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#!/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 = zfpy.compress_numpy(
random_array,
write_header=False,
**compression_kwargs
)
# 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_memview_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)
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