{
 "metadata": {
  "name": "",
  "signature": "sha256:a3176c12fa21d34700967c7971415a3ecb1067069d27e681653ecbc319c3ef9f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "rcParams['figure.figsize'] = 14, 7"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.integrate import simps, quad\n",
      "\n",
      "\n",
      "x = np.linspace(-3.0, 12.0, 300)\n",
      "R = 0.1\n",
      "sigma = 1.0  #conductance\n",
      "true_csd = 1.0*np.exp(-(x-2.)**2 / (2*np.pi*0.5)) + 0.5*np.exp(-(x-7)**2 / (2*np.pi*1.0))\n",
      "\n",
      "#print true_csd\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True)\n",
      "ax1.plot(x, true_csd)\n",
      "ax1.set_title('True CSD')\n",
      "\n",
      "def calculate_pot(csd, z, z0):\n",
      "    return 1.0/(2 *sigma) * np.trapz( (np.sqrt((z-z0)**2 + R**2 ) - np.abs(z-z0))*csd, z)\n",
      "\n",
      "pots = [calculate_pot(true_csd, x, x0) for x0 in x]\n",
      "ax2.plot(x, pots)\n",
      "ax2.set_title('Potentials')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "<matplotlib.text.Text at 0xd656290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wgzLZuNG/RmUkqW5dqXp1KTPTdiUAAFs2bzbbBtSta7uS0qEJAOAZJYaZ5ORk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\nGT1MS1MhIyKh1acPzJ4NvXvbXVuqkRkHmznTnNz8/vu2k4h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1gP7GJyUl0ahRo/M+lixZ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd12e810>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "#elec_pos = np.linspace(-3.0, 13.0, 30)\n",
      "elec_pos = [ -2.0, -1.0, 0.0, 2.2, 4.3, 7.3, 9.0]\n",
      "\n",
      "meas_pot = np.array([calculate_pot(true_csd, x, x0) for x0 in elec_pos])\n",
      "\n",
      "plot(x, pots)\n",
      "scatter(elec_pos, meas_pot)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "<matplotlib.collections.PathCollection at 0xdbd59d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XhyI0cSKMGwc77gg9eoR7xxo23Hw5JKkkFiApxY0bF25Of+212EmUSYqL4dhj\nYdttYcQISCRiJ8osy5bBE0/AI4/Aq6+G7WdHHQXdu4fSkQxWrYLXX4eHH4YxY1aQSMylYcOXGDp0\nNzp1ah87nqQMZgGSUtxhh4XHqafGTqJMs3Qp7LMPnHkm9OsXO036W7UKJk8O532NGxdKz/HHwyGH\nhHt3ktVLL73EoYeexPLl9wK7AZU58cQfGTq0AXXrxk4nKRNZgKQUtmABNG0KhYWOv1Ycn30G++4b\nViI6dIidJj0tWgQjR4Yth1tvDb16wQknQK1asZNtmM6dj+LFF7sDp/z3PY9Rv351li7tyhFHwKWX\nuj1O0ublOUBSChs1Co4+2vKjeHbdFR58MNzjMWNG7DTpo7g4DJk44YRwwPHnn4d7fN5/H849N3XK\nD0BR0Sog6zfv+YlGje5i9uwwWrt1azj99DBZTpKSkQVIShLFxeEslt69YydRpjvoILjmGujWDb76\nKnaa1FZUBI89Bm3ahJWevfaCOXPg7rvDBLdUvNfqnHNOZYstLgAeAcay5ZYXcs45p7LddnDVVTBr\nFtSsGb6+/v3hm29iJ5akNbkFTkoSr74Kf/1rGEGbij8UKf1cdhm88AK89JKrkqX1889w//1w3XWw\n3XZw0UVhoEG6nOs1btw4rr9+BIlEggsuOIPu3bv/4Zr//AcGDw7bKa+4Avr2DQexSlJZ8x4gKUWd\nfDK0bBm2w0jJoLgY+vQJ27Wefdbx2Bvi++/DvT033hgOFL3wQmjfPrNf1PjwQzj77FCIbr4Z9t8/\ndiJJ6cYCJKWgJUvCCeyzZ4cT3aVkUVQUJpP9/DM8+ihUrhw7UXL66iu45Rb45z+hUycYNAj22CN2\nquRRXBzGfA8cGLZY/uMfUKNG7FSS0oVDEKQUNGYMdO5s+VHyqVgRHnjgf+cErVgRO1FymTMn3OfS\nuHG41+Xtt8N/z5afNSUS4VyjDz+EKlWgWTMYPz52KkmZygIkJYGRIx1+oORVuXK4kb9ChfBD7PLl\nsRPF9+GHcOKJYeLZVlvB9Olwxx1hip7Wbeut4fbbw6TB884Lq4sOSZC0uVmApMjeey9snznooNhJ\npHWrXBnuv38FM2a8xzbbTKVx405MnDgxdqzN7vXX4dBDw4pt8+bh/qhrr4XatWMnSy0dOoQR4HXq\nhNWyV16JnUhSJvEeICmys86C7bcP05KkZNa379mMHv0py5c/CGRRtep+TJlyPy1atIgdrVytWgUT\nJsDQoeGi2n/2AAAgAElEQVSw4gsugJ49HQpRVp5/PowI79Ur/D2YlbXep0jSGhyCIKWQn36CevXg\n3Xdh551jp5FKVqPGTnz33RtAw/++5ztOP/0J7ryzV8xY5WblShg7FoYNC+ObL7wwbAF0lHPZW7QI\nTjkFvv0WHnoIdtkldiJJqcQhCFIKefLJcFig5UepoGrVLYH/nYxaqdJIxow5jrvuCkMS0sXSpXDT\nTZCbG+7Pu+EGmDYNjjvO8lNeatUKo9aPOw722SesuElSebEASRE5/ECpZOjQy9lyy6OB66lUqR/b\nbz+cF174mVtugRNOCGfgpLLCQvjb38JI+rfeCqs/r7wCXbpk9jk+m0uFCuEctCeeCOdPDRkSth9K\nUllzC5wUyWefwd57w7x5YSyslApefPFFnnpqAttvvw39+/+VmjVr8tNP4QfX55+Hu+5KvYEe774b\nzqXJzw/39px9dihBimfBgjB2vUYNuP9+zwySVDLvAZJSxKWXwg8/hBPjpXTw3HPQt2+YkHbttbDj\njrETrdvy5WG094gRMHduKD2nneYP2slk5Uo4//ywHe7pp8NZS5K0Nt4DJKWAX36B++5z+5vSy8EH\nw0cfQbVq0LRpKPfJdmbQjBnh/Jl69cLKwrnnhlHW559v+Uk2WVlw881w8cVhbPZLL8VOJCldWICk\nCCZMCD+ANWsWO4lUtrbeOvzQ+uqr8PLLkJMDt90WJh7G8uWXoYy1aQMdO4Yzjd5+O2zZO/JIxy4n\nu169wv1YJ5wQtlhK0qZyC5wUQbdu0KMHnHxy7CRS+XrnHbjqqjBU4NRTwzaz3Nzy/ZzFxTBrVrin\n56mnwmHDhx8efoDu2NFJbqlq1iw45BDo3h2uuw4qVoydSFKy8B4gKcnNmRNeiS4shC22iJ1G2jxm\nz4Y77oAHHwyrn8ccE7bMNWu26RPWiovDKs8778DEieFepOXLw8c/5JDwq4eWpofFi8NZTNttF/5d\n8p+rJLAASUnv4ovDdiCHHygT/fJLGC09blwoKsuWQdu20Lp1uMk9Nxfq1IEddvjjSs0PP4QXDubN\nC7/OnRvO5/nXv6CoKHyMDh1C4Wne3NHV6ernn8Pq+cKF4d8j792SZAGSktiKFVC/Pkya5EQjCeDf\n/4apU8Mo6pkzwzanhQvDK/1ZWeF+nVWrwopOpUqQnR1WkOrVC/8ttWwZik+9ehaeTLJqVRhg8fLL\nYatj3bqxE0mKyQIkJbFHH4Xbbw+vgEtat1Wrwiv9P/8c7vWoWjUUIEuOflVcHO4Fuv32UIKaNImd\nSFIspe0M3goqbUb//CeccUbsFFLyq1Ah3CPnfXJal0QCBg2C2rXhgAPCdM2WLWOnkpQKXAGSNpMZ\nM6B9+3DvQuXKsdNIUvp44gno1y/cE7T33rHTSNrcXAGSktSdd4bzLCw/klS2jjwybJPMywtbjTt0\niJ1IUjJb70Go+fn5NG7cmNzcXIYNG7bWawYMGEBubi4tWrSgoKBg9ftPPfVUatWqRfPmzde4fvHi\nxXTq1IlGjRrRuXNnlixZsolfhpTcfvoJRo+GPn1iJ5Gk9NStGzz8MBx9dDjkVpLWpcQCVFRURP/+\n/cnPz2f69OmMGTOGTz75ZI1rJkyYwOzZs5k1axZ33nkn/fr1W/1nvXr1Ij8//w8fd+jQoXTq1ImZ\nM2dy4IEHMnTo0DL6cqTk9Oij0KoV7LJL7CSSlL4OOCBsgzvppPCrJK1NiQVo6tSp5OTk0KBBA7Ky\nsujRowfjfvc3yvjx4+nZsycAbdu2ZcmSJSxcuBCA/fbbj2233fYPH/e3z+nZsydPPfVUmXwxUrK6\n9Vbo3z92CklKf/vuG86Y6tsXxoyJnUZSMiqxAM2fP5969eqtfjs7O5v58+eX+prfW7RoEbVq1QKg\nVq1aLFq0qNTBpVQxdSp8/XXYniFJKn977gkTJ8L558P998dOIynZlDgEIbGBBy78furChj7v12tL\nun7w4MGrf9+xY0c6duy4wR9bSgbDh8OZZ4azTCRJm0ezZvDii3DQQeEMqeOPj51IUlmZNGkSkyZN\n2ujnl1iA6tatS2Fh4eq3CwsLyc7OLvGaefPmUXc9RzLXqlWLhQsXUrt2bRYsWEDNmjXXee1vC5CU\nahYtgmeegZtvjp1EkjJP06bwwgvQqVN4EerYY2MnklQWfr8oMmTIkFI9v8QtcK1bt2bWrFnMnTuX\nFStWMHbsWPLy8ta4Ji8vj9GjRwMwZcoUatSosXp727rk5eUxatQoAEaNGsXhhx9eqtBSqrjrrjCR\naLvtYieRpMzUrFmYCjdgADz+eOw0kpLBeg9Cfe655zjnnHMoKiqid+/eXHTRRYwYMQKAvn37Aqye\nFFetWjXuvfdeWrVqBcDxxx/P5MmT+eabb6hZsyZXXnklvXr1YvHixRx77LH8+9//pkGDBjzyyCPU\nqFHjj+E8CFUpbOVKaNgQnn0WWrSInUaSMltBAXTtCiNGgK+7SumltJ1hvQUoJguQUtmjj4b7f159\nNXYSSRLAu++GgTR33w3du8dOI6mslLYzrPcgVEkb59Zb4ayzYqeQJP1qzz3h6aehd2+YMCF2Gkmx\nuAIklYMPPgivMs6ZA1lZsdNIkn7rrbcgLy+s1DtcVkp9rgBJSWD4cDjjDMuPJCWjffaBsWPDVLh3\n3omdRtLm5gqQVMa+/hoaNYIZM6CECe+SpMjGj4c+feCll2C33WKnkbSxStsZSjwHSFLp3XFHGH1t\n+ZGk5JaXB0uXQpcuYWDNLrvETiRpc7AASWVo+XK4/fbwaqIkKfn95S+hBB10ELz2GqznLHdJacAC\nJJWRX375hXvvLaJlyypupZCkFHLGGfDdd9CpU1gJ2mGH2IkklSeHIEhl4O9/H0bVqlvx17/OprDw\nQr799tvYkSRJpTBoEBx2WDgs9fvvY6eRVJ4sQNImevrpp7n22pEUFRUCTZk5cwmnnHJm7FiSpFK6\n5hpo2zYckvrjj7HTSCovFiBpE7366pv8+OPJwA5AgpUrL+CNN96IHUuSVEqJRDjGoH79MMxmxYrY\niSSVBwuQtInq19+JKlUWAr+OX5xC7do7xYwkSdpIFSrAPfeEc9xOOQVWrYqdSFJZ8xwgaRMtX76c\n7OyJLF36JVWqTAQm89JLz9CmTZvY0SRJG+mnn8J47JYt4aabwuqQpORU2s5gAZI20cKF0LRpMbfc\n8jwVKiymffv2ZGdnx44lSdpES5ZAhw5w3HFw8cWx00haFwuQtJkNGhRulh0+PHYSSVJZW7AA/vzn\nUIBOOy12GklrYwGSNqMlS2DXXWHaNNh559hpJEnlYdassBJ0xx1hVLak5FLazuBBqNImuO02OPRQ\ny48kpbPcXHj6aTj4YNhuO9hvv9iJJG0KV4CkjfTjj9CwIbzyCjRtGjuNJKm8TZwIf/kLvPgi7L57\n7DSSflXazuAYbGkjjRwJ++5r+ZGkTHHQQeF+z27dYM6c2GkkbSy3wEkbYeVKuOEGePTR2EkkSZvT\nscfCV1+FEdmvvw41a8ZOJKm0XAGSNsJDD0FODuy1V+wkkqTNrX//MBq7WzdYujR2Gkml5T1AUimt\nWgXNmsEtt4TtEJKkzFNcDGecAZ9/Ds88A1WqxE4kZS7vAZLK2bhxUK0aHHhg7CSSpFgSCbj9dthq\nK+jZM7w4Jik1WICkUli1CoYMgcsuC//zkyRlrooVw5bohQvh7LPDqpCk5GcBkkrhqaegUiXo3j12\nEklSMqhaNewMeO01uPrq2GkkbQinwEkbaNUqGDwYrrnG1R9J0v9ssw089xy0axemwvXpEzuRpJJY\ngKQN9Pjj4ZW+Qw6JnUSSlGzq1IHnn4f27WGHHeDII2MnkrQuFiBpA/x6789117n6I0lau5ycMBGu\na1fYdlvYf//YiSStjfcASRvg0UehenU4+ODYSSRJyaxVKxg7NpwTVFAQO42ktbEASetRVBRWfwYP\ndvVHkrR+++8Pt91WxH77fU+DBkfQtm0npkyZEjuWpP+yAEnr8cgj4QbXLl1iJ5EkpYqXXz6flSvv\n4osvxjB1ah8OOqg7M2bMiB1LEhYgqUS//BJWf4YMcfVHkrThHnzwQVasOAaoChzD8uW9GTduXOxY\nkrAASSW65x6oWxc6dYqdRJKUSrKyqgDfrX571apeJBJbxgskaTULkLQOP/wQVn6GDnX1R5JUOldc\ncSFbbnkEcAcVK55H5cqf8vLLp/PLL7GTSbIASetw003hULs2bWInkSSlmgEDzuT++6/jL38p4Kyz\nKjJjxl4UF1ehTx8oLo6dTspsieLi5P3PMJFIkMTxlMa+/hqaNIEpU8K5DpIkbaoffoADD4SOHcPu\nAkllo7SdwRUgaS2uvhp69LD8SJLKTrVq8OyzMH48/N//xU4jZa5KsQNIyebzz+H+++GTT2InkSSl\nm+23h+efD1usd9wRTjopdiIp81iApN+57DIYMABq1oydRJKUjurVg/z8cGDqdtvBIYfETiRlFu8B\nkn7jX/+C7t1h1iyoXj12GklSOnv77fD/nCefhD//OXYaKXV5D5C0kVatCis/V19t+ZEklb+2beGB\nB+DII6GgIHYaKXNYgKT/evBBWLkSTjkldhJJUqbo3BnuuAO6dfPeU2lz8R4gCVi6FC68EB57DCr4\nsoAkaTM68khYtiyUoVdfhYYNYyeS0psFSAKuuSaczbDPPrGTSJIy0cknhxJ00EGhBNWtGzuRlL4s\nQMp4s2fDXXfBBx/ETiJJymR//WvYkdCpE0yeHMZkSyp7ToFTxsvLC9N3Bg2KnUSSJLjkEnjuOXjl\nFdhmm9hppORX2s5gAVJGy8+Hs86Cjz6CKlVip5EkCYqL4eyzYdq0cGhqtWqxE0nJzQIkbaAff4Tm\nzWH48DB9R5KkZLFqFfTuDfPmwdNPQ9WqsRNJycsCJG2gCy+EuXPh4YdjJ5Ek6Y+KiuD442HFCnj0\nUcjKip1ISk4WIGkDvP9+uMn0gw+gdu3YaSRJWrsVK+CII8IB3Q8+CJUcXyX9QWk7gyeeKOMUFUGf\nPmH0teVHkpTMKleGxx+H774Lo7KLimInklKfBUgZ5/bbw17qU0+NnUSSpPWrWhWefBK+/hpOOcUS\nJG0qt8ApoxQWQsuW8Prr0Lhx7DSSJG24H3+E7t2hXj245x6o4MvYEuAWOGmdiovhzDNhwADLjyQp\n9Wy5JYwfHwb4nH56mBQnqfQsQMoYo0bBF1944KkkKXVVqwbPPAOzZsEZZ1iCpI3hFjhlhC++gNat\n4aWXYPfdY6eRJGnTLF0KXbuG/6fddpvb4ZTZ3AIn/c6qVeGm0fPPt/xIktLDVlvBc8/Bhx+GyaYO\nRpA2nAVIae/mm2HlylCAJElKF1tvDfn58Nln4YW+X36JnUhKDW6BU1qbPh3at4e334Zdd42dRpKk\nsvfjj+Gw1G22CYelZmXFTiRtXm6Bk/5r5Uo46aRw4KnlR5KUrrbcEsaNg+XL4Zhj4OefYyeSkpsF\nSGnroougTp0wKlSSpHRWtSo89hhUqhRWg376KXYiKXlZgJSWxo+HRx8No68TidhpJEkqf5Urw8MP\nw7bbwqGHwg8/xE4kJScLkNLOF1+EVZ+HH4btt4+dRpKkzadSJRg9GnbeGTp1gsWLYyeSko8FSGll\nxQo49li44ALYZ5/YaSRJ2vwqVoS774Z99w2DgObPj51ISi6VYgeQytKgQVCrFgwcGDuJJEnxVKgA\nN9wANWtCu3bw/PPQqFHsVFJysAApbTz5ZHhMm+Z9P5IkQdgRsf320KEDPPMM7Lln7ERSfBYgpYUP\nP4S+feHZZ2G77WKnkSQpefTuHf7f2LUrPPII7L9/7ERSXN4DpJT39deQlwc33QRt2sROI0lS8jni\niFB+jj0WnngidhopLleAlNJWrICjjoLjj4cTToidRpKk5LX//uFeoEMPhXnzYMCA2ImkOBLFxcXF\nsUOsSyKRIInjKbLiYujTJ6wAPfFEuOFTkiSVbM4cOOQQ6NwZ/vGPMDVOSmWl7Qzr/ZExPz+fxo0b\nk5uby7Bhw9Z6zYABA8jNzaVFixYUFBSs97mDBw8mOzubli1b0rJlS/Lz8zc4sPSr4cNhyhS4/37L\njyRJG6phQ3jjDfjgg7CLwgNTlWlK/LGxqKiI/v37k5+fz/Tp0xkzZgyffPLJGtdMmDCB2bNnM2vW\nLO6880769eu33ucmEgkGDhxIQUEBBQUFdO3atZy+PKWrJ56AoUNh/HjYaqvYaSRJSi3bbgv5+bDN\nNtCxIyxcGDuRtPmUWICmTp1KTk4ODRo0ICsrix49ejBu3Lg1rhk/fjw9e/YEoG3btixZsoSFCxeu\n97lubdPGmjwZzjgjjPNs2DB2GkmSUlPlynDffdC9O+y9N3z8cexE0uZRYgGaP38+9erVW/12dnY2\n8393nPC6rvnyyy9LfO7w4cNp0aIFvXv3ZsmSJZv8hSgzvPceHHMMPPwwtGoVO40kSaktkYDLL4cr\nrwxDEl58MXYiqfyVOAUusYGnSZZ2Nadfv35cfvnlAFx22WWcd955jBw5cq3XDh48ePXvO3bsSMeO\nHUv1uZQ+Pv883LR5++1wwAGx00iSlD5OPhl23hl69IC//Q3OPddDxZW8Jk2axKRJkzb6+SUWoLp1\n61JYWLj67cLCQrKzs0u8Zt68eWRnZ7Ny5cp1PrdmzZqr33/aaafRvXv3dWb4bQFS5lm+fDmVK1fm\nq68q0KULXHopHH107FSSJKWfDh3CcKHDD4eCArjzTthii9ippD/6/aLIkCFDSvX8ErfAtW7dmlmz\nZjF37lxWrFjB2LFjycvLW+OavLw8Ro8eDcCUKVOoUaMGtWrVKvG5CxYsWP38J598kubNm5cqtNLf\n119/zd57H0j16tuwxRaN2GOPb+nZE/47Y0OSJJWDnXcOE+J++QXat4ffvJYtpY0SV4AqVarErbfe\nSpcuXSgqKqJ37940adKEESNGANC3b1+6devGhAkTyMnJoVq1atx7770lPhdg0KBBvPfeeyQSCRo2\nbLj640m/Ou643kyb1pyiohcoKipi8eJbaNeuNdAxdjRJktLallvCQw/B9ddD27bwyCPQrl3sVFLZ\n8SBUJaVq1bbjxx9nAdsDULHiIIYM2ZpLLrkkbjBJkjJIfj707BmGJPTp431BSk5lfhCqFEONGs2B\nKv99axVVqrxLnTp1YkaSJCnjdO0Kr78eDh8/+WRYtix2ImnTWYCUdGbNguLifLKy/o/q1U+kevV9\nad68mBNPPDF2NEmSMk5uLkydGs4NatMGPvoodiJp07gFTknlnXcgLw+uugoOOOBzXnvtNWrUqEG3\nbt3IysqKHU+SpIw2ejScdx4MGwa9erklTsmhtJ3BAqSkkZ8PJ50EI0eGEiRJkpLP9OnhUPI99wxn\n81WvHjuRMp33ACkl3X9/uMnyqacsP5IkJbOmTcOWuIoVw5a4Dz6InUgqHVeAFNWqVXDZZfDggzBh\nQvhLVZIkpYZRo+D882HQIDj33FCKpM3NLXBKGd99ByeeCN9/D489BjvuGDuRJEkqrTlzwi6ORCIU\nogYNYidSpnELnFLCzJnhcLWdd4aJEy0/kiSlqoYN4ZVX4JBDwpa4++4DX79WMnMFSJvd+PFw2mlw\n9dVw+umx00iSpLLy/vthoFFODowY4Quc2jxcAVLSWr4cBgwIj3HjLD+SJKWbFi3CkRY5ObD77jB2\nrKtBSj6uAGmzmDEDjjsu/IV4991Qo0bsRJIkqTy9+WZ4sbNhwzAuu3792ImUrlwBUlIpLoZ77oF2\n7aBfP3j0UcuPJEmZYN99oaAg3PPbqhXccgsUFcVOJbkCpHJUWAh9+sCiReHk6GbNYieSJEkxfPpp\n+Jng55/DTpDmzWMnUjpxBUjRFRfDnXeGV3vatYO337b8SJKUyRo3hkmToHdvOOCAcG7Q0qWxUylT\nWYBUpmbOhE6dwqs7kybBJZdAVlbsVJIkKbYKFcIq0AcfwMKF8Kc/hXODVq2KnUyZxgKkMrFsGVx0\nUdjv261buPFxt91ip5IkScmmTp1QfJ58MgxH2GefsFtE2lwsQNokxcVhxGWTJjBvHnz4IQwcCJUq\nxU4mSZKSWdu28NZb8Ne/whFHwCmnwIIFsVMpE1iAtNHeeAP22w+uuQYeegjuvz+8qiNJkrQhKlSA\nnj3DkIRatcJwhMGD4fvvYydTOrMAqdQ+/hgOOwxOOCHM9582LRQhSZKkjbH11jBsWDhE9fPPITcX\nbrwxHKIulTULkDbYzJlhefqAA6BDh3C4ac+eULFi7GSSJCkdNGwYjs6YODEMU2rUCEaOhF9+iZ1M\n6cQCpPX68EM4/nj485/DX0wzZ4b7fKpWjZ1MkiSlo+bNYdy4cJ/x/feH4zQeftiDVFU2PAhVa1Vc\nHCa53XBDuEFx4EDo1w+22ip2MkmSlEmKi+GFF2DIEPjPf+DCC+HEE6Fy5djJlCxK2xksQFrDypXw\n6KNw003wzTdw9tlw2mmw5Zaxk0mSpExWXAyTJ8PVV4dt+H/7W/gZZYstYidTbBYglWjGjBkUFhbS\nrFkzateuvfr9X34J99wD//wn5OTAuefCoYd6f48kSUo+U6eGKbRvvw3nnBMOWN1229ipFEtpO4P3\nAGWQiy8eQsuWHTj66L+Tk9Ocp5+ewIQJcPjh4dDSwkJ4+ulw0+Fhh1l+JElSctprL3jqqbA17sMP\nYZddwnlCn34aO5lSgStAGeLdd9+lffvD+fHHacCOwHwSCdhzz53o2zdBjx5QvXrslJIkSaX35Zdh\nF8uIEdCyZdjC36VLOGdI6c8tcFqrO+54hnPP/Zaffz5p9fuysv7M/PlPseOOO0ZMJkmSVDaWLw/T\n4m6+GX76Cc48MwxMcHtcenMLnFYrLIRbb4X27eGii7pSVFQMzPvvnz7FVlt9wfbbbx8zoiRJUpmp\nWjWcWThtGtx5Z5ho27BhKEGTJoVBCpIFKI0UF8MHH8BVV8Gee4Yl4H/9C847DxYtqsStt66gSpXd\nqF59V2rU+CvPPfcEFVwbliRJaSaRCC8AjxkDn30GrVtD//7hYNVhw2DhwtgJFZNb4FLcjz/Cq69C\nfj6MHx/ed9hhYbDBn/8MlSqtef2SJUv46quvqF+/PlU9yVSSJGWI4uIwNe7uu+Hxx2HffeGEE8LP\nTd4Hndq8ByjN/brK88IL4TFlCrRqBZ07Q/fu4eTkRCJ2SkmSpOS1bBmMGwcPPQRvvAHdusFf/hJ+\nnsrKip1OpWUBSjPFxWGk46uvhsO/Xn4ZttoqTDbp3Bk6doStt46dUpIkKTV9/TU88kgoQzNnwlFH\nhUfHjpahVGEBSnFFRfDRR/8rPK++CtWqhX2sHTqE/xh32SV2SkmSpPQzZ04oQ088AbNnh0PhjzoK\nOnWCLbaInU7rYgFKMV9+Gfajvv122M42bRrstBPst18oPO3bQ/36sVNKkiRllsLCcNjqE0+En8+6\ndAn3Cx18MGy3Xex0+i0LUBL77jt4/32YOvV/peeHH2DvvaFt2/DYay9n1UuSJCWTr78O9wyNHx/G\nabdqFe69zsuD3NzY6WQBSgLFxTB/Prz3HhQUhF/few8WLYJmzaBNm1B29t4bdt3VoQWSJEmp4scf\nwz3Z48fDM8+Ee7F/LUP77PPHCbwqfxagCL74IkwQ+W3ZSSTCOTx77PG/X3NzoWLF2GklSZJUFlat\nCtvjxo+Hp58O2+YOPjiUoS5dHFS1uViAIrjjjvBKwG/LTp06ruxIkiRlkn//O6wKPf10eHF8773D\n6lD37tCgQex06csCJEmSJEW2dCm8+GIoQ88+C5dcAmefHTtVerIASZIkSUmkqAiWLw9Hm6jsWYAk\nSZIkZYzSdoYK5ZhFkiRJkpKKBUiSJElSxrAASZIkScoYFiBJkiRJGcMCJEmSJCljWIAkSZIkZQwL\nkCRJkqSMYQGSJEmSlDEsQJIkSZIyhgVIkiRJUsawAEmSJEnKGBYgSZIkSRnDAiRJkiQpY1iAJEmS\nJGUMC5AkSZKkjGEBkiRJkpQxLECSJEmSMoYFSJIkSVLGsABJkiRJyhgWIEmSJEkZwwIkSZIkKWNY\ngCRJkiRlDAuQJEmSpIxhAZIkSZKUMSxAkiRJkjKGBUiSJElSxrAASZIkScoYFiBJkiRJGWO9BSg/\nP5/GjRuTm5vLsGHD1nrNgAEDyM3NpUWLFhQUFKz3uYsXL6ZTp040atSIzp07s2TJkjL4UrShJk2a\nFDtC2vJ7W3783pYfv7flx+9t+fD7Wn783pYfv7fJo8QCVFRURP/+/cnPz2f69OmMGTOGTz75//bu\nLaSpB44D+FfJCJSyIid5LEGRNl1rZQyEHrqsQEjCpIuGonYhIboRjN56yEkSZZeHijSjwHpKiyHM\n6iGSIbHIEskLG2wu9xIaY1107P8QjdZ/rm2e03HH7wd88HiOfs9Pt52f5+z8hsLWsVgsGB0dxcjI\nCG7fvo3jx4//ddvm5mYYjUYMDw9j+/btaG5ulmj3KBI+AKXD2kqHtZUOaysd1lYarKt0WFvpsLbz\nR9QGqL+/HwUFBcjLy0NaWhoOHDiArq6usHW6u7tRW1sLADAYDJicnMTExETUbX/fpra2Fk+ePJFi\n34iIiIiIiMJEbYDGx8eRm5sb+lwQBIyPj8e0jsfjmXVbr9cLlUoFAFCpVPB6vXPfEyIiIiIior9Y\nFO2LKSkpMX2TYDAY0zqRvl9KSsqsPyc/Pz/mDBSfCxcuyB1BsVhb6bC20mFtpcPaSoN1lQ5rKx3W\nVhr5+flxrR+1AcrJyYHL5Qp97nK5IAhC1HXcbjcEQcD09PT/lufk5AD4edZnYmIC2dnZ+PTpE7Ky\nsiL+/NHR0bh2hoiIiIiIKJqol8CVlJRgZGQETqcTP378wKNHj1BeXh62Tnl5Oe7fvw8AsNlsyMzM\nhEqlirpteXk5Ojo6AAAdHR3Ys2ePFPtGREREREQUJuoZoEWLFuHGjRvYtWsXAoEAGhoaoFarcevW\nLU+aaT8AAAUdSURBVADAsWPHUFZWBovFgoKCAqSnp6O9vT3qtgBgMpmwb98+3L17F3l5eXj8+LHE\nu0lERERERASkBGN5Aw8REREREZEC/HUQ6nxw+fJlpKam4vPnz3JHUYxz585BrVZDp9OhoqICU1NT\nckdKerEMDab4uVwubN26FUVFRSguLsa1a9fkjqQogUAAer0eu3fvljuKokxOTqKyshJqtRoajQY2\nm03uSIphNptRVFQErVaLqqoqfP/+Xe5ISau+vh4qlQparTa0jMPqxRGptjz2mrtIdf0lnn5h3jdA\nLpcLVqsVa9eulTuKouzcuRODg4N49+4dCgsLYTab5Y6U1GIZGkyJSUtLw5UrVzA4OAibzYabN2+y\ntiJqbW2FRqPhHTdFdvLkSZSVlWFoaAgDAwOhS8BpbpxOJ+7cuQO73Y73798jEAigs7NT7lhJq66u\nDj09PWHLOKxeHJFqy2OvuYtUVyD+fmHeN0BnzpzBpUuX5I6hOEajEampP3/9BoMBbrdb5kTJLZah\nwZSY7OxsbNiwAQCQkZEBtVoNj8cjcyplcLvdsFgsOHz4cEzjDCg2U1NTePXqFerr6wH8fE/ssmXL\nZE6lDEuXLkVaWhr8fj9mZmbg9/tDd5il+G3ZsgXLly8PW8Zh9eKIVFsee81dpLoC8fcL87oB6urq\ngiAIWL9+vdxRFK2trQ1lZWVyx0hqsQwNprlzOp14+/YtDAaD3FEU4fTp02hpaQm9IJM4HA4HVq1a\nhbq6OmzcuBFHjhyB3++XO5YirFixAmfPnsWaNWuwevVqZGZmYseOHXLHUhQOq/83eOwlnkT6Bdlf\n9YxGI7Ra7f8+uru7YTabwwZG8T+U8Zmttk+fPg2tc/HiRSxevBhVVVUyJk1+vHxIej6fD5WVlWht\nbUVGRobccZLes2fPkJWVBb1ez+dWkc3MzMBut6OxsRF2ux3p6em8jEgkY2NjuHr1KpxOJzweD3w+\nHx4+fCh3LMWKNqyeEsdjL/H4/X40NTXF3S9EvQ32v2C1WiMu//DhAxwOB3Q6HYCfl2ps2rQJ/f39\nsw5OpXCz1faXe/fuwWKx4Pnz5/8okXLFMjSYEjc9PY29e/fi0KFDnBsmkr6+PnR3d8NiseDbt2/4\n8uULampqQnPdKHGCIEAQBGzevBkAUFlZyQZIJG/evEFpaSlWrlwJAKioqEBfXx+qq6tlTqYcsQ6r\np8Tw2EtcY2NjcDqdcfcLsp8Bmk1xcTG8Xi8cDgccDgcEQYDdbucDUSQ9PT1oaWlBV1cXlixZInec\npBfL0GBKTDAYRENDAzQaDU6dOiV3HMVoamqCy+WCw+FAZ2cntm3bxuZHJNnZ2cjNzcXw8DAAoLe3\nF0VFRTKnUoZ169bBZrPh69evCAaD6O3thUajkTuWonBYvXR47CU+rVabUL8wbxugP/EUrLhOnDgB\nn88Ho9EIvV6PxsZGuSMltd8H/2o0Guzfv593fRLJ69ev8eDBA7x8+RJ6vR56vT7iHWBobvgcK67r\n16+juroaOp0OAwMDOH/+vNyRFEGn06GmpgYlJSWh6/2PHj0qc6rkdfDgQZSWluLjx4/Izc1Fe3s7\nTCYTrFYrCgsL8eLFC5hMJrljJqU/a9vW1sZjLxH8quvw8HDob/Z3sb6WcRAqEREREREtGElzBoiI\niIiIiGiu2AAREREREdGCwQaIiIiIiIgWDDZARERERES0YLABIiIiIiKiBYMNEBERERERLRhsgIiI\niIiIaMH4D6ufVTgfPNIXAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc8f4950>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simple_csd = np.array([ -(meas_pot[i-1]+meas_pot[i+1] -2*meas_pot[i])/(elec_pos[i]-elec_pos[i-1])**2 for i in xrange(1, len(elec_pos)-1)])\n",
      "\n",
      "fig = plt.subplot(1,1,1)\n",
      "plot(elec_pos[1:len(elec_pos)-1], simple_csd, label=\"approximated csd\")\n",
      "plot(x, true_csd, label=\"true csd\")\n",
      "fig.legend(loc=(0.01,0.7))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "<matplotlib.legend.Legend at 0xdae07d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Pj4/l6elpbdiwIWW/+5WdjRs3ptyvUqWK9fnnn6fcf/31161Bg+7/JnPnzp2Wh4dHyv2w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\nAABglyg7AAAAAOzS/wNX0PTLOUfAdwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd67eed0>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "kCSD workflow"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_sources = 100\n",
      "\n",
      "source_space = linspace(min(elec_pos), max(elec_pos), n_sources)\n",
      "#source_space = linspace(-4.4, 4.4, n_sources)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#define the positions of the basis functions\n",
      "xmax=15.0\n",
      "xmin=-3.0\n",
      "estimation_area = linspace(xmin, xmax, n_sources)\n",
      "max_dist = xmax-xmin\n",
      "\n",
      "dist_density = 300\n",
      "h = 1.3*(elec_pos[1]-elec_pos[0])\n",
      "R = 0.15"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def pot_intarg(src, arg, current_pos, h, R, sigma, src_type):\n",
      "    \"\"\"\n",
      "    \"\"\"\n",
      "    if src_type == \"gaussian\":\n",
      "        y = (1./(2*sigma))*(sqrt((arg-current_pos)**2 + R**2) - abs(arg - current_pos)) * gauss_rescale(src, current_pos, h)\n",
      "        # for this formula look at formula (8) from Pettersen et al., 2006\n",
      "    if src_type == \"step\":\n",
      "        #TODO re-evaluate\n",
      "        y = (1./(2*sigma))*(sqrt((arg-current_pos)**2+R**2) - abs(arg-current_pos)) * (abs(src-current_pos)<0.5*h)\n",
      "    if src_type == \"wavelet\":\n",
      "        pass\n",
      "            \n",
      "    return y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gauss_rescale(x, mu, three_stdev):\n",
      "    \"\"\"\n",
      "    returns normalized gaussian scale function centered in mu, cut off farther than three_stdev from the center\n",
      "    \"\"\"\n",
      "    variance = (three_stdev/3.0)**2\n",
      "    g = 1./sqrt(2. * pi * variance) * exp( -(1./(2.*variance)) * (x-mu)**2 ) #* (abs(x-mu)<three_stdev)\n",
      "    return g"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def b_pot_quad(src, arg, h, R, sigma, src_type):\n",
      "    \"\"\"\n",
      "    \"\"\"\n",
      "    if src_type in [\"gaussian\", \"step\"]:\n",
      "        x = linspace(src - 4*h, src + 4*h, 150) #manipulate the resolution\n",
      "        y = [pot_intarg(src, arg, current_pos, h, R, sigma, src_type) for current_pos in x]\n",
      "        plot(x,y)\n",
      "        z = np.trapz(y, x)\n",
      "    else:\n",
      "        raise error(\"Source type not implemented!\")\n",
      "\n",
      "    return z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def create_dist_table(dist_dens, dist_max, h, R, sigma, src_type):\n",
      "    \"\"\"\n",
      "    Creates table of source contribution to overall potential as a function of distance\n",
      "    \"\"\"\n",
      "    dist_table = zeros(dist_dens+1)\n",
      "\n",
      "    for i in xrange(0, dist_dens+1):\n",
      "        dist_table[i] = b_pot_quad(0,(float(i)/dist_dens) * dist_max, h, R, sigma, src_type)\n",
      "    \n",
      "    return dist_table\n",
      "\n",
      "dist_table = create_dist_table(dist_density, max_dist, h, R, sigma, src_type = \"gaussian\")\n",
      "#print dist_table\n",
      "figure()\n",
      "plot(dist_table)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "[<matplotlib.lines.Line2D at 0xeae4310>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Jjz/+uFZXV7W4uChJmp+f14ULF/TVr35VQRDs0FcAAOxH0WUCklRtVTc2N+30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\naaSnryQ//0W8Xi1dXZAdl62Gm762NIDMsDB2VVezomIFD575IAsWLOD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g9NPLsVguDwk2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uhmEX/oLKxZc6PLQaVSqf7hqOFGpVKpVH+TQ4fgvikKHd4axSutXcn9Yge3rF7N2KtGBrzcgGH1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\nhBBCCCGEGBH+D1RsvV29qvUYAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xdbde750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PP73ffg8GXRg6koVeqV+VSiW/8zu/kzlz5uSv/uqvklikt6x6+ru/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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xdbde9d0>"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_b_pot_matrix(x, src, elec_pos, dist_table, dist_dens):\n",
      "    \"\"\" \n",
      "    compute the matrix of potentials generated by every source basis function\n",
      "    at every electrode position. This procedure can be optimized\n",
      "    bPotMatrix(j,k) = b_j(x_k) \n",
      "    with x_k electrode position, b_j j-th basis source from the paper\n",
      "    \"\"\"\n",
      "    n_elec = len(elec_pos)\n",
      "    \n",
      "    b_pot_matrix = zeros((n_sources, n_elec))\n",
      "    \n",
      "    for src_ind, current_src in enumerate(src):\n",
      "        for arg_ind, arg in enumerate(elec_pos):\n",
      "            r = abs(current_src - arg)\n",
      "            \n",
      "            #dist_table_ind = uint16(0.85 * r * float(dist_dens)/max_dist)\n",
      "            dist_table_ind = uint16(r * float(dist_dens)/max_dist)\n",
      "            #print dist_table_ind\n",
      "            b_pot_matrix[src_ind, arg_ind] = dist_table[dist_table_ind]\n",
      "            \n",
      "    return b_pot_matrix\n",
      "  \n",
      "b_pot_matrix = calculate_b_pot_matrix( estimation_area, source_space, elec_pos, dist_table, dist_density)\n",
      "#print bpm\n",
      "imshow(b_pot_matrix, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xee99248>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SMp70eDkGPIr39D8ZdB3NgwUK1CfTCDeC/RaGgodHkh07diA6Olr2PHLkyMAH2AUFBYiP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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc671210>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k_pot = dot(transpose(b_pot_matrix), b_pot_matrix) # Kernel matrix K from (2.14) from the paper"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imshow(k_pot, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xee66c68>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qKlJ1dbXS09OVmJioTZs2RewrSX/zN3+jjo4OLVq0SJI0Z84c/fznPz/n+Dzh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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd004550>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_b_src_matrix(x, src, h):\n",
      "    n_gx = len(x)\n",
      "\n",
      "    b_src_matrix = zeros((n_gx, n_sources))\n",
      "\n",
      "    for src_ind, curr_src in enumerate(src):\n",
      "        b_src_matrix[:, src_ind] = gauss_rescale(x, curr_src, h)\n",
      "\n",
      "    return b_src_matrix\n",
      "    \n",
      "b_src_matrix = calculate_b_src_matrix( estimation_area, source_space, h)\n",
      "\n",
      "imshow(b_src_matrix, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xe959a70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5e/nL7qlsRrq3ia/o4sadbnH79tZk9xPTvSlO/OC68W4DbnpfesTE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\nQRwAAJ9iEAcAwKcYxAEA8Kn/H+4PYCWNreT+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe0f1650>"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interp_cross = dot(b_src_matrix, b_pot_matrix) # cross-kernel matrix from (2.17) in the paper\n",
      "imshow(interp_cross, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xed6d560>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GAPxxcx8aGop3330X165dQ21tLfbt2zcsLQsJjQD44+beZDJh1qxZAIDw8HCk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\nqOb/AwweD4cXdaXwAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xee85950>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_b_interp_pot_matrix(x, src, dist_table, dist_dens):\n",
      "    \"\"\"\n",
      "    compute the matrix of potentials generated by every source basis function\n",
      "    at every electrode position. This procedure can be optimized\n",
      "    bPotMatrix(j,k) = b_j(x_k) \n",
      "    with x_k electrode position, b_j j-th basis source from the paper\n",
      "    \"\"\"\n",
      "    n_points = len(x)\n",
      "    dist_max = max(x) - min(x)\n",
      "\n",
      "    b_interp_pot_matrix = np.zeros((n_sources, n_points))\n",
      "    \n",
      "    for src_ind, current_src in enumerate(src):\n",
      "        for arg_ind, arg in enumerate(x):\n",
      "            r = abs(current_src - arg);\n",
      "        \n",
      "            #dist_table_ind = uint16(0.85*r*float(dist_dens)/dist_max)   \n",
      "            dist_table_ind = uint16(r*float(dist_dens)/dist_max)\n",
      "            b_interp_pot_matrix[src_ind, arg_ind] = dist_table[dist_table_ind]\n",
      "    \n",
      "    return b_interp_pot_matrix\n",
      "\n",
      "b_interp_pot_matrix = calculate_b_interp_pot_matrix( estimation_area, source_space, dist_table, dist_density)\n",
      "\n",
      "imshow(b_interp_pot_matrix, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xe6d2560>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yICIiIoYMiIiIhvhg3TsEnBAQERHddJ0TgmjRivEa6XOgd6xboReTSx7hcahz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\nKwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xed73f50>"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interp_pot = dot(transpose(b_interp_pot_matrix), b_pot_matrix)\n",
      "imshow(interp_pot, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xf4536c8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf031090>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import KFold, LeaveOneOut,ShuffleSplit\n",
      "\n",
      "lambd=0.0\n",
      "\n",
      "def calc_err(lambd, pot, k_pot, ind_test, ind_train):\n",
      "    k_train = k_pot[ind_train, ind_train]\n",
      "    \n",
      "    #print \"indtest\",ind_test\n",
      "    #print \"indtrain\", ind_train\n",
      "    pot_train = pot[ind_train]\n",
      "    pot_test = pot[ind_test]\n",
      "    \n",
      "    beta = np.dot(np.linalg.inv(k_train + lambd*identity(k_train.shape[0])) , pot_train)\n",
      "    #print \"beta.sh\", beta.shape\n",
      "    \n",
      "    #plot(beta)\n",
      "    \n",
      "    #k_cross = np.array([[k_pot[i,j] for i in ind_test] for j in ind_train])\n",
      "    #k_cross = k_pot[np.array([ind_test, ind_train])]\n",
      "    k_cross = k_pot[ind_test][:,ind_train]\n",
      "    #imshow(k_cross, interpolation=\"none\")\n",
      "    \n",
      "    pot_est = np.zeros(size(transpose(ind_test)))\n",
      "    \n",
      "    pot_est = dot(k_cross, beta)\n",
      "    \n",
      "    #plot(pot_est, pot_test)\n",
      "    print \"pottest:\", pot_test, \"pot_est:\", pot_est\n",
      "    err = linalg.norm(pot_test - pot_est)\n",
      "    \n",
      "    return err\n",
      "\n",
      "#print calc_err(0.0, estimate_pots(), k_pot, np.array([range(0,len(elec_pos)-2)]), np.array([len(elec_pos)-2]))\n",
      "\n",
      "\n",
      "def cross_validation(lambd, pot, k_pot, n_folds):\n",
      "    errors = []\n",
      "    n = len(elec_pos)\n",
      "    #print \"n:\",n\n",
      "    kf = KFold(n, n_folds=n_folds, indices=True)\n",
      "    #kf = LeaveOneOut(n, indices=True)\n",
      "    #kf = ShuffleSplit(5, n_iter=15, test_size=0.25, indices=True)\n",
      "    for ind_train, ind_test in kf:\n",
      "        #ind_test, ind_train = np.random.permutation(ind_test), np.random.permutation(ind_train)\n",
      "        err = calc_err(lambd, pot, k_pot, ind_test, ind_train)\n",
      "        #print err\n",
      "        errors.append(err)\n",
      "        \n",
      "        #print(\"%s %s\" % (ind_train, ind_test))\n",
      "        \n",
      "    error = mean(errors)\n",
      "    print \"l=\", lambd, \", err=\", error\n",
      "    return error\n",
      "\n",
      "def choose_lambda(lambdas, n_folds, n_iter):\n",
      "    \"\"\"finds the optimal regularization parameter lambda for Tikhonov regularization\"\"\"\n",
      "    n = len(lambdas)\n",
      "    errors = np.zeros(n)\n",
      "    errors_iter = np.zeros(n_iter)\n",
      "    for i, lambd in enumerate(lambdas):\n",
      "        for j in xrange(n_iter):\n",
      "            errors_iter[j] = cross_validation(lambd, meas_pot, k_pot, n_folds)\n",
      "            #errors_iter[j] = calc_cv_error(lambd, n_folds)\n",
      "        errors[i] = mean(errors_iter)\n",
      "    fig = pyplot.figure()\n",
      "    ax = fig.add_subplot(1,1,1)\n",
      "    plot(lambdas, errors)\n",
      "    ax.set_xscale('log')\n",
      "    ax.set_yscale('log')\n",
      "    return lambdas[errors == min(errors)]\n",
      "\n",
      "lambdas = np.array([10./j**2 for j in xrange(1,60) ])\n",
      "#lambdas = np.array([0,1])\n",
      "lambd = choose_lambda(lambdas, n_folds = 5,  n_iter=1)[0]\n",
      "#lambd = 0.0\n",
      "print \"lambda =\", lambd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "pottest: [ 0.00296697  0.00468031] pot_est: [  5.13806253e-06   8.68440536e-06]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [  4.93689518e-06   5.03892972e-06]\n",
        "pottest: [ 0.01113273] pot_est: [  8.63698763e-06]\n",
        "pottest: [ 0.01176185] pot_est: [  6.44005818e-06]\n",
        "pottest: [ 0.00767375] pot_est: [  5.52222352e-06]\n",
        "l= 10.0 , err= 0.0116454618352\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [  2.03703207e-05   3.44171111e-05]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [  1.95372037e-05   1.99989385e-05]\n",
        "pottest: [ 0.01113273] pot_est: [  3.42617946e-05]\n",
        "pottest: [ 0.01176185] pot_est: [  2.55108432e-05]\n",
        "pottest: [ 0.00767375] pot_est: [  2.18738925e-05]\n",
        "l= 2.5 , err= 0.0116233523446\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [  4.51664472e-05   7.62638239e-05]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [  4.31858417e-05   4.44216441e-05]\n",
        "pottest: [ 0.01113273] pot_est: [  7.60429023e-05]\n",
        "pottest: [ 0.01176185] pot_est: [  5.64877419e-05]\n",
        "pottest: [ 0.00767375] pot_est: [  4.84320279e-05]\n",
        "l= 1.11111111111 , err= 0.011587378367\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [  7.86916946e-05   1.32753945e-04]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [  7.49089149e-05   7.75812661e-05]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00013268]\n",
        "pottest: [ 0.01176185] pot_est: [  9.82375196e-05]\n",
        "pottest: [ 0.00767375] pot_est: [  8.42276376e-05]\n",
        "l= 0.625 , err= 0.0115387715222\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00011987  0.000202  ]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00011344  0.00011854]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00020252]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00014932]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00012804]\n",
        "l= 0.4 , err= 0.0114791073001\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00016748  0.00028182]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00015732  0.00016619]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00028367]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00020809]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00017848]\n",
        "l= 0.277777777778 , err= 0.0114101842978\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00022019  0.00036991]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00020496  0.00021937]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00037416]\n",
        "pottest: [ 0.01176185]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " pot_est: [ 0.00027283]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00023415]\n",
        "l= 0.204081632653 , err= 0.0113339012848\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.0002767   0.00046395]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00025474  0.00027686]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00047206]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00034186]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00029364]\n",
        "l= 0.15625 , err= 0.0112521457687\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00033574  0.00056172]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00030508  0.0003375 ]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00057554]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00041359]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.0003557]\n",
        "l= 0.123456790123 , err= 0.011166703392\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00039617  0.00066119]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00035453  0.00040019]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00068296]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.0004866]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00041921]\n",
        "l= 0.1 , err= 0.0110791925941\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00045696  0.00076057]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00040175  0.00046393]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00079292]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00055966]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00048322]\n",
        "l= 0.0826446280992 , err= 0.0109910246641\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00051725  0.00085834]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00044557  0.00052787]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00090426]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00063179]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00054699]\n",
        "l= 0.0694444444444 , err= 0.0109033862498\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00057633  0.00095324]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00048502  0.00059126]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00101604]\n",
        "pottest: [ 0.01176185]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " pot_est: [ 0.00070216]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00060991]\n",
        "l= 0.0591715976331 , err= 0.0108172397124\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00063364  0.00104427]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00051928  0.0006535 ]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00112758]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00077016]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00067158]\n",
        "l= 0.0510204081633 , err= 0.0107333362349\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00068873  0.00113068]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.0005477   0.00071412]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00123835]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00083537]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00073169]\n",
        "l= 0.0444444444444 , err= 0.0106522369432\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00074131  0.00121192]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00056981  0.00077275]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00134801]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00089748]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00079006]\n",
        "l= 0.0390625 , err= 0.0105743381043\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00079116  0.00128762]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00058524  0.00082912]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00145636]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00095632]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.0008466]\n",
        "l= 0.0346020761246 , err= 0.0104998974362\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00083816  0.00135756]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00059375  0.00088305]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.0015633]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.0010118]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00090129]\n",
        "l= 0.0308641975309 , err= 0.0104290594943\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00088225  0.00142163]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00059519  0.00093444]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00166882]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00106391]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00095416]\n",
        "l= 0.0277008310249 , err= 0.010361878885\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00092343  0.00147984]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00058949  0.00098322]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00177297]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00111272]\n",
        "pottest: [ 0.00767375]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " pot_est: [ 0.00100527]\n",
        "l= 0.025 , err= 0.0102983406665\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00096173  0.00153225]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00057665  0.0010294 ]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00187587]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.0011583]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00105473]\n",
        "l= 0.0226757369615 , err= 0.0102383777243\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00099722  0.00157897]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.0005567  0.001073 ]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00197764]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00120078]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00110264]\n",
        "l= 0.0206611570248 , err= 0.0101818851949\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00102999  0.00162017]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00052972  0.00111409]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00207844]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00124029]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00114913]\n",
        "l= 0.0189035916824 , err= 0.0101287321729\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00106014  0.00165602]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00049582  0.00115274]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00217845]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00127699]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00119433]\n",
        "l= 0.0173611111111 , err= 0.0100787710243\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.0010878   0.00168673]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00045513  0.00118904]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00227784]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00131103]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00123836]\n",
        "l= 0.016 , err= 0.0100318446511\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00111307  0.00171251]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00040777  0.00122309]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.0023768]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00134257]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00128135]\n",
        "l= 0.0147928994083 , err= 0.00998779204739\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00113607  0.00173357]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00035389  0.00125501]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00247549]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00137175]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00132343]\n",
        "l= 0.0137174211248 , err= 0.00994645245792\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00115693  0.00175011]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00029363  0.00128489]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.0025741]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00139873]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00136472]\n",
        "l= 0.0127551020408 , err= 0.00990766841169\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00117577  0.00176234]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ 0.00022715  0.00131285]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00267278]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00142366]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00140531]\n",
        "l= 0.0118906064209 , err= 0.00987128786569\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00119268  0.00177046]\n",
        "pottest: [ 0.00903358  0.0202294 ]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " pot_est: [ 0.00015458  0.001339  ]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00277169]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00144665]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00144532]\n",
        "l= 0.0111111111111 , err= 0.00983716565287\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00120779  0.00177466]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [  7.60643815e-05   1.36343547e-03]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00287097]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00146786]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00148485]\n",
        "l= 0.0104058272633 , err= 0.00980516439465\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00122119  0.00177512]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ -8.26025112e-06   1.38625998e-03]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00297077]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00148738]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00152399]\n",
        "l= 0.009765625 , err= 0.00977515500684\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00123298  0.00177201]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [ -9.82663115e-05   1.40757005e-03]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00307122]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00150534]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00156281]\n",
        "l= 0.00918273645546 , err= 0.00974701690203\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00124325  0.00176548]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00019383  0.00142746]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00317243]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00152184]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.0016014]\n",
        "l= 0.00865051903114 , err= 0.00972063796931\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00125208  0.00175568]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00029484  0.00144601]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00327451]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00153698]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00163984]\n",
        "l= 0.00816326530612 , err= 0.00969591439487\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00125956  0.00174276]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00040118  0.0014633 ]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00337758]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00155084]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00167818]\n",
        "l= 0.00771604938272 , err= 0.00967275037207\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00126575  0.00172685]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00051275  0.00147942]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00348172]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00156352]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00171648]\n",
        "l= 0.0073046018992 , err= 0.00965105773836\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127073  0.00170806]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00062946  0.00149443]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00358703]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00157508]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00175481]\n",
        "l= 0.00692520775623 , err= 0.00963075556727\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127456  0.00168651]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.0007512  0.0015084]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00369359]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.0015856]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00179322]\n",
        "l= 0.00657462195924 , err= 0.00961176973645\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.0012773  0.0016623]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00087791  0.0015214 ]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00380147]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00159514]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00183175]\n",
        "l= 0.00625 , err= 0.00959403248723\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.001279    0.00163553]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.0010095   0.00153348]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00391074]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00160376]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00187044]\n",
        "l= 0.0059488399762 , err= 0.00957748198679\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127972  0.0016063 ]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.0011459   0.00154469]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00402146]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00161153]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00190935]\n",
        "l= 0.00566893424036 , err= 0.00956206190094\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127949  0.00157467]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00128703  0.00155509]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00413371]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00161848]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00194849]\n",
        "l= 0.00540832882639 , err= 0.0095477209826\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127838  0.00154074]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00143285  0.00156473]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00424752]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00162467]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00198791]\n",
        "l= 0.0051652892562 , err= 0.00953441267966\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127641  0.00150456]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00158327  0.00157364]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00436295]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00163015]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00202765]\n",
        "l= 0.00493827160494 , err= 0.0095220947641\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127362  0.00146621]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00173826  0.00158188]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00448006]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00163494]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00206772]\n",
        "l= 0.0047258979206 , err= 0.00951072898341\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00127006  0.00142575]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00189776  0.00158947]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00459887]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.0016391]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00210815]\n",
        "l= 0.00452693526483 , err= 0.00950028073453\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00126574  0.00138324]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00206173  0.00159646]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00471944]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00164265]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00214897]\n",
        "l= 0.00434027777778 , err= 0.00949071876011\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.0012607   0.00133873]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00223011  0.00160288]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00484179]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00164563]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00219021]\n",
        "l= 0.00416493127863 , err= 0.00948201486622\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00125498  0.00129226]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00240287  0.00160875]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00496597]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00164806]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00223187]\n",
        "l= 0.004 , err= 0.00947414366086\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00124858  0.00124388]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00257997  0.00161411]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.005092]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00164997]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00227399]\n",
        "l= 0.00384467512495 , err= 0.00946708231197\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00124155  0.00119363]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00276137  0.00161898]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00521991]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.0016514]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00231657]\n",
        "l= 0.00369822485207 , err= 0.00946081032396\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00123389  0.00114156]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00294704  0.00162339]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00534973]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00165235]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00235964]\n",
        "l= 0.00355998576006 , err= 0.00945530933156\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00122564  0.0010877 ]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00313695  0.00162735]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00548149]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00165286]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00240321]\n",
        "l= 0.00342935528121 , err= 0.00945056290973\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00121681  0.00103209]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00333107  0.0016309 ]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.0056152]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00165294]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.0024473]\n",
        "l= 0.00330578512397 , err= 0.00944655639861\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00120741  0.00097474]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00352938  0.00163406]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00575089]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00165261]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00249191]\n",
        "l= 0.0031887755102 , err= 0.00944327674231\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00119747  0.0009157 ]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00373184  0.00163683]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00588859]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00165189]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00253707]\n",
        "l= 0.00307787011388 , err= 0.00944071234059\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.001187  0.000855]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00393844  0.00163924]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.0060283]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.0016508]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00258277]\n",
        "l= 0.00297265160523 , err= 0.00943885291235\n",
        "pottest: [ 0.00296697  0.00468031] pot_est: [ 0.00117601  0.00079264]\n",
        "pottest: [ 0.00903358  0.0202294 ] pot_est: [-0.00414915  0.00164131]\n",
        "pottest: [ 0.01113273] pot_est: [ 0.00617005]\n",
        "pottest: [ 0.01176185] pot_est: [ 0.00164935]\n",
        "pottest: [ 0.00767375] pot_est: [ 0.00262904]\n",
        "l= 0.00287273771905 , err= 0.00943768937015\n",
        "lambda ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.00287273771905\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAID0WhE+RVEs6ocTAQAAOZRluSif++k1PgAAQOt5jQ8AAMAVCB8AACA94QMAAKQnfAAA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\nQHr/D17ukZ1iigt2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xee69e10>"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "estimation_table = interp_pot\n",
      "#lambd=0.0\n",
      "\n",
      "k_inv = linalg.inv(k_pot + lambd * identity(k_pot.shape[0]))\n",
      "imshow(k_inv, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xfd5c518>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AgA/RQgcAwINooQMAABdQgQMAfIgWOgAAHuT9BE4LHQAAD6ICBwD4kA8WsR0+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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf5a3290>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def estimate_pots():\n",
      "    estimation_table = interp_pot\n",
      "    \n",
      "    k_inv = linalg.inv(k_pot + lambd *identity(k_pot.shape[0]))\n",
      "    beta = dot(k_inv, meas_pot)                 \n",
      "    output = dot(estimation_table, beta)\n",
      "    return output\n",
      "\n",
      "output = estimate_pots()\n",
      "fig, ax = plt.subplots()\n",
      "\n",
      "\n",
      "output_area = linspace(min(estimation_area), max(estimation_area), len(output))\n",
      "ax.plot(x, pots, label = \"true pot\")\n",
      "ax.plot(elec_pos, meas_pot, label = \"interpolated measurements\")\n",
      "ax.plot(output_area, output*max(pots)/max(output), label = \"kCSD estimated pot\")\n",
      "#ax.set_ylim([min(pots) ,max(pots)])\n",
      "ax.legend(loc=(0.01,0.7))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "<matplotlib.legend.Legend at 0xffa9450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hac2T1FC/fn06d+7Ma6+9ltGhyCMmM3yW/vtf2LED7ppBPHktWkDz5vDmm4kW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\nGH8BPXIFuKgJw0UAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xff75910>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "estimation_table = interp_cross\n",
      "\n",
      "k_inv = linalg.inv(k_pot + lambd *identity(k_pot.shape[0]))\n",
      "imshow(k_inv, interpolation=\"none\")\n",
      "colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0xfd557e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AgA/RQgcAwINooQMAABdQgQMAfIgWOgAAHuT9BE4LHQAAD6ICBwD4kA8WsR0+\nfFhlZWXKy8tTMBjUjh07EhEXAAAOOmXj5o4+K/B7771XN954o373u9+ps7NTx48fT0RcAACgF70m\n8CNHjmjbtm1atWrV6Z1TUjR48OCEBAYAgHMMb6G3tLRo2LBhqqys1Pjx43X33XfrxIkTiYoNAACH\neL+F3msC7+zs1K5du7Rw4ULt2rVLV1xxhWpra8/f8bXqs9uHISfiBAD0Y9u6ulXTeXaD83ptoQcC\nAQUCAU2cOFGSVFZWduEEXlztRGwAAI+YlmRp2r+UhI9G+nsSN7yFnp6erszMTDU1NUmSNm3apDFj\nxiQkMAAAnOP9Fnqfq9CXL1+uuXPnKhwOa9SoUVq5cmUi4gIAAL3oM4GPGzdOO3fuTEQsAAAkiPdb\n6DyJDQDgQzwLHQAAuIAKHADgQ95voVOBAwB8yL1V6MuXL1deXp7Gjh2rBx54oOf1mpoa5eTkKDc3\nVxs2bOhzHCpwAAASZPPmzVq3bp3effddpaam6uOPP5YkNTY2au3atWpsbFR7e7uuv/56NTU1KSnp\n4nU2FTgAwIc6bdyi9+STT2rx4sVKTU2VJA0bNkySVFdXp4qKCqWmpiorK0vZ2dlqaGjodSwSOAAA\nCdLc3KytW7dqypQpKioq0ltvvSVJ6ujoUCAQ6NkvEAiovb2917FooQMAfCie28j+LOm9i75bUlKi\n/fv3n/f6ww8/rM7OTv3jH//Qjh07tHPnTpWXl+vDDz+84DiWZfUaBQkcAOBD8STwr36+nfHCOe9u\n3Ljxoj/55JNP6pZbbpEkTZw4UUlJSTp48KAyMjLU2tras19bW5syMjJ6jYIWOgAACTJnzhy9/vrr\nkqSmpiaFw2FdffXVKi0t1Zo1axQOh9XS0qLm5mZNmjSp17GowAEAPuTOfeBVVVWqqqpSfn6+Lrvs\nMj333HOSpGAwqPLycgWDQaWkpGjFihV9ttCt7u7uuD7zzbIs6b/7+8fGxcH0UxzvP8ugdyfdDsBh\nR90OwDlJy7rcDsFRhy4z+4/LVZ91K8704pjTidHOD+aqdGWutNABAPAgs08BAQC4IO+3H0ngAAAf\n4tPIAACAC6jAAQA+RAsdAAAPooUOAABc4L0E/mHI7Qic9UHI7QicZfrvb2/I7Qic1RZyOwLHdHeH\n3A7BUdu6+uc92e5x59PI7OS9BN4ScjsCZ5me4Ez//f0t5HYEzjI5gWuL2yE46g2zn4tzCU7ZuLnD\newkcAACx0cmVAAAE8klEQVTYs4htfLodo0SnY5B0TQKPp+QEHkufzy8tgQeMJPBYcuH3F07gsSR1\nfEm65poEHvB4Ao8lqeNK6ZpA3/vZIWl8Yo5zRnuHlJHA311SamInaLV2KCkzgRPc8XbijnVJvL8K\nPe5noRcVFWnLFrNbTwCA2EyfPl2hUMjtMC6orw8JidWQIUN06NAhW8eMRtwJHAAAJB7XwAEA8CAS\nOAAAHkQCBwDAgzyVwOvr65Wbm6ucnBw9+uijbodjq6qqKqWlpSk/P9/tUBzR2tqqGTNmaMyYMRo7\ndqyWLVvmdki2OXnypCZPnqyCggIFg0EtXrzY7ZAcEYlEVFhYqNmzZ7sdiu2ysrL0ta99TYWFhZo0\naZLb4dju8OHDKisrU15enoLBoHbs2OF2SLCBZxaxRSIRffWrX9WmTZuUkZGhiRMnavXq1crLy3M7\nNFts27ZNgwYN0l133aU//elPbodju/3792v//v0qKCjQsWPHdO211+qll14y5vd34sQJDRw4UJ2d\nnZo6dap+/vOfa+rUqW6HZatf/OIXevvtt3X06FGtW7fO7XBsNXLkSL399tsaOnSo26E4Yt68eZo+\nfbqqqqrU2dmp48ePa/DgwW6HhTh5pgJvaGhQdna2srKylJqaqttuu011dXVuh2WbadOmaciQIW6H\n4Zj09HQVFBRIkgYNGqS8vDx1dHS4HJV9Bg4cKEkKh8OKRCLGJYK2tjatX79eCxYskEfO+WNm6ryO\nHDmibdu2qaqqSpKUkpJC8jaEZxJ4e3u7MjMze74PBAJqb293MSJcqr1792r37t2aPHmy26HYpqur\nSwUFBUpLS9OMGTMUDAbdDslW9913nx577DElJXnmT0ZMLMvS9ddfrwkTJujpp592OxxbtbS0aNiw\nYaqsrNT48eN1991368SJE26HBRt45n+j3Tfewx3Hjh1TWVmZli5dqkGDBrkdjm2SkpK0Z88etbW1\naevWrf32ARaX4pVXXtHw4cNVWFhobJW6fft27d69W6+++qp++ctfatu2bW6HZJvOzk7t2rVLCxcu\n1K5du3TFFVeotrbW7bBgA88k8IyMDLW2tvZ839raqkAgQc90hC1OnTqlW2+9VXfccYfmzJnjdjiO\nGDx4sG666Sa99dZbbodimzfffFPr1q3TyJEjVVFRoddff1133XWX22HZasSIEZKkYcOG6eabb1ZD\nQ4PLEdknEAgoEAho4sSJkqSysjLt2rXL5ahgB88k8AkTJqi5uVl79+5VOBzW2rVrVVpa6nZYiFJ3\nd7fmz5+vYDCoRYsWuR2OrQ4ePKjDhw9Lkj799FNt3LhRhYWFLkdln0ceeUStra1qaWnRmjVrdN11\n1+m5555zOyzbnDhxQkePHpUkHT9+XBs2bDDqbpD09HRlZmaqqalJkrRp0yaNGTPG5ahgB1s+zCQR\nUlJS9MQTT+iGG25QJBLR/PnzjVnBLEkVFRXasmWLPvnkE2VmZuqhhx5SZWWl22HZZvv27Xr++ed7\nbtWRpJqaGn3zm990ObL47du3T/PmzVNXV5e6urp05513qri42O2wHGPa5awDBw7o5ptvlnS63Tx3\n7lzNnDnT5ajstXz5cs2dO1fhcFijRo3SypUr3Q4JNvDMbWQAAOAsz7TQAQDAWSRwAAA8iAQOAIAH\nkcABAPAgEjgAAB5EAgcAwINI4AAAeND/B2Umr13GWIm9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf5a6d50>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def estimate_csd():\n",
      "    estimation_table = interp_cross\n",
      "\n",
      "    k_inv = linalg.inv(k_pot + lambd *identity(k_pot.shape[0]))\n",
      "    beta = dot(k_inv, meas_pot)                 \n",
      "    csd = dot(estimation_table, beta)\n",
      "    return csd\n",
      "\n",
      "output = estimate_csd()\n",
      "\n",
      "plot(linspace(min(estimation_area), max(estimation_area), len(output)), output/np.linalg.norm(output), label = \"kCSD estimated CSD\") #csd calculated with kCSD\n",
      "plot(x, true_csd/np.linalg.norm(true_csd), label = \"true CSD\")\n",
      "\n",
      "\n",
      "legend(loc = (0.01, 0.75))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "<matplotlib.legend.Legend at 0xfec75d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LFy5UhQoV1LBhQ8XHxysmJibrObt379att96qKVOmqHHjxvk6VrJHfHKIAMDP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\nvX82Xx36isd8H+PNpm/S2Lux2bEkiyw9spTh64bz2sOv8Wa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       "text": [
        "<matplotlib.figure.Figure at 0xfd6bd50>"
       ]
      }
     ],
     "prompt_number": 88
    }
   ],
   "metadata": {}
  }
 ]
}