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-rw-r--r--numpy/core/code_generators/numpy_api_order.txt2
-rw-r--r--numpy/core/numeric.py69
-rw-r--r--numpy/core/src/multiarray/multiarraymodule.c18
-rw-r--r--numpy/core/tests/test_numeric.py18
4 files changed, 48 insertions, 59 deletions
diff --git a/numpy/core/code_generators/numpy_api_order.txt b/numpy/core/code_generators/numpy_api_order.txt
index 3d1e40add..2b71fc547 100644
--- a/numpy/core/code_generators/numpy_api_order.txt
+++ b/numpy/core/code_generators/numpy_api_order.txt
@@ -173,5 +173,5 @@ PyArray_CompareString
PyArray_MultiIterFromObjects
PyArray_GetEndianness
PyArray_GetNDArrayCFeatureVersion
-PyArray_Acorrelate
+PyArray_Correlate2
PyArray_NeighborhoodIterNew
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index b497e4898..31b021b71 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -6,7 +6,7 @@ __all__ = ['newaxis', 'ndarray', 'flatiter', 'ufunc',
'set_numeric_ops', 'can_cast',
'asarray', 'asanyarray', 'ascontiguousarray', 'asfortranarray',
'isfortran', 'empty_like', 'zeros_like',
- 'correlate', 'acorrelate', 'convolve', 'inner', 'dot', 'outer', 'vdot',
+ 'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot',
'alterdot', 'restoredot', 'roll', 'rollaxis', 'cross', 'tensordot',
'array2string', 'get_printoptions', 'set_printoptions',
'array_repr', 'array_str', 'set_string_function',
@@ -22,6 +22,7 @@ __all__ = ['newaxis', 'ndarray', 'flatiter', 'ufunc',
'CLIP', 'RAISE', 'WRAP', 'MAXDIMS', 'BUFSIZE', 'ALLOW_THREADS']
import sys
+import warnings
import multiarray
import umath
from umath import *
@@ -585,7 +586,7 @@ def _mode_from_name(mode):
return _mode_from_name_dict[mode.lower()[0]]
return mode
-def correlate(a,v,mode='valid'):
+def correlate(a,v,mode='valid',old_behavior=True):
"""
Discrete, linear correlation of two 1-dimensional sequences.
@@ -602,14 +603,25 @@ def correlate(a,v,mode='valid'):
mode : {'valid', 'same', 'full'}, optional
Refer to the `convolve` docstring. Note that the default
is `valid`, unlike `convolve`, which uses `full`.
+ old_behavior : bool
+ If True, uses the old, numeric behavior (correlate(a,v) == correlate(v,
+ a), and the conjugate is not taken for complex arrays). If False, uses
+ the conventional signal processing definition (see note).
See Also
--------
convolve : Discrete, linear convolution of two
one-dimensional sequences.
- acorrelate: Discrete correlation following the usual signal processing
- definition for complex arrays, and without assuming that
- correlate(a, b) == correlate(b, a)
+
+ Note
+ ----
+ If old_behavior is False, this function computes the correlation as
+ generally defined in signal processing texts::
+
+ z[k] = sum_n a[n] * conj(v[n+k])
+
+ with a and v sequences being zero-padded where necessary and conj being the
+ conjugate.
Examples
--------
@@ -622,42 +634,17 @@ def correlate(a,v,mode='valid'):
"""
mode = _mode_from_name(mode)
- return multiarray.correlate(a,v,mode)
-
-def acorrelate(a, v, mode='valid'):
- """
- Discrete, linear correlation of two 1-dimensional sequences.
-
- This function computes the correlation as generally defined in signal
- processing texts::
-
- z[k] = sum_n a[n] * conj(v[n+k])
-
- with a and v sequences being zero-padded where necessary and conj being the
- conjugate.
-
- Parameters
- ----------
- a, v : array_like
- Input sequences.
- mode : {'valid', 'same', 'full'}, optional
- Refer to the `convolve` docstring. Note that the default
- is `valid`, unlike `convolve`, which uses `full`.
-
- See Also
- --------
- convolve : Discrete, linear convolution of two
- one-dimensional sequences.
- correlate: Deprecated function to compute correlation
-
- Notes
- -----
- This is the function which corresponds to matlab xcorr.
-
- """
- mode = _mode_from_name(mode)
- return multiarray.acorrelate(a, v, mode)
-
+ if old_behavior:
+ warnings.warn("""
+The current behavior of correlate is deprecated for 1.4.0, and will be removed
+for NumPy 1.5.0.
+
+The new behavior fits the conventional definition of correlation: inputs are
+never swapped, and the second argument is conjugated for complex arrays.""",
+ DeprecationWarning)
+ return multiarray.correlate(a,v,mode)
+ else:
+ return multiarray.correlate2(a,v,mode)
def convolve(a,v,mode='full'):
"""
diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c
index 8f71a4d14..c45952910 100644
--- a/numpy/core/src/multiarray/multiarraymodule.c
+++ b/numpy/core/src/multiarray/multiarraymodule.c
@@ -906,7 +906,7 @@ PyArray_CopyAndTranspose(PyObject *op)
}
/*
- * Implementation which is common between PyArray_Correlate and PyArray_Acorrelate
+ * Implementation which is common between PyArray_Correlate and PyArray_Correlate2
*
* inverted is set to 1 if computed correlate(ap2, ap1), 0 otherwise
*/
@@ -1065,13 +1065,13 @@ _pyarray_revert(PyArrayObject *ret)
}
/*NUMPY_API
- * acorrelate(a1,a2,mode)
+ * correlate(a1,a2,mode)
*
- * This function computes the usual correlation (acorrelate(a1, a2) !=
- * accorrelate(a2, a1), and conjugate the second argument for complex inputs
+ * This function computes the usual correlation (correlate(a1, a2) !=
+ * correlate(a2, a1), and conjugate the second argument for complex inputs
*/
NPY_NO_EXPORT PyObject *
-PyArray_Acorrelate(PyObject *op1, PyObject *op2, int mode)
+PyArray_Correlate2(PyObject *op1, PyObject *op2, int mode)
{
PyArrayObject *ap1, *ap2, *ret = NULL;
int typenum;
@@ -1796,7 +1796,7 @@ static PyObject *array_correlate(PyObject *NPY_UNUSED(dummy), PyObject *args, Py
}
static PyObject*
-array_acorrelate(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
+array_correlate2(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
{
PyObject *shape, *a0;
int mode = 0;
@@ -1806,7 +1806,7 @@ array_acorrelate(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
&a0, &shape, &mode)) {
return NULL;
}
- return PyArray_Acorrelate(a0, shape, mode);
+ return PyArray_Correlate2(a0, shape, mode);
}
static PyObject *
@@ -2416,8 +2416,8 @@ static struct PyMethodDef array_module_methods[] = {
{"correlate",
(PyCFunction)array_correlate,
METH_VARARGS | METH_KEYWORDS, NULL},
- {"acorrelate",
- (PyCFunction)array_acorrelate,
+ {"correlate2",
+ (PyCFunction)array_correlate2,
METH_VARARGS | METH_KEYWORDS, NULL},
{"frombuffer",
(PyCFunction)array_frombuffer,
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py
index 18f879cb4..9353bc960 100644
--- a/numpy/core/tests/test_numeric.py
+++ b/numpy/core/tests/test_numeric.py
@@ -896,19 +896,20 @@ class _TestCorrelate(TestCase):
def test_float(self):
self._setup(np.float)
- z = np.correlate(self.x, self.y, 'full')
- assert_array_almost_equal(z, self.z1)
- z = np.correlate(self.y, self.x, 'full')
+ z = np.correlate(self.x, self.y, 'full', old_behavior=self.old_behavior)
assert_array_almost_equal(z, self.z1)
+ z = np.correlate(self.y, self.x, 'full', old_behavior=self.old_behavior)
+ assert_array_almost_equal(z, self.z2)
def test_object(self):
self._setup(Decimal)
- z = np.correlate(self.x, self.y, 'full')
- assert_array_almost_equal(z, self.z1)
- z = np.correlate(self.y, self.x, 'full')
+ z = np.correlate(self.x, self.y, 'full', old_behavior=self.old_behavior)
assert_array_almost_equal(z, self.z1)
+ z = np.correlate(self.y, self.x, 'full', old_behavior=self.old_behavior)
+ assert_array_almost_equal(z, self.z2)
class TestCorrelate(_TestCorrelate):
+ old_behavior = True
def _setup(self, dt):
# correlate uses an unconventional definition so that correlate(a, b)
# == correlate(b, a), so force the corresponding outputs to be the same
@@ -923,7 +924,8 @@ class TestCorrelate(_TestCorrelate):
z = np.correlate(x, y, 'full')
assert_array_almost_equal(z, r_z)
-class TestAcorrelate(_TestCorrelate):
+class TestCorrelateNew(_TestCorrelate):
+ old_behavior = False
def test_complex(self):
x = np.array([1, 2, 3, 4+1j], dtype=np.complex)
y = np.array([-1, -2j, 3+1j], dtype=np.complex)
@@ -932,7 +934,7 @@ class TestAcorrelate(_TestCorrelate):
#assert_array_almost_equal(z, r_z)
r_z = r_z[::-1].conjugate()
- z = np.acorrelate(y, x, 'full')
+ z = np.correlate(y, x, 'full', old_behavior=self.old_behavior)
assert_array_almost_equal(z, r_z)
class TestArgwhere:
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