/usr/lib/python2.7/dist-packages/h5py/tests/old/test_h5t.py is in python-h5py 2.7.1-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | # This file is part of h5py, a Python interface to the HDF5 library.
#
# http://www.h5py.org
#
# Copyright 2008-2013 Andrew Collette and contributors
#
# License: Standard 3-clause BSD; see "license.txt" for full license terms
# and contributor agreement.
from __future__ import absolute_import
import sys
import numpy as np
from six import PY2, text_type
import h5py
from h5py import h5t
from ..common import TestCase, ut
class TestCompound(ut.TestCase):
"""
Feature: Compound types can be created from Python dtypes
"""
def test_ref(self):
""" Reference types are correctly stored in compound types (issue 144)
"""
ref = h5py.special_dtype(ref=h5py.Reference)
dt = np.dtype([('a', ref), ('b', '<f4')])
tid = h5t.py_create(dt, logical=True)
t1, t2 = tid.get_member_type(0), tid.get_member_type(1)
self.assertEqual(t1, h5t.STD_REF_OBJ)
self.assertEqual(t2, h5t.IEEE_F32LE)
self.assertEqual(tid.get_member_offset(0), 0)
self.assertEqual(tid.get_member_offset(1), h5t.STD_REF_OBJ.get_size())
def test_out_of_order_offsets(self):
size = 20
type_dict = {
'names': ['f1', 'f2', 'f3'],
'formats': ['<f4', '<i4', '<f8'],
'offsets': [0, 16, 8]
}
expected_dtype = np.dtype(type_dict)
tid = h5t.create(h5t.COMPOUND, size)
for name, offset, dt in zip(
type_dict["names"], type_dict["offsets"], type_dict["formats"]
):
tid.insert(
name.encode("utf8") if isinstance(name, text_type) else name,
offset,
h5t.py_create(dt)
)
self.assertEqual(tid.dtype, expected_dtype)
self.assertEqual(tid.dtype.itemsize, size)
class TestTypeFloatID(TestCase):
"""Test TypeFloatID."""
def test_custom_float_promotion(self):
"""Custom floats are correctly promoted to standard floats on read."""
test_filename = self.mktemp()
dataset = 'DS1'
dataset2 = 'DS2'
dataset3 = 'DS3'
dataset4 = 'DS4'
dataset5 = 'DS5'
# Strings are handled very differently between python2 and python3.
if not PY2:
test_filename = test_filename.encode()
dataset = dataset.encode()
dataset2 = dataset2.encode()
dataset3 = dataset3.encode()
dataset4 = dataset4.encode()
dataset5 = dataset5.encode()
DIM0 = 4
DIM1 = 7
wdata = np.array([[-1.50066626e-09, 1.40062184e-09, 1.81216819e-10,
4.01087163e-10, 4.27917257e-10, -7.04858394e-11,
5.74800652e-10],
[-1.50066626e-09, 4.86579665e-10, 3.42879503e-10,
5.12045517e-10, 5.10226528e-10, 2.24190444e-10,
3.93356459e-10],
[-1.50066626e-09, 5.24778443e-10, 8.19454726e-10,
1.28966349e-09, 1.68483894e-10, 5.71276360e-11,
-1.08684617e-10],
[-1.50066626e-09, -1.08343556e-10, -1.58934199e-10,
8.52196536e-10, 6.18456397e-10, 6.16637408e-10,
1.31694833e-09]], dtype=np.float32)
wdata2 = np.array([[-1.50066626e-09, 5.63886715e-10, -8.74251782e-11,
1.32558853e-10, 1.59161573e-10, 2.29420039e-10,
-7.24185156e-11],
[-1.50066626e-09, 1.87810656e-10, 7.74889486e-10,
3.95630195e-10, 9.42236511e-10, 8.38554115e-10,
-8.71978045e-11],
[-1.50066626e-09, 6.20275387e-10, 7.34871719e-10,
6.64840627e-10, 2.64662958e-10, 1.05319486e-09,
1.68256520e-10],
[-1.50066626e-09, 1.67347025e-10, 5.12045517e-10,
3.36513040e-10, 1.02545528e-10, 1.28784450e-09,
4.06089384e-10]], dtype=np.float32)
# Create a new file using the default properties.
fid = h5py.h5f.create(test_filename)
# Create the dataspace. No maximum size parameter needed.
dims = (DIM0, DIM1)
space = h5py.h5s.create_simple(dims)
# create a custom type with larger bias
mytype = h5t.IEEE_F16LE
mytype = h5t.IEEE_F16LE.copy()
mytype.set_fields(14, 9, 5, 0, 9)
mytype.set_size(2)
mytype.set_ebias(53)
mytype.lock()
dset = h5py.h5d.create(fid, dataset, mytype, space)
dset.write(h5py.h5s.ALL, h5py.h5s.ALL, wdata)
del dset
# create a custom type with larger exponent
mytype2 = h5t.IEEE_F16LE
mytype2 = h5t.IEEE_F16LE.copy()
mytype2.set_fields(15, 9, 6, 0, 9)
mytype2.set_size(2)
mytype2.set_ebias(53)
mytype2.lock()
dset = h5py.h5d.create(fid, dataset2, mytype2, space)
dset.write(h5py.h5s.ALL, h5py.h5s.ALL, wdata2)
del dset
# create a custom type which reimplements 16-bit floats
mytype3 = h5t.IEEE_F16LE
mytype3 = h5t.IEEE_F16LE.copy()
mytype3.set_fields(15, 10, 5, 0, 10)
mytype3.set_size(2)
mytype3.set_ebias(15)
mytype3.lock()
dset = h5py.h5d.create(fid, dataset3, mytype3, space)
dset.write(h5py.h5s.ALL, h5py.h5s.ALL, wdata2)
del dset
# create a custom type with larger bias
mytype4 = h5t.IEEE_F16LE
mytype4 = h5t.IEEE_F16LE.copy()
mytype4.set_fields(15, 10, 5, 0, 10)
mytype4.set_size(2)
mytype4.set_ebias(258)
mytype4.lock()
dset = h5py.h5d.create(fid, dataset4, mytype4, space)
dset.write(h5py.h5s.ALL, h5py.h5s.ALL, wdata2)
del dset
# create a dataset with long doubles
dset = h5py.h5d.create(fid, dataset5, h5t.NATIVE_LDOUBLE, space)
dset.write(h5py.h5s.ALL, h5py.h5s.ALL, wdata2)
# Explicitly close and release resources.
del space
del dset
del fid
f = h5py.File(test_filename, 'r')
# ebias promotion to float32
values = f[dataset][:]
self.assertTrue(np.all(values == wdata))
self.assertEqual(values.dtype, np.dtype('<f4'))
# esize promotion to float32
values = f[dataset2][:]
self.assertTrue(np.all(values == wdata2))
self.assertEqual(values.dtype, np.dtype('<f4'))
# regular half floats
dset = f[dataset3]
try:
self.assertEqual(dset.dtype, np.dtype('<f2'))
except AttributeError:
self.assertEqual(dset.dtype, np.dtype('<f4'))
# ebias promotion to float64
dset = f[dataset4]
self.assertEqual(dset.dtype, np.dtype('<f8'))
# long double floats
dset = f[dataset5]
self.assertEqual(dset.dtype, np.longdouble)
|