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"""
Tests for converting between numpy dtypes and h5py data types
"""
from __future__ import absolute_import
from itertools import count
import platform
import numpy as np
import six
import h5py
try:
import tables
except ImportError:
tables = None
from .common import ut, TestCase
UNSUPPORTED_LONG_DOUBLE = ('i386', 'i486', 'i586', 'i686', 'ppc64le')
class TestVlen(TestCase):
"""
Check that storage of vlen strings is carried out correctly.
"""
def assertVlenArrayEqual(self, dset, arr, message=None, precision=None):
assert dset.shape == arr.shape, \
"Shape mismatch (%s vs %s)%s" % (dset.shape, arr.shape, message)
for (i, d, a) in zip(count(), dset, arr):
self.assertArrayEqual(d, a, message, precision)
def test_compound(self):
fields = []
fields.append(('field_1', h5py.string_dtype()))
fields.append(('field_2', np.int32))
dt = np.dtype(fields)
self.f['mytype'] = np.dtype(dt)
dt_out = self.f['mytype'].dtype.fields['field_1'][0]
string_inf = h5py.check_string_dtype(dt_out)
self.assertEqual(string_inf.encoding, 'utf-8')
def test_compound_vlen_bool(self):
vidt = h5py.vlen_dtype(np.uint8)
def a(items):
return np.array(items, dtype=np.uint8)
f = self.f
dt_vb = np.dtype([
('foo', vidt),
('logical', np.bool)])
vb = f.create_dataset('dt_vb', shape=(4,), dtype=dt_vb)
data = np.array([(a([1, 2, 3]), True),
(a([1 ]), False),
(a([1, 5 ]), True),
(a([],), False), ],
dtype=dt_vb)
vb[:] = data
actual = f['dt_vb'][:]
self.assertVlenArrayEqual(data['foo'], actual['foo'])
self.assertArrayEqual(data['logical'], actual['logical'])
dt_vv = np.dtype([
('foo', vidt),
('bar', vidt)])
f.create_dataset('dt_vv', shape=(4,), dtype=dt_vv)
dt_vvb = np.dtype([
('foo', vidt),
('bar', vidt),
('logical', np.bool)])
vvb = f.create_dataset('dt_vvb', shape=(2,), dtype=dt_vvb)
dt_bvv = np.dtype([
('logical', np.bool),
('foo', vidt),
('bar', vidt)])
bvv = f.create_dataset('dt_bvv', shape=(2,), dtype=dt_bvv)
data = np.array([(True, a([1, 2, 3]), a([1, 2])),
(False, a([]), a([2, 4, 6])), ],
dtype=bvv)
bvv[:] = data
actual = bvv[:]
self.assertVlenArrayEqual(data['foo'], actual['foo'])
self.assertVlenArrayEqual(data['bar'], actual['bar'])
self.assertArrayEqual(data['logical'], actual['logical'])
def test_compound_vlen_enum(self):
eidt = h5py.enum_dtype({'OFF': 0, 'ON': 1}, basetype=np.uint8)
vidt = h5py.vlen_dtype(np.uint8)
def a(items):
return np.array(items, dtype=np.uint8)
f = self.f
dt_vve = np.dtype([
('foo', vidt),
('bar', vidt),
('switch', eidt)])
vve = f.create_dataset('dt_vve', shape=(2,), dtype=dt_vve)
data = np.array([(a([1, 2, 3]), a([1, 2]), 1),
(a([]), a([2, 4, 6]), 0), ],
dtype=dt_vve)
vve[:] = data
actual = vve[:]
self.assertVlenArrayEqual(data['foo'], actual['foo'])
self.assertVlenArrayEqual(data['bar'], actual['bar'])
self.assertArrayEqual(data['switch'], actual['switch'])
def test_vlen_enum(self):
fname = self.mktemp()
arr1 = [[1], [1, 2]]
dt1 = h5py.vlen_dtype(h5py.enum_dtype(dict(foo=1, bar=2), 'i'))
with h5py.File(fname, 'w') as f:
df1 = f.create_dataset('test', (len(arr1),), dtype=dt1)
df1[:] = np.array(arr1)
with h5py.File(fname, 'r') as f:
df2 = f['test']
dt2 = df2.dtype
arr2 = [e.tolist() for e in df2[:]]
self.assertEqual(arr1, arr2)
self.assertEqual(h5py.check_enum_dtype(h5py.check_vlen_dtype(dt1)),
h5py.check_enum_dtype(h5py.check_vlen_dtype(dt2)))
class TestEmptyVlen(TestCase):
def test_write_empty_vlen(self):
fname = self.mktemp()
with h5py.File(fname, 'w') as f:
d = np.core.records.fromarrays([[], []], names='a,b', formats='|V16,O')
dset = f.create_dataset('test', data=d, dtype=[('a', '|V16'), ('b', h5py.special_dtype(vlen=np.float_))])
self.assertEqual(dset.size, 0)
class TestExplicitCast(TestCase):
def test_f2_casting(self):
fname = self.mktemp()
np.random.seed(1)
A = np.random.rand(1500, 20)
# Save to HDF5 file
with h5py.File(fname, "w") as Fid:
Fid.create_dataset("Data", data=A, dtype='f2')
with h5py.File(fname, "r") as Fid:
B = Fid["Data"][:]
# Compare
self.assertTrue(np.all(A.astype('f2') == B))
class TestOffsets(TestCase):
"""
Check that compound members with aligned or manual offsets are handled
correctly.
"""
def test_compound_vlen(self):
vidt = h5py.vlen_dtype(np.uint8)
eidt = h5py.enum_dtype({'OFF': 0, 'ON': 1}, basetype=np.uint8)
for np_align in (False, True):
dt = np.dtype([
('a', eidt),
('foo', vidt),
('bar', vidt),
('switch', eidt)], align=np_align)
np_offsets = [dt.fields[i][1] for i in dt.names]
for logical in (False, True):
if logical and np_align:
# Vlen types have different size in the numpy struct
self.assertRaises(TypeError, h5py.h5t.py_create, dt,
logical=logical)
else:
ht = h5py.h5t.py_create(dt, logical=logical)
offsets = [ht.get_member_offset(i)
for i in range(ht.get_nmembers())]
if np_align:
self.assertEqual(np_offsets, offsets)
def test_aligned_offsets(self):
dt = np.dtype('i4,i8,i2', align=True)
ht = h5py.h5t.py_create(dt)
self.assertEqual(dt.itemsize, ht.get_size())
self.assertEqual(
[dt.fields[i][1] for i in dt.names],
[ht.get_member_offset(i) for i in range(ht.get_nmembers())]
)
def test_aligned_data(self):
dt = np.dtype('i4,f8,i2', align=True)
data = np.empty(10, dtype=dt)
data['f0'] = np.array(np.random.randint(-100, 100, size=data.size),
dtype='i4')
data['f1'] = np.random.rand(data.size)
data['f2'] = np.array(np.random.randint(-100, 100, size=data.size),
dtype='i2')
fname = self.mktemp()
with h5py.File(fname, 'w') as f:
f['data'] = data
with h5py.File(fname, 'r') as f:
self.assertArrayEqual(f['data'], data)
def test_compound_robustness(self):
# make an out of order compound type with gaps in it, and larger itemsize than minimum
# Idea is to be robust to type descriptions we *could* get out of HDF5 files, from custom descriptions
# of types in addition to numpy's flakey history on unaligned fields with non-standard or padded layouts.
fields = [
('f0', np.float64, 25),
('f1', np.uint64, 9),
('f2', np.uint32, 0),
('f3', np.uint16, 5)
]
lastfield = fields[np.argmax([ x[2] for x in fields ])]
itemsize = lastfield[2] + np.dtype(lastfield[1]).itemsize + 6
extract_index = lambda index, sequence: [ x[index] for x in sequence ]
dt = np.dtype({
'names' : extract_index(0, fields),
'formats' : extract_index(1, fields),
'offsets' : extract_index(2, fields),
# 'aligned': False, - already defaults to False
'itemsize': itemsize
})
self.assertTrue(dt.itemsize == itemsize)
data = np.empty(10, dtype=dt)
# don't trust numpy struct handling , keep fields out of band incase content insertion is erroneous
# yes... this has also been known to happen.
f1 = np.array([1 + i * 4 for i in range(data.shape[0])], dtype=dt.fields['f1'][0])
f2 = np.array([2 + i * 4 for i in range(data.shape[0])], dtype=dt.fields['f2'][0])
f3 = np.array([3 + i * 4 for i in range(data.shape[0])], dtype=dt.fields['f3'][0])
f0c = 3.14
data['f0'] = f0c
data['f3'] = f3
data['f1'] = f1
data['f2'] = f2
# numpy consistency checks
self.assertTrue(np.all(data['f0'] == f0c))
self.assertArrayEqual(data['f3'], f3)
self.assertArrayEqual(data['f1'], f1)
self.assertArrayEqual(data['f2'], f2)
fname = self.mktemp()
with h5py.File(fname, 'w') as fd:
fd.create_dataset('data', data=data)
with h5py.File(fname, 'r') as fd:
readback = fd['data']
self.assertTrue(readback.dtype == dt)
self.assertArrayEqual(readback, data)
self.assertTrue(np.all(readback['f0'] == f0c))
self.assertArrayEqual(readback['f1'], f1)
self.assertArrayEqual(readback['f2'], f2)
self.assertArrayEqual(readback['f3'], f3)
def test_out_of_order_offsets(self):
dt = np.dtype({
'names' : ['f1', 'f2', 'f3'],
'formats' : ['<f4', '<i4', '<f8'],
'offsets' : [0, 16, 8]
})
data = np.empty(10, dtype=dt)
data['f1'] = np.random.rand(data.size)
data['f2'] = np.random.randint(-10, 11, data.size)
data['f3'] = np.random.rand(data.size) * -1
fname = self.mktemp()
with h5py.File(fname, 'w') as fd:
fd.create_dataset('data', data=data)
with h5py.File(fname, 'r') as fd:
self.assertArrayEqual(fd['data'], data)
def test_float_round_tripping(self):
return
dtypes = set(f for f in np.typeDict.values()
if (np.issubdtype(f, np.floating) or
np.issubdtype(f, np.complexfloating)))
if platform.machine() in UNSUPPORTED_LONG_DOUBLE:
dtype_dset_map = {str(j): d
for j, d in enumerate(dtypes)
if d not in (np.float128, np.complex256)}
else:
dtype_dset_map = {str(j): d
for j, d in enumerate(dtypes)}
fname = self.mktemp()
with h5py.File(fname, 'w') as f:
for n, d in dtype_dset_map.items():
data = np.arange(10,
dtype=d)
f.create_dataset(n, data=data)
with h5py.File(fname, 'r') as f:
for n, d in dtype_dset_map.items():
ldata = f[n][:]
self.assertEqual(ldata.dtype, d)
class TestStrings(TestCase):
def test_vlen_utf8(self):
dt = h5py.string_dtype()
string_info = h5py.check_string_dtype(dt)
assert string_info.encoding == 'utf-8'
assert string_info.length is None
assert h5py.check_vlen_dtype(dt) is six.text_type
def test_vlen_ascii(self):
dt = h5py.string_dtype(encoding='ascii')
string_info = h5py.check_string_dtype(dt)
assert string_info.encoding == 'ascii'
assert string_info.length is None
assert h5py.check_vlen_dtype(dt) is bytes
def test_fixed_utf8(self):
dt = h5py.string_dtype(length=10)
string_info = h5py.check_string_dtype(dt)
assert string_info.encoding == 'utf-8'
assert string_info.length == 10
assert h5py.check_vlen_dtype(dt) is None
def test_fixed_ascii(self):
dt = h5py.string_dtype(encoding='ascii', length=10)
string_info = h5py.check_string_dtype(dt)
assert string_info.encoding == 'ascii'
assert string_info.length == 10
assert h5py.check_vlen_dtype(dt) is None
@ut.skipUnless(tables is not None, 'tables is required')
class TestB8(TestCase):
"""
Test H5T_NATIVE_B8 reading
"""
def test_b8_bool(self):
arr1 = np.array([False, True], dtype=np.bool)
self._test_b8(arr1)
self._test_b8(arr1, dtype=np.uint8)
def test_b8_bool_compound(self):
arr1 = np.array([(False,), (True,)], dtype=np.dtype([('x', '?')]))
self._test_b8(arr1)
self._test_b8(arr1, dtype=np.dtype([('x', 'u1')]))
def test_b8_bool_compound_nested(self):
arr1 = np.array(
[(True, (True, False)), (True, (False, True))],
dtype=np.dtype([('x', '?'), ('y', [('a', '?'), ('b', '?')])]),
)
self._test_b8(arr1)
self._test_b8(
arr1,
dtype=np.dtype([('x', 'u1'), ('y', [('a', 'u1'), ('b', 'u1')])]),
)
def test_b8_bool_array(self):
arr1 = np.array(
[((True, True, False),), ((True, False, True),)],
dtype=np.dtype([('x', ('?', (3,)))]),
)
self._test_b8(arr1)
self._test_b8(
arr1,
dtype=np.dtype([('x', ('?', (3,)))]),
)
def _test_b8(self, arr1, dtype=None):
path = self.mktemp()
with tables.open_file(path, 'w') as f:
if arr1.dtype.names:
f.create_table('/', 'test', obj=arr1)
else:
f.create_array('/', 'test', obj=arr1)
with h5py.File(path, 'r') as f:
dset = f['test']
# read uncast dset to make sure it raises as before
with self.assertRaises(
TypeError, msg='No NumPy equivalent for TypeBitfieldID exists'
):
dset[:]
# read cast dset and make sure it's equal
if dtype is None:
dtype = arr1.dtype
with dset.astype(dtype):
arr2 = dset[:]
self.assertArrayEqual(arr2, arr1.astype(dtype, copy=False))
# read uncast dataset again to ensure nothing changed permanantly
with self.assertRaises(
TypeError, msg='No NumPy equivalent for TypeBitfieldID exists'
):
dset[:]
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