Quantitative-Histology / Git / [171cba] /4x/reference/entropy

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Quantitative-Histology
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{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import skimage as ski\n",
      "from skimage import io, filter, draw, morphology, exposure, color, feature\n",
      "import scipy.ndimage as nd\n",
      "import scipy.stats as st\n",
      "import scipy.signal as sg\n",
      "from sklearn.svm import SVC\n",
      "import pandas as pd\n",
      "from scipy.signal import *\n",
      "import lmfit as lm\n",
      "import numpy as np\n",
      "from prettyplotlib import plt\n",
      "import pickle\n",
      "#matplotlib.rcParams['savefig.dpi'] = 3. * matplotlib.rcParams['savefig.dpi']\n",
      "#figsize(14, 8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read filenames\n",
      "files = []\n",
      "dirq = \"4xtry/\"\n",
      "for i in os.listdir(dirq) :\n",
      "    if i.endswith(\".jpg\") :\n",
      "        if not i.endswith(\"processed.jpg\") :\n",
      "            files.append(dirq + i)\n",
      "            \n",
      "print len(files)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "403\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "base = {\"entropy\" : {}, \"lacunarity\" : {}}\n",
      "data = {\"soay\" : base, \"control\" : base}\n",
      "\n",
      "entropy = []\n",
      "entropyvar = []\n",
      "entropynum = []\n",
      "lacunarity = []\n",
      "lacunarityvar = []\n",
      "t = np.unique(np.round(np.exp(np.linspace(np.log(25), np.log(600), 20)))).astype(int)\n",
      "\n",
      "\n",
      "\n",
      "quick = files[::2]\n",
      "\n",
      "#files = [files[0]]\n",
      "\n",
      "for file in files[::3] :\n",
      "    if not os.path.exists(file + \"_processed.jpg\") :\n",
      "        A = io.imread(file)\n",
      "        B = exposure.adjust_sigmoid(ski.img_as_float(A), gain=12)\n",
      "        C = color.rgb2xyz(B)[:, :, 1]\n",
      "        D = filter.threshold_adaptive(C, 351)\n",
      "        E = morphology.remove_small_objects(~morphology.remove_small_objects(~D, 100), 100)\n",
      "        io.imsave(file + \"_processed.jpg\", ski.img_as_float(E))\n",
      "        print \"Created !\"\n",
      "\n",
      "    else :\n",
      "        E = ski.img_as_float(io.imread(file + \"_processed.jpg\"))\n",
      "        print \"Existed !\"\n",
      "    \n",
      "    \n",
      "    ent = []\n",
      "    entvar = []\n",
      "    lac = []\n",
      "    entnum = []\n",
      "    \n",
      "    for i in t :\n",
      "        circle = morphology.disk(i)\n",
      "        circlesize = np.sum(circle)\n",
      "        Y = fftconvolve(E, circle, \"same\")\n",
      "        px = np.reshape(Y[int(i) : int(E.shape[0]-i), int(i) : int(E.shape[1]-i)] / circlesize, (E.shape[0]-2*i)*(E.shape[1]-2*i),)\n",
      "        py = 1. - px\n",
      "        ind = np.logical_and(px > 1./circlesize, px < 1)\n",
      "        ent.append(-np.mean(px[ind] * np.log(px[ind]) + py[ind] * np.log(py[ind])))\n",
      "        entvar.append(np.var(px[ind] * np.log(px[ind]) + py[ind] * np.log(py[ind])))\n",
      "        entnum.append(len(ind))\n",
      "        lx = np.var(Y) / np.mean(Y)**2 + 1\n",
      "        ly = np.var(1-Y) / np.mean(1-Y)**2 + 1\n",
      "        lac.append(2 - (1./lx + 1./ly))\n",
      "\n",
      "    fid = file.split(dirq + \"Sheep\")[1].split(\"-4x\")[0]\n",
      "\n",
      "    if 95 < int(fid) < 100 :\n",
      "        if fid not in data[\"control\"][\"entropy\"] :\n",
      "            data[\"control\"][\"entropy\"][fid] = []\n",
      "            data[\"control\"][\"lacunarity\"][fid] = []\n",
      "            \n",
      "        data[\"control\"][\"entropy\"][fid].append(ent) \n",
      "        data[\"control\"][\"lacunarity\"][fid].append(lac)\n",
      "  \n",
      "    else :\n",
      "        if fid not in data[\"soay\"][\"entropy\"] :\n",
      "            data[\"soay\"][\"entropy\"][fid] = []\n",
      "            data[\"soay\"][\"lacunarity\"][fid] = []\n",
      "            \n",
      "        data[\"soay\"][\"entropy\"][fid].append(ent)\n",
      "        data[\"soay\"][\"lacunarity\"][fid].append(lac)\n",
      "        \n",
      "    entropy.append(ent)\n",
      "#    entropyvar.append(entvar)\n",
      "#    entropynum.append(entnum)\n",
      "    lacunarity.append(lac)\n",
      "    print fid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Existed !\n",
        "148"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Existed !"
       ]
      },
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        "\n",
        "148"
       ]
      },
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        "\n",
        "148"
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        "21"
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        "Existed !"
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        "31"
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        "31"
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        "Existed !"
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        "\n",
        "31"
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        "\n",
        "Existed !"
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        "\n",
        "31"
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        "\n",
        "Existed !"
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        "\n",
        "56"
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        "\n",
        "Existed !"
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        "56"
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        "Existed !"
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        "56"
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        "56"
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      {
       "output_type": "stream",
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        "\n"
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      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.array([np.var(data[\"soay\"][\"lacunarity\"].values()[i], axis=0) for i in range(12)])\n",
      "X2.shape\n",
      "\n",
      "y = np.zeros((12,1))\n",
      "y[-2:-1] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Z = t.reshape(20,1)\n",
      "for i in data[\"control\"][\"lacunarity\"].keys()[:-2] :\n",
      "    for j in data[\"control\"][\"lacunarity\"][i] :\n",
      "        #Z = np.concatenate((Z, np.array(j).reshape(len(j),1)), axis=1)\n",
      "\n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 249
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = {}\n",
      "d[\"Scale\"] = Z[:, 0]\n",
      "for i in data[\"control\"][\"entropy\"].keys()[:-2] :\n",
      "    for j in data[\"control\"][\"entropy\"][i] :\n",
      "        d[\"Sheep\" + i + \"_\" + str(randint(0, 1000))] = j"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 269
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(d).to_csv(\"../datapackage/4. Raw Data/Ent_Soay.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 270
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "indlacmean = []\n",
      "indlacvar = []\n",
      "indlacsize = []\n",
      "for i in data[\"control\"][\"lacunarity\"].keys() :\n",
      "    indlacmean.append(np.mean(np.array(data[\"control\"][\"lacunarity\"][i]), axis=0))\n",
      "    indlacvar.append(np.std(t[np.argmin(np.array(data[\"control\"][\"lacunarity\"][i]), axis=1)], axis=0))\n",
      "    indlacsize.append(np.mean(t[np.argmin(np.array(data[\"control\"][\"lacunarity\"][i]), axis=1)], axis=0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.errorbar(range(10), np.array(indlacsize[:10]), np.array(indlacvar[:10]), linewidth=3, ecolor=\"k\", elinewidth=2)\n",
      "plt.errorbar(range(10,12), np.array(indlacsize[10:]), np.array(indlacvar[10:]), linewidth=3, ecolor=\"k\", elinewidth=2)\n",
      "legend([\"Soay\", \"Control\"])\n",
      "xlim([-0.5, 11.5])\n",
      "title(\"Global Minimum in Lacunarity\")\n",
      "xlabel(\"Individual\")\n",
      "ylabel(\"Scale (pixels)\")\n",
      "\n",
      "dd = {}\n",
      "dd[\"Scale\"] = t\n",
      "dd[\"Entropy_mean\"] = np.mean(np.array(data[\"control\"][\"entropy\"][\"96\"]), axis=0)\n",
      "dd[\"Entropy_std\"] = np.std(np.array(data[\"control\"][\"entropy\"][\"96\"]), axis=0)\n",
      "pd.DataFrame(dd).to_csv(\"../datapackage/3. Entropy/entropy_control.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eim7FmKD41wfObpfVtU3lzrYSoAAAAAANBbNHd26IUD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\ncUSdumvX67o+f6GmJIwPcEIAAIDPF7IGUa/Xq/3792vfvn0qLy/X8ePH5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9eM/T0dbbnSg7o6w6rqidn3IjAAAAAADg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\nerAhUTYbmbh13kr/7QMAAAAAAKkwED2FhoaGE+PQTCYT06dPjws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       "text": [
        "<matplotlib.figure.Figure at 0x125cb6710>"
       ]
      }
     ],
     "prompt_number": 279
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e = ski.img_as_float(E[1000:2000, 1000:2000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sg.correlate(e, e)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lacunarity[0, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "F = io.imread(files[325] + \"_processed.jpg\")\n",
      "print nd.correlate(F[1950:2000], F[1950:2000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(t, np.array(lacunarity[:, :325]), \"k\");\n",
      "plt.plot(t, np.array(lacunarity[:, 325:]), \"r\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = pd.read_csv(\"backup3.csv\")\n",
      "lacunarity = []\n",
      "minlac = []\n",
      "for i in range(401) :\n",
      "    lacunarity.append(eval(A.values[:, 3][i]))\n",
      "    \n",
      "lacunarity = np.array(lacunarity).T\n",
      "print lacunarity.shape\n",
      "\n",
      "print t\n",
      "for i in range(50) :\n",
      "    minlac.append(t[np.argmin(lacunarity[i, :])])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XX = np.array([lacunarity[0, :], entropy[-1, :]]).T\n",
      "X = XX\n",
      "q = SVC()\n",
      "q.fit(X, y) \n",
      "\n",
      "x_min, x_max = X[:, 0].min() - .05, X[:, 0].max() + .05\n",
      "y_min, y_max = X[:, 1].min() - .05, X[:, 1].max() + .05\n",
      "xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange(y_min, y_max, .02))\n",
      "Z = q.predict(np.c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "Z = Z.reshape(xx.shape)\n",
      "#plt.figure(1, figsize=(4, 3))\n",
      "\n",
      "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=0.7)\n",
      "plt.xlabel('Lacunarity')\n",
      "plt.ylabel('Entropy')\n",
      "\n",
      "plt.xlim(xx.min(), xx.max())\n",
      "plt.ylim(yy.min(), yy.max())\n",
      "#plt.xticks(())\n",
      "#plt.yticks(())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}