Read/process pickle file. Source from ozel.
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3888fbef3e
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witgit-proc
99
witgit-proc
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@ -10,3 +10,102 @@ https://github.com/ozel/DIY_particle_detector
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@date: January 2022
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"""
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import pandas as pd
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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import matplotlib.gridspec as gridspec
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import decimal as D
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import msgpack
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import argparse
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mpl.rcParams['font.size']=12
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parser = argparse.ArgumentParser()
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parser.add_argument("file_name", help="filename to import, e.g. pulses.pkl")
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args = parser.parse_args()
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def poly1(x, a, b):
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return a*x + b
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df = pd.read_pickle(args.file_name)
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THL = -300
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lp = []
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for i,p in df.iterrows():
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if p.pulse.min() < (THL):
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lp.append(p.pulse)
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lp = np.asarray(lp[:])
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count=0
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loopcnt =0
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areas = []
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peaks = []
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min_alpha_peak = 1243
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min_g = -20
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max_g = -3300
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min_length = 44
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max_length = 120
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min_skip = min_length
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xpulses = []
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ypulses = []
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gmax=0
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for i,y in enumerate(lp[:]):
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dydx=np.gradient(y)
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gy= dydx.min()
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gx= dydx.argmin()
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while True:
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if y[0] > THL and y[0] < np.abs(THL) and y.min() < THL and y[gx] <= np.abs(THL) and gy < min_g and gy > max_g:
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trigx=np.where(dydx < min_g)[0]
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trigy=dydx[dydx < min_g]
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peakx1=trigx[0]
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crossing_x1 = (y[peakx1+min_length:] > y[peakx1]).argmax() if (y[peakx1+min_length:] > y[peakx1]).any() else -1
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if crossing_x1 > 0:
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peakx2=peakx1+crossing_x1+min_length
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diff = np.abs(peakx2 - peakx1)
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peak = y[peakx1:peakx2].min()
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if diff >= min_length and diff <= max_length and peak <= THL :
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area = np.absolute(y[peakx1:peakx2]).sum()
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areas.append(area)
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peak = np.absolute(peak) + y[peakx1]
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peaks.append(peak)
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ypulse = np.roll(y[peakx1-100:peakx2+100],50-y[peakx1:peakx2].argmin())[130:]
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if ypulse.size > min_length:
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xpulses.append(list(range(len(ypulse))))
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ypulses.append(ypulse)
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if gy < gmax:
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gmax=gy
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count+=1
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else:
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peakx2=peakx1+min_skip
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else:
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peakx2 = peakx1 + min_skip
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else:
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peakx1=0
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remaining = len(y)
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if remaining < min_length:
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break
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else:
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peakx2=min_skip
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y = np.delete(y, slice(0,peakx2))
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if len(y) < min_length:
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break
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else:
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dydx=np.delete(dydx, slice(0,peakx2))
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gy= dydx.min()
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gx= dydx.argmin()
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loopcnt+=1
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time_diff = df.iloc[-1,0] - df.iloc[0,0]
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time_diff = time_diff.total_seconds() / 60.0
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cpm = count/time_diff
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print("detected pulses:",count, "in", round(time_diff), "minutes ->", round(cpm,3), "CPM")
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peaks=np.asarray(peaks)
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