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#!/usr/bin/env python3
"""
Extract reflectometry data from NIST.
Specifically written for https://www.nist.gov/document/spinelfilmzip.
Output columns are: ["Qz", "counts per incident count", "uncertainty", "resolution"].
"""
import io, numpy, re
angstrom = 0.1
def normalizeData(data):
"""
Removes duplicate q values from the input data,
normalizes it such that the maximum of the reflectivity is
unity and rescales the q-axis to inverse nm
"""
r0 = numpy.where(data[0] - numpy.roll(data[0], 1) == 0)
data = numpy.delete(data, r0, 1)
data[0] = data[0]/angstrom
data[3] = data[3]/angstrom
norm = numpy.max(data[1])
data[1] = data[1]/norm
data[2] = data[2]/norm
so = numpy.argsort(data[0])
data = data[:, so]
return data
def xtract(rawdata, out_fname, code):
table = re.match(
r'.*# "polarization": "' + code + r'"\n#.*?\n# "units".*?\n(.*?)#.*',
rawdata, re.DOTALL).group(1)
data1 = numpy.genfromtxt(io.BytesIO(table.encode()), unpack=True)
data2 = normalizeData(data1)
n = len(data2[0])
data3 = [(data2[0][i], data2[1][i], data2[2][i], data2[3][i]) for i in range(n)]
with open(out_fname, 'w') as f:
for a in data3:
for v in a:
f.write('%21.16e ' % v)
f.write('\n')
rawdata = open("MAFO_Saturated.refl").read()
xtract(rawdata, "MAFO_Saturated_pp.tab", r'\+\+')
xtract(rawdata, "MAFO_Saturated_mm.tab", r'\-\-')
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