mirror of https://github.com/silx-kit/pyFAI.git
95 lines
4.0 KiB
Python
Executable File
95 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python
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# coding: utf-8
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# author: Jérôme Kieffer
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#
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# Project: Fast Azimuthal integration
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# https://github.com/silx-kit/pyFAI
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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# THE SOFTWARE.
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# tests if the distribution of Chi2 is centered around 1:
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# Needs a large dataset (thousands of images)
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import os
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import sys
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import glob
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import pylab
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pylab.ion()
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import numpy
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from math import sqrt
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import fabio
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import logging
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logger = logging.getLogger(__file__)
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pyFAI = sys.modules["pyFAI"]
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from lxml import etree
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ai = pyFAI.AzimuthalIntegrator(detector="Pilatus1M")
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images = glob.glob("/data/bm29/inhouse/opd29/20140430/raw/water_008_*.edf")
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images.sort()
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img = images[0]
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xml = etree.parse(os.path.splitext(img)[0] + ".xml")
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wl = float(xml.xpath("//wavelength")[0].getchildren()[0].text)
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centerX = float(xml.xpath("//beamCenter_1")[0].getchildren()[0].text)
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centerY = float(xml.xpath("//beamCenter_2")[0].getchildren()[0].text)
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directDist = float(xml.xpath("//detectorDistance")[0].getchildren()[0].text) * 1000.0
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msk = xml.xpath("//maskFile")[0].getchildren()[0].getchildren()[0].text
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msk = numpy.logical_or(fabio.open(msk).data, ai.detector.mask)
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ai.setFit2D(directDist=directDist, centerX=centerX, centerY=centerY)
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ai.wavelength = wl
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I_splitBB = [];sigma_splitBB = [];I_splitFull = [];sigma_splitFull = [];I_nosplit = [];sigma_nosplit = []
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for fn in images[:10]:
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img = fabio.open(fn).data
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xml = etree.parse(os.path.splitext(fn)[0] + ".xml")
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monitor = float(xml.xpath("//beamStopDiode")[0].getchildren()[0].text)
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print(fn, monitor);
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variance = numpy.maximum(img, 1)
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q, i, s = ai.integrate1d(img, 1040, unit="q_nm^-1", method="numpy", variance=variance, mask=msk, normalization_factor=monitor)
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I_nosplit.append(i)
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sigma_nosplit.append(s)
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q, i, s = ai.integrate1d(img, 1040, unit="q_nm^-1", method="splitbbox", variance=variance, mask=msk, normalization_factor=monitor)
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I_splitBB.append(i)
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sigma_splitBB.append(s)
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q, i, s = ai.integrate1d(img, 1040, unit="q_nm^-1", method="splitpixel", variance=variance, mask=msk, normalization_factor=monitor)
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I_splitFull.append(i)
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sigma_splitFull.append(s)
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I_splitBB = numpy.vstack(I_splitBB)
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I_splitFull = numpy.vstack(I_splitFull)
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I_nosplit = numpy.vstack(I_nosplit)
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sigma_nosplit = numpy.vstack(sigma_nosplit)
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sigma_splitBB = numpy.vstack(sigma_splitBB)
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sigma_splitFull = numpy.vstack(sigma_splitFull)
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Chi2_splitBB = [];Chi2_splitFull = []; Chi2_nosplit = []
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Iavg_splitFull = I_splitFull.mean(axis=0)
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Iavg_splitBB = I_splitBB.mean(axis=0)
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Iavg_nosplit = I_nosplit.mean(axis=0)
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for i in range(I_splitBB.shape[0]):
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Chi2_splitBB.append((((I_splitBB[i] - Iavg_splitBB) / sigma_splitBB[i]) ** 2).mean())
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Chi2_splitFull.append((((I_splitFull[i] - Iavg_splitFull) / sigma_splitFull[i]) ** 2).mean())
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Chi2_nosplit.append((((I_nosplit[i] - Iavg_nosplit) / sigma_nosplit[i]) ** 2).mean())
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pylab.hist(Chi2_splitBB, 50, label="splitBB")
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pylab.hist(Chi2_splitFull, 50, label="splitFull")
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pylab.hist(Chi2_nosplit, 50, label="no_split")
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pylab.xlabel("$\chi^2$")
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pylab.ylabel("count")
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pylab.legend()
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pylab.show()
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