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@heborras
Created October 12, 2017 11:02
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This script analyzes the recorded serial output from an Arduino Due.
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Event/interrupt analysis\n",
"\n",
"### This script analyzes the recorded serial output from an Arduino Due. NOTE: The file must have linux style lineendings! The output contains one event from a detector per line. The script will store these values. It will calculate the efficiency of each detector. Additionally it will calculate which events happened in coincidence and tell statistical data about these events. Last it will plot the distribution of time and frequencies between events for each detector.\n",
"\n",
"### The Arduino DUE firmware for recording events/interrupts can be found here: https://gist.github.com/HenniOVP/09cb302eedfb100eca4fbe17489bded0\n",
"\n",
"### The python software for recording the output from the Arduino DUE can be found here: https://gist.github.com/HenniOVP/3ee85aee16575391f46451dbd566b0ef"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# variables for configuration\n",
"\n",
"data_file = \"20171010-1252_interrupt_output_PMT_SiPM_GM_2400_sec.txt\"\n",
"\n",
"# the V1 pulse is 2.5 ms long\n",
"# the one from the muon hunter is 1 ms long\n",
"max_coincidence_time_us = 200\n",
"# detector areas in cm^2\n",
"detector_area = {\n",
" \"Upper SiPM\": 300.0,\n",
" #\"GM\": 15.55,\n",
" \"Lower SiPM\": 300.0\n",
" }\n",
"max_Hz = 10"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from bisect import bisect_left\n",
"import itertools"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# functions\n",
"def findClosest(list_to_search, value):\n",
" # bisect only works because we know that our list is sorted\n",
" pos = bisect_left(list_to_search, value)\n",
" if pos == 0:\n",
" return list_to_search[0]\n",
" if pos == len(list_to_search):\n",
" return list_to_search[-1]\n",
" before = list_to_search[pos - 1]\n",
" after = list_to_search[pos]\n",
" if after - value < value - before:\n",
" return after\n",
" else:\n",
" return before"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create dict for storing data\n",
"inter_times = {key: [] for key in detector_area.keys()}\n",
"\n",
"rollover_counter = 0\n",
"old_unadjusted_time = 0\n",
"# read the interrupt file\n",
"with open(data_file, 'r') as f:\n",
" current_line = f.readline()\n",
" while current_line is not '':\n",
" # check for corrupted lines\n",
" if current_line.count('=') is not 3:\n",
" current_line = f.readline()\n",
" continue\n",
" time = current_line.split(':')[1]\n",
" time = int(time.split(';')[0]) \n",
" inter_pin = str(current_line.split('=')[3].split('\\n')[0])\n",
" # one last corruption check\n",
" if inter_pin not in inter_times.keys():\n",
" current_line = f.readline()\n",
" continue\n",
" \n",
" # handle signed long overflow\n",
" if time <= 0:\n",
" time = 2**32 + time\n",
" \n",
" # handle unsigned long rollover\n",
" if old_unadjusted_time > time:\n",
" rollover_counter += 1\n",
" old_unadjusted_time = time\n",
" time = 2**32 * rollover_counter + time\n",
" \n",
" # save interrupt data\n",
" inter_times[inter_pin].append(time)\n",
" current_line = f.readline()\n",
"\n",
"# calculate differences between events\n",
"# yeah, very perl like?\n",
"# I just wanted to try to push the dict comprehension syntax\n",
"inter_diffs = {key: np.diff(np.asarray(inter_times[key])) for key in detector_area.keys()}\n",
"\n",
"# calculate the frequency between events\n",
"inter_frequ = {key: (1/(inter_diffs[key]/100000.)) for key in detector_area.keys()}\n",
"# bin frequencies above max_Hz at max_Hz\n",
"for key in inter_frequ.keys():\n",
" for i in range(len(inter_frequ[key])):\n",
" if inter_frequ[key][i] >= max_Hz:\n",
" inter_frequ[key][i] = max_Hz\n",
"\n",
"# get total measurement time\n",
"max_us_time = max( [max(inter_times[key]) for key in inter_times.keys()] )\n",
"min_us_time = min( [min(inter_times[key]) for key in inter_times.keys()] )\n",
"s_time = (max_us_time - min_us_time) * 10**(-6)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# calculate coincidences\n",
"\n",
"combinations = itertools.combinations(inter_times.keys(), 2)\n",
"corr_lists = {}\n",
"\n",
"for (key1,key2) in combinations:\n",
" if not corr_lists.has_key(key1):\n",
" corr_lists[key1] = {}\n",
" if not corr_lists[key1].has_key(key2):\n",
" corr_lists[key1][key2] = {'base_time':[], 'diff':[]}\n",
" for timestamp in inter_times[key1]:\n",
" closest_time = findClosest(inter_times[key2], timestamp)\n",
" diff = timestamp - closest_time \n",
" if(abs(diff) <= max_coincidence_time_us):\n",
" corr_lists[key1][key2]['base_time'].append(timestamp)\n",
" corr_lists[key1][key2]['diff'].append(diff)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maximum coincidence time [us]: 200\n",
"Test runtime [min]: 39.9945276833\n",
"\n",
"Lower SiPM statistics:\n",
"\tDetector area [cm^2]: 300.0\n",
"\tTotal number of events: 6066\n",
"\tEfficency [events/cm^2/min]: 0.505569165864\n",
"\n",
"Upper SiPM statistics:\n",
"\tDetector area [cm^2]: 300.0\n",
"\tTotal number of events: 7141\n",
"\tEfficency [events/cm^2/min]: 0.595164756584\n",
"\n",
"Coincidence Lower SiPM <-> Upper SiPM:\n",
"\tTotal number of coincidences: 5154\n",
"\tPercent of Lower SiPM events: 84.97\n",
"\tPercent of Upper SiPM events: 72.17\n",
"\tTime from Lower SiPM event to Upper SiPM event for one coincidence:\n",
"\t\tMedian [us]: 88.0\n",
"\t\tAverage [us]: 41.69\n",
"\t\tStandard deviation [us]: 79.34\n",
"\n"
]
}
],
"source": [
"# tell statistics\n",
"print(\"Maximum coincidence time [us]: \" +str(max_coincidence_time_us))\n",
"print(\"Test runtime [min]: \" + str(s_time/60))\n",
"print(\"\")\n",
"\n",
"for key in inter_times.keys():\n",
" print(key+\" statistics:\")\n",
" print(\"\\tDetector area [cm^2]: \" + str(detector_area[key]))\n",
" print(\"\\tTotal number of events: \" + str(len(inter_times[key])))\n",
" print(\"\\tEfficency [events/cm^2/min]: \" + str(len(inter_times[key])/detector_area[key]/(s_time/60)))\n",
" print(\"\")\n",
" \n",
"for key1 in corr_lists.keys():\n",
" for key2 in corr_lists[key1].keys():\n",
" print(\"Coincidence \"+key1+\" <-> \"+key2+\":\")\n",
" \n",
" print(\"\\tTotal number of coincidences: \" +str(len(corr_lists[key1][key2]['diff'])))\n",
" print(\"\\tPercent of \"+key1+\" events: \"+str(round(float(len(corr_lists[key1][key2]['diff']))/len(inter_times[key1])*100, 2)))\n",
" print(\"\\tPercent of \"+key2+\" events: \"+str(round(float(len(corr_lists[key1][key2]['diff']))/len(inter_times[key2])*100, 2)))\n",
" \n",
" print(\"\\tTime from \"+key1+\" event to \"+key2+\" event for one coincidence:\")\n",
" print(\"\\t\\tMedian [us]: \"+str(np.median(corr_lists[key1][key2]['diff'])))\n",
" print(\"\\t\\tAverage [us]: \"+str(round(np.mean(corr_lists[key1][key2]['diff']), 2)))\n",
" print(\"\\t\\tStandard deviation [us]: \"+ str(round(np.std(corr_lists[key1][key2]['diff']), 2)))\n",
" print(\"\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7de1ef0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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j09sUg8GQTgQGBhIREeHVGcoMGEfIB7JD15jBkFyioqKIjo7OFvNsGQwGd2zz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\nAZQM8EBa51UdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7d7afd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt_keys = inter_times.keys()\n",
"\n",
"plt_list = [inter_diffs[key] for key in plt_keys]\n",
"plt_legend = [str(key)+', total: '+str(len(inter_diffs[key])) for key in plt_keys]\n",
"n, bins, patches = plt.hist(plt_list, bins=50, histtype='step', normed=True, log=True)\n",
"plt.legend(plt_legend)\n",
"plt.title(\"Distribution of time between events - log scale\\nCurves are normalized \")\n",
"plt.xlabel(\"Time between events [us]\")\n",
"plt.ylabel(\"Number of events (normalized) [1]\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7d85898>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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LD6Dv7yURaayUCvR8gf5AqYUu7/+hnzkX+l5D8TwHupdA4d58ga7H7dDPp7tb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\nzMzMOgQnPWZmZtYhOOkxMzOzDuH/A/kB9ZX8rGkuAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7b8e8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt_list = [inter_frequ[key] for key in plt_keys]\n",
"n, bins, patches = plt.hist(plt_list, bins=50, histtype='step', normed=True, log=False)\n",
"plt.legend(plt_legend)\n",
"plt.title(\"Distribution of frequencies between events\\nCurves are normalized - values above \"+str(max_Hz)+\"Hz are collected at \"+str(max_Hz)+\"Hz\")\n",
"plt.xlabel(\"Frequency between events [Hz]\")\n",
"plt.ylabel(\"Number of events (normalized) [1]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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