ENH: core: Add static caching of the callables for C to core._method calls

1 files changed, 105 insertions, 0 deletions

diff --git a/numpy/core/_methods.py b/numpy/core/_methods.py
new file mode 100644
index 000000000..3c05cf71d
--- /dev/null
+++ b/numpy/core/_methods.py
@@ -0,0 +1,105 @@
+# Array methods which are called by the both the C-code for the method
+# and the Python code for the NumPy-namespace function
+
+import multiarray as mu
+import umath as um
+from 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 _any(a, axis=None, out=None, skipna=False, keepdims=False):
+    return um.logical_or.reduce(a, axis=axis,
+                            out=out, skipna=skipna, keepdims=keepdims)
+
+def _all(a, axis=None, out=None, skipna=False, keepdims=False):
+    return um.logical_and.reduce(a, axis=axis,
+                            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):
+        um.sqrt(ret, out=ret)
+    else:
+        ret = um.sqrt(ret)
+
+    return ret