{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CandidateInfoTuple(isNodule_bool=True, diameter_mm=32.27003025, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.287966244644280690737019247886', center_xyz=(67.61451718, 85.02525992, -109.8084416))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from p2ch12.dsets import getCandidateInfoList, getCt, LunaDataset\n",
    "from util.util import xyz2irc\n",
    "\n",
    "\n",
    "candidateInfo_list = getCandidateInfoList(requireOnDisk_bool=False)\n",
    "candidateInfo_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-01-11 19:50:10,261 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CCFF70CC0>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 CandidateInfoTuple(isNodule_bool=True, diameter_mm=32.27003025, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.287966244644280690737019247886', center_xyz=(67.61451718, 85.02525992, -109.8084416))\n",
      "1 CandidateInfoTuple(isNodule_bool=True, diameter_mm=30.61040636, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.112740418331256326754121315800', center_xyz=(47.90350511, 37.60442008, -99.93417567))\n",
      "2 CandidateInfoTuple(isNodule_bool=True, diameter_mm=30.61040636, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.112740418331256326754121315800', center_xyz=(44.19, 37.79, -107.01))\n",
      "3 CandidateInfoTuple(isNodule_bool=True, diameter_mm=30.61040636, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.112740418331256326754121315800', center_xyz=(40.69, 32.19, -97.15))\n",
      "4 CandidateInfoTuple(isNodule_bool=True, diameter_mm=27.44242293, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.943403138251347598519939390311', center_xyz=(-45.29440163, 74.86925386, -97.52812481))\n",
      "5 CandidateInfoTuple(isNodule_bool=True, diameter_mm=27.07544345, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.481278873893653517789960724156', center_xyz=(-102.571208, -5.186558766, -205.1033412))\n",
      "6 CandidateInfoTuple(isNodule_bool=True, diameter_mm=26.83708074, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.487268565754493433372433148666', center_xyz=(121.152909372, 12.9136003304, -159.399497186))\n",
      "7 CandidateInfoTuple(isNodule_bool=True, diameter_mm=26.83708074, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.487268565754493433372433148666', center_xyz=(118.8539408, 11.54202797, -165.5042458))\n",
      "8 CandidateInfoTuple(isNodule_bool=True, diameter_mm=25.87269662, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.177086402277715068525592995222', center_xyz=(-66.628286875, 57.151972075, -110.12035075))\n",
      "9 CandidateInfoTuple(isNodule_bool=True, diameter_mm=25.41540526, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.219618492426142913407827034169', center_xyz=(-101.7504204, -95.65460516, -138.4943211))\n"
     ]
    }
   ],
   "source": [
    "from p2ch12.vis import findPositiveSamples, showCandidate\n",
    "positiveSample_list = findPositiveSamples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-01-11 19:50:10,361 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD49BD470>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n",
      "2020-01-11 19:50:10,440 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD1BB1C18>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n",
      "2020-01-11 19:50:10,521 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD49BD320>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n",
      "2020-01-11 19:50:10,603 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD49BD518>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n",
      "2020-01-11 19:50:10,682 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD49BD5C0>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n",
      "2020-01-11 19:50:10,762 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD49BD5F8>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n",
      "2020-01-11 19:50:10,843 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD49BDF60>: 551065 training samples, 549714 neg, 1351 pos, unbalanced ratio\n"
     ]
    }
   ],
   "source": [
    "augmentation_dict = {}\n",
    "augmentation_list = [\n",
    "    ('None', {}),\n",
    "    ('flip', {'flip': True}),\n",
    "    ('offset', {'offset': 0.1}),\n",
    "    ('scale', {'scale': 0.2}),\n",
    "    ('rotate', {'rotate': True}),\n",
    "    ('noise', {'noise': 25.0}),    \n",
    "]\n",
    "ds_list = [\n",
    "    LunaDataset(sortby_str='label_and_size', augmentation_dict=augmentation_dict) \n",
    "    for title_str, augmentation_dict in augmentation_list\n",
    "]\n",
    "\n",
    "all_dict = {}\n",
    "for title_str, augmentation_dict in augmentation_list:\n",
    "    all_dict.update(augmentation_dict)\n",
    "all_ds = LunaDataset(sortby_str='label_and_size', augmentation_dict=all_dict)\n",
    "\n",
    "augmentation_list.extend([('All', augmentation_dict)] * 3)\n",
    "ds_list.extend([all_ds] * 3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 32, 48, 48]) (tensor([0, 1]), '1.3.6.1.4.1.14519.5.2.1.6279.6001.173106154739244262091404659845', tensor([110, 355, 189]))\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2160 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_ndx = 100\n",
    "sample_ndx = 154\n",
    "sample_ndx = 155\n",
    "\n",
    "\n",
    "sample_tup = all_ds[sample_ndx]\n",
    "print(sample_tup[0].shape, sample_tup[1:])\n",
    "\n",
    "fig = plt.figure(figsize=(30, 30))\n",
    "\n",
    "clim=(-1000.0, 300)\n",
    "\n",
    "for i, ((title_str, _), ds) in enumerate(zip(augmentation_list, ds_list)):\n",
    "    sample_tup = ds[sample_ndx]\n",
    "    subplot = fig.add_subplot(3, 3, i+1)\n",
    "    subplot.set_title(title_str, fontsize=30)\n",
    "    for label in (subplot.get_xticklabels() + subplot.get_yticklabels()):\n",
    "        label.set_fontsize(20)\n",
    "    plt.imshow(sample_tup[0][0][16], clim=clim, cmap='gray')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-01-11 19:50:12,409 INFO     pid:9592 p2ch12.dsets:289:__init__ <p2ch12.dsets.LunaDataset object at 0x0000020CD90EDAC8>: 911 training samples, 904 neg, 7 pos, unbalanced ratio\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Tensor' object has no attribute 'index'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-9ed54f9450c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mseries_uid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpositiveSample_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mshowCandidate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_uid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\linux-home\\edit\\book\\code\\p2ch12\\vis.py\u001b[0m in \u001b[0;36mshowCandidate\u001b[1;34m(series_uid, batch_ndx, **kwargs)\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[0msubplot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m     \u001b[0msubplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'index {}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcenter_irc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_xticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msubplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_yticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m         \u001b[0mlabel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_fontsize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'index'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_uid = positiveSample_list[1][2]\n",
    "showCandidate(series_uid)"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}