[pypy-commit] pypy numpy-back-to-applevel: Add missing files, to progress, we need out attribute (Which is pointless

[fijal]

Author: Maciej Fijalkowski <fijall at gmail.com>
Branch: numpy-back-to-applevel
Changeset: r51577:1ff91b2e045c
Date: 2012-01-21 15:41 +0200
http://bitbucket.org/pypy/pypy/changeset/1ff91b2e045c/

Log:	Add missing files, to progress, we need out attribute (Which is
	pointless on pypy btw)

diff --git a/lib_pypy/numpypy/core/_methods.py b/lib_pypy/numpypy/core/_methods.py
new file mode 100644
--- /dev/null
+++ b/lib_pypy/numpypy/core/_methods.py
@@ -0,0 +1,98 @@
+# Array methods which are called by the both the C-code for the method
+# and the Python code for the NumPy-namespace function
+
+import _numpypy as mu
+um = mu
+#from numpypy.core import umath as um
+from numpypy.core.numeric import asanyarray
+
+def _amax(a, axis=None, out=None, skipna=False, keepdims=False):
+    return um.maximum.reduce(a, axis=axis,
+                            out=out, skipna=skipna, keepdims=keepdims)
+
+def _amin(a, axis=None, out=None, skipna=False, keepdims=False):
+    return um.minimum.reduce(a, axis=axis,
+                            out=out, skipna=skipna, keepdims=keepdims)
+
+def _sum(a, axis=None, dtype=None, out=None, skipna=False, keepdims=False):
+    return um.add.reduce(a, axis=axis, dtype=dtype,
+                            out=out, skipna=skipna, keepdims=keepdims)
+
+def _prod(a, axis=None, dtype=None, out=None, skipna=False, keepdims=False):
+    return um.multiply.reduce(a, axis=axis, dtype=dtype,
+                            out=out, skipna=skipna, keepdims=keepdims)
+
+def _mean(a, axis=None, dtype=None, out=None, skipna=False, keepdims=False):
+    arr = asanyarray(a)
+
+    # Upgrade bool, unsigned int, and int to float64
+    if dtype is None and arr.dtype.kind in ['b','u','i']:
+        ret = um.add.reduce(arr, axis=axis, dtype='f8',
+                            out=out, skipna=skipna, keepdims=keepdims)
+    else:
+        ret = um.add.reduce(arr, axis=axis, dtype=dtype,
+                            out=out, skipna=skipna, keepdims=keepdims)
+    rcount = mu.count_reduce_items(arr, axis=axis,
+                            skipna=skipna, keepdims=keepdims)
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(ret, rcount,
+                        out=ret, casting='unsafe', subok=False)
+    else:
+        ret = ret / float(rcount)
+    return ret
+
+def _var(a, axis=None, dtype=None, out=None, ddof=0,
+                            skipna=False, keepdims=False):
+    arr = asanyarray(a)
+
+    # First compute the mean, saving 'rcount' for reuse later
+    if dtype is None and arr.dtype.kind in ['b','u','i']:
+        arrmean = um.add.reduce(arr, axis=axis, dtype='f8',
+                            skipna=skipna, keepdims=True)
+    else:
+        arrmean = um.add.reduce(arr, axis=axis, dtype=dtype,
+                            skipna=skipna, keepdims=True)
+    rcount = mu.count_reduce_items(arr, axis=axis,
+                            skipna=skipna, keepdims=True)
+    if isinstance(arrmean, mu.ndarray):
+        arrmean = um.true_divide(arrmean, rcount,
+                            out=arrmean, casting='unsafe', subok=False)
+    else:
+        arrmean = arrmean / float(rcount)
+
+    # arr - arrmean
+    x = arr - arrmean
+
+    # (arr - arrmean) ** 2
+    if arr.dtype.kind == 'c':
+        x = um.multiply(x, um.conjugate(x), out=x).real
+    else:
+        x = um.multiply(x, x, out=x)
+
+    # add.reduce((arr - arrmean) ** 2, axis)
+    ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out,
+                                skipna=skipna, keepdims=keepdims)
+
+    # add.reduce((arr - arrmean) ** 2, axis) / (n - ddof)
+    if not keepdims and isinstance(rcount, mu.ndarray):
+        rcount = rcount.squeeze(axis=axis)
+    rcount -= ddof
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(ret, rcount,
+                        out=ret, casting='unsafe', subok=False)
+    else:
+        ret = ret / float(rcount)
+
+    return ret
+
+def _std(a, axis=None, dtype=None, out=None, ddof=0,
+                            skipna=False, keepdims=False):
+    ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
+                                skipna=skipna, keepdims=keepdims)
+
+    if isinstance(ret, mu.ndarray):
+        ret = um.sqrt(ret, out=ret)
+    else:
+        ret = um.sqrt(ret)
+
+    return ret
diff --git a/lib_pypy/numpypy/core/numeric.py b/lib_pypy/numpypy/core/numeric.py
new file mode 100644
--- /dev/null
+++ b/lib_pypy/numpypy/core/numeric.py
@@ -0,0 +1,62 @@
+
+from _numpypy import array
+
+def asanyarray(a, dtype=None, order=None, maskna=None, ownmaskna=False):
+    """
+    Convert the input to an ndarray, but pass ndarray subclasses through.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data, in any form that can be converted to an array.  This
+        includes scalars, lists, lists of tuples, tuples, tuples of tuples,
+        tuples of lists, and ndarrays.
+    dtype : data-type, optional
+        By default, the data-type is inferred from the input data.
+    order : {'C', 'F'}, optional
+        Whether to use row-major ('C') or column-major ('F') memory
+        representation.  Defaults to 'C'.
+   maskna : bool or None, optional
+        If this is set to True, it forces the array to have an NA mask.
+        If this is set to False, it forces the array to not have an NA
+        mask.
+    ownmaskna : bool, optional
+        If this is set to True, forces the array to have a mask which
+        it owns.
+
+    Returns
+    -------
+    out : ndarray or an ndarray subclass
+        Array interpretation of `a`.  If `a` is an ndarray or a subclass
+        of ndarray, it is returned as-is and no copy is performed.
+
+    See Also
+    --------
+    asarray : Similar function which always returns ndarrays.
+    ascontiguousarray : Convert input to a contiguous array.
+    asfarray : Convert input to a floating point ndarray.
+    asfortranarray : Convert input to an ndarray with column-major
+                     memory order.
+    asarray_chkfinite : Similar function which checks input for NaNs and
+                        Infs.
+    fromiter : Create an array from an iterator.
+    fromfunction : Construct an array by executing a function on grid
+                   positions.
+
+    Examples
+    --------
+    Convert a list into an array:
+
+    >>> a = [1, 2]
+    >>> np.asanyarray(a)
+    array([1, 2])
+
+    Instances of `ndarray` subclasses are passed through as-is:
+
+    >>> a = np.matrix([1, 2])
+    >>> np.asanyarray(a) is a
+    True
+
+    """
+    return array(a, dtype, copy=False, order=order, subok=True,
+                                maskna=maskna, ownmaskna=ownmaskna)
diff --git a/pypy/module/micronumpy/appbridge.py b/pypy/module/micronumpy/appbridge.py
new file mode 100644
--- /dev/null
+++ b/pypy/module/micronumpy/appbridge.py
@@ -0,0 +1,28 @@
+
+from pypy.rlib.objectmodel import specialize
+
+class AppBridgeCache(object):
+    w__var = None
+    w__std = None
+    w_module = None
+
+    def __init__(self, space):
+        self.w_import = space.appexec([], """():
+        def f():
+           mod = __import__('numpypy.core._methods', {}, {}, [''])
+           return mod
+        return f
+        """)
+    
+    @specialize.arg(2)
+    def call_method(self, space, name, *args):
+        w_meth = getattr(self, 'w_' + name)
+        if w_meth is None:
+            if self.w_module is None:
+                self.w_module = space.call_function(self.w_import)
+            w_meth = space.getattr(self.w_module, space.wrap(name))
+            setattr(self, 'w_' + name, w_meth)
+        return space.call_function(w_meth, *args)
+
+def get_appbridge_cache(space):
+    return space.fromcache(AppBridgeCache)