forked from llnl/zfp
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_numpy.py
More file actions
198 lines (178 loc) · 8.27 KB
/
test_numpy.py
File metadata and controls
198 lines (178 loc) · 8.27 KB
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
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
#!/usr/bin/env python
import unittest
import zfpy
import test_utils
import numpy as np
try:
from packaging.version import parse as version_parse
except ImportError:
version_parse = None
class TestNumpy(unittest.TestCase):
def lossless_round_trip(self, orig_array):
compressed_array = zfpy.compress_numpy(orig_array, write_header=True)
decompressed_array = zfpy.decompress_numpy(compressed_array)
self.assertIsNone(np.testing.assert_array_equal(decompressed_array, orig_array))
def test_different_dimensions(self):
for dimensions in range(1, 5):
shape = [5] * dimensions
c_array = np.random.rand(*shape)
self.lossless_round_trip(c_array)
shape = range(2, 2 + dimensions)
c_array = np.random.rand(*shape)
self.lossless_round_trip(c_array)
def test_different_dtypes(self):
shape = (5, 5)
num_elements = shape[0] * shape[1]
for dtype in [np.float32, np.float64]:
elements = np.random.random_sample(num_elements)
elements = elements.astype(dtype, casting="same_kind")
array = np.reshape(elements, newshape=shape)
self.lossless_round_trip(array)
if (version_parse is not None and
(version_parse(np.__version__) >= version_parse("1.11.0"))
):
for dtype in [np.int32, np.int64]:
array = np.random.randint(2**30, size=shape, dtype=dtype)
self.lossless_round_trip(array)
else:
array = np.random.randint(2**30, size=shape)
self.lossless_round_trip(array)
def test_advanced_decompression_checksum(self):
ndims = 2
ztype = zfpy.type_float
random_array = test_utils.getRandNumpyArray(ndims, ztype)
mode = zfpy.mode_fixed_accuracy
compress_param_num = 1
compression_kwargs = {
"tolerance": test_utils.computeParameterValue(
mode,
compress_param_num
),
}
compressed_array_tmp = zfpy.compress_numpy(
random_array,
write_header=False,
**compression_kwargs
)
mem = memoryview(compressed_array_tmp)
compressed_array = np.array(mem, copy=False)
# Decompression using the "advanced" interface which enforces no header,
# and the user must provide all the metadata
decompressed_array = np.empty_like(random_array)
zfpy._decompress(
compressed_array,
ztype,
random_array.shape,
out=decompressed_array,
**compression_kwargs
)
decompressed_array_dims = decompressed_array.shape + tuple(0 for i in range(4 - decompressed_array.ndim))
decompressed_checksum = test_utils.getChecksumDecompArray(
decompressed_array_dims,
ztype,
mode,
compress_param_num
)
actual_checksum = test_utils.hashNumpyArray(
decompressed_array
)
self.assertEqual(decompressed_checksum, actual_checksum)
def test_advanced_decompression_nonsquare(self):
for dimensions in range(1, 5):
shape = range(2, 2 + dimensions)
random_array = np.random.rand(*shape)
decompressed_array = np.empty_like(random_array)
compressed_array = zfpy.compress_numpy(
random_array,
write_header=False,
)
zfpy._decompress(
compressed_array,
zfpy.dtype_to_ztype(random_array.dtype),
random_array.shape,
out= decompressed_array,
)
self.assertIsNone(np.testing.assert_array_equal(decompressed_array, random_array))
def test_utils(self):
for ndims in range(1, 5):
for ztype, ztype_str in [
(zfpy.type_float, "float"),
(zfpy.type_double, "double"),
(zfpy.type_int32, "int32"),
(zfpy.type_int64, "int64"),
]:
orig_random_array = test_utils.getRandNumpyArray(ndims, ztype)
orig_random_array_dims = orig_random_array.shape + tuple(0 for i in range(4 - orig_random_array.ndim))
orig_checksum = test_utils.getChecksumOrigArray(orig_random_array_dims, ztype)
actual_checksum = test_utils.hashNumpyArray(orig_random_array)
self.assertEqual(orig_checksum, actual_checksum)
for stride_str, stride_config in [
("as_is", test_utils.stride_as_is),
("permuted", test_utils.stride_permuted),
("interleaved", test_utils.stride_interleaved),
#("reversed", test_utils.stride_reversed),
]:
# permuting a 1D array is not supported
if stride_config == test_utils.stride_permuted and ndims == 1:
continue
random_array = test_utils.generateStridedRandomNumpyArray(
stride_config,
orig_random_array
)
random_array_dims = random_array.shape + tuple(0 for i in range(4 - random_array.ndim))
self.assertTrue(np.equal(orig_random_array, random_array).all())
for compress_param_num in range(3):
modes = [(zfpy.mode_fixed_accuracy, "tolerance"),
(zfpy.mode_fixed_precision, "precision"),
(zfpy.mode_fixed_rate, "rate")]
if ztype in [zfpy.type_int32, zfpy.type_int64]:
modes = [modes[-1]] # only fixed-rate is supported for integers
for mode, mode_str in modes:
# Compression
compression_kwargs = {
mode_str: test_utils.computeParameterValue(
mode,
compress_param_num
),
}
compressed_array = zfpy.compress_numpy(
random_array,
write_header=False,
**compression_kwargs
)
compressed_checksum = test_utils.getChecksumCompArray(
random_array_dims,
ztype,
mode,
compress_param_num
)
actual_checksum = test_utils.hashCompressedArray(
compressed_array
)
self.assertEqual(compressed_checksum, actual_checksum)
# Decompression
decompressed_checksum = test_utils.getChecksumDecompArray(
random_array_dims,
ztype,
mode,
compress_param_num
)
# Decompression using the "public" interface
# requires a header, so re-compress with the header
# included in the stream
compressed_array_tmp = zfpy.compress_numpy(
random_array,
write_header=True,
**compression_kwargs
)
mem = memoryview(compressed_array_tmp)
compressed_array = np.array(mem, copy=False)
decompressed_array = zfpy.decompress_numpy(
compressed_array,
)
actual_checksum = test_utils.hashNumpyArray(
decompressed_array
)
self.assertEqual(decompressed_checksum, actual_checksum)
if __name__ == "__main__":
unittest.main(verbosity=2)