multifractal curves test chamber

mandel.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Multifractal curves"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "1. Import necessary modules and enable inline function plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "from pylab import *\n",
      "from numpy import interp\n",
      "import random"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "2. Define a simple function to map intervals"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def remap (value, from1, to1, from2, to2):\n",
      "    return (value - from1) / (to1 - from1) * (to2 - from2) + from2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "3. Define a function for creating initial curves (both time and price kinds)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gene():\n",
      "    nodes = range(4,6)\n",
      "    tNodes = random.sample(nodes, 1)[0]\n",
      "    \n",
      "    tx = [random.random() for _ in range(tNodes)]\n",
      "    ty = [random.random() for _ in range(tNodes)]\n",
      "    tx.sort()\n",
      "    ty.sort()\n",
      "    tx[0] = 0.0\n",
      "    tx[-1] = 1.0\n",
      "    ty[0] = 0.0\n",
      "    ty[-1] = 1.0\n",
      "    \n",
      "    px = [random.random() for _ in range(tNodes)]\n",
      "    py = [random.random() for _ in range(tNodes)]\n",
      "    px.sort()\n",
      "    px[0] = 0.0\n",
      "    px[-1] = 1.0\n",
      "    \n",
      "    return [tx, ty], [px, py]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "4. Define a function for recursing initial curve"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def recurse(array, numRecursion):\n",
      "    tempArray = []\n",
      "    for n in array:\n",
      "        tempArray.append(n)\n",
      "    newArray = []\n",
      "    for m in range(len(tempArray)):\n",
      "        for k in range(numRecursion):\n",
      "            for i in range(len(tempArray[m]) - 1):\n",
      "                for j in range(len(tempArray[m])):\n",
      "                    newArray.append(remap(tempArray[m][j], 0.0, 1.0, tempArray[m][i], tempArray[m][i+1]))\n",
      "            tempArray[m] = newArray\n",
      "            newArray = []\n",
      "    \n",
      "    return tempArray[0], tempArray[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "5. Create a one time and one price curve, and plot their graphs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time, price = gene()\n",
      "\n",
      "fig, axes = plt.subplots(1, 2, figsize=(20,5))\n",
      "axis([0.0,1.0, 0.0,1.0])\n",
      "# default grid appearance\n",
      "axes[0].plot(time[0], time[1], lw=2)\n",
      "axes[0].grid(True)\n",
      "axes[0].set_autoscale_on(False)\n",
      "\n",
      "# custom grid appearance\n",
      "axes[1].plot(price[0], price[1], lw=2)\n",
      "axes[1].grid(True)\n",
      "axes[1].set_autoscale_on(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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3XLmM5FB6sog+pjmT07meFUJAQKVfL/JQOAoAAABA0p9/SjffLM2eLcXG2iqh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\nwdwV6hospE8IJSYmau7cuRo9erSampo0ZcoUZWdna968eZK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       "text": [
        "<matplotlib.figure.Figure at 0x107cbdd90>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "6. Recurse each curve 3 times and plot resulting graphs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "newt = recurse(time, 3)\n",
      "newp = recurse(price, 3)\n",
      "\n",
      "fig, axes = plt.subplots(1, 2, figsize=(20,5))\n",
      "axis([0.0,1.0, 0.0,1.0])\n",
      "# default grid appearance\n",
      "axes[0].plot(newt[0], newt[1], lw=2)\n",
      "axes[0].grid(True)\n",
      "axes[0].set_autoscale_on(False)\n",
      "\n",
      "# custom grid appearance\n",
      "axes[1].plot(newp[0], newp[1], lw=2)\n",
      "axes[1].grid(True)\n",
      "axes[1].set_autoscale_on(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8917575uVVfr9f/6zNG6ctSsYdOs7zc0324in0A/wW7faVLqtW6XVq+2+goK4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\nYquDHZoMuv12ado06fnnLXm0ZIm0fr0lld54w+5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       "text": [
        "<matplotlib.figure.Figure at 0x10755e350>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "7. Plot the baby"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "baby = []\n",
      "baby.append(newt[1])\n",
      "baby.append(newp[1])\n",
      "\n",
      "fig, axes = plt.subplots(1, 2, figsize=(20,5))\n",
      "axis([0.0,1.0, 0.0,1.0])\n",
      "# custom grid appearance\n",
      "axes[1].plot(baby[0], baby[1], lw=2)\n",
      "axes[1].grid(True)\n",
      "axes[1].set_autoscale_on(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1075337d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "TODO: There will be many initial price/time curves with a random probability (weight) for each pair.\n",
      "A string of probabilities combined with a string of initial time/price curves will be the DNA of a single creature\n",
      "TODO: Should create a class that combines time, prices, probability triplets\n",
      "TODO: Write a population generator and a population recurser. Use OpenCL if iPython Notebook supports it\n",
      "TODO: Find a fitness function already!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}