{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To run the models in cerebmodels the user must be in the root of `~/cerebmodels/`**\n",
    "\n",
    "Since this notebook is in `~/cerebmodels/docs/notebooks/` the user must move up two directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/main-dev/cerebmodels/docs\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/main-dev/cerebmodels\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Execution - Case1: no stimulation, raw-mode (i.e. without evoking capability)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Set-up\n",
    "\n",
    "### Import `ExecutiveControl` of cerebmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from executive import ExecutiveControl as ec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Search for models and pick a desired model\n",
    "\n",
    "### 2.1. See available model scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['subcell', 'synapses', 'cells', 'microcircuits', 'layers']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec.list_modelscales()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. See available models for a particular scale\n",
    "Below shows the available models at the level of cellular modelling scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PC2010Genet',\n",
       " 'GoC2007Solinas',\n",
       " 'GrC1994Gabbiani',\n",
       " 'PC2009Akemann',\n",
       " 'PC2013Marasco',\n",
       " 'PC2006Akemann',\n",
       " 'GoC2010Botta',\n",
       " 'PC2001Miyasho',\n",
       " 'PC2003Khaliq',\n",
       " 'GoC2011Souza',\n",
       " 'GrC2016Dover',\n",
       " 'PC2018Zang',\n",
       " 'PC1997aHausser',\n",
       " 'PC2015Masoli',\n",
       " 'PC2011Brown',\n",
       " 'PC2015aForrest',\n",
       " 'GrC2011Souza',\n",
       " 'PC2015bForrest',\n",
       " 'GrC2001DAngelo',\n",
       " 'PC1997bHausser',\n",
       " 'DCN2011Luthman',\n",
       " 'GrC2009Diwakar']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec.list_models( modelscale = \"cells\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Pick a desired model\n",
    "Although it is not essential to instantiate `ExecutiveControl` to launch the model, it is recommended if one were to visualize the response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "exc = ec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "desired_model = exc.choose_model( modelscale = \"cells\", modelname = \"PC2003Khaliq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Launching (executing) the desired model\n",
    "### 3.1. Setting the parameters\n",
    "For our cases of launching the model without stimulation and without ``CerbUnit``'s capability, one is just required to define the run-time parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtimeparam = { \"dt\": 0.025, \"celsius\": 37, \"tstop\": 500, \"v_init\": -65 }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Executing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 0.15529299999999946 seconds ---\n"
     ]
    }
   ],
   "source": [
    "desired_model = exc.launch_model(parameters = runtimeparam, onmodel = desired_model )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Saving the response\n",
    "In ``CerebUnit`` the model response are saved in an HDF5 file based on NWB:2.0 scheme. Execution of ``save_response()`` returns the filename and its path. Below, this return full path and filename is assigned to the attribute **``fullfilename``** of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "desired_model.fullfilename = exc.save_response()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualizing the response\n",
    "### 4.1. List model regions of the chosen model\n",
    "Before one calls the visualization function the user must define the the region of interest (roi) from which the response will be visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['soma v']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exc.list_modelregions( chosenmodel = desired_model )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Visualize all events\n",
    "The method ``visualize_all`` plots all the events in respective sub-plot. In our particular case with not stimulation the simulation has only one event therefore only one sub-plot is seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exc.visualize_all(chosenmodel = desired_model, roi=\"soma v\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Visualize all events in one plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ottcCIvgCO6bV0gnbZepIjHESVdy3WtFdD9+BqywGV3vdQgRfMJawgDLdg2xiZKBmmHIsnJBOyMBj7AYi+ILQgHDtc2b72uxptRbfrikhnTRn4hWbE6DXnpRWEJKOWOHQf4mb4n06YQYTwwJcbda/xCGP3Uh9JfPOXTQi+IKr6LxfuAVUFkePYI7gOx6+Ge2VmbZCUsMhHFyFsrjiwCFDAszRx5XtWGgP6fDYdTDk1DWJCL7ATrS3xXHDmJbpYWJUwJSnKCeGr729ytxwXDQi+AI70beIzhuGS0DDKXghvXbD9rXbcyEPn+lY6HYAiNnD5xrXcQsRfIGd6JuEw0Hiumm44rWmeInRx1Vi+BamnLumEMEXXIXjhmHzxA3xEt3AFMF3YvhBzS5+ZFzHwJMXhQi+4AI8nmIkX96smzto4HJMfAPYeu1FYvh67ToENbdXsnSEpIM724OrNg1X2RRjJl7FhOLM8PC57EbGi8TDF4Rm4Zp4xZ31ol2YnY6EaZBZN7GD7Ty/oT2Gb79ylUfmmtDlFiL4AjtcHj5X6IWryJlq8KrPLn/KpCmzmbmydCID7nrtuo0IvsBOtCDpHEzjE2Zexdf/5KDVXFy7ugdBHfR7+Dx5+DJoKwithCsW7Agzl4BydSRcTw664U6nBcyZEcs350Py8IUkRmtIhzv0wjbwp9WsK6tRGTO4yuUESAxfEFpHbAxfp4ff2L4Wu8wdiW64B8UBc0orOOa0h6DCMXzzUmqjEcEX2GHzFJVh3pwLis8xKK7bbjRc9eVNsev2mIAIvsAOVyyY7ebmCr24sCg6x8S2hu91or9T5RrX4R0vAtypoiqCL7gKR/E0U1IG2QY+o2Z/coTMLLvazMbA54nzCDPnilduePsi+AI7fHn4ljHtdVMMiVeH7cb8hka7LsTwdXu1XMLs2OO81tzI8dci+ER0FxEpIsqz/yYi+hMRFRHRJiI6U8fvCOZjQnlkttK6PGYZJ0iZmKXDZZcnpBNyoVONJmHBJ6IhAC4BsD9q82UARtv/FgB4PNHfEcwlOsasNw/fwpS6KaaVQDAyD5+5fIV+ZyD63tBtuzE6PPxHANyD2OvuSgBLlcVnAHoR0UANvyUYCFtIhymGzzZByoWxBq4YvikzbR1rxrTXJA+fiK4EUKKU2tjgo0EADkT9fdDeJqQgXMIReczWZjLGnjkp11FeosbyvSbm4Tvt5Mr+MX3QNr2lLxDR+wAGxPnofgA/hhXOaTdEtABW2AdDhw5NxJTQSWGLMbN5+M6rISEdNg+fZ0CRc6CSO5uGq4Oy3ms1HZcWBV8pdXG87UQ0EcBwABvtZcUGA1hHRFMBlAAYEvX1wfa2ePYXAlgIAAUFBS7ssuA2bFkk9itXHFj7Yhd6zcW1q3NxFbYaSIxPDtzjL5whHTfq6rQ7pKOU2qyU6qeUyldK5cMK25yplDoC4A0AN9nZOtMBVCqlDutpsmAabB4o1+N7A/va7BoWw+fyPrkGmaNtc43r8IZ09NqOR4sefjt5B8DlAIoA1AK4hel3BMNgKZ7GFhYw44FTMWV6cHfUDd/rtM0XKuJzAjpFDL+12F6+814BuFOXbcF0eC9q/QtW84qGblyZIMU1g9eUbBrnlVGTO32WjiC0BrZYsP3KN42eb4CO4zhYv6HNLFsePudAJd9TH0/4MPaa0Go6LiL4AjvcgsSXkcFjF9C98leUgDKkvQI8M6R12wUibdZfAgGsdgHx8IUkIeaiZsnDN8PD55qA1tRv6LRlyiLm3OeOa7wIMKiWjiA0B1u2B9fNzba6kVkeM1esnVPk+J7OeDPCACmPLCQJ0Y/BOr1xx67+PHyw2OWKibtRD59vQhdXlo4ZTw4hpmPcFCL4AjvRgq9TRAMhnps7PD2f0ePSmi8fUw9fm9kGlRz12eXq+KLt8VXh1Go2xsWXkI6QFETP/tQ5E9QRZN2iwdWRBII83lxsh9r57Qai7WpWuQDTU18wfK2Jhy8IzRJkEjouYeYKFQWjXHGOJx3Lrs7jG2kvRygOMEeYuTqSAFO4sylE8AV2or16nRe1I266U+W4bkI/21hGVEeisf4PWyguGN3x6T53IduuVrPhNnM5F5ZtrabjIoIvsBNzUWsUpABXSCfI44lz3dx+5icoTru6vVrnGHM5AbpDUH7Gzi8eIvgCO1yx4BBTSIdN6FyI4es8FLGhOI12Gb1a7jCfbk3mcoaaQgRfYIcrDz/y+M75mM0UejHCE+eJ4Ue3V+cgPhDppIwZyGc6d00hgi+wE+3ZmpCHH2DymP1M3pxxHQljGMPP5AQ4bdbeQTFda00hgi+wE2Ty8NkmdHGFXlyJtWszG3N8dS4Gw9WhAtFOgCFZOhLDF5INV/K52dIcuexyxfDNGnNI9Tx8CekISUe9Pxh+r/OirvPx2I2OXevM9oiNiWszC1/QjfZqTE9lzIKKFDnjCeno7ki4jkVTcK141SnwBoKY+3Qhyqq9Hd0UV8nrloUn5xSgS4ano5sCAKj38whdfYC/I9EpHFwdVH2MXW1mG7RXo10uB8DP095o27q98Og2uzHxKqkFv/SkF58UlWHCoB4Y1Cu7o5vjCiUn6vBJURlKT3oxtE9ORzcHAJ+HH92R6BwE5RION+zqFI1atvPG1F5fIPxedx6+c4yVstpMRHrs+sTD187NZw/HN84a3NHNcIVX1h7EXS9vjKlI2NFEe+I6b0Quj7mWya477dVmlu3Jgau9XE9mjW0DmvQedf5IJyUx/ARxjp+mc2MEzoXoRopXa/EyhXS8LoR0uITZhFARV3vZOlTGkI5pTkBTJLfgdyIv1206055HP2rrvKhrvEGkkWNXm1nU+YPw2IZ13oPRdrnaq1Xwo+0yPJl50ojlySE9jbSvC8B17mp9wbCTJjNtEyTs4aeQi98Z9/V4rR+9czIA6LtZ6v1B1PmD6NMtC4A+D1QphZN1fvTKdtqr7+4+WedHTwa7lVHt1dlBxbZXr10A6NElXeuTQ6Vtt2d2htb2VnkDUAps5y43JxOA/kld8UhqwXfojCLIBcHxTDuPj3+i1hcW5qAmN+Z4rQ+AlZEExOYzJ8LJugACIYW+3W27QT12gyGFilof+mm2CwDl1b5we/0aZ0iVV/vQv0cXALEpmgnbrfGhe5d05GSmxxR+S5SKauua6Neji7brDLCOA4CoY6Hx3NVEHWOds9uaIKkF3zktlEJR/HAMv2ObEcPxWn9Y6HTd4BU1zk2oV+jKaqwU3lPsrC5ddo/X+qAUMLCndXP7NNlVSqGixhe2q1eMvGG7OoW5rNqLvG5ZyPCQ3g7KPncDe3bR2t7y6ohdAPAH9LQ5FFI4XuPDAIZj3BTJLfidyMt1m86062XVXgzooVfoSqsa3oR6drjMtttfc3sdL3FAT7sj0SQaJ+sD8AVDUaKhT0DLojx8n6b2AtaxyO2aicz0NO1PJJnpaeiVk6G1vWXhc6f3GFfW+REIqfAx1nksmiK5Bd9+TaWQTmejqt6PE7V+jOzXDYA+odtfXgsAGNWvOwDAFww29/XW262w7I7s2xWAvo7EsTsiz7aryZs7EG6vdXy9mo5vVb0fFTU+DM3N0e6J76+oxeDe2cjwpGkV5uJyy25Wepq2jhqIHOPhfaxzp8t2o2tNBF9oK5EJIZ3DxT9QUQcAyO/TFUT6bpZ95TXIyfRgUC/HA9Wzv3vLapDhIYzoq/fm3ltWDQA4bYDeDmpPWU2MXV2isa/MFrm8rsjw6PPE6/1BHKqsw4i8bpbgaxS5vWU1GKG5vYB1jHO7ZobHSXR1Unvtc3dqf73nrjmSWvAjWTqp4+KH5b5z6D12lVYBAEb07ar1Bt9+uAqj+nVDZrp1Ceuyu/NoNYbm5oTLUui6CXccqUZet6zwILOuDmrnkSp40gij7ScdXU9QO49a522kc9402S0qrYZSwMh+XbWGdLyBIPaW1WBk327I9KRpOw6AdSyc4wDoezrbcbQK6WkUfvr1SQw/UawDmDpy3/kGbTceqESXjDSM7tcNWZqEIxAMYcOBE5g8pFfUTZi43VBIYW1xBc4c2huZtl1dQmfZ7YXMdOsE6RK6wuIKjBvYA927WJPmdXV8hcUV6NElHSP7Op64nitqzb4KAAgfY13Hd0tJJXzBECYP7Y0MjSGden8Qmw9WWu1N13etAcDafccxYVBPdMu0zp1k6SRIZ/FyU5k1+ypw+qBeSPekIUOTR7fhwAnU+YMoyM8NC7MOj277kSocr/VjSn6u1pu75EQd9pXXYkp+rtYOqtYXwPr9J1CQ31ur96mUwqe7y1GQn4u0NEKWRk/8093lGNQrG6f0yrbHBvTcpJ8WlQNA+Fj4g0pL0sba4uPwBUMoyM9FhsfqrHV0JtXeADYcPIEp+b2R7tHrBDRHcgu+/ZpCEZ2oPPwObgiAQyfqsLmkEheM6QcA9qN24g17e/NhZKan4fzT+iLDFmavhpvlrU2H4EkjXDi2n1ZhfnvTIQDAl8b1j4SgNHRQ739eCm8ghEvHD4iIkQa7W0pOori8FpeM6w8A2gZtT9b78fHOY/iSbVdnSOetTYdRMKw38rplIStdX+f31qZD6JrpwTmj8rQ+9a3YdgS+8LnTGypqjoQFn4j+i4i2E9FWIvp11Pb7iKiIiHYQ0aWJ/k5CbUyhoE4kpNPxir98QwkA4NLxtnCkU8LeUY03gFfXleCiMf3QvUuGNg+/3h/EK+sO4pxReXaOuN2RJGg3GFJ4Yc0BnDGkF/LzumrtSJZ9VoyBPbtgSn4uiMgKkWiw+9zqYmSmp2HmhAEAoC2G/+rag/AFQrhq8iCtdgv3VWDH0SpcOekU264eT7yyzo83Nx7GJeMHIDvTE3YuEj13Siks+2w/BvfOxplDe2vtrFsiIcEnogsAXAngDKXUeAC/tbePA3A9gPEAZgJ4jIhcL86ekqUV7NeO9vB9gRCWflqMc0blYYSdMqhDkJauLEZlnR8LvjjCsqlp0PblwgM4etKL+eeOCLcVSNzremvTIew5VoPb7PY6gp9oTHz13gqs2luBeeeOCNd4yfBQwh3fweO1+Nvag7iuYAh62VP+dXji9f4gnvjXHkzNz8WkIb0suxquB6UU/vjBLuR2zcTX7Yq4GZqcgMWf7EW1NxDnmkjM7srd5SgsPo75545AWhqBiLSnvjZFoh7+twD8UinlBQClVKm9/UoALyilvEqpvQCKAExN8LfajErlQdsOFvwln+7DkZP1YWEGEvfoDh6vxZ8+2IWLxvTD5KG9wzaBxG7C0qp6/Oa9HZg6PBdfGNUHALTE8Cvr/Hjo7c8xbmAPzBxvectZGuz6AiE8sHwzBvbsgllTh4S3JzpGopTCA8u3ID0tDbefPzJiV8Og7SPv78Thynr84JJTw9t0dCRvbz6Mf+8qwx3nj0SOPfip49ztOVaNxz/ejSsmDsS4U3oAiOqsEwhL1vuDeOD1LRjUKxvXTYmcu5vPzseZ9jXNSaKCfyqAc4loFRF9TERT7O2DAByI+t5Be5urdLTopSr7y2vxh/d34sIx/fDFU/uGtycy+OcLhPDdFzaACPjplePD28M3dztvwmBI4Ycvb0J9IIRffG1iOIU30cdspRR+/NpmlFV78auvn460sBeeeBz4V+9ux86j1fj5VRPCIgck7jE/+1kxPtpxDHdfelrMgkFWNk375w38p6gMf/3XHlxXMATTR/QJb0/UAThQUYv/Xr4FEwf1xM1n58fYBdofjqv3B/G9FzegS3oafvKVceHtToZVIsf4529vw55jNXj4axNjVqS7/4pxuNge2+CkxQVQiOh9AAPifHS//f9zAUwHMAXAS0Q0Is53m7O/AMACABg6dGhb/muLpGJIx3me6agYfmWdH7cuWYN0Txoe/Mr4mM/ae4OHQgoPLN+MtcXH8edvTsbg3pGVvBxhbu+g7S/e+Rwf7zyGh6+eGJ6tCiDheO3//bMIb286jHsvG4OJg3uGt3vSCGnUfrsvrtmPRZ/sxc1n5+OisbECYR3f9p33/xSV4cE3t+GiMf1ixBOwxl6iVxdrC3uOVeOOZeswsm83PPDlsY3a296QWVW9H/OXFiIYUvjTrMlI90R810RCL6GQwg//tgmbSyqxcHYB+tllD5w6c10pAAAVfUlEQVT2Au0PFS1duQ/PfrYft31xBM6LcoTcpEXBV0pd3NRnRPQtAK8qK/9pNRGFAOQBKAEwJOqrg+1t8ewvBLAQAAoKCphUKnUUvyNDOoFgCN9+bh32ldXgmbnTGi2xmJme1mavyx8M4Z6/bcJr60vwnQtH4cunnxLzeVa65SV5/W3zQJVS+PV7O/DkJ3sxZ8YwfHNarLPhhF7a2l6lFB79sAi/X7ETX5s8KBy7b9jm9nifz6/ejx+/thnnjs7DA1eMbfR5VkZazKIwreWjHaW4/dm1GNm3K/5w/aTwmEB0e53Sw21h+5GTmL1oNdIIeHJOAbp3ydDS3vJqL25+ag2KSqvx1C1TMNwuVxFpb/vOnT8Ywl0vbcSbGw/hnpmnhbOJInY97bILWOMBP3vL6lDvmTmmzf9fF4mGdJYDuAAAiOhUAJkAygC8AeB6IsoiouEARgNYneBvtZlwDD919L7DujalFB58cyv+vasMD109ATNG9mn0nZxMT8wKPy1R7w/i9mfW4rX1Jfjhpafh+186tdF3su3H4vo2CL71xLAFj3+0G7OmDsX/NHgSASwvMY3QpvYqpfDwO5/jt/+wxP7X3zg97izv7DYeBwB44uPduO/VzTjv1L5YOLsgxqMN283wtOk4AMCbGw9h3pJCjOzbDc/Nn95IlB27bW3v2uLjuPYvK+Ehwku3zcCwPl0bfSc7wwN/ULXJEy85UYdrnliJXaVV+OtNBTh3dGNPuUumdU3UteFY1PmCWLC0EG9sPIQfzRyDO84fFbe9bbWrlMLvV+zEz97ahkvH98ejN5zZqEN1k0TXtF0MYDERbQHgAzDH9va3EtFLALYBCAC4Uymlp3hIG0jNJQ47Zm+f/jTyuHrdlPihuezM9FbfLCfr/Zj3dCHWFFfg51dNwI3Th8X9XmZ6GtLTKGYJuubwB0O4++WNeH3DIdx23gjcO3NM3GNGRMjJTG+13WBI4cevbsaLhQcwZ8Yw/OQr48Nx+4ZkZ3habdd5Enn8o9348ukD8ftrJ4XHLRqSk9l6uwCwbFUxHli+BVPyc/HknAL0iCP2QNs7qH/tPIbbnlmL/j2y8MzcaRiSmxP3ezlRwpwRpwNrSFFpNWYvWoVqbwDPzJ2GKfm58e06wtzKNlfW+TFvyRoUFh/Hw1dPbPS055DttDdqBbfmCIUsJ2jpymJcWzAYD189MW5H7SYJCb5SygfgxiY+ewjAQ4nYF9qPmyGdD7eX4n/f2oZLxvXHj5p5XM3J8MQsd9gUx6q8mLN4NXYercKfrp+Mr5xxSrPfz26l0NX7g7hz2Tp8sL0U98w8La4X19Bu9CLTTeENBPH9Fzfgnc1H8J0LR+H7Xzq12Y43p5V2gyGF/3l9C5at2o9ZU4fi51dNaNY7zM5MD68m1RKPfVSEX7+7AxeO6YfHbjgzZgAxXntrW9lRv73pML734nqM7tcdS26dGi44Fr+9EWFuqrNx2HywEnOeWo00Iry4YEY4cyZ+ey1Za801UVpVjzmL16CotAp/nnUmrjh9YJPfbYtz4QtYjsUbGw9hwRdH4L7L4jsWbpOoh9+pSeniaS4N2m4/chL/9fx6jB3YA3+4flKTXi3QOmE+eLwWsxetxuHKOjw5pwDnn9avxTa0JpRRVe/HvCWFWL2vAv971QTMbuKJoaHdltpb6wvgtmfW4t+7yvDAFWMx79yWcxZacxz8wRB+YMeTbz9vJH4087QWr+PsjDQcrWzerlIKv/z7djzxrz24ctIp+O01Z7ToXbf2ieT51ftx/2ubcdaw3nhyzpTwkoDN2QVa9sRX7i7H/KWF6JWTgWfnTkN+XuPwUIzdTGt/WnIuDlTU4sZFq1B60otFc6bEZJQ1bbvlY1HnC+Jby9biox3H8KOZY/CtqBTXjiapBd8hdeTe3UHbsmov5j5diK5ZHiyaMyUmRTAeLcXwdx2twuxFq1HrC2DZvGk4a1j8R/Z4dpu7CStqfLj5qdXYeugk/nDdJFw5qXUZwi3Zrazz49an12D9/uP49ddPx7VRedXN0ZKA1vmCuGPZWnzYRsHIyUxHbTNPDtFhp5tmDMODzYSdYtqb6YEvEEIwpJp8wvjLx7vxy79vx/mn9cXjN5wV9t6bb6/1neaOxYptR3Hnc+swLDcHz8ydFl6EpPn2Wtdhc9fajiNVmL1oFbyBEJbNn9bqHPiWxjMqa60stXX7j+MXX5uIWVP1Zh4mSlILfkoO2rq0r75ACHc8uw5l1V68fPuMVt2IOZkeBEIKvkCoURx688FK3LR4FdI9aXjxthkYO7DpR/aGZDcTay89WY8bF61CcXktFs4+q1EqY/N2m35yKK/2Yvai1dhVWoVHv3kmLpvYdCigITmZHpTbSzQ2pKrej7lLCrFmXwUeunoCbpjW8pNIdHvrfPEHQH2BEL7/4ga8vflwq8JODdsLWLH2blmxkqGUwm/e24HHPtqNr55hPTE0NcbQuL22MDfRSb22/iDufnkTJgzqiadvnoLeXTNb194WBlc3HjiBOU+tRqYnDS/dNiO8lkCrbGd6mrR7rMqL2YtWYfexajz6zTNxeRuuCbdIbsFPwTz8cPE05t958M2tWL2vAn+8fhJOH9yrVf8n2vOKFoW1xRW4efEa9MzJwLJ50+JmdDRHUzHxkhN1uOGvn6G0younbpmCs0fmtdluvI7kSGU9bnjyM5ScqMOTc6a0Oac6JzMdB47XNdp+otaHOYvb/iQStpvhiTugWO8P4o5l6/DP7aW4//KxmB8nVbQ5ssMx8UCM4IdCCj99cyuWrCzGN6cNxc+vnNCqJ4aw3XBIp3En9exnxfjv17dgxog++OtNBeia1Xqpym7myeGzPeWY+/Qa5HbLxLK50xulDrdsO75zcehEHW58chUOVda1OjzUESRFtcyPdx7DJY98jOLympjtqTzRlnM93+XrS/Dcqv24/byRbRIl5waPDjtsOngCsxetRl73rCbT91pjt+Fj9uHKOlz7l5Uor/Hh2XnT2iz2jt2GN3fpyXpc+8RKHD3pxZJbprZrAk2XOO2trPNj1l9X4fPDVXj8xrPaLPaAHV/2B2POvTcQxLwlhfhwRykevnpim8UeiB9rV0rh/uVbsGRlMeafOxwPXdU2sQeiQzqxndTT/9mLB5ZvwUVj+mHxzVPaJPaAlYdPcVJqPy0qw5zFqzGwVzZevu3sNou90+aGzsWhE3W45i8rcazKi2fmTuu0Yg8kiYdf4w1g59HqJmcDplK1TGdXueTemc5eMKw37r6kcV58czSM2ZacqMOtTxcit2smXlwwPWZWY1vIzvSgrNob/ruyzo+bF69BZZ0fLyyYjgmDejbzv5uzmx7jMVfV+zHnqTUoq/Zi2bxp4Xo+bcV6cojYrfcHMX9pIYpKqxLyDrMzPVDKmhjUJcODUEjhBy9txCdFZfjtNWfgG3Zxsfa0F4j1mH/3j514fvV+3HnBSNx9ScsDyk21F4gNvby58RAefNPK+Hr0hjNbla7ZECKyM8IidreUVGLBM2sxrE8Onp8/HX26NZ091Bw5mR5UeyPn7kStDzctXo2TdX48N396zKzqzkhSePiOYxEMxcqcSsFEfM5dtQRkAwDgkesmtTmnODoNLxRSuPuljaj3B/H0LVPaLfZAbFxVKYV7/rYRe8qqsXD2We0WewAxoqGUwr2vbsbOo5YH3l6xd9obLUYPv/M5Vu+twO+unZSQd+jErh3bT/xrD97edBj3XTam3WIPNA6RvLvlCP78YRGunzKk3WIPNO5Idhypwl0vb8TU/Fz8adbkdol9pM3pYU+8ss6P255Zi+5d0rHk1qntFnsg9mlSKatD3V9ei4U3FXR6sQeSxMNPsy+4UIMwRngBFJfb05E4Nx9HROeFNQewZt9x/PaaM5qcTNMc3e1H86r6AJZvKMHKPeX4xdcmYlS/1g+axSMnMx01ttf12voSvLf1KH58+RicPartYZwYu1mesN23Nh3G25sO44eXnpZwHZSczHR4AyEEgiF8tqcCS1cWY945w/HVFuYbtNxe6/jWeAM4VuXF7/6xA1dMHBhTsbQ9dM2M2K2o8eHeVzdh4qCe+NmVExJKec6JshsMKXzvxQ3o0SUdj93Y/LyAVrU5y4NqryXMP3tzG46crMffbp+BgT2zW/ifLdlNR439dPbCmgP45/ZSPPiVcXFnlndGklvwUzmIrzmoU17txa/e3Y7pI3Lx9TPbV/jUmYRTWlWPxz/ajTEDuuP6VqYyNkdet0xU1PjgDQTxh/d3YeKgnph3TmIiZ9nNQo0viBpvAI+8vxNjBnTH7eclnlPdp5uVbVJe48PvVuzAoF7ZuPvS0xK2m2fbLav24q//3oPsDA9+flViohzd3ooaHxZ9sgeVdX68uKD12ThN0aNLOjI8hPIaH97ZfBifHz6J/5s1ObzQe0Jt7pqJ8movikqr8er6g5h/7oiEnspi7VrX2h/f34WCYb0xp0Gxuc5MUoR0nNzghiGd8CLmKZSmw7UAyq/f3YEabyAhAXEE/4XVB7CrtBq3nzdSy7np2z0LIWXV4N9fUYvvXDS6zQOITdkFrEVX9hyrwX9dOFpLHRRH0JavL8H6/SfwrfNHJuzRAkDfblZY7NPd5fj7liOYc3Z+q1MZm7VrH4ei0mos+bQYl08Y2KZUxqYgIuR1y8LRk/X4v3/uwuh+3XCFplTGvt2zUFbtxWMfFqFLuifhpxyHvO5ZqPUFsfTTYhw5WY/vXjzaKH1JDg8/zfHw439uzulIHGIYtC0qrcbLaw/gli8MTyj80jPbWpJw5R5rIesvNzONvS30tQX04Xe2Y+zAHrh4bMuzc1tl1xa6X727HaP6dcNlE+JVCW+/3V/8fTsG9OiCawraH1+PZ/c37+1ATqYHt54zXIvd7lnpyEpPw58/LAIAfPvC5ktStIW+3bPw6jqrkO4fW5ip3Va77209it3HanDL2flanhqAyLX20DufY9KQXjgnwbCh2ySFh+9cI02FdAzqgBOGIyPpkRU7kZ3hwR0JThEnIozqZ9Wcv/28EdoKSUVPtf/OhaO0eVz5USmi375glDYxGhaVDnjbeSPCZXcTxQnpAMDs6cOQq8G7B6zzNri3Ffu+ZFz/Nk2KawnnGA/P69qo9LUOu8GQ0ubdA0B+XuTcfecifdeaWySF4HucGH7DLB37NZXSMnWXVthSUom3Nx/G3HNHJJTd4PDQ1RPw7QtGNVlRsz2c1r87Jg3phS+e2heXjtfjhQNAfp8czBjRBzNG9GmxgFtbyOuWhYvH9sf4U3o0WZmxPaR70nDz2fnI75OjZawhmllThyK3aybumZn4WEM0V08ehK6ZHtx/+VitZYMvHT8Aed2y8KOZYxLKAGvI6YN74bT+3XHx2H64oBV1njobSRHScXrZoAzahtE18eovH+9G96x0zDtXT3hg8tDeWgbPoklLI7x2x9kA9I7XEBGemz9Nu13AWhBEKaXd7oNfHQ+lxmm3O+/cEZh7znDtdi8Y0w9bfnqpdrtDcnOw5v6LtNvN8KTh3e+dC8DMscHk8PDT4qciOqJn4HlpN5FqmYlz8Hgt/r7lCGZNG9pi+dqOhohYbkAuu45tsWumXRPFHkgawbdeG2fpWJh5atqJxp19+j/7AMCotDNBEJomqUI6976yKabuRng2YwopvjNecddLG5GVkQYoy9tXSiGkrAqiSkWehpRS9udRn9l/H6/14YqJAzGoV2KTVQRB6BwkheCPHdAD1xUMQZW38Wo/Z4/sk9D0etOYMKgHrjlrsNXZkdXXEZH9Gvs3yJq0FvmMrFf7wwwPaZnAJAhC54A4qyq2lYKCAlVYWNjRzRAEQTAKIlqrlCpo6XtJEcMXBEEQWkYEXxAEIUUQwRcEQUgRRPAFQRBSBBF8QRCEFEEEXxAEIUUQwRcEQUgRRPAFQRBShE418YqIjgEobud/zwNQprE5JiD7nBrIPqcGiezzMKVUi4stdyrBTwQiKmzNTLNkQvY5NZB9Tg3c2GcJ6QiCIKQIIviCIAgpQjIJ/sKObkAHIPucGsg+pwbs+5w0MXxBEASheZLJwxcEQRCaISkEn4hmEtEOIioions7uj26IKLFRFRKRFuituUS0Qoi2mW/9ra3ExH9yT4Gm4jozI5refsgoiFE9CERbSOirUT0XXt7Mu9zFyJaTUQb7X3+qb19OBGtsvftRSLKtLdn2X8X2Z/nd2T7E4GIPES0nojesv9O6n0mon1EtJmINhBRob3N1WvbeMEnIg+ARwFcBmAcgFlENK5jW6WNpwHMbLDtXgAfKKVGA/jA/huw9n+0/W8BgMddaqNOAgDuUkqNAzAdwJ32uUzmffYCuFApdQaASQBmEtF0AL8C8IhSahSA4wDm2t+fC+C4vf0R+3um8l0An0f9nQr7fIFSalJU+qW717ZSyuh/AGYAeC/q7/sA3NfR7dK4f/kAtkT9vQPAQPv9QAA77PdPAJgV73um/gPwOoAvpco+A8gBsA7ANFgTcNLt7eFrHMB7AGbY79Pt71FHt70d+zoYlsBdCOAtWKtvJvs+7wOQ12Cbq9e28R4+gEEADkT9fdDelqz0V0odtt8fAdDffp9Ux8F+bJ8MYBWSfJ/t0MYGAKUAVgDYDeCEUipgfyV6v8L7bH9eCaCPuy3Wwh8A3AMgZP/dB8m/zwrAP4hoLREtsLe5em0nxSLmqYpSShFR0qVZEVE3AK8A+J5S6iRZq6oDSM59VkoFAUwiol4AXgMwpoObxAoRfRlAqVJqLRGd39HtcZFzlFIlRNQPwAoi2h79oRvXdjJ4+CUAhkT9PdjelqwcJaKBAGC/ltrbk+I4EFEGLLFfppR61d6c1PvsoJQ6AeBDWOGMXkTkOGTR+xXeZ/vzngDKXW5qonwBwFeJaB+AF2CFdf6I5N5nKKVK7NdSWB37VLh8bSeD4K8BMNoe4c8EcD2ANzq4TZy8AWCO/X4OrDi3s/0me3R/OoDKqEdFIyDLlV8E4HOl1O+jPkrmfe5re/YgomxYYxafwxL+b9hfa7jPzrH4BoB/KjvIawpKqfuUUoOVUvmw7td/KqVuQBLvMxF1JaLuznsAlwDYArev7Y4eyNA0GHI5gJ2wYp/3d3R7NO7X8wAOA/DDiuHNhRW7/ADALgDvA8i1v0uwspV2A9gMoKCj29+O/T0HVpxzE4AN9r/Lk3yfTwew3t7nLQD+x94+AsBqAEUAXgaQZW/vYv9dZH8+oqP3IcH9Px/AW8m+z/a+bbT/bXV0yu1rW2baCoIgpAjJENIRBEEQWoEIviAIQooggi8IgpAiiOALgiCkCCL4giAIKYIIviAIQooggi8IgpAiiOALgiCkCP8PpRo+Csd0VHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exc.visualize_aio(chosenmodel = desired_model, roi=\"soma v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}