04_minimal_working_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use scipy.optimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vol = np.array([ 49.14835132,  48.95387966,  48.8291772,   57.47423436,  53.40459584,  39.85244873,  39.84663685,  49.2232604,\n",
    "       48.882031, 38.79104303])\n",
    "cur = np.array([ 1.99044514,  0.9919535,   0.69317581,  2.18901812,  0.99226583,  0.99202458, 0.49062936,  0.49075445,  1.99087761,\n",
    "       1.99084474])\n",
    "rate = np.array([ 0.89986609, 0.42571558,  0.2892334,  1.52508731,  0.55962977,  0.21261142,  0.10024236,  0.20604203,  0.88737798,\n",
    "        0.38697821])\n",
    "rate_err = np.array([ 0.01588928,  0.00614813,  0.00418711,  0.02155168,  0.01026643,  0.00312938, 0.00153864,  0.00294942,\n",
    "            0.01257681,  0.00570924])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def f_rate(vi, prefactor, v_power, i_power):\n",
    "    v, i = vi\n",
    "    return prefactor*(v**v_power)*i**i_power\n",
    "\n",
    "p0 = [1E-6, 3.5, 1.]\n",
    "popt, pcov = curve_fit(f_rate, (vol, cur), rate, p0=p0, sigma=rate_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result for Eriks Windows python version: popt = array([  1.08470160e-06,   3.30763171e+00,   1.02735433e+00])\n",
      "Result for Eriks Windows python version: popt = array([  1.08470160e-06,   3.30763171e+00,   1.02735433e+00])\n"
     ]
    }
   ],
   "source": [
    "print('Result for Eriks Windows python version: popt = array([  1.08470160e-06,   3.30763171e+00,   1.02735433e+00])')\n",
    "print('Result for Eriks Windows python version: popt = array([  1.08470160e-06,   3.30763171e+00,   1.02735433e+00])')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try unreasonable start values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.08470128e-06   3.30763179e+00   1.02735433e+00] [9.837130529641048e-06, 1.0567106454160702, 0.8938885573504451]\n",
      "[  1.08470138e-06   3.30763177e+00   1.02735433e+00] [1.3334648389036865e-07, 5.03113594900288, 5.520354368670098]\n",
      "[  1.08470146e-06   3.30763175e+00   1.02735433e+00] [9.363003079396648e-06, 5.633492164792334, 8.018353750289196]\n",
      "[  1.08470140e-06   3.30763176e+00   1.02735433e+00] [1.3723699806951563e-06, 2.624842103512192, 1.123543424174912]\n",
      "[  1.08470129e-06   3.30763179e+00   1.02735433e+00] [7.394255860861325e-06, 1.2921558900147478, 4.254120223186519]\n",
      "[  1.08470129e-06   3.30763179e+00   1.02735433e+00] [9.192466462952326e-06, 1.7291472849471845, 4.057352891528611]\n",
      "[  1.08470127e-06   3.30763179e+00   1.02735433e+00] [5.911553723094813e-06, 1.8271854143295607, 6.654895685211928]\n",
      "[  1.08470123e-06   3.30763180e+00   1.02735433e+00] [4.043961364202612e-06, 8.938060400973152, 8.17218481099107]\n",
      "[  1.08470113e-06   3.30763183e+00   1.02735433e+00] [7.68091228594578e-06, 0.0989614265021832, 8.441164196262772]\n",
      "[  1.08470124e-06   3.30763180e+00   1.02735433e+00] [8.918401790139066e-06, 5.509519342636862, 9.467965223753591]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erik/anaconda3/envs/pax/lib/python3.4/site-packages/ipykernel/__main__.py:3: RuntimeWarning: overflow encountered in multiply\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "# Check for dependence on initial values\n",
    "# Take random start values in range 0-10 for I and U exponent, in range 0 - 1e-5 for prefactor\n",
    "for i in range(10):\n",
    "    p0 = [np.random.rand()*10e-6, np.random.rand()*10, np.random.rand()*10]\n",
    "    popt, pcov = curve_fit(f_rate, (vol, cur), rate, p0=p0, sigma=rate_err)\n",
    "    print(popt, p0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use a different minimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from iminuit import Minuit, describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f_rate_minuit(v, i, prefactor, v_power, i_power):\n",
    "    return prefactor*(v**v_power)*i**i_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Chi2:\n",
    "    def __init__(self,f,vol,cur,rate, rate_err):\n",
    "        self.f = f\n",
    "        self.vol = vol\n",
    "        self.cur = cur\n",
    "        self.rate = rate\n",
    "        self.rate_err = rate_err\n",
    "        f_sig = describe(f)\n",
    "        \n",
    "        #this is how you fake function \n",
    "        #signature dynamically\n",
    "#         self.func_code = make_func_code( f_sig[1:] )#docking off independent variable\n",
    "#         self.func_defaults = None #this keeps np.vectorize happy\n",
    "\n",
    "    def __call__(self,*arg):\n",
    "        #notice that it accept variable length\n",
    "        #positional arguments\n",
    "        chi2 = sum( ( rate - self.f(vol, cur, *arg) )**2 / rate_err**2  \n",
    "                   for rate, vol, cur, rate_err in zip(self.rate,self.vol,self.cur, self.rate_err))\n",
    "        return chi2    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "chi2 = Chi2(f_rate_minuit, vol, cur, rate, rate_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'prefactor': 1e-06, 'i_power': 1.5, 'v_power': 4.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erik/anaconda3/envs/pax/lib/python3.4/site-packages/ipykernel/__main__.py:3: InitialParamWarning: errordef is not given. Default to 1.\n",
      "  app.launch_new_instance()\n",
      "/home/erik/anaconda3/envs/pax/lib/python3.4/site-packages/ipykernel/__main__.py:3: InitialParamWarning: Parameter prefactor is floating but does not have initial step size. Assume 1.\n",
      "  app.launch_new_instance()\n",
      "/home/erik/anaconda3/envs/pax/lib/python3.4/site-packages/ipykernel/__main__.py:3: InitialParamWarning: Parameter v_power is floating but does not have initial step size. Assume 1.\n",
      "  app.launch_new_instance()\n",
      "/home/erik/anaconda3/envs/pax/lib/python3.4/site-packages/ipykernel/__main__.py:3: InitialParamWarning: Parameter i_power is floating but does not have initial step size. Assume 1.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
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       "<hr>"
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       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td title=\"Minimum value of function\">FCN = 32.86873310384783</td>\n",
       "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 421</td>\n",
       "                <td title=\"Number of call in last migrad\">NCALLS = 421</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td title=\"Estimated distance to minimum\">EDM = 2.991595619610985e-05</td>\n",
       "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
       "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "                UP = 1.0</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        \n",
       "        <table>\n",
       "            <tr>\n",
       "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "                <td align=\"center\"></td>\n",
       "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "                <td align=\"center\"></td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><a href=\"#\" onclick=\"$('#bdHrODoyuj').toggle()\">+</a></td>\n",
       "                <td title=\"Variable name\">Name</td>\n",
       "                <td title=\"Value of parameter\">Value</td>\n",
       "                <td title=\"Parabolic error\">Parab Error</td>\n",
       "                <td title=\"Minos lower error\">Minos Error-</td>\n",
       "                <td title=\"Minos upper error\">Minos Error+</td>\n",
       "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
       "            </tr>\n",
       "        \n",
       "            <tr>\n",
       "                <td>1</td>\n",
       "                <td>prefactor</td>\n",
       "                <td>1.08398e-06</td>\n",
       "                <td>1.61952e-07</td>\n",
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>2</td>\n",
       "                <td>v_power</td>\n",
       "                <td>3.3078</td>\n",
       "                <td>0.0388421</td>\n",
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>3</td>\n",
       "                <td>i_power</td>\n",
       "                <td>1.02737</td>\n",
       "                <td>0.0086296</td>\n",
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            </table>\n",
       "        \n",
       "            <pre id=\"bdHrODoyuj\" style=\"display:none;\">\n",
       "            <textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
       "\\hline\n",
       "1 & prefactor & 1.084e-06 & 1.620e-07 &  &  &  &  & \\\\\n",
       "\\hline\n",
       "2 & $v_{power}$ & 3.308e+00 & 3.884e-02 &  &  &  &  & \\\\\n",
       "\\hline\n",
       "3 & $i_{power}$ & 1.027e+00 & 8.630e-03 &  &  &  &  & \\\\\n",
       "\\hline\n",
       "\\end{tabular}</textarea>\n",
       "            </pre>\n",
       "            "
      ]
     },
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     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<hr>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values found:  {'prefactor': 1.0839774916972308e-06, 'i_power': 1.0273675755168616, 'v_power': 3.307804223124495}\n",
      "Errors found:  {'prefactor': 1.6195234595439715e-07, 'i_power': 0.008629603987238735, 'v_power': 0.038842071148113236}\n"
     ]
    }
   ],
   "source": [
    "myvals = {'prefactor': 1e-6, 'v_power': 4.0, 'i_power': 1.5}\n",
    "forced_parameters=['prefactor','v_power','i_power']\n",
    "m=Minuit(chi2, forced_parameters = forced_parameters, **myvals) # m=Minuit(chi2) # \n",
    "\n",
    "print(m.values)\n",
    "m.migrad()\n",
    "print('Values found: ', m.values)\n",
    "print('Errors found: ', m.errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Don't give the errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p0 = [1E-6, 3.5, 1.]\n",
    "popt, pcov = curve_fit(f_rate, (vol, cur), rate, p0=p0, sigma=rate_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.08470160e-06,   3.30763171e+00,   1.02735433e+00])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Convert to actual numbers in a quick-and-dirty way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d_eff = 150.0 + 4.06 + 7.2 + 13.8/2 # Effective distance: distance outside NG-Cd shield + effective center + distance Cd - reference plane + radius NG\n",
    "s_eff = 4*np.pi*d_eff**2 # Effective surface area of sphere\n",
    "phi = np.array(rate)/3.515 # Flux at 0 degrees and this distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gauss(x, A, sigma):\n",
    "    return A*np.exp(-x**2/(2*sigma**2))\n",
    "popt_g = np.array([  0.98970953,  50.42659426])\n",
    "# average over all angles\n",
    "rate_v_angle_gauss = [gauss(angle, 1., popt_g[1]) for angle in np.linspace(-90,90,200)]\n",
    "angular_corr = np.average(rate_v_angle_gauss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conversion_factor = s_eff * angular_corr * 1/3.515"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_per_sec = phi * s_eff * angular_corr\n",
    "n_per_sec_err = np.array(rate_err)/np.array(rate) * n_per_sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 58932.88041235,  27880.42092553,  18942.10434517,  99879.06984985,\n",
       "        36650.55798536,  13924.07551346,   6564.94458429,  13493.84141579,\n",
       "        58115.02506544,  25343.48257541])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_factor * np.array(rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%run function_definitions.ipynb\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v_min = 35\n",
    "v_max = 60\n",
    "i_min = 0.4\n",
    "i_max = 2.5\n",
    "rate_min = 5e3\n",
    "rate_max = 1e5\n",
    "n_gridpoints = 101\n",
    "log_color = True\n",
    "x_label = 'Voltage (kV)'\n",
    "y_label = 'Current (mA)'\n",
    "point_size = 200\n",
    "#c_ticks = None #  None is automatic\n",
    "#c_ticks = [0.1, 0.17, 0.3, 0.5, 1, 2]\n",
    "c_ticks = [5e3, 1e4, 3e4, 1e5]\n",
    "c_tick_labels = ['5e3', '1e4', '3e4', '1e5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Initialize background colors given by fit\n",
    "xx, yy = pylab.meshgrid(\n",
    "    pylab.linspace(v_min,v_max, n_gridpoints),\n",
    "    pylab.linspace(i_min,i_max,n_gridpoints))\n",
    "\n",
    "zz = pylab.zeros(xx.shape)\n",
    "for i in range(xx.shape[0]):\n",
    "    for j in range(xx.shape[1]):\n",
    "        zz[i,j] = conversion_factor * f_rate((xx[i,j], yy[i,j]), *popt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4288ca90f0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lEaWQkF4IIYQQogEGOkdtaYya9NdK8c4Kxa6uzmNdNSYbXK+AqKWbd1eHbxek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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4288d46b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.set_c\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "ax1 = fig.gca()\n",
    "# Plot fit \n",
    "if log_color:\n",
    "    pylab.pcolor(xx,yy,zz, vmin=rate_min, vmax=rate_max, norm=LogNorm())\n",
    "else:\n",
    "    pylab.pcolor(xx,yy,zz, vmin=rate_min, vmax=rate_max)\n",
    "\n",
    "if log_color:\n",
    "    plt.scatter(vol, cur, c = conversion_factor * rate, edgecolors='white', s=point_size, vmin=rate_min, vmax=rate_max, norm=LogNorm(), linewidth=3)\n",
    "else:\n",
    "    plt.scatter(vol, cur, c = conversion_factor * rate, edgecolors='white', s=point_size, vmin=rate_min, vmax=rate_max, linewidth=3)\n",
    "\n",
    "cbar = plt.colorbar(label='Neutron emission', ticks = c_ticks)\n",
    "cbar.ax.set_yticklabels(c_tick_labels)   \n",
    "plt.xlim(v_min, v_max)\n",
    "plt.ylim(i_min, i_max)\n",
    "plt.xlabel(x_label)\n",
    "plt.ylabel(y_label)\n",
    "plt.title(\"Floating exponents, $f = V^{\\ \\ %.2f}  I^{\\ \\ %.2f}$ \" % (popt[1], popt[2]))\n",
    "# plt.savefig('PTB_rate_UI.png', bbox_inches = 'tight')\n",
    "# plt.savefig('PTB_rate_UI.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 58932.88041235,  27880.42092553,  18942.10434517,  99879.06984985,\n",
       "        36650.55798536,  13924.07551346,   6564.94458429,  13493.84141579,\n",
       "        58115.02506544,  25343.48257541])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 49.14835132,  48.95387966,  48.8291772 ,  57.47423436,\n",
       "        53.40459584,  39.85244873,  39.84663685,  49.2232604 ,\n",
       "        48.882031  ,  38.79104303])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.99044514,  0.9919535 ,  0.69317581,  2.18901812,  0.99226583,\n",
       "        0.99202458,  0.49062936,  0.49075445,  1.99087761,  1.99084474])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.64781328461944265"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_v_angle_gauss = [gauss(angle, 1., popt_g[1]) for angle in np.linspace(-90,90,200)]\n",
    "rate_v_angle_gauss_cos = [gauss(angle, 1., popt_g[1]) * np.cos(angle/180*np.pi) for angle in np.linspace(-90,90,200)]\n",
    "angular_corr = np.average(rate_v_angle_gauss)\n",
    "\n",
    "angular_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f42882a1f60>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lELsZcZM35rxB/+f7qxDzAY8nf5xRL42iwXcNYnR/5f2yZ3c2enTuDHPnujCgiMSaRsZE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\nqRgTERERcUjFmIiIiIhD/w8EI2ILsNwqSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f428821c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rate_v_angle_gauss)\n",
    "plt.plot(rate_v_angle_gauss_cos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.4.5"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}