{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:11:38.071793Z",
     "start_time": "2019-02-27T02:11:35.747112Z"
    },
    "scrolled": true
   },
   "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\n",
    "from birdbrain.visualization.plotting_2d import widget_controllers_2d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data, create atlas object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:12:00.750063Z",
     "start_time": "2019-02-27T02:11:38.074799Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already download\n",
      "Getting voxel data from .img files...\n",
      "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=66), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atlas created\n"
     ]
    }
   ],
   "source": [
    "dset_dir = '../../data/processed/canary/'\n",
    "\n",
    "canary_atlas = atlas(\n",
    "    species = 'canary',\n",
    "    dset_dir = dset_dir,\n",
    "    um_mult = 1,\n",
    "    smoothing = []#['Brain', 'Brainregions']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:12:00.757720Z",
     "start_time": "2019-02-27T02:12:00.752191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Nuclei', 'Skull', 'Tracts', 'Brain', 'Combined_set', 'Subdivisions',\n",
       "       'T2_rare_3d', 'T2_flash_3d_hr'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canary_atlas.voxel_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:12:00.853489Z",
     "start_time": "2019-02-27T02:12:00.759296Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 401, 478)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(canary_atlas.voxel_data.loc['Brain', 'voxels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:12:00.941436Z",
     "start_time": "2019-02-27T02:12:00.855543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 401, 478)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(canary_atlas.voxel_data.loc['T2_rare_3d', 'voxels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-27T02:12:01.103432Z",
     "start_time": "2019-02-27T02:12:00.943244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89c7cfb286a542978f6fa2d0e69da7ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=9000.0, description='medial-lateral:', max=9000.0, min=-8880.0, step=100.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e0654eb98ed4735b1bbae7559f5fd38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=10980.0, description='posterior-anterior:', max=10980.0, min=-13020.0, step=100.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce79d8b214e84aa1a67add32f100a5be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=2760.0, description='ventral-dorsal:', max=2760.0, min=-25860.0, step=100.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "257b000268e741e18be80e333b338903",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Region:', index=24, options=('L2', 'HVC', 'OB', 'E', 'lMAN', 'mMAN', 'area X', 'Bas', 'R…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "390eb782e32b40f3a295792c44947cef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Generate graph', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widget_controllers_2d(canary_atlas, ['Nuclei'], brain_masked_image='T2_rare_3d', region_alpha = 0.5)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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