cone_contact_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cone contact classification\n",
    "This notebook contains the code to reproduce the classification of contacts between cones and bipolar cells, including the cross validation and the plots shown in figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import itemfreq\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import loadmat\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn import cross_validation\n",
    "from sklearn import svm\n",
    "from sklearn.decomposition import PCA\n",
    "%matplotlib inline\n",
    "matplotlib.rc('font',**{'family':'sans-serif','sans-serif':['Arial']})\n",
    "matplotlib.rcParams.update({'mathtext.default': 'regular'})\n",
    "matplotlib.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "BC_ids=np.loadtxt('data/BC_IDs_new').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_contacts=pd.read_pickle('data/cone_contact_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cut_off_cones=np.array([73,72,71,74,201,202,211,203,88,204,142,34,4,30,199])+2000\n",
    "cones_outside_cbc9=np.array([193,209,200,198,197,199,73,72,71,74,69,67,66,211,86,88,87,120,85,204,84,207,128,114,126,127,125,142,130,104,106,175,135])+2000\n",
    "BC_excluded=np.array([691,709,827,836])\n",
    "blue_cones=np.array([2006,2007,2009,2024,2028,2029])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "contact_summary=all_contacts[np.in1d(all_contacts['cone'],cut_off_cones,invert=True)\\\n",
    "                             &np.in1d(all_contacts['cell'],BC_excluded,invert=True)][['cell','cone','type']]\\\n",
    "                            .drop_duplicates().reset_index().drop('index',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "contact_summary.loc[(contact_summary['type']<63),'group']='off'\n",
    "contact_summary.loc[(contact_summary['type']>62),'group']='on'\n",
    "contact_summary.loc[(contact_summary['type']==71),'group']='rbc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    contact_summary.loc[i,'area']=np.sum(all_contacts['area'][(all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    contact_summary.loc[i,'excentricity']=np.min(all_contacts['excentricity'][(all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    contact_summary.loc[i,'angle']=np.max(all_contacts['angle'][(all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    contact_summary.loc[i,'branch_dist']=np.min(all_contacts['branch_dist'][(all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    contact_summary.loc[i,'end_dist']=np.min(all_contacts['end_dist'][(all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    if (contact_summary['group'][i]=='on'):\n",
    "        contact_summary.loc[i,'height']=all_contacts['height'][(all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i])\\\n",
    "                                                                   &(all_contacts['angle']==contact_summary['angle'][i])].values[0]\n",
    "    else:\n",
    "        contact_summary.loc[i,'height']=all_contacts['height'][(all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i])\\\n",
    "                                                                   &(all_contacts['excentricity']==contact_summary['excentricity'][i])].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    contact_summary.loc[i,'count']=np.sum((all_contacts['cell']==contact_summary['cell'][i])&(all_contacts['cone']==contact_summary['cone'][i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "contact_summary=contact_summary[contact_summary['area']>0].reset_index().drop('index',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "contact_summary['area']/=1e6\n",
    "contact_summary['branch_dist']/=1e3\n",
    "contact_summary['branch_dist']=np.clip(contact_summary['branch_dist'],1e-2,1e2)\n",
    "contact_summary['end_dist']/=1e3\n",
    "contact_summary['end_dist']=np.clip(contact_summary['end_dist'],1e-2,1e2)\n",
    "contact_summary['excentricity']/=1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "contact_summary_log_norm=contact_summary.copy()\n",
    "contact_summary_log_norm['excentricity']=np.log(contact_summary_log_norm['excentricity'])\n",
    "contact_summary_log_norm['area']=np.log(contact_summary_log_norm['area'])\n",
    "contact_summary_log_norm['branch_dist']=np.log(contact_summary_log_norm['branch_dist'])\n",
    "for i in range(4,contact_summary.shape[1]):\n",
    "    contact_summary_log_norm.ix[:,i]-=np.mean(contact_summary_log_norm.ix[:,i])\n",
    "    contact_summary_log_norm.ix[:,i]/=np.std(contact_summary_log_norm.ix[:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "classified_contacts=np.loadtxt('data/cone_training_data.csv',delimiter=',',usecols=range(4))\n",
    "classified_contacts=pd.DataFrame(classified_contacts,columns=['cell','cone','type','class'])\n",
    "classified_contacts=classified_contacts[np.in1d(classified_contacts['cone'],cut_off_cones,invert=True)\\\n",
    "                                        &np.in1d(classified_contacts['cell'],BC_excluded,invert=True)].reset_index().drop('index',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rows_to_del=[]\n",
    "for i in range(classified_contacts.shape[0]):\n",
    "    if np.sum((contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i]))==0:\n",
    "        rows_to_del+=[i]\n",
    "classified_contacts=classified_contacts.drop(classified_contacts.index[rows_to_del]).reset_index().drop('index',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# classified_contacts.loc[:,'type']=''\n",
    "classified_contacts.loc[:,'group']=''\n",
    "classified_contacts.loc[:,'area']=0\n",
    "classified_contacts.loc[:,'excentricity']=0\n",
    "classified_contacts.loc[:,'angle']=0\n",
    "classified_contacts.loc[:,'branch_dist']=0\n",
    "classified_contacts.loc[:,'end_dist']=0\n",
    "classified_contacts.loc[:,'height']=0\n",
    "classified_contacts.loc[:,'count']=0\n",
    "cc_log_norm=classified_contacts.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(classified_contacts.shape[0]):\n",
    "    classified_contacts.loc[i,'group']=contact_summary['group'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "    classified_contacts.loc[i,'area']=contact_summary['area'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "    classified_contacts.loc[i,'excentricity']=contact_summary['excentricity'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "    classified_contacts.loc[i,'angle']=contact_summary['angle'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "    classified_contacts.loc[i,'branch_dist']=contact_summary['branch_dist'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "    classified_contacts.loc[i,'end_dist']=contact_summary['end_dist'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "    classified_contacts.loc[i,'height']=contact_summary['height'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "    classified_contacts.loc[i,'count']=contact_summary['count'][(contact_summary['cell']==classified_contacts['cell'][i])&(contact_summary['cone']==classified_contacts['cone'][i])].values[0]\n",
    "for i in range(cc_log_norm.shape[0]):\n",
    "    cc_log_norm.loc[i,'group']=contact_summary_log_norm['group'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]\n",
    "    cc_log_norm.loc[i,'area']=contact_summary_log_norm['area'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]\n",
    "    cc_log_norm.loc[i,'excentricity']=contact_summary_log_norm['excentricity'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]\n",
    "    cc_log_norm.loc[i,'angle']=contact_summary_log_norm['angle'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]\n",
    "    cc_log_norm.loc[i,'branch_dist']=contact_summary_log_norm['branch_dist'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]\n",
    "    cc_log_norm.loc[i,'end_dist']=contact_summary_log_norm['end_dist'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]\n",
    "    cc_log_norm.loc[i,'height']=contact_summary_log_norm['height'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]\n",
    "    cc_log_norm.loc[i,'count']=contact_summary_log_norm['count'][(contact_summary_log_norm['cell']==cc_log_norm['cell'][i])&(contact_summary_log_norm['cone']==cc_log_norm['cone'][i])].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cc_plots=classified_contacts.copy()\n",
    "for i in range(cc_plots.shape[0]):\n",
    "    cc_plots.loc[i,'min_height']=np.min(all_contacts['height'][(all_contacts['cell']==cc_plots['cell'][i])&(all_contacts['cone']==cc_plots['cone'][i])])\n",
    "for i in range(cc_plots.shape[0]):\n",
    "    cc_plots.loc[i,'mean_height']=np.mean(all_contacts['height'][(all_contacts['cell']==cc_plots['cell'][i])&(all_contacts['cone']==cc_plots['cone'][i])])\n",
    "for i in range(cc_plots.shape[0]):\n",
    "    cc_plots.loc[i,'excent_min_height']=all_contacts['excentricity'][(all_contacts['cell']==cc_plots['cell'][i])&(all_contacts['cone']==cc_plots['cone'][i])\\\n",
    "                                                                   &(all_contacts['height']==cc_plots['min_height'][i])].values[0]\n",
    "for i in range(cc_plots.shape[0]):\n",
    "    cc_plots.loc[i,'area_log']=np.log10(cc_plots.ix[i,'area'])\n",
    "for i in range(cc_plots.shape[0]):\n",
    "    cc_plots.loc[i,'end_dist_log']=np.log10(cc_plots.ix[i,'end_dist'])\n",
    "for i in range(cc_plots.shape[0]):\n",
    "    cc_plots.loc[i,'branch_dist_log']=np.log10(cc_plots.ix[i,'branch_dist'])\n",
    "cc_plots['excent_min_height']/=1e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures for poster/paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d3a75c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5/2.54,3/2.54))\n",
    "sns.set(context=\"paper\",style='white',font='Arial',rc={\"xtick.major.size\": 0, \"ytick.major.size\": 4,\"lines.markeredgewidth\": 0.02,\"lines.markersize\":10})\n",
    "ax = sns.violinplot(x=\"group\",y=\"min_height\", data=cc_plots,hue='class',hue_order=[0,1],order=['off','on','rbc'],orient=\"v\",inner=None,\\\n",
    "                    linewidth=0,palette=sns.light_palette('gray',n_colors=3,reverse=True))\n",
    "ax2=sns.stripplot(x=\"group\", y=\"min_height\", data=cc_plots, hue='class',hue_order=[0,1],order=['off','on','rbc'],jitter=0.05,split=True, marker='x',size=2,linewidth=0.5,palette=['black','black'])\n",
    "ax.set(xlabel='',xticklabels=['OFF-\\nCBCs','ON-\\nCBCs','RBCs'],xticks=[-0.05,1.1,2],ylim=(-0.7,1.5))\n",
    "plt.legend([])\n",
    "ax.set_ylabel('Contact height')\n",
    "sns.despine(offset=3)\n",
    "# plt.savefig('figures/cc_stats_min_height.svg',bbox_inches='tight',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gEB4eDj6fDw8Pj35VswZqOUzZLw8ePADQs8ZrSkQdar0XtVoNhUKBmJgYTJ48Gd7e3rS6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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d3a6b5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5/2.54,3/2.54))\n",
    "sns.set(context=\"paper\",style='white',font='Arial',rc={\"xtick.major.size\": 0, \"ytick.major.size\": 4,\"lines.markeredgewidth\": 0.02,\"lines.markersize\":10})\n",
    "ax = sns.violinplot(x=\"group\",y=\"excentricity\", data=cc_plots,hue='class',hue_order=[0,1],order=['off','on','rbc'],orient=\"v\",inner=None,\\\n",
    "                    linewidth=0,palette=sns.light_palette('gray',n_colors=3,reverse=True))\n",
    "ax = sns.stripplot(x=\"group\", y=\"excentricity\", data=cc_plots, hue='class',hue_order=[0,1],order=['off','on','rbc'],jitter=0.05,split=True,\\\n",
    "                   size=2,marker='x',linewidth=0.5,palette=['black','black'])\n",
    "ax.set(xlabel='',xticklabels=['OFF-\\nCBCs','ON-\\nCBCs','RBCs'],xticks=[-0.05,1.1,2],ylim=(0,8),yticks=[0,2,4,6,8])\n",
    "plt.legend([])\n",
    "ax.set_ylabel('Eccentricity [$\\mu m$]')\n",
    "sns.despine(offset=3)\n",
    "# plt.savefig('figures/cc_stats_eccentricity_min.svg',bbox_inches='tight',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SCV5//XVYW1tj6tSpFhniB4qOj369873jiNpVwMDOzg7+/v6oqKjAF198AaFQCBcXF7S1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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d380f1a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5/2.54,3/2.54))\n",
    "sns.set(context=\"paper\",style='white',font='Arial',rc={\"xtick.major.size\": 0, \"ytick.major.size\": 4,\"lines.markeredgewidth\": 0.02,\"lines.markersize\":10})\n",
    "ax = sns.stripplot(x=\"group\", y=\"area\", data=cc_plots, hue='class',hue_order=[0,1],order=['off','on','rbc'],jitter=0, size=0,marker='x',palette=['black','black'])\n",
    "plt.legend([],loc='upper left')\n",
    "ax2 = ax.twinx()\n",
    "sns.violinplot(x=\"group\",y=\"area_log\", data=cc_plots,hue='class',hue_order=[0,1],order=['off','on','rbc'],orient=\"v\",inner=None,linewidth=0,palette=sns.light_palette('gray',n_colors=3,reverse=True),ax=ax2)\n",
    "sns.stripplot(x=\"group\", y=\"area_log\", data=cc_plots, hue='class',hue_order=[0,1],order=['off','on','rbc'],jitter=0.05,split=True,size=2,marker='x',linewidth=0.5,palette=['black','black',],ax=ax2)\n",
    "ax.set(yscale='log',ylim=(1e-4,1e2),xlabel='',xticklabels=['OFF-\\nCBCs','ON-\\nCBCs','RBCs'],xticks=[-0.05,1.1,2],ylabel='Contact area [$\\mu m^2$]')\n",
    "ax.set_yticks([1e-4,1e-2,1,1e2])\n",
    "ax2.set(ylim=(-4,2),ylabel='',yticks=[])\n",
    "sns.despine(offset=3)\n",
    "plt.legend([])\n",
    "# plt.savefig('figures/cc_stats_area.svg',bbox_inches='tight',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crossvalidation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001 1.0 0.68 1.0 0.0\n",
      "0.0001 10.0 0.68 1.0 0.0\n",
      "0.0001 100.0 0.88 0.3125 0.0294117647059\n",
      "0.0001 1000.0 0.92 0.125 0.0588235294118\n",
      "0.0001 10000.0 0.9 0.1875 0.0588235294118\n",
      "0.0001 100000.0 0.9 0.125 0.0882352941176\n",
      "0.0001 1000000.0 0.9 0.125 0.0882352941176\n",
      "0.001 1.0 0.68 1.0 0.0\n",
      "0.001 10.0 0.88 0.3125 0.0294117647059\n",
      "0.001 100.0 0.92 0.125 0.0588235294118\n",
      "0.001 1000.0 0.9 0.1875 0.0588235294118\n",
      "0.001 10000.0 0.9 0.125 0.0882352941176\n",
      "0.001 100000.0 0.9 0.125 0.0882352941176\n",
      "0.001 1000000.0 0.9 0.125 0.0882352941176\n",
      "0.01 1.0 0.88 0.3125 0.0294117647059\n",
      "0.01 10.0 0.92 0.125 0.0588235294118\n",
      "0.01 100.0 0.9 0.1875 0.0588235294118\n",
      "0.01 1000.0 0.86 0.25 0.0882352941176\n",
      "0.01 10000.0 0.86 0.25 0.0882352941176\n",
      "0.01 100000.0 0.86 0.25 0.0882352941176\n",
      "0.01 1000000.0 0.86 0.25 0.0882352941176\n",
      "0.1 1.0 0.9 0.1875 0.0588235294118\n",
      "0.1 10.0 0.84 0.375 0.0588235294118\n",
      "0.1 100.0 0.84 0.3125 0.0882352941176\n",
      "0.1 1000.0 0.84 0.3125 0.0882352941176\n",
      "0.1 10000.0 0.84 0.3125 0.0882352941176\n",
      "0.1 100000.0 0.84 0.3125 0.0882352941176\n",
      "0.1 1000000.0 0.84 0.3125 0.0882352941176\n"
     ]
    }
   ],
   "source": [
    "#OFF-CBCs\n",
    "for gamma in np.logspace(-4,-1,4):\n",
    "    for C in np.logspace(0,6,7):\n",
    "        loo=cross_validation.LeaveOneOut(cc_log_norm.ix[cc_log_norm['group']=='off',:].shape[0])\n",
    "        clf_svm=svm.SVC(gamma=gamma,C=C)\n",
    "        scores_svm_loo = cross_validation.cross_val_score(clf_svm,cc_log_norm.ix[cc_log_norm['group']=='off',5:].as_matrix(),\\\n",
    "                                                          cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix(),cv=loo)\n",
    "        prediction_svm_loo = cross_validation.cross_val_predict(clf_svm,cc_log_norm.ix[cc_log_norm['group']=='off',5:].as_matrix(),\\\n",
    "                                                                cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix(),cv=loo)\n",
    "        print(gamma,C,np.mean(scores_svm_loo),\\\n",
    "              np.sum(prediction_svm_loo>cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix())/np.sum(cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix()==0)\\\n",
    "              ,np.sum(prediction_svm_loo<cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix())/np.sum(cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix()==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma C score false positive false negative\n",
      "0.0001 0.1 1.0 0.601851851852 1.0 0.0\n",
      "0.0001 0.1 1.5 0.0 1.0 1.0\n",
      "0.0001 0.1 2.0 0.398148148148 0.0 1.0\n",
      "0.0001 0.1 2.5 0.398148148148 0.0 1.0\n",
      "0.0001 0.1 3.0 0.398148148148 0.0 1.0\n",
      "0.0001 1.0 1.0 0.601851851852 1.0 0.0\n",
      "0.0001 1.0 1.5 0.0 1.0 1.0\n",
      "0.0001 1.0 2.0 0.398148148148 0.0 1.0\n",
      "0.0001 1.0 2.5 0.398148148148 0.0 1.0\n",
      "0.0001 1.0 3.0 0.398148148148 0.0 1.0\n",
      "0.0001 10.0 1.0 0.712962962963 0.720930232558 0.0\n",
      "0.0001 10.0 1.5 0.740740740741 0.651162790698 0.0\n",
      "0.0001 10.0 2.0 0.601851851852 0.093023255814 0.6\n",
      "0.0001 10.0 2.5 0.435185185185 0.0 0.938461538462\n",
      "0.0001 10.0 3.0 0.398148148148 0.0 1.0\n",
      "0.0001 100.0 1.0 0.842592592593 0.325581395349 0.0461538461538\n",
      "0.0001 100.0 1.5 0.861111111111 0.209302325581 0.0923076923077\n",
      "0.0001 100.0 2.0 0.851851851852 0.139534883721 0.153846153846\n",
      "0.0001 100.0 2.5 0.851851851852 0.116279069767 0.169230769231\n",
      "0.0001 100.0 3.0 0.759259259259 0.116279069767 0.323076923077\n",
      "0.0001 1000.0 1.0 0.851851851852 0.255813953488 0.0769230769231\n",
      "0.0001 1000.0 1.5 0.87037037037 0.186046511628 0.0923076923077\n",
      "0.0001 1000.0 2.0 0.87962962963 0.162790697674 0.0923076923077\n",
      "0.0001 1000.0 2.5 0.861111111111 0.116279069767 0.153846153846\n",
      "0.0001 1000.0 3.0 0.805555555556 0.0697674418605 0.276923076923\n",
      "0.001 0.1 1.0 0.601851851852 1.0 0.0\n",
      "0.001 0.1 1.5 0.0 1.0 1.0\n",
      "0.001 0.1 2.0 0.398148148148 0.0 1.0\n",
      "0.001 0.1 2.5 0.398148148148 0.0 1.0\n",
      "0.001 0.1 3.0 0.398148148148 0.0 1.0\n",
      "0.001 1.0 1.0 0.712962962963 0.720930232558 0.0\n",
      "0.001 1.0 1.5 0.740740740741 0.651162790698 0.0\n",
      "0.001 1.0 2.0 0.592592592593 0.093023255814 0.615384615385\n",
      "0.001 1.0 2.5 0.425925925926 0.0 0.953846153846\n",
      "0.001 1.0 3.0 0.398148148148 0.0 1.0\n",
      "0.001 10.0 1.0 0.842592592593 0.325581395349 0.0461538461538\n",
      "0.001 10.0 1.5 0.861111111111 0.209302325581 0.0923076923077\n",
      "0.001 10.0 2.0 0.851851851852 0.162790697674 0.138461538462\n",
      "0.001 10.0 2.5 0.851851851852 0.116279069767 0.169230769231\n",
      "0.001 10.0 3.0 0.759259259259 0.116279069767 0.323076923077\n",
      "0.001 100.0 1.0 0.851851851852 0.255813953488 0.0769230769231\n",
      "0.001 100.0 1.5 0.87037037037 0.186046511628 0.0923076923077\n",
      "0.001 100.0 2.0 0.87962962963 0.162790697674 0.0923076923077\n",
      "0.001 100.0 2.5 0.861111111111 0.116279069767 0.153846153846\n",
      "0.001 100.0 3.0 0.777777777778 0.116279069767 0.292307692308\n",
      "0.001 1000.0 1.0 0.87037037037 0.209302325581 0.0769230769231\n",
      "0.001 1000.0 1.5 0.861111111111 0.186046511628 0.107692307692\n",
      "0.001 1000.0 2.0 0.851851851852 0.162790697674 0.138461538462\n",
      "0.001 1000.0 2.5 0.842592592593 0.139534883721 0.169230769231\n",
      "0.001 1000.0 3.0 0.796296296296 0.139534883721 0.246153846154\n",
      "0.01 0.1 1.0 0.694444444444 0.767441860465 0.0\n",
      "0.01 0.1 1.5 0.731481481481 0.651162790698 0.0153846153846\n",
      "0.01 0.1 2.0 0.472222222222 0.0697674418605 0.830769230769\n",
      "0.01 0.1 2.5 0.398148148148 0.0 1.0\n",
      "0.01 0.1 3.0 0.398148148148 0.0 1.0\n",
      "0.01 1.0 1.0 0.833333333333 0.348837209302 0.0461538461538\n",
      "0.01 1.0 1.5 0.87037037037 0.209302325581 0.0769230769231\n",
      "0.01 1.0 2.0 0.861111111111 0.162790697674 0.123076923077\n",
      "0.01 1.0 2.5 0.851851851852 0.116279069767 0.169230769231\n",
      "0.01 1.0 3.0 0.777777777778 0.116279069767 0.292307692308\n",
      "0.01 10.0 1.0 0.87037037037 0.232558139535 0.0615384615385\n",
      "0.01 10.0 1.5 0.87962962963 0.162790697674 0.0923076923077\n",
      "0.01 10.0 2.0 0.87962962963 0.162790697674 0.0923076923077\n",
      "0.01 10.0 2.5 0.851851851852 0.139534883721 0.153846153846\n",
      "0.01 10.0 3.0 0.814814814815 0.139534883721 0.215384615385\n",
      "0.01 100.0 1.0 0.861111111111 0.209302325581 0.0923076923077\n",
      "0.01 100.0 1.5 0.861111111111 0.209302325581 0.0923076923077\n",
      "0.01 100.0 2.0 0.842592592593 0.209302325581 0.123076923077\n",
      "0.01 100.0 2.5 0.833333333333 0.209302325581 0.138461538462\n",
      "0.01 100.0 3.0 0.842592592593 0.186046511628 0.138461538462\n",
      "0.01 1000.0 1.0 0.851851851852 0.255813953488 0.0769230769231\n",
      "0.01 1000.0 1.5 0.833333333333 0.232558139535 0.123076923077\n",
      "0.01 1000.0 2.0 0.87037037037 0.139534883721 0.123076923077\n",
      "0.01 1000.0 2.5 0.87037037037 0.139534883721 0.123076923077\n",
      "0.01 1000.0 3.0 0.861111111111 0.162790697674 0.123076923077\n",
      "0.1 0.1 1.0 0.842592592593 0.325581395349 0.0461538461538\n",
      "0.1 0.1 1.5 0.888888888889 0.162790697674 0.0769230769231\n",
      "0.1 0.1 2.0 0.851851851852 0.162790697674 0.138461538462\n",
      "0.1 0.1 2.5 0.861111111111 0.116279069767 0.153846153846\n",
      "0.1 0.1 3.0 0.833333333333 0.116279069767 0.2\n",
      "0.1 1.0 1.0 0.87037037037 0.209302325581 0.0769230769231\n",
      "0.1 1.0 1.5 0.861111111111 0.209302325581 0.0923076923077\n",
      "0.1 1.0 2.0 0.87037037037 0.186046511628 0.0923076923077\n",
      "0.1 1.0 2.5 0.851851851852 0.186046511628 0.123076923077\n",
      "0.1 1.0 3.0 0.851851851852 0.186046511628 0.123076923077\n",
      "0.1 10.0 1.0 0.851851851852 0.255813953488 0.0769230769231\n",
      "0.1 10.0 1.5 0.842592592593 0.232558139535 0.107692307692\n",
      "0.1 10.0 2.0 0.824074074074 0.232558139535 0.138461538462\n",
      "0.1 10.0 2.5 0.833333333333 0.209302325581 0.138461538462\n",
      "0.1 10.0 3.0 0.824074074074 0.209302325581 0.153846153846\n",
      "0.1 100.0 1.0 0.833333333333 0.232558139535 0.123076923077\n",
      "0.1 100.0 1.5 0.842592592593 0.209302325581 0.123076923077\n",
      "0.1 100.0 2.0 0.842592592593 0.209302325581 0.123076923077\n",
      "0.1 100.0 2.5 0.842592592593 0.209302325581 0.123076923077\n",
      "0.1 100.0 3.0 0.842592592593 0.209302325581 0.123076923077\n",
      "0.1 1000.0 1.0 0.833333333333 0.209302325581 0.138461538462\n",
      "0.1 1000.0 1.5 0.814814814815 0.209302325581 0.169230769231\n",
      "0.1 1000.0 2.0 0.814814814815 0.209302325581 0.169230769231\n",
      "0.1 1000.0 2.5 0.814814814815 0.209302325581 0.169230769231\n",
      "0.1 1000.0 3.0 0.814814814815 0.209302325581 0.169230769231\n"
     ]
    }
   ],
   "source": [
    "#ON-CBCs\n",
    "print('gamma','C','score','false positive','false negative')\n",
    "for gamma in np.logspace(-4,-1,4):\n",
    "    for C in np.logspace(-1,3,5):\n",
    "        for false_weight in range(2,7):\n",
    "            loo=cross_validation.LeaveOneOut(cc_log_norm.ix[cc_log_norm['group']=='on',:].shape[0])\n",
    "            clf_svm=svm.SVC(gamma=gamma,C=C,class_weight={0:false_weight/2,1:1})\n",
    "            scores_svm_loo = cross_validation.cross_val_score(clf_svm,cc_log_norm.ix[cc_log_norm['group']=='on',5:].as_matrix(),\\\n",
    "                                                              cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix(),cv=loo)\n",
    "            prediction_svm_loo = cross_validation.cross_val_predict(clf_svm,cc_log_norm.ix[cc_log_norm['group']=='on',5:].as_matrix(),\\\n",
    "                                                                    cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix(),cv=loo)\n",
    "            print(gamma,C,false_weight/2,np.mean(scores_svm_loo),\\\n",
    "                  np.sum(prediction_svm_loo>cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix())/np.sum(cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix()==0),\\\n",
    "                  np.sum(prediction_svm_loo<cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix())/np.sum(cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix()==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001 1.0 0.641791044776 0.0 1.0\n",
      "0.0001 10.0 0.641791044776 0.0 1.0\n",
      "0.0001 100.0 0.805970149254 0.0697674418605 0.416666666667\n",
      "0.0001 1000.0 0.805970149254 0.093023255814 0.375\n",
      "0.0001 10000.0 0.805970149254 0.139534883721 0.291666666667\n",
      "0.0001 100000.0 0.820895522388 0.116279069767 0.291666666667\n",
      "0.001 1.0 0.641791044776 0.0 1.0\n",
      "0.001 10.0 0.805970149254 0.0697674418605 0.416666666667\n",
      "0.001 100.0 0.805970149254 0.093023255814 0.375\n",
      "0.001 1000.0 0.820895522388 0.116279069767 0.291666666667\n",
      "0.001 10000.0 0.805970149254 0.162790697674 0.25\n",
      "0.001 100000.0 0.850746268657 0.116279069767 0.208333333333\n",
      "0.01 1.0 0.791044776119 0.0697674418605 0.458333333333\n",
      "0.01 10.0 0.805970149254 0.116279069767 0.333333333333\n",
      "0.01 100.0 0.791044776119 0.162790697674 0.291666666667\n",
      "0.01 1000.0 0.820895522388 0.139534883721 0.25\n",
      "0.01 10000.0 0.89552238806 0.093023255814 0.125\n",
      "0.01 100000.0 0.89552238806 0.093023255814 0.125\n",
      "0.1 1.0 0.805970149254 0.116279069767 0.333333333333\n",
      "0.1 10.0 0.791044776119 0.139534883721 0.333333333333\n",
      "0.1 100.0 0.865671641791 0.093023255814 0.208333333333\n",
      "0.1 1000.0 0.865671641791 0.093023255814 0.208333333333\n",
      "0.1 10000.0 0.865671641791 0.093023255814 0.208333333333\n",
      "0.1 100000.0 0.865671641791 0.093023255814 0.208333333333\n",
      "1.0 1.0 0.746268656716 0.116279069767 0.5\n",
      "1.0 10.0 0.776119402985 0.162790697674 0.333333333333\n",
      "1.0 100.0 0.776119402985 0.162790697674 0.333333333333\n",
      "1.0 1000.0 0.776119402985 0.162790697674 0.333333333333\n",
      "1.0 10000.0 0.776119402985 0.162790697674 0.333333333333\n",
      "1.0 100000.0 0.776119402985 0.162790697674 0.333333333333\n"
     ]
    }
   ],
   "source": [
    "#RBCs\n",
    "for gamma in np.logspace(-4,0,5):\n",
    "    for C in np.logspace(0,5,6):\n",
    "        loo=cross_validation.LeaveOneOut(cc_log_norm.ix[cc_log_norm['group']=='rbc',:].shape[0])\n",
    "        clf_svm=svm.SVC(gamma=gamma,C=C)\n",
    "        scores_svm_loo = cross_validation.cross_val_score(clf_svm,cc_log_norm.ix[cc_log_norm['group']=='rbc',5:].as_matrix(),\\\n",
    "                                                          cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix(),cv=loo)\n",
    "        prediction_svm_loo = cross_validation.cross_val_predict(clf_svm,cc_log_norm.ix[cc_log_norm['group']=='rbc',5:].as_matrix(),\\\n",
    "                                                                cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix(),cv=loo)\n",
    "        print(gamma,C,np.mean(scores_svm_loo),\\\n",
    "              np.sum(prediction_svm_loo>cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix())/np.sum(cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix()==0),\\\n",
    "              np.sum(prediction_svm_loo<cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix())/np.sum(cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix()==1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "off_svm_cv=svm.SVC(gamma=1e-3,C=100)\n",
    "on_svm_cv=svm.SVC(gamma=1e-2,C=1000,class_weight={0:2,1:1})\n",
    "rbc_svm_cv=svm.SVC(gamma=1e-2,C=10000)\n",
    "loo=cross_validation.LeaveOneOut(cc_log_norm.ix[cc_log_norm['group']=='off',:].shape[0])\n",
    "off_scores_svm_cv = cross_validation.cross_val_score(off_svm_cv,cc_log_norm.ix[cc_log_norm['group']=='off',5:].as_matrix(),\\\n",
    "                                                        cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix(),cv=loo)\n",
    "off_prediction_cv = cross_validation.cross_val_predict(off_svm_cv,cc_log_norm.ix[cc_log_norm['group']=='off',5:].as_matrix(),\\\n",
    "                                                        cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix(),cv=loo)\n",
    "loo=cross_validation.LeaveOneOut(cc_log_norm.ix[cc_log_norm['group']=='on',:].shape[0])\n",
    "on_scores_svm_cv = cross_validation.cross_val_score(on_svm_cv,cc_log_norm.ix[cc_log_norm['group']=='on',5:].as_matrix(),\\\n",
    "                                                        cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix(),cv=loo)\n",
    "on_prediction_cv = cross_validation.cross_val_predict(on_svm_cv,cc_log_norm.ix[cc_log_norm['group']=='on',5:].as_matrix(),\\\n",
    "                                                        cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix(),cv=loo)\n",
    "loo=cross_validation.LeaveOneOut(cc_log_norm.ix[cc_log_norm['group']=='rbc',:].shape[0])\n",
    "rbc_scores_svm_cv = cross_validation.cross_val_score(rbc_svm_cv,cc_log_norm.ix[cc_log_norm['group']=='rbc',5:].as_matrix(),\\\n",
    "                                                        cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix(),cv=loo)\n",
    "rbc_prediction_cv = cross_validation.cross_val_predict(rbc_svm_cv,cc_log_norm.ix[cc_log_norm['group']=='rbc',5:].as_matrix(),\\\n",
    "                                                        cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix(),cv=loo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cc_cv_plot=classified_contacts.copy()\n",
    "cc_cv_plot.loc[:,'prediction']=0\n",
    "cc_cv_plot.loc[cc_cv_plot['group']=='off','prediction']=off_prediction_cv\n",
    "cc_cv_plot.loc[cc_cv_plot['group']=='on','prediction']=on_prediction_cv\n",
    "cc_cv_plot.loc[cc_cv_plot['group']=='rbc','prediction']=rbc_prediction_cv\n",
    "cc_cv_plot.loc[:,'error']=np.abs(cc_cv_plot['class']-cc_cv_plot['prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4TVAsixBrdZxauieffJLZhTBXcMNF9z/zzDMQiUQ4duwYvL29ERERwfiOUSyLUGt1gsSx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\nFR2Fd6joKLxDRUfhHSo6Cu/8HxAFWmFYHlhtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d37fddf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4/2.54,4/2.54))\n",
    "sns.set(font='Arial',style='white',context='paper',rc={\"xtick.major.size\": 4, \"ytick.major.size\": 4})\n",
    "ax=plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==1)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==1)]['area'],marker='o',c='k',s=20,lw=0.3,edgecolor='w').axes\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==0)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==0)]['area'],marker='^',c='k',s=20,lw=0.3,edgecolor='w')\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==0)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==0)]['area'],marker='o',c='w',s=17,lw=0.3)\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==1)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='off')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==1)]['area'],marker='^',c='w',s=17,lw=0.3)\n",
    "ax.set(xscale='log',xlim=(1e-1,1e1),yscale='log',ylim=(5e-3,1e1))\n",
    "plt.title('OFF-CBCs')\n",
    "ax.set_xlabel('Eccentricity [$\\mu m$]')\n",
    "ax.set_ylabel('Contact area [$\\mu m^2$]')\n",
    "sns.despine(offset=3)\n",
    "# plt.savefig('figures/classification_off.svg',bbox_inches='tight',dpi=300,transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1q4vIqFIUqvdXvEdISAi2b99On+Pn5wdnZ2dYW1vjyZMnsLGxQWpqKubOnYv4+HiUl5ej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\nc3jR8XAOLzoezuFFx8M5vOh4OOf/AGheHW+NqJ85AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d3a6c0b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4/2.54,4/2.54))\n",
    "sns.set(font='Arial',style='white',context='paper',rc={\"xtick.major.size\": 4, \"ytick.major.size\": 4})\n",
    "ax=plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==1)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==1)]['area'],marker='o',c='k',s=20,lw=0.3,edgecolor='w').axes\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==0)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==0)]['area'],marker='^',c='k',s=20,lw=0.3,edgecolor='w')\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==0)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==0)]['area'],marker='o',c='w',s=17,lw=0.3)\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==1)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='on')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==1)]['area'],marker='^',c='w',s=17,lw=0.3)\n",
    "ax.set(xscale='log',xlim=(1e-1,1e1),yscale='log',ylim=(5e-3,1e1))\n",
    "plt.title('ON-CBCs')\n",
    "ax.set_xlabel('Eccentricity [$\\mu m$]')\n",
    "ax.set_ylabel('Contact area [$\\mu m^2$]')\n",
    "sns.despine(offset=3)\n",
    "# plt.savefig('figures/classification_on.svg',bbox_inches='tight',dpi=300,transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1dcTJyUkjBXqdTRrMzc2RmJiIjo4OfP/993jppZeeSuSqzCvi5uam0rW0MbeIJhiJB+pf\nNjtz5oxCL5HRlDSw8nBtbW3k1KlTJDw8nHz99dekvb19RGpX57wihOiuhxuJB+rvGQsLC0lycvKg\nc49cv35dbTY/Law83IYNGxAfH682sevLvCIjZSQeqL9nLC0tRUBAAOLi4mBqasp4vrKyMqxZswax\nsbEqdxTVCGzU6efnRyIjI0l8fDxJSEhQmDhwNKCrHm4o5JXBg9HfM/7www/kxo0bhBBCSktLyZtv\nvsl4vtDQUAKA/PWvf9WdMlz/+jA6TFCzDFeVUVlZifnz58Pe3h5GRkYoKChASkoKOjs7YWxsDD6f\nj+TkZGY8KwBcvHgRzs7Og/bD4wJWscvNzQ0WFhYwMzPD2LFj8ejRI03ZRYFiuByMmzdv4ttvv4W9\nvT0iIyPh6uoKPp+PcePGQSKRYObMmYiKikJ1dTW8vLzg7e0NJycn1NTUaG1eGFYtDQEBAQMa7+Vr\ntY8GdLmloT99WxAGG+8w1P7BxjcAGLBNG2U5ViHV0NAQO3bsQEhICPbt2zfslAWUp2e4RGKo/coq\ng7WaLPw/tPF+lDJcCwKbYYmjCVZluMjISBgZGWHLli24fPkyQkNDNWUXRU9h5eFeeuklpnWBio3y\nNDxbNawUrcNKcNevX1f4fuXKFbUaQ9F/VAqpmZmZyMvLQ1paGjO3LCEEOTk5zDhLCkUVVBLcrFmz\nYGJigtraWkyfPh2Wlpbg8XhYvXq1pu2j6BkqhVRzc3PMnTsXpqamaGlpwdy5c5GXl4cjR45o2j6K\nnsGqDFdTU8NMy7Vp0yZaD0dhDatqETMzMyQlJWH69OkoLy/HmDFjNGUXRU9h5eEOHDiA9vZ2JCYm\norW1VSNrb1GGpre3F0KhECkpKRAKhTo35oOVh+PxeLC3t4ednR0IIfjuu+/oVA8ck5KSgrfffhtm\nZmZobW1VmFBaF6BTPegYBgYGMDMzA/CkiMO2T6K2x6ayCqny3iJ2dnb48ssv0dDQoCm7KEro7e1l\nlk5qbW0Fi95lA+aR0wa0t4iO4eHhAZFIBB6PB0IIli5dqvK5o2FsKqsOmNeuXYOxsTEz1cPvf//7\nAavTaBN96oCpbobr0MkVT920ZWhoiIMHD2pUcI8ePUJYWBi2bt06KqeA1yVUHRmm6TIeq6aturo6\nLFy4kFkNWtNNW42NjZg+fbpG7/GsoEqHTS7mH2HVtPXpp5+isbEREokEdXV1uHr16lPdVCwWM/MG\nNzQ04OOPP8bevXuZRUbOnDmD1NRU2NnZMYuZUTTPcIN21AGrLPVvf/sbKisr8fDhQ+bDlqamJiQl\nJcHExATAk6kaAgICEBYWhqysLMhkMqxatQru7u4A6FBErug/Jb+mMllWWeoLL7yAbdu2jeiGqswr\n0rd+b7gVbvRlbhFtw9X8I6wEN3bsWLUt0CtPjq2trSGVSjF+/Hi0trYylZqqsmLFCmbWbnmWShme\n/skBV4NyWAlOvlSlvA5oJNB5RbSHVicnZDMvRE9PDxEKheTQoUMkMTGRdHd3q3XeiZGij3OLaILB\nJp7mClbuJDQ0FHfu3MEbb7wBqVSKnTt3auhnQNEUEokEjY2NADSbHCiDVUitq6vD/v37AQALFix4\n5tdI1UVu3boFoVCIgwcP4t69e5xPTsjKwxkZGSEnJwctLS3Izs6GsbGxpuyiaIjy8nIUFRUxS5tz\nPXqfVVtqU1MTjh07BrFYjN/85jf48MMPYWlpqUn7WEHbUodmNLSnqhRSOzo6cPjwYXz00UcIDQ1F\neno6cnNzYWpqqmn7KGpksLq2vutycYIqmcWWLVtIYmIi6e3tJYQQ8vjxY/LNN9+QzZs3azCfYQ/N\nUtmhyZnTlaFSGe7+/ftYvnw5U3dmaGgIHx8fZsUTim7CRdtpf1ROGvoP1pDJZJDJZGo3iMINXLWd\n9kelMpyvry82btyIDz74AJaWlqitrcWFCxcUFoKl6BbaWrtB5Sz1559/RmpqKpqammBpaYl33nkH\nkydP1rR9rKBZ6uhH5YrfiRMnYsuWLZq0hfIMQFvKKZxCBUfhFCo4CqdQwVE4hQqOwimsGu9HOz09\nPZBKpbCysmJWlKaMLvRKcH2Ri4+iWdj+uPXWDUilUjqghgPYVrLrreCsrKwwadIkHD16VNumqMz6\n9et1zl4rKytW5+it4AwNDfH888/rVBOXLtrLtqxMs1QKp+i14HStN8uzYK/eZqmU0YleezjK6IMK\njsIpVHAUTqGCo3AKFRyFU6jgKJzyTAnu0aNH2Llzp1YXxlCV9PR0nD59GhcuXNC2KSqh6rt9pgSn\nS7OiV1RUwNfXV2d6vKj6bvVCcLo4K7oym8+cOQMATBvlaFktcLh3rOq71XnB6eKs6EPZnJmZCZlM\nhokTJyI+Ph52dnZatla1dwyo9m51XnDyWdHlL2OwWdH74uXlpfVVbVSx2c3NDf7+/qNiaUpV37Eq\n71bnBSeH9JsVHcBTzYrOJbpmszrs1Zv+cLo4K7qu2awOe2lvEQqnjM6fEkVvoYKjcAoVHIVTqOAo\nnEIFR+EUKjgKp1DBUTiFCo7CKVRwFE6hgqNwChVcH/Lz8/GHP/wBAQEB8Pf3R0BAAMrLy9V2/ebm\nZmRmZipsu3z5MtLT0wc9Xr5vsPP6IxAI8Kc//Qm5ubms7dq9ezez2rfG4WyRJR0gLy+PfP755xq9\n/v79+zVy3oULF0hCQsLTmkbee++9pz6XDXrTW0RdkH59Gdra2rB9+3a0trbCwcEB+/btG7AtPDwc\nwcHBaG5uhpWVFT777DOkpKQgKysLbW1t6Orqwr///W8kJCSgtLQUpqamyMvLAwAsXboUjx8/hoeH\nx4D7CAQCdHR0IC8vDyUlJSgvL0dUVBRsbW3xySefYN26dXBwcFCwV36Or68vjhw5gpkzZ+L+/fv4\n8ccf8fDhQ5ibm8PGxgY5OTnw9vaGv78/Z+8WoCF1AN9//71CSD179izeeecdnD17Fr/97W9x9+5d\nnD9/XmHbV199hfnz5+PUqVOYNm0aUlNTAQDm5uY4fvw43njjDfz000/w9/fH4sWLYWtri6lTpyI+\nPh5GRkYAMOCad+/eZWzy9/eHu7s7fHx8kJaWhp6eHkgkkgFiGwoLCwscP34czc3NcHZ2xtmzZyES\nidT78lSACq4f7u7uiIuLQ3x8POLi4iCRSJjBIb6+vrC0tIRYLFbY1tTUxHS7TktLY8QiX4fU0tIS\n3d3dIIQwHvTVV19VuO/t27cH3EeO/Jy3334b2dnZuHLlCubNm6f0GeTH9118T26LqakpXn31VTz/\n/PNaGS9BBdeP/iHV1taWSRyOHDmCyspK2NnZKWx75ZVXsGbNGsTFxWHz5s2YM2fOoNeWd2AkhAzo\n/z/YffqeRwjBCy+8gPHjx+Pbb7/FkiVLlD5DTU2Nwr+qPisXUMH1Iy0tTSGkOjg4ICMjA/7+/pBK\npZgyZQo++OADhW2BgYFITU2Fn58fjh8/Dnt7+0GvbWdnh5ycHPT09AzY1/+aU6ZMGXDelStXsGTJ\nEjQ1NQ05duD69evw9fVFfX09AMXBLcr+5gra41fHyMjIQH19Pfz8/BS2y5MFExMTJmlgw3vvvYfz\n58+r09RBoR5Oh0hKSsLJkyexbNmyQffHxcUN6j2HY/fu3Zyt7k09HIVTqIejcAoVHIVTqOAonEIF\nR+EUKjgKp1DBUTiFCo7CKf8HVZVj3zTeDq0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d37dfce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4/2.54,4/2.54))\n",
    "sns.set(font='Arial',style='white',context='paper',rc={\"xtick.major.size\": 4, \"ytick.major.size\": 4})\n",
    "ax=plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==1)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==1)]['area'],marker='o',c='k',s=20,lw=0.3,edgecolor='w').axes\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==0)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==0)]['area'],marker='^',c='k',s=20,lw=0.3,edgecolor='w')\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==0)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==1)&(cc_cv_plot['prediction']==0)]['area'],marker='o',c='w',s=17,lw=0.3)\n",
    "plt.scatter(cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==1)]['excentricity'],\\\n",
    "               cc_cv_plot[(cc_cv_plot['group']=='rbc')&(cc_cv_plot['class']==0)&(cc_cv_plot['prediction']==1)]['area'],marker='^',c='w',s=17,lw=0.3)\n",
    "ax.set(xscale='log',xlim=(1e-1,1e1),yscale='log',ylim=(2e-2,3e1))\n",
    "plt.title('RBCs')\n",
    "ax.set_xlabel('Eccentricity [$\\mu m$]')\n",
    "ax.set_ylabel('Contact area [$\\mu m^2$]')\n",
    "sns.despine(offset=3)\n",
    "# plt.savefig('figures/classification_rbc.svg',bbox_inches='tight',dpi=300,transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d37e79a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4/2.54,4/2.54))\n",
    "sns.set(font='Arial',context='paper',style='white',rc={\"xtick.major.size\": 0, \"ytick.major.size\": 4})\n",
    "ax=sns.barplot(x='group',y='error',hue='class',data=cc_cv_plot,ci=None,palette=[np.array([0.8,0.8,0.8,1.]),np.array([0.4,0.4,0.4,1.])])\n",
    "ax.set(ylabel='error rate',xlabel='',xticklabels=['Off CBC','On CBC','RBC'],ylim=(0,0.185),yticks=[0,0.05,0.1,0.15])\n",
    "plt.legend(bbox_to_anchor=(1, 1.08),labels=['false negative','false positive'])\n",
    "sns.despine()\n",
    "# plt.savefig('figures/classication_performance.svg',bbox_inches='tight',dpi=300,transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10000, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_svm_off=svm.SVC(gamma=1e-3,C=100)\n",
    "clf_svm_on=svm.SVC(gamma=1e-2,C=1000,class_weight={0:2,1:1})\n",
    "clf_svm_rbc=svm.SVC(gamma=1e-2,C=10000)\n",
    "clf_svm_off.fit(cc_log_norm.ix[cc_log_norm['group']=='off',5:].as_matrix(),cc_log_norm.ix[cc_log_norm['group']=='off']['class'].as_matrix())\n",
    "clf_svm_on.fit(cc_log_norm.ix[cc_log_norm['group']=='on',5:].as_matrix(),cc_log_norm.ix[cc_log_norm['group']=='on']['class'].as_matrix())\n",
    "clf_svm_rbc.fit(cc_log_norm.ix[cc_log_norm['group']=='rbc',5:].as_matrix(),cc_log_norm.ix[cc_log_norm['group']=='rbc']['class'].as_matrix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "contact_summary.loc[:,'prediction']=0\n",
    "contact_summary.ix[contact_summary['group']=='off','prediction']=clf_svm_off.predict(contact_summary_log_norm.ix[contact_summary_log_norm['group']=='off',4:11])\n",
    "contact_summary.ix[contact_summary['group']=='on','prediction']=clf_svm_on.predict(contact_summary_log_norm.ix[contact_summary_log_norm['group']=='on',4:11])\n",
    "contact_summary.ix[contact_summary['group']=='rbc','prediction']=clf_svm_rbc.predict(contact_summary_log_norm.ix[contact_summary_log_norm['group']=='rbc',4:11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "blue_cones=np.array([2006,2007,2009,2024,2028,2029])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_cones=np.unique(contact_summary['cone'].as_matrix())\n",
    "green_cones=all_cones[(np.in1d(all_cones,blue_cones,invert=True))&(np.in1d(all_cones,cones_outside_cbc9,invert=True))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(contact_summary.shape[0]):\n",
    "    if contact_summary.ix[i,'cone'] in blue_cones:\n",
    "        contact_summary.loc[i,'cone_type']='blue'\n",
    "    elif contact_summary.ix[i,'cone'] in green_cones:\n",
    "        contact_summary.loc[i,'cone_type']='green'\n",
    "    else:\n",
    "        contact_summary.loc[i,'cone_type']='outside'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# contact_summary.to_pickle('data/cone_contact_predictions')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}