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import numpy as np
def process(shots, names, kinds):
channel_indicies = [i for i, x in enumerate(kinds) if x == 'channel']
out = np.full(len(channel_indicies)+1, np.nan)
channel_indicies.pop(0)
out_names = []
# signal diff
# A B C D
# chopper 1: - + + -
# chopper 2: - - + +
# we want A-B+C-D
c1 = shots[-2]
c2 = shots[-1]
a = np.mean(shots[0, (c1==-1)*(c2==-1)])
b = np.mean(shots[0, (c1==+1)*(c2==-1)])
c = np.mean(shots[0, (c1==+1)*(c2==+1)])
d = np.mean(shots[0, (c1==-1)*(c2==+1)])
out[0] = a-b+c-d
out_names.append('signal_diff')
# signal mean
out[1] = np.mean(shots[0])
out_names.append('signal_mean')
# others
for i in channel_indicies:
out[i+1] = np.mean(shots[i])
out_names.append(names[i])
# finish
return [out, out_names]
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