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check2_hulin.py
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check2_hulin.py
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#!/usr/bin/env python3
from multiprocessing import Pool
import matplotlib.pyplot as plt
# from scipy import signal
from scipy.signal import find_peaks, peak_prominences
import numpy as np
from sys import argv
from glob import glob
import os, time, re
import ROOT
from root_numpy import root2array, array2root
#signIt = lambda t: t if t<127 else t-256
signIt = lambda t: t
myOutPutDir = './'
class findrate(object):
def __init__(self, filename="tek0001CH2.isf"):
self.filename = filename
self.timelap = 10. #second
self.inputarray = np.array([])
self.npeaks = 0
self.date = ""
self.time = ""
self.diffpeakmean = 0
self.heightmean = 0
self.prominencemean = 0
def getinput(self):
with open(self.filename,'rb') as f1:
b0 = f1.read(1024)
b1i = b0.find(b':CURVE')
b = b0[:b1i]
c = b.split(b';')
b2 = b0[b1i:]
c += [b2]
pars = {}
for v in c:
a = v.find(b" ")
print(v,a)
pars[v[:a]] = v[a+1:]
# print(pars)
# Nbyte = pars[b'BIT_NR']
# print(type(Nbyte),Nbyte)
# return
self.timelap = float(pars[b'WFID'].split(b',')[3].split(b'/')[0][1:6])
self.timelap *=10
self.date = pars[b':DATE'].decode("utf-8")
self.time = pars[b':TIME'].decode("utf-8")
wav = pars[b':CURVE'][10:-1]
wav += f1.read()
wav1 = [signIt(int(x)) for x in wav[::2]] if pars[b'BIT_NR'] == b'16' else [signIt(int(x)) for x in wav]
# wav1 = [from_bytes(x,'big',signed=True) for x in wav[::2]]
# wav1 = [ax(j) for j in wav[::2]]
print(len(wav1), wav1[:10])
self.inputarray = np.asarray(wav1)
def processinput(self, N):
basename = os.path.basename(self.filename)
arwav2 = self.inputarray[:N] if N>0 else self.inputarray[:]
self.inputarray = None
# peaks = signal.find_peaks(wav2)
#peaks, _ = find_peaks(arwav2, height=8, width=150, distance=850, prominence=None)
peaks, properties = find_peaks(arwav2, height=None, width=100, prominence=10, distance=500)
print(peaks)
#wav3 = [wav2[ix] for ix in peaks]
promsall = peak_prominences(arwav2, peaks, wlen=None)
proms = promsall[0]
promxmins = promsall[1]
promxmaxs = promsall[2]
lpromscallow = []
for i in range(len(proms)-1):
if arwav2[promxmins[i]] < arwav2[promxmaxs[i]]:
lpromscallow.append(arwav2[peaks[i]] - arwav2[promxmins[i]])
else:
lpromscallow.append(arwav2[peaks[i]] - arwav2[promxmaxs[i]])
promscallow = np.asarray(lpromscallow)
print(proms[:10])
print(promscallow[:10])
contour_heights = arwav2[peaks] - proms
plt.plot(arwav2)
plt.plot(peaks, arwav2[peaks], "x")
plt.vlines(x=peaks, ymin=contour_heights, ymax=arwav2[peaks], color = "C1")
plt.vlines(promxmins, ymin=contour_heights, ymax=arwav2[peaks], color = "C2", linestyles = "dashed")
plt.vlines(promxmaxs, ymin=contour_heights, ymax=arwav2[peaks], color = "C2", linestyles = "dotted")
plt.hlines(y=arwav2[peaks], xmin=promxmins, xmax=promxmaxs, color = "C2", linestyles = "dashdot")
plt.hlines(y=properties["width_heights"], xmin=properties["left_ips"], xmax=properties["right_ips"], color = "C1")
#x2 = [peaks[i+1]-peaks[i] for i in range(len(peaks)-1)]
fig0, ax0 = plt.subplots(num="diffpeak_"+basename)
num_bins = 50
# the histogram of the data
n, bins, patches = ax0.hist(np.diff(peaks), num_bins)
fig1, ax1 = plt.subplots(num="peakheight_"+basename)
#n, bins, patches = ax.hist([arwav1[ix] for ix in peaks], num_bins)
ax1.hist(arwav2[peaks], num_bins)
fig2, ax2 = plt.subplots(num="peakprom_"+basename)
#plt.plot(proms)
ax2.hist(proms, num_bins)
#plt.show()
self.npeaks = len(peaks)
self.diffpeakmean = np.mean(np.diff(peaks))
self.heightmean = np.mean(arwav2[peaks])
self.prominencemean = np.mean(proms)
# artmp = np.column_stack((proms, arwav2[peaks]))
# print(dir(artmp))
# artmp.dtype = [['a', np.float32], ['b', np.float32]]
#rootfile = array2root(np.array(zip(proms, arwav2[peaks]), dtype=[('a', np.float32), ('b', np.float32)]) , basename + '.root', mode="recreate")
#rootfile = array2root( artmp, basename + '.root', mode="recreate")
# rootfile = array2root( np.array(np.column_stack((proms, arwav2[peaks])), dtype=[('a', np.float32), ('b', np.float32)]) , basename + '.root', mode="recreate")
df = np.append(np.array([-1]),np.diff(peaks))
pks = arwav2[peaks]
bigx = np.array([(df[i],pks[i],proms[i]) for i in range(len(df))],dtype=[('dt',np.float32),('pk',np.float32),('proms',np.float32)])
rootfile = array2root( bigx, myOutPutDir+"/"+basename[:-4] + '.root', mode="recreate")
def processor(inputName):
myrate = findrate(inputName)
myrate.getinput()
myrate.processinput(-1)
return myrate
def multi_run(mydir='./test1'):
script, pattern, Nfiles = argv
if not os.path.exists(mydir): os.makedirs(mydir)
global myOutPutDir
myOutPutDir = mydir
filesall = sorted([f for f in glob(pattern) if not os.path.exists(mydir+'/'+os.path.basename(f)[:-4]+'.root')], key=lambda f:os.path.getmtime(f))
files = filesall[:int(Nfiles)] if int(Nfiles)>0 else filesall[:]
print(files)
myrates = []
print(len(files))
p = Pool(6)
myrates = p.map(processor, files)
newfile = open(mydir+"/summary.txt","a")
print("filename", "date", "time", "npeaks", "duration", "rates", "heightmean", "prominencemean")
#newfile.write(" ".join(["filename", "date", "time", "npeaks", "duration", "rates", "heightmean", "prominencemean"]))
for i in range(len(myrates)):
print(os.path.basename(myrates[i].filename), myrates[i].date, myrates[i].time, myrates[i].npeaks, myrates[i].timelap, myrates[i].npeaks/myrates[i].timelap, myrates[i].heightmean, myrates[i].prominencemean)
newfile.write(f"{os.path.basename(myrates[i].filename)} {myrates[i].date} {myrates[i].time} {myrates[i].npeaks} {myrates[i].timelap} {myrates[i].npeaks/myrates[i].timelap} {myrates[i].heightmean} {myrates[i].prominencemean}\n")
def test():
script, pattern, Nfiles = argv
filesall = sorted([f for f in glob(pattern)], key=lambda f:os.path.getmtime(f))
files = filesall[:int(Nfiles)] if int(Nfiles)>0 else filesall[:]
print(files)
myrates = []
print(len(files))
for i in range(len(files)):
print(f"processing the {i} of {len(files)} file: {files[i]}")
myrate = findrate(files[i])
myrate.getinput()
myrate.processinput(-1)
myrates.append(myrate)
print("filename", "date", "time", "npeaks", "duration", "rates", "heightmean", "prominencemean")
for i in range(len(myrates)):
print(os.path.basename(myrates[i].filename), myrates[i].date, myrates[i].time, myrates[i].npeaks, myrates[i].timelap, myrates[i].npeaks/myrates[i].timelap, myrates[i].heightmean, myrates[i].prominencemean)
plt.show()
#summary plots
#ROOT.TH1F
if __name__ == '__main__':
test()
#multi_run()