{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:10.686466Z",
     "start_time": "2019-02-27T02:28:08.305819Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/cube/tsainbur/conda_envs/tpy3/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    }
   ],
   "source": [
    "from birdbrain.atlas import atlas\n",
    "from birdbrain.utils import um_to_vox\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data, create atlas object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:15.735789Z",
     "start_time": "2019-02-27T02:28:10.689495Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "qform_code 32 not valid; setting to 0\n",
      "qform_code 32 not valid; setting to 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already download\n",
      "Loading brain labels...\n",
      "Getting voxel data from .img files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "qform_code 32 not valid; setting to 0\n",
      "qform_code 32 not valid; setting to 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting location for each nucleus/region from voxel data...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=45), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Atlas created\n"
     ]
    }
   ],
   "source": [
    "dset_dir = '../../data/processed/starling/'\n",
    "\n",
    "starling_atlas = atlas(\n",
    "    species = 'starling',\n",
    "    dset_dir = dset_dir,\n",
    "    um_mult = 100,\n",
    "    smoothing =[],\n",
    "    updated_y_sinus = [0, 1500, 0]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate the number of neurons in a region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:15.741543Z",
     "start_time": "2019-02-27T02:28:15.738250Z"
    }
   },
   "outputs": [],
   "source": [
    "# Estimate of total number of neurons from https://www.pnas.org/content/113/26/7255/tab-figures-data\n",
    "starling_n_neurons = 482.50*10**6\n",
    "starling_n_neurons_std = 88.29*10**6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:15.833237Z",
     "start_time": "2019-02-27T02:28:15.743499Z"
    }
   },
   "outputs": [],
   "source": [
    "nucleus = 'CMM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:16.024719Z",
     "start_time": "2019-02-27T02:28:15.837596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0026046479908523462"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_brain_voxels = np.sum(starling_atlas.voxel_data.loc['Brain', 'voxels'])\n",
    "n_region_voxels = np.sum(starling_atlas.voxel_data.loc['Nuclei', 'voxels']==starling_atlas.brain_labels.loc[nucleus,'label'])\n",
    "n_region_voxels/n_brain_voxels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:16.031214Z",
     "start_time": "2019-02-27T02:28:16.027497Z"
    }
   },
   "outputs": [],
   "source": [
    "estimated_region_num_neurons = int((n_region_voxels/n_brain_voxels) * starling_n_neurons)\n",
    "estimated_region_num_neurons_std = int((n_region_voxels/n_brain_voxels) * starling_n_neurons_std) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:16.124715Z",
     "start_time": "2019-02-27T02:28:16.033145Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CMM has an estimated 1,256,742+=229,964 neurons\n"
     ]
    }
   ],
   "source": [
    "print(nucleus+\" has an estimated {:,}+={:,} neurons\".format(estimated_region_num_neurons, estimated_region_num_neurons_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:28:16.215488Z",
     "start_time": "2019-02-27T02:28:16.128516Z"
    }
   },
   "outputs": [],
   "source": [
    "##TODO - print region volume in mL"
   ]
  }
 ],
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