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
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
|
# Copyright 2012 Knowledge Economy Developments Ltd
#
# Henry Gomersall
# heng@kedevelopments.co.uk
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from pyfftw import (
FFTW, n_byte_align_empty, n_byte_align)
from .test_pyfftw_base import run_test_suites
import numpy
import unittest
class FFTWCallTest(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(FFTWCallTest, self).__init__(*args, **kwargs)
if not hasattr(self, 'assertRaisesRegex'):
self.assertRaisesRegex = self.assertRaisesRegexp
def setUp(self):
self.input_array = n_byte_align_empty((256, 512), 16,
dtype='complex128')
self.output_array = n_byte_align_empty((256, 512), 16,
dtype='complex128')
self.fft = FFTW(self.input_array, self.output_array)
self.input_array[:] = (numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape))
def test_call(self):
'''Test a call to an instance of the class.
'''
self.input_array[:] = (numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape))
output_array = self.fft()
self.assertTrue(numpy.alltrue(output_array == self.output_array))
def test_call_with_positional_input_update(self):
'''Test the class call with a positional input update.
'''
input_array = n_byte_align(
(numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape)), 16)
output_array = self.fft(n_byte_align(input_array.copy(), 16)).copy()
self.fft.update_arrays(input_array, self.output_array)
self.fft.execute()
self.assertTrue(numpy.alltrue(output_array == self.output_array))
def test_call_with_keyword_input_update(self):
'''Test the class call with a keyword input update.
'''
input_array = n_byte_align(
numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape), 16)
output_array = self.fft(
input_array=n_byte_align(input_array.copy(), 16)).copy()
self.fft.update_arrays(input_array, self.output_array)
self.fft.execute()
self.assertTrue(numpy.alltrue(output_array == self.output_array))
def test_call_with_keyword_output_update(self):
'''Test the class call with a keyword output update.
'''
output_array = n_byte_align(
(numpy.random.randn(*self.output_array.shape)
+ 1j*numpy.random.randn(*self.output_array.shape)), 16)
returned_output_array = self.fft(
output_array=n_byte_align(output_array.copy(), 16)).copy()
self.fft.update_arrays(self.input_array, output_array)
self.fft.execute()
self.assertTrue(
numpy.alltrue(returned_output_array == output_array))
def test_call_with_positional_updates(self):
'''Test the class call with a positional array updates.
'''
input_array = n_byte_align((numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape)), 16)
output_array = n_byte_align((numpy.random.randn(*self.output_array.shape)
+ 1j*numpy.random.randn(*self.output_array.shape)), 16)
returned_output_array = self.fft(
n_byte_align(input_array.copy(), 16),
n_byte_align(output_array.copy(), 16)).copy()
self.fft.update_arrays(input_array, output_array)
self.fft.execute()
self.assertTrue(numpy.alltrue(returned_output_array == output_array))
def test_call_with_keyword_updates(self):
'''Test the class call with a positional output update.
'''
input_array = n_byte_align(
(numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape)), 16)
output_array = n_byte_align(
(numpy.random.randn(*self.output_array.shape)
+ 1j*numpy.random.randn(*self.output_array.shape)), 16)
returned_output_array = self.fft(
output_array=n_byte_align(output_array.copy(), 16),
input_array=n_byte_align(input_array.copy(), 16)).copy()
self.fft.update_arrays(input_array, output_array)
self.fft.execute()
self.assertTrue(numpy.alltrue(returned_output_array == output_array))
def test_call_with_different_input_dtype(self):
'''Test the class call with an array with a different input dtype
'''
input_array = n_byte_align(numpy.complex64(
numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape)), 16)
output_array = self.fft(n_byte_align(input_array.copy(), 16)).copy()
_input_array = n_byte_align(numpy.asarray(input_array,
dtype=self.input_array.dtype), 16)
self.assertTrue(_input_array.dtype != input_array.dtype)
self.fft.update_arrays(_input_array, self.output_array)
self.fft.execute()
self.assertTrue(numpy.alltrue(output_array == self.output_array))
def test_call_with_list_input(self):
'''Test the class call with a list rather than an array
'''
output_array = self.fft().copy()
test_output_array = self.fft(self.input_array.tolist()).copy()
self.assertTrue(numpy.alltrue(output_array == test_output_array))
def test_call_with_invalid_update(self):
'''Test the class call with an invalid update.
'''
new_shape = self.input_array.shape + (2, )
invalid_array = (numpy.random.randn(*new_shape)
+ 1j*numpy.random.randn(*new_shape))
self.assertRaises(ValueError, self.fft,
*(),
**{'output_array':invalid_array})
self.assertRaises(ValueError, self.fft,
*(),
**{'input_array':invalid_array})
def test_call_with_auto_input_alignment(self):
'''Test the class call with a keyword input update.
'''
input_array = (numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape))
output_array = self.fft(
input_array=n_byte_align(input_array.copy(), 16)).copy()
# Offset by one from 16 byte aligned to guarantee it's not
# 16 byte aligned
a = input_array
a__ = n_byte_align_empty(
numpy.prod(a.shape)*a.itemsize+1, 16, dtype='int8')
a_ = a__[1:].view(dtype=a.dtype).reshape(*a.shape)
a_[:] = a
# Just confirm that a usual update will fail
self.assertRaisesRegex(ValueError, 'Invalid input alignment',
self.fft.update_arrays, *(a_, self.output_array))
self.fft(a_, self.output_array)
self.assertTrue(numpy.alltrue(output_array == self.output_array))
# now try with a single byte offset and SIMD off
ar, ai = numpy.float32(numpy.random.randn(2, 257))
a = ar[1:] + 1j*ai[1:]
b = a.copy()
a_size = len(a.ravel())*a.itemsize
update_array = numpy.frombuffer(
numpy.zeros(a_size + 1, dtype='int8')[1:].data,
dtype=a.dtype).reshape(a.shape)
fft = FFTW(a, b, flags=('FFTW_UNALIGNED',))
# Confirm that a usual update will fail (it's not on the
# byte boundary)
self.assertRaisesRegex(ValueError, 'Invalid input alignment',
fft.update_arrays, *(update_array, b))
fft(update_array, b)
def test_call_with_invalid_output_striding(self):
'''Test the class call with an invalid strided output update.
'''
# Add an extra dimension to bugger up the striding
new_shape = self.output_array.shape + (2,)
output_array = n_byte_align(numpy.random.randn(*new_shape)
+ 1j*numpy.random.randn(*new_shape), 16)
self.assertRaisesRegex(ValueError, 'Invalid output striding',
self.fft, **{'output_array': output_array[:,:,1]})
def test_call_with_different_striding(self):
'''Test the input update with different strides to internal array.
'''
shape = self.input_array.shape + (2,)
input_array = n_byte_align(numpy.random.randn(*shape)
+ 1j*numpy.random.randn(*shape), 16)
fft = FFTW(input_array[:,:,0], self.output_array)
test_output_array = fft().copy()
new_input_array = n_byte_align(
input_array[:, :, 0].copy(), 16)
new_output = fft(new_input_array).copy()
# Test the test!
self.assertTrue(new_input_array.strides != input_array[:,:,0].strides)
self.assertTrue(numpy.alltrue(test_output_array == new_output))
def test_call_with_copy_with_missized_array_error(self):
'''Force an input copy with a missized array.
'''
shape = list(self.input_array.shape + (2,))
shape[0] += 1
input_array = n_byte_align(numpy.random.randn(*shape)
+ 1j*numpy.random.randn(*shape), 16)
fft = FFTW(self.input_array, self.output_array)
self.assertRaisesRegex(ValueError, 'Invalid input shape',
self.fft, **{'input_array': input_array[:,:,0]})
def test_call_with_unaligned(self):
'''Make sure the right thing happens with unaligned data.
'''
input_array = (numpy.random.randn(*self.input_array.shape)
+ 1j*numpy.random.randn(*self.input_array.shape))
output_array = self.fft(
input_array=n_byte_align(input_array.copy(), 16)).copy()
input_array = n_byte_align(input_array, 16)
output_array = n_byte_align(output_array, 16)
# Offset by one from 16 byte aligned to guarantee it's not
# 16 byte aligned
a = n_byte_align(input_array.copy(), 16)
a__ = n_byte_align_empty(
numpy.prod(a.shape)*a.itemsize+1, 16, dtype='int8')
a_ = a__[1:].view(dtype=a.dtype).reshape(*a.shape)
a_[:] = a
# Create a different second array the same way
b = n_byte_align(output_array.copy(), 16)
b__ = n_byte_align_empty(
numpy.prod(b.shape)*a.itemsize+1, 16, dtype='int8')
b_ = b__[1:].view(dtype=b.dtype).reshape(*b.shape)
b_[:] = a
# Set up for the first array
fft = FFTW(input_array, output_array)
a_[:] = a
output_array = fft().copy()
# Check a_ is not aligned...
self.assertRaisesRegex(ValueError, 'Invalid input alignment',
self.fft.update_arrays, *(a_, output_array))
# and b_ too
self.assertRaisesRegex(ValueError, 'Invalid output alignment',
self.fft.update_arrays, *(input_array, b_))
# But it should still work with the a_
fft(a_)
# However, trying to update the output will raise an error
self.assertRaisesRegex(ValueError, 'Invalid output alignment',
self.fft.update_arrays, *(input_array, b_))
# Same with SIMD off
fft = FFTW(input_array, output_array, flags=('FFTW_UNALIGNED',))
fft(a_)
self.assertRaisesRegex(ValueError, 'Invalid output alignment',
self.fft.update_arrays, *(input_array, b_))
def test_call_with_normalisation_on(self):
_input_array = n_byte_align_empty((256, 512), 16,
dtype='complex128')
ifft = FFTW(self.output_array, _input_array,
direction='FFTW_BACKWARD')
self.fft(normalise_idft=True) # Shouldn't make any difference
ifft(normalise_idft=True)
self.assertTrue(numpy.allclose(self.input_array, _input_array))
def test_call_with_normalisation_off(self):
_input_array = n_byte_align_empty((256, 512), 16,
dtype='complex128')
ifft = FFTW(self.output_array, _input_array,
direction='FFTW_BACKWARD')
self.fft(normalise_idft=True) # Shouldn't make any difference
ifft(normalise_idft=False)
_input_array /= ifft.N
self.assertTrue(numpy.allclose(self.input_array, _input_array))
def test_call_with_normalisation_default(self):
_input_array = n_byte_align_empty((256, 512), 16,
dtype='complex128')
ifft = FFTW(self.output_array, _input_array,
direction='FFTW_BACKWARD')
self.fft()
ifft()
# Scaling is performed by default
self.assertTrue(numpy.allclose(self.input_array, _input_array))
test_cases = (
FFTWCallTest,)
test_set = None
if __name__ == '__main__':
run_test_suites(test_cases, test_set)
|
