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author | Julian Taylor <jtaylor.debian@googlemail.com> | 2014-10-27 20:30:19 +0100 |
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committer | Julian Taylor <jtaylor.debian@googlemail.com> | 2014-10-27 20:30:19 +0100 |
commit | 7be18edfaae4d0ba6a888160d1cc57c453b568f2 (patch) | |
tree | 85c0f49236a2158d9679fdd8ee5b0fa8db4ad5d3 /numpy/lib | |
parent | 16575443239fa84615fc795692a79ef27f25c216 (diff) | |
parent | 3d5f499fd6807186baf669eec491034766caab34 (diff) | |
download | numpy-7be18edfaae4d0ba6a888160d1cc57c453b568f2.tar.gz |
Merge pull request #5214 from mkowoods/np.average
Update to average calculation
Diffstat (limited to 'numpy/lib')
-rw-r--r-- | numpy/lib/function_base.py | 5 | ||||
-rw-r--r-- | numpy/lib/tests/test_function_base.py | 5 |
2 files changed, 7 insertions, 3 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 35195338f..36ce94bad 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -515,8 +515,7 @@ def average(a, axis=None, weights=None, returned=False): scl = avg.dtype.type(a.size/avg.size) else: a = a + 0.0 - wgt = np.array(weights, dtype=a.dtype, copy=0) - + wgt = np.asarray(weights) # Sanity checks if a.shape != wgt.shape: if axis is None: @@ -533,7 +532,7 @@ def average(a, axis=None, weights=None, returned=False): # setup wgt to broadcast along axis wgt = np.array(wgt, copy=0, ndmin=a.ndim).swapaxes(-1, axis) - scl = wgt.sum(axis=axis) + scl = wgt.sum(axis=axis, dtype=np.result_type(a.dtype, wgt.dtype)) if (scl == 0.0).any(): raise ZeroDivisionError( "Weights sum to zero, can't be normalized") diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index eb0b04057..80faf85a6 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -124,6 +124,11 @@ class TestAverage(TestCase): assert_array_equal(average(y1, weights=w2, axis=1), desired) assert_equal(average(y1, weights=w2), 5.) + y3 = rand(5).astype(np.float32) + w3 = rand(5).astype(np.float64) + + assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3)) + def test_returned(self): y = np.array([[1, 2, 3], [4, 5, 6]]) |