Paper_Figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "drive_path = 'c:/'\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ks_2samp\n",
    "from scipy.stats import anderson_ksamp\n",
    "from scipy.stats import kruskal\n",
    "from scipy.stats import variation\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from scipy.stats import zscore\n",
    "from scipy.stats import nanmean\n",
    "from scipy.stats import nanstd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\Analysis\\\\Odor_Panel\\\\Odor_Trials.csv'\n",
    "trials=pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 1: Graph individual traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VMHBswBEOTVX3fm0uZsQFWnzcaALOj9qXzRqD0YTD54Qfxg2tPThT2DiuPtrq\nXFEzzl5uGv65qlGDT76ssPKM8bnS0DV870bMx0fKnBJ0UwEDxwYMHJqqlB4KPHHHEoQEWB6xny1q\nwsn8ery26xKOna+zWslWWqO2WCL88dHyCffXGrHjtN/bX+Lw9zkkEqgjdXYP4PMTjiu+mErGvBt4\nuqARpwoaIZMB0aG+6OnX4WJZK6obNQgP9sZ3b1yAOSmhUvTVZVilRlNZVKgvXnokB/tOVePI+VrB\nt/fD52rNRi0lNTNwz3Vz0duvQ0NrD6LDfOHjpQQAnClsgiWXytvw8ZEybMxOhJ+30uF/jrySFkFb\n/4ABvf264f5NlMFowtHz1gMHAN4/UIrNy5Lg563EgM6ANz8rxJnCJiRE+ePBG+cjNNDbIf2ZbKwG\nzrHzdfjVzrMWH69u1OD5v53Bn57Y4LB/UHckGjh+DByaOoL9vXDzxjR8be0M3Pr0bgzoDBav/fjI\n4Ejl8LladHRr4eutxBN3LEZmWsSY02av7SrAm58VYtvKZNx73VyHVa4ZjCaLN+svlbeJHuUwHpfK\nW9E2qrJPTHefDh8eLMWdV2dg554i7Do2OLXX0NaD+tYevPxIDpQecvQP6KFSKqZUBZ81VqfU9p+t\nHvMFOjRanMxvcFiH3JHYPmqcUqOpSKVUYH5q2JjXfXykHB3dgwuie/p0ePGdXDS09qCyvmvM5+oN\nJuw6WoF9p8f+fLFVbbMG/QPiIZlXKhz5jJfY/ansjChsEjmlddfRctS3dGP3qOm1miYN3tlbhCf/\n8CVuevIzfPsXe/Hu/uJpsYbHauAE+dlWiXU4txZ6gxH1rd3o7Bauyh/JYDShoq5zzOuGVDd24bk3\nTuO/Xz6Kj484v9JlNKPRhC6xdTicUqMpanF6hN3Pae/S4uV3z9v1nHf3FTusdLmspsPiY3klwqKH\nPq0eu46W4/0DJWjr7LPpPbQ6A07k1wvac7LicOuWdHh6mH+cDuiNePTlo6JB+P6BUlwqbwMAtHX2\nY+fuItz9i71449MCqLv6YTCaUNOkQU2TZkrt/mB1Su22Lem4WNoiWBw2Wl5JC+7+xV6oNVqoPBW4\n/+vzsHlpouC6Do0Wj+84hvrWHngoZLj3urm4elWKxdft7dfhmb98hRb14C9EcbUa7V1a3H3tHFv+\nbA7R268ThJy3SgFPbtxJU1TW7Ejgo3y7n1dQ0SZoi4/0Q21zN8Q+M5vVfTh0tgabRD4r7GUtcGqa\nutHW2TfFV78pAAAOTklEQVR830RvMOKRl46gtrkbAPDBwVL84Yfrx7yvcqawEb395tVn3ioPZM+J\ngkqpwLWrU/DhoTKzx+0ZtfRp9fjwUJngNeakhOKZe5fBSzX51+lbHeFEhPjgz09twtP3LMXtV81G\nzqI4rBE52AkA1JrBEYt2wIBXP85Hh0Y4gtnxfh7qW3sADA6rX/ukYDhMxOw5WSV4/KPDZfjkWAUO\nnq3BrqPlaGzrsf4nnCDR0Q3X4NAUFhXqi8gQH4e81sO3LMQrj2/A7VfNFn38vQMlDhnllFoJHAC4\nMGJa7cTF+uGwAQZ3g//Mhi19xKbTVsyPHt41/hvrZ8LXCcUQBRVt+OiIcyv8pDJmWbTSQ47sjCjc\nvDENj96WhcduX4ybNsy0+pz+AYPgTPWaJg1OFZjfUNTpjTht4SajTm/AvyyUUf7543y8+E4uXv3X\nJTz0q4MoqVaP9ccYN/EKtalbIEEEAMvmRk/4NcKCvDEzPhgx4X64eWMaXnh4jeCaxrZefHioFIYJ\nhI7eYERFvfBguZFefOc8vv2LvfjgYCk+/VIYLu8fsH7qaVfPAM4VCSvw1i2KH/5vPx9P3LguVXCN\nI4ze2SC/rBV//aQAB8/WoH9g8qz5Gdc6nJxFcWNec2HUjTpL4XH28uAWFCXVaqi7/jN1d/hcLdq7\nxq4GGdAb8ccPLzhtnpO7DNB0tHV5IpQj7knI5TL8/P7luG6N5Snw0e66OgPyEaeOpiUEI0vk/tDO\n3UV46NcHsftE5bi2halq6IJOP3ZgtXb04c3PCnH5Srvo49Y+Q45fqIPeYP54SIAKc0cVWFyzKsWu\ngiK5DFiTGWv2dy2mrqUbNU2D5eon8xvw1B+P46PDZXjxnVw88tJRVDWOXazhDhTPPPPMM/Y+KdBP\nhVOXGoen0cR09QwgPSkEn35ZgbySFnwi8q0CGNy3af/panx8pBz/OloOHy8l0uKD8cLb52zeUqO9\nS4uU2EDERQh3wJ2oy5XtgpHZrMRgrJgf4/D3InIXAb4qpMQGolndh8gQH9x73RwsyYhCVnokrlmV\ngmtXpeDWLbPw6ZcVgg9iYPBD9Nat6YL2qDBf7DslrE7T9Opw5nITPvmyAq0dfZiVGAKVp233Sc8U\nNuK0lfU/ttqUnWhxSuzPH+UL7mVvWZaErPRIszalhxwyiK8JEvOd6+fhrmvnYPPSRMjlMlxp6LI4\nxRgcoMLspFA885eTZveSunoGsP9MDUIDvJASa3nXCHcw7rtQaxfFWh3GtnX247HfHbPptYbq2o0m\n4K+7LsFgMJrNsdrirT1FyM6IMvtG5QjcZYCmqyUZUViSIVy/MvL3PycrHntOXjF7PDTQCw9YOIMn\nPTEEi2dHmm1BM5J2wIDdJ6/gXHEzfvztbCTHjP0BOtb9G1tV1nciPFhYOFBW24GiKuG0/bqseEEb\nAGxbkYy3vigWrGXy91HimfuW48NDpejXGrB5aSJWLhj84hoc4IW7rpmDb26ahYr6Thw8W4MvRm14\n+lV+A1LjgkTvew/oDHj53fPo1epw3eoZNv+ZpTburW3WZcXD18vxVRNGE/D6p4WC9ogQH6t7Pl1p\n6HLKeiCxowkYOESDrs+ZAW/Vf0YicrkMP/jmQqvLBh6+ZSEWz460+DgANLf34rH/O4aTImXII5lM\nJhRWCqfIvndzJrJFwtIaS1+gxc4ISo4JQHJMgOj1XioPbBcZ3S2bG420hGA8eWc2fnb/8uGwGf3c\njORQ3LhOeJ+8rLYTb+0psvpnePXjS9h3SpqducdjXFNqwGA5YHJs4PAN+wGd87YCB4Afbl+M27fN\nxurMWNywbiZKqtWCIW5VowZXLU9yyKrdfq0en52oxE6Rf+CVC2IwMz54wu9BNNn5+3giIzkUmt4B\nxEX44YHr5yMzzfo6Hi+VB3IWxSEzLRytHX1obOsVvc5gMOFkfgOy0iMsliznl7fio8PC+8MPfWMB\nti5Pwjc3zUJBRRua1eLvMVJ5bQdOFzTiw4OlOFXQgMLKdjSre/HBoTJBWff2relItfIZkBIbiEvl\nrWjpGByN+Pso8fgdS2zekcXfxxMn8xuGF9cOseW+9pnCRiRFByI+0vG3GCZKZprg3XaTyQSZTIbC\nyjY8vuNLR/XLTHJMAF5+JMcsSPJKmvH0n04Krn32gRVYMDNc9HX6tXp09Qyg7987uUaF+Q6XNA4x\nmQZ/yV/91yW0doiXbD9+x2KsWiBeHk5E9qms78Ter6pw4GzN8P+bIy2aFYGf3b98+OcOjRZltR1I\njgnA797LQ+6og9DSEoLwwsNrh39+d38xdu62PjKwh6+3Em88vXnMdTG9/Tp8dakRXT1arM6MtXv/\ntLe/KMI7e4vH1cdgfxX+/NTGcR2e50wT7s1QCKQlBMNbpUCf1vIeTACQEhMIncE4XHFhixvWzRSM\nWhbMDMeclFDBYrNjeXWCwOnT6vHyP87j5KUGs0WccrkMcRF+yEwLx43rZsJH5YHfvHVOUCQwWoIb\nfnMgmqySYwLxnRvm47o1M/Ds66cEm4fmFjejrLYDqXFBuFDagv95/ZTVz5nrRx2nvSA1HDvhuMDZ\nuCTBpkWYPl5KrF8sfp/HFsvnRY8ZODeuS0Vjey+OXzCfelRrtLhU3jbm1KXUHHY8gYdCjjkp4nsw\nLZ8XjTd/ugU7HluHlx5Zi3VZY5dVD4kI8cFqkblOmUyGq5YnCdpPXKxHb78OxVXtaPj3ItN/HirD\n8Yv1gh0DjEYTqhs12HW0Aj/980m89UXRmGGzbG4UEqLE526JaPyiw3zx6++tRnSor+CxDw6WorNb\ni1/9/azVsIkK9cHyeeafF6nxQfCyseLNFttWJjnstaxJig5AVKj1BbhbliXh0VuzMG+G8LN39FpI\nd+DQ8VZmWrho9cmtW9IREuCFkAAvAIM3z3buvgxbtkW7fu0MKBTiubh0ThRUngpoR+xVpOnV4ZYf\nfQ5gsMb91i3povsfjXaloQtXGizXsqclBGFTdqLolj1E5Bg+XkrcsC4Vv//ggln78Qv1qGvuHnOp\nxPU5qVCMqlQd/DIcinNFE/8AXpQegZgwvwm/ji1kMhm+sT4NO97PE318fmoYosMGw3nr8kTkl7ea\nPZ5b1ARgnrO7aReHHsC2dE4UPBTm/9hrFsYiKdp8RBAf6Y9bNs3C0CxZTJiv6Apdfx9PbFwi3IV1\niJfKw2olitEE7NxTZPV0vrEsSo/AjsfW4YWH12Lr8iSHl10TkbkNS+JFD4Sz9oUQAAL9PLHBwueF\nLTtgjxTkp8KiWebFDx4KOW7bIqw+c6YtyxLxxB1LBJ+hQ1+mh2SmRWB0rVRdSw9e+kcuHnnpCH6z\n8xzKax1TPj4RDh3hRIX64s6rM/C3zy9DpzciNT4ID964QPTaW7ekY11WPDq7tUiKCYDeYMKR3Fqz\nyrObN6aNOVe6OjNW9KQ/R1idGYvHtmdNm7MqiNyB0kOBr61JxeufFtj1vGtXpQiKgIbMsyNwZDLg\nf7+3CjFhfiioaMOxvDr09OmweVki0hKkr05duSAGK+ZHI7+8FV9eqEd3rw6bshPMDr4M8PVEWnww\nikdt83XgTA2AwbVKx/JqsW1lMrZvne2UPd9sMeEqNTEdGi36tHpEhvjYNSKoqOvE33dfRrO6F2sW\nxuLmDWljftjr9Abc/tM96Om3fT+hq5Yn4bvfWID7nttnsSQTAJ5/aNWUP82UyB319utw97P70GPj\nbsuLZ0fiR9/OhoeF6XeD0YTbnv7cps+JrPQIPHPf8jGvczdv7SnCP/aNXdUWG+6HXz60EsH+XhL0\nyty41+FY46XygL+Pp90jg+AAL+QsisPVK5MxNyXMpucr5HLUtnTbdPDTkOtzZiAxOmC41l1MfKQf\n7ro6g6MbIhdQeijg76O0uGVNdkYU/vj4emxdnoRtK5JxzaoUq19u5TIZ+gcMZlWt2RlR6OoZEOwI\n8OAN84fvjUwmnh5ymw610/QOwFulFC00cDaH3sNxlTWZtle9ARgetaxaEGPxkLmtDlpASkTjs2VZ\nEp57cCVWzo8xG7lEh/nioZsWQCaTISTAy+ZwuGVTGu7YNhuZaeG4dnUK/t+ti7BhiXnZ8uykEGSm\nia/jc3cz44PgZ+NUWZeNB2A6mnutChqnzDTxNTliokN9hxdgKT0U2LI8Ee/uKzG7xtNDjvUW9kki\nIunMSw3DvNQwdPcO4EJZK/R6IxbPjhzXPQilhwI3bUjDTRvShtu2//ucnotlrUiKDsAd22ZP2i+a\nCoUcmWnh+PKC9apcpYccOXYsTXGkKRE4crkMP757KT4/Xgl1Vz+USgU+Olwmeu3cGeb3ZK5anoR/\nHSk3OwZ2w5IE+PEIaSK34efjiZVO2KFdpVTgnuvmOvx1XWXRrAhB4HirPPDMfctwqbwNnT1arF0Y\n55LiB2CKBA4A+HkrcfPGwW8uWp0Bnx2vFMzNAhAUAYQGeuOx2xfj/97LQ2e3FgvTInD3ddIdYU1E\n5CirMmPx9t5is225HrhhPjKSQ5GR7PoCKKdUqbmDH/3xOC6WtQraX31qI6JEVjIbjSb09us4siGi\nSa2mSYP39pegT6vH+sXxbnV215QZ4Yy2fF60IHDCAr0sntUul8sYNkQ06cVH+uPR27Jc3Q1RU6JK\nTcyK+THwHLUIbPn8mEl7Q5CIaLKbsoETEuCFB2+YN7xpX3piMG4eUZ1CRETSmrL3cIZ09+nQ269D\nWKA390EjInKhKR84RETkHqbslBoREbkXBg4REUmCgUNERJJg4BARkSQYOEREJAkGDhERSYKBQ0RE\nkmDgEBGRJBg4REQkCQYOERFJgoFDRESSYOAQEZEkGDhERCQJBg4REUmCgUNERJJg4BARkSQYOERE\nJAkGDhERSYKBQ0REkmDgEBGRJBg4REQkif8P/pZpFhmMZLoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc8ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell=5\n",
    "trial=39\n",
    "filename='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\160328_1\\\\%sdt.txt'%trial\n",
    "comp=pd.read_csv(filename,index_col=0)\n",
    "ncomp=pd.DataFrame(comp[1:150]['G PMT (%s)'%cell])\n",
    "ncomp['frame'] = ncomp.index\n",
    "ncomp['time']=ncomp.frame*0.157\n",
    "sns.set(style=\"white\")\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\",font_scale=3);\n",
    "f,ax=plt.subplots()\n",
    "ax.plot(ncomp['time'],ncomp.iloc[:,0],linewidth=5.0)\n",
    "sns.despine(left=True,bottom=True)\n",
    "plt.ylim(-0.15, 1);\n",
    "# plt.xticks(np.arange(0, 25.197, 5.0))\n",
    "ax.set_yticks([]);\n",
    "ax.set_xticks([]);\n",
    "plt.tight_layout();\n",
    "# f.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\SampleTraces\\HighRes\\Ticks\\cell%s_%s.pdf\"%(cell,trial))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Figure 2: Baseline and Peak, Integral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullbaseline_WL.csv'\n",
    "baseline=pd.read_csv(x)\n",
    "y='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullintegral_WL.csv'\n",
    "integral=pd.read_csv(y)\n",
    "z='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullpeak_WL.csv'\n",
    "peak=pd.read_csv(z)\n",
    "s='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullsuccess_WI.csv'\n",
    "success=pd.read_csv(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "melt_base=pd.melt(baseline,('Mouse','Group'),var_name='Odor')\n",
    "melt_int=pd.melt(integral,('Mouse','Group'),var_name='Odor')\n",
    "melt_success=pd.melt(success,('Mouse','Group'),var_name='Odor')\n",
    "melt_peak=pd.melt(peak,('Mouse','Group'),var_name='Odor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#THE ENTIRE DATAFRAME\n",
    "completedf=pd.concat([melt_base[['Mouse','Group','Odor','value']],melt_int['value'],melt_peak['value'],melt_success['value']],axis=1)\n",
    "completedf.columns=['Mouse','Group','Odor','Baseline','Integral','Peak','Success']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6AoqCb5tg9O6tzT67AIkpGSSm/DcK3L56IIaU/axfv57akXZm++vio8jQxGHr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\nBy3X13jHYPIyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc38f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('Peak','Success',completedf[completedf.Group=='Hexanal']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E6nkJzYPteh+H8svx0pLTyNp4wW4KsO00ZfOMLnfZ9XRUChbpyeecfuNyc3Mx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\nmKFUAABmPqbRJl9CAPxaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbebbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#GRAPH THE INTERACTIONS\n",
    "sns.set(style='white',palette=\"muted\", color_codes=True);\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\",font_scale=2);\n",
    "sns.jointplot('Integral','Peak',completedf).set_axis_labels(\"Integral\", \"Peak DF/F\");\n",
    "# sns.despine(right=True,top=True);\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\Correlations\\Ticks\\Integral_Peak.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6PatXr9YSjlzvORWpSmuQoaGhqKpKfHy81fGEhATCwsJKvOfs2bNERlrn57zl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\nCCGEsOP/AARmf0UlofijAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8aca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='white');\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\",font_scale=2);\n",
    "x=melt_peak[melt_peak.Group=='Control'].value.dropna();\n",
    "y=melt_peak[melt_peak.Group=='Hexanal'].value.dropna();\n",
    "z=melt_peak[melt_peak.Group=='Mint'].value.dropna();\n",
    "sns.kdeplot(x,cumulative=True,color='black',lw=3,label='Control')\n",
    "sns.kdeplot(y,cumulative=True,color=sns.xkcd_rgb[\"azure\"],lw=3,label='Hexanal')\n",
    "sns.kdeplot(z,cumulative=True,color=sns.xkcd_rgb['grey'],lw=3,label='Mint')\n",
    "# sns.kdeplot(y,cumulative=True)\n",
    "# sns.kdeplot(z,cumulative=True)\n",
    "# sns.distplot(x,hist=False,color=sns.xkcd_rgb[\"black\"],kde_kws={'lw':3,'label':'Control'},norm_hist=True);\n",
    "# sns.distplot(y,hist=False,color=sns.xkcd_rgb[\"azure\"],kde_kws={'lw':3,'label':'Hexanal'});\n",
    "# sns.distplot(z,hist=False,color=sns.xkcd_rgb[\"grey\"],kde_kws={'lw':3,'label':'Mint'});\n",
    "sns.despine();\n",
    "plt.tight_layout();\n",
    "plt.xlabel('Peak');\n",
    "plt.ylabel('Density Function');\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\Correlations\\Ticks\\Density.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GhlJaWordbsfHx8fZpqCggKioKABCQ0P561//6nJuh8PBpUuXmDRpUoOfn5iYSGJioktZ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\n/wcU0vNmUFto9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd914160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=completedf.Peak.dropna()\n",
    "sns.set(style='white',palette=\"muted\", color_codes=True);\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\",font_scale=2);\n",
    "sns.distplot(x,kde=False);\n",
    "sns.despine(right=True,top=True);\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\Correlations\\Ticks\\Hist_Peak.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 3: Habituation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\HabituationFiles\\NormalizedHabituation.csv'\n",
    "habituation=pd.read_csv(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XN3E5kzVmRHVBjaZgpaWlYcyYMapp7kQNSTtPGyx8osZsefx13GaNGRlQ+i0J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb9dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h=pd.melt(habituation,var_name='Time',value_name='Normalized Change');\n",
    "sns.set(style='white',palette=\"muted\", color_codes=True);\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\",font_scale=2);\n",
    "sns.pointplot(x='Time',y='Normalized Change',data=h);\n",
    "plt.ylabel('Normalized Change')\n",
    "sns.despine();\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\Habituation_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prep Peaks Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullpeak.csv'\n",
    "comp=pd.read_csv(filename)\n",
    "comp_sorted=comp.reindex_axis(comp.mean().sort_values().index, axis=1)\n",
    "comp_labels=pd.DataFrame(comp.Mouse)\n",
    "comp_group=pd.DataFrame(comp.Group)\n",
    "tmp=[comp_group,comp_labels,comp_sorted]\n",
    "composite_full=pd.concat(tmp,axis=1)\n",
    "cfull=pd.melt(composite_full,['Group','Mouse'],var_name=\"Odor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "groupcolors={'Control':sns.xkcd_rgb[\"black\"],'Mint':sns.xkcd_rgb[\"grey\"],'Hexanal':sns.xkcd_rgb[\"azure\"]};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Make separate excitatory and inhibitory dataframes\n",
    "mindf=pd.DataFrame([])\n",
    "maxdf=pd.DataFrame([])\n",
    "zdf=pd.DataFrame([])\n",
    "for x in cfull.index:\n",
    "    if cfull.value[x]>0:\n",
    "        a=pd.DataFrame(cfull.iloc[x]).T\n",
    "        maxdf=maxdf.append(a,ignore_index=True)\n",
    "    elif cfull.value[x]<0:\n",
    "        a=pd.DataFrame(cfull.iloc[x]).T\n",
    "        mindf=mindf.append(a,ignore_index=True)\n",
    "    else:\n",
    "        a=pd.DataFrame(cfull.iloc[x]).T\n",
    "        zdf=zdf.append(a,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "one_mindf=mindf[['Group','Odor','value']][mindf.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]\n",
    "conc_mindf=mindf[['Group','Odor','value']][mindf.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]\n",
    "conc_mindf=conc_mindf.sort_values(by='Odor')\n",
    "\n",
    "one_maxdf=maxdf[['Group','Odor','value']][maxdf.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]\n",
    "conc_maxdf=maxdf[['Group','Odor','value']][maxdf.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]\n",
    "\n",
    "one_mindf=one_mindf.replace(to_replace='MS01',value='MS')\n",
    "one_mindf=one_mindf.replace(to_replace='Hexanal01',value='Hexanal')\n",
    "one_mindf=one_mindf.replace(to_replace='IAA01',value='IAA')\n",
    "\n",
    "one_maxdf=one_maxdf.replace(to_replace='MS01',value='MS')\n",
    "one_maxdf=one_maxdf.replace(to_replace='Hexanal01',value='Hexanal')\n",
    "one_maxdf=one_maxdf.replace(to_replace='IAA01',value='IAA')\n",
    "\n",
    "sorter=('MS01','MS05','MS10','IAA01','IAA05','IAA10','Hexanal01','Hexanal05','Hexanal10')\n",
    "conc_maxdf.Odor=conc_maxdf.Odor.astype('category')\n",
    "conc_maxdf.Odor.cat.set_categories(sorter,inplace=True)\n",
    "conc_maxdf=conc_maxdf.sort_values(['Odor'])\n",
    "\n",
    "conc_mindf.Odor=conc_mindf.Odor.astype('category')\n",
    "conc_mindf.Odor.cat.set_categories(sorter,inplace=True)\n",
    "conc_mindf=conc_mindf.sort_values(['Odor'])\n",
    "\n",
    "conc_mindf=conc_mindf.replace(to_replace='MS01',value='MS 1%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='MS05',value='MS 5%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='MS10',value='MS 10%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='IAA01',value='IAA 1%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='IAA05',value='IAA 5%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='IAA10',value='IAA 10%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='Hexanal01',value='Hexanal 1%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='Hexanal05',value='Hexanal 5%')\n",
    "conc_mindf=conc_mindf.replace(to_replace='Hexanal10',value='Hexanal 10%')\n",
    "\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='MS01',value='MS 1%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='MS05',value='MS 5%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='MS10',value='MS 10%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='IAA01',value='IAA 1%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='IAA05',value='IAA 5%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='IAA10',value='IAA 10%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='Hexanal01',value='Hexanal 1%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='Hexanal05',value='Hexanal 5%')\n",
    "conc_maxdf=conc_maxdf.replace(to_replace='Hexanal10',value='Hexanal 10%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cat=('Control','Mint','Hexanal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "amp=pd.read_csv("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 4-5: Amplitude of excitatory responses/BOXPLOTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R0iipAshATaRSztTVaA3OoyZ7UEJLWsBATaRiztLVKJXSFqYgsgfOoyYip8V5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\nbXq8fv36wcPDQ5YWtWEtv6n6O1Vw8fR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Ao9Fg4sSJePrppzFu3Dh069YNXl5e0Ov1+Ne/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\nRERECvb/ASFoSJHgKzB3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe6430b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1% CONCENTRATION\n",
    "cat=('Control','Mint','Hexanal')\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(7,5));\n",
    "sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=one_maxdf);\n",
    "sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=one_mindf);\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('DF/F', fontsize=12);\n",
    "plt.title('Peak DF/F, 1%', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(-1,5)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Box\\\\box_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MS_conc_maxdf=conc_maxdf[conc_maxdf.Odor.isin(['MS 1%','MS 5%','MS 10%'])]\n",
    "hex_conc_maxdf=conc_maxdf[conc_maxdf.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]\n",
    "IAA_conc_maxdf=conc_maxdf[conc_maxdf.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]\n",
    "\n",
    "MS_conc_mindf=conc_mindf[conc_mindf.Odor.isin(['MS 1%','MS 5%','MS 10%'])]\n",
    "hex_conc_mindf=conc_mindf[conc_mindf.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]\n",
    "IAA_conc_mindf=conc_mindf[conc_mindf.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Odor</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Control</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.015671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.216320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.200734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.126636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.146380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.209309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.299622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.148053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.086919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.024138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.097680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.202925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.184702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.051542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.044623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.133760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.030670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.118389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.055969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.340597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.130009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.270302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.280088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.153363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.118162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.331770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.157791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.417632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.033887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>MS 1%</td>\n",
       "      <td>0.194419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8040</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.351417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8039</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.294391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8049</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.760009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8037</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.291506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8062</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.154878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8064</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.152494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8086</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.218386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8085</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.124959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8084</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.081698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8083</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.098549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8082</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.368336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8081</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.129421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8080</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.770505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8079</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.323544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8078</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.086767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8077</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.134787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8063</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.179797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8076</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.603242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8074</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.429644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8073</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.281141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8072</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.149075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8071</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.220699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.130865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8069</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.019291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8068</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.288933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8067</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.223579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8066</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.031014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8065</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.114366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8075</th>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.579442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7960</th>\n",
       "      <td>Control</td>\n",
       "      <td>Hexanal 10%</td>\n",
       "      <td>0.189300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Group         Odor     value\n",
       "0     Control        MS 1%  0.015671\n",
       "512   Hexanal        MS 1%  0.216320\n",
       "513   Hexanal        MS 1%  0.200734\n",
       "514   Hexanal        MS 1%  0.126636\n",
       "515   Hexanal        MS 1%  0.146380\n",
       "516   Hexanal        MS 1%  0.209309\n",
       "517   Hexanal        MS 1%  0.299622\n",
       "518   Hexanal        MS 1%  0.148053\n",
       "519   Hexanal        MS 1%  0.086919\n",
       "520   Hexanal        MS 1%  0.024138\n",
       "511   Hexanal        MS 1%  0.097680\n",
       "521   Hexanal        MS 1%  0.202925\n",
       "523   Hexanal        MS 1%  0.184702\n",
       "524   Hexanal        MS 1%  0.051542\n",
       "525   Hexanal        MS 1%  0.044623\n",
       "526   Hexanal        MS 1%  0.133760\n",
       "527   Hexanal        MS 1%  0.030670\n",
       "528   Hexanal        MS 1%  0.118389\n",
       "529   Hexanal        MS 1%  0.055969\n",
       "530   Hexanal        MS 1%  0.340597\n",
       "531   Hexanal        MS 1%  0.130009\n",
       "522   Hexanal        MS 1%  0.270302\n",
       "532   Hexanal        MS 1%  0.280088\n",
       "510   Hexanal        MS 1%  0.153363\n",
       "508   Hexanal        MS 1%  0.118162\n",
       "488   Hexanal        MS 1%  0.331770\n",
       "489   Hexanal        MS 1%  0.157791\n",
       "490   Hexanal        MS 1%  0.417632\n",
       "491   Hexanal        MS 1%  0.033887\n",
       "492   Hexanal        MS 1%  0.194419\n",
       "...       ...          ...       ...\n",
       "8040  Hexanal  Hexanal 10%  0.351417\n",
       "8039  Hexanal  Hexanal 10%  0.294391\n",
       "8049  Hexanal  Hexanal 10%  0.760009\n",
       "8037  Hexanal  Hexanal 10%  0.291506\n",
       "8062  Hexanal  Hexanal 10%  0.154878\n",
       "8064  Hexanal  Hexanal 10%  0.152494\n",
       "8086  Hexanal  Hexanal 10%  0.218386\n",
       "8085  Hexanal  Hexanal 10%  0.124959\n",
       "8084  Hexanal  Hexanal 10%  0.081698\n",
       "8083  Hexanal  Hexanal 10%  0.098549\n",
       "8082  Hexanal  Hexanal 10%  0.368336\n",
       "8081  Hexanal  Hexanal 10%  0.129421\n",
       "8080  Hexanal  Hexanal 10%  0.770505\n",
       "8079  Hexanal  Hexanal 10%  0.323544\n",
       "8078  Hexanal  Hexanal 10%  0.086767\n",
       "8077  Hexanal  Hexanal 10%  0.134787\n",
       "8063  Hexanal  Hexanal 10%  0.179797\n",
       "8076  Hexanal  Hexanal 10%  0.603242\n",
       "8074  Hexanal  Hexanal 10%  0.429644\n",
       "8073  Hexanal  Hexanal 10%  0.281141\n",
       "8072  Hexanal  Hexanal 10%  0.149075\n",
       "8071  Hexanal  Hexanal 10%  0.220699\n",
       "8070  Hexanal  Hexanal 10%  0.130865\n",
       "8069  Hexanal  Hexanal 10%  0.019291\n",
       "8068  Hexanal  Hexanal 10%  0.288933\n",
       "8067  Hexanal  Hexanal 10%  0.223579\n",
       "8066  Hexanal  Hexanal 10%  0.031014\n",
       "8065  Hexanal  Hexanal 10%  0.114366\n",
       "8075  Hexanal  Hexanal 10%  0.579442\n",
       "7960  Control  Hexanal 10%  0.189300\n",
       "\n",
       "[7150 rows x 3 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conc_maxdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FJUOKTgCwdOlSjWGhExYWJomuK3WMfd9ubm4aw0JBp3Dv3buHwYMHv/Jv0KBByMrKMvih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\nuLKbYRhMnjwZiYm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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf0211d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Multiple Concentration\n",
    "cat=('Control','Mint','Hexanal')\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(3.5,3));\n",
    "sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=IAA_conc_mindf);\n",
    "ax=sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=IAA_conc_maxdf);\n",
    "ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('DF/F', fontsize=12);\n",
    "plt.title('Peak DF/F, 1%', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(-1,4)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Box\\\\IAA_conc_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 4-5: MEDIAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#MEDIANS for 1%\n",
    "medians=pd.DataFrame([])\n",
    "df=one_maxdf\n",
    "for x in df.Group.unique():\n",
    "    tmp=pd.DataFrame([])\n",
    "    for y in df.Odor.unique():\n",
    "        a=df[(df.Group==x)&(df.Odor==y)]['value'].dropna().median()\n",
    "        m=pd.DataFrame({y:[a]})\n",
    "        tmp=pd.concat([tmp,m],axis=1)\n",
    "    b=pd.DataFrame({'Group':[x]})\n",
    "#     c=pd.DataFrame({'Group':[df[df.Mouse==x]['Group'].iloc[0]]})\n",
    "#     c=c.reset_index(drop=True)\n",
    "    tmp=pd.concat([b,tmp],axis=1)\n",
    "    medians=medians.append(tmp)\n",
    "maxmedianmelt=pd.melt(medians,\"Group\",var_name=\"Odor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GuYJzTH1oqlOWWpWgFkK4hK3Z1166EaB/kIbX+9X9Q3Lv/r3EH4oHwNfHl0nj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb3b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot everything by group\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=maxmedianmelt)\n",
    "# sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=minmedianmelt)\n",
    "ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('DF/F', fontsize=12);\n",
    "plt.title('Median Peak', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Median\\\\Median_one_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#MEDIANS\n",
    "medians=pd.DataFrame([])\n",
    "# df=MS_conc_maxdf\n",
    "df=hex_conc_maxdf\n",
    "# df=IAA_conc_maxdf\n",
    "for x in df.Group.unique():\n",
    "    tmp=pd.DataFrame([])\n",
    "    for y in df.Odor.unique():\n",
    "        a=df[(df.Group==x)&(df.Odor==y)]['value'].dropna().median()\n",
    "        m=pd.DataFrame({y:[a]})\n",
    "        tmp=pd.concat([tmp,m],axis=1)\n",
    "    b=pd.DataFrame({'Group':[x]})\n",
    "#     c=pd.DataFrame({'Group':[df[df.Mouse==x]['Group'].iloc[0]]})\n",
    "#     c=c.reset_index(drop=True)\n",
    "    tmp=pd.concat([b,tmp],axis=1)\n",
    "    medians=medians.append(tmp)\n",
    "maxmedianmelt=pd.melt(medians,\"Group\",var_name=\"Odor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zyk0zzDqew/zTOYxvruflZu6kmhQ+OZnD9jgzWjT0rufGa83dqVWlfOykOrUURU3Pmic9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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6e6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot everything by group\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(3.5,3));\n",
    "ax=sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=maxmedianmelt)\n",
    "# sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=minmedianmelt)\n",
    "ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('DF/F', fontsize=12);\n",
    "plt.title('Median Peak', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(0,0.6)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Median\\\\Median_hex_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 6: Number of excitatory events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullpeak.csv'\n",
    "comp=pd.read_csv(filename)\n",
    "comp_sorted=comp.reindex_axis(comp.mean().sort_values().index, axis=1)\n",
    "comp_labels=pd.DataFrame(comp.Mouse)\n",
    "comp_group=pd.DataFrame(comp.Group)\n",
    "tmp=[comp_group,comp_labels,comp_sorted]\n",
    "composite_full=pd.concat(tmp,axis=1)\n",
    "cfull=pd.melt(composite_full,['Group','Mouse'],var_name=\"Odor\")\n",
    "\n",
    "maxdf=pd.DataFrame([])\n",
    "zdf=pd.DataFrame([])\n",
    "for x in cfull.index:\n",
    "    if cfull.value[x]>0:\n",
    "        a=pd.DataFrame(cfull.iloc[x]).T\n",
    "        maxdf=maxdf.append(a,ignore_index=True)\n",
    "    else:\n",
    "        a=pd.DataFrame(cfull.iloc[x]).T\n",
    "        zdf=zdf.append(a,ignore_index=True)\n",
    "zdf=zdf.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ratio=pd.DataFrame([])\n",
    "for x in maxdf.Group.unique():\n",
    "    for y in maxdf.Odor.unique():\n",
    "        a=float(len(maxdf[(maxdf.Group==x) & (maxdf.Odor==y)]['value']))\n",
    "        b=float(len(zdf[(zdf.Group==x) & (zdf.Odor==y)]['value']))\n",
    "        E=a/(a+b)\n",
    "        ratio=ratio.append(pd.DataFrame({'Group':[x],'Odor':[y],'Ratio':[E]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "one_ratio=ratio[['Group','Odor','Ratio']][ratio.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]\n",
    "conc_ratio=ratio[['Group','Odor','Ratio']][ratio.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]\n",
    "conc_ratio=conc_ratio.sort_values(by='Odor')\n",
    "\n",
    "one_ratio=one_ratio.replace(to_replace='MS01',value='MS')\n",
    "one_ratio=one_ratio.replace(to_replace='Hexanal01',value='Hexanal')\n",
    "one_ratio=one_ratio.replace(to_replace='IAA01',value='IAA')\n",
    "\n",
    "sorter=('MS01','MS05','MS10','IAA01','IAA05','IAA10','Hexanal01','Hexanal05','Hexanal10')\n",
    "conc_ratio.Odor=conc_ratio.Odor.astype('category')\n",
    "conc_ratio.Odor.cat.set_categories(sorter,inplace=True)\n",
    "conc_ratio=conc_ratio.sort_values(['Odor'])\n",
    "\n",
    "conc_ratio=conc_ratio.replace(to_replace='MS01',value='MS 1%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='MS05',value='MS 5%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='MS10',value='MS 10%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='IAA01',value='IAA 1%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='IAA05',value='IAA 5%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='IAA10',value='IAA 10%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='Hexanal01',value='Hexanal 1%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='Hexanal05',value='Hexanal 5%')\n",
    "conc_ratio=conc_ratio.replace(to_replace='Hexanal10',value='Hexanal 10%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MS_conc_ratio=conc_ratio[conc_ratio.Odor.isin(['MS 1%','MS 5%','MS 10%'])]\n",
    "hex_conc_ratio=conc_ratio[conc_ratio.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]\n",
    "IAA_conc_ratio=conc_ratio[conc_ratio.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "one_ratio.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Ratio_One.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MS_conc_ratio.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Ratio_MS.csv')\n",
    "hex_conc_ratio.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Ratio_hex.csv')\n",
    "IAA_conc_ratio.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Ratio_IAA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NCktPmfkuzkTMVcfXV0kLQ+pqiWyko0v1u3ttiqKQlpZGwoXznE9I4HzCOQwGQ5Gew93N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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe736940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1%\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.pointplot(x='Odor',y='Ratio',hue='Group',palette=groupcolors,data=one_ratio)\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Ratio', fontsize=12);\n",
    "plt.title('Excitatory Responses', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "# plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Excitatory\\\\Excitatory_One_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NmjV7tPpqRDnWrl0rhBBiyZIlpf67l927d4sJEyYIIYRIT08XXbt2FWazWZnf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb89f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Different concentrations\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(3.5,3));\n",
    "ax=sns.pointplot(x='Odor',y='Ratio',hue='Group',palette=groupcolors,data=hex_conc_ratio)\n",
    "ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Ratio', fontsize=12);\n",
    "plt.title('Excitatory Responses', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(0.8,1)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Excitatory\\\\Excitatory_hex_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of odorants responded to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "composite_full=composite_full.drop('Blank',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "EI=pd.DataFrame([])\n",
    "for x in composite_full.index:\n",
    "    a=0\n",
    "    b=0\n",
    "    for value in composite_full.iloc[x][2:]:\n",
    "        if value>0:\n",
    "            a=a+1\n",
    "        elif value<0:\n",
    "            b=b+1\n",
    "    c=pd.DataFrame(composite_full.iloc[x][['Group','Mouse']]).T\n",
    "    c=c.reset_index(drop=True)\n",
    "    d=pd.concat([c,pd.DataFrame({'E':[a]}),pd.DataFrame({'I':[b]})],axis=1)\n",
    "    EI=EI.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "EI.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Odor_Number.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "groupcolors={'Control':sns.xkcd_rgb[\"black\"],'Mint':sns.xkcd_rgb[\"grey\"],'Hexanal':sns.xkcd_rgb[\"azure\"]};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NxumKoqDRaPj2229VdXzgwAHi4+MpKSnBz8+P2bNnq2pPCGuoNEBq7vcx5fLly8ybN4/P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\nJIQK/w9zBiXD2zirygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbae19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "sns.set_context('paper')\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(3,3))\n",
    "sns.barplot(x='Group', y=\"E\", data=EI,\n",
    "            label=\"E\", palette=groupcolors);\n",
    "sns.despine()\n",
    "plt.ylabel('Odor Number', fontsize=12);\n",
    "plt.title('Responses', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.xticks(fontsize=11);\n",
    "plt.yticks(fontsize=11);\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Excitatory\\\\Odors_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 7: Reliability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullpeak_WL.csv'\n",
    "peak=pd.read_csv(z)\n",
    "s='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullsuccess_WI.csv'\n",
    "success=pd.read_csv(s)\n",
    "melt_success=pd.melt(success,('Mouse','Group'),var_name='Odor')\n",
    "melt_peak=pd.melt(peak,('Mouse','Group'),var_name='Odor')\n",
    "rdf=pd.concat([melt_peak[['Mouse','Group','Odor','value']],melt_success['value']],axis=1)\n",
    "rdf.columns=['Mouse','Group','Odor','Peak','Success']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse</th>\n",
       "      <th>Group</th>\n",
       "      <th>Odor</th>\n",
       "      <th>Peak</th>\n",
       "      <th>Success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12940</th>\n",
       "      <td>G PMT (22)_160626_2</td>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal05</td>\n",
       "      <td>2.629784</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12941</th>\n",
       "      <td>G PMT (23)_160626_2</td>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal05</td>\n",
       "      <td>2.228448</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12942</th>\n",
       "      <td>G PMT (24)_160626_2</td>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal05</td>\n",
       "      <td>2.581529</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12943</th>\n",
       "      <td>G PMT (25)_160626_2</td>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal05</td>\n",
       "      <td>1.906673</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12944</th>\n",
       "      <td>G PMT (26)_160626_2</td>\n",
       "      <td>Hexanal</td>\n",
       "      <td>Hexanal05</td>\n",
       "      <td>2.273161</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Mouse    Group       Odor      Peak  Success\n",
       "12940  G PMT (22)_160626_2  Hexanal  Hexanal05  2.629784      1.0\n",
       "12941  G PMT (23)_160626_2  Hexanal  Hexanal05  2.228448      1.0\n",
       "12942  G PMT (24)_160626_2  Hexanal  Hexanal05  2.581529      1.0\n",
       "12943  G PMT (25)_160626_2  Hexanal  Hexanal05  1.906673      1.0\n",
       "12944  G PMT (26)_160626_2  Hexanal  Hexanal05  2.273161      1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "smalldf=pd.DataFrame([])\n",
    "xdf=pd.DataFrame([])\n",
    "for x in rdf.Mouse.unique():\n",
    "    for y in rdf.Odor.unique():\n",
    "        b=rdf[(rdf.Mouse==x)&(rdf.Odor==y)].Peak.values[0]\n",
    "        if b>0:\n",
    "            c=rdf[(rdf.Mouse==x)&(rdf.Odor==y)]\n",
    "            smalldf=smalldf.append(c,ignore_index=True)\n",
    "        else:\n",
    "            a=rdf[(rdf.Mouse==x)&(rdf.Odor==y)]\n",
    "            xdf=xdf.append(a,ignore_index=True)\n",
    "xdf=xdf.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "one_r=smalldf[['Group','Odor','Success']][smalldf.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]\n",
    "conc_r=smalldf[['Group','Odor','Success']][smalldf.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]\n",
    "conc_r=conc_r.sort_values(by='Odor')\n",
    "\n",
    "sorting=('MS01','THA','AP','PA','IAA01','Hexanone','Hexanal01','EB')\n",
    "one_r.Odor=one_r.Odor.astype('category')\n",
    "one_r.Odor.cat.set_categories(sorting,inplace=True)\n",
    "one_r=one_r.sort_values(['Odor'])\n",
    "\n",
    "one_r=one_r.replace(to_replace='MS01',value='MS')\n",
    "one_r=one_r.replace(to_replace='Hexanal01',value='Hexanal')\n",
    "one_r=one_r.replace(to_replace='IAA01',value='IAA')\n",
    "\n",
    "sorter=('MS01','MS05','MS10','IAA01','IAA05','IAA10','Hexanal01','Hexanal05','Hexanal10')\n",
    "conc_r.Odor=conc_r.Odor.astype('category')\n",
    "conc_r.Odor.cat.set_categories(sorter,inplace=True)\n",
    "conc_r=conc_r.sort_values(['Odor'])\n",
    "\n",
    "conc_r=conc_r.replace(to_replace='MS01',value='MS 1%')\n",
    "conc_r=conc_r.replace(to_replace='MS05',value='MS 5%')\n",
    "conc_r=conc_r.replace(to_replace='MS10',value='MS 10%')\n",
    "conc_r=conc_r.replace(to_replace='IAA01',value='IAA 1%')\n",
    "conc_r=conc_r.replace(to_replace='IAA05',value='IAA 5%')\n",
    "conc_r=conc_r.replace(to_replace='IAA10',value='IAA 10%')\n",
    "conc_r=conc_r.replace(to_replace='Hexanal01',value='Hexanal 1%')\n",
    "conc_r=conc_r.replace(to_replace='Hexanal05',value='Hexanal 5%')\n",
    "conc_r=conc_r.replace(to_replace='Hexanal10',value='Hexanal 10%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "groupcolors={'Control':sns.xkcd_rgb[\"black\"],'Mint':sns.xkcd_rgb[\"grey\"],'Hexanal':sns.xkcd_rgb[\"azure\"]};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gCMCqPUdIvGZONHSXyti1HsK0to75fgr89ttvmTx5MgAtW7bk8OHDODg4ADxV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9a1c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1%\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.pointplot(x='Odor',y='Success',hue='Group',palette=groupcolors,data=one_r)\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Ratio', fontsize=12);\n",
    "plt.title('Successful Trials', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "# plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Excitatory\\\\Successes_One_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MS_conc_r=conc_r[conc_r.Odor.isin(['MS 1%','MS 5%','MS 10%'])]\n",
    "hex_conc_r=conc_r[conc_r.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]\n",
    "IAA_conc_r=conc_r[conc_r.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xr732mjh16lSNtvdRU6XgxsbGin79+onevXuL559/Xrz44ovi/Pnz+m7bQzNU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\nDkOGDAGKv7cOGDCAGTNmaN+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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc363c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1%\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(3.5,3));\n",
    "ax=sns.pointplot(x='Odor',y='Success',hue='Group',palette=groupcolors,data=IAA_conc_r)\n",
    "ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Ratio', fontsize=12);\n",
    "plt.title('Successful Trials', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(0.65,1)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Excitatory\\\\Successes_IAA_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V6XT6n+3s7OjQoUO1Jq+sBQk86DO7Z88edu7cWa25hajPDAZXq9Vy+/ZtdDodOp1O//ND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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde4bbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplots\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(3.5,3));\n",
    "ax=sns.barplot(x='Group',y='Success',palette=groupcolors,data=smalldf)\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Ratio', fontsize=12);\n",
    "plt.title('Successful Trials', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(0.5,1)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Excitatory\\\\Successes_Summary_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# smalldf.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Success.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# mint=one_r[one_r.Group=='Mint']\n",
    "# hexanal=one_r[one_r.Group=='Hexanal']\n",
    "# control=one_r[one_r.Group=='Control']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MS_conc_r.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Success_MS.csv')\n",
    "hex_conc_r.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Success_hex.csv')\n",
    "# IAA_conc_r.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Stats\\\\Success_IAA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Make some ranks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ranking Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AtHbmLDsX9a2ip1xmAf+x++24pFstbuCPk1d70ldCumtFCemCqGUpHTseDqRf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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9175f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Amplitude\n",
    "filename='C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\Ranks\\\\Example_amp.csv'\n",
    "comp=pd.read_csv(filename)\n",
    "groupcolors={'Control':sns.xkcd_rgb[\"black\"],'Mint':sns.xkcd_rgb[\"grey\"],'Hexanal':sns.xkcd_rgb[\"azure\"]};\n",
    "x=pd.melt(comp,('Mouse'),var_name='Odor',value_name='Rank')\n",
    "\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.pointplot(x='Odor',y='Rank',hue='Mouse',palette=groupcolors,data=x,order=['PA','AP','MS 1%','THA','IAA 1%','EB','Hexanal 1%','Hexanone'])\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Rank', fontsize=12);\n",
    "plt.title('Odor Ranking', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "# plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "# plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_One_Control_Ordered.tiff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullpeak.csv'\n",
    "comp=pd.read_csv(filename)\n",
    "comp_sorted=comp.reindex_axis(comp.mean().sort_values().index, axis=1)\n",
    "comp_labels=pd.DataFrame(comp.Mouse)\n",
    "comp_group=pd.DataFrame(comp.Group)\n",
    "tmp=[comp_group,comp_labels,comp_sorted]\n",
    "composite_full=pd.concat(tmp,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# composite_full=composite_full.drop('Blank',axis=1)\n",
    "# labels=composite_full.iloc[:,0:2]\n",
    "# df=composite_full.iloc[:,2:].rank(axis=1)\n",
    "# rankdf=pd.concat((labels,df),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "composite_full=composite_full.drop(['Blank','MS05','MS10','Hexanal05','Hexanal10'],axis=1)\n",
    "# composite_full=composite_full.drop(['Blank'],axis=1)\n",
    "labels=composite_full.iloc[:,0:2]\n",
    "df=composite_full.iloc[:,2:].rank(axis=1)\n",
    "rankdf=pd.concat((labels,df),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rankdf=rankdf.rename(index=str,columns={'MS01':'MS 1%','MS05':'MS 5%','MS10':'MS 10%','IAA01':'IAA 1%','IAA05':'IAA 5%','IAA10':'IAA 10%','Hexanal01':'Hexanal 1%','Hexanal05':'Hexanal 5%','Hexanal10':'Hexanal 10%'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rankdf=rankdf.rename(index=str,columns={'MS01':'MS 1%','IAA01':'IAA 1%','Hexanal01':'Hexanal 1%'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "allrank=pd.melt(rankdf,('Mouse','Group'),var_name='Odor',value_name='Rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "control=rankdf[rankdf.Group=='Control'].iloc[:,2:]\n",
    "mint=rankdf[rankdf.Group=='Mint'].iloc[:,2:]\n",
    "hexanal=rankdf[rankdf.Group=='Hexanal'].iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MS 1%</th>\n",
       "      <th>THA</th>\n",
       "      <th>MS 10%</th>\n",
       "      <th>AP</th>\n",
       "      <th>MS 5%</th>\n",
       "      <th>IAA 5%</th>\n",
       "      <th>PA</th>\n",
       "      <th>IAA 1%</th>\n",
       "      <th>IAA 10%</th>\n",
       "      <th>Hexanal 10%</th>\n",
       "      <th>Hexanone</th>\n",
       "      <th>Hexanal 1%</th>\n",
       "      <th>Hexanal 5%</th>\n",
       "      <th>EB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MS 1%   THA  MS 10%    AP  MS 5%  IAA 5%   PA  IAA 1%  IAA 10%  \\\n",
       "0    2.0   7.0    12.0   4.0   13.0     3.0  5.0    14.0     10.0   \n",
       "1    1.0  11.0    10.0   9.0   13.0     4.0  3.0    14.0      7.0   \n",
       "2    1.0   7.0    10.0   3.0    4.0     5.0  9.0    14.0     12.0   \n",
       "3   11.0   7.0     5.0  10.0   12.0     9.0  4.0    14.0      2.0   \n",
       "4    9.0   2.0     6.0   3.0    7.0     8.0  5.0    12.0     10.0   \n",
       "\n",
       "   Hexanal 10%  Hexanone  Hexanal 1%  Hexanal 5%    EB  \n",
       "0          8.0       9.0         6.0        11.0   1.0  \n",
       "1          6.0       2.0         5.0        12.0   8.0  \n",
       "2         11.0       2.0         6.0        13.0   8.0  \n",
       "3          3.0       1.0         8.0        13.0   6.0  \n",
       "4          4.0       1.0        11.0        13.0  14.0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rankms=rankdf[['Group','Mouse','MS 1%','MS 5%','MS 10%']]\n",
    "rankms=pd.melt(rankms,('Mouse','Group'),var_name='Odor',value_name='Rank')\n",
    "rankhex=rankdf[['Group','Mouse','Hexanal 1%','Hexanal 5%','Hexanal 10%']]\n",
    "rankhex=pd.melt(rankhex,('Mouse','Group'),var_name='Odor',value_name='Rank')\n",
    "rankiaa=rankdf[['Group','Mouse','IAA 1%','IAA 5%','IAA 10%']]\n",
    "rankiaa=pd.melt(rankiaa,('Mouse','Group'),var_name='Odor',value_name='Rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rankone=rankdf[['Group','Mouse','MS 1%','THA','AP','PA','IAA 1%','Hexanone','Hexanal 1%','EB']]\n",
    "rankone=pd.melt(rankone,('Mouse','Group'),var_name='Odor',value_name='Rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# rankone.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_One.csv')\n",
    "# rankms.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_ms.csv')\n",
    "# rankhex.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_hex.csv')\n",
    "# rankiaa.to_csv('C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_iaa.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "groupcolors={'Control':sns.xkcd_rgb[\"black\"],'Mint':sns.xkcd_rgb[\"grey\"],'Hexanal':sns.xkcd_rgb[\"azure\"]};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Vy3RVvW3bNnr06GHal1OTOzExkUWLFrFq1SoWLVqEvb09r732Gj169Cg1IQ0S1EIIIazI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\n8PBwVq9ezeHDhwG4cOFCkRPP5s6dS//+/Q22sYsQwvCk3/QhV69dJTH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xg9dff50lS5YU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\nFbgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3afb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1%\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.pointplot(x='Odor',y='Rank',hue='Group',palette=groupcolors,data=rankone)\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Rank', fontsize=12);\n",
    "plt.title('Odor Ranking', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "# plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_One_170523_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vzZs3GRUwivPnH2Z+e/PNN1m6dGmR5vqWNl555RVCQ0Px8/MjNTWVESNGIEkS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\ndAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbe4f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "sns.set_style(\"ticks\")\n",
    "plt.figure(figsize=(3.5,3));\n",
    "ax=sns.pointplot(x='Odor',y='Rank',hue='Group',palette=groupcolors,data=rankhex)\n",
    "ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Rank', fontsize=12);\n",
    "plt.title('Odor Ranking', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "plt.ylim(8,12)\n",
    "plt.yticks((8,9,10,11,12))\n",
    "plt.tight_layout();\n",
    "plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_hex_ticks.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Within cell comparison of concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filename='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullpeak_WL.csv'\n",
    "comp=pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "groupcolors={'Control':sns.xkcd_rgb[\"black\"],'Mint':sns.xkcd_rgb[\"grey\"],'Hexanal':sns.xkcd_rgb[\"azure\"]};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Mouse</th>\n",
       "      <th>IAA 1%</th>\n",
       "      <th>IAA 10%</th>\n",
       "      <th>IAA 5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (1)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.528683</td>\n",
       "      <td>0.181840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (2)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.220899</td>\n",
       "      <td>0.009612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (3)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.602885</td>\n",
       "      <td>0.267893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (4)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.140682</td>\n",
       "      <td>0.419780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (5)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.786623</td>\n",
       "      <td>0.610787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group               Mouse  IAA 1%   IAA 10%    IAA 5%\n",
       "0  Control  G PMT (1)_160321_1     1.0  0.528683  0.181840\n",
       "1  Control  G PMT (2)_160321_1     1.0  0.220899  0.009612\n",
       "2  Control  G PMT (3)_160321_1     1.0  0.602885  0.267893\n",
       "3  Control  G PMT (4)_160321_1     1.0  0.140682  0.419780\n",
       "4  Control  G PMT (5)_160321_1     1.0  0.786623  0.610787"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IAAonly=comp[['Group','Mouse','IAA01','IAA05','IAA10']]\n",
    "values=pd.DataFrame({'IAA 1%':IAAonly['IAA01']/IAAonly['IAA01'],'IAA 5%':IAAonly['IAA05']/IAAonly['IAA01'],'IAA 10%':IAAonly['IAA10']/IAAonly['IAA01']})\n",
    "temp=IAAonly[['Group','Mouse']]\n",
    "IAAnorm=pd.concat((temp,values),axis=1)\n",
    "IAAmelt=pd.melt(IAAnorm,('Mouse','Group'),var_name='Odor',value_name='Peak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qwEUIagDVxveVAdcjqAEAMBhBDaDaGFEDrsfJZACqjSuAAa5HUAM4J4ykAdciqAGcE0bS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\nBgDAYAQ1AAAG+3+ard59cW/PXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3c8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.boxplot(x='Odor',y='Peak',hue='Group',palette=groupcolors,data=IAAmelt)\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Peak', fontsize=12);\n",
    "plt.title('Normalized IAA', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "# plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "# plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_One.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MSonly=comp[['Group','Mouse','MS01','MS05','MS10']]\n",
    "values=pd.DataFrame({'MS 1%':MSonly['MS01']/MSonly['MS01'],'MS 5%':MSonly['MS05']/MSonly['MS01'],'MS 10%':MSonly['MS10']/MSonly['MS01']})\n",
    "temp=MSonly[['Group','Mouse']]\n",
    "MSnorm=pd.concat((temp,values),axis=1)\n",
    "MSmelt=pd.melt(MSnorm,('Mouse','Group'),var_name='Odor',value_name='Peak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H65FHHnGFtCS1b99eycnJkiS73a7Q0FDZbDYFBgZq3759kqRt27apQ4cOv+t1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\nhcR0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbb702b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.boxplot(x='Odor',y='Peak',hue='Group',palette=groupcolors,data=MSmelt)\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Peak', fontsize=12);\n",
    "plt.title('Normalized MS', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "# plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "# plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_One.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Hexonly=comp[['Group','Mouse','Hexanal01','Hexanal05','Hexanal10']]\n",
    "values=pd.DataFrame({'Hexanal 1%':Hexonly['Hexanal01']/Hexonly['Hexanal01'],'Hexanal 5%':Hexonly['Hexanal05']/Hexonly['Hexanal01'],'Hexanal 10%':Hexonly['Hexanal10']/Hexonly['Hexanal01']})\n",
    "temp=Hexonly[['Group','Mouse']]\n",
    "Hexnorm=pd.concat((temp,values),axis=1)\n",
    "Hexmelt=pd.melt(Hexnorm,('Mouse','Group'),var_name='Odor',value_name='Peak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qaiqhoaEMGTKEyMhICgoKnFWuiIiIUzgtqP/617/ae5EvWbKEwMBAEhISGDx4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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd186e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True);\n",
    "sns.set_context(\"paper\", font_scale=1.3);\n",
    "plt.figure(figsize=(7,5));\n",
    "ax=sns.boxplot(x='Odor',y='Peak',hue='Group',palette=groupcolors,data=Hexmelt)\n",
    "# ax.legend_.remove()\n",
    "sns.despine()\n",
    "plt.ylabel('Peak', fontsize=12);\n",
    "plt.title('Normalized Hexanal', fontsize=12);\n",
    "plt.xlabel('Odor', fontsize=12);\n",
    "# plt.ylim(-0.2,0.6)\n",
    "plt.tight_layout();\n",
    "# plt.savefig(\"C:\\Users\\Annie\\Desktop\\Plasticity\\Figures\\\\Ranks\\\\Ranks_One.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Normtotal=pd.concat((IAAnorm,MSnorm[['MS 1%','MS 5%','MS 10%']],Hexnorm[['Hexanal 1%','Hexanal 5%','Hexanal 10%']]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Normtotal[Normtotal.Group=='Hexanal'].to_csv('C:\\Users\\Annie\\Desktop\\Hexanal.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmp=IAAnorm[IAAnorm.Group=='Control']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Mouse</th>\n",
       "      <th>IAA 1%</th>\n",
       "      <th>IAA 10%</th>\n",
       "      <th>IAA 5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (1)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.528683</td>\n",
       "      <td>0.181840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (2)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.220899</td>\n",
       "      <td>0.009612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (3)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.602885</td>\n",
       "      <td>0.267893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (4)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.140682</td>\n",
       "      <td>0.419780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (5)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.786623</td>\n",
       "      <td>0.610787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group               Mouse  IAA 1%   IAA 10%    IAA 5%\n",
       "0  Control  G PMT (1)_160321_1     1.0  0.528683  0.181840\n",
       "1  Control  G PMT (2)_160321_1     1.0  0.220899  0.009612\n",
       "2  Control  G PMT (3)_160321_1     1.0  0.602885  0.267893\n",
       "3  Control  G PMT (4)_160321_1     1.0  0.140682  0.419780\n",
       "4  Control  G PMT (5)_160321_1     1.0  0.786623  0.610787"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Mouse</th>\n",
       "      <th>IAA 1%</th>\n",
       "      <th>IAA 10%</th>\n",
       "      <th>IAA 5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (1)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.528683</td>\n",
       "      <td>0.181840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (2)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.220899</td>\n",
       "      <td>0.009612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (3)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.602885</td>\n",
       "      <td>0.267893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (4)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.140682</td>\n",
       "      <td>0.419780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Control</td>\n",
       "      <td>G PMT (5)_160321_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.786623</td>\n",
       "      <td>0.610787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group               Mouse  IAA 1%   IAA 10%    IAA 5%\n",
       "0  Control  G PMT (1)_160321_1     1.0  0.528683  0.181840\n",
       "1  Control  G PMT (2)_160321_1     1.0  0.220899  0.009612\n",
       "2  Control  G PMT (3)_160321_1     1.0  0.602885  0.267893\n",
       "3  Control  G PMT (4)_160321_1     1.0  0.140682  0.419780\n",
       "4  Control  G PMT (5)_160321_1     1.0  0.786623  0.610787"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio\n",
      "0.238482384824\n"
     ]
    }
   ],
   "source": [
    "tmp=Hexnorm[Hexnorm.Group=='Hexanal']\n",
    "a=3\n",
    "b=4\n",
    "values=[]\n",
    "for x in range(len(tmp)):\n",
    "    if tmp.iloc[x,a]>1:\n",
    "        values.append(tmp.iloc[x,a])\n",
    "print 'ratio'\n",
    "print float(len(values))/float(len(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "both ratio\n",
      "0.253333333333\n"
     ]
    }
   ],
   "source": [
    "tmp=Hexnorm[Hexnorm.Group=='Control']\n",
    "a=3\n",
    "b=4\n",
    "values=[]\n",
    "for x in range(len(tmp)):\n",
    "    if tmp.iloc[x,a]>1 and tmp.iloc[x,b]>1:\n",
    "        values.append(tmp.iloc[x,a])\n",
    "print 'both ratio'\n",
    "print float(len(values))/float(len(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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