Average.ipynb

Kernel: Python 2 (SageMath)

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.

%matplotlib inline

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]

0 - 102.csv
1 - 122.csv
2 - 142.csv
3 - 162.csv
4 - 22.csv
5 - 2mm.csv
6 - 42.csv
7 - 62.csv
8 - 82.csv
9 - RM60-201802201527.csv
10 - instruct.csv

Enter the number of the data file: 

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

Read 995 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.

avg0 = np.average(dataArray)
print "The average is: ", avg0

The average is:  0.666677488442

nPoints =  len(dataArray)
med = np.median(dataArray)
print "The median is: ", med

The median is:  0.430046

std0 = np.std(dataArray)
print "The standard deviation is: ", std0

The standard deviation is:  0.677830875697

print "Now there are %d data points" %(nPoints)

Now there are 995 data points

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]))

The data range is from 0.003 to 4.711
The average and standard deviation are 0.66668 +/- 0.67783
The standard deviation of the mean is 0.02149
The median of the data is 0.430
Average Counts per Minute = 89.998539
There were 0 zero intervals

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)")

<matplotlib.text.Text at 0x7fcab702eed0>

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:  0.430046
0.666677488442
Now there are 995 data points
Here are the first 20 data points:
[ 1.082612  0.11592   1.417281  1.145258  2.981797  0.118352  0.639712
  0.077353  0.043021  0.132457  0.088408  2.162083  0.114384  0.54984
  0.623566  0.299211  0.118747  2.462249  0.827654  0.385706]
The data range is from 0.003 to 4.711
The average and standard deviation are 0.66668 +/- 0.67783
The standard deviation of the mean is 0.02149
The median of the data is 0.430
Average Counts per Minute = 89.998539
There were 0 zero intervals

<matplotlib.text.Text at 0x7fcab70528d0>

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

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)

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 =  0.238184944104
Using 19 bins

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npts = []
avgs = []
stds = []
distances = []
files = glob.glob('*.csv')
for dataFile in files:
    print dataFile
    data = np.genfromtxt(dataFile, comments = '#')
    npts.append(len(data))
    avg = np.average(data)
    avgs.append(avg)
    stds.append(np.std(data))
    distances.append(dataFile)
avgs = np.array(avgs)
stds = np.array(stds)
unAvgs = stds/np.sqrt(npts)
r = []
for dist in distances:
    r.append(float(dist.split(',')[0]))

142.csv
102.csv
62.csv
22.csv
2.csv
162.csv
82.csv
122.csv
42.csv

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-41-b4eb8083715d> in <module>()
     17 r = []
     18 for dist in distances:
---> 19     r.append(float(dist.split(',')[0]))

ValueError: invalid literal for float(): 142.csv

model(Io)= (Io + r**2)+ 4.3
defmodel(params, r)
I0 = params[0]
return I0/(r**2 + 4.3)

  File "<ipython-input-31-c1dadf6b4163>", line 1
    model(Io)= (Io + r**2)+ 4.3
SyntaxError: can't assign to function call