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
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Project: Fast Azimuthal Integration
# https://github.com/pyFAI/pyFAI
#
# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# 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/>.
#
"test suite for dark_current / flat_field correction"
__author__ = "Jérôme Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "GPLv3+"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "20/10/2014"
import unittest
import os
import numpy
import logging
import time
import sys
import fabio
from utilstest import UtilsTest, Rwp, getLogger
logger = getLogger(__file__)
pyFAI = sys.modules["pyFAI"]
from pyFAI.opencl import ocl
if logger.getEffectiveLevel() <= logging.INFO:
import pylab
class TestFlat1D(unittest.TestCase):
shape = 640, 480
flat = 1 + numpy.random.random(shape)
dark = numpy.random.random(shape)
raw = flat + dark
eps = 1e-6
ai = pyFAI.AzimuthalIntegrator()
ai.setFit2D(directDist=1, centerX=shape[1] // 2, centerY=shape[0] // 2, pixelX=1, pixelY=1)
bins = 500
def test_no_correct(self):
r, I = self.ai.integrate1d(self.raw, self.bins, unit="r_mm", correctSolidAngle=False)
logger.info("1D Without correction Imin=%s Imax=%s <I>=%s std=%s" % (I.min(), I.max(), I.mean(), I.std()))
self.assertNotAlmostEqual(I.mean(), 1, 2, "Mean should not be 1")
self.assertFalse(I.max() - I.min() < self.eps, "deviation should be large")
def test_correct(self):
all_methods = ["numpy", "cython", "splitbbox", "splitpix", "lut", "csr"]
if ocl:
for device in ["cpu","gpu","acc"]:
if ocl.select_device(dtype=device):
all_methods.append("lut_ocl_%s" % device)
all_methods.append("csr_ocl_%s" % device)
for meth in all_methods:
r, I = self.ai.integrate1d(self.raw, self.bins, unit="r_mm", method=meth, correctSolidAngle=False, dark=self.dark, flat=self.flat)
logger.info("1D method:%s Imin=%s Imax=%s <I>=%s std=%s" % (meth, I.min(), I.max(), I.mean(), I.std()))
self.assertAlmostEqual(I.mean(), 1, 2, "Mean should be 1 in %s" % meth)
self.assert_(I.max() - I.min() < self.eps, "deviation should be small with meth %s, got %s" % (meth, I.max() - I.min()))
for meth in ["xrpd_numpy", "xrpd_cython", "xrpd_splitBBox", "xrpd_splitPixel"]: # , "xrpd_OpenCL" ]: bug with 32 bit GPU and request 64 bit integration
r, I = self.ai.__getattribute__(meth)(self.raw, self.bins, correctSolidAngle=False, dark=self.dark, flat=self.flat)
logger.info("1D method:%s Imin=%s Imax=%s <I>=%s std=%s" % (meth, I.min(), I.max(), I.mean(), I.std()))
self.assertAlmostEqual(I.mean(), 1, 2, "Mean should be 1 in %s" % meth)
self.assert_(I.max() - I.min() < self.eps, "deviation should be small with meth %s, got %s" % (meth, I.max() - I.min()))
if ocl and pyFAI.opencl.ocl.select_device("gpu", extensions=["cl_khr_fp64"]):
meth = "xrpd_OpenCL"
r, I = self.ai.__getattribute__(meth)(self.raw, self.bins, correctSolidAngle=False, dark=self.dark, flat=self.flat)
logger.info("1D method:%s Imin=%s Imax=%s <I>=%s std=%s" % (meth, I.min(), I.max(), I.mean(), I.std()))
self.assertAlmostEqual(I.mean(), 1, 2, "Mean should be 1 in %s" % meth)
self.assert_(I.max() - I.min() < self.eps, "deviation should be small with meth %s, got %s" % (meth, I.max() - I.min()))
class TestFlat2D(unittest.TestCase):
shape = 640, 480
flat = 1 + numpy.random.random(shape)
dark = numpy.random.random(shape)
raw = flat + dark
eps = 1e-6
ai = pyFAI.AzimuthalIntegrator()
ai.setFit2D(directDist=1, centerX=shape[1] // 2, centerY=shape[0] // 2, pixelX=1, pixelY=1)
bins = 500
azim = 360
def test_no_correct(self):
I, _ , _ = self.ai.integrate2d(self.raw, self.bins, self.azim, unit="r_mm", correctSolidAngle=False)
I = I[numpy.where(I > 0)]
logger.info("2D Without correction Imin=%s Imax=%s <I>=%s std=%s" % (I.min(), I.max(), I.mean(), I.std()))
self.assertNotAlmostEqual(I.mean(), 1, 2, "Mean should not be 1")
self.assertFalse(I.max() - I.min() < self.eps, "deviation should be large")
def test_correct(self):
test2d = {"numpy": self.eps,
"cython": self.eps,
"splitbbox": self.eps,
"splitpix": self.eps,
"lut": self.eps,
}
if ocl:
for device in ["cpu","gpu","acc"]:
if ocl.select_device(dtype=device):
test2d["lut_ocl_%s" % device] = self.eps
test2d["csr_ocl_%s" % device] = self.eps
test2d_direct = {"xrpd2_numpy": 0.3, # histograms are very noisy in 2D
"xrpd2_histogram": 0.3, # histograms are very noisy in 2D
"xrpd2_splitBBox": self.eps,
"xrpd2_splitPixel": self.eps}
for meth in test2d:
logger.info("About to test2d %s" % meth)
try:
I, _, _ = self.ai.integrate2d(self.raw, self.bins, self.azim, unit="r_mm", method=meth, correctSolidAngle=False, dark=self.dark, flat=self.flat)
except (MemoryError, pyFAI.opencl.pyopencl.MemoryError):
logger.warning("Got MemoryError from OpenCL device")
continue
I = I[numpy.where(I > 0)]
logger.info("2D method:%s Imin=%s Imax=%s <I>=%s std=%s" % (meth, I.min(), I.max(), I.mean(), I.std()))
self.assertAlmostEqual(I.mean(), 1, 2, "Mean should be 1 in %s" % meth)
self.assert_(I.max() - I.min() < test2d[meth], "deviation should be small with meth %s, got %s" % (meth, I.max() - I.min()))
for meth in test2d_direct:
logger.info("About to test2d_direct %s" % meth)
I, _, _ = self.ai.__getattribute__(meth)(self.raw, self.bins, self.azim, correctSolidAngle=False, dark=self.dark, flat=self.flat)
I = I[numpy.where(I > 0)]
logger.info("1D method:%s Imin=%s Imax=%s <I>=%s std=%s" % (meth, I.min(), I.max(), I.mean(), I.std()))
self.assert_(abs(I.mean() - 1) < test2d_direct[meth], "Mean should be 1 in %s" % meth)
self.assert_(I.max() - I.min() < test2d_direct[meth], "deviation should be small with meth %s, got %s" % (meth, I.max() - I.min()))
def test_suite_all_Flat():
testSuite = unittest.TestSuite()
testSuite.addTest(TestFlat1D("test_no_correct"))
testSuite.addTest(TestFlat1D("test_correct"))
testSuite.addTest(TestFlat2D("test_no_correct"))
testSuite.addTest(TestFlat2D("test_correct"))
return testSuite
if __name__ == '__main__':
mysuite = test_suite_all_Flat()
runner = unittest.TextTestRunner()
runner.run(mysuite)
|
