{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brain Observatory Trace Analysis\n",
    "This notebook demonstrates how to run the stimulus-specific tuning analysis code in the SDK.  First let's instantiate a `BrainObservatoryCache` instance.\n",
    "\n",
    "Download this notebook in .ipynb format <a href='brain_observatory_analysis.ipynb'>here</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
    "boc =  BrainObservatoryCache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drifting Gratings\n",
    "In this example, we'll show how you can plot a heatmap of a cell's response organized by orientation and temporal frequency.  Here we start with a known experiment ID.  Take a look at the other notebook to see how you can find experiments of interest.  You can run the drifting grating analysis code on that experiment's NWB file as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "%matplotlib inline\n",
    "\n",
    "from allensdk.brain_observatory.drifting_gratings import DriftingGratings\n",
    "\n",
    "data_set = boc.get_ophys_experiment_data(502376461)\n",
    "dg = DriftingGratings(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you know which cell you're interested in, here's how you can find out where it is in the NWB File."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Specimen ID:', 517425074)\n",
      "('Cell loc:', 97)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "specimen_id = 517425074\n",
    "cell_loc = data_set.get_cell_specimen_indices([specimen_id])[0]\n",
    "\n",
    "print(\"Specimen ID:\", specimen_id)\n",
    "print(\"Cell loc:\", cell_loc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `response` property of the stimulus-specific analysis objects is 4-D array organized with the following dimensions:\n",
    "    \n",
    "    0: num. grating directions\n",
    "    1: num. grating temporal frequencies + 1 (0=blank sweep)\n",
    "    2: num. cells + 1 (running speed)\n",
    "    3: 0=response mean, 1=response standard error of the mean, 2=number of signficant trials\n",
    "\n",
    "Dimension 2 of the `response` array has one index per cell in the experiment, plus one.  The final index of that dimension is the running speed (`response[:,:,-1,:]`).  This organization allows users to examine whether the mouse ran more for some specific stimulus conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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l8OxshwJfl7SpmeWOnh4E3Uwly+MJ4Mnal78IDC16vUI61yPU9HkkJ82luNC3\nAZviya2XNbNlgEHA5vhMzDDgkqxC1MpVm+oUcs/OKjnOLqqzgKSzU9KntyVdI2n5rPIEQauo5ONY\nFY/rWTgqcC+wiqSVJC2AByNvSjS/PNTjMN0cWA8PeryJmd1iZrPDEpjZ+2Y2HvgSHqT485I2yyhH\nrVy1MCf37DLp+Aruc/lDUZ0z8Ty5OwIb4rldrpekjPIEQUtoJJKYmc0EvodngpgEXG5mj7Va5krU\nM2z5OvA+cGC1QMJmZpK+hyeh3h4f5tRFrVy1qc7LJXW+CjxZyB8jaTCwN7CHmd1S1M5zuLVUtzxB\n0CoaXedhZjcCqzVDlkapx/JYB7iznjwp6Qd+B43nUqqaq1bSQNy6OLfo9Lq4MpytJFJQ5MfwRFRB\n0ON0UzCgepTHisydhb4Wk4CV8okzm6q5avH8LPPjKRcKLAPMNLPXSupOS2VB0OP0tQDIg/HAx/Xy\nBu5EzUWtXLWJfYCryyiKzBSnplsCT0ITBJPT0Wz62lTtAmRThrPSNZmplas21RmBO3APKymaCvSX\ntGSJUhmCzwSVZdU8ggZdzwrpKDCxSe32FquiHupdnt7yzXApV+2OwMblctUWsR/wdMEpWsT9+CzN\n7JkeSSsAaxDpJoMOoZt8HvWuMB0raWyrhKiVq7ao3kLAzsBJpW2Y2ZuSzgNOkfQK8DpwOvAQ86ax\nDIIeoZssj3qVR9Z1ElktlVq5agvsCCwMXFihnYPxYeXlwELAeGC3yFUbdAp9SnnUyiPbDOrtw8wu\npLLiIO00PDgdQdBx9JYhST1EtK8gaCN9yvIIgqB59LWp2iAImkRYHkEQ5CJ8HkEQ5CIsjyAIchHK\nIwiCXMSwJQiCXITl0UUMb2Nf/dvYF8D6be5v8zb29ULtKk3lrCa1E1O1QRDkIiyPIAhyET6PIAhy\nEZZHEAS56CblEblqg6CNtCoYkKRTJD0m6SFJV6VsAi0llEcQtJEWBkC+CVjTzEbgoXkPb1za6oTy\nCII28mGdR1bMbLyZFYyWu5k7BGtL6AjlIek7kv6Z0k1Ol3SXpK2Lyr8m6caUinKWpFFl2phQkoZy\npqTL2vtOgqA6bUq9sDcwrvFmqtMpDtMXgB/h5lY/YE/gaknrmNkjwEA8iPHvmTtXSzEGnI+ba4Ww\niTNaKHMQZKaRqVpJN+PZAGafwu/7I83sulTnSOBDM2v5H2dHKI/CGy/iKEkH4LlrHzGzSwAkLUn1\neKrv1pPVwrwaAAAPuUlEQVTZLgh6ikpWxRvUTo5kZlVzQEvaE9gazxvdcjpi2FKMpH6SdsKtjbsy\nXr6TpFckPSLpVEmLtEDEIMhNpWHKIDw1Y+HIiqQtgR8C25jZ+82RtjodYXkASPoUnl5yAPAW8DUz\ny5Lm8lI8qfUUYE08PcOngS2bLGoQ5KaFK0zPxpOt3SwJ4G4z+07ruusg5QE8DqwFLApsD1wsaSMz\ne7Sei83sd0UvJ0l6GpgoaYSZPdR8cYMgO63aGGdmbU9+2DHKw8w+Ap5OLx+U9DngEGDfnE3ej1uE\nq+KJn8pSnEZw+XQEwYNUuWkaoJtWmHaM8ihDP2DBBq7/DL4L/qVqlT7XQAdB97J2Ogpc2KR2Q3k0\nGUknAn/Bp2wHAbsAG+GeYyQtDgwFFk+XrCppOjDVzKZJWjldcwPwKu7zOA23PiJPbdAxxK7a5rMM\nvoZjGWA68DCwpZmNT+XbABfgc9oGnJvOF9JRfgBsAhwELIIroeuB4yLVZNBJhOXRZMxsrxrlFwEX\nVSmfDIxuslhB0HRCeQRBkIsYtgRBkIuIYRoEQS5i2BIEQS5CeQRBkItu8nl03Ma43sCLbe7vuTb3\n90Sb+7u7jX092Ma+ytGmeB5tIZRHDtqtPJ5vc39Ptrm/diqPnt7k1E3KI4YtQdBGumnY0ueVx4rr\nrJP5miemTGHF5ZbLfN3AzFc4g6ZMYbkc/S2Vs7+Fp0xhqRz9LZCzv/5TprBAxv7yBmpZYMoUFsnx\n3njggZw9zk03TdWqL6/eltR333yQGTOrFsWuJpKeBVaqs/pzZjaskf5aTZ9WHkEQ5CccpkEQ5CKU\nRxAEuQjlEQRBLkJ51ImkL0q6RtLklFRq9xb2dbikiSkB1suSrpW0Zqv6q9D/LElntaj9fpKOl/S0\npBnp8XhJTbkfa31Xki4oSRA2S1LWSP19nlAe9bMI8C884NC7Le5rFHAOnrdmY+AjYLykxVrcL5LW\nx+PG/rOF3RwGHAB8D1gN/0y/Q/Pyq9bzXRUSKC2Tjq0r1Asq0OfXedSLmY0jpfCTVDEwUZP62qr4\ntaTd8AhrI/FwjS1B0qLAJcBewNhW9YMrxevM7Ib0+nlJ1wGfb0bjdX5X70eCsMYIy6N3MBj/rv7b\n4n7OBa4ws9ta3M8dwMaSVgOQ9Ek8y1nLFGMZNpQ0TdITks6V9LE29t0VhOXROzgTeABPitUSJO0L\nrAx8s1V9FDCzkyUNAh6VNBOPcn+Cmf2m1X0nxgFXAc8Aw4ATgL9JWtfMumkRaEsJ5dHhSPo58AVg\nZKuCOUv6BP4DGmlmLd9+kdKJ7gbsBDwKjADOkvSMmV3Q6v7N7Iqil5MkPYBvXh4DXN3q/ruFUB4d\njKQzgG8Ao82slTvzNwCWxC2Bwrn+wChJ3wYGNvkf+RTgFDP7Y3o9SdIw3GHacuVRipm9JGkyniAs\nqJNQHh2KpDOBHXDF8VSLu/szcG/JuQvx3fkntMCUX5h5N5jOood8cMnfsTw1EoQFcxPKo04kDQRW\nAYTf5EMlrQW8bmYvNLmvXwK7AtsC0yUNSUVvm9k7zewLwMzexIcPxTK8g7+3x5rdH3AdcFjaKDYJ\nWAdPLXphMxqv9l2lYyzu83gJGA78DJiKK9GgXswsjjoOPIPdLOaN23J+C/oq189M4Og2vt9bgLNa\n1PZA4Oe4w/Id4N/A8cACrf6ugAHAjbiyeC/JcB6wfE/fY73tiF21QRDkItZ5BEGQi1AeQRDkIpRH\nEAS5COURBEEuQnkEQZCLUB5BEOQilEcQBLkI5dEhSBqYIlpdm/G6H0p6TNK76fq9WyVj0DiSFpL0\ngqTLe6DvwZJek/S/zWivqvIoE6qt1tGy0HzBvEjaBzgZeANfsTkW37ofdC4/xiOXHV18UtJ3a4V+\nlDQmzx9MAfNtCKcD+0haI08bxdTa2zK2zLlD8OA0Z+I3bTE9nQq0rzEGX4a9mZm93dPCBNVJYSR/\nAFxtZu1OCVzgLOAIfDvA9o00VFV5mNlxpeck7YUrj1+YWbtzMAdzsxzwbiiOXsNewEI0aQNgHszs\nbUl/AnaStJyZTcnbVst8HpKWknSapMdThOzXJd0oaaMydQsm23aSviLpLklvS5oq6VdplySSPp/a\n+G+KLH6lpHkSj0q6T9KbaXx5iqRnJb0n6UlJP5bUv4LMW0san9qfkXwJxxb6r9DHAEk/lfSUpPcL\nZqekJSQdJmmCpBdT2dQkc/YEuXP3faqkWcBngUWKho1vpvLZ/hNJK0i6WNIUSR9J2q6onYGSjpb0\nsKR30vu5XdLXKvS7oDzK+TPp8/y3pKMkLVrOnE7vdZakJcq0VTDBDy1Tlvfe2VLS3yW9JekNSX+W\n9PEK72Vgkv3BVP9NSY9IOj1ZCKTPb5aktSu0sUcq/1m58jJ8C3gL35jXFCStqdruhO1KLrscNxz2\naKTvlmzJl0emugVYFrgVuB63VrbBw73tamalDiMDdsZN8WvxOJcbAfsDy8sjat0AjAd+i2/j3g5Y\nAVi/TFsCrgE+AfwpnfsacCKwVuqrWOZDgdPwodgV+NbtTYGfAGMkjTKz4kjchivf61MffwVewyNS\nAawNHANMwKNTTce3f28DfFnSpmZ2R9UPsjJ/xW/C/fEgPiek9/t+Sb1lgHuAaek99UsyImkp4DZg\ndWAiHr90AWAr4CpJh5nZKUWfj/Ct9JsCj+PD1oXxCOjrVZDT0lGJecoauHd2Ar6aZPxf/DveFlhX\n0ieLrTN5/I7b8Mjtk9J7n4kHA9ofuBT3Hf0K+DKwHx7tvZT98WFjzfCJ8rAKnwRuNrOZtepn4GXK\nuxf6Af8DDGLeCPJ34Z/ZZvjvIR85tjs/g3/QQ6vUuQ/4ABhTcn5x4DH8Bzq46Px38S9hBrBu0Xnh\nSmQW8Crw5ZL2Lk+ybFxy/t50zUPAwkXnF8ZvipnAtkXnV8MTmL9c+r5wE3MmcFqFPu4GBpX5DBYD\nFi1zfjjwCnBPyfmBqb1rM3wX9wJvljlfaGsm8MsK116Zyr9dcn4ArvA+AFYpOr9favMmoH/R+Y8B\nL6S2ri1p64/p/BJl+i/4aw5t4r3z+ZJrzqrwHq9N508uI9cgPHJa4f77D674B5bUWzP1e0Od39XX\nU/0TKpQX3sfd+J9OuePSeu8R3DE6C7igQvmTuFLpX4/8ZdvIfEEN5YHH25wFnFehfOd0/a5lPrhz\nytQ/IJVdV6Zs61R2SJkf1Uxgmyo37TVF505M9X9cpv6Q9CG/VqGP0Tk+w/PStYsVnWuF8niT8opt\n+dT/32p8h0cVnftHumadKjd+Q8qjwXtnHiUJfCqVnV90bqV07t/AfHV8xj9Kfe5bcv5sSv6EarRz\nSKr/vQrlhfdRLo5L6VH1HsFz4MwC/lbpPQK3p7aWy3r/Fo5WDFs2SI9LSzqmTPkKuEYvnSoy4P4y\n9QsOnXJTkC8WtVmO28ucm5Aei8exhee3llY2s2mSHgXWljTczJ4pqVIavm82kjYGDsR9E0sD8xc3\njf+IS2esmskTZvZWmfPr49/B/BW+o0XSY/F3NAJ3zpb7HiY0JOUcmn3vFCK8LV50rpAb5hYz+6gO\nmc4DjsWHKL8FkDQA2AWPRHZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f844e095510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# skip the blank sweep column of the temporal frequency dimension\n",
    "plt.imshow(dg.response[:,1:,cell_loc,0], cmap='hot', interpolation='none')\n",
    "plt.xticks(range(5), dg.tfvals[1:])\n",
    "plt.yticks(range(8), dg.orivals)\n",
    "plt.xlabel(\"Temporal frequency (Hz)\", fontsize=20)\n",
    "plt.ylabel(\"Direction (deg)\", fontsize=20)\n",
    "plt.tick_params(labelsize=14)\n",
    "cbar= plt.colorbar()\n",
    "cbar.set_label(\"DF/F (%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `peak` property of the analysis object is a Pandas DataFrame of peak conditions (direction and temporal frequency) as well as computed response metrics.  For drifting gratings this includes:\n",
    "\n",
    "    ori_dg: preferred direction (index into dg.orivals)\n",
    "    tf_dg: preferred temporal frequency (index into tf.tfvals)\n",
    "    response_reliability_dg: response reliability \n",
    "    osi_dg: orientation selectivity index\n",
    "    dsi_dg: direction selectivity index\n",
    "    ptest_dg: number of signficant cells\n",
    "    p_run_dg: K-S statistic comparing running trials to stationary trials\n",
    "    run_modulation_dg: ratio of mean fluorescence during running vs static\n",
    "    cv_dg: circular variance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ori_dg                         4\n",
       "tf_dg                          2\n",
       "reliability_dg          0.194066\n",
       "osi_dg                   1.21454\n",
       "dsi_dg                  0.340134\n",
       "peak_dff_dg              11.6263\n",
       "ptest_dg             3.03061e-21\n",
       "p_run_dg               0.0638074\n",
       "run_modulation_dg        -0.6221\n",
       "cv_os_dg                       1\n",
       "cv_ds_dg                0.340134\n",
       "tf_index_dg             0.297836\n",
       "cell_specimen_id       517425074\n",
       "Name: 97, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg.peak.loc[cell_loc]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let's plot all trials for a given cell's preferred condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Preferred direction:', 180)\n",
      "('Preferred temporal frequency:', 2)\n"
     ]
    }
   ],
   "source": [
    "pref_ori = dg.orivals[dg.peak.ori_dg[cell_loc]]\n",
    "pref_tf = dg.tfvals[dg.peak.tf_dg[cell_loc]]\n",
    "print(\"Preferred direction:\", pref_ori)\n",
    "print(\"Preferred temporal frequency:\", pref_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temporal_frequency</th>\n",
       "      <th>orientation</th>\n",
       "      <th>blank_sweep</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>836</td>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>7156</td>\n",
       "      <td>7216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>7337</td>\n",
       "      <td>7397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>9775</td>\n",
       "      <td>9834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>13477</td>\n",
       "      <td>13536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>16548</td>\n",
       "      <td>16607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>55013</td>\n",
       "      <td>55073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>56819</td>\n",
       "      <td>56878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>59708</td>\n",
       "      <td>59768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>97818</td>\n",
       "      <td>97878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>99082</td>\n",
       "      <td>99142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>102603</td>\n",
       "      <td>102663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>104499</td>\n",
       "      <td>104559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>105312</td>\n",
       "      <td>105371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>113258</td>\n",
       "      <td>113318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     temporal_frequency  orientation  blank_sweep   start     end\n",
       "1                     2          180            0     836     896\n",
       "71                    2          180            0    7156    7216\n",
       "73                    2          180            0    7337    7397\n",
       "100                   2          180            0    9775    9834\n",
       "141                   2          180            0   13477   13536\n",
       "175                   2          180            0   16548   16607\n",
       "271                   2          180            0   55013   55073\n",
       "291                   2          180            0   56819   56878\n",
       "323                   2          180            0   59708   59768\n",
       "435                   2          180            0   97818   97878\n",
       "449                   2          180            0   99082   99142\n",
       "488                   2          180            0  102603  102663\n",
       "509                   2          180            0  104499  104559\n",
       "518                   2          180            0  105312  105371\n",
       "606                   2          180            0  113258  113318"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pref_trials = dg.stim_table[(dg.stim_table.orientation==pref_ori)&(dg.stim_table.temporal_frequency==pref_tf)]\n",
    "pref_trials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sweep_response` is a DataFrame that contains the DF/F of each cell during each stimulus trial. It shares its index with `stim_table`. Each cell contains a timeseries that extends from 1 second prior to the start of the trial to 1 second after the end of the trial. The final column of `sweep_response`, named `dx`, is the running speed of the mouse during each trial. The data in this DataFrame is used to create another DataFrame called `mean_sweep_response` that contains the mean DF/F during the trial for each cell (and the mean running speed in the last column)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = dg.sweep_response[(dg.stim_table.orientation==pref_ori)&(dg.stim_table.temporal_frequency==pref_tf)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we look at the mean running speed during trials that presented the preferred condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       0.920868\n",
       "71      0.060151\n",
       "73      0.027268\n",
       "100     4.897258\n",
       "141    -0.000856\n",
       "175    -0.002599\n",
       "271    38.010730\n",
       "291     0.000437\n",
       "323    36.139884\n",
       "435    -0.012866\n",
       "449    11.234307\n",
       "488     5.972907\n",
       "509     1.967057\n",
       "518    -0.004841\n",
       "606     6.747042\n",
       "Name: dx, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_mean = dg.mean_sweep_response[(dg.stim_table.orientation==pref_ori)&(dg.stim_table.temporal_frequency==pref_tf)]\n",
    "subset_mean['dx']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the response to each trial of the preferred condition, labeled with the mean running speed during the trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+jo4O/Pz8CAj4R122sbGRp556iv/+7/+mtrZHwzQhISEsXLiQW265hYSEBPz9/fH392fY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kiKjiY8OJiP9uxhVUEBqwoKKKur4/KcHK6+6CKWzp5NgL8/a7ZvZ+327Xy4Zw81zc3H3SsOCw4m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SpUtZs2YNs2fPntJzUp/NRGd3u+wCu4hYgGLgOuAk8ClwtzHm+Ig8GtivZBrYp0RrVxf7Skv5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\nadomohO7pmmapm0i/w9E3a6VotWm3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f844da126d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trial_timestamps = np.arange(-1*dg.interlength, dg.interlength+dg.sweeplength, 1.)/dg.acquisition_rate\n",
    "plt.figure(figsize=(8,20))\n",
    "for i in range(len(subset)):\n",
    "    plt.subplot(len(pref_trials),1,i+1)\n",
    "    plt.plot(trial_timestamps, subset[str(cell_loc)].iloc[i], color='k', lw=2)\n",
    "    plt.axvspan(0,2,color='red', alpha=0.3)\n",
    "    plt.ylabel(\"DF/F (%)\")\n",
    "    plt.ylim(-10,600)\n",
    "    plt.yticks(range(0,700,200))\n",
    "    plt.text(2.5, 300, str(round(subset_mean['dx'].iloc[i],2))+\" cm/s\")\n",
    "    if i<(len(subset)-1):\n",
    "        plt.xticks([])\n",
    "    else:\n",
    "        plt.xticks([-1,0,1,2,3])\n",
    "        plt.xlabel(\"Time (s)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Static Gratings\n",
    "The static gratings analysis object is quite similar to the drifting gratings analysis object.  Here we'll just take a look at the `peak` table, which contains information about the preferred orientation, spatial frequency, phase, as well as a number of other metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from allensdk.brain_observatory.static_gratings import StaticGratings\n",
    "\n",
    "# example loading drifing grating data\n",
    "data_set = boc.get_ophys_experiment_data(510938357)\n",
    "\n",
    "sg = StaticGratings(data_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ori_sg</th>\n",
       "      <th>sf_sg</th>\n",
       "      <th>phase_sg</th>\n",
       "      <th>reliability_sg</th>\n",
       "      <th>osi_sg</th>\n",
       "      <th>peak_dff_sg</th>\n",
       "      <th>ptest_sg</th>\n",
       "      <th>time_to_peak_sg</th>\n",
       "      <th>cell_specimen_id</th>\n",
       "      <th>p_run_sg</th>\n",
       "      <th>cv_os_sg</th>\n",
       "      <th>run_modulation_sg</th>\n",
       "      <th>sf_index_sg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0113189</td>\n",
       "      <td>0.285308</td>\n",
       "      <td>3.36311</td>\n",
       "      <td>0.559699</td>\n",
       "      <td>0.6634</td>\n",
       "      <td>517399188</td>\n",
       "      <td>0.898808</td>\n",
       "      <td>0.313745</td>\n",
       "      <td>0.193421</td>\n",
       "      <td>0.193012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0328444</td>\n",
       "      <td>0.856061</td>\n",
       "      <td>17.1593</td>\n",
       "      <td>5.6698e-07</td>\n",
       "      <td>0.46438</td>\n",
       "      <td>517399994</td>\n",
       "      <td>0.102605</td>\n",
       "      <td>0.792459</td>\n",
       "      <td>0.716304</td>\n",
       "      <td>0.272816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0140488</td>\n",
       "      <td>0.918923</td>\n",
       "      <td>4.73641</td>\n",
       "      <td>0.81548</td>\n",
       "      <td>0.36487</td>\n",
       "      <td>517399857</td>\n",
       "      <td>0.118354</td>\n",
       "      <td>0.602649</td>\n",
       "      <td>0.870513</td>\n",
       "      <td>0.218487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00940717</td>\n",
       "      <td>1.05556</td>\n",
       "      <td>2.29522</td>\n",
       "      <td>0.0635452</td>\n",
       "      <td>0.36487</td>\n",
       "      <td>517399727</td>\n",
       "      <td>0.259146</td>\n",
       "      <td>0.914524</td>\n",
       "      <td>1.05157</td>\n",
       "      <td>0.145557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.00875107</td>\n",
       "      <td>0.378599</td>\n",
       "      <td>3.99094</td>\n",
       "      <td>0.0794371</td>\n",
       "      <td>0.86242</td>\n",
       "      <td>517399442</td>\n",
       "      <td>0.112734</td>\n",
       "      <td>0.318078</td>\n",
       "      <td>0.958211</td>\n",
       "      <td>0.204495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ori_sg sf_sg phase_sg reliability_sg    osi_sg peak_dff_sg    ptest_sg  \\\n",
       "0      4     2        1      0.0113189  0.285308     3.36311    0.559699   \n",
       "1      5     3        3      0.0328444  0.856061     17.1593  5.6698e-07   \n",
       "2      4     3        3      0.0140488  0.918923     4.73641     0.81548   \n",
       "3      5     5        1    -0.00940717   1.05556     2.29522   0.0635452   \n",
       "4      3     5        0    -0.00875107  0.378599     3.99094   0.0794371   \n",
       "\n",
       "  time_to_peak_sg cell_specimen_id  p_run_sg  cv_os_sg run_modulation_sg  \\\n",
       "0          0.6634        517399188  0.898808  0.313745          0.193421   \n",
       "1         0.46438        517399994  0.102605  0.792459          0.716304   \n",
       "2         0.36487        517399857  0.118354  0.602649          0.870513   \n",
       "3         0.36487        517399727  0.259146  0.914524           1.05157   \n",
       "4         0.86242        517399442  0.112734  0.318078          0.958211   \n",
       "\n",
       "  sf_index_sg  \n",
       "0    0.193012  \n",
       "1    0.272816  \n",
       "2    0.218487  \n",
       "3    0.145557  \n",
       "4    0.204495  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg.peak.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Natural Scenes\n",
    "The natural scenes analysis object is again similar to the others.  In addition to computing the `sweep_response` and `mean_sweep_response` arrays, `NaturalScenes` reports the cell's preferred scene, running modulation, time to peak response, and other metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done analyzing natural scenes\n"
     ]
    }
   ],
   "source": [
    "from allensdk.brain_observatory.natural_scenes import NaturalScenes\n",
    "\n",
    "data_set = boc.get_ophys_experiment_data(510938357)\n",
    "\n",
    "ns = NaturalScenes(data_set)\n",
    "print(\"done analyzing natural scenes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scene_ns</th>\n",
       "      <th>reliability_ns</th>\n",
       "      <th>peak_dff_ns</th>\n",
       "      <th>ptest_ns</th>\n",
       "      <th>p_run_ns</th>\n",
       "      <th>run_modulation_ns</th>\n",
       "      <th>time_to_peak_ns</th>\n",
       "      <th>cell_specimen_id</th>\n",
       "      <th>image_selectivity_ns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48</td>\n",
       "      <td>0.0183184</td>\n",
       "      <td>4.91692</td>\n",
       "      <td>0.241369</td>\n",
       "      <td>0.0814777</td>\n",
       "      <td>-1.04747</td>\n",
       "      <td>0.76291</td>\n",
       "      <td>517399188</td>\n",
       "      <td>0.307441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96</td>\n",
       "      <td>0.151659</td>\n",
       "      <td>7.58865</td>\n",
       "      <td>2.77309e-08</td>\n",
       "      <td>0.0145825</td>\n",
       "      <td>-1.24205</td>\n",
       "      <td>0.36487</td>\n",
       "      <td>517399994</td>\n",
       "      <td>0.523847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>-0.0106162</td>\n",
       "      <td>2.51431</td>\n",
       "      <td>0.0333866</td>\n",
       "      <td>0.0991697</td>\n",
       "      <td>1.01418</td>\n",
       "      <td>0.3317</td>\n",
       "      <td>517399857</td>\n",
       "      <td>-0.000474576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>0.0047568</td>\n",
       "      <td>1.65234</td>\n",
       "      <td>0.577975</td>\n",
       "      <td>0.156953</td>\n",
       "      <td>1.04387</td>\n",
       "      <td>0.03317</td>\n",
       "      <td>517399727</td>\n",
       "      <td>0.176847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>0.000218728</td>\n",
       "      <td>1.55928</td>\n",
       "      <td>0.0684794</td>\n",
       "      <td>0.428506</td>\n",
       "      <td>0.539678</td>\n",
       "      <td>0.56389</td>\n",
       "      <td>517399442</td>\n",
       "      <td>0.242186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  scene_ns reliability_ns peak_dff_ns     ptest_ns   p_run_ns  \\\n",
       "0       48      0.0183184     4.91692     0.241369  0.0814777   \n",
       "1       96       0.151659     7.58865  2.77309e-08  0.0145825   \n",
       "2      103     -0.0106162     2.51431    0.0333866  0.0991697   \n",
       "3       15      0.0047568     1.65234     0.577975   0.156953   \n",
       "4       21    0.000218728     1.55928    0.0684794   0.428506   \n",
       "\n",
       "  run_modulation_ns time_to_peak_ns cell_specimen_id image_selectivity_ns  \n",
       "0          -1.04747         0.76291        517399188             0.307441  \n",
       "1          -1.24205         0.36487        517399994             0.523847  \n",
       "2           1.01418          0.3317        517399857         -0.000474576  \n",
       "3           1.04387         0.03317        517399727             0.176847  \n",
       "4          0.539678         0.56389        517399442             0.242186  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ns.peak.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Locally Sparse Noise\n",
    "The locally sparse noise stimulus object is a bit different from the others.  It does not have a peak condition table, instead providing a method to retrieve the \"on\" and \"off\" receptive fields of all cells.  The receptive field of a cell is computed by averaging responses to trials in which a given sparse noise grid location is on/off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done analyzing locally sparse noise\n"
     ]
    }
   ],
   "source": [
    "from allensdk.brain_observatory.locally_sparse_noise import LocallySparseNoise\n",
    "import allensdk.brain_observatory.stimulus_info as stim_info\n",
    "\n",
    "specimen_id = 587179530\n",
    "cell = boc.get_cell_specimens(ids=[specimen_id])[0]\n",
    "\n",
    "exp = boc.get_ophys_experiments(experiment_container_ids=[cell['experiment_container_id']],\n",
    "                                stimuli=[stim_info.LOCALLY_SPARSE_NOISE])[0]\n",
    "                                         \n",
    "data_set = boc.get_ophys_experiment_data(exp['id'])\n",
    "lsn = LocallySparseNoise(data_set)\n",
    "print(\"done analyzing locally sparse noise\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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McUL7EOCKOe9/3O2TJK2RbetRyczMzN7tXq9Hr9dbj2olqRn9fp9+v79suXHmtI8AZqrq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\nqZApXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f844cde5410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f844dfb8cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "cell_idx = data_set.get_cell_specimen_indices([specimen_id])[0]\n",
    "\n",
    "plt.imshow(lsn.receptive_field[:,:,cell_idx,0], interpolation='nearest', cmap='PuRd', origin='lower')\n",
    "plt.title(\"on receptive field\")\n",
    "plt.show()\n",
    "plt.imshow(lsn.receptive_field[:,:,cell_idx,1], interpolation='nearest', cmap='Blues', origin='lower')\n",
    "plt.title(\"off receptive field\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}