append(dtype.itemtype.str_format(self.getitem(item)))
item += self.strides[0]
- # Add a comma only if comma is False - this prevents adding two
- # commas
- builder.append(spacer + '...' + ',' * (1 - comma))
- # Ugly, but can this be done with an iterator?
- item = self.start + self.backstrides[0] - 2 * self.strides[0]
- i = self.shape[0] - 3
+ if i < self.shape[0] - 1:
+ # Add a comma only if comma is False - this prevents adding
+ # two commas
+ builder.append(spacer + '...' + ncomma)
+ # Ugly, but can this be done with an iterator?
+ item = self.start + self.backstrides[0] - 2 * self.strides[0]
+ i = self.shape[0] - 3
+ else:
+ i += 1
while i < self.shape[0]:
- if start:
- start = False
- else:
+ if i > 0:
builder.append(spacer)
builder.append(dtype.itemtype.str_format(self.getitem(item)))
item += self.strides[0]
i += 1
- else:
- builder.append('[')
builder.append(']')
@jit.unroll_safe
@@ -1185,6 +1201,7 @@
shape = GetSetProperty(BaseArray.descr_get_shape,
BaseArray.descr_set_shape),
size = GetSetProperty(BaseArray.descr_get_size),
+ ndim = GetSetProperty(BaseArray.descr_get_ndim),
T = GetSetProperty(BaseArray.descr_get_transpose),
flat = GetSetProperty(BaseArray.descr_get_flatiter),
@@ -1199,6 +1216,8 @@
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
@@ -3,7 +3,7 @@
class AppTestDtypes(BaseNumpyAppTest):
def test_dtype(self):
- from numpypy import dtype
+ from _numpypy import dtype
d = dtype('?')
assert d.num == 0
@@ -14,7 +14,7 @@
raises(TypeError, dtype, 1042)
def test_dtype_with_types(self):
- from numpypy import dtype
+ from _numpypy import dtype
assert dtype(bool).num == 0
assert dtype(int).num == 7
@@ -22,13 +22,13 @@
assert dtype(float).num == 12
def test_array_dtype_attr(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
a = array(range(5), long)
assert a.dtype is dtype(long)
def test_repr_str(self):
- from numpypy import dtype
+ from _numpypy import dtype
assert repr(dtype) == "<type 'numpypy.dtype'>"
d = dtype('?')
@@ -36,7 +36,7 @@
assert str(d) == "bool"
def test_bool_array(self):
- from numpypy import array, False_, True_
+ from _numpypy import array, False_, True_
a = array([0, 1, 2, 2.5], dtype='?')
assert a[0] is False_
@@ -44,7 +44,7 @@
assert a[i] is True_
def test_copy_array_with_dtype(self):
- from numpypy import array, False_, True_, int64
+ from _numpypy import array, False_, True_, int64
a = array([0, 1, 2, 3], dtype=long)
# int on 64-bit, long in 32-bit
@@ -58,35 +58,35 @@
assert b[0] is False_
def test_zeros_bool(self):
- from numpypy import zeros, False_
+ from _numpypy import zeros, False_
a = zeros(10, dtype=bool)
for i in range(10):
assert a[i] is False_
def test_ones_bool(self):
- from numpypy import ones, True_
+ from _numpypy import ones, True_
a = ones(10, dtype=bool)
for i in range(10):
assert a[i] is True_
def test_zeros_long(self):
- from numpypy import zeros, int64
+ from _numpypy import zeros, int64
a = zeros(10, dtype=long)
for i in range(10):
assert isinstance(a[i], int64)
assert a[1] == 0
def test_ones_long(self):
- from numpypy import ones, int64
+ from _numpypy import ones, int64
a = ones(10, dtype=long)
for i in range(10):
assert isinstance(a[i], int64)
assert a[1] == 1
def test_overflow(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
assert array([128], 'b')[0] == -128
assert array([256], 'B')[0] == 0
assert array([32768], 'h')[0] == -32768
@@ -98,7 +98,7 @@
raises(OverflowError, "array([2**64], 'Q')")
def test_bool_binop_types(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
types = [
'?', 'b', 'B', 'h', 'H', 'i', 'I', 'l', 'L', 'q', 'Q', 'f', 'd'
]
@@ -107,7 +107,7 @@
assert (a + array([0], t)).dtype is dtype(t)
def test_binop_types(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
tests = [('b','B','h'), ('b','h','h'), ('b','H','i'), ('b','i','i'),
('b','l','l'), ('b','q','q'), ('b','Q','d'), ('B','h','h'),
('B','H','H'), ('B','i','i'), ('B','I','I'), ('B','l','l'),
@@ -129,7 +129,7 @@
assert (array([1], d1) + array([1], d2)).dtype is dtype(dout)
def test_add_int8(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
a = array(range(5), dtype="int8")
b = a + a
@@ -138,7 +138,7 @@
assert b[i] == i * 2
def test_add_int16(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
a = array(range(5), dtype="int16")
b = a + a
@@ -147,7 +147,7 @@
assert b[i] == i * 2
def test_add_uint32(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
a = array(range(5), dtype="I")
b = a + a
@@ -156,19 +156,28 @@
assert b[i] == i * 2
def test_shape(self):
- from numpypy import dtype
+ from _numpypy import dtype
assert dtype(long).shape == ()
def test_cant_subclass(self):
- from numpypy import dtype
+ from _numpypy import dtype
# 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
+ import _numpypy as numpy
raises(TypeError, numpy.generic, 0)
raises(TypeError, numpy.number, 0)
raises(TypeError, numpy.integer, 0)
@@ -181,7 +190,7 @@
raises(TypeError, numpy.inexact, 0)
def test_bool(self):
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.bool_.mro() == [numpy.bool_, numpy.generic, object]
assert numpy.bool_(3) is numpy.True_
@@ -196,7 +205,7 @@
assert numpy.bool_("False") is numpy.True_
def test_int8(self):
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.int8.mro() == [numpy.int8, numpy.signedinteger, numpy.integer, numpy.number, numpy.generic, object]
@@ -218,7 +227,7 @@
assert numpy.int8('128') == -128
def test_uint8(self):
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.uint8.mro() == [numpy.uint8, numpy.unsignedinteger, numpy.integer, numpy.number, numpy.generic, object]
@@ -241,7 +250,7 @@
assert numpy.uint8('256') == 0
def test_int16(self):
- import numpypy as numpy
+ import _numpypy as numpy
x = numpy.int16(3)
assert x == 3
@@ -251,7 +260,7 @@
assert numpy.int16('32768') == -32768
def test_uint16(self):
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.uint16(65535) == 65535
assert numpy.uint16(65536) == 0
@@ -260,7 +269,7 @@
def test_int32(self):
import sys
- import numpypy as numpy
+ import _numpypy as numpy
x = numpy.int32(23)
assert x == 23
@@ -275,7 +284,7 @@
def test_uint32(self):
import sys
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.uint32(10) == 10
@@ -286,14 +295,14 @@
assert numpy.uint32('4294967296') == 0
def test_int_(self):
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.int_ is numpy.dtype(int).type
assert numpy.int_.mro() == [numpy.int_, numpy.signedinteger, numpy.integer, numpy.number, numpy.generic, int, object]
def test_int64(self):
import sys
- import numpypy as numpy
+ import _numpypy as numpy
if sys.maxint == 2 ** 63 -1:
assert numpy.int64.mro() == [numpy.int64, numpy.signedinteger, numpy.integer, numpy.number, numpy.generic, int, object]
@@ -315,7 +324,7 @@
def test_uint64(self):
import sys
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.uint64.mro() == [numpy.uint64, numpy.unsignedinteger, numpy.integer, numpy.number, numpy.generic, object]
@@ -330,7 +339,7 @@
raises(OverflowError, numpy.uint64(18446744073709551616))
def test_float32(self):
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.float32.mro() == [numpy.float32, numpy.floating, numpy.inexact, numpy.number, numpy.generic, object]
@@ -339,7 +348,7 @@
raises(ValueError, numpy.float32, '23.2df')
def test_float64(self):
- import numpypy as numpy
+ import _numpypy as numpy
assert numpy.float64.mro() == [numpy.float64, numpy.floating, numpy.inexact, numpy.number, numpy.generic, float, object]
@@ -352,7 +361,7 @@
raises(ValueError, numpy.float64, '23.2df')
def test_subclass_type(self):
- import numpypy as numpy
+ import _numpypy as numpy
class X(numpy.float64):
def m(self):
diff --git a/pypy/module/micronumpy/test/test_module.py b/pypy/module/micronumpy/test/test_module.py
--- a/pypy/module/micronumpy/test/test_module.py
+++ b/pypy/module/micronumpy/test/test_module.py
@@ -3,33 +3,33 @@
class AppTestNumPyModule(BaseNumpyAppTest):
def test_mean(self):
- from numpypy import array, mean
+ from _numpypy import array, mean
assert mean(array(range(5))) == 2.0
assert mean(range(5)) == 2.0
def test_average(self):
- from numpypy import array, average
+ from _numpypy import array, average
assert average(range(10)) == 4.5
assert average(array(range(10))) == 4.5
def test_sum(self):
- from numpypy import array, sum
+ from _numpypy import array, sum
assert sum(range(10)) == 45
assert sum(array(range(10))) == 45
def test_min(self):
- from numpypy import array, min
+ from _numpypy import array, min
assert min(range(10)) == 0
assert min(array(range(10))) == 0
def test_max(self):
- from numpypy import array, max
+ from _numpypy import array, max
assert max(range(10)) == 9
assert max(array(range(10))) == 9
def test_constants(self):
import math
- from numpypy import inf, e, pi
+ from _numpypy import inf, e, pi
assert type(inf) is float
assert inf == float("inf")
assert e == math.e
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
@@ -158,9 +158,10 @@
assert calc_new_strides([24], [2, 4, 3], [48, 6, 1]) is None
assert calc_new_strides([24], [2, 4, 3], [24, 6, 2]) == [2]
+
class AppTestNumArray(BaseNumpyAppTest):
def test_ndarray(self):
- from numpypy import ndarray, array, dtype
+ from _numpypy import ndarray, array, dtype
assert type(ndarray) is type
assert type(array) is not type
@@ -175,12 +176,26 @@
assert a.dtype is dtype(int)
def test_type(self):
- from numpypy import array
+ from _numpypy import array
ar = array(range(5))
assert type(ar) is type(ar + ar)
+ def test_ndim(self):
+ from _numpypy import array
+ x = array(0.2)
+ assert x.ndim == 0
+ x = array([1, 2])
+ assert x.ndim == 1
+ x = array([[1, 2], [3, 4]])
+ assert x.ndim == 2
+ x = array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+ assert x.ndim == 3
+ # numpy actually raises an AttributeError, but _numpypy raises an
+ # TypeError
+ raises(TypeError, 'x.ndim = 3')
+
def test_init(self):
- from numpypy import zeros
+ from _numpypy import zeros
a = zeros(15)
# Check that storage was actually zero'd.
assert a[10] == 0.0
@@ -189,7 +204,7 @@
assert a[13] == 5.3
def test_size(self):
- from numpypy import array
+ from _numpypy import array
assert array(3).size == 1
a = array([1, 2, 3])
assert a.size == 3
@@ -200,13 +215,13 @@
Test that empty() works.
"""
- from numpypy import empty
+ from _numpypy import empty
a = empty(2)
a[1] = 1.0
assert a[1] == 1.0
def test_ones(self):
- from numpypy import ones
+ from _numpypy import ones
a = ones(3)
assert len(a) == 3
assert a[0] == 1
@@ -215,7 +230,7 @@
assert a[2] == 4
def test_copy(self):
- from numpypy import arange, array
+ from _numpypy import arange, array
a = arange(5)
b = a.copy()
for i in xrange(5):
@@ -232,12 +247,12 @@
assert (c == b).all()
def test_iterator_init(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
assert a[3] == 3
def test_getitem(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
raises(IndexError, "a[5]")
a = a + a
@@ -246,7 +261,7 @@
raises(IndexError, "a[-6]")
def test_getitem_tuple(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
raises(IndexError, "a[(1,2)]")
for i in xrange(5):
@@ -256,7 +271,7 @@
assert a[i] == b[i]
def test_setitem(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
a[-1] = 5.0
assert a[4] == 5.0
@@ -264,7 +279,7 @@
raises(IndexError, "a[-6] = 3.0")
def test_setitem_tuple(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
raises(IndexError, "a[(1,2)] = [0,1]")
for i in xrange(5):
@@ -275,7 +290,7 @@
assert a[i] == i
def test_setslice_array(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = array(range(2))
a[1:4:2] = b
@@ -286,7 +301,7 @@
assert b[1] == 0.
def test_setslice_of_slice_array(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
a = zeros(5)
a[::2] = array([9., 10., 11.])
assert a[0] == 9.
@@ -305,7 +320,7 @@
assert a[0] == 3.
def test_setslice_list(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5), float)
b = [0., 1.]
a[1:4:2] = b
@@ -313,14 +328,14 @@
assert a[3] == 1.
def test_setslice_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5), float)
a[1:4:2] = 0.
assert a[1] == 0.
assert a[3] == 0.
def test_scalar(self):
- from numpypy import array, dtype
+ from _numpypy import array, dtype
a = array(3)
raises(IndexError, "a[0]")
raises(IndexError, "a[0] = 5")
@@ -329,13 +344,13 @@
assert a.dtype is dtype(int)
def test_len(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
assert len(a) == 5
assert len(a + a) == 5
def test_shape(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
assert a.shape == (5,)
b = a + a
@@ -344,7 +359,7 @@
assert c.shape == (3,)
def test_set_shape(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
a = array([])
a.shape = []
a = array(range(12))
@@ -364,7 +379,7 @@
a.shape = (1,)
def test_reshape(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
a = array(range(12))
exc = raises(ValueError, "b = a.reshape((3, 10))")
assert str(exc.value) == "total size of new array must be unchanged"
@@ -377,7 +392,7 @@
a.shape = (12, 2)
def test_slice_reshape(self):
- from numpypy import zeros, arange
+ from _numpypy import zeros, arange
a = zeros((4, 2, 3))
b = a[::2, :, :]
b.shape = (2, 6)
@@ -413,13 +428,13 @@
raises(ValueError, arange(10).reshape, (5, -1, -1))
def test_reshape_varargs(self):
- from numpypy import arange
+ from _numpypy import arange
z = arange(96).reshape(12, -1)
y = z.reshape(4, 3, 8)
assert y.shape == (4, 3, 8)
def test_add(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a + a
for i in range(5):
@@ -432,7 +447,7 @@
assert c[i] == bool(a[i] + b[i])
def test_add_other(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = array([i for i in reversed(range(5))])
c = a + b
@@ -440,20 +455,20 @@
assert c[i] == 4
def test_add_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a + 5
for i in range(5):
assert b[i] == i + 5
def test_radd(self):
- from numpypy import array
+ from _numpypy import array
r = 3 + array(range(3))
for i in range(3):
assert r[i] == i + 3
def test_add_list(self):
- from numpypy import array, ndarray
+ from _numpypy import array, ndarray
a = array(range(5))
b = list(reversed(range(5)))
c = a + b
@@ -462,14 +477,14 @@
assert c[i] == 4
def test_subtract(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a - a
for i in range(5):
assert b[i] == 0
def test_subtract_other(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = array([1, 1, 1, 1, 1])
c = a - b
@@ -477,34 +492,34 @@
assert c[i] == i - 1
def test_subtract_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a - 5
for i in range(5):
assert b[i] == i - 5
def test_scalar_subtract(self):
- from numpypy import int32
+ from _numpypy import int32
assert int32(2) - 1 == 1
assert 1 - int32(2) == -1
def test_mul(self):
- import numpypy
+ import _numpypy
- a = numpypy.array(range(5))
+ a = _numpypy.array(range(5))
b = a * a
for i in range(5):
assert b[i] == i * i
- a = numpypy.array(range(5), dtype=bool)
+ a = _numpypy.array(range(5), dtype=bool)
b = a * a
- assert b.dtype is numpypy.dtype(bool)
- assert b[0] is numpypy.False_
+ assert b.dtype is _numpypy.dtype(bool)
+ assert b[0] is _numpypy.False_
for i in range(1, 5):
- assert b[i] is numpypy.True_
+ assert b[i] is _numpypy.True_
def test_mul_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a * 5
for i in range(5):
@@ -512,7 +527,7 @@
def test_div(self):
from math import isnan
- from numpypy import array, dtype, inf
+ from _numpypy import array, dtype, inf
a = array(range(1, 6))
b = a / a
@@ -544,7 +559,7 @@
assert c[2] == -inf
def test_div_other(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = array([2, 2, 2, 2, 2], float)
c = a / b
@@ -552,14 +567,14 @@
assert c[i] == i / 2.0
def test_div_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a / 5.0
for i in range(5):
assert b[i] == i / 5.0
def test_pow(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5), float)
b = a ** a
for i in range(5):
@@ -569,7 +584,7 @@
assert (a ** 2 == a * a).all()
def test_pow_other(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5), float)
b = array([2, 2, 2, 2, 2])
c = a ** b
@@ -577,14 +592,14 @@
assert c[i] == i ** 2
def test_pow_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5), float)
b = a ** 2
for i in range(5):
assert b[i] == i ** 2
def test_mod(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(1, 6))
b = a % a
for i in range(5):
@@ -597,7 +612,7 @@
assert b[i] == 1
def test_mod_other(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = array([2, 2, 2, 2, 2])
c = a % b
@@ -605,14 +620,14 @@
assert c[i] == i % 2
def test_mod_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a % 2
for i in range(5):
assert b[i] == i % 2
def test_pos(self):
- from numpypy import array
+ from _numpypy import array
a = array([1., -2., 3., -4., -5.])
b = +a
for i in range(5):
@@ -623,7 +638,7 @@
assert a[i] == i
def test_neg(self):
- from numpypy import array
+ from _numpypy import array
a = array([1., -2., 3., -4., -5.])
b = -a
for i in range(5):
@@ -634,7 +649,7 @@
assert a[i] == -i
def test_abs(self):
- from numpypy import array
+ from _numpypy import array
a = array([1., -2., 3., -4., -5.])
b = abs(a)
for i in range(5):
@@ -645,7 +660,7 @@
assert a[i + 5] == abs(i)
def test_auto_force(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a - 1
a[2] = 3
@@ -659,7 +674,7 @@
assert c[1] == 4
def test_getslice(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
s = a[1:5]
assert len(s) == 4
@@ -673,7 +688,7 @@
assert s[0] == 5
def test_getslice_step(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(10))
s = a[1:9:2]
assert len(s) == 4
@@ -681,7 +696,7 @@
assert s[i] == a[2 * i + 1]
def test_slice_update(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
s = a[0:3]
s[1] = 10
@@ -691,7 +706,7 @@
def test_slice_invaidate(self):
# check that slice shares invalidation list with
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
s = a[0:2]
b = array([10, 11])
@@ -705,13 +720,13 @@
assert d[1] == 12
def test_mean(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
assert a.mean() == 2.0
assert a[:4].mean() == 1.5
def test_sum(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
assert a.sum() == 10.0
assert a[:4].sum() == 6.0
@@ -720,52 +735,52 @@
assert a.sum() == 5
def test_identity(self):
- from numpypy import identity, array
- from numpypy import int32, float64, dtype
+ from _numpypy import identity, array
+ from _numpypy import int32, float64, dtype
a = identity(0)
assert len(a) == 0
assert a.dtype == dtype('float64')
- assert a.shape == (0,0)
+ assert a.shape == (0, 0)
b = identity(1, dtype=int32)
assert len(b) == 1
assert b[0][0] == 1
- assert b.shape == (1,1)
+ assert b.shape == (1, 1)
assert b.dtype == dtype('int32')
c = identity(2)
- assert c.shape == (2,2)
- assert (c == [[1,0],[0,1]]).all()
+ assert c.shape == (2, 2)
+ assert (c == [[1, 0], [0, 1]]).all()
d = identity(3, dtype='int32')
- assert d.shape == (3,3)
+ assert d.shape == (3, 3)
assert d.dtype == dtype('int32')
- assert (d == [[1,0,0],[0,1,0],[0,0,1]]).all()
+ assert (d == [[1, 0, 0], [0, 1, 0], [0, 0, 1]]).all()
def test_prod(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(1, 6))
assert a.prod() == 120.0
assert a[:4].prod() == 24.0
def test_max(self):
- from numpypy import array
+ from _numpypy import array
a = array([-1.2, 3.4, 5.7, -3.0, 2.7])
assert a.max() == 5.7
b = array([])
raises(ValueError, "b.max()")
def test_max_add(self):
- from numpypy import array
+ from _numpypy import array
a = array([-1.2, 3.4, 5.7, -3.0, 2.7])
assert (a + a).max() == 11.4
def test_min(self):
- from numpypy import array
+ from _numpypy import array
a = array([-1.2, 3.4, 5.7, -3.0, 2.7])
assert a.min() == -3.0
b = array([])
raises(ValueError, "b.min()")
def test_argmax(self):
- from numpypy import array
+ from _numpypy import array
a = array([-1.2, 3.4, 5.7, -3.0, 2.7])
r = a.argmax()
assert r == 2
@@ -786,14 +801,14 @@
assert a.argmax() == 2
def test_argmin(self):
- from numpypy import array
+ from _numpypy import array
a = array([-1.2, 3.4, 5.7, -3.0, 2.7])
assert a.argmin() == 3
b = array([])
raises(ValueError, "b.argmin()")
def test_all(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
assert a.all() == False
a[0] = 3.0
@@ -802,7 +817,7 @@
assert b.all() == True
def test_any(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
a = array(range(5))
assert a.any() == True
b = zeros(5)
@@ -811,7 +826,7 @@
assert c.any() == False
def test_dot(self):
- from numpypy import array, dot
+ from _numpypy import array, dot
a = array(range(5))
assert a.dot(a) == 30.0
@@ -821,14 +836,14 @@
assert (dot(5, [1, 2, 3]) == [5, 10, 15]).all()
def test_dot_constant(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
b = a.dot(2.5)
for i in xrange(5):
assert b[i] == 2.5 * a[i]
def test_dtype_guessing(self):
- from numpypy import array, dtype, float64, int8, bool_
+ from _numpypy import array, dtype, float64, int8, bool_
assert array([True]).dtype is dtype(bool)
assert array([True, False]).dtype is dtype(bool)
@@ -845,7 +860,7 @@
def test_comparison(self):
import operator
- from numpypy import array, dtype
+ from _numpypy import array, dtype
a = array(range(5))
b = array(range(5), float)
@@ -864,7 +879,7 @@
assert c[i] == func(b[i], 3)
def test_nonzero(self):
- from numpypy import array
+ from _numpypy import array
a = array([1, 2])
raises(ValueError, bool, a)
raises(ValueError, bool, a == a)
@@ -874,7 +889,7 @@
assert not bool(array([0]))
def test_slice_assignment(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
a[::-1] = a
assert (a == [0, 1, 2, 1, 0]).all()
@@ -884,8 +899,8 @@
assert (a == [8, 6, 4, 2, 0]).all()
def test_debug_repr(self):
- from numpypy import zeros, sin
- from numpypy.pypy import debug_repr
+ from _numpypy import zeros, sin
+ from _numpypy.pypy import debug_repr
a = zeros(1)
assert debug_repr(a) == 'Array'
assert debug_repr(a + a) == 'Call2(add, Array, Array)'
@@ -899,8 +914,8 @@
assert debug_repr(b) == 'Array'
def test_remove_invalidates(self):
- from numpypy import array
- from numpypy.pypy import remove_invalidates
+ from _numpypy import array
+ from _numpypy.pypy import remove_invalidates
a = array([1, 2, 3])
b = a + a
remove_invalidates(a)
@@ -908,7 +923,7 @@
assert b[0] == 28
def test_virtual_views(self):
- from numpypy import arange
+ from _numpypy import arange
a = arange(15)
c = (a + a)
d = c[::2]
@@ -926,7 +941,7 @@
assert b[1] == 2
def test_tolist_scalar(self):
- from numpypy import int32, bool_
+ from _numpypy import int32, bool_
x = int32(23)
assert x.tolist() == 23
assert type(x.tolist()) is int
@@ -934,13 +949,13 @@
assert y.tolist() is True
def test_tolist_zerodim(self):
- from numpypy import array
+ from _numpypy import array
x = array(3)
assert x.tolist() == 3
assert type(x.tolist()) is int
def test_tolist_singledim(self):
- from numpypy import array
+ from _numpypy import array
a = array(range(5))
assert a.tolist() == [0, 1, 2, 3, 4]
assert type(a.tolist()[0]) is int
@@ -948,41 +963,55 @@
assert b.tolist() == [0.2, 0.4, 0.6]
def test_tolist_multidim(self):
- from numpypy import array
+ from _numpypy import array
a = array([[1, 2], [3, 4]])
assert a.tolist() == [[1, 2], [3, 4]]
def test_tolist_view(self):
- from numpypy import array
- a = array([[1,2],[3,4]])
+ from _numpypy import array
+ a = array([[1, 2], [3, 4]])
assert (a + a).tolist() == [[2, 4], [6, 8]]
def test_tolist_slice(self):
- from numpypy import array
+ from _numpypy import array
a = array([[17.1, 27.2], [40.3, 50.3]])
- assert a[:,0].tolist() == [17.1, 40.3]
+ 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):
- import numpypy
- a = numpypy.zeros((2, 2))
+ import _numpypy
+ a = _numpypy.zeros((2, 2))
assert len(a) == 2
def test_shape(self):
- import numpypy
- assert numpypy.zeros(1).shape == (1,)
- assert numpypy.zeros((2, 2)).shape == (2, 2)
- assert numpypy.zeros((3, 1, 2)).shape == (3, 1, 2)
- assert numpypy.array([[1], [2], [3]]).shape == (3, 1)
- assert len(numpypy.zeros((3, 1, 2))) == 3
- raises(TypeError, len, numpypy.zeros(()))
- raises(ValueError, numpypy.array, [[1, 2], 3])
+ import _numpypy
+ assert _numpypy.zeros(1).shape == (1,)
+ assert _numpypy.zeros((2, 2)).shape == (2, 2)
+ assert _numpypy.zeros((3, 1, 2)).shape == (3, 1, 2)
+ assert _numpypy.array([[1], [2], [3]]).shape == (3, 1)
+ assert len(_numpypy.zeros((3, 1, 2))) == 3
+ raises(TypeError, len, _numpypy.zeros(()))
+ raises(ValueError, _numpypy.array, [[1, 2], 3])
def test_getsetitem(self):
- import numpypy
- a = numpypy.zeros((2, 3, 1))
+ import _numpypy
+ a = _numpypy.zeros((2, 3, 1))
raises(IndexError, a.__getitem__, (2, 0, 0))
raises(IndexError, a.__getitem__, (0, 3, 0))
raises(IndexError, a.__getitem__, (0, 0, 1))
@@ -993,8 +1022,8 @@
assert a[1, -1, 0] == 3
def test_slices(self):
- import numpypy
- a = numpypy.zeros((4, 3, 2))
+ import _numpypy
+ a = _numpypy.zeros((4, 3, 2))
raises(IndexError, a.__getitem__, (4,))
raises(IndexError, a.__getitem__, (3, 3))
raises(IndexError, a.__getitem__, (slice(None), 3))
@@ -1027,51 +1056,51 @@
assert a[1][2][1] == 15
def test_init_2(self):
- import numpypy
- raises(ValueError, numpypy.array, [[1], 2])
- raises(ValueError, numpypy.array, [[1, 2], [3]])
- raises(ValueError, numpypy.array, [[[1, 2], [3, 4], 5]])
- raises(ValueError, numpypy.array, [[[1, 2], [3, 4], [5]]])
- a = numpypy.array([[1, 2], [4, 5]])
+ import _numpypy
+ raises(ValueError, _numpypy.array, [[1], 2])
+ raises(ValueError, _numpypy.array, [[1, 2], [3]])
+ raises(ValueError, _numpypy.array, [[[1, 2], [3, 4], 5]])
+ raises(ValueError, _numpypy.array, [[[1, 2], [3, 4], [5]]])
+ a = _numpypy.array([[1, 2], [4, 5]])
assert a[0, 1] == 2
assert a[0][1] == 2
- a = numpypy.array(([[[1, 2], [3, 4], [5, 6]]]))
+ a = _numpypy.array(([[[1, 2], [3, 4], [5, 6]]]))
assert (a[0, 1] == [3, 4]).all()
def test_setitem_slice(self):
- import numpypy
- a = numpypy.zeros((3, 4))
+ import _numpypy
+ a = _numpypy.zeros((3, 4))
a[1] = [1, 2, 3, 4]
assert a[1, 2] == 3
raises(TypeError, a[1].__setitem__, [1, 2, 3])
- a = numpypy.array([[1, 2], [3, 4]])
+ a = _numpypy.array([[1, 2], [3, 4]])
assert (a == [[1, 2], [3, 4]]).all()
- a[1] = numpypy.array([5, 6])
+ a[1] = _numpypy.array([5, 6])
assert (a == [[1, 2], [5, 6]]).all()
- a[:, 1] = numpypy.array([8, 10])
+ a[:, 1] = _numpypy.array([8, 10])
assert (a == [[1, 8], [5, 10]]).all()
- a[0, :: -1] = numpypy.array([11, 12])
+ a[0, :: -1] = _numpypy.array([11, 12])
assert (a == [[12, 11], [5, 10]]).all()
def test_ufunc(self):
- from numpypy import array
+ from _numpypy import array
a = array([[1, 2], [3, 4], [5, 6]])
assert ((a + a) == \
array([[1 + 1, 2 + 2], [3 + 3, 4 + 4], [5 + 5, 6 + 6]])).all()
def test_getitem_add(self):
- from numpypy import array
+ from _numpypy import array
a = array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
assert (a + a)[1, 1] == 8
def test_ufunc_negative(self):
- from numpypy import array, negative
+ from _numpypy import array, negative
a = array([[1, 2], [3, 4]])
b = negative(a + a)
assert (b == [[-2, -4], [-6, -8]]).all()
def test_getitem_3(self):
- from numpypy import array
+ from _numpypy import array
a = array([[1, 2], [3, 4], [5, 6], [7, 8],
[9, 10], [11, 12], [13, 14]])
b = a[::2]
@@ -1082,37 +1111,37 @@
assert c[1][1] == 12
def test_multidim_ones(self):
- from numpypy import ones
+ from _numpypy import ones
a = ones((1, 2, 3))
assert a[0, 1, 2] == 1.0
def test_multidim_setslice(self):
- from numpypy import zeros, ones
+ from _numpypy import zeros, ones
a = zeros((3, 3))
b = ones((3, 3))
- a[:,1:3] = b[:,1:3]
+ a[:, 1:3] = b[:, 1:3]
assert (a == [[0, 1, 1], [0, 1, 1], [0, 1, 1]]).all()
a = zeros((3, 3))
b = ones((3, 3))
- a[:,::2] = b[:,::2]
+ a[:, ::2] = b[:, ::2]
assert (a == [[1, 0, 1], [1, 0, 1], [1, 0, 1]]).all()
def test_broadcast_ufunc(self):
- from numpypy import array
+ from _numpypy import array
a = array([[1, 2], [3, 4], [5, 6]])
b = array([5, 6])
c = ((a + b) == [[1 + 5, 2 + 6], [3 + 5, 4 + 6], [5 + 5, 6 + 6]])
assert c.all()
def test_broadcast_setslice(self):
- from numpypy import zeros, ones
+ from _numpypy import zeros, ones
a = zeros((10, 10))
b = ones(10)
a[:, :] = b
assert a[3, 5] == 1
def test_broadcast_shape_agreement(self):
- from numpypy import zeros, array
+ from _numpypy import zeros, array
a = zeros((3, 1, 3))
b = array(((10, 11, 12), (20, 21, 22), (30, 31, 32)))
c = ((a + b) == [b, b, b])
@@ -1126,7 +1155,7 @@
assert c.all()
def test_broadcast_scalar(self):
- from numpypy import zeros
+ from _numpypy import zeros
a = zeros((4, 5), 'd')
a[:, 1] = 3
assert a[2, 1] == 3
@@ -1137,14 +1166,14 @@
assert a[3, 2] == 0
def test_broadcast_call2(self):
- from numpypy import zeros, ones
+ from _numpypy import zeros, ones
a = zeros((4, 1, 5))
b = ones((4, 3, 5))
b[:] = (a + a)
assert (b == zeros((4, 3, 5))).all()
def test_broadcast_virtualview(self):
- from numpypy import arange, zeros
+ from _numpypy import arange, zeros
a = arange(8).reshape([2, 2, 2])
b = (a + a)[1, 1]
c = zeros((2, 2, 2))
@@ -1152,13 +1181,13 @@
assert (c == [[[12, 14], [12, 14]], [[12, 14], [12, 14]]]).all()
def test_argmax(self):
- from numpypy import array
+ from _numpypy import array
a = array([[1, 2], [3, 4], [5, 6]])
assert a.argmax() == 5
assert a[:2, ].argmax() == 3
def test_broadcast_wrong_shapes(self):
- from numpypy import zeros
+ from _numpypy import zeros
a = zeros((4, 3, 2))
b = zeros((4, 2))
exc = raises(ValueError, lambda: a + b)
@@ -1166,7 +1195,7 @@
" together with shapes (4,3,2) (4,2)"
def test_reduce(self):
- from numpypy import array
+ from _numpypy import array
a = array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
assert a.sum() == (13 * 12) / 2
b = a[1:, 1::2]
@@ -1174,7 +1203,7 @@
assert c.sum() == (6 + 8 + 10 + 12) * 2
def test_transpose(self):
- from numpypy import array
+ from _numpypy import array
a = array(((range(3), range(3, 6)),
(range(6, 9), range(9, 12)),
(range(12, 15), range(15, 18)),
@@ -1193,7 +1222,7 @@
assert(b[:, 0] == a[0, :]).all()
def test_flatiter(self):
- from numpypy import array, flatiter
+ from _numpypy import array, flatiter
a = array([[10, 30], [40, 60]])
f_iter = a.flat
assert f_iter.next() == 10
@@ -1208,23 +1237,23 @@
assert s == 140
def test_flatiter_array_conv(self):
- from numpypy import array, dot
+ from _numpypy import array, dot
a = array([1, 2, 3])
assert dot(a.flat, a.flat) == 14
def test_flatiter_varray(self):
- from numpypy import ones
+ from _numpypy import ones
a = ones((2, 2))
assert list(((a + a).flat)) == [2, 2, 2, 2]
def test_slice_copy(self):
- from numpypy import zeros
+ from _numpypy import zeros
a = zeros((10, 10))
b = a[0].copy()
assert (b == zeros(10)).all()
def test_array_interface(self):
- from numpypy import array
+ from _numpypy import array
a = array([1, 2, 3])
i = a.__array_interface__
assert isinstance(i['data'][0], int)
@@ -1233,6 +1262,7 @@
assert isinstance(i['data'][0], int)
raises(TypeError, getattr, array(3), '__array_interface__')
+
class AppTestSupport(BaseNumpyAppTest):
def setup_class(cls):
import struct
@@ -1245,7 +1275,7 @@
def test_fromstring(self):
import sys
- from numpypy import fromstring, array, uint8, float32, int32
+ from _numpypy import fromstring, array, uint8, float32, int32
a = fromstring(self.data)
for i in range(4):
@@ -1275,17 +1305,17 @@
assert g[1] == 2
assert g[2] == 3
h = fromstring("1, , 2, 3", dtype=uint8, sep=",")
- assert (h == [1,0,2,3]).all()
+ assert (h == [1, 0, 2, 3]).all()
i = fromstring("1 2 3", dtype=uint8, sep=" ")
- assert (i == [1,2,3]).all()
+ assert (i == [1, 2, 3]).all()
j = fromstring("1\t\t\t\t2\t3", dtype=uint8, sep="\t")
- assert (j == [1,2,3]).all()
+ assert (j == [1, 2, 3]).all()
k = fromstring("1,x,2,3", dtype=uint8, sep=",")
- assert (k == [1,0]).all()
+ assert (k == [1, 0]).all()
l = fromstring("1,x,2,3", dtype='float32', sep=",")
- assert (l == [1.0,-1.0]).all()
+ assert (l == [1.0, -1.0]).all()
m = fromstring("1,,2,3", sep=",")
- assert (m == [1.0,-1.0,2.0,3.0]).all()
+ assert (m == [1.0, -1.0, 2.0, 3.0]).all()
n = fromstring("3.4 2.0 3.8 2.2", dtype=int32, sep=" ")
assert (n == [3]).all()
o = fromstring("1.0 2f.0f 3.8 2.2", dtype=float32, sep=" ")
@@ -1309,7 +1339,7 @@
assert (u == [1, 0]).all()
def test_fromstring_types(self):
- from numpypy import (fromstring, int8, int16, int32, int64, uint8,
+ from _numpypy import (fromstring, int8, int16, int32, int64, uint8,
uint16, uint32, float32, float64)
a = fromstring('\xFF', dtype=int8)
@@ -1333,9 +1363,8 @@
j = fromstring(self.ulongval, dtype='L')
assert j[0] == 12
-
def test_fromstring_invalid(self):
- from numpypy import fromstring, uint16, uint8, int32
+ from _numpypy import fromstring, uint16, uint8, int32
#default dtype is 64-bit float, so 3 bytes should fail
raises(ValueError, fromstring, "\x01\x02\x03")
#3 bytes is not modulo 2 bytes (int16)
@@ -1346,7 +1375,8 @@
class AppTestRepr(BaseNumpyAppTest):
def test_repr(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
+ int_size = array(5).dtype.itemsize
a = array(range(5), float)
assert repr(a) == "array([0.0, 1.0, 2.0, 3.0, 4.0])"
a = array([], float)
@@ -1354,14 +1384,26 @@
a = zeros(1001)
assert repr(a) == "array([0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0])"
a = array(range(5), long)
- assert repr(a) == "array([0, 1, 2, 3, 4])"
+ if a.dtype.itemsize == int_size:
+ assert repr(a) == "array([0, 1, 2, 3, 4])"
+ else:
+ assert repr(a) == "array([0, 1, 2, 3, 4], dtype=int64)"
+ a = array(range(5), 'int32')
+ if a.dtype.itemsize == int_size:
+ assert repr(a) == "array([0, 1, 2, 3, 4])"
+ else:
+ assert repr(a) == "array([0, 1, 2, 3, 4], dtype=int32)"
a = array([], long)
assert repr(a) == "array([], dtype=int64)"
a = array([True, False, True, False], "?")
assert repr(a) == "array([True, False, True, False], dtype=bool)"
+ a = zeros([])
+ assert repr(a) == "array(0.0)"
+ a = array(0.2)
+ assert repr(a) == "array(0.2)"
def test_repr_multi(self):
- from numpypy import array, zeros
+ from _numpypy import arange, zeros
a = zeros((3, 4))
assert repr(a) == '''array([[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
@@ -1374,9 +1416,19 @@
[[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]])'''
+ a = arange(1002).reshape((2, 501))
+ assert repr(a) == '''array([[0, 1, 2, ..., 498, 499, 500],
+ [501, 502, 503, ..., 999, 1000, 1001]])'''
+ assert repr(a.T) == '''array([[0, 501],
+ [1, 502],
+ [2, 503],
+ ...,
+ [498, 999],
+ [499, 1000],
+ [500, 1001]])'''
def test_repr_slice(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
a = array(range(5), float)
b = a[1::2]
assert repr(b) == "array([1.0, 3.0])"
@@ -1391,7 +1443,7 @@
assert repr(b) == "array([], shape=(0, 5), dtype=int16)"
def test_str(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
a = array(range(5), float)
assert str(a) == "[0.0 1.0 2.0 3.0 4.0]"
assert str((2 * a)[:]) == "[0.0 2.0 4.0 6.0 8.0]"
@@ -1417,14 +1469,14 @@
a = zeros((400, 400), dtype=int)
assert str(a) == "[[0 0 0 ..., 0 0 0]\n [0 0 0 ..., 0 0 0]\n" \
- " [0 0 0 ..., 0 0 0]\n ..., \n [0 0 0 ..., 0 0 0]\n" \
+ " [0 0 0 ..., 0 0 0]\n ...,\n [0 0 0 ..., 0 0 0]\n" \
" [0 0 0 ..., 0 0 0]\n [0 0 0 ..., 0 0 0]]"
a = zeros((2, 2, 2))
r = str(a)
assert r == '[[[0.0 0.0]\n [0.0 0.0]]\n\n [[0.0 0.0]\n [0.0 0.0]]]'
def test_str_slice(self):
- from numpypy import array, zeros
+ from _numpypy import array, zeros
a = array(range(5), float)
b = a[1::2]
assert str(b) == "[1.0 3.0]"
@@ -1440,7 +1492,7 @@
class AppTestRanges(BaseNumpyAppTest):
def test_arange(self):
- from numpypy import arange, array, dtype
+ from _numpypy import arange, array, dtype
a = arange(3)
assert (a == [0, 1, 2]).all()
assert a.dtype is dtype(int)
@@ -1462,7 +1514,7 @@
class AppTestRanges(BaseNumpyAppTest):
def test_app_reshape(self):
- from numpypy import arange, array, dtype, reshape
+ from _numpypy import arange, array, dtype, reshape
a = arange(12)
b = reshape(a, (3, 4))
assert b.shape == (3, 4)
diff --git a/pypy/module/micronumpy/test/test_ufuncs.py b/pypy/module/micronumpy/test/test_ufuncs.py
--- a/pypy/module/micronumpy/test/test_ufuncs.py
+++ b/pypy/module/micronumpy/test/test_ufuncs.py
@@ -4,14 +4,14 @@
class AppTestUfuncs(BaseNumpyAppTest):
def test_ufunc_instance(self):
- from numpypy import add, ufunc
+ from _numpypy import add, ufunc
assert isinstance(add, ufunc)
assert repr(add) == "<ufunc 'add'>"
assert repr(ufunc) == "<type 'numpypy.ufunc'>"
def test_ufunc_attrs(self):
- from numpypy import add, multiply, sin
+ from _numpypy import add, multiply, sin
assert add.identity == 0
assert multiply.identity == 1
@@ -22,7 +22,7 @@
assert sin.nin == 1
def test_wrong_arguments(self):
- from numpypy import add, sin
+ from _numpypy import add, sin
raises(ValueError, add, 1)
raises(TypeError, add, 1, 2, 3)
@@ -30,14 +30,14 @@
raises(ValueError, sin)
def test_single_item(self):
- from numpypy import negative, sign, minimum
+ from _numpypy import negative, sign, minimum
assert negative(5.0) == -5.0
assert sign(-0.0) == 0.0
assert minimum(2.0, 3.0) == 2.0
def test_sequence(self):
- from numpypy import array, ndarray, negative, minimum
+ from _numpypy import array, ndarray, negative, minimum
a = array(range(3))
b = [2.0, 1.0, 0.0]
c = 1.0
@@ -71,7 +71,7 @@
assert min_c_b[i] == min(b[i], c)
def test_negative(self):
- from numpypy import array, negative
+ from _numpypy import array, negative
a = array([-5.0, 0.0, 1.0])
b = negative(a)
@@ -86,7 +86,7 @@
assert negative(a + a)[3] == -6
def test_abs(self):
- from numpypy import array, absolute
+ from _numpypy import array, absolute
a = array([-5.0, -0.0, 1.0])
b = absolute(a)
@@ -94,7 +94,7 @@
assert b[i] == abs(a[i])
def test_add(self):
- from numpypy import array, add
+ from _numpypy import array, add
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
@@ -103,7 +103,7 @@
assert c[i] == a[i] + b[i]
def test_divide(self):
- from numpypy import array, divide
+ from _numpypy import array, divide
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
@@ -114,7 +114,7 @@
assert (divide(array([-10]), array([2])) == array([-5])).all()
def test_fabs(self):
- from numpypy import array, fabs
+ from _numpypy import array, fabs
from math import fabs as math_fabs
a = array([-5.0, -0.0, 1.0])
@@ -123,7 +123,7 @@
assert b[i] == math_fabs(a[i])
def test_minimum(self):
- from numpypy import array, minimum
+ from _numpypy import array, minimum
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
@@ -132,7 +132,7 @@
assert c[i] == min(a[i], b[i])
def test_maximum(self):
- from numpypy import array, maximum
+ from _numpypy import array, maximum
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
@@ -145,7 +145,7 @@
assert isinstance(x, (int, long))
def test_multiply(self):
- from numpypy import array, multiply
+ from _numpypy import array, multiply
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
@@ -154,7 +154,7 @@
assert c[i] == a[i] * b[i]
def test_sign(self):
- from numpypy import array, sign, dtype
+ from _numpypy import array, sign, dtype
reference = [-1.0, 0.0, 0.0, 1.0]
a = array([-5.0, -0.0, 0.0, 6.0])
@@ -173,7 +173,7 @@
assert a[1] == 0
def test_reciporocal(self):
- from numpypy import array, reciprocal
+ from _numpypy import array, reciprocal
reference = [-0.2, float("inf"), float("-inf"), 2.0]
a = array([-5.0, 0.0, -0.0, 0.5])
@@ -182,7 +182,7 @@
assert b[i] == reference[i]
def test_subtract(self):
- from numpypy import array, subtract
+ from _numpypy import array, subtract
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
@@ -191,7 +191,7 @@
assert c[i] == a[i] - b[i]
def test_floor(self):
- from numpypy import array, floor
+ from _numpypy import array, floor
reference = [-2.0, -1.0, 0.0, 1.0, 1.0]
a = array([-1.4, -1.0, 0.0, 1.0, 1.4])
@@ -200,7 +200,7 @@
assert b[i] == reference[i]
def test_copysign(self):
- from numpypy import array, copysign
+ from _numpypy import array, copysign
reference = [5.0, -0.0, 0.0, -6.0]
a = array([-5.0, 0.0, 0.0, 6.0])
@@ -216,7 +216,7 @@
def test_exp(self):
import math
- from numpypy import array, exp
+ from _numpypy import array, exp
a = array([-5.0, -0.0, 0.0, 12345678.0, float("inf"),
-float('inf'), -12343424.0])
@@ -230,7 +230,7 @@
def test_sin(self):
import math
- from numpypy import array, sin
+ from _numpypy import array, sin
a = array([0, 1, 2, 3, math.pi, math.pi*1.5, math.pi*2])
b = sin(a)
@@ -243,7 +243,7 @@
def test_cos(self):
import math
- from numpypy import array, cos
+ from _numpypy import array, cos
a = array([0, 1, 2, 3, math.pi, math.pi*1.5, math.pi*2])
b = cos(a)
@@ -252,7 +252,7 @@
def test_tan(self):
import math
- from numpypy import array, tan
+ from _numpypy import array, tan
a = array([0, 1, 2, 3, math.pi, math.pi*1.5, math.pi*2])
b = tan(a)
@@ -262,7 +262,7 @@
def test_arcsin(self):
import math
- from numpypy import array, arcsin
+ from _numpypy import array, arcsin
a = array([-1, -0.5, -0.33, 0, 0.33, 0.5, 1])
b = arcsin(a)
@@ -276,7 +276,7 @@
def test_arccos(self):
import math
- from numpypy import array, arccos
+ from _numpypy import array, arccos
a = array([-1, -0.5, -0.33, 0, 0.33, 0.5, 1])
b = arccos(a)
@@ -291,7 +291,7 @@
def test_arctan(self):
import math
- from numpypy import array, arctan
+ from _numpypy import array, arctan
a = array([-3, -2, -1, 0, 1, 2, 3, float('inf'), float('-inf')])
b = arctan(a)
@@ -304,7 +304,7 @@
def test_arcsinh(self):
import math
- from numpypy import arcsinh, inf
+ from _numpypy import arcsinh, inf
for v in [inf, -inf, 1.0, math.e]:
assert math.asinh(v) == arcsinh(v)
@@ -312,7 +312,7 @@
def test_arctanh(self):
import math
- from numpypy import arctanh
+ from _numpypy import arctanh
for v in [.99, .5, 0, -.5, -.99]:
assert math.atanh(v) == arctanh(v)
@@ -323,7 +323,7 @@
def test_sqrt(self):
import math
- from numpypy import sqrt
+ from _numpypy import sqrt
nan, inf = float("nan"), float("inf")
data = [1, 2, 3, inf]
@@ -333,13 +333,13 @@
assert math.isnan(sqrt(nan))
def test_reduce_errors(self):
- from numpypy import sin, add
+ from _numpypy import sin, add
raises(ValueError, sin.reduce, [1, 2, 3])
raises(TypeError, add.reduce, 1)
def test_reduce(self):
- from numpypy import add, maximum
+ from _numpypy import add, maximum
assert add.reduce([1, 2, 3]) == 6
assert maximum.reduce([1]) == 1
@@ -348,7 +348,7 @@
def test_comparisons(self):
import operator
- from numpypy import equal, not_equal, less, less_equal, greater, greater_equal
+ from _numpypy import equal, not_equal, less, less_equal, greater, greater_equal
for ufunc, func in [
(equal, operator.eq),
diff --git a/pypy/module/pypyjit/test_pypy_c/test_00_model.py b/pypy/module/pypyjit/test_pypy_c/test_00_model.py
--- a/pypy/module/pypyjit/test_pypy_c/test_00_model.py
+++ b/pypy/module/pypyjit/test_pypy_c/test_00_model.py
@@ -8,10 +8,12 @@
from pypy.tool import logparser
from pypy.jit.tool.jitoutput import parse_prof
from pypy.module.pypyjit.test_pypy_c.model import (Log, find_ids_range,
- find_ids, TraceWithIds,
+ find_ids,
OpMatcher, InvalidMatch)
class BaseTestPyPyC(object):
+ log_string = 'jit-log-opt,jit-log-noopt,jit-log-virtualstate,jit-summary'
+
def setup_class(cls):
if '__pypy__' not in sys.builtin_module_names:
py.test.skip("must run this test with pypy")
@@ -52,8 +54,7 @@
cmdline += ['--jit', ','.join(jitcmdline)]
cmdline.append(str(self.filepath))
#
- print cmdline, logfile
- env={'PYPYLOG': 'jit-log-opt,jit-log-noopt,jit-log-virtualstate,jit-summary:' + str(logfile)}
+ env={'PYPYLOG': self.log_string + ':' + str(logfile)}
pipe = subprocess.Popen(cmdline,
env=env,
stdout=subprocess.PIPE,
diff --git a/pypy/module/pypyjit/test_pypy_c/test__ffi.py b/pypy/module/pypyjit/test_pypy_c/test__ffi.py
--- a/pypy/module/pypyjit/test_pypy_c/test__ffi.py
+++ b/pypy/module/pypyjit/test_pypy_c/test__ffi.py
@@ -98,7 +98,8 @@
end = time.time()
return end - start
#
- log = self.run(main, [get_libc_name(), 200], threshold=150)
+ log = self.run(main, [get_libc_name(), 200], threshold=150,
+ import_site=True)
assert 1 <= log.result <= 1.5 # at most 0.5 seconds of overhead
loops = log.loops_by_id('sleep')
assert len(loops) == 1 # make sure that we actually JITted the loop
@@ -121,7 +122,7 @@
return fabs._ptr.getaddr(), x
libm_name = get_libm_name(sys.platform)
- log = self.run(main, [libm_name])
+ log = self.run(main, [libm_name], import_site=True)
fabs_addr, res = log.result
assert res == -4.0
loop, = log.loops_by_filename(self.filepath)
diff --git a/pypy/module/pypyjit/test_pypy_c/test_string.py b/pypy/module/pypyjit/test_pypy_c/test_string.py
--- a/pypy/module/pypyjit/test_pypy_c/test_string.py
+++ b/pypy/module/pypyjit/test_pypy_c/test_string.py
@@ -15,7 +15,7 @@
i += letters[i % len(letters)] == uletters[i % len(letters)]
return i
- log = self.run(main, [300])
+ log = self.run(main, [300], import_site=True)
assert log.result == 300
loop, = log.loops_by_filename(self.filepath)
assert loop.match("""
@@ -55,7 +55,7 @@
i += int(long(string.digits[i % len(string.digits)], 16))
return i
- log = self.run(main, [1100])
+ log = self.run(main, [1100], import_site=True)
assert log.result == main(1100)
loop, = log.loops_by_filename(self.filepath)
assert loop.match("""
diff --git a/pypy/module/sys/__init__.py b/pypy/module/sys/__init__.py
--- a/pypy/module/sys/__init__.py
+++ b/pypy/module/sys/__init__.py
@@ -42,7 +42,7 @@
'argv' : 'state.get(space).w_argv',
'py3kwarning' : 'space.w_False',
'warnoptions' : 'state.get(space).w_warnoptions',
- 'builtin_module_names' : 'state.w_None',
+ 'builtin_module_names' : 'space.w_None',
'pypy_getudir' : 'state.pypy_getudir', # not translated
'pypy_initial_path' : 'state.pypy_initial_path',
diff --git a/pypy/module/sys/app.py b/pypy/module/sys/app.py
--- a/pypy/module/sys/app.py
+++ b/pypy/module/sys/app.py
@@ -66,11 +66,11 @@
return None
copyright_str = """
-Copyright 2003-2011 PyPy development team.
+Copyright 2003-2012 PyPy development team.
All Rights Reserved.
For further information, see <http://pypy.org>
-Portions Copyright (c) 2001-2008 Python Software Foundation.
+Portions Copyright (c) 2001-2012 Python Software Foundation.
All Rights Reserved.
Portions Copyright (c) 2000 BeOpen.com.
diff --git a/pypy/objspace/fake/checkmodule.py b/pypy/objspace/fake/checkmodule.py
--- a/pypy/objspace/fake/checkmodule.py
+++ b/pypy/objspace/fake/checkmodule.py
@@ -1,8 +1,10 @@
from pypy.objspace.fake.objspace import FakeObjSpace, W_Root
+from pypy.config.pypyoption import get_pypy_config
def checkmodule(modname):
- space = FakeObjSpace()
+ config = get_pypy_config(translating=True)
+ space = FakeObjSpace(config)
mod = __import__('pypy.module.%s' % modname, None, None, ['__doc__'])
# force computation and record what we wrap
module = mod.Module(space, W_Root())
diff --git a/pypy/objspace/fake/objspace.py b/pypy/objspace/fake/objspace.py
--- a/pypy/objspace/fake/objspace.py
+++ b/pypy/objspace/fake/objspace.py
@@ -93,9 +93,9 @@
class FakeObjSpace(ObjSpace):
- def __init__(self):
+ def __init__(self, config=None):
self._seen_extras = []
- ObjSpace.__init__(self)
+ ObjSpace.__init__(self, config=config)
def float_w(self, w_obj):
is_root(w_obj)
@@ -135,6 +135,9 @@
def newfloat(self, x):
return w_some_obj()
+ def newcomplex(self, x, y):
+ return w_some_obj()
+
def marshal_w(self, w_obj):
"NOT_RPYTHON"
raise NotImplementedError
@@ -215,6 +218,10 @@
expected_length = 3
return [w_some_obj()] * expected_length
+ def unpackcomplex(self, w_complex):
+ is_root(w_complex)
+ return 1.1, 2.2
+
def allocate_instance(self, cls, w_subtype):
is_root(w_subtype)
return instantiate(cls)
@@ -232,6 +239,11 @@
def exec_(self, *args, **kwds):
pass
+ def createexecutioncontext(self):
+ ec = ObjSpace.createexecutioncontext(self)
+ ec._py_repr = None
+ return ec
+
# ----------
def translates(self, func=None, argtypes=None, **kwds):
@@ -267,18 +279,21 @@
ObjSpace.ExceptionTable +
['int', 'str', 'float', 'long', 'tuple', 'list',
'dict', 'unicode', 'complex', 'slice', 'bool',
- 'type', 'basestring']):
+ 'type', 'basestring', 'object']):
setattr(FakeObjSpace, 'w_' + name, w_some_obj())
#
for (name, _, arity, _) in ObjSpace.MethodTable:
args = ['w_%d' % i for i in range(arity)]
+ params = args[:]
d = {'is_root': is_root,
'w_some_obj': w_some_obj}
+ if name in ('get',):
+ params[-1] += '=None'
exec compile2("""\
def meth(self, %s):
%s
return w_some_obj()
- """ % (', '.join(args),
+ """ % (', '.join(params),
'; '.join(['is_root(%s)' % arg for arg in args]))) in d
meth = func_with_new_name(d['meth'], name)
setattr(FakeObjSpace, name, meth)
@@ -301,9 +316,12 @@
pass
FakeObjSpace.default_compiler = FakeCompiler()
-class FakeModule(object):
+class FakeModule(Wrappable):
+ def __init__(self):
+ self.w_dict = w_some_obj()
def get(self, name):
name + "xx" # check that it's a string
return w_some_obj()
FakeObjSpace.sys = FakeModule()
FakeObjSpace.sys.filesystemencoding = 'foobar'
+FakeObjSpace.builtin = FakeModule()
diff --git a/pypy/objspace/fake/test/test_objspace.py b/pypy/objspace/fake/test/test_objspace.py
--- a/pypy/objspace/fake/test/test_objspace.py
+++ b/pypy/objspace/fake/test/test_objspace.py
@@ -40,7 +40,7 @@
def test_constants(self):
space = self.space
space.translates(lambda: (space.w_None, space.w_True, space.w_False,
- space.w_int, space.w_str,
+ space.w_int, space.w_str, space.w_object,
space.w_TypeError))
def test_wrap(self):
@@ -72,3 +72,9 @@
def test_newlist(self):
self.space.newlist([W_Root(), W_Root()])
+
+ def test_default_values(self):
+ # the __get__ method takes either 2 or 3 arguments
+ space = self.space
+ space.translates(lambda: (space.get(W_Root(), W_Root()),
+ space.get(W_Root(), W_Root(), W_Root())))
diff --git a/pypy/rlib/clibffi.py b/pypy/rlib/clibffi.py
--- a/pypy/rlib/clibffi.py
+++ b/pypy/rlib/clibffi.py
@@ -30,6 +30,9 @@
_MAC_OS = platform.name == "darwin"
_FREEBSD_7 = platform.name == "freebsd7"
+_LITTLE_ENDIAN = sys.byteorder == 'little'
+_BIG_ENDIAN = sys.byteorder == 'big'
+
if _WIN32:
from pypy.rlib import rwin32
@@ -360,12 +363,36 @@
cast_type_to_ffitype._annspecialcase_ = 'specialize:memo'
def push_arg_as_ffiptr(ffitp, arg, ll_buf):
- # this is for primitive types. For structures and arrays
- # would be something different (more dynamic)
+ # This is for primitive types. Note that the exact type of 'arg' may be
+ # different from the expected 'c_size'. To cope with that, we fall back
+ # to a byte-by-byte copy.
TP = lltype.typeOf(arg)
TP_P = lltype.Ptr(rffi.CArray(TP))
- buf = rffi.cast(TP_P, ll_buf)
- buf[0] = arg
+ TP_size = rffi.sizeof(TP)
+ c_size = intmask(ffitp.c_size)
+ # if both types have the same size, we can directly write the
+ # value to the buffer
+ if c_size == TP_size:
+ buf = rffi.cast(TP_P, ll_buf)
+ buf[0] = arg
+ else:
+ # needs byte-by-byte copying. Make sure 'arg' is an integer type.
+ # Note that this won't work for rffi.FLOAT/rffi.DOUBLE.
+ assert TP is not rffi.FLOAT and TP is not rffi.DOUBLE
+ if TP_size <= rffi.sizeof(lltype.Signed):
+ arg = rffi.cast(lltype.Unsigned, arg)
+ else:
+ arg = rffi.cast(lltype.UnsignedLongLong, arg)
+ if _LITTLE_ENDIAN:
+ for i in range(c_size):
+ ll_buf[i] = chr(arg & 0xFF)
+ arg >>= 8
+ elif _BIG_ENDIAN:
+ for i in range(c_size-1, -1, -1):
+ ll_buf[i] = chr(arg & 0xFF)
+ arg >>= 8
+ else:
+ raise AssertionError
push_arg_as_ffiptr._annspecialcase_ = 'specialize:argtype(1)'
diff --git a/pypy/rlib/jit.py b/pypy/rlib/jit.py
--- a/pypy/rlib/jit.py
+++ b/pypy/rlib/jit.py
@@ -386,6 +386,18 @@
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_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 enable 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/tool/jitlogparser/parser.py b/pypy/tool/jitlogparser/parser.py
--- a/pypy/tool/jitlogparser/parser.py
+++ b/pypy/tool/jitlogparser/parser.py
@@ -185,7 +185,10 @@
return self.code.map[self.bytecode_no]
def getlineno(self):
- return self.getopcode().lineno
+ code = self.getopcode()
+ if code is None:
+ return None
+ return code.lineno
lineno = property(getlineno)
def getline_starts_here(self):
diff --git a/pypy/tool/jitlogparser/storage.py b/pypy/tool/jitlogparser/storage.py
--- a/pypy/tool/jitlogparser/storage.py
+++ b/pypy/tool/jitlogparser/storage.py
@@ -6,7 +6,6 @@
import py
import os
from lib_pypy.disassembler import dis
-from pypy.tool.jitlogparser.parser import Function
from pypy.tool.jitlogparser.module_finder import gather_all_code_objs
class LoopStorage(object):
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() { }
diff --git a/pypy/translator/goal/app_main.py b/pypy/translator/goal/app_main.py
--- a/pypy/translator/goal/app_main.py
+++ b/pypy/translator/goal/app_main.py
@@ -139,8 +139,8 @@
items = pypyjit.defaults.items()
items.sort()
for key, value in items:
- print ' --jit %s=N %slow-level JIT parameter (default %s)' % (
- key, ' '*(18-len(key)), value)
+ print ' --jit %s=N %s%s (default %s)' % (
+ key, ' '*(18-len(key)), pypyjit.PARAMETER_DOCS[key], value)
print ' --jit off turn off the JIT'
def print_version(*args):
More information about the pypy-commit
mailing list