From 58463a00cac849c448154b79977f582b911c3ab6 Mon Sep 17 00:00:00 2001
From: Christopher Whelan <topherwhelan@gmail.com>
Date: Fri, 3 Apr 2020 23:16:34 -0700
Subject: [PATCH] CLN: Add float and double versions of BN_NAN
---
bottleneck/include/bottleneck.h | 10 +++--
bottleneck/src/move_template.c | 42 +++++++++---------
bottleneck/src/nonreduce_axis_template.c | 2 +-
bottleneck/src/reduce_template.c | 48 +++++++++++----------
bottleneck/tests/list_input_test.py | 54 +++++++++++++++++-------
bottleneck/tests/nonreduce_axis_test.py | 18 +++++---
bottleneck/tests/nonreduce_test.py | 12 ++++--
7 files changed, 110 insertions(+), 76 deletions(-)
diff --git a/bottleneck/include/bottleneck.h b/bottleneck/include/bottleneck.h
index adf93da41c..9dc679894c 100644
--- a/bottleneck/include/bottleneck.h
+++ b/bottleneck/include/bottleneck.h
@@ -87,10 +87,12 @@ static inline float __bn_nanf(void) {
return __bint.__f;
}
-#define BN_INFINITYF __bn_inff()
-#define BN_NANF __bn_nanf()
-#define BN_INFINITY ((npy_double)BN_INFINITYF)
-#define BN_NAN ((npy_double)BN_NANF)
+#define BN_INFINITYF __bn_inff()
+#define BN_NANF __bn_nanf()
+#define BN_INFINITY ((npy_double)BN_INFINITYF)
+#define BN_NAN ((npy_double)BN_NANF)
+#define BN_NAN_float32 BN_NANF
+#define BN_NAN_float64 BN_NAN
/* WIRTH ----------------------------------------------------------------- */
diff --git a/bottleneck/src/move_template.c b/bottleneck/src/move_template.c
index cb4caf9ce6..7d2cc425d7 100644
--- a/bottleneck/src/move_template.c
+++ b/bottleneck/src/move_template.c
@@ -83,7 +83,7 @@ MOVE(move_sum, DTYPE0) {
asum += ai;
count += 1;
}
- YI(DTYPE0) = BN_NAN;
+ YI(DTYPE0) = BN_NAN_DTYPE0;
}
WHILE1 {
const npy_DTYPE0 ai = AI(DTYPE0);
@@ -91,7 +91,7 @@ MOVE(move_sum, DTYPE0) {
asum += ai;
count += 1;
}
- YI(DTYPE0) = count >= min_count ? asum : BN_NAN;
+ YI(DTYPE0) = count >= min_count ? asum : BN_NAN_DTYPE0;
}
WHILE2 {
const npy_DTYPE0 ai = AI(DTYPE0);
@@ -109,7 +109,7 @@ MOVE(move_sum, DTYPE0) {
count--;
}
}
- YI(DTYPE0) = count >= min_count ? asum : BN_NAN;
+ YI(DTYPE0) = count >= min_count ? asum : BN_NAN_DTYPE0;
}
NEXT2
}
@@ -127,7 +127,7 @@ MOVE(move_sum, DTYPE0) {
asum = 0;
WHILE0 {
asum += AI(DTYPE0);
- YI(DTYPE1) = BN_NAN;
+ YI(DTYPE1) = BN_NAN_DTYPE1;
}
WHILE1 {
asum += AI(DTYPE0);
@@ -162,7 +162,7 @@ MOVE(move_mean, DTYPE0) {
asum += ai;
count += 1;
}
- YI(DTYPE0) = BN_NAN;
+ YI(DTYPE0) = BN_NAN_DTYPE0;
}
WHILE1 {
const npy_DTYPE0 ai = AI(DTYPE0);
@@ -170,7 +170,7 @@ MOVE(move_mean, DTYPE0) {
asum += ai;
count += 1;
}
- YI(DTYPE0) = count >= min_count ? asum / count : BN_NAN;
+ YI(DTYPE0) = count >= min_count ? asum / count : BN_NAN_DTYPE0;
}
count_inv = 1.0 / count;
WHILE2 {
@@ -191,7 +191,7 @@ MOVE(move_mean, DTYPE0) {
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_DTYPE0;
}
NEXT2
}
@@ -209,7 +209,7 @@ MOVE(move_mean, DTYPE0) {
asum = 0;
WHILE0 {
asum += AI(DTYPE0);
- YI(DTYPE1) = BN_NAN;
+ YI(DTYPE1) = BN_NAN_DTYPE1;
}
WHILE1 {
asum += AI(DTYPE0);
@@ -249,7 +249,7 @@ MOVE(NAME, DTYPE0) {
amean += delta / count;
assqdm += delta * (ai - amean);
}
- YI(DTYPE0) = BN_NAN;
+ YI(DTYPE0) = BN_NAN_DTYPE0;
}
WHILE1 {
const npy_DTYPE0 ai = AI(DTYPE0);
@@ -265,7 +265,7 @@ MOVE(NAME, DTYPE0) {
}
yi = FUNC(assqdm / (count - ddof));
} else {
- yi = BN_NAN;
+ yi = BN_NAN_DTYPE0;
}
YI(DTYPE0) = yi;
}
@@ -310,7 +310,7 @@ MOVE(NAME, DTYPE0) {
}
yi = FUNC(assqdm * ddof_inv);
} else {
- yi = BN_NAN;
+ yi = BN_NAN_DTYPE0;
}
YI(DTYPE0) = yi;
}
@@ -335,7 +335,7 @@ MOVE(NAME, DTYPE0) {
const npy_DTYPE1 delta = ai - amean;
amean += delta / (INDEX + 1);
assqdm += delta * (ai - amean);
- YI(DTYPE1) = BN_NAN;
+ YI(DTYPE1) = BN_NAN_DTYPE1;
}
WHILE1 {
const npy_DTYPE1 ai = AI(DTYPE0);
@@ -457,16 +457,16 @@ MOVE(NAME, DTYPE0) {
extreme_pair->death = window;
WHILE0 {
MACRO_FLOAT(DTYPE0,
- BN_NAN, )
+ BN_NAN_DTYPE0, )
}
WHILE1 {
MACRO_FLOAT(DTYPE0,
- count >= min_count ? VALUE : BN_NAN, )
+ count >= min_count ? VALUE : BN_NAN_DTYPE0, )
}
WHILE2 {
MACRO_FLOAT(
DTYPE0,
- count >= min_count ? VALUE : BN_NAN,
+ count >= min_count ? VALUE : BN_NAN_DTYPE0,
const npy_DTYPE0 aold = AOLD(DTYPE0);
if (!bn_isnan(aold)) count--;
if (extreme_pair->death == INDEX) {
@@ -501,7 +501,7 @@ MOVE(NAME, DTYPE0) {
WHILE0 {
MACRO_INT(DTYPE0,
DTYPE1,
- BN_NAN, )
+ BN_NAN_DTYPE1, )
}
WHILE1 {
MACRO_INT(DTYPE0,
@@ -625,7 +625,7 @@ MOVE_MAIN(move_median, 0)
} \
} \
if (n < min_count) { \
- r = BN_NAN; \
+ r = BN_NAN_##dtype1; \
} else if (n == 1) { \
r = 0.0; \
} else { \
@@ -634,7 +634,7 @@ MOVE_MAIN(move_median, 0)
r = 2.0 * (r - 0.5); \
} \
} else { \
- r = BN_NAN; \
+ r = BN_NAN_##dtype1; \
}
/* dtype = [['float64', 'float64'], ['float32', 'float32']] */
@@ -643,7 +643,7 @@ MOVE(move_rank, DTYPE0) {
BN_BEGIN_ALLOW_THREADS
WHILE {
WHILE0 {
- YI(DTYPE1) = BN_NAN;
+ YI(DTYPE1) = BN_NAN_DTYPE1;
}
WHILE1 {
MOVE_RANK(DTYPE0, DTYPE1, 0)
@@ -668,7 +668,7 @@ MOVE(move_rank, DTYPE0) {
BN_BEGIN_ALLOW_THREADS
WHILE {
WHILE0 {
- YI(DTYPE1) = BN_NAN;
+ YI(DTYPE1) = BN_NAN_DTYPE1;
}
WHILE1 {
const npy_DTYPE0 ai = AI(DTYPE0);
@@ -683,7 +683,7 @@ MOVE(move_rank, DTYPE0) {
}
}
if (INDEX < min_count - 1) {
- r = BN_NAN;
+ r = BN_NAN_DTYPE1;
} else if (INDEX == 0) {
r = 0.0;
} else {
diff --git a/bottleneck/src/nonreduce_axis_template.c b/bottleneck/src/nonreduce_axis_template.c
index 142945b7af..d1b5a065ca 100644
--- a/bottleneck/src/nonreduce_axis_template.c
+++ b/bottleneck/src/nonreduce_axis_template.c
@@ -363,7 +363,7 @@ NRA(push, DTYPE0) {
BN_BEGIN_ALLOW_THREADS
WHILE {
index = 0;
- ai_last = BN_NAN;
+ ai_last = BN_NAN_DTYPE0;
FOR {
ai = AI(DTYPE0);
if (!bn_isnan(ai)) {
diff --git a/bottleneck/src/reduce_template.c b/bottleneck/src/reduce_template.c
index f80d96ab03..5a19b2f11d 100644
--- a/bottleneck/src/reduce_template.c
+++ b/bottleneck/src/reduce_template.c
@@ -228,7 +228,7 @@ REDUCE_ALL(nanmean, DTYPE0) {
if (count > 0) {
return PyFloat_FromDouble(asum / count);
} else {
- return PyFloat_FromDouble(BN_NAN);
+ return PyFloat_FromDouble(BN_NAN_DTYPE0);
}
}
@@ -236,7 +236,7 @@ REDUCE_ONE(nanmean, DTYPE0) {
INIT_ONE(DTYPE0, DTYPE0)
BN_BEGIN_ALLOW_THREADS
if (LENGTH == 0) {
- FILL_Y(BN_NAN)
+ FILL_Y(BN_NAN_DTYPE0)
} else {
WHILE {
Py_ssize_t count = 0;
@@ -251,7 +251,7 @@ REDUCE_ONE(nanmean, DTYPE0) {
if (count > 0) {
asum /= count;
} else {
- asum = BN_NAN;
+ asum = BN_NAN_DTYPE0;
}
YPP = asum;
NEXT
@@ -279,7 +279,7 @@ REDUCE_ALL(nanmean, DTYPE0) {
if (total_length > 0) {
return PyFloat_FromDouble(asum / total_length);
} else {
- return PyFloat_FromDouble(BN_NAN);
+ return PyFloat_FromDouble(BN_NAN_DTYPE1);
}
}
@@ -287,7 +287,7 @@ REDUCE_ONE(nanmean, DTYPE0) {
INIT_ONE(DTYPE1, DTYPE1)
BN_BEGIN_ALLOW_THREADS
if (LENGTH == 0) {
- FILL_Y(BN_NAN)
+ FILL_Y(BN_NAN_DTYPE1)
} else {
WHILE {
npy_DTYPE1 asum = 0;
@@ -297,7 +297,7 @@ REDUCE_ONE(nanmean, DTYPE0) {
if (LENGTH > 0) {
asum /= LENGTH;
} else {
- asum = BN_NAN;
+ asum = BN_NAN_DTYPE1;
}
YPP = asum;
NEXT
@@ -346,7 +346,7 @@ REDUCE_ALL(NAME, DTYPE0) {
}
out = FUNC(asum / (count - ddof));
} else {
- out = BN_NAN;
+ out = BN_NAN_DTYPE0;
}
BN_END_ALLOW_THREADS
return PyFloat_FromDouble(out);
@@ -356,7 +356,7 @@ REDUCE_ONE(NAME, DTYPE0) {
INIT_ONE(DTYPE0, DTYPE0)
BN_BEGIN_ALLOW_THREADS
if (LENGTH == 0) {
- FILL_Y(BN_NAN)
+ FILL_Y(BN_NAN_DTYPE0)
} else {
WHILE {
Py_ssize_t count = 0;
@@ -380,7 +380,7 @@ REDUCE_ONE(NAME, DTYPE0) {
}
asum = FUNC(asum / (count - ddof));
} else {
- asum = BN_NAN;
+ asum = BN_NAN_DTYPE0;
}
YPP = asum;
NEXT
@@ -418,7 +418,7 @@ REDUCE_ALL(NAME, DTYPE0) {
}
out = FUNC(asum / (size - ddof));
} else {
- out = BN_NAN;
+ out = BN_NAN_DTYPE1;
}
BN_END_ALLOW_THREADS
return PyFloat_FromDouble(out);
@@ -430,7 +430,7 @@ REDUCE_ONE(NAME, DTYPE0) {
const npy_DTYPE1 length_inv = 1.0 / LENGTH;
const npy_DTYPE1 length_ddof_inv = 1.0 / (LENGTH - ddof);
if (LENGTH == 0) {
- FILL_Y(BN_NAN)
+ FILL_Y(BN_NAN_DTYPE1)
} else {
WHILE {
npy_DTYPE1 asum = 0;
@@ -446,7 +446,7 @@ REDUCE_ONE(NAME, DTYPE0) {
}
asum = FUNC(asum * length_ddof_inv);
} else {
- asum = BN_NAN;
+ asum = BN_NAN_DTYPE1;
}
YPP = asum;
NEXT
@@ -537,7 +537,7 @@ REDUCE_ALL(NAME, DTYPE0) {
}
}
if (is_allnan) {
- extreme = BN_NAN;
+ extreme = BN_NAN_DTYPE0;
}
BN_END_ALLOW_THREADS
return PyFloat_FromDouble(extreme);
@@ -591,9 +591,9 @@ REDUCE_ONE(NAME, DTYPE0) {
}
for (npy_intp k = 0; k < LOOP_SIZE; k++) {
if (allnans[k]) {
- py[k] = BN_NAN;
+ py[k] = BN_NAN_DTYPE0;
} else {
- py[k] = extremes[k];
+ py[k] = extremes[i];
}
}
free(extremes);
@@ -642,7 +642,7 @@ REDUCE_ONE(NAME, DTYPE0) {
}
if (is_allnan) {
- extreme = BN_NAN;
+ extreme = BN_NAN_DTYPE0;
}
YPP = extreme;
NEXT
@@ -658,7 +658,9 @@ REDUCE_ONE(NAME, DTYPE0) {
is_allnan = 0;
}
}
- if (is_allnan) extreme = BN_NAN;
+ if (is_allnan) {
+ extreme = BN_NAN_DTYPE0;
+ }
YPP = extreme;
NEXT
}
@@ -1012,7 +1014,7 @@ REDUCE_MAIN(ss, 0)
} \
} \
if (l != LENGTH) { \
- med = BN_NAN; \
+ med = BN_NAN_##dtype; \
goto done; \
} \
k = LENGTH >> 1; \
@@ -1055,7 +1057,7 @@ REDUCE_MAIN(ss, 0)
l = 0; \
r = n - 1; \
if (n == 0) { \
- med = BN_NAN; \
+ med = BN_NAN_##dtype; \
goto done; \
} \
PARTITION(dtype) \
@@ -1077,7 +1079,7 @@ REDUCE_ALL(NAME, DTYPE0) {
INIT_ALL_RAVEL_ANY_ORDER
BN_BEGIN_ALLOW_THREADS
if (LENGTH == 0) {
- med = BN_NAN;
+ med = BN_NAN_DTYPE1;
} else {
BUFFER_NEW(DTYPE0, LENGTH)
FUNC(DTYPE0)
@@ -1093,7 +1095,7 @@ REDUCE_ONE(NAME, DTYPE0) {
INIT_ONE(DTYPE1, DTYPE1)
BN_BEGIN_ALLOW_THREADS
if (LENGTH == 0) {
- FILL_Y(BN_NAN)
+ FILL_Y(BN_NAN_DTYPE1)
} else {
npy_DTYPE1 med = BN_NAN;
BUFFER_NEW(DTYPE0, LENGTH)
@@ -1118,7 +1120,7 @@ REDUCE_ALL(median, DTYPE0) {
INIT_ALL_RAVEL_ANY_ORDER
BN_BEGIN_ALLOW_THREADS
if (LENGTH == 0) {
- med = BN_NAN;
+ med = BN_NAN_DTYPE1;
} else {
BUFFER_NEW(DTYPE0, LENGTH)
MEDIAN_INT(DTYPE0)
@@ -1134,7 +1136,7 @@ REDUCE_ONE(median, DTYPE0) {
INIT_ONE(DTYPE1, DTYPE1)
BN_BEGIN_ALLOW_THREADS
if (LENGTH == 0) {
- FILL_Y(BN_NAN)
+ FILL_Y(BN_NAN_DTYPE1)
} else {
npy_DTYPE1 med;
BUFFER_NEW(DTYPE0, LENGTH)
diff --git a/bottleneck/tests/list_input_test.py b/bottleneck/tests/list_input_test.py
index 0dd943ae91..11bec90a08 100644
--- a/bottleneck/tests/list_input_test.py
+++ b/bottleneck/tests/list_input_test.py
@@ -2,12 +2,13 @@
import warnings
+import hypothesis
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
import bottleneck as bn
-from .util import DTYPES
+from .util import DTYPES, get_functions, hy_lists
def lists(dtypes=DTYPES):
@@ -27,8 +28,11 @@ def lists(dtypes=DTYPES):
yield a.astype(dtype).tolist()
-@pytest.mark.parametrize("func", bn.get_functions("all"), ids=lambda x: x.__name__)
-def test_list_input(func):
+@hypothesis.given(input_list=hy_lists())
+@pytest.mark.parametrize(
+ "func", get_functions("all"), ids=lambda x: x.__name__,
+)
+def test_list_input(func, input_list) -> None:
"""Test that bn.xxx gives the same output as bn.slow.xxx for list input."""
msg = "\nfunc %s | input %s (%s) | shape %s\n"
msg += "\nInput array:\n%s\n"
@@ -36,16 +40,34 @@ def test_list_input(func):
if name == "replace":
return
func0 = eval("bn.slow.%s" % name)
- for i, a in enumerate(lists()):
- with warnings.catch_warnings():
- warnings.simplefilter("ignore")
- try:
- actual = func(a)
- desired = func0(a)
- except TypeError:
- actual = func(a, 2)
- desired = func0(a, 2)
- a = np.array(a)
- tup = (name, "a" + str(i), str(a.dtype), str(a.shape), a)
- err_msg = msg % tup
- assert_array_almost_equal(actual, desired, err_msg=err_msg)
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+
+ actual_raised = False
+ desired_raised = False
+
+ try:
+ if any(x in func.__name__ for x in ["move", "partition"]):
+ actual = func(input_list, 2)
+ else:
+ actual = func(input_list)
+ except ValueError:
+ actual_raised = True
+
+ try:
+ if any(x in func.__name__ for x in ["move", "partition"]):
+ desired = func0(input_list, 2)
+ else:
+ desired = func0(input_list)
+ except ValueError:
+ desired_raised = True
+
+ if actual_raised and desired_raised:
+ return
+
+ assert not (actual_raised or desired_raised)
+
+ a = np.array(input_list)
+ tup = (name, "a", str(a.dtype), str(a.shape), a)
+ err_msg = msg % tup
+ assert_array_almost_equal(actual, desired, err_msg=err_msg)
diff --git a/bottleneck/tests/nonreduce_axis_test.py b/bottleneck/tests/nonreduce_axis_test.py
index 831ce15b19..f503347eec 100644
--- a/bottleneck/tests/nonreduce_axis_test.py
+++ b/bottleneck/tests/nonreduce_axis_test.py
@@ -1,5 +1,6 @@
import numpy as np
import pytest
+import hypothesis
from numpy.testing import (
assert_array_almost_equal,
assert_array_equal,
@@ -10,7 +11,7 @@
import bottleneck as bn
from .reduce_test import unit_maker as reduce_unit_maker
from .reduce_test import unit_maker_argparse as unit_maker_parse_rankdata
-from .util import DTYPES, array_order, arrays
+from .util import DTYPES, array_order, arrays, hy_array_gen
# ---------------------------------------------------------------------------
# partition, argpartition
@@ -62,8 +63,8 @@ def test_partition_and_argpartition(func) -> None:
assert_array_equal(s1, s0, err_msg)
-def complete_the_partition(a, n, axis):
- def func1d(a, n):
+def complete_the_partition(a: np.ndarray, n: int, axis: int) -> np.ndarray:
+ def func1d(a: np.ndarray, n: int) -> np.ndarray:
a[:n] = np.sort(a[:n])
a[n + 1 :] = np.sort(a[n + 1 :])
return a
@@ -127,11 +128,14 @@ def complete_the_argpartition(index, a, n, axis):
return a
-def test_transpose() -> None:
+@hypothesis.given(array=hy_array_gen)
+def test_transpose(array) -> None:
"""partition transpose test"""
- a = np.arange(12).reshape(4, 3)
- actual = bn.partition(a.T, 2, -1).T
- desired = bn.slow.partition(a.T, 2, -1).T
+ if min(array.shape) < 3:
+ return
+
+ actual = bn.partition(array.T, 2, -1).T
+ desired = bn.slow.partition(array.T, 2, -1).T
assert_equal(actual, desired, "partition transpose test")
diff --git a/bottleneck/tests/nonreduce_test.py b/bottleneck/tests/nonreduce_test.py
index b1081b2ff7..d62d871e1a 100644
--- a/bottleneck/tests/nonreduce_test.py
+++ b/bottleneck/tests/nonreduce_test.py
@@ -80,10 +80,14 @@ def test_replace_unsafe_cast() -> None:
def test_non_array() -> None:
"""Test that non-array input raises"""
- a = [1, 2, 3]
- assert_raises(TypeError, bn.replace, a, 0, 1)
- a = (1, 2, 3)
- assert_raises(TypeError, bn.replace, a, 0, 1)
+ list_input = [1, 2, 3]
+ assert_raises(TypeError, bn.replace, list_input, 0, 1)
+
+ tuple_input = (1, 2, 3)
+ assert_raises(TypeError, bn.replace, tuple_input, 0, 1)
+
+ integer_input = 1
+ assert_raises(TypeError, bn.replace, integer_input, 0, 1)
# ---------------------------------------------------------------------------