rankCorrelation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from scipy.io import loadmat\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import spearmanr\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "matplotlib.style.use('default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# loading output of 'Analyze_behavior_jans.m'\n",
    "output = loadmat('/run/user/1000/gvfs/smb-share:server=cup.pni.princeton.edu,share=murthy/Kyle/code/pc2_tnt/output_2.mat')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['__header__', '__version__', '__globals__', 'Folders', 'MaleSong_FliesKinematics', 'param'])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# structure of output\n",
    "output.keys()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[array(['pc2_tnt'], dtype='<U7'),\n        array(['pc2_control'], dtype='<U11')]], dtype=object)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note the order of the genotypes!\n",
    "output['Folders']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "array([array(['Pulse amount'], dtype='<U12'),\n       array(['Sine amount'], dtype='<U11'),\n       array(['Bout amount'], dtype='<U11'),\n       array(['Pulse onsets'], dtype='<U12'),\n       array(['Sine onsets'], dtype='<U11'),\n       array(['Bout onsets'], dtype='<U11'), array(['IPI'], dtype='<U3'),\n       array(['Pulse duration'], dtype='<U14'),\n       array(['Sine duration'], dtype='<U13'),\n       array(['Bout duration'], dtype='<U13'),\n       array(['Pause'], dtype='<U5')], dtype=object)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns 0-10\n",
    "np.concatenate(output['MaleSong_FliesKinematics']['SongFeatures'][0][0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "array([array(['Female forward velocity'], dtype='<U23'),\n       array(['Female lateral velocity'], dtype='<U23'),\n       array(['Female rotational velocity'], dtype='<U26'),\n       array(['Female absolute velocity'], dtype='<U24'),\n       array(['Male forward velocity'], dtype='<U21'),\n       array(['Male lateral velocity'], dtype='<U21'),\n       array(['Male rotational velocity'], dtype='<U24'),\n       array(['Male absolute velocity'], dtype='<U22'),\n       array(['Male-Female distance'], dtype='<U20')], dtype=object)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns 11-22\n",
    "np.concatenate(output['MaleSong_FliesKinematics']['FliesKinematics'][0][0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(1076, 23)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pc2_tnt\n",
    "output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][0].shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "(475, 23)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pc2_control\n",
    "output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][1].shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Pulse amount\n",
      "1 Sine amount\n",
      "2 Bout amount\n",
      "3 Pulse onsets\n",
      "4 Sine onsets\n",
      "5 Bout onsets\n",
      "6 IPI\n",
      "7 Pulse duration\n",
      "8 Sine duration\n",
      "9 Bout duration\n",
      "10 Pause\n"
     ]
    }
   ],
   "source": [
    "controlIDX = 1\n",
    "tntIDX = 0\n",
    "songftrs = output['MaleSong_FliesKinematics']['SongFeatures'][0][0][0]\n",
    "\n",
    "# setting up dataframe for easier plotting\n",
    "df = pd.DataFrame(columns=['label', 'coeff', 'condition'])\n",
    "df_change = pd.DataFrame(columns=['label', 'dcoeff'])\n",
    "\n",
    "for idx, ftr in enumerate(songftrs):\n",
    "    print(idx, ftr[0])\n",
    "    fSpeed_control = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][controlIDX][:,14]\n",
    "    ftr_control = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][controlIDX][:,idx]\n",
    "    stat_c, _ = spearmanr(fSpeed_control, ftr_control)\n",
    "    df.loc[len(df.index)] = [ftr[0], stat_c, 'pc2_control']\n",
    "\n",
    "    fSpeed_tnt = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][tntIDX][:,14]\n",
    "    ftr_tnt = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][tntIDX][:,idx]\n",
    "    stat_t, _ = spearmanr(fSpeed_tnt, ftr_tnt)\n",
    "    df.loc[len(df.index)] = [ftr[0], stat_t, 'pc2_tnt']\n",
    "\n",
    "    df_change.loc[len(df_change.index)] = [ftr[0], stat_t-stat_c]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "order = ['IPI', 'Pulse amount', 'Pulse onsets', 'Pulse duration', 'Sine amount', 'Sine onsets', 'Sine duration', 'Bout amount', 'Bout onsets', 'Bout duration']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "\n",
    "ax = fig.add_subplot(121)\n",
    "sns.barplot(x='coeff', y='label', data=df, hue='condition', order= order, ax=ax)\n",
    "ax.legend().set_visible(False)\n",
    "\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('')\n",
    "ax.set_title('rank correlation\\nsong and speed', fontsize=10)\n",
    "ax.axvline(0, c='k', lw=0.5)\n",
    "\n",
    "ax.set_xticks([-0.25, 0, 0.25])\n",
    "\n",
    "ax1 = fig.add_subplot(122)\n",
    "sns.barplot(x='dcoeff', y='label', data=df_change, order= order, ax=ax1, color='darkgray')\n",
    "ax1.set_yticks([])\n",
    "ax1.set_ylabel('')\n",
    "ax1.legend().set_visible(False)\n",
    "\n",
    "ax1.axvline(0, c='k', lw=0.5)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_title('change in\\nrank correlation', fontsize=11)\n",
    "\n",
    "ax1.set_xticks([-0.2,-0.1, 0, 0.1])\n",
    "\n",
    "\n",
    "sns.despine(left=True, bottom=True, top=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(r'/run/user/1000/gvfs/smb-share:server=cup.pni.princeton.edu,share=murthy/Kyle/code/pc2_tnt/plots/rankCorrelation.svg')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "fSpeed_control = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][controlIDX][:,14]\n",
    "ipi_control = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][controlIDX][:,6]/10 # change to milliseconds\n",
    "fSpeed_tnt = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][tntIDX][:,14]\n",
    "ipi_tnt = output['MaleSong_FliesKinematics']['MaleSong_FemaleSpeed_Binned'][0][tntIDX][:,6]/10 # change to milliseconds"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "_, bin_edges_control = np.histogram(ipi_control)\n",
    "_, bin_edges_tnt = np.histogram(ipi_tnt)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "ipiDF = pd.DataFrame(columns=['ipi', 'fspeed', 'condition'])\n",
    "for be in range(len(bin_edges_control)):\n",
    "    if be==len(bin_edges_control)-1:\n",
    "        continue\n",
    "    bin_center = np.mean((bin_edges_control[be], bin_edges_control[be+1]))\n",
    "    idxs = np.where((ipi_control>=bin_edges_control[be]) & (ipi_control<=bin_edges_control[be+1]))[0]\n",
    "    for idx in idxs:\n",
    "        ipiDF.loc[len(ipiDF.index)] = [bin_center, fSpeed_control[idx], 'pC2l control']\n",
    "\n",
    "for be in range(len(bin_edges_tnt)):\n",
    "    if be==len(bin_edges_tnt)-1:\n",
    "        continue\n",
    "    bin_center = np.mean((bin_edges_tnt[be], bin_edges_tnt[be+1]))\n",
    "    idxs = np.where((ipi_tnt>=bin_edges_tnt[be]) & (ipi_tnt<=bin_edges_tnt[be+1]))[0]\n",
    "    for idx in idxs:\n",
    "        ipiDF.loc[len(ipiDF.index)] = [bin_center, fSpeed_tnt[idx], 'pC2l TNT']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 300x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(3,5))\n",
    "sns.lineplot(data=ipiDF, x='ipi', y='fspeed', hue='condition', errorbar='se', err_style='bars',ax=ax, lw=2, err_kws={'elinewidth':1, 'capsize':0})\n",
    "ax.grid(False)\n",
    "ax.set_ylabel('female speed [mm/s]')\n",
    "ax.set_xlabel('inter-pulse interval [ms]')\n",
    "plt.setp(ax.spines.values(), linewidth=0.5, color='k')\n",
    "ax.legend(frameon=False, loc='lower right')\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.savefig(r'/run/user/1000/gvfs/smb-share:server=cup.pni.princeton.edu,share=murthy/Kyle/code/pc2_tnt/plots/ipi_speed.svg')"
   ],
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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