docs/first-order/receptive_fields.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7fd117a1",
   "metadata": {},
   "source": [
    "# Showing Receptive Fields\n",
    "Note: some of this content is adapted from the [Allen SDK](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_receptive_fields.html).\n",
    "\n",
    "One of the most important features of visually responsive neurons is the location and extent of their *receptive field*. This is the region within the visual field in which stimulation can affect the neuron's response. Is it highly localized or spatially distributed? Is it centered on the stimulus display, or is it on an edge? How much does it overlap with the receptive fields of neurons in other regions? Obtaining answers to these questions is a crucial step for interpreting results related to neurons' visual coding properties. This notebook demonstrates how to use the 'gabors' stimulus table and the `Units` table to compute the receptive fields."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7a34503c",
   "metadata": {},
   "source": [
    "### Environment Setup\n",
    "⚠️**Note: If running on a new environment, run this cell once and then restart the kernel**⚠️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38d2089c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "try:\n",
    "    from databook_utils.dandi_utils import dandi_download_open\n",
    "except:\n",
    "    !git clone https://github.com/AllenInstitute/openscope_databook.git\n",
    "    %cd openscope_databook\n",
    "    %pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25a0c4cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12bb9c25",
   "metadata": {},
   "source": [
    "### Download Ecephys File\n",
    " In this example, we use the Allen Institute's **Visual Coding - Neuropixels** dataset. Change the code to download the file you're interested in. Set `dandiset_id` and `dandi_filepath` to correspond to the dandiset id and filepath of the file, respectively. If you're accessing an embargoed dataset, and set `dandi_api_key` to your DANDI API key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89d50dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "dandiset_id = \"000021\"\n",
    "dandi_filepath = \"sub-716813540/sub-716813540_ses-739448407.nwb\"\n",
    "download_loc = \".\"\n",
    "dandi_api_key = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "787cb7a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A newer version (0.62.2) of dandi/dandi-cli is available. You are using 0.55.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File already exists\n",
      "Opening file\n"
     ]
    }
   ],
   "source": [
    "# This can sometimes take a while depending on the size of the file\n",
    "io = dandi_download_open(dandiset_id, dandi_filepath, download_loc, dandi_api_key=dandi_api_key)\n",
    "nwb = io.read()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eed9f8ea",
   "metadata": {},
   "source": [
    "### Reading Data\n",
    "We need two components of data for this analysis; The `Units` table, which is discussed more in depth in [Visualizing Unit Quality Metrics](../visualization/visualize_unit_metrics.ipynb). In particular we need the `spike_times` property for the units. We also need the stimulus table from the NWB File's `Intervals` section. Because receptive field analysis is so central to interpreting results related to visual coding, experiments include some [Gabor patches](http://neuroanatody.com/2016/05/whats-in-a-gabor-patch/) which are used to reliably drive neural responses, shown repeatedly at a set of different coordinates within the mouse's visual field. This stimulus is usually shown at the beginning of the session, and typically uses the same parameters for every mouse. So for our purposes, we select the stimulus table named `gabors_presentations`. Below the `Units` table and the *gabors stimulus table* are shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f2dbb74",
   "metadata": {
    "scrolled": false,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
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       "      <th>peak_channel_id</th>\n",
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      "text/plain": [
       "           peak_channel_id quality  waveform_halfwidth   amplitude  \\\n",
       "id                                                                   \n",
       "951791104        850216277    good            0.247236   90.180675   \n",
       "951790926        850216277   noise            0.549414   96.975255   \n",
       "951790903        850216277    good            0.219765  244.056540   \n",
       "951790894        850216277    good            0.164824  367.934580   \n",
       "951790884        850216277    good            0.329648  188.990015   \n",
       "951790873        850216277   noise            1.249916  392.734333   \n",
       "951790864        850216277   noise            1.318593  277.943055   \n",
       "951790850        850216277   noise            1.648241  189.359235   \n",
       "951790836        850216277    good            0.329648  106.165605   \n",
       "951790820        850216277    good            0.178559  190.294455   \n",
       "\n",
       "           velocity_below       l_ratio       snr  amplitude_cutoff  spread  \\\n",
       "id                                                                            \n",
       "951791104       -0.343384  1.383148e-02  0.763260          0.500000   100.0   \n",
       "951790926        0.000000  2.362854e-01  0.676006          0.306994   140.0   \n",
       "951790903       -0.686767  6.284489e-02  0.867047          0.500000    60.0   \n",
       "951790894       -0.274707  3.215116e-05  3.188863          0.001455    70.0   \n",
       "951790884        0.537968  7.274902e-02  0.588863          0.074423   160.0   \n",
       "951790873        1.041597  2.236291e-07  0.446848          0.500000   140.0   \n",
       "951790864        6.364042           NaN  1.465630          0.011230   160.0   \n",
       "951790850       -0.457845  2.436327e-01  0.850077          0.172105   160.0   \n",
       "951790836       -0.529792  3.770473e-02  0.775795          0.500000    90.0   \n",
       "951790820       -0.480737  3.121674e-02  1.488400          0.110186    70.0   \n",
       "\n",
       "           nn_miss_rate  ...  isolation_distance  silhouette_score  \\\n",
       "id                       ...                                         \n",
       "951791104      0.004499  ...           47.188066          0.104118   \n",
       "951790926      0.009977  ...           25.190298          0.029867   \n",
       "951790903      0.000905  ...           30.063412         -0.036832   \n",
       "951790894      0.000100  ...           72.605985          0.005048   \n",
       "951790884      0.000000  ...           24.436355               NaN   \n",
       "951790873      0.000000  ...           94.253021               NaN   \n",
       "951790864      0.000000  ...                 NaN         -0.056607   \n",
       "951790850      0.001341  ...           16.992300         -0.070661   \n",
       "951790836      0.006340  ...           36.216320          0.042926   \n",
       "951790820      0.007238  ...           36.992078          0.047044   \n",
       "\n",
       "           velocity_above  cumulative_drift  firing_rate  waveform_duration  \\\n",
       "id                                                                            \n",
       "951791104        0.392438            418.06     2.341833           0.741709   \n",
       "951790926        0.171692            392.19     2.613414           1.167504   \n",
       "951790903        0.343384            483.90     0.744933           0.865327   \n",
       "951790894       -0.343384             71.55     0.640379           0.824121   \n",
       "951790884        1.291776              0.00     0.034851           2.060302   \n",
       "951790873       -0.196219              0.00     0.004855           1.565829   \n",
       "951790864       -8.328692           1810.38     0.542730           2.225126   \n",
       "951790850        2.583553            999.03     0.765002           1.881742   \n",
       "951790836       -0.892797            280.11     2.340107           0.769179   \n",
       "951790820       -0.480737            232.88     1.997528           0.796650   \n",
       "\n",
       "           recovery_slope                                        spike_times  \\\n",
       "id                                                                             \n",
       "951791104       -0.022793  [3.2095355441515783, 6.124035954291701, 17.051...   \n",
       "951790926       -0.032515  [2.7148354745354113, 3.1582355369324366, 3.193...   \n",
       "951790903       -0.044864  [1.6110686525422746, 2.9030355010196693, 2.907...   \n",
       "951790894       -0.033171  [1281.742282131046, 1349.6264583506447, 1405.8...   \n",
       "951790884             NaN  [7.834769528366286, 245.91720303227598, 438.41...   \n",
       "951790873             NaN  [488.6720371937045, 1581.39612429953, 1760.856...   \n",
       "951790864       -4.514536  [51.122208953280754, 65.47991097375385, 65.491...   \n",
       "951790850       -3.234939  [51.113175618676216, 51.12444228692837, 62.794...   \n",
       "951790836       -0.020140  [0.6329351815620978, 0.6378018489136213, 1.041...   \n",
       "951790820       -0.037071  [8.167536241861212, 56.18294299878077, 56.1983...   \n",
       "\n",
       "                                            spike_amplitudes  \\\n",
       "id                                                             \n",
       "951791104  [0.00010742085654993536, 0.0001035669805512930...   \n",
       "951790926  [9.173462781002884e-05, 8.000884789421282e-05,...   \n",
       "951790903  [0.00026998475697199466, 0.0002846940383350162...   \n",
       "951790894  [0.00025149672984953213, 0.0004496466043690103...   \n",
       "951790884  [9.431834274924923e-05, 0.00010953675617822429...   \n",
       "951790873  [0.00011043706542136272, 8.223885328261097e-05...   \n",
       "951790864  [0.00017703212276047242, 0.0001768216167308391...   \n",
       "951790850  [0.00012096045820642862, 0.0001094972500522143...   \n",
       "951790836  [0.0001462638799394556, 0.00011085512769588882...   \n",
       "951790820  [0.00017325089170459892, 0.0002418117123045365...   \n",
       "\n",
       "                                               waveform_mean  \n",
       "id                                                            \n",
       "951791104  [[0.0, -0.3348150000001908, 10.006815000000017...  \n",
       "951790926  [[0.0, -5.414564999999925, 5.5407299999999395,...  \n",
       "951790903  [[0.0, -13.258049999999884, -18.07259999999988...  \n",
       "951790894  [[0.0, 0.71097000000006, -3.5579700000000116, ...  \n",
       "951790884  [[0.0, -13.237058823529424, -24.38345201238388...  \n",
       "951790873  [[0.0, 15.990000000000009, 46.18466666666663, ...  \n",
       "951790864  [[0.0, -1.7390099999999222, -5.484569999999962...  \n",
       "951790850  [[0.0, -0.875939999999872, -4.936424999999701,...  \n",
       "951790836  [[0.0, 0.19090500000024235, -2.333954999999832...  \n",
       "951790820  [[0.0, 2.8191149999998117, -4.932915000000207,...  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units = nwb.units\n",
    "units[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "008d6c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['drifting_gratings_presentations', 'flashes_presentations', 'gabors_presentations', 'invalid_times', 'natural_movie_one_presentations', 'natural_movie_three_presentations', 'natural_scenes_presentations', 'spontaneous_presentations', 'static_gratings_presentations'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwb.intervals.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e79af47",
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>stimulus_name</th>\n",
       "      <th>stimulus_block</th>\n",
       "      <th>temporal_frequency</th>\n",
       "      <th>x_position</th>\n",
       "      <th>y_position</th>\n",
       "      <th>color</th>\n",
       "      <th>mask</th>\n",
       "      <th>opacity</th>\n",
       "      <th>phase</th>\n",
       "      <th>size</th>\n",
       "      <th>units</th>\n",
       "      <th>stimulus_index</th>\n",
       "      <th>orientation</th>\n",
       "      <th>spatial_frequency</th>\n",
       "      <th>contrast</th>\n",
       "      <th>tags</th>\n",
       "      <th>timeseries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.684670</td>\n",
       "      <td>88.918188</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(1, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.918188</td>\n",
       "      <td>89.168385</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(2, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.168385</td>\n",
       "      <td>89.418582</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(3, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.418582</td>\n",
       "      <td>89.668780</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(4, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89.668780</td>\n",
       "      <td>89.918990</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(5, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>89.918990</td>\n",
       "      <td>90.169200</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(6, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>90.169200</td>\n",
       "      <td>90.419410</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(7, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>90.419410</td>\n",
       "      <td>90.669620</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(8, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90.669620</td>\n",
       "      <td>90.919822</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(9, 1, timestamps pynwb.base.TimeSeries at 0x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90.919822</td>\n",
       "      <td>91.170025</td>\n",
       "      <td>gabors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>circle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[3644.93333333, 3644.93333333]</td>\n",
       "      <td>[20.0, 20.0]</td>\n",
       "      <td>deg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[stimulus_time_interval]</td>\n",
       "      <td>[(10, 1, timestamps pynwb.base.TimeSeries at 0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    start_time  stop_time stimulus_name  stimulus_block  temporal_frequency  \\\n",
       "id                                                                            \n",
       "0    88.684670  88.918188        gabors             0.0                 4.0   \n",
       "1    88.918188  89.168385        gabors             0.0                 4.0   \n",
       "2    89.168385  89.418582        gabors             0.0                 4.0   \n",
       "3    89.418582  89.668780        gabors             0.0                 4.0   \n",
       "4    89.668780  89.918990        gabors             0.0                 4.0   \n",
       "5    89.918990  90.169200        gabors             0.0                 4.0   \n",
       "6    90.169200  90.419410        gabors             0.0                 4.0   \n",
       "7    90.419410  90.669620        gabors             0.0                 4.0   \n",
       "8    90.669620  90.919822        gabors             0.0                 4.0   \n",
       "9    90.919822  91.170025        gabors             0.0                 4.0   \n",
       "\n",
       "    x_position  y_position            color    mask  opacity  \\\n",
       "id                                                             \n",
       "0         30.0       -40.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "1        -20.0        30.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "2        -20.0        20.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "3        -20.0        40.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "4         10.0        20.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "5         10.0        10.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "6        -30.0        30.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "7         20.0         0.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "8         30.0       -40.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "9        -10.0        40.0  [1.0, 1.0, 1.0]  circle      1.0   \n",
       "\n",
       "                             phase          size units  stimulus_index  \\\n",
       "id                                                                       \n",
       "0   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "1   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "2   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "3   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "4   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "5   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "6   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "7   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "8   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "9   [3644.93333333, 3644.93333333]  [20.0, 20.0]   deg             0.0   \n",
       "\n",
       "    orientation spatial_frequency  contrast                      tags  \\\n",
       "id                                                                      \n",
       "0           0.0              0.08       0.8  [stimulus_time_interval]   \n",
       "1          90.0              0.08       0.8  [stimulus_time_interval]   \n",
       "2          90.0              0.08       0.8  [stimulus_time_interval]   \n",
       "3           0.0              0.08       0.8  [stimulus_time_interval]   \n",
       "4          90.0              0.08       0.8  [stimulus_time_interval]   \n",
       "5          45.0              0.08       0.8  [stimulus_time_interval]   \n",
       "6          45.0              0.08       0.8  [stimulus_time_interval]   \n",
       "7           0.0              0.08       0.8  [stimulus_time_interval]   \n",
       "8          90.0              0.08       0.8  [stimulus_time_interval]   \n",
       "9           0.0              0.08       0.8  [stimulus_time_interval]   \n",
       "\n",
       "                                           timeseries  \n",
       "id                                                     \n",
       "0   [(1, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "1   [(2, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "2   [(3, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "3   [(4, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "4   [(5, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "5   [(6, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "6   [(7, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "7   [(8, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "8   [(9, 1, timestamps pynwb.base.TimeSeries at 0x...  \n",
       "9   [(10, 1, timestamps pynwb.base.TimeSeries at 0...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_stim_table = nwb.intervals[\"gabors_presentations\"].to_dataframe()\n",
    "rf_stim_table[:10]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b84a9775",
   "metadata": {},
   "source": [
    "### Selecting Units\n",
    "It would be inconvenient to try to view and generate the receptive fields for all units at once. Below you can select units based on their quality metrics and the brain region they appear in. If you'd like, redefine the `select_condition` function to set different criteria for selecting the units you'd like to view. For more information on the useful metrics of units in the `Units` table, see [Visualizing Unit Quality Metrics](../visualization/visualize_unit_metrics.ipynb). Because it can be very useful to select units by brain region or by probe, you may use the `get_unit_location` and `get_unit_probe` functions below, which take information from the `Electrodes` table to get the location and probes for each unit respectively. The sets of probe names and location names are printed for your convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e3d0de2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'', 'VISp', 'VIS', 'VISl', 'grey', 'VISam', 'VISrl'}\n",
      "{'probeD', 'probeE', 'probeC', 'probeA', 'probeF', 'probeB'}\n"
     ]
    }
   ],
   "source": [
    "### helper functions made from electrodes table to get brain location of unit or probe name of unit\n",
    "\n",
    "# map channel ids to brain location and probe group name\n",
    "# creates a dictionary that iterates over indices of the dataset, assigns \"id\" as the key, and makes the location the value\n",
    "channel_locations = {nwb.electrodes[\"id\"][i]: nwb.electrodes[\"location\"][i] for i in range(len(nwb.electrodes))}\n",
    "# creates similar dictionary as previous line, but with the group name/probe name as the value\n",
    "channel_probes = {nwb.electrodes[\"id\"][i]: nwb.electrodes[\"group_name\"][i] for i in range(len(nwb.electrodes))}\n",
    "\n",
    "# function retrieves peak channel ID for given unit index from \"units\" dataset and looks up corresponding probe from dictionary\n",
    "def get_unit_location(unit_idx):\n",
    "    peak_channel_id = units[\"peak_channel_id\"][unit_idx]\n",
    "    return channel_locations[peak_channel_id]\n",
    "\n",
    "# function retrieves peak channel ID from \"units\" dataset, looks up corresponding group name, returns probe associated with peak channel\n",
    "def get_unit_probe(unit_idx):\n",
    "    peak_channel_id = units[\"peak_channel_id\"][unit_idx]\n",
    "    return channel_probes[peak_channel_id]\n",
    "\n",
    "print(set(channel_locations.values()))\n",
    "print(set(channel_probes.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eab69dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111\n"
     ]
    }
   ],
   "source": [
    "### select units to view the receptive fields of, change the conditional function below to select different units\n",
    "\n",
    "def select_condition(unit_idx):\n",
    "    # the values below are recommended thresholds for these quality metrics\n",
    "    return units[\"snr\"][unit_idx] > 1 and \\\n",
    "            units[\"isi_violations\"][unit_idx] < 1 and \\\n",
    "            units[\"firing_rate\"][unit_idx] > 0.1 and \\\n",
    "            get_unit_location(unit_idx) == \"VISam\"\n",
    "            # get_unit_probe(unit_idx) == \"probeC\"\n",
    "            # change the line above an option above to select a different brain location or probe\n",
    "\n",
    "selected_unit_idxs = []\n",
    "for unit_idx in range(len(units)):\n",
    "    if select_condition(unit_idx):\n",
    "        selected_unit_idxs.append(unit_idx)\n",
    "        \n",
    "if len(selected_unit_idxs) == 0:\n",
    "    raise IndexError(\"There are no units for this selection\")\n",
    "\n",
    "print(len(selected_unit_idxs))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3a24abc8",
   "metadata": {},
   "source": [
    "### Getting Fields\n",
    "Below the receptive fields for each selected unit are calculated and a visualization for each one is generated. First, we get the dimensions of the receptive field plots from the set of possible coordinates of the gabor patch stimulus. Next we identify how many spikes happen in response to every stimulus for each coordinate of each unit's receptive field. Finally, we plot each receptive field as a 2D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f354ea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-40. -30. -20. -10.   0.  10.  20.  30.  40.]\n",
      "[-40. -30. -20. -10.   0.  10.  20.  30.  40.]\n",
      "deg\n"
     ]
    }
   ],
   "source": [
    "### get x and y coordinates of gabors displayed to build receptive field\n",
    "\n",
    "xs = np.sort(list(set(rf_stim_table.x_position)))\n",
    "ys = np.sort(list(set(rf_stim_table.y_position)))\n",
    "field_units = rf_stim_table.units[0]\n",
    "print(xs)\n",
    "print(ys)\n",
    "print(field_units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c865b942",
   "metadata": {},
   "outputs": [],
   "source": [
    "### get receptive field of a unit using its spike times and the stim table\n",
    "\n",
    "def get_rf(spike_times):\n",
    "    # creates 2D array that stores response spike counts for each coordinate of the receptive field\n",
    "    unit_rf = np.zeros([ys.size, xs.size])\n",
    "    # for every x and y coordinate in the field\n",
    "    for xi, x in enumerate(xs):\n",
    "        for yi, y in enumerate(ys):\n",
    "            \n",
    "            # for this coordinate of the rf, count all the times that this neuron responds to a stimulus time with a spike\n",
    "            stim_times = rf_stim_table[(rf_stim_table.x_position == x) & (rf_stim_table.y_position == y)].start_time\n",
    "            response_spike_count = 0\n",
    "            for stim_time in stim_times:\n",
    "                # any spike within 0.2 seconds after stim time is considered a response\n",
    "                start_idx, end_idx = np.searchsorted(spike_times, [stim_time, stim_time+0.2])\n",
    "                response_spike_count += end_idx-start_idx\n",
    "\n",
    "            unit_rf[yi, xi] = response_spike_count\n",
    "    \n",
    "    return unit_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff8698e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### compute receptive fields for each unit in selected units\n",
    "\n",
    "unit_rfs = []\n",
    "for unit_idx in selected_unit_idxs:\n",
    "    unit_spike_times = units[\"spike_times\"][unit_idx]\n",
    "    unit_rfs.append(get_rf(unit_spike_times))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79afdd8f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 120 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### display the receptive fields for each unit in a 2D plot\n",
    "\n",
    "n_rows = len(unit_rfs)//10\n",
    "fig, axes = plt.subplots(n_rows+1, 10)\n",
    "fig.set_size_inches(12, n_rows+1)\n",
    "\n",
    "# handle case where there's <= 10 rfs\n",
    "if len(axes.shape) == 1:\n",
    "    axes = axes.reshape((1, axes.shape[0]))\n",
    "\n",
    "for irf, rf in enumerate(unit_rfs):\n",
    "    ax_row = int(irf/10)\n",
    "    ax_col = irf%10\n",
    "    axes[ax_row][ax_col].imshow(rf, origin=\"lower\")\n",
    "for ax in axes.flat[1:]:\n",
    "    ax.axis('off')\n",
    "\n",
    "# making axis labels for first receptive field\n",
    "axes[0][0].set_xlabel(field_units)\n",
    "axes[0][0].set_ylabel(field_units)\n",
    "axes[0][0].xaxis.set_label_position(\"top\") \n",
    "axes[0][0].xaxis.set_ticks_position(\"top\")\n",
    "axes[0][0].set_xticks(range(len(xs)), xs, rotation=90, fontsize=6)\n",
    "axes[0][0].set_yticks(range(len(ys)), ys, fontsize=6)\n",
    "\n",
    "for i, l in enumerate(axes[0][0].xaxis.get_ticklabels()):\n",
    "    if i % 2 != 0:\n",
    "        l.set_visible(False)\n",
    "for i, l in enumerate(axes[0][0].yaxis.get_ticklabels()):\n",
    "    if i % 2 != 0:\n",
    "        l.set_visible(False)"
   ]
  }
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