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BUG: Fix of var method for complex arrays #13177

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Merged
merged 9 commits into from
Apr 1, 2019
Merged

BUG: Fix of var method for complex arrays #13177

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d5aae9c
BUG: Closes #13110. Fixed the incorrect optimization for complex arrays
cnighut Mar 22, 2019
d68e620
Closes #13110. Removedd incorrect optimisation for complex type array…
cnighut Mar 22, 2019
494ed9d
Closes #13110. Removedd incorrect optimisation for complex type array.
cnighut Mar 22, 2019
7ec463c
BUG: Closes #13110. Fixed the incorrect optimization for variance cal…
cnighut Mar 22, 2019
9edffa3
BUG: Closes #13110. Fix of method for complex arrays
cnighut Mar 25, 2019
6bc7126
Added test case for method for complex arrays
cnighut Mar 25, 2019
fadda39
Added comment to define the newly added test cases for imaginary data…
cnighut Apr 1, 2019
3b14a2c
Added comment to define the newly added test cases for imaginary data…
cnighut Apr 1, 2019
d64cb8c
Added test for imaginary data in object arrays
cnighut Apr 1, 2019
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7 changes: 4 additions & 3 deletions numpy/core/_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,10 +115,11 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
# Note that x may not be inexact and that we need it to be an array,
# not a scalar.
x = asanyarray(arr - arrmean)
if issubclass(arr.dtype.type, nt.complexfloating):
x = um.multiply(x, um.conjugate(x), out=x).real
else:
if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
x = um.multiply(x, x, out=x)
else:
x = um.multiply(x, um.conjugate(x), out=x).real

ret = umr_sum(x, axis, dtype, out, keepdims)

# Compute degrees of freedom and make sure it is not negative.
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3 changes: 3 additions & 0 deletions numpy/core/tests/test_numeric.py
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Expand Up @@ -216,6 +216,9 @@ def test_var(self):
assert_(np.isnan(np.var([])))
assert_(w[0].category is RuntimeWarning)

B = np.array([None, 0])
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The test is great. As a final suggestion, could you add a comment along the lines of

# Regression test for gh-13177, that object arrays are treated correctly.
B = np.array([1j, 0], dtype=object)

(Note: might as well simplify the definition...)

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I don't see that this was resolved

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strange, I could have sworn that I saw the comment there when I approved... Anyway, not too big a deal.

B[0] = 1j
assert_almost_equal(np.var(B), 0.25)

class TestIsscalar(object):
def test_isscalar(self):
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