remove venv
This commit is contained in:
Tykayn
tykayn
parent
056387013d
commit
0680c7594e
13999 changed files with 0 additions and 2895688 deletions
|
|
@ -1,12 +0,0 @@
|
|||
import numpy as np
|
||||
|
||||
# list of numarray data types
|
||||
integer_types: list[str] = [
|
||||
"int8", "uint8", "int16", "uint16",
|
||||
"int32", "uint32", "int64", "uint64"]
|
||||
|
||||
float_types: list[str] = ["float32", "float64"]
|
||||
|
||||
complex_types: list[str] = ["complex64", "complex128"]
|
||||
|
||||
types: list[str] = integer_types + float_types
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -1,21 +0,0 @@
|
|||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 0 1 1 1
|
||||
1 1 0 0 0 1 1
|
||||
1 0 1 0 1 0 1
|
||||
0 0 0 1 0 0 0
|
||||
1 0 1 0 1 0 1
|
||||
1 1 0 0 0 1 1
|
||||
1 1 1 0 1 1 1
|
||||
1 0 1 1 1 0 1
|
||||
0 0 0 1 0 0 0
|
||||
1 0 0 1 0 0 1
|
||||
1 1 1 1 1 1 1
|
||||
1 0 0 1 0 0 1
|
||||
0 0 0 1 0 0 0
|
||||
1 0 1 1 1 0 1
|
||||
|
|
@ -1,294 +0,0 @@
|
|||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
2 2 2 2 2 2 2
|
||||
3 3 3 3 3 3 3
|
||||
4 4 4 4 4 4 4
|
||||
5 5 5 5 5 5 5
|
||||
6 6 6 6 6 6 6
|
||||
7 7 7 7 7 7 7
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 2 3 4 5 6 7
|
||||
8 9 10 11 12 13 14
|
||||
15 16 17 18 19 20 21
|
||||
22 23 24 25 26 27 28
|
||||
29 30 31 32 33 34 35
|
||||
36 37 38 39 40 41 42
|
||||
43 44 45 46 47 48 49
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 2 3 4 5 6 7
|
||||
8 1 2 3 4 5 6
|
||||
9 8 1 2 3 4 5
|
||||
10 9 8 1 2 3 4
|
||||
11 10 9 8 1 2 3
|
||||
12 11 10 9 8 1 2
|
||||
13 12 11 10 9 8 1
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 2 1 2 1 2 1
|
||||
2 1 2 1 2 1 2
|
||||
1 2 1 2 1 2 1
|
||||
2 1 2 1 2 1 2
|
||||
1 2 1 2 1 2 1
|
||||
2 1 2 1 2 1 2
|
||||
1 2 1 2 1 2 1
|
||||
1 2 3 4 5 6 7
|
||||
2 3 4 5 6 7 8
|
||||
3 4 5 6 7 8 9
|
||||
4 5 6 7 8 9 10
|
||||
5 6 7 8 9 10 11
|
||||
6 7 8 9 10 11 12
|
||||
7 8 9 10 11 12 13
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
1 0 3 0 2 0 4
|
||||
0 0 0 2 0 0 0
|
||||
5 0 2 0 6 0 7
|
||||
2 2 0 0 0 7 7
|
||||
2 2 2 0 7 7 7
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
3 0 1 0 4 0 2
|
||||
0 0 0 1 0 0 0
|
||||
5 0 6 0 1 0 7
|
||||
5 5 0 0 0 1 1
|
||||
5 5 5 0 1 1 1
|
||||
1 1 1 0 2 2 2
|
||||
3 3 0 0 0 4 4
|
||||
5 0 6 0 7 0 8
|
||||
0 0 0 9 0 0 0
|
||||
10 0 11 0 12 0 13
|
||||
14 14 0 0 0 15 15
|
||||
16 16 16 0 17 17 17
|
||||
1 1 1 0 2 3 3
|
||||
1 1 0 0 0 3 3
|
||||
1 0 4 0 3 0 3
|
||||
0 0 0 3 0 0 0
|
||||
3 0 3 0 5 0 6
|
||||
3 3 0 0 0 6 6
|
||||
3 3 7 0 6 6 6
|
||||
1 2 3 0 4 5 6
|
||||
7 8 0 0 0 9 10
|
||||
11 0 12 0 13 0 14
|
||||
0 0 0 15 0 0 0
|
||||
16 0 17 0 18 0 19
|
||||
20 21 0 0 0 22 23
|
||||
24 25 26 0 27 28 29
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
1 0 3 0 2 0 2
|
||||
0 0 0 2 0 0 0
|
||||
2 0 2 0 4 0 5
|
||||
2 2 0 0 0 5 5
|
||||
2 2 2 0 5 5 5
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
1 0 3 0 4 0 2
|
||||
0 0 0 5 0 0 0
|
||||
6 0 7 0 8 0 9
|
||||
6 6 0 0 0 9 9
|
||||
6 6 6 0 9 9 9
|
||||
1 2 3 0 4 5 6
|
||||
7 1 0 0 0 4 5
|
||||
8 0 1 0 9 0 4
|
||||
0 0 0 1 0 0 0
|
||||
10 0 11 0 1 0 12
|
||||
13 10 0 0 0 1 14
|
||||
15 13 10 0 16 17 1
|
||||
1 2 3 0 4 5 6
|
||||
1 2 0 0 0 5 6
|
||||
1 0 7 0 8 0 6
|
||||
0 0 0 9 0 0 0
|
||||
10 0 11 0 12 0 13
|
||||
10 14 0 0 0 15 13
|
||||
10 14 16 0 17 15 13
|
||||
1 1 1 0 1 1 1
|
||||
1 1 0 0 0 1 1
|
||||
1 0 1 0 1 0 1
|
||||
0 0 0 1 0 0 0
|
||||
1 0 1 0 1 0 1
|
||||
1 1 0 0 0 1 1
|
||||
1 1 1 0 1 1 1
|
||||
1 1 2 0 3 3 3
|
||||
1 1 0 0 0 3 3
|
||||
1 0 1 0 4 0 3
|
||||
0 0 0 1 0 0 0
|
||||
5 0 6 0 1 0 1
|
||||
5 5 0 0 0 1 1
|
||||
5 5 5 0 7 1 1
|
||||
1 2 1 0 1 3 1
|
||||
2 1 0 0 0 1 3
|
||||
1 0 1 0 1 0 1
|
||||
0 0 0 1 0 0 0
|
||||
1 0 1 0 1 0 1
|
||||
4 1 0 0 0 1 5
|
||||
1 4 1 0 1 5 1
|
||||
1 2 3 0 4 5 6
|
||||
2 3 0 0 0 6 7
|
||||
3 0 8 0 6 0 9
|
||||
0 0 0 6 0 0 0
|
||||
10 0 6 0 11 0 12
|
||||
13 6 0 0 0 12 14
|
||||
6 15 16 0 12 14 17
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
1 0 1 0 3 0 2
|
||||
0 0 0 1 0 0 0
|
||||
4 0 5 0 1 0 1
|
||||
4 4 0 0 0 1 1
|
||||
4 4 4 0 1 1 1
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
4 0 0 5 0 0 5
|
||||
5 5 5 5 5 5 5
|
||||
5 0 0 5 0 0 6
|
||||
0 0 0 7 0 0 0
|
||||
8 0 7 7 7 0 9
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
4 0 0 4 0 0 5
|
||||
4 4 4 4 4 4 4
|
||||
6 0 0 4 0 0 4
|
||||
0 0 0 7 0 0 0
|
||||
8 0 7 7 7 0 9
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 4 0 0 0
|
||||
5 0 0 6 0 0 7
|
||||
8 8 8 8 8 8 8
|
||||
9 0 0 10 0 0 11
|
||||
0 0 0 12 0 0 0
|
||||
13 0 14 14 14 0 15
|
||||
1 0 2 3 3 0 4
|
||||
0 0 0 3 0 0 0
|
||||
5 0 0 3 0 0 6
|
||||
5 5 3 3 3 6 6
|
||||
5 0 0 3 0 0 6
|
||||
0 0 0 3 0 0 0
|
||||
7 0 3 3 8 0 9
|
||||
1 0 2 3 4 0 5
|
||||
0 0 0 6 0 0 0
|
||||
7 0 0 8 0 0 9
|
||||
10 11 12 13 14 15 16
|
||||
17 0 0 18 0 0 19
|
||||
0 0 0 20 0 0 0
|
||||
21 0 22 23 24 0 25
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
2 0 0 2 0 0 2
|
||||
2 2 2 2 2 2 2
|
||||
2 0 0 2 0 0 2
|
||||
0 0 0 2 0 0 0
|
||||
4 0 2 2 2 0 5
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
2 0 0 2 0 0 2
|
||||
2 2 2 2 2 2 2
|
||||
2 0 0 2 0 0 2
|
||||
0 0 0 2 0 0 0
|
||||
4 0 2 2 2 0 5
|
||||
1 0 2 3 4 0 5
|
||||
0 0 0 2 0 0 0
|
||||
6 0 0 7 0 0 8
|
||||
9 6 10 11 7 12 13
|
||||
14 0 0 10 0 0 12
|
||||
0 0 0 15 0 0 0
|
||||
16 0 17 18 15 0 19
|
||||
1 0 2 3 4 0 5
|
||||
0 0 0 3 0 0 0
|
||||
6 0 0 3 0 0 7
|
||||
6 8 9 3 10 11 7
|
||||
6 0 0 3 0 0 7
|
||||
0 0 0 3 0 0 0
|
||||
12 0 13 3 14 0 15
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
2 0 0 2 0 0 2
|
||||
2 2 2 2 2 2 2
|
||||
2 0 0 2 0 0 2
|
||||
0 0 0 2 0 0 0
|
||||
4 0 2 2 2 0 5
|
||||
1 0 2 2 3 0 4
|
||||
0 0 0 2 0 0 0
|
||||
5 0 0 2 0 0 6
|
||||
5 5 2 2 2 6 6
|
||||
5 0 0 2 0 0 6
|
||||
0 0 0 2 0 0 0
|
||||
7 0 8 2 2 0 9
|
||||
1 0 2 3 2 0 4
|
||||
0 0 0 2 0 0 0
|
||||
5 0 0 6 0 0 7
|
||||
8 5 6 9 6 7 10
|
||||
5 0 0 6 0 0 7
|
||||
0 0 0 11 0 0 0
|
||||
12 0 11 13 11 0 14
|
||||
1 0 2 3 4 0 5
|
||||
0 0 0 4 0 0 0
|
||||
6 0 0 7 0 0 8
|
||||
9 10 7 11 12 8 13
|
||||
10 0 0 12 0 0 14
|
||||
0 0 0 15 0 0 0
|
||||
16 0 15 17 18 0 19
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
2 0 0 2 0 0 2
|
||||
2 2 2 2 2 2 2
|
||||
2 0 0 2 0 0 2
|
||||
0 0 0 2 0 0 0
|
||||
4 0 2 2 2 0 5
|
||||
|
|
@ -1,42 +0,0 @@
|
|||
0 0 1
|
||||
1 1 1
|
||||
1 0 0
|
||||
1 0 0
|
||||
1 1 1
|
||||
0 0 1
|
||||
0 0 0
|
||||
1 1 1
|
||||
0 0 0
|
||||
0 1 1
|
||||
0 1 0
|
||||
1 1 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 1 1
|
||||
1 1 1
|
||||
1 1 0
|
||||
0 1 0
|
||||
1 1 1
|
||||
0 1 0
|
||||
1 0 0
|
||||
0 1 0
|
||||
0 0 1
|
||||
0 1 0
|
||||
0 1 0
|
||||
0 1 0
|
||||
1 1 1
|
||||
1 1 1
|
||||
1 1 1
|
||||
1 1 0
|
||||
0 1 0
|
||||
0 1 1
|
||||
1 0 1
|
||||
0 1 0
|
||||
1 0 1
|
||||
0 0 1
|
||||
0 1 0
|
||||
1 0 0
|
||||
1 1 0
|
||||
1 1 1
|
||||
0 1 1
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 2.1 KiB |
|
|
@ -1,102 +0,0 @@
|
|||
import numpy as np
|
||||
from scipy._lib._array_api import xp_assert_close
|
||||
|
||||
from scipy import ndimage
|
||||
from scipy.ndimage import _ctest
|
||||
from scipy.ndimage import _cytest
|
||||
from scipy._lib._ccallback import LowLevelCallable
|
||||
|
||||
FILTER1D_FUNCTIONS = [
|
||||
lambda filter_size: _ctest.filter1d(filter_size),
|
||||
lambda filter_size: _cytest.filter1d(filter_size, with_signature=False),
|
||||
lambda filter_size: LowLevelCallable(
|
||||
_cytest.filter1d(filter_size, with_signature=True)
|
||||
),
|
||||
lambda filter_size: LowLevelCallable.from_cython(
|
||||
_cytest, "_filter1d",
|
||||
_cytest.filter1d_capsule(filter_size),
|
||||
),
|
||||
]
|
||||
|
||||
FILTER2D_FUNCTIONS = [
|
||||
lambda weights: _ctest.filter2d(weights),
|
||||
lambda weights: _cytest.filter2d(weights, with_signature=False),
|
||||
lambda weights: LowLevelCallable(_cytest.filter2d(weights, with_signature=True)),
|
||||
lambda weights: LowLevelCallable.from_cython(_cytest,
|
||||
"_filter2d",
|
||||
_cytest.filter2d_capsule(weights),),
|
||||
]
|
||||
|
||||
TRANSFORM_FUNCTIONS = [
|
||||
lambda shift: _ctest.transform(shift),
|
||||
lambda shift: _cytest.transform(shift, with_signature=False),
|
||||
lambda shift: LowLevelCallable(_cytest.transform(shift, with_signature=True)),
|
||||
lambda shift: LowLevelCallable.from_cython(_cytest,
|
||||
"_transform",
|
||||
_cytest.transform_capsule(shift),),
|
||||
]
|
||||
|
||||
|
||||
def test_generic_filter():
|
||||
def filter2d(footprint_elements, weights):
|
||||
return (weights*footprint_elements).sum()
|
||||
|
||||
def check(j):
|
||||
func = FILTER2D_FUNCTIONS[j]
|
||||
|
||||
im = np.ones((20, 20))
|
||||
im[:10,:10] = 0
|
||||
footprint = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
|
||||
footprint_size = np.count_nonzero(footprint)
|
||||
weights = np.ones(footprint_size)/footprint_size
|
||||
|
||||
res = ndimage.generic_filter(im, func(weights),
|
||||
footprint=footprint)
|
||||
std = ndimage.generic_filter(im, filter2d, footprint=footprint,
|
||||
extra_arguments=(weights,))
|
||||
xp_assert_close(res, std, err_msg=f"#{j} failed")
|
||||
|
||||
for j, func in enumerate(FILTER2D_FUNCTIONS):
|
||||
check(j)
|
||||
|
||||
|
||||
def test_generic_filter1d():
|
||||
def filter1d(input_line, output_line, filter_size):
|
||||
for i in range(output_line.size):
|
||||
output_line[i] = 0
|
||||
for j in range(filter_size):
|
||||
output_line[i] += input_line[i+j]
|
||||
output_line /= filter_size
|
||||
|
||||
def check(j):
|
||||
func = FILTER1D_FUNCTIONS[j]
|
||||
|
||||
im = np.tile(np.hstack((np.zeros(10), np.ones(10))), (10, 1))
|
||||
filter_size = 3
|
||||
|
||||
res = ndimage.generic_filter1d(im, func(filter_size),
|
||||
filter_size)
|
||||
std = ndimage.generic_filter1d(im, filter1d, filter_size,
|
||||
extra_arguments=(filter_size,))
|
||||
xp_assert_close(res, std, err_msg=f"#{j} failed")
|
||||
|
||||
for j, func in enumerate(FILTER1D_FUNCTIONS):
|
||||
check(j)
|
||||
|
||||
|
||||
def test_geometric_transform():
|
||||
def transform(output_coordinates, shift):
|
||||
return output_coordinates[0] - shift, output_coordinates[1] - shift
|
||||
|
||||
def check(j):
|
||||
func = TRANSFORM_FUNCTIONS[j]
|
||||
|
||||
im = np.arange(12).reshape(4, 3).astype(np.float64)
|
||||
shift = 0.5
|
||||
|
||||
res = ndimage.geometric_transform(im, func(shift))
|
||||
std = ndimage.geometric_transform(im, transform, extra_arguments=(shift,))
|
||||
xp_assert_close(res, std, err_msg=f"#{j} failed")
|
||||
|
||||
for j, func in enumerate(TRANSFORM_FUNCTIONS):
|
||||
check(j)
|
||||
|
|
@ -1,67 +0,0 @@
|
|||
""" Testing data types for ndimage calls
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
from scipy._lib._array_api import assert_array_almost_equal
|
||||
import pytest
|
||||
|
||||
from scipy import ndimage
|
||||
|
||||
|
||||
def test_map_coordinates_dts():
|
||||
# check that ndimage accepts different data types for interpolation
|
||||
data = np.array([[4, 1, 3, 2],
|
||||
[7, 6, 8, 5],
|
||||
[3, 5, 3, 6]])
|
||||
shifted_data = np.array([[0, 0, 0, 0],
|
||||
[0, 4, 1, 3],
|
||||
[0, 7, 6, 8]])
|
||||
idx = np.indices(data.shape)
|
||||
dts = (np.uint8, np.uint16, np.uint32, np.uint64,
|
||||
np.int8, np.int16, np.int32, np.int64,
|
||||
np.intp, np.uintp, np.float32, np.float64)
|
||||
for order in range(0, 6):
|
||||
for data_dt in dts:
|
||||
these_data = data.astype(data_dt)
|
||||
for coord_dt in dts:
|
||||
# affine mapping
|
||||
mat = np.eye(2, dtype=coord_dt)
|
||||
off = np.zeros((2,), dtype=coord_dt)
|
||||
out = ndimage.affine_transform(these_data, mat, off)
|
||||
assert_array_almost_equal(these_data, out)
|
||||
# map coordinates
|
||||
coords_m1 = idx.astype(coord_dt) - 1
|
||||
coords_p10 = idx.astype(coord_dt) + 10
|
||||
out = ndimage.map_coordinates(these_data, coords_m1, order=order)
|
||||
assert_array_almost_equal(out, shifted_data)
|
||||
# check constant fill works
|
||||
out = ndimage.map_coordinates(these_data, coords_p10, order=order)
|
||||
assert_array_almost_equal(out, np.zeros((3,4)))
|
||||
# check shift and zoom
|
||||
out = ndimage.shift(these_data, 1)
|
||||
assert_array_almost_equal(out, shifted_data)
|
||||
out = ndimage.zoom(these_data, 1)
|
||||
assert_array_almost_equal(these_data, out)
|
||||
|
||||
|
||||
@pytest.mark.xfail(True, reason="Broken on many platforms")
|
||||
def test_uint64_max():
|
||||
# Test interpolation respects uint64 max. Reported to fail at least on
|
||||
# win32 (due to the 32 bit visual C compiler using signed int64 when
|
||||
# converting between uint64 to double) and Debian on s390x.
|
||||
# Interpolation is always done in double precision floating point, so
|
||||
# we use the largest uint64 value for which int(float(big)) still fits
|
||||
# in a uint64.
|
||||
# This test was last enabled on macOS only, and there it started failing
|
||||
# on arm64 as well (see gh-19117).
|
||||
big = 2**64 - 1025
|
||||
arr = np.array([big, big, big], dtype=np.uint64)
|
||||
# Tests geometric transform (map_coordinates, affine_transform)
|
||||
inds = np.indices(arr.shape) - 0.1
|
||||
x = ndimage.map_coordinates(arr, inds)
|
||||
assert x[1] == int(float(big))
|
||||
assert x[2] == int(float(big))
|
||||
# Tests zoom / shift
|
||||
x = ndimage.shift(arr, 0.1)
|
||||
assert x[1] == int(float(big))
|
||||
assert x[2] == int(float(big))
|
||||
File diff suppressed because it is too large
Load diff
|
|
@ -1,187 +0,0 @@
|
|||
import math
|
||||
import numpy as np
|
||||
|
||||
from scipy._lib._array_api import (
|
||||
xp_assert_equal,
|
||||
assert_array_almost_equal,
|
||||
assert_almost_equal,
|
||||
is_cupy,
|
||||
)
|
||||
|
||||
import pytest
|
||||
|
||||
from scipy import ndimage
|
||||
|
||||
skip_xp_backends = pytest.mark.skip_xp_backends
|
||||
pytestmark = [skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'])]
|
||||
|
||||
|
||||
@skip_xp_backends('jax.numpy', reason="jax-ml/jax#23827")
|
||||
class TestNdimageFourier:
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("float32", 6), ("float64", 14)])
|
||||
def test_fourier_gaussian_real01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
a = np.zeros(shape, dtype=dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = xp.asarray(a)
|
||||
|
||||
a = fft.rfft(a, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.irfft(a, n=shape[0], axis=0)
|
||||
assert_almost_equal(ndimage.sum(a), xp.asarray(1), decimal=dec,
|
||||
check_0d=False)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("complex64", 6), ("complex128", 14)])
|
||||
def test_fourier_gaussian_complex01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
a = np.zeros(shape, dtype=dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = xp.asarray(a)
|
||||
|
||||
a = fft.fft(a, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.ifft(a, n=shape[0], axis=0)
|
||||
assert_almost_equal(ndimage.sum(xp.real(a)), xp.asarray(1.0), decimal=dec,
|
||||
check_0d=False)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("float32", 6), ("float64", 14)])
|
||||
def test_fourier_uniform_real01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
a = np.zeros(shape, dtype=dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = xp.asarray(a)
|
||||
|
||||
a = fft.rfft(a, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.irfft(a, n=shape[0], axis=0)
|
||||
assert_almost_equal(ndimage.sum(a), xp.asarray(1.0), decimal=dec,
|
||||
check_0d=False)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("complex64", 6), ("complex128", 14)])
|
||||
def test_fourier_uniform_complex01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
a = np.zeros(shape, dtype=dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = xp.asarray(a)
|
||||
|
||||
a = fft.fft(a, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.ifft(a, n=shape[0], axis=0)
|
||||
assert_almost_equal(ndimage.sum(xp.real(a)), xp.asarray(1.0), decimal=dec,
|
||||
check_0d=False)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("float32", 4), ("float64", 11)])
|
||||
def test_fourier_shift_real01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
expected = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
|
||||
expected = xp.asarray(expected)
|
||||
|
||||
a = fft.rfft(expected, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_shift(a, [1, 1], shape[0], 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.irfft(a, n=shape[0], axis=0)
|
||||
assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1], decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("complex64", 4), ("complex128", 11)])
|
||||
def test_fourier_shift_complex01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
expected = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
|
||||
expected = xp.asarray(expected)
|
||||
|
||||
a = fft.fft(expected, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_shift(a, [1, 1], -1, 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.ifft(a, n=shape[0], axis=0)
|
||||
assert_array_almost_equal(xp.real(a)[1:, 1:], expected[:-1, :-1], decimal=dec)
|
||||
assert_array_almost_equal(xp.imag(a), xp.zeros(shape), decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("float32", 5), ("float64", 14)])
|
||||
def test_fourier_ellipsoid_real01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
a = np.zeros(shape, dtype=dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = xp.asarray(a)
|
||||
|
||||
a = fft.rfft(a, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], shape[0], 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.irfft(a, n=shape[0], axis=0)
|
||||
assert_almost_equal(ndimage.sum(a), xp.asarray(1.0), decimal=dec,
|
||||
check_0d=False)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec', [("complex64", 5), ("complex128", 14)])
|
||||
def test_fourier_ellipsoid_complex01(self, shape, dtype, dec, xp):
|
||||
fft = getattr(xp, 'fft')
|
||||
|
||||
a = np.zeros(shape, dtype=dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = xp.asarray(a)
|
||||
|
||||
a = fft.fft(a, n=shape[0], axis=0)
|
||||
a = fft.fft(a, n=shape[1], axis=1)
|
||||
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, n=shape[1], axis=1)
|
||||
a = fft.ifft(a, n=shape[0], axis=0)
|
||||
assert_almost_equal(ndimage.sum(xp.real(a)), xp.asarray(1.0), decimal=dec,
|
||||
check_0d=False)
|
||||
|
||||
def test_fourier_ellipsoid_unimplemented_ndim(self, xp):
|
||||
# arrays with ndim > 3 raise NotImplementedError
|
||||
x = xp.ones((4, 6, 8, 10), dtype=xp.complex128)
|
||||
with pytest.raises(NotImplementedError):
|
||||
ndimage.fourier_ellipsoid(x, 3)
|
||||
|
||||
def test_fourier_ellipsoid_1d_complex(self, xp):
|
||||
# expected result of 1d ellipsoid is the same as for fourier_uniform
|
||||
for shape in [(32, ), (31, )]:
|
||||
for type_, dec in zip([xp.complex64, xp.complex128], [5, 14]):
|
||||
x = xp.ones(shape, dtype=type_)
|
||||
a = ndimage.fourier_ellipsoid(x, 5, -1, 0)
|
||||
b = ndimage.fourier_uniform(x, 5, -1, 0)
|
||||
assert_array_almost_equal(a, b, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(0, ), (0, 10), (10, 0)])
|
||||
@pytest.mark.parametrize('dtype', ["float32", "float64",
|
||||
"complex64", "complex128"])
|
||||
@pytest.mark.parametrize('test_func',
|
||||
[ndimage.fourier_ellipsoid,
|
||||
ndimage.fourier_gaussian,
|
||||
ndimage.fourier_uniform])
|
||||
def test_fourier_zero_length_dims(self, shape, dtype, test_func, xp):
|
||||
if (
|
||||
is_cupy(xp)
|
||||
and test_func.__name__ == "fourier_ellipsoid"
|
||||
and math.prod(shape) == 0
|
||||
):
|
||||
pytest.xfail("CuPy's fourier_ellipsoid does not accept size==0 arrays")
|
||||
dtype = getattr(xp, dtype)
|
||||
a = xp.ones(shape, dtype=dtype)
|
||||
b = test_func(a, 3)
|
||||
xp_assert_equal(a, b)
|
||||
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
|
|
@ -1,78 +0,0 @@
|
|||
import pytest
|
||||
|
||||
import numpy as np
|
||||
from .._ni_support import _get_output
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
'dtype',
|
||||
[
|
||||
# String specifiers
|
||||
'f4', 'float32', 'complex64', 'complex128',
|
||||
# Type and dtype specifiers
|
||||
np.float32, float, np.dtype('f4'),
|
||||
# Derive from input
|
||||
None,
|
||||
],
|
||||
)
|
||||
def test_get_output_basic(dtype):
|
||||
shape = (2, 3)
|
||||
|
||||
input_ = np.zeros(shape, dtype='float32')
|
||||
|
||||
# For None, derive dtype from input
|
||||
expected_dtype = 'float32' if dtype is None else dtype
|
||||
|
||||
# Output is dtype-specifier, retrieve shape from input
|
||||
result = _get_output(dtype, input_)
|
||||
assert result.shape == shape
|
||||
assert result.dtype == np.dtype(expected_dtype)
|
||||
|
||||
# Output is dtype specifier, with explicit shape, overriding input
|
||||
result = _get_output(dtype, input_, shape=(3, 2))
|
||||
assert result.shape == (3, 2)
|
||||
assert result.dtype == np.dtype(expected_dtype)
|
||||
|
||||
# Output is pre-allocated array, return directly
|
||||
output = np.zeros(shape, dtype=dtype)
|
||||
result = _get_output(output, input_)
|
||||
assert result is output
|
||||
|
||||
|
||||
@pytest.mark.thread_unsafe
|
||||
def test_get_output_complex():
|
||||
shape = (2, 3)
|
||||
|
||||
input_ = np.zeros(shape)
|
||||
|
||||
# None, promote input type to complex
|
||||
result = _get_output(None, input_, complex_output=True)
|
||||
assert result.shape == shape
|
||||
assert result.dtype == np.dtype('complex128')
|
||||
|
||||
# Explicit type, promote type to complex
|
||||
with pytest.warns(UserWarning, match='promoting specified output dtype to complex'):
|
||||
result = _get_output(float, input_, complex_output=True)
|
||||
assert result.shape == shape
|
||||
assert result.dtype == np.dtype('complex128')
|
||||
|
||||
# String specifier, simply verify complex output
|
||||
result = _get_output('complex64', input_, complex_output=True)
|
||||
assert result.shape == shape
|
||||
assert result.dtype == np.dtype('complex64')
|
||||
|
||||
|
||||
def test_get_output_error_cases():
|
||||
input_ = np.zeros((2, 3), 'float32')
|
||||
|
||||
# Two separate paths can raise the same error
|
||||
with pytest.raises(RuntimeError, match='output must have complex dtype'):
|
||||
_get_output('float32', input_, complex_output=True)
|
||||
with pytest.raises(RuntimeError, match='output must have complex dtype'):
|
||||
_get_output(np.zeros((2, 3)), input_, complex_output=True)
|
||||
|
||||
with pytest.raises(RuntimeError, match='output must have numeric dtype'):
|
||||
_get_output('void', input_)
|
||||
|
||||
with pytest.raises(RuntimeError, match='shape not correct'):
|
||||
_get_output(np.zeros((3, 2)), input_)
|
||||
|
|
@ -1,70 +0,0 @@
|
|||
"""Tests for spline filtering."""
|
||||
import pytest
|
||||
|
||||
import numpy as np
|
||||
from scipy._lib._array_api import assert_almost_equal
|
||||
|
||||
from scipy import ndimage
|
||||
|
||||
skip_xp_backends = pytest.mark.skip_xp_backends
|
||||
pytestmark = [skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'])]
|
||||
|
||||
|
||||
def get_spline_knot_values(order):
|
||||
"""Knot values to the right of a B-spline's center."""
|
||||
knot_values = {0: [1],
|
||||
1: [1],
|
||||
2: [6, 1],
|
||||
3: [4, 1],
|
||||
4: [230, 76, 1],
|
||||
5: [66, 26, 1]}
|
||||
|
||||
return knot_values[order]
|
||||
|
||||
|
||||
def make_spline_knot_matrix(xp, n, order, mode='mirror'):
|
||||
"""Matrix to invert to find the spline coefficients."""
|
||||
knot_values = get_spline_knot_values(order)
|
||||
|
||||
# NB: do computations with numpy, convert to xp as the last step only
|
||||
|
||||
matrix = np.zeros((n, n))
|
||||
for diag, knot_value in enumerate(knot_values):
|
||||
indices = np.arange(diag, n)
|
||||
if diag == 0:
|
||||
matrix[indices, indices] = knot_value
|
||||
else:
|
||||
matrix[indices, indices - diag] = knot_value
|
||||
matrix[indices - diag, indices] = knot_value
|
||||
|
||||
knot_values_sum = knot_values[0] + 2 * sum(knot_values[1:])
|
||||
|
||||
if mode == 'mirror':
|
||||
start, step = 1, 1
|
||||
elif mode == 'reflect':
|
||||
start, step = 0, 1
|
||||
elif mode == 'grid-wrap':
|
||||
start, step = -1, -1
|
||||
else:
|
||||
raise ValueError(f'unsupported mode {mode}')
|
||||
|
||||
for row in range(len(knot_values) - 1):
|
||||
for idx, knot_value in enumerate(knot_values[row + 1:]):
|
||||
matrix[row, start + step*idx] += knot_value
|
||||
matrix[-row - 1, -start - 1 - step*idx] += knot_value
|
||||
|
||||
return xp.asarray(matrix / knot_values_sum)
|
||||
|
||||
|
||||
@pytest.mark.parametrize('order', [0, 1, 2, 3, 4, 5])
|
||||
@pytest.mark.parametrize('mode', ['mirror', 'grid-wrap', 'reflect'])
|
||||
def test_spline_filter_vs_matrix_solution(order, mode, xp):
|
||||
n = 100
|
||||
eye = xp.eye(n, dtype=xp.float64)
|
||||
spline_filter_axis_0 = ndimage.spline_filter1d(eye, axis=0, order=order,
|
||||
mode=mode)
|
||||
spline_filter_axis_1 = ndimage.spline_filter1d(eye, axis=1, order=order,
|
||||
mode=mode)
|
||||
matrix = make_spline_knot_matrix(xp, n, order, mode=mode)
|
||||
assert_almost_equal(eye, spline_filter_axis_0 @ matrix)
|
||||
assert_almost_equal(eye, spline_filter_axis_1 @ matrix.T)
|
||||
Loading…
Add table
Add a link
Reference in a new issue