{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",

      "  return f(*args, **kwds)\n",

      "/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",

      "  return f(*args, **kwds)\n"

     ]

    }

   ],

   "source": [

    "from __future__ import absolute_import\n",

    "from __future__ import division\n",

    "from __future__ import print_function\n",

    "\n",

    "import glob, pylab, pandas as pd\n",

    "import pydicom, numpy as np\n",

    "from os import listdir\n",

    "from os.path import isfile, join\n",

    "import matplotlib.pylab as plt\n",

    "import os\n",

    "import seaborn as sns\n",

    "import warnings\n",

    "warnings.filterwarnings(action='once')\n",

    "import pickle\n",

    "from tqdm import tqdm, tqdm_notebook\n",

    "%load_ext autoreload\n",

    "%autoreload 2\n",

    "%matplotlib notebook\n",

    "from helper import *\n",

    "import time\n",

    "from IPython.core.interactiveshell import InteractiveShell\n",

    "InteractiveShell.ast_node_interactivity = \"all\"\n",

    "from defenitions import *"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 65,

   "metadata": {},

   "outputs": [],

   "source": [

    "train_base_df = pd.read_csv(train_images_dir)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 67,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "4045572"

      ]

     },

     "execution_count": 67,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "train_base_df.shape[0]"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "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",

       "        vertical-align: top;\n",

       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        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>ID</th>\n",

       "      <th>Label</th>\n",

       "      <th>Sub_type</th>\n",

       "      <th>PatientID</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>ID_28fbab7eb_epidural</td>\n",

       "      <td>0.5</td>\n",

       "      <td>epidural</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>ID_28fbab7eb_intraparenchymal</td>\n",

       "      <td>0.5</td>\n",

       "      <td>intraparenchymal</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>ID_28fbab7eb_intraventricular</td>\n",

       "      <td>0.5</td>\n",

       "      <td>intraventricular</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>ID_28fbab7eb_subarachnoid</td>\n",

       "      <td>0.5</td>\n",

       "      <td>subarachnoid</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>ID_28fbab7eb_subdural</td>\n",

       "      <td>0.5</td>\n",

       "      <td>subdural</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                              ID  Label          Sub_type  PatientID\n",

       "0          ID_28fbab7eb_epidural    0.5          epidural  28fbab7eb\n",

       "1  ID_28fbab7eb_intraparenchymal    0.5  intraparenchymal  28fbab7eb\n",

       "2  ID_28fbab7eb_intraventricular    0.5  intraventricular  28fbab7eb\n",

       "3      ID_28fbab7eb_subarachnoid    0.5      subarachnoid  28fbab7eb\n",

       "4          ID_28fbab7eb_subdural    0.5          subdural  28fbab7eb"

      ]

     },

     "execution_count": 4,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "train_base_df['Sub_type'] = train_base_df['ID'].str.split(\"_\", n = 3, expand = True)[2]\n",

    "train_base_df['PatientID'] = train_base_df['ID'].str.split(\"_\", n = 3, expand = True)[1]\n",

    "train_base_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 26,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "array(['epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid',\n",

       "       'subdural', 'any'], dtype=object)"

      ]

     },

     "execution_count": 26,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "sub_types=train_base_df.Sub_type.unique()\n",

    "sub_types"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 27,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "application/vnd.jupyter.widget-view+json": {

       "model_id": "ae4afd5b0d824567b34dd59b1b81e430",

       "version_major": 2,

       "version_minor": 0

      },

      "text/plain": [

       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "\n"

     ]

    }

   ],

   "source": [

    "dfs =[]\n",

    "for sub_type in tqdm_notebook(sub_types):\n",

    "    df = train_base_df[train_base_df.Sub_type==sub_type][['PatientID','Label']].copy()\n",

    "    df=df.rename(columns={\"Label\": sub_type}).reset_index(drop=True)\n",

    "    dfs.append(df)\n",

    "train_df=dfs[0]\n",

    "for df in tqdm_notebook(dfs[1:]):\n",

    "    train_df=test_df.merge(df,on='PatientID')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "application/vnd.jupyter.widget-view+json": {

       "model_id": "5fe21e059b0f4fff9decf2a356119816",

       "version_major": 2,

       "version_minor": 0

      },

      "text/plain": [

       "HBox(children=(IntProgress(value=0, max=78545), HTML(value='')))"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "\n"

     ]

    }

   ],

   "source": [

    "PID = np.zeros(train_df.shape[0],dtype=object)\n",

    "StudyI = np.zeros(train_df.shape[0],dtype=object)\n",

    "SeriesI = np.zeros(train_df.shape[0],dtype=object)\n",

    "WindowCenter = np.zeros(train_df.shape[0],dtype=object)\n",

    "WindowWidth = np.zeros(train_df.shape[0],dtype=object)\n",

    "ImagePositionX = np.zeros(train_df.shape[0],dtype=np.float)\n",

    "ImagePositionY = np.zeros(train_df.shape[0],dtype=np.float)\n",

    "ImagePositionZ = np.zeros(train_df.shape[0],dtype=np.float)\n",

    "\n",

    "for i,row in tqdm_notebook(train_df.iterrows(),total=train_df.shape[0]):\n",

    "    ds = pydicom.dcmread(train_images_dir + 'ID_{}.dcm'.format(row['PatientID']))\n",

    "    SeriesI[i]=ds.SeriesInstanceUID\n",

    "    PID[i]=ds.PatientID\n",

    "    StudyI[i]=ds.StudyInstanceUID \n",

    "    WindowCenter[i]=ds.WindowCenter\n",

    "    WindowWidth[i]=ds.WindowWidth\n",

    "    ImagePositionX[i]=float(ds.ImagePositionPatient[0])\n",

    "    ImagePositionY[i]=float(ds.ImagePositionPatient[1])\n",

    "    ImagePositionZ[i]=float(ds.ImagePositionPatient[2])\n",

    "train_df['SeriesI']=SeriesI\n",

    "train_df['PID']=PID\n",

    "train_df['StudyI']=StudyI\n",

    "train_df['WindowCenter']=WindowCenter\n",

    "train_df['WindowWidth']=WindowWidth\n",

    "train_df['ImagePositionZ']=ImagePositionZ\n",

    "train_df['ImagePositionX']=ImagePositionX\n",

    "train_df['ImagePositionY']=ImagePositionY\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "train_df.to_csv(data_dir+'train.csv',index=False)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

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       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

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       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        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>ID</th>\n",

       "      <th>Label</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>ID_28fbab7eb_epidural</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>ID_28fbab7eb_intraparenchymal</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>ID_28fbab7eb_intraventricular</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>ID_28fbab7eb_subarachnoid</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>ID_28fbab7eb_subdural</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                              ID  Label\n",

       "0          ID_28fbab7eb_epidural    0.5\n",

       "1  ID_28fbab7eb_intraparenchymal    0.5\n",

       "2  ID_28fbab7eb_intraventricular    0.5\n",

       "3      ID_28fbab7eb_subarachnoid    0.5\n",

       "4          ID_28fbab7eb_subdural    0.5"

      ]

     },

     "execution_count": 3,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "sample_submission=pd.read_csv(data_dir+'stage_1_sample_submission.csv')\n",

    "sample_submission.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

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       "\n",

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       "    }\n",

       "\n",

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       "    }\n",

       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>ID</th>\n",

       "      <th>Label</th>\n",

       "      <th>Sub_type</th>\n",

       "      <th>PatientID</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>ID_28fbab7eb_epidural</td>\n",

       "      <td>0.5</td>\n",

       "      <td>epidural</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>ID_28fbab7eb_intraparenchymal</td>\n",

       "      <td>0.5</td>\n",

       "      <td>intraparenchymal</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>ID_28fbab7eb_intraventricular</td>\n",

       "      <td>0.5</td>\n",

       "      <td>intraventricular</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>ID_28fbab7eb_subarachnoid</td>\n",

       "      <td>0.5</td>\n",

       "      <td>subarachnoid</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>ID_28fbab7eb_subdural</td>\n",

       "      <td>0.5</td>\n",

       "      <td>subdural</td>\n",

       "      <td>28fbab7eb</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                              ID  Label          Sub_type  PatientID\n",

       "0          ID_28fbab7eb_epidural    0.5          epidural  28fbab7eb\n",

       "1  ID_28fbab7eb_intraparenchymal    0.5  intraparenchymal  28fbab7eb\n",

       "2  ID_28fbab7eb_intraventricular    0.5  intraventricular  28fbab7eb\n",

       "3      ID_28fbab7eb_subarachnoid    0.5      subarachnoid  28fbab7eb\n",

       "4          ID_28fbab7eb_subdural    0.5          subdural  28fbab7eb"

      ]

     },

     "execution_count": 4,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "test_base_df=sample_submission.copy()\n",

    "test_base_df['Sub_type'] = test_base_df['ID'].str.split(\"_\", n = 3, expand = True)[2]\n",

    "test_base_df['PatientID'] = test_base_df['ID'].str.split(\"_\", n = 3, expand = True)[1]\n",

    "test_base_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 234,

   "metadata": {},

   "outputs": [],

   "source": [

    "test_ids=test_df.PatientID.unique()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 26,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "array(['epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid',\n",

       "       'subdural', 'any'], dtype=object)"

      ]

     },

     "execution_count": 26,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "sub_types=test_base_df.Sub_type.unique()\n",

    "sub_types"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "application/vnd.jupyter.widget-view+json": {

       "model_id": "f153620a2b61441bb7cb7f1acf392844",

       "version_major": 2,

       "version_minor": 0

      },

      "text/plain": [

       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "\n"

     ]

    },

    {

     "data": {

      "application/vnd.jupyter.widget-view+json": {

       "model_id": "189e8ac701514c2c963161608d43a459",

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      },

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       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"

      ]

     },

     "metadata": {},

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    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "\n"

     ]

    }

   ],

   "source": [

    "dfs =[]\n",

    "for sub_type in tqdm_notebook(sub_types):\n",

    "    df = test_base_df[test_base_df.Sub_type==sub_type][['PatientID','Label']].copy()\n",

    "    df=df.rename(columns={\"Label\": sub_type}).reset_index(drop=True)\n",

    "    dfs.append(df)\n",

    "test_df=dfs[0]\n",

    "for df in tqdm_notebook(dfs[1:]):\n",

    "    test_df=test_df.merge(df,on='PatientID')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 8,

   "metadata": {},

   "outputs": [

    {

     "data": {

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       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>PatientID</th>\n",

       "      <th>epidural</th>\n",

       "      <th>intraparenchymal</th>\n",

       "      <th>intraventricular</th>\n",

       "      <th>subarachnoid</th>\n",

       "      <th>subdural</th>\n",

       "      <th>any</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

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       "      <th>0</th>\n",

       "      <td>28fbab7eb</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>877923b8b</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

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       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

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       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>a591477cb</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>42217c898</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>a130c4d2f</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "   PatientID  epidural  intraparenchymal  intraventricular  subarachnoid  \\\n",

       "0  28fbab7eb       0.5               0.5               0.5           0.5   \n",

       "1  877923b8b       0.5               0.5               0.5           0.5   \n",

       "2  a591477cb       0.5               0.5               0.5           0.5   \n",

       "3  42217c898       0.5               0.5               0.5           0.5   \n",

       "4  a130c4d2f       0.5               0.5               0.5           0.5   \n",

       "\n",

       "   subdural  any  \n",

       "0       0.5  0.5  \n",

       "1       0.5  0.5  \n",

       "2       0.5  0.5  \n",

       "3       0.5  0.5  \n",

       "4       0.5  0.5  "

      ]

     },

     "execution_count": 8,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "test_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "application/vnd.jupyter.widget-view+json": {

       "model_id": "5fe21e059b0f4fff9decf2a356119816",

       "version_major": 2,

       "version_minor": 0

      },

      "text/plain": [

       "HBox(children=(IntProgress(value=0, max=78545), HTML(value='')))"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    },

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "\n"

     ]

    }

   ],

   "source": [

    "PID = np.zeros(test_df.shape[0],dtype=object)\n",

    "StudyI = np.zeros(test_df.shape[0],dtype=object)\n",

    "SeriesI = np.zeros(test_df.shape[0],dtype=object)\n",

    "WindowCenter = np.zeros(test_df.shape[0],dtype=object)\n",

    "WindowWidth = np.zeros(test_df.shape[0],dtype=object)\n",

    "ImagePositionX = np.zeros(test_df.shape[0],dtype=np.float)\n",

    "ImagePositionY = np.zeros(test_df.shape[0],dtype=np.float)\n",

    "ImagePositionZ = np.zeros(test_df.shape[0],dtype=np.float)\n",

    "\n",

    "for i,row in tqdm_notebook(test_df.iterrows(),total=test_df.shape[0]):\n",

    "    ds = pydicom.dcmread(test_images_dir + 'ID_{}.dcm'.format(row['PatientID']))\n",

    "    SeriesI[i]=ds.SeriesInstanceUID\n",

    "    PID[i]=ds.PatientID\n",

    "    StudyI[i]=ds.StudyInstanceUID \n",

    "    WindowCenter[i]=ds.WindowCenter\n",

    "    WindowWidth[i]=ds.WindowWidth\n",

    "    ImagePositionX[i]=float(ds.ImagePositionPatient[0])\n",

    "    ImagePositionY[i]=float(ds.ImagePositionPatient[1])\n",

    "    ImagePositionZ[i]=float(ds.ImagePositionPatient[2])\n",

    "test_df['SeriesI']=SeriesI\n",

    "test_df['PID']=PID\n",

    "test_df['StudyI']=StudyI\n",

    "test_df['WindowCenter']=WindowCenter\n",

    "test_df['WindowWidth']=WindowWidth\n",

    "test_df['ImagePositionZ']=ImagePositionZ\n",

    "test_df['ImagePositionX']=ImagePositionX\n",

    "test_df['ImagePositionY']=ImagePositionY\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 23,

   "metadata": {},

   "outputs": [],

   "source": [

    "test_df.to_csv(data_dir+'test.csv',index=False)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": []

  }

 ],

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