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Fix move_mean to handle window=1 without floating-point errors #488

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Fix move_mean to handle window=1 without floating-point errors #488

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55 changes: 36 additions & 19 deletions bottleneck/src/move_template.c
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Original file line number Diff line number Diff line change
Expand Up @@ -178,25 +178,34 @@ MOVE(move_mean, DTYPE0) {
YI(DTYPE0) = count >= min_count ? asum / count : BN_NAN;
}
count_inv = 1.0 / count;
WHILE2 {
ai = AI(DTYPE0);
aold = AOLD(DTYPE0);
if (ai == ai) {
if (aold == aold) {
asum += ai - aold;
/* Special case for window=1: just copy the input value to avoid
floating-point errors from unnecessary arithmetic */
if (window == 1) {
WHILE2 {
ai = AI(DTYPE0);
YI(DTYPE0) = ai;
}
} else {
WHILE2 {
ai = AI(DTYPE0);
aold = AOLD(DTYPE0);
if (ai == ai) {
if (aold == aold) {
asum += ai - aold;
} else {
asum += ai;
count++;
count_inv = 1.0 / count;
}
} else {
asum += ai;
count++;
count_inv = 1.0 / count;
}
} else {
if (aold == aold) {
asum -= aold;
count--;
count_inv = 1.0 / count;
if (aold == aold) {
asum -= aold;
count--;
count_inv = 1.0 / count;
}
}
YI(DTYPE0) = count >= min_count ? asum * count_inv : BN_NAN;
}
YI(DTYPE0) = count >= min_count ? asum * count_inv : BN_NAN;
}
NEXT2
}
Expand All @@ -222,9 +231,17 @@ MOVE(move_mean, DTYPE0) {
*(npy_DTYPE1*)(it.py + it.i * it.ystride) = (npy_DTYPE1)asum / (it.i + 1);
it.i++;
}
WHILE2 {
asum += AI(DTYPE0) - AOLD(DTYPE0);
YI(DTYPE1) = (npy_DTYPE1)asum * window_inv;
/* Special case for window=1: just copy the input value to avoid
floating-point errors from unnecessary arithmetic */
if (window == 1) {
WHILE2 {
YI(DTYPE1) = (npy_DTYPE1)AI(DTYPE0);
}
} else {
WHILE2 {
asum += AI(DTYPE0) - AOLD(DTYPE0);
YI(DTYPE1) = (npy_DTYPE1)asum * window_inv;
}
}
NEXT2
}
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27 changes: 27 additions & 0 deletions bottleneck/tests/move_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,3 +210,30 @@ def test_move_std_sqrt():
a3 = np.array([[a, a], [a, a]])
b = bn.move_std(a3, window=3, axis=2)
assert np.isfinite(b[:, :, 2:]).all(), err_msg % 3


# ----------------------------------------------------------------------------
# Regression test for issue #437


def test_move_mean_window_1():
"""Test move_mean with window=1, min_count=1 (issue #437).

When window=1 and min_count=1, move_mean should return the input array
unchanged, since a moving average with window size 1 is the identity
function. However, the current implementation introduces small floating
point errors.

See: https://github.com/pydata/bottleneck/issues/437
"""
# Minimal test case from issue #437 (simplified from 119 to 3 elements)
a = [0.008196721311475436, -0.01626016260162607, 0.012396694214876205]

b = bn.move_mean(a, window=1, min_count=1)

# With window=1, the result should be exactly equal to the input
err_msg = (
"move_mean with window=1 and min_count=1 should return the input array "
"unchanged, but got differences. This is issue #437."
)
assert_equal(b, a, err_msg)
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