[Methods]monitor_positioning.ipynb

{
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading local settings from pipeline_config.json\n",
      "Connecting pfahey@at-database.ad.bcm.edu:3306\n",
      "Bokeh could not be loaded. Either it is not installed or you are not running within a notebook\n",
      "numba not found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skimage import feature\n",
    "from itertools import product\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.ndimage import gaussian_filter\n",
    "from scipy.interpolate import griddata, interp1d\n",
    "\n",
    "\n",
    "import scanreader\n",
    "import static_nda as nda\n",
    "from stimline import tune\n",
    "from stimulus import stimulus\n",
    "from pipeline.utils import h5,caiman_interface\n",
    "from pipeline import meso, experiment, stack, collection\n",
    "\n",
    "import datajoint as dj\n",
    "anat = dj.create_virtual_module('anatomy', 'pipeline_anatomy')\n",
    "maps = dj.create_virtual_module('maps','pipeline_map')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7949ce4d",
   "metadata": {},
   "source": [
    "# Monitor Specs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02c7a506",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "angular width of monitor if perfectly centered and perpendicular\n",
      "angle to location 1:  -62.032884493880616\n",
      "angle to location 2:  62.032884493880616\n",
      "absolute angular distance between two points:  124.06576898776123\n",
      "\n",
      "angular height of monitor if perfectly cenetered and perpendicular\n",
      "angle to location 1:  -46.66833744829331\n",
      "angle to location 2:  46.668337448293315\n",
      "absolute angular distance between two points:  93.33667489658663\n",
      "\n",
      "angular diameter of 1 cm at nearest point\n",
      "angle to location 1:  -1.9091524329963767\n",
      "angle to location 2:  1.9091524329963767\n",
      "absolute angular distance between two points:  3.8183048659927534\n",
      "\n",
      "angular diameter of 1 cm at most remote point\n",
      "angle to location 1:  64.82804423538926\n",
      "angle to location 2:  65.50175223568995\n",
      "absolute angular distance between two points:  0.6737080003006923\n"
     ]
    }
   ],
   "source": [
    "# Display Viewing Area(HxV) : 565.0 x 330.7 mm\n",
    "# Measured display area: (22 + 3/16) * 2.54, 12.5 * 2.54\n",
    "# w,h = 55.3,31.1 # assumed specs for monitor diagonal (25 in)\n",
    "\n",
    "\n",
    "d = 15 # centimeters from eye to screen\n",
    "w,h = 56.5,31.8 # full width and height of screen in centimeters\n",
    "\n",
    "theta = np.deg2rad(90) #angle of screen relative to eye, 90 is tangent to the nearest surface of the eye\n",
    "\n",
    "def eye2loc(x):\n",
    "    ## distance from eye to screen location at x cm according to law of cosines\n",
    "    c = np.sqrt(d**2 + x**2 - 2*d*x*np.cos(theta))\n",
    "    return c\n",
    "\n",
    "def dist2deg(x):\n",
    "    ## visual angle from nearest point to location at x cm according to line of sines\n",
    "    a = np.arcsin(np.sin(theta)*x/eye2loc(x))\n",
    "    a = np.rad2deg(a)\n",
    "    return a\n",
    "\n",
    "def two_point_dist(x1,x2):\n",
    "    ## visual angle between two monitor points at x1,x2 cm from the center\n",
    "    print('angle to location 1: ',dist2deg(x1))\n",
    "    print('angle to location 2: ',dist2deg(x2))\n",
    "    print('absolute angular distance between two points: ',np.abs(dist2deg(x2) - dist2deg(x1)))\n",
    "\n",
    "\n",
    "print('\\nangular width of monitor if perfectly centered and perpendicular')\n",
    "two_point_dist(-w/2,w/2)\n",
    "\n",
    "print('\\nangular height of monitor if perfectly cenetered and perpendicular')\n",
    "two_point_dist(-h/2,h/2)\n",
    "\n",
    "print('\\nangular diameter of 1 cm at nearest point')\n",
    "x1 = -0.5\n",
    "x2 = 0.5\n",
    "two_point_dist(x1,x2)\n",
    "\n",
    "print('\\nangular diameter of 1 cm at most remote point')\n",
    "farthest = ((w/2)**2 + (h/2)**2)**0.5\n",
    "x1 = (farthest-0.5)\n",
    "x2 = (farthest+0.5)\n",
    "two_point_dist(x1,x2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97628ee7",
   "metadata": {},
   "source": [
    "# Dotmapping Scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "58f6fb4a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two dotmapping stimulus protocols were applied across the 7 animals, with no one policy applied across all 7\n"
     ]
    },
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       "                                <p id=\"primary\">bg_level</p>\n",
       "                                <span class=\"djtooltiptext\">(0-255) the index of the background luminance, 0 is black</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">dot_level</p>\n",
       "                                <span class=\"djtooltiptext\">(0-255) the index of the dot luminance, 0 is black</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">dot_xsize</p>\n",
       "                                <span class=\"djtooltiptext\">calculated attribute</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">dot_ysize</p>\n",
       "                                <span class=\"djtooltiptext\">calculated attribute</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">dot_shape</p>\n",
       "                                <span class=\"djtooltiptext\">shape of the dot</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">dot_time</p>\n",
       "                                <span class=\"djtooltiptext\">calculated attribute</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>254</td>\n",
       "<td>1</td>\n",
       "<td>0.05</td>\n",
       "<td>0.05</td>\n",
       "<td>rect</td>\n",
       "<td>0.30</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*bg_level    *dot_level    *dot_xsize    *dot_ysize    *dot_shape    *dot_time   \n",
       "+----------+ +-----------+ +-----------+ +-----------+ +-----------+ +----------+\n",
       "254          1             0.05          0.05          rect          0.30        \n",
       " (Total: 1)"
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      "grid x  10  positions  (-0.25, -0.194444, -0.138889, -0.0833333, -0.0277778, 0.0277778, 0.0833333, 0.138889, 0.194444, 0.25)\n",
      "\n",
      "grid y  10  positions  (-0.25, -0.194444, -0.138889, -0.0833333, -0.0277778, 0.0277778, 0.0833333, 0.138889, 0.194444, 0.25)\n",
      "\n",
      "conditions:  100\n",
      "\n",
      "trials per condition:  (10,)\n",
      "\n",
      "angular width of gridded area\n",
      "angle to location 1:  -46.00837664401536\n",
      "angle to location 2:  46.00837664401537\n",
      "absolute angular distance between two points:  92.01675328803073\n",
      "\n",
      "angular height of gridded area\n",
      "angle to location 1:  -46.061026203017654\n",
      "angle to location 2:  46.06102620301767\n",
      "absolute angular distance between two points:  92.12205240603532\n"
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       "                            </div></th><th><div class=\"djtooltip\">\n",
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       "                                <span class=\"djtooltiptext\">(0-255) the index of the dot luminance, 0 is black</span>\n",
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       "                                <p id=\"primary\">dot_xsize</p>\n",
       "                                <span class=\"djtooltiptext\">calculated attribute</span>\n",
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       "                                <p id=\"primary\">dot_ysize</p>\n",
       "                                <span class=\"djtooltiptext\">calculated attribute</span>\n",
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       "                                <p id=\"primary\">dot_shape</p>\n",
       "                                <span class=\"djtooltiptext\">shape of the dot</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">dot_time</p>\n",
       "                                <span class=\"djtooltiptext\">calculated attribute</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>254</td>\n",
       "<td>1</td>\n",
       "<td>0.10</td>\n",
       "<td>0.10</td>\n",
       "<td>rect</td>\n",
       "<td>0.20</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
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       "*bg_level    *dot_level    *dot_xsize    *dot_ysize    *dot_shape    *dot_time   \n",
       "+----------+ +-----------+ +-----------+ +-----------+ +-----------+ +----------+\n",
       "254          1             0.10          0.10          rect          0.20        \n",
       " (Total: 1)"
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     "text": [
      "grid x  8  positions  (-0.4, -0.285714, -0.171429, -0.0571429, 0.0571429, 0.171429, 0.285714, 0.4)\n",
      "\n",
      "grid y  5  positions  (-0.25, -0.125, 0.0, 0.125, 0.25)\n",
      "\n",
      "conditions:  40\n",
      "\n",
      "trials per condition:  (20,)\n",
      "\n",
      "angular width of gridded area\n",
      "angle to location 1:  -59.460648887287945\n",
      "angle to location 2:  59.460648887287945\n",
      "absolute angular distance between two points:  118.92129777457589\n",
      "\n",
      "angular height of gridded area\n",
      "angle to location 1:  -46.66833744829331\n",
      "angle to location 2:  46.668337448293315\n",
      "absolute angular distance between two points:  93.33667489658663\n",
      "\n",
      "\n",
      "\n",
      "Optimal dotmapping scans were recorded by Taliah\n"
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       "      <th></th>\n",
       "      <th>study_name</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>session</th>\n",
       "      <th>scan_idx</th>\n",
       "      <th>scan_purpose</th>\n",
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       "      <th>dot_time</th>\n",
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       "      <td>imagenet_V1</td>\n",
       "      <td>21067</td>\n",
       "      <td>9</td>\n",
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       "      <td>ROI_dotmapping</td>\n",
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       "      <td>dotmapfine</td>\n",
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       "      <td>21067</td>\n",
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       "      <td>2021-09-08 19:26:15</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>21067</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>21067</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22620</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22620</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22620</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22620</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22846</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22846</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22846</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22846</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22846</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>22846</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>23343</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>23343</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>23555</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:21:44</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>23656</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:42:01</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.3</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmapfine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>23656</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:42:01</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>imagenet_V1</td>\n",
       "      <td>23964</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>ROI_dotmapping</td>\n",
       "      <td>2021-09-02 16:42:01</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>rect</td>\n",
       "      <td>0.2</td>\n",
       "      <td>taliah</td>\n",
       "      <td>5</td>\n",
       "      <td>dotmap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     study_name  animal_id  session  scan_idx    scan_purpose  \\\n",
       "0   imagenet_V1      21067        9        13  ROI_dotmapping   \n",
       "1   imagenet_V1      21067        9        18  ROI_dotmapping   \n",
       "2   imagenet_V1      21067       10        17  ROI_dotmapping   \n",
       "3   imagenet_V1      21067       13         7  ROI_dotmapping   \n",
       "4   imagenet_V1      22620        4        13  ROI_dotmapping   \n",
       "5   imagenet_V1      22620        4        22  ROI_dotmapping   \n",
       "6   imagenet_V1      22620        5         5  ROI_dotmapping   \n",
       "7   imagenet_V1      22620        5         9  ROI_dotmapping   \n",
       "8   imagenet_V1      22846        2        18  ROI_dotmapping   \n",
       "9   imagenet_V1      22846        2        22  ROI_dotmapping   \n",
       "10  imagenet_V1      22846        7        12  ROI_dotmapping   \n",
       "11  imagenet_V1      22846        7        16  ROI_dotmapping   \n",
       "12  imagenet_V1      22846       10        14  ROI_dotmapping   \n",
       "13  imagenet_V1      22846       10        15  ROI_dotmapping   \n",
       "14  imagenet_V1      23343        5        14  ROI_dotmapping   \n",
       "15  imagenet_V1      23343        5        16  ROI_dotmapping   \n",
       "16  imagenet_V1      23555        5        10  ROI_dotmapping   \n",
       "17  imagenet_V1      23656       14        20  ROI_dotmapping   \n",
       "18  imagenet_V1      23656       14        21  ROI_dotmapping   \n",
       "19  imagenet_V1      23964        4        20  ROI_dotmapping   \n",
       "\n",
       "              score_ts  bg_level  dot_level  dot_xsize  dot_ysize dot_shape  \\\n",
       "0  2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "1  2021-09-08 19:26:15       254          1       0.10       0.10      rect   \n",
       "2  2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "3  2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "4  2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "5  2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "6  2021-09-02 16:21:44       254          1       0.10       0.10      rect   \n",
       "7  2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "8  2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "9  2021-09-02 16:21:44       254          1       0.10       0.10      rect   \n",
       "10 2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "11 2021-09-02 16:21:44       254          1       0.10       0.10      rect   \n",
       "12 2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "13 2021-09-02 16:21:44       254          1       0.10       0.10      rect   \n",
       "14 2021-09-02 16:21:44       254          1       0.10       0.10      rect   \n",
       "15 2021-09-02 16:21:44       254          1       0.05       0.05      rect   \n",
       "16 2021-09-02 16:21:44       254          1       0.10       0.10      rect   \n",
       "17 2021-09-02 16:42:01       254          1       0.05       0.05      rect   \n",
       "18 2021-09-02 16:42:01       254          1       0.10       0.10      rect   \n",
       "19 2021-09-02 16:42:01       254          1       0.10       0.10      rect   \n",
       "\n",
       "    dot_time username  score       notes  \n",
       "0        0.3   taliah      5  dotmapfine  \n",
       "1        0.2   taliah      5      dotmap  \n",
       "2        0.3   taliah      5  dotmapfine  \n",
       "3        0.3   taliah      5  dotmapfine  \n",
       "4        0.3   taliah      5  dotmapfine  \n",
       "5        0.3   taliah      5      dotmap  \n",
       "6        0.2   taliah      5      dotmap  \n",
       "7        0.3   taliah      5  dotmapfine  \n",
       "8        0.3   taliah      5  dotmapfine  \n",
       "9        0.2   taliah      5      dotmap  \n",
       "10       0.3   taliah      5  dotmapfine  \n",
       "11       0.2   taliah      5      dotmap  \n",
       "12       0.3   taliah      5  dotmapfine  \n",
       "13       0.2   taliah      5      dotmap  \n",
       "14       0.2   taliah      5      dotmap  \n",
       "15       0.3   taliah      5  dotmapfine  \n",
       "16       0.2   taliah      5      dotmap  \n",
       "17       0.3   taliah      5  dotmapfine  \n",
       "18       0.2   taliah      5      dotmap  \n",
       "19       0.2   taliah      5      dotmap  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_atts = list(stimulus.SingleDot.heading)\n",
    "shared_atts = unique_atts.copy()[1:]\n",
    "shared_atts.remove('dot_x')\n",
    "shared_atts.remove('dot_y')\n",
    "\n",
    "dot_rest = 'scan_purpose = \"ROI_dotmapping\" and study_name = \"imagenet_V1\"'\n",
    "data_sessions = dj.U('animal_id','session') & nda.Scan()\n",
    "cur_dot_rel = collection.CuratedScan() & data_sessions & dot_rest\n",
    "dot_params = (dj.U('animal_id','session','scan_idx',*shared_atts) & \n",
    "              ((stimulus.Trial & cur_dot_rel) * stimulus.SingleDot))\n",
    "\n",
    "recast_shared_atts = (dj.U(*shared_atts) & dot_params.proj(dot_xsize='CAST(dot_xsize AS DECIMAL(3,2))',\n",
    "                                                           dot_ysize='CAST(dot_ysize AS DECIMAL(3,2))',\n",
    "                                                           dot_time ='CAST(dot_time AS DECIMAL(3,2))'))\n",
    "recast_singledot = stimulus.SingleDot.proj(*unique_atts,\n",
    "                                           dot_xsize='CAST(dot_xsize AS DECIMAL(3,2))',\n",
    "                                           dot_ysize='CAST(dot_ysize AS DECIMAL(3,2))',\n",
    "                                           dot_time ='CAST(dot_time AS DECIMAL(3,2))')\n",
    "\n",
    "print('Two dotmapping stimulus protocols were applied across the 7 animals, with no one policy applied across all 7')\n",
    "for recast_shared_att in recast_shared_atts:\n",
    "    display(recast_shared_atts & recast_shared_att)\n",
    "    recast_att_rel = (((stimulus.Trial & cur_dot_rel) * recast_singledot) & recast_shared_att)\n",
    "    x_set,y_set = [set([tuple(sorted(set((recast_att_rel & dot_key).fetch(k)))) \n",
    "                    for dot_key in cur_dot_rel & recast_att_rel]) for k in ['dot_x','dot_y']]\n",
    "    n_conds = set([len(dj.U(*unique_atts) & (recast_att_rel & dot_key)) for dot_key in cur_dot_rel & recast_att_rel])\n",
    "    n_repeats = set([tuple(set(dj.U(*unique_atts).aggr(recast_att_rel & dot_key,\n",
    "                                                       n_trials='count(*)').fetch('n_trials')))\n",
    "                     for dot_key in cur_dot_rel & recast_att_rel])\n",
    "    \n",
    "    assert len(x_set) == 1\n",
    "    assert len(y_set) == 1\n",
    "    assert len(n_conds) == 1\n",
    "    assert len(n_repeats) == 1\n",
    "    x_set = list(x_set)[0]\n",
    "    y_set = list(y_set)[0]\n",
    "    n_conds = list(n_conds)[0]\n",
    "    n_repeats = list(n_repeats)[0]\n",
    "    \n",
    "    print('grid x ', len(x_set), ' positions ',x_set)\n",
    "    print('\\ngrid y ', len(y_set), ' positions ',y_set)\n",
    "    print('\\nconditions: ', n_conds)\n",
    "    print('\\ntrials per condition: ', n_repeats)\n",
    "    \n",
    "    mon_aspect = 1.78\n",
    "    xsize,ysize = float(recast_shared_att['dot_xsize']),float(recast_shared_att['dot_ysize'])\n",
    "    xmin = np.max((np.min(x_set) - xsize/2,-0.5)) * w\n",
    "    xmax = np.min((np.max(x_set) + xsize/2,0.5)) * w\n",
    "    ymax = np.min((np.max(y_set)*mon_aspect + (ysize*mon_aspect)/2,0.5)) * h\n",
    "    ymin = np.max((np.min(y_set)*mon_aspect - (ysize*mon_aspect)/2,-0.5)) * h \n",
    "\n",
    "    print('\\nangular width of gridded area')\n",
    "    two_point_dist(xmin,xmax)\n",
    "\n",
    "    print('\\nangular height of gridded area')\n",
    "    two_point_dist(ymin,ymax)\n",
    "\n",
    "print('\\n\\n\\nOptimal dotmapping scans were recorded by Taliah')\n",
    "display(pd.DataFrame((cur_dot_rel * dot_params).fetch(as_dict=True)))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c20d688e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_dotmap_dataframe(dot_keys):\n",
    "    dotmap_dicts = []\n",
    "    for dot_key in dot_keys:\n",
    "        dotmap_dict = dot_key.copy()\n",
    "        print('fetching scan') # fetches scan and averages \n",
    "        scan_filenames = (experiment.Scan & dot_key).local_filenames_as_wildcard\n",
    "        scan = scanreader.read_scan(scan_filenames) # for mesoscope, returns (fields x pixel_height x pixel_width x channel x frame) matrix, vertically concatenating all scan fields in the same plane\n",
    "        av_scan = np.average(scan,(0,1,2,3)) # average across all depths, pixels, and channels per frame\n",
    "        dotmap_dict['av_scan'] = av_scan\n",
    "\n",
    "\n",
    "        print('deconvolving scan')\n",
    "        warnings.filterwarnings(\"ignore\")\n",
    "        deconvolved_scan,_ = caiman_interface.deconvolve(av_scan)\n",
    "        dotmap_dict['deconvolved_scan'] = deconvolved_scan\n",
    "\n",
    "        print('fetching_dots')\n",
    "        dot_rel = (stimulus.Trial & dot_key) * stimulus.SingleDot\n",
    "\n",
    "        xs,xsizes,ys,ysizes = dot_rel.fetch('dot_x','dot_xsize','dot_y','dot_ysize')\n",
    "\n",
    "        x_set = sorted(list(set(xs))) #ordered set of x dot coordinates (low to high = left to right)\n",
    "        y_set = sorted(list(set(ys))) #ordered set of y dot coordinates (low to high = top to bottom)\n",
    "        x_num,y_num = len(x_set),len(y_set)\n",
    "        x_mat,y_mat = np.meshgrid(x_set,y_set)\n",
    "\n",
    "        xsize = list(set(xsizes))\n",
    "        ysize = list(set(ysizes))\n",
    "        assert len(xsize) == 1\n",
    "        assert len(ysize) == 1\n",
    "        xsize = xsize[0]\n",
    "        ysize = ysize[0]\n",
    "\n",
    "        dotmap_dict.update({'x_set':x_set,'y_set':y_set,'x_size':xsize,'y_size':ysize})\n",
    "\n",
    "        print('syncing trace')\n",
    "        stim_sync = (stimulus.Sync & dot_key).fetch1('frame_times')\n",
    "        nfields,fps = (meso.ScanInfo & dot_key).fetch1('nfields','fps')\n",
    "        s2f = interp1d(stim_sync.squeeze(),np.arange(np.size(stim_sync))/(nfields)) #frame to stimulus clock\n",
    "        dotmap_dict['fps'] = fps\n",
    "\n",
    "        print('computing stas')\n",
    "        stim_stack = []\n",
    "        for y,x in product(y_set,x_set):\n",
    "            x_restrict = f'dot_x > {x - 0.01} and dot_x < {x + 0.01}'\n",
    "            y_restrict = f'dot_y > {y - 0.01} and dot_y < {y + 0.01}'\n",
    "            flip_times = (dot_rel & x_restrict & y_restrict).fetch('flip_times')\n",
    "            stim_trace = np.zeros_like(deconvolved_scan)\n",
    "            for flip_time in flip_times[1:]:\n",
    "                flip_time = np.round(s2f(flip_time[0])).astype(int)\n",
    "                stim_trace[flip_time[0]:flip_time[1]] = np.ones(np.diff(flip_time))\n",
    "            stim_stack.append(stim_trace)\n",
    "        stim_stack = np.vstack(stim_stack)\n",
    "\n",
    "    #     max_time = 0.217\n",
    "    #     num_lags = int(np.ceil((max_time * fps)/5) * 5)\n",
    "        num_lags = 5\n",
    "        num_timepoints = stim_stack.shape[-1] - (num_lags - 1) # length of movie minus lag time\n",
    "        weighted_sums = [np.tensordot(deconvolved_scan[..., lag:lag + num_timepoints], stim_stack[..., :num_timepoints],\n",
    "                                      axes=(-1, -1)) for lag in range(num_lags)]\n",
    "        stas = np.stack(weighted_sums, -1) / num_timepoints # num_traces x height x width x num_lags\n",
    "        stas = [np.vstack([sta[i:i+len(x_set)] for i in len(x_set) * np.arange(len(y_set))]) for sta in stas.T]\n",
    "        dotmap_dict['num_lags'] = num_lags\n",
    "        dotmap_dict['times'] = np.round(np.arange(num_lags+1) * 1/fps,3)\n",
    "        dotmap_dict['stas'] = stas\n",
    "\n",
    "        print('projecting dots to monitor')\n",
    "        mon_width = 55.3 # width of monitor in centimeters\n",
    "        mon_rel = experiment.DisplayGeometry() & 'animal_id = 18765 and session = 4'\n",
    "        mon_aspect = float(mon_rel.fetch1('monitor_aspect'))\n",
    "\n",
    "        y_mat_size = 1000\n",
    "        x_mat_size = int(y_mat_size * mon_aspect)\n",
    "        mat = np.full((y_mat_size,x_mat_size),np.nan)\n",
    "\n",
    "        def dot2mon(dot_images):\n",
    "            mon_images = []\n",
    "            for dot_image in dot_images:\n",
    "                mon_mat = mat.copy()\n",
    "                for (y,x),resp in zip(product(y_set,x_set), np.hstack(dot_image)):\n",
    "                    xmin = int((np.max((x - xsize/2,-0.5))+0.5)*x_mat_size)\n",
    "                    xmax = int((np.min((x + xsize/2,0.5))+0.5)*x_mat_size)\n",
    "                    ymax = int((np.min((y*mon_aspect + (ysize*mon_aspect)/2,0.5))+0.5)*y_mat_size)\n",
    "                    ymin = int((np.max((y*mon_aspect - (ysize*mon_aspect)/2,-0.5))+0.5)*y_mat_size)\n",
    "                    mon_mat[ymin:ymax,xmin:xmax] = np.full((np.abs(ymax-ymin),np.abs(xmax-xmin)),resp)\n",
    "                mon_images.append(mon_mat)\n",
    "            return mon_images\n",
    "\n",
    "        stas = dot2mon(stas)\n",
    "        dotmap_dict['sta_ims'] = stas\n",
    "        dotmap_dicts.append(dotmap_dict)\n",
    "    dotmap_df = pd.DataFrame(dotmap_dicts)\n",
    "    dotmap_df.to_pickle('dotmap_df.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62bc4b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gen_dotmap_dataframe(cur_dot_rel.fetch('KEY'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8f823b9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1922.4x4320 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dotmap_df = pd.read_pickle('dotmap_df.pkl')\n",
    "num_lags = np.shape(np.vstack(dotmap_df['times']))[1]-1\n",
    "\n",
    "r,c = len(dotmap_df),num_lags\n",
    "fig,axes_set = plt.subplots(r,c,figsize = (c*3*1.78, r*3))\n",
    "for sta_ims,times,scan_label,axes in zip(dotmap_df['sta_ims'],dotmap_df['times'],\n",
    "                                         np.array(dotmap_df[['animal_id','session','scan_idx']]),\n",
    "                                         axes_set):\n",
    "    sta_max = np.nanmax(np.abs(sta_ims))\n",
    "    for sta_im,ax,t0,t1 in zip(sta_ims,axes,times[:-1],times[1:]):\n",
    "        ax.imshow(sta_im,vmin=-sta_max,vmax=sta_max)\n",
    "        ax.set_xlabel(str(int(t0*1000))+'ms to ' + str(int(t1*1000)) + 'ms',fontsize=20)\n",
    "    axes[0].set_ylabel('_'.join([str(s) for s in scan_label]),fontsize=20)\n",
    "for ax in axes_set.ravel():\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xticks([])\n",
    "plt.show()\n",
    "plt.close()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}