speed_ahv_scoring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speed cells scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trial data\n",
    "speed=pd.read_csv(\"TrialData/speed.csv\")\n",
    "speedMask=pd.read_csv(\"TrialData/speedFilterMask.csv\")\n",
    "trialMetadata=pd.read_csv(\"TrialData/metadata.csv\",index_col=0)\n",
    "#cells data\n",
    "rate=pd.read_csv(\"CellData/firingRate.csv\")\n",
    "cellMetadata=pd.read_csv(\"CellData/metadata.csv\",index_col=0)\n",
    "speed.columns=trialMetadata[\"trialIndex\"].values\n",
    "speedMask.columns=trialMetadata[\"trialIndex\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def speedFilter(array,mask):\n",
    "    out=array\n",
    "    for i in range(len(array)):\n",
    "        if mask[i]==0:\n",
    "            out[i]=np.nan\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#select cells and trials for analysis\n",
    "speedScore=np.zeros(len(rate.T))\n",
    "for i in range(len(rate.columns)):\n",
    "        trialId=cellMetadata.loc[rate.columns[i],:][\"trialIndex\"]\n",
    "        if cellMetadata[\"include\"][rate.columns[i]]==1:\n",
    "            x = rate[rate.columns[i]].values\n",
    "            y = speedFilter(speed[trialId].values,speedMask[trialId])\n",
    "            bad = ~np.logical_or(np.isnan(x), np.isnan(y))\n",
    "            a=np.compress(bad, x)  \n",
    "            b=np.compress(bad, y) \n",
    "            speedScore[i]=pearsonr(a,b)[0]\n",
    "        else:\n",
    "            speedScore[i]=np.nan\n",
    "        if i %100==0:\n",
    "            print(\"Scored cell: \"+str(i))\n",
    "cellMetadata[\"speedScore\"] = speedScore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "cellMetadata.to_csv(\"CellData/metadata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(8,8))\n",
    "hist([x for x in speedScore if not isnan(x)]);\n",
    "xlabel(\"Speed Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "savefig(\"imgs/speedScore_distr.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divided by region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "MECcells=cellMetadata[cellMetadata[\"region\"]=='MEC']\n",
    "PAScells=cellMetadata[cellMetadata[\"region\"]=='PaS']\n",
    "PRScells=cellMetadata[cellMetadata[\"region\"]=='PrS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of MEC cells: 630\n",
      "# of PaS cells: 528\n",
      "# of PrS cells: 653\n"
     ]
    }
   ],
   "source": [
    "print(\"# of MEC cells: \"+str(len(MECcells)))\n",
    "print(\"# of PaS cells: \"+str(len(PAScells)))\n",
    "print(\"# of PrS cells: \"+str(len(PRScells)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize(15,6))\n",
    "ax=subplot(1,3,1)\n",
    "title(\"mEC\",fontsize=20)\n",
    "xlabel(\"Speed Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in MECcells[\"speedScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "ax=subplot(1,3,2)\n",
    "title(\"PaS\",fontsize=20)\n",
    "xlabel(\"Speed Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in PAScells[\"speedScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "ax=subplot(1,3,3)\n",
    "title(\"PrS\",fontsize=20)\n",
    "xlabel(\"Speed Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in PRScells[\"speedScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata=pd.read_csv(\"CellData/metadata.csv\",index_col=0)\n",
    "MECspsc=concatenate((np.load(\"CellData/ShuffledSpeedScoresMEC1.npy\"),np.load(\"CellData/ShuffledSpeedScoresMEC2.npy\")))\n",
    "PaSspsc=concatenate((np.load(\"CellData/ShuffledSpeedScoresPaS1.npy\"),np.load(\"CellData/ShuffledSpeedScoresPaS2.npy\")))\n",
    "PrSspsc=concatenate((np.load(\"CellData/ShuffledSpeedScoresPrS1.npy\"),np.load(\"CellData/ShuffledSpeedScoresPrS2.npy\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "MECth=percentile(MECspsc,99)\n",
    "PaSth=percentile([x for x in PaSspsc if not isnan(x)],99)\n",
    "PrSth=percentile(PrSspsc,99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add speed threshold to metadata\n",
    "SpeedTh=[]\n",
    "SpeedModulated=[]\n",
    "for i in range(len(cellMetadata.index)):\n",
    "    if cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"MEC\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        SpeedTh.append(MECth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"speedScore\"]>MECth:\n",
    "            SpeedModulated.append(1)\n",
    "        else:\n",
    "            SpeedModulated.append(0)\n",
    "        \n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PaS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        SpeedTh.append(PaSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"speedScore\"]>PaSth:\n",
    "            SpeedModulated.append(1)\n",
    "        else:\n",
    "            SpeedModulated.append(0)\n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PrS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        SpeedTh.append(PrSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"speedScore\"]>PrSth:\n",
    "            SpeedModulated.append(1)\n",
    "        else:\n",
    "            SpeedModulated.append(0)\n",
    "    else:\n",
    "        SpeedTh.append(np.nan)\n",
    "        SpeedModulated.append(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata[\"speedTh\"]=SpeedTh\n",
    "cellMetadata[\"speedModulated\"]=SpeedModulated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "cellMetadata.to_csv(\"CellData/metadata.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AHV cell scoring: unidirectional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trial data\n",
    "angularVelocity=pd.read_csv(\"TrialData/filteredAngularVelocity.csv\")\n",
    "trialMetadata=pd.read_csv(\"TrialData/metadata.csv\",index_col=0)\n",
    "#cells data\n",
    "rate=pd.read_csv(\"CellData/firingRate.csv\")\n",
    "cellMetadata=pd.read_csv(\"CellData/metadata.csv\",index_col=0)\n",
    "angularVelocity.columns=trialMetadata[\"trialIndex\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#select cells and trials for analysis\n",
    "angularVelocityScore=np.zeros(len(rate.T))\n",
    "for i in range(len(rate.columns)):\n",
    "        trialId=cellMetadata.loc[rate.columns[i],:][\"trialIndex\"]\n",
    "        if cellMetadata[\"include\"][rate.columns[i]]==1:\n",
    "            x = rate[rate.columns[i]].values\n",
    "            y = angularVelocity[trialId].values\n",
    "            bad = ~np.logical_or(np.isnan(x), np.isnan(y))\n",
    "            a=np.compress(bad, x)  \n",
    "            b=np.compress(bad, y) \n",
    "            angularVelocityScore[i]=pearsonr(a,b)[0]\n",
    "        else:\n",
    "            angularVelocityScore[i]=np.nan\n",
    "        if i %100==0:\n",
    "            print(\"Scored cell: \"+str(i))\n",
    "cellMetadata[\"angularVelocityScore\"]=angularVelocityScore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "cellMetadata.to_csv(\"CellData/metadata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of cells')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(8,8))\n",
    "hist([x for x in angularVelocityScore if not isnan(x)]);\n",
    "xlabel(\"Speed Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "#savefig(\"imgs/speedScore_distr.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DIVIDED BY REGION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "MECcells=cellMetadata[cellMetadata[\"region\"]=='MEC']\n",
    "PAScells=cellMetadata[cellMetadata[\"region\"]=='PaS']\n",
    "PRScells=cellMetadata[cellMetadata[\"region\"]=='PrS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize(15,6))\n",
    "ax=subplot(1,3,1)\n",
    "title(\"mEC\",fontsize=20)\n",
    "xlabel(\"Angular Velocity Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in MECcells[\"angularVelocityScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "ax=subplot(1,3,2)\n",
    "title(\"PaS\",fontsize=20)\n",
    "xlabel(\"Angular Velocity Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in PAScells[\"angularVelocityScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "ax=subplot(1,3,3)\n",
    "title(\"PrS\",fontsize=20)\n",
    "xlabel(\"Angular Velocity Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in PRScells[\"angularVelocityScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## threshold CW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata=pd.read_csv(\"CellData/metadata.csv\",index_col=0)\n",
    "MECspsc=concatenate((np.load(\"CellData/ShuffledAngularVelocityScoresMEC1.npy\"),np.load(\"CellData/ShuffledAngularVelocityScoresMEC2.npy\")))\n",
    "PaSspsc=concatenate((np.load(\"CellData/ShuffledAngularVelocityScoresPaS1.npy\"),np.load(\"CellData/ShuffledAngularVelocityScoresPaS2.npy\")))\n",
    "PrSspsc=concatenate((np.load(\"CellData/ShuffledAngularVelocityScoresPrS1.npy\"),np.load(\"CellData/ShuffledAngularVelocityScoresPrS2.npy\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "MECth=percentile(MECspsc,99)\n",
    "PaSth=percentile([x for x in PaSspsc if not isnan(x)],99)\n",
    "PrSth=percentile(PrSspsc,99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add AngularVelocitythreshold to metadata\n",
    "AngularVelocityTh=[]\n",
    "AngularVelocityModulated=[]\n",
    "for i in range(len(cellMetadata.index)):\n",
    "    if cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"MEC\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AngularVelocityTh.append(MECth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"angularVelocityScore\"]>MECth:\n",
    "            AngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AngularVelocityModulated.append(0)\n",
    "        \n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PaS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AngularVelocityTh.append(PaSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"angularVelocityScore\"]>PaSth:\n",
    "            AngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AngularVelocityModulated.append(0)\n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PrS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AngularVelocityTh.append(PrSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"angularVelocityScore\"]>PrSth:\n",
    "            AngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AngularVelocityModulated.append(0)\n",
    "    else:\n",
    "        AngularVelocityTh.append(np.nan)\n",
    "        AngularVelocityModulated.append(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata[\"angularVelocityTh\"]=AngularVelocityTh\n",
    "cellMetadata[\"angularVelocityModulated\"]=AngularVelocityModulated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "cellMetadata.to_csv(\"CellData/metadata.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## threshold CCW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata=pd.read_csv(\"CellData/metadata.csv\",index_col=0)\n",
    "MECspsc=concatenate((np.load(\"CellData/ShuffledAngularVelocityScoresMEC1.npy\"),np.load(\"CellData/ShuffledAngularVelocityScoresMEC2.npy\")))\n",
    "PaSspsc=concatenate((np.load(\"CellData/ShuffledAngularVelocityScoresPaS1.npy\"),np.load(\"CellData/ShuffledAngularVelocityScoresPaS2.npy\")))\n",
    "PrSspsc=concatenate((np.load(\"CellData/ShuffledAngularVelocityScoresPrS1.npy\"),np.load(\"CellData/ShuffledAngularVelocityScoresPrS2.npy\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "MECth=percentile(MECspsc,1)\n",
    "PaSth=percentile([x for x in PaSspsc if not isnan(x)],1)\n",
    "PrSth=percentile(PrSspsc,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add AngularVelocitythreshold to metadata\n",
    "AngularVelocityTh=[]\n",
    "AngularVelocityModulated=[]\n",
    "for i in range(len(cellMetadata.index)):\n",
    "    if cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"MEC\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AngularVelocityTh.append(MECth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"angularVelocityScore\"]<MECth:\n",
    "            AngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AngularVelocityModulated.append(0)\n",
    "        \n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PaS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AngularVelocityTh.append(PaSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"angularVelocityScore\"]<PaSth:\n",
    "            AngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AngularVelocityModulated.append(0)\n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PrS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AngularVelocityTh.append(PrSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"angularVelocityScore\"]<PrSth:\n",
    "            AngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AngularVelocityModulated.append(0)\n",
    "    else:\n",
    "        AngularVelocityTh.append(np.nan)\n",
    "        AngularVelocityModulated.append(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata[\"angularVelocityCCWTh\"]=AngularVelocityTh\n",
    "cellMetadata[\"angularVelocityCCWModulated\"]=AngularVelocityModulated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Angular velocity scoring: bidirectional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trial data\n",
    "angularVelocity=pd.read_csv(\"TrialData/filteredAngularVelocity.csv\")\n",
    "trialMetadata=pd.read_csv(\"TrialData/metadata.csv\",index_col=0)\n",
    "#cells data\n",
    "rate=pd.read_csv(\"CellData/firingRate.csv\")\n",
    "cellMetadata=pd.read_csv(\"CellData/metadata.csv\",index_col=0)\n",
    "angularVelocity.columns=trialMetadata[\"trialIndex\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select cells and trials for analysis\n",
    "angularVelocityScore=np.zeros(len(rate.T))\n",
    "for i in range(len(rate.columns)):\n",
    "        trialId=cellMetadata.loc[rate.columns[i],:][\"trialIndex\"]\n",
    "        if cellMetadata[\"include\"][rate.columns[i]]==1:\n",
    "            x = rate[rate.columns[i]].values\n",
    "            y = abs(angularVelocity[trialId].values)\n",
    "            bad = ~np.logical_or(np.isnan(x), np.isnan(y))\n",
    "            a=np.compress(bad, x)  \n",
    "            b=np.compress(bad, y) \n",
    "            angularVelocityScore[i]=pearsonr(a,b)[0]\n",
    "        else:\n",
    "            angularVelocityScore[i]=np.nan\n",
    "        if i %100==0:\n",
    "            print(\"Scored cell: \"+str(i))\n",
    "cellMetadata[\"absAngularVelocityScore\"]=angularVelocityScore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "cellMetadata.to_csv(\"CellData/metadata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of cells')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(8,8))\n",
    "hist([x for x in angularVelocityScore if not isnan(x)]);\n",
    "xlabel(\"Absolute Angular Velocity Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "#savefig(\"imgs/speedScore_distr.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DIVIDED BY REGION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "MECcells=cellMetadata[cellMetadata[\"region\"]=='MEC']\n",
    "PAScells=cellMetadata[cellMetadata[\"region\"]=='PaS']\n",
    "PRScells=cellMetadata[cellMetadata[\"region\"]=='PrS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize(15,6))\n",
    "ax=subplot(1,3,1)\n",
    "title(\"mEC\",fontsize=20)\n",
    "xlabel(\"abs Angular Velocity Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in MECcells[\"absAngularVelocityScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "ax=subplot(1,3,2)\n",
    "title(\"PaS\",fontsize=20)\n",
    "xlabel(\"abs Angular Velocity Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in PAScells[\"absAngularVelocityScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "ax=subplot(1,3,3)\n",
    "title(\"PrS\",fontsize=20)\n",
    "xlabel(\"abs Angular Velocity Score\",fontsize=20)\n",
    "ylabel(\"# of cells\",fontsize=20)\n",
    "scores=[x for x in PRScells[\"absAngularVelocityScore\"].values if not isnan(x)]\n",
    "ax.hist(scores);\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata=pd.read_csv(\"CellData/metadata.csv\",index_col=0)\n",
    "MECspsc=concatenate((np.load(\"CellData/ShuffledAbsAngularVelocityScoresMEC1.npy\"),np.load(\"CellData/ShuffledAbsAngularVelocityScoresMEC2.npy\")))\n",
    "PaSspsc=concatenate((np.load(\"CellData/ShuffledAbsAngularVelocityScoresPaS1.npy\"),np.load(\"CellData/ShuffledAbsAngularVelocityScoresPaS2.npy\")))\n",
    "PrSspsc=concatenate((np.load(\"CellData/ShuffledAbsAngularVelocityScoresPrS1.npy\"),np.load(\"CellData/ShuffledAbsAngularVelocityScoresPrS2.npy\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "MECth=percentile(MECspsc,99)\n",
    "PaSth=percentile([x for x in PaSspsc if not isnan(x)],99)\n",
    "PrSth=percentile(PrSspsc,99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add AbsAngularVelocitythreshold to metadata\n",
    "AbsAngularVelocityTh=[]\n",
    "AbsAngularVelocityModulated=[]\n",
    "for i in range(len(cellMetadata.index)):\n",
    "    if cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"MEC\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AbsAngularVelocityTh.append(MECth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"absAngularVelocityScore\"]>MECth:\n",
    "            AbsAngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AbsAngularVelocityModulated.append(0)\n",
    "        \n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PaS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AbsAngularVelocityTh.append(PaSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"absAngularVelocityScore\"]>PaSth:\n",
    "            AbsAngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AbsAngularVelocityModulated.append(0)\n",
    "    elif cellMetadata.loc[cellMetadata.index[i]][\"region\"]==\"PrS\" and cellMetadata.loc[cellMetadata.index[i]][\"include\"]==1:\n",
    "        AbsAngularVelocityTh.append(PrSth)\n",
    "        if cellMetadata.loc[cellMetadata.index[i]][\"absAngularVelocityScore\"]>PrSth:\n",
    "            AbsAngularVelocityModulated.append(1)\n",
    "        else:\n",
    "            AbsAngularVelocityModulated.append(0)\n",
    "    else:\n",
    "        AbsAngularVelocityTh.append(np.nan)\n",
    "        AbsAngularVelocityModulated.append(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellMetadata[\"absAngularVelocityTh\"]=AbsAngularVelocityTh\n",
    "cellMetadata[\"absAngularVelocityModulated\"]=AbsAngularVelocityModulated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "cellMetadata.to_csv(\"CellData/metadata.csv\")"
   ]
  }
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