gistfile1.json

{
 "metadata": {
  "language": "Julia",
  "name": "Heatmap"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "using Base.Graphics\nusing Cairo\nusing Tk",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#in this grid the gui is set up\n#width and height of the image\nwidth=1000\nheight=1000\n\n#resolution of the colour grid\ndensity=1000\n\n#some Tk code to create a window and a cairo context\nwin = Toplevel(\"Heatmap\", width, height)\ncanvas= Canvas(win)\npack(canvas, expand=true, fill=\"both\")\nctx=getgc(canvas)\n\n#this is interesting: we adress the window pixels from 0 to width\n#and from 0 to height with coordinates from 0 to density\nset_coords(ctx, 0, 0, width, height, 0, density, 0 , density)\n\n#blue colour\nset_source_rgb(ctx,0,0,0.5)\npaint(ctx)\nreveal(canvas)\nTk.update()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": "\"\""
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#in this grid i can test the colour function\nusing PyPlot\n\nnumpoints=100\ndelta_colour=0.2\noffset=0.05\n\nfunction effect(r)\n    e^(-r)*delta_colour-offset\nend\n\nx=linspace(0,30,10000)\ny=[effect(r) for r in x]\nplot(x,y,color=\"red\", linewidth=1)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6EL4AAAAuQfjWJbjiGwjYPQkAAACaiPCtS1KSZBhcvQ0AAMAFCN+6dOhg3rLdAQAAIOIRvnVJSjJvObMDAABAxCN860L4AgAAuAbhW5dg+LLVAQAAIOIRvnVp21Zq0YIVXwAAABcgfOvi8ZirvoQvAABAxCN868PV2wAAAFyB8K1Phw6s+AIAALgA4VsftjoAAAC4AuFbH7Y6AAAAuALhWx+2OgAAALgC4VsfVnwBAABcgfCtD3t8AQAAXIHwrU+HDlJFhXTmjN2TAAAAoAkI3/pw2WIAAABXIHzrEwxftjsAAABENMK3Ph06mLeELwAAQEQjfOvDVgcAAABXIHzrw4ovAACAKxC+9YmLk9q2JXwBAAAiHOHbEFzEAgAAIOIRvg3BRSwAAAAiHuHbEIQvAABAxCN8G6JDB7Y6AAAARDjCtyFY8QUAAIh4hG9DEL4AAAARj/BtiA4dCF8AAIAIR/g2RHKydOKEdP683ZMAAADgEhG+DeHzSYbBF9wAAAAiGOHbEMnJ5u2xY/bOAQAAgEtG+DaEz2feHj1q7xwAAAC4ZIRvQwRXfAlfAACAiEX4NkSHDpLHw1YHAACACEb4NkRsrOT1suILAAAQwQjfhkpOJnwBAAAiGOHbUMnJbHUAAACIYIRvQ/l8rPgCAABEMMK3odjqAAAAENEI34by+djqAAAAEMEI34ZixRcAACCiEb4NlZwsHT8uXbhg9yQAAAC4BIRvQ/l8kmGY8QsAAICIQ/g2FJctBgAAiGiEb0P5fOYtX3ADAACISIRvQ7HiCwAAENEI34ZKSjJvCV8AAICIRPg2VGys5PWy1QEAACBCEb6Nwbl8AQAAIhbh2xg+H+ELAAAQoQjfxkhOZqsDAABAhCJ8G4OtDgAAABGL8G0MtjoAAABELMK3MdjqAAAAELEI38bw+aTSUunCBbsnAQAAQCMRvo2RnCwZhnT8uN2TAAAAoJEI38bo2NG8PXLE3jkAAADQaIRvY3TqZN4SvgAAABGH8G2MYPgePmzvHAAAAGg0wrcxvF4pNpbwBQAAiECEb2N4POY+X8IXAAAg4hC+jdWpE+ELAAAQgQjfxurUiS+3AQAARCDCt7FY8QUAAIhIhG9jEb4AAAARifBtLL7cBgAAEJEsD9/y8nIVFBTI7/crPj5eGRkZWrp0aYMee/jwYd11113q2LGjEhISdM0112jdunXV7jdixAjFxMRU+7npppvC/XLMFd/SUunbb8P/twEAAGCZWKufICcnR1u2bNHcuXPVp08fFRYWKi8vT4FAQHl5ebU+7uzZs8rKylJZWZmef/55derUSS+88IKys7NVXFys4cOHV7l/z549VVhYWOWY1+sN/wsKXsTi2DGpc+fw/30AAABYwtLwXbVqlYqLi1VUVKSJEydKkjIzM3XgwAFNnz5dEydOVExMzYvOr7zyinbu3KkPP/xQgwcPlmSu7F511VWaMWOGNmzYUOX+8fHxuvrqq618OabKV28jfAEAACKGpVsdVqxYocTEROXm5lY5np+fr0OHDmnjxo11PrZv376h6JWkFi1aaNKkSdq0aZMOHTpU5f6GYYR3+Npw2WIAAICIZGn47tixQ6mpqdVWddPS0iRJO3furPOx6enp1Y4HH/vxxx9XOb537175fD7FxcWpV69eevjhh3XmzJmmvoTqCF8AAICIZOlWh2PHjqlXr17VjiclJYV+X5vS0tLQ/ep77LBhw5SXl6e+ffuqoqJCq1at0lNPPaUPPvhA69evl8fjqfV5CgoKqu0FzsvLq33/cUKCFB/PRSwAAADCqKioSEVFRVWOnThxIqzPYfmX25rDnDlzqvx7dna2unfvrmnTpmnlypW69dZba33svHnzNGDAgMY9IefyBQAACKuaFh63bt2qgQMHhu05LN3q4PP5alzVLS0tDf2+rscG79fYx0rSpEmTJKnOfcSXjPAFAACIOJaGb3p6unbt2qVAIFDl+Pbt2yVJ/fr1q/WxaWlp2rZtW7XjDXmsZPGX3QhfAACAiGNp+I4bN07l5eVatmxZleMLFy6U3++vcsaGmh67e/dubdq0KXTs/PnzWrx4sYYMGaLO9ZxK7LXXXpMkDR06tAmvoBaELwAAQMSxdI9vdna2Ro0apalTp6qsrEw9e/ZUUVGR1qxZo8LCwtCXzqZMmaJFixZp37596tatmyTp7rvv1vz585Wbm6snn3xSHTt21Isvvqg9e/aouLg49Bzvv/++nnjiCY0fP14pKSmqqKjQ6tWr9fLLLysrK0tjx44N/wvr2FH605/C/3cBAABgGcu/3LZ8+XLNmjVLs2fPVmlpqVJTU7VkyRJNmDAhdJ9AIKBAIFBle0LLli21du1azZgxQ/fff79Onz6tjIwMrV69WsOGDQvdr2vXroqJidGjjz6qo0ePyuPxqE+fPpozZ44efPBBa14UK74AAAARx2M025UfnCX4LcGSkpLGn9Xht7+VfvhD6fRp89RmAAAACLsm9VoNLN3j61rBi1hwLl8AAICIQfheCq7eBgAAEHEI30sRDN+vv7Z3DgAAADQY4XspCF8AAICIQ/heirg4KTlZ+uoruycBAABAAxG+l6pzZ+nLL+2eAgAAAA1E+F6qLl1Y8QUAAIgghO+l6tyZ8AUAAIgghO+lInwBAAAiCuF7qdjjCwAAEFEI30vVpYv0zTdSebndkwAAAKABCN9L1bmzect2BwAAgIhA+F4qwhcAACCiEL6XKhi+7PMFAACICITvpfJ6pVatWPEFAACIEITvpfJ4OKUZAABABCF8m4LwBQAAiBiEb1NwLl8AAICIQfg2BSu+AAAAEYPwbYouXQhfAACACEH4NkXnztLhw9KFC3ZPAgAAgHoQvk3RubMZvUeP2j0JAAAA6kH4NgVXbwMAAIgYhG9TdOli3hK+AAAAjkf4NsVll5m3hC8AAIDjEb5N0aqV1KGDdOiQ3ZMAAACgHoRvU/n90hdf2D0FAAAA6kH4NhXhCwAAEBEI36by+9nqAAAAEAEI36ZixRcAACAiEL5N5febZ3Xg6m0AAACORvg2ld9vRu/XX9s9CQAAAOpA+DaV32/est0BAADA0Qjfpura1bwlfAEAAByN8G2qjh2luDjCFwAAwOEI36aKiZG6dCF8AQAAHI7wDQfO5QsAAOB4hG84cC5fAAAAxyN8w4HwBQAAcDzCNxwIXwAAAMcjfMPB75fKyqTycrsnAQAAQC0I33DgIhYAAACOR/iGA+ELAADgeIRvOHD1NgAAAMcjfMOhTRvJ6+VcvgAAAA5G+IaL3y99/rndUwAAAKAWhG+4dOtG+AIAADgY4RsuKSnSwYN2TwEAAIBaEL7hQvgCAAA4GuEbLikp0tGjUkWF3ZMAAACgBoRvuKSkmLd//7u9cwAAAKBGhG+4BMOX7Q4AAACORPiGi98veTyELwAAgEMRvuHSsqXUuTPhCwAA4FCEbzhxZgcAAADHInzDifAFAABwLMI3nFJSOKsDAACAQxG+4RRc8TUMuycBAADARQjfcEpJkc6cMS9kAQAAAEchfMOJc/kCAAA4FuEbToQvAACAYxG+4eTzSa1bE74AAAAORPiGk8fDKc0AAAAcivANN8IXAADAkQjfcCN8AQAAHInwDbcePaT9++2eAgAAABchfMOtRw/pyBGpvNzuSQAAAFAJ4RtuPXqYt6z6AgAAOArhG26ELwAAgCMRvuHWubN5Ll/CFwAAwFEI33DzeKTu3QlfAAAAhyF8rdCjh7Rvn91TAAAAoBLC1wpXXMGKLwAAgMMQvlYInsvXMOyeBAAAAP9A+FqhRw/pm2+ko0ftngQAAAD/QPhagVOaAQAAOA7ha4Vg+PIFNwAAAMcgfK3g9Zo/rPgCAAA4BuFrleAX3AAAAOAIhK9VCF8AAABHIXytcsUV7PEFAABwEMLXKldcIR08KH37rd2TAAAAQISvdXr1ks6flw4csHsSAAAAiPC1Tu/e5u2ePfbOAQAAAEmEr3W6dZNatiR8AQAAHILwtUqLFlLPnoQvAACAQxC+VurdW/r0U7unAAAAgAhfa/XuzYovAACAQxC+VurVS/rsM05pBgAA4ACEr5V695YuXOAKbgAAAA5A+FqJU5oBAAA4BuFrpX/6J6l1a8IXAADAAQhfK8XEmKc048wOAAAAtiN8rcaZHQAAAByB8LUa4QsAAOAIhK/VeveWDhyQzp2zexIAAICoRvharXdvKRCQ9u61exIAAICoRvhaLTXVvN21y945AAAAohzha7VOnaQOHQhfAAAAmxG+VvN4zFVfwhcAAMBWhG9z6NuX8AUAALAZ4dscUlOl3bvNL7kBAADAFoRvc0hNlU6flv7+d7snAQAAiFqEb3PgzA4AAAC2I3ybw+WXS61bE74AAAA2InybQ4sW0ve+R/gCAADYiPBtLsEvuAEAAMAWlodveXm5CgoK5Pf7FR8fr4yMDC1durRBjz18+LDuuusudezYUQkJCbrmmmu0bt26Gu9bXFysoUOHKiEhQR07dlR+fr6OHDkSzpfSNJzLFwAAwFaWh29OTo4WLVqkxx57TO+8844GDRqkvLw8FRUV1fm4s2fPKisrS+vXr9fzzz+vlStX6rLLLlN2drb+9Kc/Vbnve++9pxtvvFFdunTRypUr9dxzz6m4uFhZWVk6d+6clS+v4VJTpaNHzR8AAAA0u1gr//iqVatUXFysoqIiTZw4UZKUmZmpAwcOaPr06Zo4caJiYmpu71deeUU7d+7Uhx9+qMGDB0uSRowYoauuukozZszQhg0bQvedPn26+vbtq2XLloX+Xo8ePXTttdfq1Vdf1b333mvly2yY4JkdPv5YGj7c3lkAAACikKUrvitWrFBiYqJyc3OrHM/Pz9ehQ4e0cePGOh/bt2/fUPRKUosWLTRp0iRt2rRJX375pSTpiy++0JYtWzR58uQqET106FD16dNHK1asCPOrukR9+kixsdLOnXZPAgAAEJUsDd8dO3YoNTW12qpuWlqaJGlnHRG4Y8cOpaenVzt+8WN37NghSbXeN/h727VsaV66eNs2uycBAACISpZudTh27Jh69epV7XhSUlLo97UpLS0N3a+uxwZva7tvXc8hSQUFBfJ6vVWO5eXlKS8vr87HXZK0NGn79vD/XQAAgAhXVFRU7TtgJ06cCOtzWBq+TuDxeOr8/bx58zRgwIDmGSY9XXr7bckwpHrmAgAAiCY1LTxu3bpVAwcODNtzWLrVwefz1bjiWlpaGvp9XY8N3q+uxwZva7tvXc/R7NLTpbIy6eBBuycBAACIOpaGb3p6unbt2qVAIFDl+PZ//M/9/fr1q/WxaWlp2lbDftiLHxu8re2+dT1Hs/vH/mT2+QIAADQ/S8N33LhxKi8v17Jly6ocX7hwofx+f5UzNtT02N27d2vTpk2hY+fPn9fixYs1ZMgQde7cWZLk9/t19dVXa/HixVUCe8OGDfrkk0+Uk5MT5lfVBP/0T5LXyz5fAAAAG1gavtnZ2Ro1apSmTp2qBQsWaP369brnnnu0Zs0aPfXUU6H9t1OmTFFcXJz+/ve/hx57991368orr1Rubq6KiopUXFysCRMmaM+ePZo7d26V55k7d652796t3NxcFRcX6/XXX9eECROUlpam/Px8K19i43g85qovK74AAADNzvIrty1fvlyTJ0/W7NmzdeONN2rz5s1asmRJlc3LgUBAgUBAhmGEjrVs2VJr167VyJEjdf/99+uWW27R119/rdWrV2vYsGFVniMzM1OrVq3Sl19+qVtuuUU//elPlZWVpbVr1youLs7ql9g46ems+AIAANjAY1SuzSgS/JZgSUlJ853VQZJeekm67z7pm2+kVq2a73kBAAAiTLh7zfIVX1wkPV26cEHatcvuSQAAAKIK4dvcgmeZYJ8vAABAsyJ8m1tionTFFdJHH9k9CQAAQFQhfO0wcKC0davdUwAAAEQVwtcOAwaY4XvRhT0AAABgHcLXDgMHSqdOSXv32j0JAABA1CB87RA8HUdJib1zAAAARBHC1w4+n3T55ezbo3nqAAAVK0lEQVTzBQAAaEaEr10GDGDFFwAAoBkRvnYJntkhOi+cBwAA0OwIX7sMGCCdOCHt32/3JAAAAFGB8LVL8Atu7PMFAABoFoSvXS67TPL72ecLAADQTAhfO/3zP0ubN9s9BQAAQFQgfO00ZIi0aZN04YLdkwAAALge4WunIUPMK7h9/LHdkwAAALge4WunQYOkmBhpwwa7JwEAAHA9wtdOCQlSejrhCwAA0AwIX7sNGUL4AgAANAPC125Dhph7fE+csHsSAAAAVyN87TZkiHnLac0AAAAsRfjarXdvqUMHtjsAAABYjPC1W0yMNHgw4QsAAGAxwtcJhg6V/vIXKRCwexIAAADXInydYPhw88tt27fbPQkAAIBrEb5OMHiw1LKl9N57dk8CAADgWoSvE8THm/H7pz/ZPQkAAIBrEb5OMXy4Gb6GYfckAAAArkT4OkVmpnTkiLRrl92TAAAAuBLh6xTXXCPFxrLdAQAAwCKEr1MkJEgDB/IFNwAAAIsQvk6SmWmGL/t8AQAAwo7wdZLMTOnLL6VPPrF7EgAAANchfJ1k+HApLk76wx/sngQAAMB1CF8nadtWuvZawhcAAMAChK/TjBolrVsnffut3ZMAAAC4CuHrNDfcIJWXSxs22D0JAACAqxC+TpORIfl8bHcAAAAIM8LXaVq0kLKypDVr7J4EAADAVQhfJ7rhBmnzZun4cbsnAQAAcA3C14lGjZICAam42O5JAAAAXIPwdaKUFCktTXrrLbsnAQAAcA3C16luuUV6+23p/Hm7JwEAAHAFwtepxo6VSkulDz+0exIAAABXIHydatAg6bLLpJUr7Z4EAADAFQhfp4qJMVd9CV8AAICwIHydbOxY6ZNPpL/9ze5JAAAAIh7h62TXXy+1bs2qLwAAQBgQvk7Wpo2UnS0tW2b3JAAAABGP8HW6CROkTZukzz6zexIAAICIRvg63dixUny89MYbdk8CAAAQ0Qhfp2vbVhozRlq61O5JAAAAIhrhGwkmTJC2bpU+/dTuSQAAACIW4RsJxowxv+jGdgcAAIBLRvhGgjZtpFtukYqKJMOwexoAAICIRPhGismTpR07pL/+1e5JAAAAIhLhGyluuEHq0kX6zW/sngQAACAiEb6RIjbWXPV9/XXp7Fm7pwEAAIg4hG8kufNOqbRUeustuycBAACIOIRvJPn+96Wrr5YWLrR7EgAAgIhD+Eaa/Hxp9Wrp88/tngQAACCiEL6R5vbbzdObvfSS3ZMAAABEFMI30rRrZ+71/fWv+ZIbAABAIxC+kejHP5YOH5Z+9zu7JwEAAIgYhG8k+v73peuuk+bPt3sSAACAiEH4Rqqf/ET6y1+krVvtngQAACAiEL6RauxYqXt36emn7Z4EAAAgIhC+kSo2Vpo+XXrjDWnvXrunAQAAcDzCN5Ll50vJydIzz9g9CQAAgOMRvpEsPl4qKJB+8xvpq6/sngYAAMDRCN9IN3Wq1LKl9Itf2D0JAACAoxG+kc7rlX76U+mFF6Qvv7R7GgAAAMcifN1g2jSpdWvp5z+3exIAAADHInzdwOuVHnrIvIzx/v12TwMAAOBIhK9b3H+/5PNJjz5q9yQAAACORPi6RZs20uzZ0uLF0pYtdk8DAADgOISvm/zoR1K/fubqbyBg9zQAAACOQvi6SWys9MtfShs2SL/9rd3TAAAAOArh6zaZmdLEieaX3U6etHsaAAAAxyB83ejpp6XycmnmTLsnAQAAcAzC1426dZPmzpV+9Stp3Tq7pwEAAHAEwtetpk6Vhg+X/uVfpG++sXsaAAAA2xG+bhUTI73yivTVV9KMGXZPAwAAYDvC18169TL3+774orRihd3TAAAA2Irwdbsf/1gaN066+27pwAG7pwEAALAN4et2Ho+55aF9eykvTzp3zu6JAAAAbEH4RoMOHaQlS6SSEum++yTDsHsiAACAZkf4RoshQ6Rf/1pasEB67jm7pwEAAGh2sXYPgGZ0553Szp3Sgw9KvXtLY8bYPREAAECzYcU32jzxhDR2rJSbK73/vt3TAAAANBvCN9q0aGHu9x0yxFzxLSmxeyIAAIBmQfhGo9atpf/9Xyk1VRo9Wtq61e6JAAAALEf4RqvERGn1aumKK6SRI6UPPrB7IgAAAEsRvtEsKUlau1bKyJBuuEF65x27JwIAALAM4Rvtgiu/118v3Xyz9Mtfcp5fAADgSoQvpPh4acUKqaBA+ulPpX/9V67wBgAAXIfwhalFC+mZZ6RXX5UWLpSuvVbas8fuqQAAAMKG8EVV+fnShx9KJ06Ye39fe42tDwAAwBUIX1Q3cKB5irPx46W77jL3/h44YPdUAAAATUL4omaJieaWh//5H+n//k/6/velX/yCvb8AACBiEb6o2623Sh9/LE2ZIk2fbgbwm2+y/QEAAEQcwhf1a9dOev55c+W3b19pwgTzkse//70UCNg9HQAAQIMQvmi4fv3M2F23ToqNlcaOla66Slq8WPr2W7unAwAAqBPhi8YbOVL685+l99+XLr9cmjxZ6tZN+o//kPbutXs6AACAGhG+uHT/7/+ZK8A7dkgTJ0q/+pXUq5cZxr/6lXT4sN0TAgAAhBC+aLorr5See046dEhatEiKi5N+8hOpSxcpK8u8DPInn/CFOAAAYCvCF+ETH29ue1izRvrqK3PVNyZGevBB6Xvfk664Qrr3XvOsEJ9/Xu+fKyoqaoahcSl4b5yL98bZeH+ci/cmOlgevuXl5SooKJDf71d8fLwyMjK0dOnSBj/+8OHDuuuuu9SxY0clJCTommuu0bp166rdb8SIEYqJian2c9NNN4Xz5aChkpOlH/1I+sMfpNJS6a23zAthrFtnnhWiWzcpJcXcIjFvnvTee+b9KuE/hJyL98a5eG+cjffHuXhvokOs1U+Qk5OjLVu2aO7cuerTp48KCwuVl5enQCCgvLy8Oh979uxZZWVlqaysTM8//7w6deqkF154QdnZ2SouLtbw4cOr3L9nz54qLCyscszr9Yb9NaGR2rY1o/fmm81//+or87LIf/mLeTtzpnT2rPk7v19KSzN/Dh6U1q839w37/ebqMQAAwCWyNHxXrVql4uJiFRUVaeLEiZKkzMxMHThwQNOnT9fEiRMVU0fMvPLKK9q5c6c+/PBDDR48WJK5snvVVVdpxowZ2rBhQ5X7x8fH6+qrr7buBSE8OneWxo0zfyTzVGiffCJt3y5t22b+vPGGeZnk664z79OqldSjh7ldwu+Xunat/pOcbJ5mDQAAoAaWVsKKFSuUmJio3NzcKsfz8/N1++23a+PGjRo6dGidj+/bt28oeiWpRYsWmjRpkv7zP/9Thw4dUteuXUO/M/jyVGSKizO/IHflldJtt313/OabpWeeMU+RFvzZv1/autU8m8TXX1e/gIbXKyUlST6f+RP856Qk80Icbdual2Nu2/a7n8r/npBgxrPH07z/NwAAAJazNHx37Nih1NTUaqu6aWlpkqSdO3fWGb47duxQZmZmtePBx3/88cdVwnfv3r3y+XwqKyvT5Zdfrttuu00PP/ywWrduXe1vnDlzRpK0a9euxr8wNIsTZWXaevq0eXaILl3M06dVdv68dPy4dOSIeeq048elkye/+ykrk3btMm9PnpROnzZ/GqJly6o/cXG1H2vRou6fmBgzpmv65+B9gqEdE2P+c10/wc9T8J/re2zl+wd/LtaY0Pd4dGL/fm19/vm6H1/b32zkc13yscY+l0uc+OwzbX3pJbvHQC14f5yL98aZdh08KEmqqKgIy9+zNHyPHTumXr16VTuelJQU+n1dSktLQ/et7/HDhg1TXl6e+vbtq4qKCq1atUpPPfWUPvjgA61fv16ei/4LcP/+/ZKkSZMmNe5FoVkNHDjQnic+d878Qa0GPvCA3SOgFgPvvdfuEVAH3h/n4r1xrs8++0zXXnttk/9Og8P3j3/8o64L7resx0cffaT09PRLHupSzJkzp8q/Z2dnq3v37po2bZpWrlypW2+9tcrvR48ercWLF6t79+6Kj49vzlEBAADQAGfOnNH+/fs1evTosPy9Bodv3759tWDBggbdNyUlRZLk8/lqXNUt/cdpq3w+X51/x+fzhe57KY+fNGmSpk2bpo0bN1YL3+TkZN1xxx11Ph4AAAD2uuaaa8L2txocvp07d9bdd9/dqD+enp6uoqIiBQKBKvt8t2/fLknq169fnY9PS0vTtm3bqh1v6OP5shsAAACCLD0x6rhx41ReXq5ly5ZVOb5w4UL5/f4qZ2uo7fG7d+/Wpk2bQsfOnz+vxYsXa8iQIercuXOdj3/ttdckqc4v0AEAACA6eAyLl0VHjx4duoBFz549VVRUpAULFoQuZBE0ZcoULVq0SPv27VO3bt0kSefOndPAgQNVVlamJ598Uh07dtSLL76ot99+W8XFxRo2bJgk6f3339cTTzyh8ePHKyUlRRUVFVq9erVefvlljRgxQn/4wx+sfIkAAACIAJaf7X/58uWaNWuWZs+erdLSUqWmpmrJkiWaMGFClfsFAgEFAoEq2xNatmyptWvXasaMGbr//vt1+vRpZWRkaPXq1aHolaSuXbsqJiZGjz76qI4ePSqPx6M+ffpozpw5evDBB61+iQAAAIgERpQ5deqU8cADDxhdu3Y1WrdubfTv399YsmSJ3WPBMIz169cbHo+nxp+NGzfaPV7UOHXqlDF9+nRj1KhRRnJysuHxeIzHHnusxvuWlJQYWVlZRtu2bQ2v12vk5OQY+/bta+aJo0dD35s777yzxs9RamqqDVNHh+LiYuOHP/yh0bt3b6NNmzaG3+83br31VqOkpKTaffncNL+Gvj98dprfX//6V+Omm24yUlJSjPj4eCMpKckYOnSosXjx4mr3DcdnJ+qu75qTkxPaetGnT5/QlotAIFBl6wXs88QTT2jkyJFVjl155ZU2TRN9jh49qpdffln9+/fXuHHjtGDBgmrnwZak3bt3a8SIERowYIDefPNNVVRUaPbs2Ro2bJg++ugjJScn2zC9uzX0vZHMS7ivX7++2jFY46WXXtKRI0f0b//2b7ryyit15MgRPfvssxoyZIjefffd0H+m8bmxR0PfH4nPTnM7efKkUlJSdMcdd8jv96u8vFyFhYWaPHmyPvvsM82aNUtSGD874Sr2SPD2228bHo+n2grvDTfcYPj9fuPChQs2TQbD+G7F93e/+53do+Afjh49ang8HuPxxx+v9rvc3FyjU6dOxqlTp0LHDhw4YLRs2dJ46KGHmnPMqFTXe3PnnXcaiYmJNkwVvb7++utqx8rLy43OnTsb119/fegYnxt7NPT94bPjHEOGDDFSUlJC/x6uz46lZ3VwmhUrVigxMVG5ublVjufn5+vQoUPauHGjTZOhMoPT0DlGbe/F+fPn9fvf/14/+MEP1LZt29DxlJQUjRw5UitWrGiuEaNWfZ8TPkfNq1OnTtWOJSQkKDU1VZ9//rkkPjd2asj7E8Rnxxl8Pp9iY82NCeH87ERV+O7YsUOpqalVziksmecLlqSdO3faMRYuct999ykuLk7t27dXdna2/vznP9s9Ei6yd+9enTlzpsYrNKalpenTTz/VOS75bKuKigp16dJFsbGx6tatm+6//34dP37c7rGiysmTJ7V169bQVi0+N85y8fsTxGfHHoZh6Pz58zpy5IhefPFFvfvuu5o2bZqk8H52omqP77Fjx9SrV69qx5OSkkK/h328Xq8KCgo0YsQI+Xw+7dmzR08//bRGjBiht99+WzfccIPdI+Ifgp+V4GensqSkJBmGoePHj+uyyy5r7tEgqX///srIyAhd5OePf/yj/vu//1tr167V5s2blZCQYPOE0eG+++5TRUVFaI8inxtnufj9kfjs2Gnq1Kn69a9/LUlq0aKFnnnmGU2dOlVSeD87URW+cLb+/furf//+oX+/9tprNW7cOKWlpemhhx4ifIEGKigoqPLvWVlZysjI0Pjx47VgwQI98MADNk0WPR555BG9/vrreuGFF5SRkWH3OLhIbe8Pnx37zJo1S/fcc48OHz6slStX6t///d915swZPfTQQ2F9nqgKX5/PV+Oqbmlpaej3cJb27dtrzJgxeumll3T27Fm1atXK7pGg7z4rwc9OZaWlpfJ4POrQoUNzj4U6jBs3TgkJCXyXoRk8/vjj+vnPf67/+q//0o9//OPQcT43zlDb+1MbPjvNo1u3bqELmGVnZ0sy/x+UKVOmhPWzE1V7fNPT07Vr1y4FAoEqx7dv3y5Jof9pA85U22mb0Px69uyp+Ph4bdu2rdrvtm/frt69e6tly5Y2TIbaGIZR7T/7EH6PP/546GfmzJlVfsfnxn51vT+14bNjj0GDBun8+fPat29fWD87URW+48aNU3l5uZYtW1bl+MKFC+X3+zV48GCbJkNtjh8/rrfeeksZGRn8F4KDxMbGauzYsVq+fLnKy8tDxw8ePKj169crJyfHxulQk2XLlqmiokJDhw61exTXmjNnjh5//HE98sgjeuSRR6r9ns+Nvep7f2rDZ8ce69evV4sWLdSzZ8+wfnY8RpSdt2P06NGhC1j07NlTRUVFWrBgQehCFrDPHXfcoR49emjAgAFKSkrSnj179Oyzz2r//v1avXq1rrvuOrtHjBqrV6/WN998o1OnTmnKlCnKzc0NnQZwzJgxio+P19/+9jcNGjRIAwYM0MyZM0MnEz9x4oQ++ugjtg5ZpL735vDhw5o0aZJuv/12XXHFFTIMQ++9956ee+459erVSxs3buRk/BZ49tlnNX36dGVnZ+vRRx+tdkqsIUOGSBKfG5s05P05cOAAnx0b3HPPPWrfvr0GDRqkyy67TEePHtWbb76pN954QzNmzNCTTz4pKYyfnaacXDgSlZeXGw888IDRpUsXo1WrVkb//v2NpUuX2j0WDMN48sknjYyMDMPr9RqxsbFGp06djB/84AfGli1b7B4t6nTv3j10qc6YmJgq/3zgwIHQ/UpKSozrr7/eSEhIMNq3b8+lV5tBfe/N8ePHjZycHKNHjx5GmzZtjFatWhnf+973jJkzZxplZWV2j+9aI0aMqPJ+VP6JiYmpcl8+N82vIe8Pnx17/OY3vzGGDx9udOzY0YiLizM6dOhgjBw50igsLKx233B8dqJuxRcAAADRKar2+AIAACB6Eb4AAACICoQvAAAAogLhCwAAgKhA+AIAACAqEL4AAACICoQvAAAAogLhCwAAgKjw/wENIYP64CLcFgAAAABJRU5ErkJggg==",
       "text": "Figure(PyObject <matplotlib.figure.Figure object at 0xad6b310>)"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": "1-element Array{Any,1}:\n PyObject <matplotlib.lines.Line2D object at 0xf734dd0>"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#two arrays with random numbers\npoints_x=[floor(rand()*density) for x in 1:numpoints]\npoints_y=[floor(rand()*density) for x in 1:numpoints]\n\n\npixels=Array(Float32, (density,density))\n\nfor p in 1:numpoints\n    new_x=points_x[p]\n    new_y=points_y[p]\n    \n    #this is where the actual algorithm starts\n    for i in 1:density\n        for j in 1:density\n            delta_x=i-new_x\n            delta_y=j-new_x\n            r=sqrt(delta_x^2+delta_y^2)\n            pixels[i,j]+=effect(r)\n            if(pixels[i,j]<0)\n                pixels[i,j]=0\n            end\n        end\n    end\nend",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}