112 lines
3.0 KiB
Python
Executable File
112 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
Script to process a data file from a DIY / low cost spectrometer.
|
|
|
|
Based on software from the DIY particle detector by Oliver Keller.
|
|
https://github.com/ozel/DIY_particle_detector
|
|
|
|
@author: Jeff Moe
|
|
@date: January 2022
|
|
"""
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
import matplotlib as mpl
|
|
import matplotlib.pyplot as plt
|
|
from scipy.optimize import curve_fit
|
|
import matplotlib.gridspec as gridspec
|
|
import decimal as D
|
|
import msgpack
|
|
import argparse
|
|
|
|
mpl.rcParams['font.size']=12
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("file_name", help="filename to import, e.g. pulses.pkl")
|
|
args = parser.parse_args()
|
|
|
|
def poly1(x, a, b):
|
|
return a*x + b
|
|
|
|
df = pd.read_pickle(args.file_name)
|
|
|
|
THL = -300
|
|
|
|
lp = []
|
|
for i,p in df.iterrows():
|
|
if p.pulse.min() < (THL):
|
|
lp.append(p.pulse)
|
|
|
|
lp = np.asarray(lp[:])
|
|
|
|
count=0
|
|
loopcnt =0
|
|
areas = []
|
|
peaks = []
|
|
min_alpha_peak = 1243
|
|
|
|
min_g = -20
|
|
max_g = -3300
|
|
min_length = 44
|
|
max_length = 120
|
|
min_skip = min_length
|
|
|
|
xpulses = []
|
|
ypulses = []
|
|
|
|
gmax=0
|
|
for i,y in enumerate(lp[:]):
|
|
dydx=np.gradient(y)
|
|
gy= dydx.min()
|
|
gx= dydx.argmin()
|
|
|
|
while True:
|
|
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:
|
|
trigx=np.where(dydx < min_g)[0]
|
|
trigy=dydx[dydx < min_g]
|
|
peakx1=trigx[0]
|
|
crossing_x1 = (y[peakx1+min_length:] > y[peakx1]).argmax() if (y[peakx1+min_length:] > y[peakx1]).any() else -1
|
|
if crossing_x1 > 0:
|
|
peakx2=peakx1+crossing_x1+min_length
|
|
diff = np.abs(peakx2 - peakx1)
|
|
peak = y[peakx1:peakx2].min()
|
|
if diff >= min_length and diff <= max_length and peak <= THL :
|
|
area = np.absolute(y[peakx1:peakx2]).sum()
|
|
areas.append(area)
|
|
peak = np.absolute(peak) + y[peakx1]
|
|
peaks.append(peak)
|
|
ypulse = np.roll(y[peakx1-100:peakx2+100],50-y[peakx1:peakx2].argmin())[130:]
|
|
if ypulse.size > min_length:
|
|
xpulses.append(list(range(len(ypulse))))
|
|
ypulses.append(ypulse)
|
|
if gy < gmax:
|
|
gmax=gy
|
|
count+=1
|
|
else:
|
|
peakx2=peakx1+min_skip
|
|
else:
|
|
peakx2 = peakx1 + min_skip
|
|
else:
|
|
peakx1=0
|
|
remaining = len(y)
|
|
if remaining < min_length:
|
|
break
|
|
else:
|
|
peakx2=min_skip
|
|
y = np.delete(y, slice(0,peakx2))
|
|
if len(y) < min_length:
|
|
break
|
|
else:
|
|
dydx=np.delete(dydx, slice(0,peakx2))
|
|
gy= dydx.min()
|
|
gx= dydx.argmin()
|
|
loopcnt+=1
|
|
|
|
time_diff = df.iloc[-1,0] - df.iloc[0,0]
|
|
time_diff = time_diff.total_seconds() / 60.0
|
|
cpm = count/time_diff
|
|
print("detected pulses:",count, "in", round(time_diff), "minutes ->", round(cpm,3), "CPM")
|
|
peaks=np.asarray(peaks)
|
|
|