scatter_vendor-dataset.ipynb 176 KB
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{
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import umap\n",
    "from bokeh.plotting import Figure\n",
    "from bokeh.io import output_notebook, show, output_file, curdoc, export_svgs, export_png\n",
    "from bokeh.layouts import row, column\n",
    "from bokeh.resources import CDN\n",
    "from bokeh.models.widgets import Slider, TextInput\n",
    "from bokeh.models.callbacks import CustomJS\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.palettes import Spectral6, Inferno, Magma, Viridis, magma, viridis\n",
    "from bokeh.transform import factor_cmap, factor_mark\n",
    "from bokeh.embed import file_html\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "First 4 elements in the data folder val : ['HN-CHUM-001.npy', 'HN-CHUM-002.npy', 'HN-CHUM-003.npy', 'HN-CHUM-004.npy']\nFirst 4 elements in the data folder BZ : ['HN100.npy', 'HN101.npy', 'HN103.npy', 'HN105.npy']\n"
    }
   ],
   "source": [
    "# Explicit current directory\n",
    "data_valieres_path = '../../data/HN_val/processed/bbox/bbox_64'.split('/')\n",
    "data_bz_path = '../../data/HN_BZ/processed/bbox/bbox_64'.split('/')\n",
    "info_df_path = '../../data/HN_val/processed/path_original_data.csv'.split('/')  # path to 'path_original_data.csv'\n",
    "# set variables\n",
    "DATAPATH = os.path.join(*data_valieres_path)\n",
    "DATAPATH_BZ = os.path.join(*data_bz_path)\n",
    "# Lookup folder data and sort\n",
    "file_list_val = os.listdir(DATAPATH)\n",
    "file_list_bz = os.listdir(DATAPATH_BZ)\n",
    "file_list_val.sort()\n",
    "file_list_bz.sort()\n",
    "print(f\"First 4 elements in the data folder val : {file_list_val[:4]}\")\n",
    "print(f\"First 4 elements in the data folder BZ : {file_list_bz[:4]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect an element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Example shape: (2, 64, 64, 64)\n"
    }
   ],
   "source": [
    "example_gross = np.load(os.path.join(DATAPATH, file_list_val[0]))\n",
    "example_mod_a = example_gross[0,:,:,:]\n",
    "example_mod_b = example_gross[1,:,:,:]\n",
    "print(f\"Example shape: {example_gross.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize slice from example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "\n\n\n\n\n\n  <div class=\"bk-root\" id=\"0eed9097-1f01-4b05-841e-d6856fc23f0d\" data-root-id=\"1080\"></div>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function embed_document(root) {\n    \n  var docs_json = 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Application\",\"version\":\"1.4.0\"}};\n  var render_items = [{\"docid\":\"524298e3-40ad-4ca5-9776-1b29b58a0a82\",\"roots\":{\"1080\":\"0eed9097-1f01-4b05-841e-d6856fc23f0d\"}}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    var attempts = 0;\n    var timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1080"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = Figure(plot_width = 300, plot_height = 300)\n",
    "p2 = Figure(plot_width = 300, plot_height = 300)\n",
    "slice_id = 20  # select slice index on first dimension\n",
    "p1.image(image = [example_mod_a[slice_id,:,:]], x = 0, y = 0, dw = 64, dh = 64)\n",
    "p2.image(image = [example_mod_b[slice_id,:,:]], x = 0, y = 0, dw = 64, dh = 64)\n",
    "show(row(p1, p2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Dataframe shape: (894, 12)\nDataframe attributes: Index(['Subject ID', 'Study UID', 'Study Description', 'Study Date',\n       'Series ID', 'Series Description', 'Number of images',\n       'File Size (Bytes)', 'Collection Name', 'Modality', 'Manufacturer',\n       'dicom_folder'],\n      dtype='object')\n"
    }
   ],
   "source": [
    "# read file from current directory\n",
    "path_orig_df = pd.read_csv(os.path.join(*info_df_path))\n",
    "print(f\"Dataframe shape: {path_orig_df.shape}\")\n",
    "print(f\"Dataframe attributes: {path_orig_df.columns}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(298, 296)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# diff number of subj in vallieres\n",
    "len(np.unique(path_orig_df[\"Subject ID\"])), len(file_list_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series ID</th>\n      <th>Series Description</th>\n      <th>Number of images</th>\n      <th>File Size (Bytes)</th>\n      <th>Collection Name</th>\n      <th>Modality</th>\n      <th>Manufacturer</th>\n      <th>dicom_folder</th>\n      <th>dataset_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>HN-CHUM-001</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.301393959337...</td>\n      <td>PANC. avec C.A. SPHÈRE ORL ( + tête et cou ) -TP</td>\n      <td>08-27-1885</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.280813723089...</td>\n      <td>TETE_COU_AC_2D</td>\n      <td>91</td>\n      <td>3570574</td>\n      <td>Head-Neck-PET-CT</td>\n      <td>PT</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>HN-CHUM-001/08-27-1885-PANC. avec C.A. SPHRE O...</td>\n      <td>CHUM</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>HN-CHUM-001</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.301393959337...</td>\n      <td>PANC. avec C.A. SPHÈRE ORL ( + tête et cou ) -TP</td>\n      <td>08-27-1885</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.690941750701...</td>\n      <td>RTstruct_CTsim-&gt;CT(PET-CT)</td>\n      <td>1</td>\n      <td>26765338</td>\n      <td>Head-Neck-PET-CT</td>\n      <td>RTSTRUCT</td>\n      <td>MIM Software Inc.</td>\n      <td>HN-CHUM-001/08-27-1885-PANC. avec C.A. SPHRE O...</td>\n      <td>CHUM</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HN-CHUM-001</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.301393959337...</td>\n      <td>PANC. avec C.A. SPHÈRE ORL ( + tête et cou ) -TP</td>\n      <td>08-27-1885</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.182039413725...</td>\n      <td>Standard/Full</td>\n      <td>90</td>\n      <td>47562550</td>\n      <td>Head-Neck-PET-CT</td>\n      <td>CT</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>HN-CHUM-001/08-27-1885-PANC. avec C.A. SPHRE O...</td>\n      <td>CHUM</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>HN-CHUM-002</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.278462739048...</td>\n      <td>PANC. avec C.A.  POUMON / PLÈVRE -TP</td>\n      <td>08-27-1885</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.172804297965...</td>\n      <td>Standard/Full</td>\n      <td>90</td>\n      <td>47574102</td>\n      <td>Head-Neck-PET-CT</td>\n      <td>CT</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>HN-CHUM-002/08-27-1885-PANC. avec C.A.  POUMON...</td>\n      <td>CHUM</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>HN-CHUM-002</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.278462739048...</td>\n      <td>PANC. avec C.A.  POUMON / PLÈVRE -TP</td>\n      <td>08-27-1885</td>\n      <td>1.3.6.1.4.1.14519.5.2.1.5168.2407.154839094381...</td>\n      <td>TETE_COU_AC_2D</td>\n      <td>91</td>\n      <td>3581908</td>\n      <td>Head-Neck-PET-CT</td>\n      <td>PT</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>HN-CHUM-002/08-27-1885-PANC. avec C.A.  POUMON...</td>\n      <td>CHUM</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "    Subject ID                                          Study UID  \\\n0  HN-CHUM-001  1.3.6.1.4.1.14519.5.2.1.5168.2407.301393959337...   \n1  HN-CHUM-001  1.3.6.1.4.1.14519.5.2.1.5168.2407.301393959337...   \n2  HN-CHUM-001  1.3.6.1.4.1.14519.5.2.1.5168.2407.301393959337...   \n3  HN-CHUM-002  1.3.6.1.4.1.14519.5.2.1.5168.2407.278462739048...   \n4  HN-CHUM-002  1.3.6.1.4.1.14519.5.2.1.5168.2407.278462739048...   \n\n                                  Study Description  Study Date  \\\n0  PANC. avec C.A. SPHÈRE ORL ( + tête et cou ) -TP  08-27-1885   \n1  PANC. avec C.A. SPHÈRE ORL ( + tête et cou ) -TP  08-27-1885   \n2  PANC. avec C.A. SPHÈRE ORL ( + tête et cou ) -TP  08-27-1885   \n3              PANC. avec C.A.  POUMON / PLÈVRE -TP  08-27-1885   \n4              PANC. avec C.A.  POUMON / PLÈVRE -TP  08-27-1885   \n\n                                           Series ID  \\\n0  1.3.6.1.4.1.14519.5.2.1.5168.2407.280813723089...   \n1  1.3.6.1.4.1.14519.5.2.1.5168.2407.690941750701...   \n2  1.3.6.1.4.1.14519.5.2.1.5168.2407.182039413725...   \n3  1.3.6.1.4.1.14519.5.2.1.5168.2407.172804297965...   \n4  1.3.6.1.4.1.14519.5.2.1.5168.2407.154839094381...   \n\n           Series Description  Number of images  File Size (Bytes)  \\\n0              TETE_COU_AC_2D                91            3570574   \n1  RTstruct_CTsim->CT(PET-CT)                 1           26765338   \n2               Standard/Full                90           47562550   \n3               Standard/Full                90           47574102   \n4              TETE_COU_AC_2D                91            3581908   \n\n    Collection Name  Modality        Manufacturer  \\\n0  Head-Neck-PET-CT        PT  GE MEDICAL SYSTEMS   \n1  Head-Neck-PET-CT  RTSTRUCT   MIM Software Inc.   \n2  Head-Neck-PET-CT        CT  GE MEDICAL SYSTEMS   \n3  Head-Neck-PET-CT        CT  GE MEDICAL SYSTEMS   \n4  Head-Neck-PET-CT        PT  GE MEDICAL SYSTEMS   \n\n                                        dicom_folder dataset_id  \n0  HN-CHUM-001/08-27-1885-PANC. avec C.A. SPHRE O...       CHUM  \n1  HN-CHUM-001/08-27-1885-PANC. avec C.A. SPHRE O...       CHUM  \n2  HN-CHUM-001/08-27-1885-PANC. avec C.A. SPHRE O...       CHUM  \n3  HN-CHUM-002/08-27-1885-PANC. avec C.A.  POUMON...       CHUM  \n4  HN-CHUM-002/08-27-1885-PANC. avec C.A.  POUMON...       CHUM  "
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add column for dataset id\n",
    "path_orig_df[\"dataset_id\"] = [x.split(\"-\")[1] for x in path_orig_df[\"Subject ID\"]]\n",
    "path_orig_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Subject ID : unique elements = 298\nStudy UID : unique elements = 300\nStudy Description : unique elements = 93\nStudy Date : unique elements = 1\nSeries ID : unique elements = 894\nSeries Description : unique elements = 21\nNumber of images : unique elements = 73\nFile Size (Bytes) : unique elements = 861\nCollection Name : unique elements = 1\nModality : unique elements = 3\nManufacturer : unique elements = 4\ndicom_folder : unique elements = 894\ndataset_id : unique elements = 4\n"
    }
   ],
   "source": [
    "# Count unique elements for each col\n",
    "for col in path_orig_df.columns:\n",
    "    print(f\"{col} : unique elements = {len(np.unique(path_orig_df[col]))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Manufacturers:\n['GE MEDICAL SYSTEMS' 'MIM Software Inc.' 'Philips'\n 'Philips Medical Systems']\n"
    }
   ],
   "source": [
    "manufacturers_u = np.unique( path_orig_df['Manufacturer'])\n",
    "print(f\"Manufacturers:\\n{ manufacturers_u }\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_lbl(path, file_list, path_orig_df, modality):\n",
    "    \"\"\"\n",
    "    Extract data and manufacturer label from data folder and info file\n",
    "    IN:\n",
    "    path: data folder\n",
    "    file_list: list of files with extension\n",
    "    path_orig_df: our dataset with file info\n",
    "    modality: either PT (for PET) or CT\n",
    "    OUT:\n",
    "    data: list of 3d-numpy arrays \n",
    "    lbl: list of strings\n",
    "    \"\"\"\n",
    "    if modality == \"CT\":\n",
    "        mod_scan = 0\n",
    "    elif modality == \"PT\":\n",
    "        mod_scan = 1\n",
    "    else:\n",
    "        print(\"Please enter a valid modality parameter\")\n",
    "    #\n",
    "    df_sel_mod = path_orig_df.loc[path_orig_df[\"Modality\"]==modality ,:]  \n",
    "    data = []\n",
    "    lbl = []\n",
    "    dataset_id = []\n",
    "    for elem in file_list:\n",
    "        # subj id removing extension\n",
    "        subj_id = elem.split(\".\")[0]\n",
    "        dataset_tmp = subj_id.split(\"-\")[1]\n",
    "        # load patient data\n",
    "        data_tmp = np.load(os.path.join(path, elem))[mod_scan,:,:,:]\n",
    "        # save manufacturer\n",
    "        vendor = df_sel_mod.loc[df_sel_mod[\"Subject ID\"] == subj_id, \"Manufacturer\"].values[0]\n",
    "        # save to lists\n",
    "        data.append(data_tmp)\n",
    "        lbl.append(vendor)\n",
    "        dataset_id.append(dataset_tmp)\n",
    "    return (data, lbl, dataset_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ct, vendor_ct, datasets_ct = get_data_lbl(DATAPATH, file_list_val, path_orig_df, \"CT\")\n",
    "data_pt, vendor_pt, datasets_pt = get_data_lbl(DATAPATH, file_list_val, path_orig_df, \"PT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flatten 3d arrays for subsequent analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flatten array row-major\n",
    "data_ct_f = [x.flatten('C') for x in data_ct]\n",
    "data_pt_f = [x.flatten('C') for x in data_pt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Flattened data shape: (262144,)\n"
    }
   ],
   "source": [
    "print(f\"Flattened data shape: {data_ct_f[0].shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DIM RED PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "umap_euclid = umap.UMAP( metric=\"euclidean\",\n",
    "                            n_components = 2,\n",
    "                            n_neighbors = 20,\n",
    "                            min_dist = .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "CPU times: user 10.6 s, sys: 1.94 s, total: 12.5 s\nWall time: 11.4 s\n"
    }
   ],
   "source": [
    "%time u_ct = umap_euclid.fit_transform(data_ct_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "CPU times: user 6.82 s, sys: 1.06 s, total: 7.88 s\nWall time: 5.91 s\n"
    }
   ],
   "source": [
    "%time u_pt = umap_euclid.fit_transform(data_pt_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter_2d_2dim(plot_title, x_red, vendors_id , ds_id, color_palette, markers, point_size=10, annot_set = \"standard\"):\n",
    "    #\n",
    "    source = pd.DataFrame(data=dict(x = x_red[:,0],\n",
    "                                    y = x_red[:,1],\n",
    "                                    labels=vendors_id,\n",
    "                                    datasets=ds_id ))\n",
    "\n",
    "    p = Figure(title = plot_title, plot_width = 800, plot_height = 600)\n",
    "    # plot as different set of data\n",
    "    comb = list(zip(source[\"labels\"], source[\"datasets\"]))\n",
    "    comb = [f\"{x[0]}-{x[1]}\" for x in comb]\n",
    "    source[\"comb_lbl\"] = comb\n",
    "    unique_comb = np.unique(source[\"comb_lbl\"])\n",
    "    unique_lbl = np.unique(source[\"labels\"])\n",
    "    unique_ds = np.unique(source[\"datasets\"])\n",
    "    # adapt color palette and markers set length\n",
    "\n",
    "\n",
    "    if annot_set==\"standard\":\n",
    "        colors = {label: color for label, color in zip(unique_lbl, color_palette)}\n",
    "        markers = {dataset: marker for dataset, marker in zip(unique_ds, markers)}\n",
    "        for lbl in unique_lbl:\n",
    "            for ds in unique_ds:\n",
    "                df = source.loc[(source[\"labels\"]==lbl) & (source[\"datasets\"]==ds)]\n",
    "                p.scatter(x = 'x', y= 'y', legend_group = 'comb_lbl',\n",
    "                          fill_alpha = 0.5,\n",
    "                          size = point_size,\n",
    "                          color = colors[lbl],\n",
    "                          marker = markers[ds],\n",
    "                          source = df)\n",
    "    elif annot_set==\"reverse\":\n",
    "        colors = {dataset: color for dataset, color in zip(unique_ds, color_palette)}\n",
    "        markers = {label: marker for label, marker in zip(unique_lbl, markers)}\n",
    "        for lbl in unique_lbl:\n",
    "            for ds in unique_ds:\n",
    "                df = source.loc[(source[\"labels\"]==lbl) & (source[\"datasets\"]==ds)]\n",
    "                p.scatter(x = 'x', y= 'y', legend_group = 'comb_lbl',\n",
    "                          fill_alpha = 0.5,\n",
    "                          size = point_size,\n",
    "                          color = colors[ds],\n",
    "                          marker = markers[lbl],\n",
    "                          source = df)\n",
    "    else:\n",
    "        print(\"ann_set parameter is invalid\")\n",
    "            \n",
    "            \n",
    "    ### set plot style params\n",
    "    p.xgrid.grid_line_color = None  #  turn off grid\n",
    "    p.ygrid.grid_line_color = None\n",
    "    p.xaxis.major_label_text_font_size = '0pt'  # turn off x-axis tick labels\n",
    "    p.yaxis.major_label_text_font_size = '0pt'  # turn off y-axis tick labels\n",
    "    p.xaxis.major_tick_line_color = None  # turn off x-axis major ticks\n",
    "    p.xaxis.minor_tick_line_color = None  # turn off x-axis minor ticks\n",
    "    p.yaxis.major_tick_line_color = None  # turn off y-axis major ticks\n",
    "    p.yaxis.minor_tick_line_color = None  # turn off y-axis minor ticks\n",
    "    p.legend.label_text_font_size = '6pt'\n",
    "    p.legend.location = (0,0)\n",
    "    p.legend.orientation = \"horizontal\"\n",
    "    p.legend.click_policy='hide'\n",
    "    p.legend.label_text_font_size='6pt'\n",
    "    p.legend.border_line_alpha = 0.8\n",
    "    p.legend.click_policy=\"hide\"\n",
    "    \n",
    "    return p\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create output dir for the plots\n",
    "out_dir = 'out_plot'\n",
    "os.makedirs(out_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example for pet data, annotation for manufacturer and dataset\n",
    "markers = [\"triangle\", \"circle_x\", \"hex\", \"square\", \"asterisk\", \"circle_cross\", \"diamond\"]\n",
    "pt1 = plot_scatter_2d_2dim(\"PET-DR marker:dataset - color:manufacturer\", u_pt, vendor_pt, datasets_pt, Spectral6, markers, 8, annot_set=\"standard\")\n",
    "pt2 = plot_scatter_2d_2dim(\"PET-DR marker:manufacturer - color:dataset\", u_pt, vendor_pt, datasets_pt, Spectral6, markers, 8, annot_set=\"reverse\")\n",
    "layout_pt = row(pt1, pt2)\n",
    "html_pt = file_html(layout_pt, CDN, \"test_PET\")\n",
    "\n",
    "with open(os.path.join(out_dir, \"annotation_test_PET_DR.html\"), \"w+\") as f:\n",
    "    f.write(html_pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example for ct data, annotation for manufacturer and dataset\n",
    "markers = [\"triangle\", \"circle_x\", \"hex\", \"square\", \"asterisk\", \"circle_cross\", \"diamond\"]\n",
    "ct1 = plot_scatter_2d_2dim(\"CT-DR marker:dataset - color:manufacturer\", u_ct, vendor_ct, datasets_ct, Spectral6, markers, 8, annot_set=\"standard\")\n",
    "ct2 = plot_scatter_2d_2dim(\"CT-DR marker:manufacturer - color:dataset\", u_ct, vendor_ct, datasets_ct, Spectral6, markers, 8, annot_set=\"reverse\")\n",
    "layout_ct = row(ct1, ct2)\n",
    "html_ct = file_html(layout_ct, CDN, \"test_CT\")\n",
    "\n",
    "with open(os.path.join(out_dir, \"annotation_test_CT_DR.html\"), \"w+\") as f:\n",
    "    f.write(html_ct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dim red and plot together  \n",
    "To be updated to use multiple levels of annotations..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter_2d(plot_title, x_red, lbl, color_palette, markers, point_size=10):\n",
    "    #\n",
    "    source = pd.DataFrame(data=dict(x = x_red[:,0],\n",
    "                                    y = x_red[:,1],\n",
    "                                    labels=lbl))\n",
    "\n",
    "    p = Figure(title = plot_title, plot_width =600, plot_height = 400)\n",
    "    # plot as different set of data\n",
    "    unique_lbl = np.unique(source[\"labels\"])\n",
    "                           \n",
    "    colors = color_palette[:len(unique_lbl)]\n",
    "    markers = markers[:len(unique_lbl)]\n",
    "    \n",
    "    \n",
    "    for lbl, col, mark in zip(unique_lbl, colors, markers):\n",
    "        df = source.loc[source[\"labels\"]==lbl]\n",
    "        p.scatter(x = 'x', y= 'y', legend_group = 'labels',\n",
    "                  fill_alpha = 0.5,\n",
    "                  size = point_size,\n",
    "                  color = col,\n",
    "                  marker = mark,\n",
    "                  source = df)\n",
    "    ### set plot style params\n",
    "    p.xgrid.grid_line_color = None  #  turn off grid\n",
    "    p.ygrid.grid_line_color = None\n",
    "    p.xaxis.major_label_text_font_size = '0pt'  # turn off x-axis tick labels\n",
    "    p.yaxis.major_label_text_font_size = '0pt'  # turn off y-axis tick labels\n",
    "    p.xaxis.major_tick_line_color = None  # turn off x-axis major ticks\n",
    "    p.xaxis.minor_tick_line_color = None  # turn off x-axis minor ticks\n",
    "    p.yaxis.major_tick_line_color = None  # turn off y-axis major ticks\n",
    "    p.yaxis.minor_tick_line_color = None  # turn off y-axis minor ticks\n",
    "    p.legend.label_text_font_size = '6pt'\n",
    "    p.legend.location = (0,0)\n",
    "    p.legend.orientation = \"horizontal\"\n",
    "    p.legend.click_policy='hide'\n",
    "    p.legend.label_text_font_size='6pt'\n",
    "    p.legend.border_line_alpha = 0.8\n",
    "    p.legend.click_policy=\"hide\"\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dr_and_plot(modality, data, lbl1, n_neighbors=20, min_dist=0.2, n_components=2, point_size=10): # plot param\n",
    "    markers = [\"triangle\", \"circle_x\", \"hex\", \"square\", \"asterisk\", \"circle_cross\", \"diamond\"]\n",
    "    flat_data = [x.flatten('C') for x in data]\n",
    "    umap_euclid = umap.UMAP(metric=\"euclidean\",\n",
    "                            n_components = n_components,\n",
    "                            n_neighbors = n_neighbors,\n",
    "                            min_dist = min_dist)\n",
    "    t1 = time.time()\n",
    "    u = umap_euclid.fit_transform(flat_data)\n",
    "    eta = time.time() - t1\n",
    "    \n",
    "    info_df = path_orig_df.loc[path_orig_df[\"Modality\"]==modality,:]\n",
    "    print(f\"datashape {u.shape} df shape:{info_df.shape}\")\n",
    "    p = plot_scatter_2d(modality, u, lbl1, Spectral6, markers, point_size)\n",
    "    print(f\"Time for DR: {eta}s\")\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "datashape (296, 2) df shape:(298, 13)\nTime for DR: 6.118700981140137s\n"
    },
    {
     "data": {
      "text/html": "\n\n\n\n\n\n  <div class=\"bk-root\" id=\"535bf8f6-beb4-40be-a41f-cff5c6d96456\" data-root-id=\"3035\"></div>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function embed_document(root) {\n    \n  var docs_json = 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   "source": [
    "p_ct = dr_and_plot(\"CT\", data_ct, vendor_ct)\n",
    "show(p_ct)"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "datashape (296, 2) df shape:(0, 13)\nTime for DR: 5.477960109710693s\n"
    },
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      "application/vnd.bokehjs_exec.v0+json": ""
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     "output_type": "display_data"
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   ],
   "source": [
    "p_pt = dr_and_plot(\"PET\", data_pt, vendor_pt)\n",
    "show(p_pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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