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#! /usr/bin/env python3
###################################################
# The example shows how to correlate spectra data #
# with xy-coordinate from the line scane #
###################################################
import numpy as np
from renishawWiRE import WDFReader
from _path import curdir, imgdir
try:
import matplotlib.pyplot as plt
plot = True
except ImportError:
plot = False
def main():
filename = curdir / "spectra_files" / "line.wdf"
reader = WDFReader(filename)
assert reader.measurement_type == 3
# For mapping, xdata is still wavenumber
wn = reader.xdata
spectra = reader.spectra
x = reader.xpos
y = reader.ypos
print(x.shape, y.shape)
assert x.shape[0] == y.shape[0] == spectra.shape[0]
# Distance
d = np.sqrt(x**2 + y**2)
wn = reader.xdata
spectra = reader.spectra
spectra = spectra - spectra.min(axis=1, keepdims=True)
# Simply get accumulated counts between 1560 and 1620 cm^-1
pos = np.where((wn >= 1560) & (wn <= 1620))[0]
cut = spectra[:, pos]
sum_cnt = np.sum(cut, axis=1)
if plot is True:
# Level the spectra with baseline intensity
plt.figure()
plt.plot(d - d[0], sum_cnt, "-o")
plt.xlabel("Distance (μm)")
plt.ylabel("Sum. Intensity (ccd counts)")
plt.title("Results from line.wdf")
plt.show(block=False)
plt.pause(3)
plt.savefig(imgdir / "linxy.png", dpi=100)
plt.close()
else:
print("Wavenumber is like:", wn)
print("Distance is like:", d)
print("Spectra matrix is like: \n", spectra)
return
if __name__ == "__main__":
main()
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