{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data from a gamma detector\n",
    "Indentify and extract peaks from the data in a specific range of energies.\n",
    "\n",
    "Note you will need a copy of the Eu152.mat file which can be downloaded from where you got this file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n6RYefm9lvXkT1qBa4O+SIHQzz1QZxBouTUBlqgbVHVgsIrOBykiiqp6Xof1l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\nr7tFt7fxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6fffccf3710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load plotting system\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "# add numpy\n",
    "import numpy as np\n",
    "# use scipy routine to load a matlab format datafile\n",
    "from scipy.io import loadmat\n",
    "Eu152=loadmat('Eu152.mat',mat_dtype=True,variable_names=['channel2','count2','energy2'])\n",
    "# convert the data just loaded into three arrays\n",
    "channel = np.array(Eu152['channel2'][0],dtype=int)\n",
    "count = Eu152['count2'][0]\n",
    "energy = Eu152['energy2'][0]\n",
    "# plot the data to see what was there, linear and logy plot\n",
    "p1 = plt.subplot(211)\n",
    "p1.plot(channel,count)\n",
    "p1.set_xlabel('channel')\n",
    "p1.set_ylabel('count')\n",
    "p2 = plt.subplot(212)\n",
    "p2.plot(energy,count)\n",
    "# Note channel number is linear in energy\n",
    "p2.set_xlabel('energy')\n",
    "p2.set_ylabel('count')\n",
    "p2.semilogy()\n",
    "p2.set_ylim([0.9,1e4])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now zoom into 800 - 3000 in channel numbers, we need to find peaks here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OHnoxk6tWkDvHzYauQU0ckqblvfwI0DPiailwpaRaSXOAucAzwLPAXElzJNWQ60BfOph1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\n+AOpp5mp52/Wo7M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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6fffc4f2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(channel[800:3001],count[800:3001])\n",
    "plt.xlabel('channel')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code is wrong and you need to fix it...\n",
      "Peaks are at energies [41, 46, 54, 57, 62, 66, 68, 73, 78, 81, 83, 85, 87, 92, 97, 104, 107, 109, 112, 115, 126, 132, 148, 154, 158, 165, 167, 177, 179, 189, 194, 200, 203, 208, 210, 213, 218, 220, 222, 224, 226, 229, 232, 235, 238, 243, 247, 250, 253, 255, 258, 261, 264, 269, 272, 275, 278, 280, 285, 289, 291, 293, 296, 298, 303, 305, 308, 310, 313, 318, 321, 323, 326, 333, 335, 340, 342, 344, 348, 351, 357, 360, 365, 368, 371, 374, 377, 380, 382, 385, 388, 390, 395, 397, 399, 404, 408, 410, 412, 414, 416, 422, 424, 427, 430, 432, 435, 439, 441, 448, 450, 455, 457, 463, 467, 471, 473, 476, 480, 483, 488, 492, 496, 498, 502, 508, 515, 517, 522, 525, 527, 530, 534, 536, 538, 541, 543, 545, 557, 563, 566, 568, 570, 572, 579, 582, 585, 588, 591, 596, 598, 602, 604, 606, 608, 610, 613, 615, 617, 620, 622, 625, 628, 632, 634, 638, 640, 643, 646, 653, 656, 658, 661, 666, 668, 676, 681, 683, 686, 691, 693, 698, 703, 705, 709, 714, 717, 720, 722, 724, 727, 730, 732, 735, 737, 739, 743, 748, 750, 754, 756, 758, 761, 763, 768, 770, 773, 775, 778, 780, 782, 785, 791, 793, 795, 798] keV\n"
     ]
    }
   ],
   "source": [
    "def myPeak(energy,count,minEnergy,maxEnergy):\n",
    "    # write code to find the biggest four peaks in the above spectruum and the energy they are at\n",
    "    # this is deliberately not the right answer...\n",
    "    peaks = []\n",
    "    for i in range(len(energy)):\n",
    "        if(energy[i]>minEnergy) : continue\n",
    "        if(energy[i]>maxEnergy) : continue\n",
    "        if(count[i]>count[i-1] and count[i]>count[i+1]):\n",
    "            peaks.append(i)\n",
    "    return peaks\n",
    "\n",
    "# want four most significant peaks between energy in bin 800 and 3000\n",
    "print(\"This code is wrong and you need to fix it...\")\n",
    "print(\"Peaks are at energies\",myPeak(energy,count,energy[800],energy[3000]),'keV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extensions to make the code more complete if you want to\n",
    "### Correct for efficiency: the gamma detector is not equally efficient for all energies\n",
    "If you want to continue this code example you can do a proper correction for the efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "calibEff = {59.5  : 0.042293, ## {energy : efficiency} for several points in the detector's range\n",
    "            661.7 : 0.012586, \n",
    "            88.0  : 0.047077, \n",
    "            898.0 : 0.008659,\n",
    "            122.1 : 0.051100, \n",
    "            1173.2: 0.006979,\n",
    "            165.9 : 0.042985, \n",
    "            1332.0: 0.006235,\n",
    "            391.7 :0.019621, \n",
    "            1836.1:0.005067}\n",
    "# write code to interpolate between the calibrated energy points to correct the number of entries for 1/efficiency "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demonstrate 1/eff scaling between points\n",
    "Sanity check to see that the code works between the points properly, plot 1/eff for interpolation and raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale data points with inverse efficiency\n",
    "Scale number of counts (n) and Poisson errors (sqrt(n)) by efficiency factor for each bin and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find local maxima\n",
    "Define as where one bin is locally the highest and above 10^4 corrected counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit each of the maxima found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
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 },
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