Analyzing Geiger Counter Data

This python script is designed for the AWARE RM-60 Micro Roentgen Radiation Monitor though it should work with data from other counters.

The RM-60 is connected to and powered by an Arduino Uno.

The Arduino code is an interrupt driven counter that for each count outputs the time since the previous count. The data are in seconds. My script that reads the RM-60 saves there time differences to the data file. It also outputs the counts per minute once a minute, along with the cumulative average CPM. This data starts with a # character so it can be filtered out as a comment.

This script reads the data files from disk and puts the arrival time differences into a numpy array. Right now the script prints out the files with the suffix .csv. Right now there is a maximum of 200000 data points.

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%matplotlib inline

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import matplotlib.pylab as plt
import numpy as np
import glob
fileList = glob.glob('*.csv')
fileList.sort()
for (i,fileName) in enumerate(fileList):
    print "%d - %s" % (i, fileName)
iFile = int(raw_input("Enter the number of the data file: "))
fileName = fileList[iFile]
MAX_DATA = 200000

0 - FirstRun.csv
1 - RM60-201802201527.csv

Enter the number of the data file: 

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fpData = open(fileName, 'r')
dataArray = np.genfromtxt(fileName, dtype=type(0.), comments='#', delimiter=',')
fpData.close()
nPoints = len(dataArray)
print "Read %d arrival time intervals" % (nPoints)

Read 139211 arrival time intervals

Now print out some information about the data.

There often seems to be a very rogue data point. Try to edit those out.

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avg0 = np.average(dataArray)
iBad = np.where(dataArray > 20*avg0)
print "iBad[0] = ", iBad[0]
print "There are %d suspicious points" % (len(iBad))
dataArray = np.delete(dataArray, iBad)
nPoints =  len(dataArray)
med = np.median(dataArray)
print "The median is: ", med
print avg0
std0 = np.std(dataArray)
print "Now there are %d data points" %(nPoints)
print "Here are the first 20 data points:\n", dataArray[:20]
print "The data range is from %.3f to %.3f" % (np.min(dataArray), np.max(dataArray))
print "The average and standard deviation are %.5f +/- %.5f" % (avg0, std0)
print "The standard deviation of the mean is %.5f" % (np.std(dataArray)/np.sqrt(nPoints))
print "The median of the data is %.3f" % (np.median(dataArray))
print "Average Counts per Minute = %f" % (60.0 / np.average(dataArray))
print "There were %d zero intervals" % (len(np.where(dataArray < 0.001)[0]))
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax.plot(dataArray, 'r.')
ax.set_xlabel("Count Number")
ax.set_ylabel("Wait Time (s)")

iBad[0] =  []
There are 1 suspicious points
The median is:  2.969377
4.30269441879
Now there are 139211 data points
Here are the first 20 data points:
[  3.46000000e-04   3.41967200e+00   9.17830000e-01   1.61388850e+01
   1.00373830e+01   1.74591800e+00   1.68036600e+00   8.70353000e-01
   2.64288000e+00   5.01537000e-01   1.23822100e+00   8.00409500e+00
   6.99217900e+00   1.85055500e+00   6.95972700e+00   9.30153000e-01
   5.63838900e+00   1.98919100e+00   1.35911920e+01   3.88681400e+00]
The data range is from 0.000 to 52.334
The average and standard deviation are 4.30269 +/- 4.30669
The standard deviation of the mean is 0.01154
The median of the data is 2.969
Average Counts per Minute = 13.944750
There were 85 zero intervals

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Finally plot the histogram on a linear and a logarithmic scale.

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fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
histOut = plt.hist(dataArray,23,histtype='step', log=False)
ax.set_xlabel("Wait Time (s)")
ax.set_ylabel("Counts (N)")

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fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
histOut = plt.hist(dataArray,100,histtype='step', log=True)
ax.set_xlabel("Wait Time (s)")
ax.set_ylabel("Counts (N)")
ax.set_ylim(1,100)

(1, 100)

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width = 3.5 * std0 / nPoints**(.333)
print "width = ", width
nBins = int(np.max(dataArray) / width)
print "Using %d bins" % (nBins)
bins = np.arange(0, np.max(dataArray), width)
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
histOut = plt.hist(dataArray,nBins,histtype='step', log=True)
ax.set_xlabel("Wait Time (s)")
ax.set_ylabel("Counts (N)")

width =  0.291990497959
Using 179 bins

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