gesellkammer/time_talea_test

## time_talea_test
{
 "metadata": {
  "name": "time_talea_test"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Objetivo: crear una taleacolor que se comprime y dilata en el tiempo\n",
      "\n",
      "## Definir la taleacolor normalmente"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "from e.comp import taleacolor\n",
      "from bpf4 import bpf\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Python27\\lib\\site-packages\\scikits\\__init__.py:1: UserWarning: Module argparse was already imported from C:\\Python27\\lib\\argparse.pyc, but c:\\python27\\lib\\site-packages is being added to sys.path\n",
        "  __import__('pkg_resources').declare_namespace(__name__)\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Definir la curva de compresion temporal (pace)\n",
      "\n",
      "NB: pace es la derivada de tiempo\n",
      "\n",
      "### pace\n",
      "\n",
      "* x: el tiempo de la talea\n",
      "* y: el grado de compresion temporal\n",
      "\n",
      "- si y < 1, significa que el tiempo se comprimio, es decir, que transcurrion mas rapido\n",
      "- si y > 1, significa que el tiempo se dilato\n",
      "\n",
      "### time\n",
      "\n",
      "* x: el tiempo de la talea\n",
      "* y: el tiempo \"real\"\n",
      "\n",
      "El tiempo \"normal\" seria con un pace constante de 1\n",
      "\n",
      "**time es la integrada de pace**\n",
      "\n",
      "- - -\n",
      "\n",
      "## Tarea: convertir una serie de duraciones en tiempo \"virtual\" en sus duraciones en tiempo \"real\"\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "talea = taleacolor.Talea([1, 0.5, 2], \"A B C D\".split())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pace: x --> \n",
      "from e import elib\n",
      "reload(elib)\n",
      "pace = bpf.linear(0, 0.5, 5, 0.5, 5, 1, 10, 1)\n",
      "virt2real = (1/pace).integrated().inverted()  # inverted rotates the curve, switching x for y\n",
      "virt2real.plot()\n",
      "real2virt = (1/pace).integrated()\n",
      "real2virt.plot()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "_BpfIntegrate[0.0:10.0]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Get enough items to fill a real duration of 7 seconds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_items_between(talea, t0, t1):\n",
      "    out = []\n",
      "    virt0 = real2virt(t0)\n",
      "    virt1 = real2virt(t1)\n",
      "    items = talea.take_between(virt0, virt1*1.01) # we scale up to account for rounding errors\n",
      "    for item in items:\n",
      "        start = virt2real(item.start)\n",
      "        if start > t1:\n",
      "            break\n",
      "        dur = virt2real(item.start+item.dur) - start\n",
      "        if start+dur > t1:\n",
      "            dur = t1 - start\n",
      "        out.append(taleacolor.Item(item.color, start, dur))\n",
      "    return elib.RecordList(out)\n",
      "    \n",
      "print get_items_between(talea, 0, 7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      color       start       dur       \n",
        "----------------------------------------\n",
        "0     A           0.000000    0.500000  \n",
        "1     B           0.500000    0.250000  \n",
        "2     C           0.750000    1.000000  \n",
        "3     D           1.750000    0.500000  \n",
        "4     A           2.250000    0.250000  \n",
        "5     B           2.500000    1.000000  \n",
        "6     C           3.500000    0.500000  \n",
        "7     D           4.000000    0.250000  \n",
        "8     A           4.250000    1.249464  \n",
        "9     B           5.499464    1.000714  \n",
        "10    C           6.500178    0.499822  \n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from e.elib import EPS\n",
      "def translate_coord(x0, x1, shape, N=1000):\n",
      "    dx = (x1-x0) / float(N)\n",
      "    y = x0\n",
      "    for j in range(N):\n",
      "        shape_y = shape(y)\n",
      "        trap = dx * shape_y #+ dx*(shape(y+dx)-shape_y)*0.5\n",
      "        y += trap\n",
      "    return y\n",
      "\n",
      "def translate_coord(x0, shape):\n",
      "    c = (1/shape).integrated()\n",
      "    \n",
      "def translate_durations(talea, pace, time_range=(0, 10)):\n",
      "    frames = talea.take_between(*time_range)\n",
      "    now = 0\n",
      "    durs = []\n",
      "    starts = []\n",
      "    endtime = time_range[1]\n",
      "    for frame in frames:\n",
      "        t0 = now\n",
      "        t1 = t0\n",
      "        N = 200\n",
      "        dx = frame.dur/N\n",
      "        for j in range(N):\n",
      "            trap = dx * pace(t1)\n",
      "            t1 += trap\n",
      "        dur = t1 - t0\n",
      "        now += dur\n",
      "        durs.append(dur)\n",
      "        starts.append(t0)\n",
      "        if now >= endtime:\n",
      "            break\n",
      "    return starts, durs\n",
      "X = 8\n",
      "#translate_durations(talea, pace, (0, 10))\n",
      "pace2i = pace2.integrated()\n",
      "pace2i.use_optimization=0\n",
      "pace2.plot(color='blue', alpha=0.3, linewidth=2); pace2i.plot(color='red', linewidth=2, alpha=0.3); (pace2i/pace2).plot(color='green', linewidth=2, alpha=0.3)\n",
      "# plt.gca().set_xticks(numpy.arange(0, 20, 1))\n",
      "f = bpf.asbpf(lambda x: translate_coord(0, x, pace2))[0:20]\n",
      "#g = f.render(2000)\n",
      "#g.plot(color='orange')\n",
      "ipace2 = 1/pace2\n",
      "ipace2.plot(color='black', linewidth=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 385,
       "text": [
        "_BpfLambdaRDivConst[0.0:10.0]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ITDJpqtgXqdwS1o4XAACILE9DvTZ/+Z5fn0uSLhp6qYZNmGLSVGgvghcAABZTW1muwuVv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\n"
      }
     ],
     "prompt_number": 385
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ipace2i = ipace2.integrated()\n",
      "ipace2i.use_optimization = 0\n",
      "ipace2i_ = ipace2i.inverted()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 386
    }
   ],
   "metadata": {}
  }
 ]
}