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test_dataset.py
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test_dataset.py
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##############################################################################
# Copyright by The HDF Group. #
# All rights reserved. #
# #
# This file is part of H5Serv (HDF5 REST Server) Service, Libraries and #
# Utilities. The full HDF5 REST Server copyright notice, including #
# terms governing use, modification, and redistribution, is contained in #
# the file COPYING, which can be found at the root of the source code #
# distribution tree. If you do not have access to this file, you may #
# request a copy from help@hdfgroup.org. #
##############################################################################
"""
Dataset testing operations.
Tests all dataset operations, including creation, with the exception of:
1. Slicing operations for read and write, handled by module test_slicing
2. Type conversion for read and write (currently untested)
"""
import logging
import pathlib
import sys
import numpy as np
import platform
from common import ut, TestCase
import config
if config.get("use_h5py"):
from h5py import File, Dataset
import h5py
else:
from h5pyd import File, Dataset, MultiManager
import h5pyd as h5py
def is_empty_dataspace(obj):
if config.get('use_h5py'):
space = obj.get_space()
return (space.get_simple_extent_type() == h5py.h5s.NULL)
else:
shape_json = obj.shape_json
if "class" not in shape_json:
raise KeyError()
if shape_json["class"] == 'H5S_NULL':
return True
else:
return False
class BaseDataset(TestCase):
def setUp(self):
filename = self.getFileName("dataset_test")
print("filename:", filename)
self.f = File(filename, 'w')
def tearDown(self):
if self.f:
self.f.close()
def check_h5_string(self, dset, cset, length):
if config.get('use_h5py'):
type_obj = dset.id.get_type()
self.assertEqual(type_obj.get_class(), h5py.h5t.STRING)
if cset == 'H5T_CSET_ASCII':
self.assertEqual(type_obj.get_cset(), h5py.h5t.CSET_ASCII)
elif cset == 'H5T_CSET_UTF8':
self.assertEqual(type_obj.get_cset(), h5py.h5t.CSET_UTF8)
else:
self.assertEqual(type_obj.get_cset(), h5py.h5t.CSET_ERROR)
if length:
self.assertEqual(type_obj.get_size(), length)
else:
type_json = dset.id.type_json
if "class" not in type_json:
raise TypeError()
self.assertEqual(type_json["class"], 'H5T_STRING')
if "charSet" not in type_json:
raise TypeError()
self.assertEqual(type_json["charSet"], cset)
if "length" not in type_json:
raise TypeError()
if length is None:
self.assertEqual(type_json["length"], 'H5T_VARIABLE')
else:
self.assertTrue(isinstance(type_json["length"], int))
self.assertEqual(type_json["length"], length)
class TestRepr(BaseDataset):
"""
Feature: repr(Dataset) behaves sensibly
"""
def test_repr_open(self):
""" repr() works on live and dead datasets """
ds = self.f.create_dataset('foo', (4,))
self.assertIsInstance(repr(ds), str)
self.f.close()
self.assertIsInstance(repr(ds), str)
class TestCreateShape(BaseDataset):
"""
Feature: Datasets can be created from a shape only
"""
def test_create_scalar(self):
""" Create a scalar dataset """
dset = self.f.create_dataset('foo', ())
self.assertEqual(dset.shape, ())
def test_create_simple(self):
""" Create a size-1 dataset """
dset = self.f.create_dataset('foo', (1,))
self.assertEqual(dset.shape, (1,))
def test_create_integer(self):
""" Create a size-1 dataset with integer shape"""
dset = self.f.create_dataset('foo', 1)
self.assertEqual(dset.shape, (1,))
def test_create_extended(self):
""" Create an extended dataset """
dset = self.f.create_dataset('foo', (63,))
self.assertEqual(dset.shape, (63,))
self.assertEqual(dset.size, 63)
dset = self.f.create_dataset('bar', (6, 10))
self.assertEqual(dset.shape, (6, 10))
self.assertEqual(dset.size, (60))
def test_create_integer_extended(self):
""" Create an extended dataset """
dset = self.f.create_dataset('foo', 63)
self.assertEqual(dset.shape, (63,))
self.assertEqual(dset.size, 63)
dset = self.f.create_dataset('bar', (6, 10))
self.assertEqual(dset.shape, (6, 10))
self.assertEqual(dset.size, (60))
def test_default_dtype(self):
""" Confirm that the default dtype is float """
dset = self.f.create_dataset('foo', (63,))
self.assertEqual(dset.dtype, np.dtype('=f4'))
def test_missing_shape(self):
""" Missing shape raises TypeError """
with self.assertRaises(TypeError):
self.f.create_dataset('foo')
@ut.expectedFailure
def test_long_double(self):
""" Confirm that the default dtype is float """
# Expected failure on HSDS; skip with h5py
if config.get('use_h5py') or platform.system() == 'Windows':
self.assertTrue(False)
dset = self.f.create_dataset('foo', (63,), dtype=np.longdouble)
if platform.machine() in ['ppc64le']:
print(f"Storage of long double deactivated on {platform.machine()}")
else:
self.assertEqual(dset.dtype, np.longdouble)
@ut.skipIf(not hasattr(np, "complex256"), "No support for complex256")
@ut.expectedFailure
def test_complex256(self):
""" Confirm that the default dtype is float """
# Expected failure on HSDS; skip with h5py
if config.get('use_h5py'):
self.assertTrue(False)
dset = self.f.create_dataset('foo', (63,),
dtype=np.dtype('complex256'))
self.assertEqual(dset.dtype, np.dtype('complex256'))
def test_name_bytes(self):
dset = self.f.create_dataset(b'foo', (1,))
self.assertEqual(dset.shape, (1,))
dset2 = self.f.create_dataset(b'bar/baz', (2,))
self.assertEqual(dset2.shape, (2,))
class TestCreateData(BaseDataset):
"""
Feature: Datasets can be created from existing data
"""
def test_create_scalar(self):
""" Create a scalar dataset from existing array """
data = np.ones((), 'f')
dset = self.f.create_dataset('foo', data=data)
self.assertEqual(dset.shape, data.shape)
def test_create_extended(self):
""" Create an extended dataset from existing data """
data = np.ones((63,), 'f')
dset = self.f.create_dataset('foo', data=data)
self.assertEqual(dset.shape, data.shape)
def test_dataset_intermediate_group(self):
""" Create dataset with missing intermediate groups """
ds = self.f.create_dataset("/foo/bar/baz", shape=(10, 10), dtype='<i4')
self.assertIsInstance(ds, h5py.Dataset)
self.assertTrue("/foo/bar/baz" in self.f)
def test_reshape(self):
""" Create from existing data, and make it fit a new shape """
data = np.arange(30, dtype='f')
dset = self.f.create_dataset('foo', shape=(10, 3), data=data)
self.assertEqual(dset.shape, (10, 3))
self.assertArrayEqual(dset[...], data.reshape((10, 3)))
def test_appropriate_low_level_id(self):
" Binding Dataset to a non-DatasetID identifier fails with ValueError "
with self.assertRaises(ValueError):
Dataset(self.f['/'].id)
def test_create_bytestring(self):
""" Creating dataset with byte string yields vlen ASCII dataset """
def check_vlen_ascii(dset):
self.check_h5_string(dset, 'H5T_CSET_ASCII', length=None)
check_vlen_ascii(self.f.create_dataset('a', data=b'abc'))
check_vlen_ascii(self.f.create_dataset('b', data=[b'abc', b'def']))
check_vlen_ascii(self.f.create_dataset('c', data=[[b'abc'], [b'def']]))
check_vlen_ascii(self.f.create_dataset(
'd', data=np.array([b'abc', b'def'], dtype=object)
))
def test_create_np_s(self):
dset = self.f.create_dataset('a', data=np.array([b'abc', b'def'], dtype='S3'))
self.check_h5_string(dset, 'H5T_CSET_ASCII', length=3)
def test_create_strings(self):
def check_vlen_utf8(dset):
self.check_h5_string(dset, 'H5T_CSET_UTF8', length=None)
check_vlen_utf8(self.f.create_dataset('a', data='abc'))
check_vlen_utf8(self.f.create_dataset('b', data=['abc', 'def']))
check_vlen_utf8(self.f.create_dataset('c', data=[['abc'], ['def']]))
check_vlen_utf8(self.f.create_dataset(
'd', data=np.array(['abc', 'def'], dtype=object)
))
def test_create_np_u(self):
with self.assertRaises(TypeError):
self.f.create_dataset('a', data=np.array([b'abc', b'def'], dtype='U3'))
def test_empty_create_via_None_shape(self):
self.f.create_dataset('foo', dtype='f')
self.assertTrue(is_empty_dataspace(self.f['foo'].id))
def test_empty_create_via_Empty_class(self):
self.f.create_dataset('foo', data=h5py.Empty(dtype='f'))
self.assertTrue(is_empty_dataspace(self.f['foo'].id))
def test_create_incompatible_data(self):
# Shape tuple is incompatible with data
with self.assertRaises(ValueError):
self.f.create_dataset('bar', shape=4, data=np.arange(3))
class TestReadDirectly(BaseDataset):
"""
Feature: Read data directly from Dataset into a Numpy array
"""
source_shapes = ((100,), (70,), (30, 10), (5, 7, 9))
dest_shapes = ((100,), (100,), (20, 20), (6,))
source_sels = (np.s_[0:10], np.s_[50:60], np.s_[:20, :], np.s_[2, :6, 3])
dest_sels = (np.s_[50:60], np.s_[90:], np.s_[:, :10], np.s_[:])
def test_read_direct(self):
for i in range(len(self.source_shapes)):
source_shape = self.source_shapes[i]
dest_shape = self.dest_shapes[i]
source_sel = self.source_sels[i]
dest_sel = self.dest_sels[i]
source_values = np.arange(np.prod(source_shape), dtype="int64").reshape(source_shape)
dset = self.f.create_dataset(f"dset_{i}", source_shape, data=source_values)
arr = np.full(dest_shape, -1, dtype="int64")
expected = arr.copy()
expected[dest_sel] = source_values[source_sel]
dset.read_direct(arr, source_sel, dest_sel)
np.testing.assert_array_equal(arr, expected)
def test_no_sel(self):
dset = self.f.create_dataset("dset", (10,), data=np.arange(10, dtype="int64"))
arr = np.ones((10,), dtype="int64")
dset.read_direct(arr)
np.testing.assert_array_equal(arr, np.arange(10, dtype="int64"))
def test_empty(self):
empty_dset = self.f.create_dataset("edset", dtype='int64')
arr = np.ones((100,), 'int64')
with self.assertRaises(TypeError):
empty_dset.read_direct(arr, np.s_[0:10], np.s_[50:60])
def test_wrong_shape(self):
dset = self.f.create_dataset("dset", (100,), dtype='int64')
arr = np.ones((200,))
with self.assertRaises(TypeError):
dset.read_direct(arr)
def test_not_c_contiguous(self):
dset = self.f.create_dataset("dset", (10, 10), dtype='int64')
arr = np.ones((10, 10), order='F')
with self.assertRaises(TypeError):
dset.read_direct(arr)
class TestWriteDirectly(BaseDataset):
"""
Feature: Write Numpy array directly into Dataset
"""
source_shapes = ((100,), (70,), (30, 10), (5, 7, 9))
dest_shapes = ((100,), (100,), (20, 20), (6,))
source_sels = (np.s_[0:10], np.s_[50:60], np.s_[:20, :], np.s_[2, :6, 3])
dest_sels = (np.s_[50:60], np.s_[90:], np.s_[:, :10], np.s_[:])
def test_write_direct(self):
count = len(self.source_shapes)
for i in range(count):
source_shape = self.source_shapes[i]
dest_shape = self.dest_shapes[i]
source_sel = self.source_sels[i]
dest_sel = self.dest_sels[i]
dset = self.f.create_dataset(f'dset_{i}', dest_shape, dtype='int32', fillvalue=-1)
arr = np.arange(np.prod(source_shape)).reshape(source_shape)
expected = np.full(dest_shape, -1, dtype='int32')
expected[dest_sel] = arr[source_sel]
dset.write_direct(arr, source_sel, dest_sel)
np.testing.assert_array_equal(dset[:], expected)
def test_empty(self):
empty_dset = self.f.create_dataset("edset", dtype='int64')
with self.assertRaises(TypeError):
empty_dset.write_direct(np.ones((100,)), np.s_[0:10], np.s_[50:60])
def test_wrong_shape(self):
dset = self.f.create_dataset("dset", (100,), dtype='int64')
arr = np.ones((200,))
with self.assertRaises(TypeError):
dset.write_direct(arr)
def test_not_c_contiguous(self):
dset = self.f.create_dataset("dset", (10, 10), dtype='int64')
arr = np.ones((10, 10), order='F')
with self.assertRaises(TypeError):
dset.write_direct(arr)
def test_no_selection(self):
dset = self.f.create_dataset("dset", (10, 10), dtype='int64')
arr = np.ones((10, 10), order='C')
dset.write_direct(arr)
class TestCreateRequire(BaseDataset):
"""
Feature: Datasets can be created only if they don't exist in the file
"""
def test_create(self):
""" Create new dataset with no conflicts """
dset = self.f.require_dataset('foo', (10, 3), 'f')
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.shape, (10, 3))
def test_create_existing(self):
""" require_dataset yields existing dataset """
dset = self.f.require_dataset('foo', (10, 3), 'f')
dset2 = self.f.require_dataset('foo', (10, 3), 'f')
self.assertEqual(dset, dset2)
def test_create_1D(self):
""" require_dataset with integer shape yields existing dataset"""
dset = self.f.require_dataset('foo', 10, 'f')
dset2 = self.f.require_dataset('foo', 10, 'f')
self.assertEqual(dset, dset2)
dset = self.f.require_dataset('bar', (10,), 'f')
dset2 = self.f.require_dataset('bar', 10, 'f')
self.assertEqual(dset, dset2)
dset = self.f.require_dataset('baz', 10, 'f')
dset2 = self.f.require_dataset(b'baz', (10,), 'f')
self.assertEqual(dset, dset2)
def test_shape_conflict(self):
""" require_dataset with shape conflict yields TypeError """
self.f.create_dataset('foo', (10, 3), 'f')
with self.assertRaises(TypeError):
self.f.require_dataset('foo', (10, 4), 'f')
def test_type_conflict(self):
""" require_dataset with object type conflict yields TypeError """
self.f.create_group('foo')
with self.assertRaises(TypeError):
self.f.require_dataset('foo', (10, 3), 'f')
def test_dtype_conflict(self):
""" require_dataset with dtype conflict (strict mode) yields TypeError
"""
self.f.create_dataset('foo', (10, 3), 'f')
with self.assertRaises(TypeError):
self.f.require_dataset('foo', (10, 3), 'S10')
def test_dtype_exact(self):
""" require_dataset with exactly dtype match """
dset = self.f.create_dataset('foo', (10, 3), 'f')
dset2 = self.f.require_dataset('foo', (10, 3), 'f', exact=True)
self.assertEqual(dset, dset2)
def test_dtype_close(self):
""" require_dataset with convertible type succeeds (non-strict mode)
"""
dset = self.f.create_dataset('foo', (10, 3), 'i4')
dset2 = self.f.require_dataset('foo', (10, 3), 'i2', exact=False)
self.assertEqual(dset, dset2)
self.assertEqual(dset2.dtype, np.dtype('i4'))
class TestCreateChunked(BaseDataset):
"""
Feature: Datasets can be created by manually specifying chunks
Note: HSDS defaults to 1MB min/4MB max chunk size, so chunk shapes
have been modified from h5py test
"""
def test_create_chunks(self):
""" Create via chunks tuple """
dset = self.f.create_dataset('foo', shape=(1024 * 1024,), chunks=(1024 * 1024,), dtype='i4')
self.assertEqual(dset.chunks, (1024 * 1024,))
def test_create_chunks_integer(self):
""" Create via chunks integer """
dset = self.f.create_dataset('foo', shape=(1024 * 1024,), chunks=1024 * 1024, dtype='i4')
self.assertEqual(dset.chunks, (1024 * 1024,))
def test_chunks_mismatch(self):
""" Illegal chunk size raises ValueError """
with self.assertRaises(ValueError):
self.f.create_dataset('foo', shape=(100,), chunks=(200,))
def test_chunks_false(self):
""" Chunked format required for given storage options """
with self.assertRaises(ValueError):
self.f.create_dataset('foo', shape=(10,), maxshape=100, chunks=False)
def test_chunks_scalar(self):
""" Attempting to create chunked scalar dataset raises TypeError """
with self.assertRaises(TypeError):
self.f.create_dataset('foo', shape=(), chunks=(50,))
def test_auto_chunks(self):
""" Auto-chunking of datasets """
dset = self.f.create_dataset('foo', shape=(20, 100), chunks=True)
self.assertIsInstance(dset.chunks, tuple)
self.assertEqual(len(dset.chunks), 2)
def test_auto_chunks_abuse(self):
""" Auto-chunking with pathologically large element sizes """
dset = self.f.create_dataset('foo', shape=(3,), dtype='S100000000', chunks=True)
self.assertEqual(dset.chunks, (1,))
def test_scalar_assignment(self):
""" Test scalar assignment of chunked dataset """
dset = self.f.create_dataset('foo', shape=(3, 50, 50),
dtype=np.int32, chunks=(1, 50, 50))
# test assignment of selection smaller than chunk size
dset[1, :, 40] = 10
self.assertTrue(np.all(dset[1, :, 40] == 10))
# test assignment of selection equal to chunk size
dset[1] = 11
self.assertTrue(np.all(dset[1] == 11))
# test assignment of selection bigger than chunk size
dset[0:2] = 12
self.assertTrue(np.all(dset[0:2] == 12))
def test_auto_chunks_no_shape(self):
""" Auto-chunking of empty datasets not allowed"""
with self.assertRaises(TypeError):
self.f.create_dataset('foo', dtype='S100', chunks=True)
with self.assertRaises(TypeError):
self.f.create_dataset('foo', dtype='S100', maxshape=20)
class TestCreateFillvalue(BaseDataset):
"""
Feature: Datasets can be created with fill value
"""
def test_create_fillval(self):
""" Fill value is reflected in dataset contents """
dset = self.f.create_dataset('foo', (10,), fillvalue=4.0)
self.assertEqual(dset[0], 4.0)
self.assertEqual(dset[7], 4.0)
def test_property(self):
""" Fill value is recoverable via property """
dset = self.f.create_dataset('foo', (10,), fillvalue=3.0)
self.assertEqual(dset.fillvalue, 3.0)
self.assertNotIsInstance(dset.fillvalue, np.ndarray)
def test_property_none(self):
""" .fillvalue property works correctly if not set """
dset = self.f.create_dataset('foo', (10,))
self.assertEqual(dset.fillvalue, 0)
def test_compound(self):
""" Fill value works with compound types """
dt = np.dtype([('a', 'f4'), ('b', 'i8')])
v = np.ones((1,), dtype=dt)[0]
dset = self.f.create_dataset('foo', (10,), dtype=dt, fillvalue=v)
self.assertEqual(dset.fillvalue, v)
self.assertAlmostEqual(dset[4], v)
def test_exc(self):
""" Bogus fill value raises ValueError """
with self.assertRaises(ValueError):
self.f.create_dataset('foo', (10,),
dtype=[('a', 'i'), ('b', 'f')], fillvalue=42)
class TestCreateNamedType(BaseDataset):
"""
Feature: Datasets created from an existing named type
"""
def test_named(self):
""" Named type object works and links the dataset to type """
self.f['type'] = np.dtype('f8')
dset = self.f.create_dataset('x', (100,), dtype=self.f['type'])
self.assertEqual(dset.dtype, np.dtype('f8'))
dset_type = dset.id.get_type()
if isinstance(dset.id.id, str):
# h5pyd
ref_type = self.f['type'].id.get_type()
else:
# h5py
ref_type = self.f['type'].id
self.assertEqual(dset_type, ref_type)
if isinstance(dset.id.id, str):
# h5pyd
pass # TBD: don't support committed method
else:
self.assertTrue(dset.id.get_type().committed())
class TestCreateGzip(BaseDataset):
"""
Feature: Datasets created with gzip compression
"""
def test_gzip(self):
""" Create with explicit gzip options """
dset = self.f.create_dataset('foo', (20, 30), compression='gzip',
compression_opts=9)
self.assertEqual(dset.compression, 'gzip')
self.assertEqual(dset.compression_opts, 9)
def test_gzip_implicit(self):
""" Create with implicit gzip level (level 4) """
dset = self.f.create_dataset('foo', (20, 30), compression='gzip')
self.assertEqual(dset.compression, 'gzip')
self.assertEqual(dset.compression_opts, 4)
@ut.skip
def test_gzip_number(self):
""" Create with gzip level by specifying integer """
# legacy compression not supported
dset = self.f.create_dataset('foo', (20, 30), compression=7)
self.assertEqual(dset.compression, 'gzip')
self.assertEqual(dset.compression_opts, 7)
original_compression_vals = h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS
try:
h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS = tuple()
with self.assertRaises(ValueError):
dset = self.f.create_dataset('foo2', (20, 30), compression=7)
finally:
h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS = original_compression_vals
def test_gzip_exc(self):
""" Illegal gzip level (explicit or implicit) raises ValueError """
with self.assertRaises((ValueError, RuntimeError)):
self.f.create_dataset('foo', (20, 30), compression=14)
with self.assertRaises(ValueError):
self.f.create_dataset('foo', (20, 30), compression=-4)
with self.assertRaises(ValueError):
self.f.create_dataset('foo', (20, 30), compression='gzip',
compression_opts=14)
class TestCreateCompressionNumber(BaseDataset):
"""
Feature: Datasets created with a compression code
"""
def test_compression_number(self):
""" Create with compression number of gzip (h5py.h5z.FILTER_DEFLATE) and a compression level of 7"""
original_compression_vals = h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS
if self.is_hsds():
compression = 'gzip'
else:
compression = h5py.h5z.FILTER_DEFLATE
try:
h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS = tuple()
dset = self.f.create_dataset('foo', (20, 30), compression=compression, compression_opts=(7,))
finally:
h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS = original_compression_vals
self.assertEqual(dset.compression, 'gzip')
self.assertEqual(dset.compression_opts, 7)
def test_compression_number_invalid(self):
""" Create with invalid compression numbers """
with self.assertRaises(ValueError) as e:
self.f.create_dataset('foo', (20, 30), compression=-999)
self.assertIn("Invalid filter", str(e.exception))
with self.assertRaises(ValueError) as e:
self.f.create_dataset('foo', (20, 30), compression=100)
self.assertIn("Unknown compression", str(e.exception))
original_compression_vals = h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS
try:
h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS = tuple()
# Using gzip compression requires a compression level specified in compression_opts
if self.is_hsds():
# Index error not being raised for HSDS
self.f.create_dataset('foo', (20, 30), compression='gzip')
else:
with self.assertRaises(IndexError):
self.f.create_dataset('foo', (20, 30), compression=h5py.h5z.FILTER_DEFLATE)
finally:
h5py._hl.dataset._LEGACY_GZIP_COMPRESSION_VALS = original_compression_vals
class TestCreateLZF(BaseDataset):
"""
Feature: Datasets created with LZF compression
"""
def test_lzf(self):
""" Create with explicit lzf """
if self.is_hsds():
# use lz4 instead of lzf for HSDS
compression = "lz4"
else:
compression = "lzf"
dset = self.f.create_dataset('foo', (20, 30), compression=compression)
self.assertEqual(dset.compression, compression)
self.assertEqual(dset.compression_opts, None)
testdata = np.arange(100)
dset = self.f.create_dataset('bar', data=testdata, compression=compression)
self.assertEqual(dset.compression, compression)
self.assertEqual(dset.compression_opts, None)
self.f.flush() # Actually write to file
readdata = self.f['bar'][()]
self.assertArrayEqual(readdata, testdata)
@ut.skip
def test_lzf_exc(self):
""" Giving lzf options raises ValueError """
with self.assertRaises(ValueError):
self.f.create_dataset('foo', (20, 30), compression='lzf',
compression_opts=4)
class TestCreateSZIP(BaseDataset):
"""
Feature: Datasets created with SZIP compression
"""
def test_szip(self):
""" Create with explicit szip """
if self.is_hsds():
compressors = self.f.compressors
else:
compressors = h5py.filters.encode
if "szip" in compressors:
self.f.create_dataset('foo', (20, 30), compression='szip',
compression_opts=('ec', 16))
else:
pass # szip not supported
class TestCreateShuffle(BaseDataset):
"""
Feature: Datasets can use shuffling filter
"""
def test_shuffle(self):
""" Enable shuffle filter """
dset = self.f.create_dataset('foo', (20, 30), shuffle=True)
self.assertTrue(dset.shuffle)
class TestCreateFletcher32(BaseDataset):
"""
Feature: Datasets can use the fletcher32 filter
TBD: not supported in HSDS
"""
@ut.skip
def test_fletcher32(self):
""" Enable fletcher32 filter """
dset = self.f.create_dataset('foo', (20, 30), fletcher32=True)
self.assertTrue(dset.fletcher32)
class TestCreateScaleOffset(BaseDataset):
"""
Feature: Datasets can use the scale/offset filter
"""
def test_float_fails_without_options(self):
""" Ensure that a scale factor is required for scaleoffset compression of floating point data """
with self.assertRaises(ValueError):
self.f.create_dataset('foo', (20, 30), dtype=float, scaleoffset=True)
def test_non_integer(self):
""" Check when scaleoffset is negetive"""
with self.assertRaises(ValueError):
self.f.create_dataset('foo', (20, 30), dtype=float, scaleoffset=-0.1)
def test_unsupport_dtype(self):
""" Check when dtype is unsupported type"""
with self.assertRaises(TypeError):
self.f.create_dataset('foo', (20, 30), dtype=bool, scaleoffset=True)
def test_float(self):
""" Scaleoffset filter works for floating point data """
scalefac = 4
shape = (100, 300)
range = 20 * 10 ** scalefac
testdata = (np.random.rand(*shape) - 0.5) * range
dset = self.f.create_dataset('foo', shape, dtype=float, scaleoffset=scalefac)
# Dataset reports that scaleoffset is in use
assert dset.scaleoffset is not None
# Dataset round-trips
dset[...] = testdata
filename = self.f.filename
self.f.close()
self.f = h5py.File(filename, 'r')
readdata = self.f['foo'][...]
# Test that data round-trips to requested precision
self.assertArrayEqual(readdata, testdata, precision=10 ** (-scalefac))
# Test that the filter is actually active (i.e. compression is lossy)
if self.is_hsds():
# TBD: scaleoffset is a NOP in HSDS
assert (readdata == testdata).all()
else:
assert not (readdata == testdata).all()
def test_int(self):
""" Scaleoffset filter works for integer data with default precision """
nbits = 12
shape = (100, 300)
testdata = np.random.randint(0, 2 ** nbits - 1, size=shape, dtype=np.int32)
# Create dataset; note omission of nbits (for library-determined precision)
dset = self.f.create_dataset('foo', shape, dtype=np.int32, scaleoffset=True)
# Dataset reports scaleoffset enabled
assert dset.scaleoffset is not None
# Data round-trips correctly and identically
dset[...] = testdata
filename = self.f.filename
self.f.close()
self.f = h5py.File(filename, 'r')
readdata = self.f['foo'][...]
self.assertArrayEqual(readdata, testdata)
def test_int_with_minbits(self):
""" Scaleoffset filter works for integer data with specified precision """
nbits = 12
shape = (100, 300)
testdata = np.random.randint(0, 2 ** nbits, size=shape, dtype=np.int32)
dset = self.f.create_dataset('foo', shape, scaleoffset=nbits, dtype=np.int32)
# Dataset reports scaleoffset enabled with correct precision
self.assertTrue(dset.scaleoffset == 12)
# Data round-trips correctly
dset[...] = testdata
filename = self.f.filename
self.f.close()
self.f = h5py.File(filename, 'r')
readdata = self.f['foo'][...]
self.assertArrayEqual(readdata, testdata)
def test_int_with_minbits_lossy(self):
""" Scaleoffset filter works for integer data with specified precision """
nbits = 12
shape = (100, 300)
testdata = np.random.randint(0, 2 ** (nbits + 1) - 1, size=shape)
dset = self.f.create_dataset('foo', shape, dtype=np.int32, scaleoffset=nbits)
# Dataset reports scaleoffset enabled with correct precision
self.assertTrue(dset.scaleoffset == 12)
# Data can be written and read
dset[...] = testdata
filename = self.f.filename
self.f.close()
self.f = h5py.File(filename, 'r')
readdata = self.f['foo'][...]
# Compression is lossy
if self.is_hsds():
# TBD: scaleoffset is a NOP in HSDS
assert (readdata == testdata).all()
else:
assert not (readdata == testdata).all()
@ut.skip("external dataset option not supported")
class TestExternal(BaseDataset):
"""
Feature: Datasets with the external storage property
TBD: external option not supported in HSDS. Use
external link instead
"""
def test_contents(self):
""" Create and access an external dataset """
shape = (6, 100)
testdata = np.random.random(shape)
# create a dataset in an external file and set it
ext_file = self.mktemp()
# TBD: h5f undefined
# external = [(ext_file, 0, h5f.UNLIMITED)]
# TBD: external undefined
# dset = self.f.create_dataset('foo', shape, dtype=testdata.dtype, external=external)
# dset[...] = testdata
# assert dset.external is not None
# verify file's existence, size, and contents
with open(ext_file, 'rb') as fid:
contents = fid.read()
assert contents == testdata.tobytes()
def test_name_str(self):
""" External argument may be a file name str only """
self.f.create_dataset('foo', (6, 100), external=self.mktemp())
def test_name_path(self):
""" External argument may be a file name path only """
self.f.create_dataset('foo', (6, 100),
external=pathlib.Path(self.mktemp()))
def test_iter_multi(self):
""" External argument may be an iterable of multiple tuples """
ext_file = self.mktemp()
N = 100
external = iter((ext_file, x * 1000, 1000) for x in range(N))
dset = self.f.create_dataset('poo', (6, 100), external=external)
assert len(dset.external) == N
def test_invalid(self):
""" Test with invalid external lists """
shape = (6, 100)
ext_file = self.mktemp()
for exc_type, external in [
(TypeError, [ext_file]),
(TypeError, [ext_file, 0]),
# TBD: h5f undefined
# (TypeError, [ext_file, 0, h5f.UNLIMITED]),
(ValueError, [(ext_file,)]),
(ValueError, [(ext_file, 0)]),
# TBD: h5f undefined
# (ValueError, [(ext_file, 0, h5f.UNLIMITED, 0)]),
(TypeError, [(ext_file, 0, "h5f.UNLIMITED")]),
]:
with self.assertRaises(exc_type):
self.f.create_dataset('foo', shape, external=external)
class TestAutoCreate(BaseDataset):
"""
Feature: Datasets auto-created from data produce the correct types
"""
def assert_string_type(self, ds, cset, variable=True):
if config.get('use_h5py'):
type_obj = ds.id.get_type()
self.assertEqual(type_obj.get_class(), h5py.h5t.STRING)
dset_cset = type_obj.get_cset()
if cset == 'H5T_CSET_ASCII':
expected_cset = h5py.h5t.CSET_ASCII
elif cset == 'H5T_CSET_UTF8':
expected_cset = h5py.h5t.CSET_UTF8
else:
expected_cset = h5py.h5t.CSET_ERROR
self.assertEqual(dset_cset, expected_cset)
else:
type_json = ds.id.type_json
if "class" not in type_json:
raise TypeError()
self.assertEqual(type_json["class"], 'H5T_STRING')
if "charSet" not in type_json:
raise TypeError()
self.assertEqual(type_json["charSet"], cset)
if variable:
if "length" not in type_json:
raise TypeError()
self.assertEqual(type_json["length"], 'H5T_VARIABLE')
def test_vlen_bytes(self):
"""Assigning byte strings produces a vlen string ASCII dataset """
self.f['x'] = b"Hello there"
self.assert_string_type(self.f['x'], 'H5T_CSET_ASCII')
self.f['y'] = [b"a", b"bc"]
self.assert_string_type(self.f['y'], 'H5T_CSET_ASCII')
self.f['z'] = np.array([b"a", b"bc"], dtype=np.object_)
self.assert_string_type(self.f['z'], 'H5T_CSET_ASCII')
def test_vlen_unicode(self):
"""Assigning unicode strings produces a vlen string UTF-8 dataset """
self.f['x'] = "Hello there" + chr(0x2034)
self.assert_string_type(self.f['x'], 'H5T_CSET_UTF8')
self.f['y'] = ["a", "bc"]
self.assert_string_type(self.f['y'], 'H5T_CSET_UTF8')
# 2D array; this only works with an array, not nested lists
self.f['z'] = np.array([["a", "bc"]], dtype=np.object_)
self.assert_string_type(self.f['z'], 'H5T_CSET_UTF8')
def test_string_fixed(self):
""" Assignment of fixed-length byte string produces a fixed-length
ascii dataset """
self.f['x'] = np.bytes_("Hello there")
ds = self.f['x']
self.assert_string_type(ds, 'H5T_CSET_ASCII', variable=False)
if self.is_hsds():
type_size = ds.id.get_type().itemsize
else:
type_size = ds.id.get_type().get_size()
self.assertEqual(type_size, 11)
class TestCreateLike(BaseDataset):
def get_object_mtime(self, obj):
if self.is_hsds():