Code/Image-Processing-and-Quantification/msd_lung_2D_3D_metrics_extraction.ipynb

{
  "cells": [
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
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        }
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import nibabel as nib\n",
        "import numpy as np\n",
        "import skimage\n",
        "import SimpleITK as sitk\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2, pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1697513090957
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [],
      "source": [
        "# Prototype with one subject segmentation first\n",
        "print(os.getcwd())\n",
        "image_path = r'/home/azureuser/cloudfiles/code/Users/data/msd/Task06_Lung/imagesTr'\n",
        "tumor_path = r'/home/azureuser/cloudfiles/code/Users/data/msd/Task06_Lung/labelsTr'\n",
        "\n",
        "# Organ segmentation files obtained from TotalSegmentator v1.5.7\n",
        "# Results are generated from the run_total_segmentator.py script\n",
        "label_organs_path = r'/home/azureuser/cloudfiles/code/Users/data/msd_segmentation/Task06_Lung/labelsTr'\n",
        "# Labels for lung:\n",
        "# 13\tlung_upper_lobe_left\tsuperior lobe of left lung\n",
        "# 14\tlung_lower_lobe_left\tinferior lobe of left lung\n",
        "# 15\tlung_upper_lobe_right\tsuperior lobe of right lung\n",
        "# 16\tlung_middle_lobe_right\tmiddle lobe of right lung\n",
        "# 17\tlung_lower_lobe_right\tinferior lobe of right lung\n",
        "\n",
        "# List all the files in the image path and label path, exclude files that start with ._ \n",
        "image_list = [f for f in os.listdir(image_path) if not f.startswith('._') and  'nii.gz' in f]\n",
        "tumor_label_value = 1\n",
        "number_of_organs = 104 \n",
        "\n",
        "# Sort the list\n",
        "image_list.sort()\n",
        "print(len(image_list))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1697513177223
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved file:  /home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_2D_per_organ.pkl\n"
          ]
        }
      ],
      "source": [
        "image_id_dict ={}\n",
        "for image_name in image_list:\n",
        "    label_organ_file = os.path.join(label_organs_path, image_name)\n",
        "    label_organ = sitk.ReadImage(label_organ_file)\n",
        "    img_emb_list = []\n",
        "\n",
        "    # Get 3D matrix from the image\n",
        "    label_organ_data = sitk.GetArrayFromImage(label_organ)\n",
        "\n",
        "    for i in range(label_organ_data.shape[0]):\n",
        "        label_slice = label_organ_data[i,:,:]\n",
        "        # For label slice, create a dictionary where key is the pixel value and value is the number of pixels different from 0\n",
        "        label_dict = dict(zip(*np.unique(label_slice, return_counts=True)))\n",
        "        label_vector = np.zeros(104, dtype=int)\n",
        "        for key, value in label_dict.items():\n",
        "            if key > 0:\n",
        "                label_vector[key-1] = value\n",
        "\n",
        "        img_emb_list.append(label_vector)\n",
        "\n",
        "    # concatenate the vectors into a matrix\n",
        "    full_emb = np.stack(img_emb_list, axis=0)\n",
        "    image_id_dict[image_name] = full_emb.copy()\n",
        "    \n",
        "output_file = '/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_2D_per_organ.pkl'\n",
        "with open(output_file, \"wb\") as f:\n",
        "    pickle.dump(image_id_dict, f)\n",
        "print(\"Saved file: \", output_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1697513177369
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['lung_001.nii.gz', 'lung_003.nii.gz', 'lung_004.nii.gz', 'lung_005.nii.gz', 'lung_006.nii.gz', 'lung_009.nii.gz', 'lung_010.nii.gz', 'lung_014.nii.gz', 'lung_015.nii.gz', 'lung_016.nii.gz', 'lung_018.nii.gz', 'lung_020.nii.gz', 'lung_022.nii.gz', 'lung_023.nii.gz', 'lung_025.nii.gz', 'lung_026.nii.gz', 'lung_027.nii.gz', 'lung_028.nii.gz', 'lung_029.nii.gz', 'lung_031.nii.gz', 'lung_033.nii.gz', 'lung_034.nii.gz', 'lung_036.nii.gz', 'lung_037.nii.gz', 'lung_038.nii.gz', 'lung_041.nii.gz', 'lung_042.nii.gz', 'lung_043.nii.gz', 'lung_044.nii.gz', 'lung_045.nii.gz', 'lung_046.nii.gz', 'lung_047.nii.gz', 'lung_048.nii.gz', 'lung_049.nii.gz', 'lung_051.nii.gz', 'lung_053.nii.gz', 'lung_054.nii.gz', 'lung_055.nii.gz', 'lung_057.nii.gz', 'lung_058.nii.gz', 'lung_059.nii.gz', 'lung_061.nii.gz', 'lung_062.nii.gz', 'lung_064.nii.gz', 'lung_065.nii.gz', 'lung_066.nii.gz', 'lung_069.nii.gz', 'lung_070.nii.gz', 'lung_071.nii.gz', 'lung_073.nii.gz', 'lung_074.nii.gz', 'lung_075.nii.gz', 'lung_078.nii.gz', 'lung_079.nii.gz', 'lung_080.nii.gz', 'lung_081.nii.gz', 'lung_083.nii.gz', 'lung_084.nii.gz', 'lung_086.nii.gz', 'lung_092.nii.gz', 'lung_093.nii.gz', 'lung_095.nii.gz', 'lung_096.nii.gz'])"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output_file = '/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_2D_per_organ.pkl'\n",
        "# Load pickle file\n",
        "with open(output_file, \"rb\") as f:\n",
        "    data_dict = pickle.load(f)\n",
        "data_dict.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1697513546702
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "import tqdm\n",
        "\n",
        "# Create a dataframe to store the metrics per file and 2D image\n",
        "df2d = pd.DataFrame(columns=['filename', 'slice', 'cancer_stage', 'tumor_area'])\n",
        "df3d = pd.DataFrame(columns=['filename','cancer_stage'])\n",
        "\n",
        "# #iterate through the list of files image_list and label_list\n",
        "# for image_name, label_name in tqdm.tqdm(zip(image_list, label_list)):\n",
        "(stats_list_2d, stats_list_3d) = ([], [])\n",
        "for image_name in image_list: \n",
        "#for image_name in tqdm.tqdm(image_list):\n",
        "\n",
        "    (image_file, tumor_file, organs_file) = (os.path.join(image_path, image_name), os.path.join(tumor_path, image_name), os.path.join(label_organs_path, image_name))\n",
        "    (image, tumor, lung) = (sitk.ReadImage(image_file), sitk.ReadImage(tumor_file), sitk.ReadImage(organs_file))\n",
        "    (image_data, tumor_data, organs_data) = (sitk.GetArrayFromImage(image), sitk.GetArrayFromImage(tumor), sitk.GetArrayFromImage(lung))\n",
        "    spacing = image.GetSpacing()\n",
        "\n",
        "    # For tumo image make zero voxels except for tumor label\n",
        "    tumor_data[tumor_data != tumor_label_value] = 0\n",
        "    tumor_data[tumor_data == tumor_label_value] = 1\n",
        "\n",
        "    tumor = sitk.GetImageFromArray(tumor_data)\n",
        "    cc = sitk.ConnectedComponent(tumor)\n",
        "    stats = sitk.LabelIntensityStatisticsImageFilter()\n",
        "    stats.Execute(cc, tumor)\n",
        "    print(tumor_file, stats.GetNumberOfLabels())\n",
        "    # Get array of connected component numbers\n",
        "    cc_tumor_array = sitk.GetArrayFromImage(cc)\n",
        "\n",
        "    # Creates dictionary where key is tuple consisting of subject ID and label number\n",
        "    #tumor_size_list = []\n",
        "    for l in stats.GetLabels():\n",
        "        # Ignore if number of voxels is less than 6\n",
        "\n",
        "        # Remove spurius or flat objects objects \n",
        "        if stats.GetNumberOfPixels(l) < 3 or stats.GetBoundingBox(l)[-1] == 1:\n",
        "            cc_tumor_array[cc_tumor_array == l] = 0\n",
        "            continue\n",
        "\n",
        "        # Count number of voxels in this connected component\n",
        "        voxel_count = np.sum(cc_tumor_array == l)\n",
        "        #Initialize dictionary containing different metrics\n",
        "        cc_dictionary = {}\n",
        "        cc_dictionary['image_name'] = image_name\n",
        "        cc_dictionary['PhysicalSize_cm3'] = stats.GetPhysicalSize(l) * spacing[0] * spacing[1] * spacing[2] / 1000\n",
        "        cc_dictionary['NumberOfVoxels'] = stats.GetNumberOfPixels(l)\n",
        "        cc_dictionary['Roundness'] = stats.GetRoundness(l)\n",
        "        cc_dictionary['Elongation'] = stats.GetElongation(l)\n",
        "        cc_dictionary['Flatness'] = stats.GetFlatness(l)\n",
        "        cc_dictionary['EquivalentSphericalRadius_cm'] = stats.GetEquivalentSphericalRadius(l)/10\n",
        "        cc_dictionary['EquivalentSphericalPerimeter_cm'] = stats.GetEquivalentSphericalPerimeter(l)/10\n",
        "        cc_dictionary['EquivalentEllipsoidDiameter'] = stats.GetEquivalentEllipsoidDiameter(l)\n",
        "        cc_dictionary['major_axis_length_cm'] = max(stats.GetEquivalentEllipsoidDiameter(l)) / 10\n",
        "        cc_dictionary['centroid'] = stats.GetCentroid(l)\n",
        "        cc_dictionary['bounding_box'] = stats.GetBoundingBox(l)\n",
        "        cc_dictionary['spacing']= spacing\n",
        "\n",
        "        # Get the number of voxels in the tumor that are in the organ\n",
        "        tumor_bbox = stats.GetBoundingBox(l)\n",
        "        # Get voxels only in the tumor bounding box\n",
        "        (x0,y0,z0,x1,y1,z1)= tumor_bbox\n",
        "        # Add the width, height and depth to the x0, y0, z0\n",
        "        (x1,y1,z1) = (x1+x0, y1+y0, z1+z0)\n",
        "        # Coordinate systems ITK - Numpy are different:\n",
        "        (organ_bbox_array, tumor_bbox_array)  = (organs_data[z0:z1, y0:y1, x0:x1], cc_tumor_array[z0:z1, y0:y1, x0:x1])\n",
        "\n",
        "        # Quantifies tumor in each organ\n",
        "        for i in range(number_of_organs+1):\n",
        "            cc_dictionary['organ_'+str(i)+'_voxel_count'] = 0\n",
        "            # organ_voxel_count = np.sum(np.logical_and(cc_tumor_array == l, organs_data == i))\n",
        "\n",
        "        (voxel_values, voxel_counts) = np.unique(organ_bbox_array[tumor_bbox_array == l], return_counts=True)\n",
        "        for (v,c) in zip(voxel_values, voxel_counts):\n",
        "            cc_dictionary['organ_'+str(v)+'_voxel_count'] = c\n",
        "        stats_list_3d.append(cc_dictionary.copy())\n",
        "\n",
        "    # 2D Report -- Iterate through the slices\n",
        "    for i in range(organs_data.shape[0]):\n",
        "        tumor_slice = cc_tumor_array[i,:,:]\n",
        "        organs_slice = organs_data[i,:,:]\n",
        "\n",
        "        if not np.any(tumor_slice):\n",
        "            continue\n",
        "\n",
        "        number_of_tumors = 0\n",
        "        # OpenCV returns number of objects + 1\n",
        "        number_of_tumors = cv2.connectedComponents(tumor_slice.astype('uint8'),connectivity=8)[0] - 1\n",
        "\n",
        "        # Calculate area of the tumors\n",
        "        tumor_area_cm2 = np.sum(tumor_slice == 1) * spacing[0] * spacing[1] / 100\n",
        "\n",
        "        # Localize where in the organs the tumor is\n",
        "        organ_voxel_count_2d_dict = {}\n",
        "        organ_voxel_count_2d_dict['filename']= image_name\n",
        "        organ_voxel_count_2d_dict['slice']= i\n",
        "        organ_voxel_count_2d_dict['number_of_tumors']= number_of_tumors\n",
        "        organ_voxel_count_2d_dict['tumor_area_cm2']= tumor_area_cm2\n",
        "        organ_voxel_count_2d_dict['spacing'] = spacing\n",
        "        for i in range(number_of_organs+1):\n",
        "            organ_voxel_count = np.sum(np.logical_and(tumor_slice > 0, organs_slice == i))\n",
        "            organ_voxel_count_2d_dict['organ_'+str(i)+'_voxel_count'] = organ_voxel_count\n",
        "\n",
        "        stats_list_2d.append(organ_voxel_count_2d_dict.copy())\n",
        "\n",
        "df_tumor_report_2d = pd.DataFrame(stats_list_2d)\n",
        "df_tumor_report_3d = pd.DataFrame(stats_list_3d)\n",
        "\n",
        "df_tumor_report_3d.to_csv('/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_3D_tumor_per_organ.csv', index=False)\n",
        "df_tumor_report_2d.to_csv('/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_2D_tumor_per_organ.csv', index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1697513546884
        }
      },
      "outputs": [],
      "source": [
        "# Read the data frames\n",
        "df3d = pd.read_csv('/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_3D_tumor_per_organ.csv')\n",
        "df2d = pd.read_csv('/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_2D_tumor_per_organ.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1697513547033
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['image_name', 'PhysicalSize_cm3', 'NumberOfVoxels', 'Roundness',\n",
              "       'Elongation', 'Flatness', 'EquivalentSphericalRadius_cm',\n",
              "       'EquivalentSphericalPerimeter_cm', 'EquivalentEllipsoidDiameter',\n",
              "       'major_axis_length_cm',\n",
              "       ...\n",
              "       'organ_95_voxel_count', 'organ_96_voxel_count', 'organ_97_voxel_count',\n",
              "       'organ_98_voxel_count', 'organ_99_voxel_count', 'organ_100_voxel_count',\n",
              "       'organ_101_voxel_count', 'organ_102_voxel_count',\n",
              "       'organ_103_voxel_count', 'organ_104_voxel_count'],\n",
              "      dtype='object', length=118)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df3d.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1697513547233
        }
      },
      "outputs": [],
      "source": [
        "# # Iterate through the slices\n",
        "# for index, row in df3d.iterrows():\n",
        "#     # Get index of colums starting with organ\n",
        "#     organ_columns = df3d.columns[df3d.columns.str.startswith('organ')]\n",
        "#     NumberOfVoxels = row.NumberOfVoxels\n",
        "\n",
        "#     # Normalize the number of voxels in each organ by the total number of voxels\n",
        "#     for organ in organ_columns:\n",
        "#         df3d.loc[index, organ] = df3d.loc[index, organ] / NumberOfVoxels\n",
        "\n",
        "image_name_list_with_tumor = df3d.image_name.unique().tolist()\n",
        "image_name_list_all_subjects = [f for f in data_dict.keys()]\n",
        "image_name_list_without_tumor = list(set(image_name_list_all_subjects) - set(image_name_list_with_tumor))\n",
        "\n",
        "subject_list = []\n",
        "# Group by image name and iterate through the groups\n",
        "for name, group in df3d.groupby('image_name'):\n",
        "    subject_dict = {}\n",
        "    # Number of tumors\n",
        "    number_tumors_group = len(group)    \n",
        "\n",
        "    max_tumor_size_group = max(group.major_axis_length_cm)\n",
        "    # Cancer stage https://www.cancerresearchuk.org/about-cancer/lung-cancer/stages-types-grades/tnm-staging\n",
        "    # Value stage 1: 1 tumor less than 3cm\n",
        "    # Value stage 2: 1 tumor between 3cm and  5cm\n",
        "    # Value stage 3: between 5cm to 7cm or more than 1 tumor of the lobe of the lung\n",
        "    # Value stage 4: larger than 7cm or more than 1 tumor of the lung\n",
        "    cancer_stage = -1 # No Cancer\n",
        "    if max_tumor_size_group < 3 and  number_tumors_group == 1:\n",
        "        cancer_stage = 1\n",
        "    elif (max_tumor_size_group > 3 and max_tumor_size_group < 5) and number_tumors_group == 1:\n",
        "            cancer_stage = 2\n",
        "    elif max_tumor_size_group > 5 and max_tumor_size_group < 7 or number_tumors_group >= 1:\n",
        "            cancer_stage = 3\n",
        "    elif max_tumor_size_group > 7 or number_tumors_group >= 1 :\n",
        "        cancer_stage = 4\n",
        "\n",
        "    subject_dict['name'] = name\n",
        "    subject_dict['max_tumor_size'] = max_tumor_size_group\n",
        "    subject_dict['number_tumors'] = number_tumors_group\n",
        "    subject_dict['cancer_stage'] = cancer_stage\n",
        "    \n",
        "    subject_list.append(subject_dict.copy())\n",
        "\n",
        "for name in image_name_list_without_tumor:\n",
        "    subject_dict = {}\n",
        "    subject_dict['name'] = name\n",
        "    subject_dict['max_tumor_size'] = 0\n",
        "    subject_dict['number_tumors'] = 0\n",
        "    subject_dict['cancer_stage'] = -1\n",
        "    subject_list.append(subject_dict.copy())\n",
        "\n",
        "df_subject = pd.DataFrame(subject_list)\n",
        "df_subject.to_csv('/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_3D_subject.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1697513547394
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f9143ba00>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Distribution of tumor vs organ number where values are non-zero\n",
        "df3d_organ = df3d.loc[:, df3d.columns.str.startswith('organ')]\n",
        "# Remove columns with all zeros\n",
        "df3d_organ = df3d_organ.loc[:, (df3d_organ != 0).any(axis=0)]\n",
        "\n",
        "# # Remove columns with less than 100 voxel counts\n",
        "df3d_organ = df3d_organ.loc[:, df3d_organ.sum() > 400]\n",
        "\n",
        "# Rename columns as follows: \n",
        "# organ 0 is backbround\n",
        "# organ 7 is aorta\n",
        "# organ 13\tlung_upper_lobe_left\tsuperior lobe of left lung\n",
        "# organ 14\tlung_lower_lobe_left\tinferior lobe of left lung\n",
        "# organ 15\tlung_upper_lobe_right\tsuperior lobe of right lung\n",
        "# organ 16\tlung_middle_lobe_right\tmiddle lobe of right lung\n",
        "# organ 17\tlung_lower_lobe_right\tinferior lobe of right lung\n",
        "\n",
        "df3d_organ.columns = ['background', 'aorta', 'superior lobe of left lung', 'inferior lobe of left lung', 'superior lobe of right lung', 'middle lobe of right lung', 'inferior lobe of right lung']\n",
        "\n",
        "# Normalize the number of voxels as cm3\n",
        "df3d_organ = df3d_organ.div(df3d_organ.sum(axis=1), axis=0)\n",
        "\n",
        "# Plot histogram of number of voxels per organ\n",
        "df3d_organ.sum().plot(kind='bar', title='Percentage of voxel tumor count per organ')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1697513547637
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f8ae5cc40>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot distribution of cancer stage for integer values vs tumor size\n",
        "df_subject.plot.scatter(x='cancer_stage', y='max_tumor_size')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1697513549482
        }
      },
      "outputs": [],
      "source": [
        "# Iterate through the slices\n",
        "for index, row in df3d.iterrows():\n",
        "    # Get index of colums starting with organ\n",
        "    organ_columns = df3d.columns[df3d.columns.str.startswith('organ')]\n",
        "    # Normalize the number of voxels in each organ by the total number of voxels\n",
        "    for organ in organ_columns:\n",
        "        df3d.loc[index, organ] = df3d.loc[index, organ] / row.NumberOfVoxels\n",
        "\n",
        "\n",
        "subject_list = []\n",
        "# Group by image name and iterate through the groups\n",
        "for name, group in df3d.groupby('image_name'):\n",
        "    subject_dict = {}\n",
        "    # Number of tumors\n",
        "    number_tumors_group = len(group)    \n",
        "\n",
        "    max_tumor_size_group = max(group.major_axis_length_cm)\n",
        "    # Cancer stage https://www.cancerresearchuk.org/about-cancer/lung-cancer/stages-types-grades/tnm-staging\n",
        "    # Value stage 1: 1 tumor less than 3cm\n",
        "    # Value stage 2: 1 tumor between 3cm and  5cm\n",
        "    # Value stage 3: between 5cm to 7cm or more than 1 tumor of the lobe of the lung\n",
        "    # Value stage 4: larger than 7cm or more than 1 tumor of the lung\n",
        "    cancer_stage = -1 # No Cancer\n",
        "    if number_tumors_group == 1:\n",
        "        if max_tumor_size_group < 3:\n",
        "            cancer_stage = 1\n",
        "        elif max_tumor_size_group > 3 or max_tumor_size_group < 5:\n",
        "            cancer_stage = 2\n",
        "    elif max_tumor_size_group > 5 or max_tumor_size_group < 7 or number_tumors_group >= 1:\n",
        "            cancer_stage = 3\n",
        "    elif max_tumor_size_group > 7 or number_tumors_group >= 1 :\n",
        "        cancer_stage = 4\n",
        "\n",
        "    subject_dict['name'] = name\n",
        "    subject_dict['max_tumor_size'] = max_tumor_size_group\n",
        "    subject_dict['number_tumors'] = number_tumors_group\n",
        "    subject_dict['cancer_stage'] = cancer_stage\n",
        "    \n",
        "    subject_list.append(subject_dict.copy())\n",
        "\n",
        "df_subject = pd.DataFrame(subject_list)\n",
        "df_subject.to_csv('/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_3D_subject.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1697513549752
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Tumor length (less than 20cm) per subject')"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df3d.columns\n",
        "#Plot largest_tumor_length_cm histogram, where tumor_length_cm is less than 20cm and gretar than zero, using 100 bins\n",
        "df3d[ (df3d.major_axis_length_cm > 0) &  (df3d.major_axis_length_cm < 20)].major_axis_length_cm.hist(bins=50)\n",
        "# Title\n",
        "plt.title(\"Histogram of largest 3D tumor length less than 20cm per subject\")\n",
        "# Y label\n",
        "plt.ylabel(\"Frequency\")\n",
        "# X label\n",
        "plt.xlabel(\"Tumor length (less than 20cm) per subject\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "gather": {
          "logged": 1697513552724
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Tumor area vs number of tumors')"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# cluster the tumor_area and number_of_tumors\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(df2d[['tumor_area_cm2', 'number_of_tumors']])\n",
        "df2d['tumor_cluster'] = kmeans.predict(df2d[['tumor_area_cm2', 'number_of_tumors']])\n",
        "df2d.plot.scatter(x='tumor_area_cm2', y='number_of_tumors', c='tumor_cluster', colormap='viridis')\n",
        "\n",
        "# Axis title\n",
        "plt.xlabel('Tumor area (cm^2)')\n",
        "plt.ylabel('Number of tumors')\n",
        "plt.title('Tumor area vs number of tumors')\n",
        "\n",
        "# # show density plot of tumor_area and number_of_tumors\n",
        "import seaborn as sns\n",
        "sns.jointplot(x='tumor_area_cm2', y='number_of_tumors', data=df2d, kind='kde')\n",
        "\n",
        "# Axis title\n",
        "plt.xlabel('Tumor area (cm^2)')\n",
        "plt.ylabel('Number of tumors')\n",
        "plt.title('Tumor area vs number of tumors')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1697513552870
        }
      },
      "outputs": [],
      "source": [
        "# Create a new column for cluster of tumor_area and number_of_tumors\n",
        "df2d['tumor_cluster'] = kmeans.predict(df2d[['tumor_area_cm2', 'number_of_tumors']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "gather": {
          "logged": 1697513553034
        }
      },
      "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>tumor_area_cm2</th>\n",
              "      <th>number_of_tumors</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tumor_cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.788059</td>\n",
              "      <td>1.031348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>52.251725</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.267455</td>\n",
              "      <td>1.062598</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               tumor_area_cm2  number_of_tumors\n",
              "tumor_cluster                                  \n",
              "0                   14.788059          1.031348\n",
              "1                   52.251725          1.000000\n",
              "2                    2.267455          1.062598"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2d['tumor_cluster'].value_counts()\n",
        "\n",
        "# Get mean value of tumor_area and number_of_tumors for each cluster\n",
        "df2d.groupby('tumor_cluster')[['tumor_area_cm2', 'number_of_tumors']].mean()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "gather": {
          "logged": 1697513553310
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['name', 'max_tumor_size', 'number_tumors', 'cancer_stage'], dtype='object')\n"
          ]
        },
        {
          "data": {
            "image/png": 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y5fIMIP/rX/+qSpUqqV27doqJiVFCQkKJ34ertCDcmFh+H3pZWVl22729vZ1qv/7D3dExFs5cifDGG2/o+++/19ixY3Xp0iUNHz5cjRs31q+//lrgcleuXNG9994rX19fffTRRzb/4snOzpYkjRkzRitXrrT7uP5AkltISIgk+x+ijipuX+fefu7Lf+2pUaOGbr75Zn300UeSfr+y7tChQ7r//vtt5itqX19v4MCBqlevXr5nb0r6fWnP9TVkZ2fLYrFo+fLldt8P1w8yLe1X05SGPrbHXr9ZLBZ9/PHH2rRpk4YOHaojR47o4YcfVuvWrW0ueXeWO45Hxdmus31flD5u2LChUlJStHjxYt1000365JNPdNNNN1n/0XYjIdyYWJUqVfJcsSDJ7pUzxVGnTh1lZ2dr3759Nu3Hjx/XuXPn7N6IzBlNmzbVuHHjtGHDBn311Vc6cuSIZs2aVeAyw4cPV3Jysj755JM8AzPr1q0rSfLx8VH37t3tPgq6d0zt2rXl5+en/fv327RHR0dLknbv3l2U3XTa/v37VbVq1TxngOy5//779d133yklJUX//Oc/VbFiRbuXsRelr6+X++yNvau5cv5lev1709Xvy9yuf2+mpqYqOzvb+i/f6OhoGYahqKgou++HDh06FGm7Oe/9lJSUPNN+/PFHVa1aVf7+/kVad0FK8m9fyrt/V65c0f79+5362+/QoYNeffVVbdu2TQsXLtSePXu0ePHiItXkzuNRYVzd9zn1Xn8F1bVr1/JccShJ/v7+uv/++zVnzhwdOnRIvXv31quvvmr36kAzI9yYWHR0tDZv3qwrV65Y25YtW5bn0tbiuv322yX9fnOx3HK+Curdu3eR1puenm4d15OjadOm8vLyKvCU9Zw5czR79mzNmDFD7dq1yzM9NDRUXbp00ezZs3X06NE80wv7WQMfHx+1adNG27Zts2lv1aqVoqKi9NZbb+U5uLlq/Elu27dvV2xsrEPz3nPPPfL29taHH36oJUuWqE+fPjYfqkXt6/w8+OCDqlevniZMmJBnWk4I3LBhg7UtKytL77zzjtPbcVTOZfY5pk2bJknq1auXJOnuu++Wt7e3JkyYkOe1MgzDOs7KWdWrV1eLFi00b948m/fE7t279eWXX1r/dlwtOjpaP/74o817+bvvvnP5VxTdu3dX+fLlNXXqVJt+e++995SWlubQ3/7Zs2fz9HnOzygU5b3nruORo1x93G3Tpo1CQkL07rvv2vyNLly4MM/Z4+vfp+XLl1ejRo1kGIZLxjOVJVwKbmKPPvqoPv74Y912223q16+ffv75Zy1YsMD64eIqzZs31+DBg/XOO+/o3Llz6ty5s7799lvNmzdP8fHx6tq1a5HWu2bNGg0dOlT33Xef/vSnP+natWv64IMP5O3tne9dcE+dOqWnnnpKjRo1kq+vrxYsWGAz/a677pK/v79mzJihm266SU2bNtVjjz2munXr6vjx49q0aZN+/fVXfffddwXW1rdvXz333HNKT0+3jlvx8vLSzJkzdccdd6hFixZ66KGHVL16df3444/as2ePVqxYUaR+sOfEiRP6/vvv8wyUzU9oaKi6du2qN998U+fPn8/zlVRR+rog3t7eeu655+wOum3cuLE6dOigxMREnTlzRsHBwVq8eHGecOVK+/fv15133qnbbrtNmzZt0oIFC/TAAw+oefPmkn7/QHrllVeUmJhovcw2ICBA+/fv19KlS/V///d/GjNmTJG2PWXKFPXq1UuxsbF65JFHrJeCBwUFafz48S7cyz88/PDDevPNN9WzZ0898sgjOnHihGbNmqXGjRsrPT3dZdupVq2aEhMTNWHCBN1222268847lZKSor///e9q27atQzcVnTdvnv7+97/rrrvuUnR0tM6fP693331XgYGBRQp/7joe5bh69apeeeWVPO3BwcF66qmnXH7cLV++vMaPH69hw4bplltuUb9+/XTgwAHNnTtX0dHRNl+D9ejRQ+Hh4YqLi1NYWJj27t2r6dOnq3fv3qXmTuYlpmQvzoK72LsU3DAM44033jBq1qxp+Pr6GnFxcca2bdvyvSTx+ktQcy7Zvf4SzZzLa0+ePGltu3r1qjFhwgQjKirK8PHxMSIiIozExESbS60N4/fLJHv37u3QPv3yyy/Gww8/bERHRxsVKlQwgoODja5duxqrVq3Ks86cSy9zLm3N75H7ctmff/7ZGDRokBEeHm74+PgYNWvWNPr06WN8/PHHhdZ2/Phxo1y5csYHH3yQZ9rXX39t3HrrrUZAQIDh7+9vNGvWzOay8fwuJ+3cubPdyzjt9dnMmTONihUrGunp6YXWmuPdd981JBkBAQHGpUuXbKY52tf25Lc/V69eNaKjo/NcCm4Yv/d99+7dDV9fXyMsLMwYO3assXLlSruXgjvaJ4aR97LznPfqDz/8YNx7771GQECAUaVKFWPo0KF5+sAwDOOTTz4xbrrpJsPf39/w9/c3GjRoYCQkJBgpKSmF1lSQVatWGXFxcYafn58RGBho3HHHHcYPP/xgM48rLwU3DMNYsGCBUbduXaN8+fJGixYtjBUrVuR7KfiUKVMcqiW/Y8L06dONBg0aGD4+PkZYWJjx5JNP5rkdQn79tmPHDmPAgAFG7dq1DV9fXyM0NNTo06ePsW3btkL3vaD3nquPRznby+/YEh0dbZ2vOMfdnNck9y0NDMMwpk6datSpU8fw9fU12rVrZ2zcuNFo3bq1cdttt1nnmT17ttGpUycjJCTE8PX1NaKjo41nnnnGSEtLc3gfzcJiGG44Xw7cAB555BH99NNP+uqrr0p82y1btlSXLl30t7/9rcS3DcDzsrOzVa1aNd1999169913PV1OqcOYG6CIXnzxRetdWEvS8uXLtW/fPiUmJpbodgF4xuXLl/OMS5o/f77OnDlTrJ/pMDPO3AAAUIqtW7dOo0aN0n333aeQkBDt2LFD7733nho2bKjt27fzi+h2MKAYAIBSLDIyUhEREZo6dap1EP6gQYM0adIkgk0+OHMDAABMhTE3AADAVAg3AADAVG64MTfZ2dn67bffFBAQ4PBvjwAAAM8yDEPnz59XjRo1bH6F3p4bLtz89ttveX7pFgAAlA2HDx9WrVq1Cpznhgs3ObegPnz4sPW2+QAAoHRLT09XRESEQz8lccOFm5yvogIDAwk3AACUMY4MKWFAMQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMJVSE24mTZoki8WikSNHFjjfkiVL1KBBA1WoUEFNmzbVF198UUIVAijMLycvaG3KCe0/leHpUgDcwErFb0tt3bpVs2fPVrNmzQqc75tvvtGAAQOUlJSkPn36aNGiRYqPj9eOHTvUpEmTEqoWwPXOXbyi4R8ma8O+k9a2TjHVNG1ASwVV9PFgZQBuRB4/c3PhwgUNHDhQ7777rqpUqVLgvG+//bZuu+02PfPMM2rYsKFefvlltWrVStOnTy+hagHYM/zDZG1MPWXTtjH1lIZ9uNNDFQG4kXk83CQkJKh3797q3r17ofNu2rQpz3w9e/bUpk2b8l0mMzNT6enpNg8ArvPLyQvasO+ksgzDpj3LMLRh30m+ogJQ4jwabhYvXqwdO3YoKSnJofmPHTumsLAwm7awsDAdO3Ys32WSkpIUFBRkfURERBSrZgC2Dp65WOD0A6cJNwBKlsfCzeHDhzVixAgtXLhQFSpUcNt2EhMTlZaWZn0cPnzYbdsCbkR1gisWOD0yxL+EKgGA33lsQPH27dt14sQJtWrVytqWlZWlDRs2aPr06crMzJS3t7fNMuHh4Tp+/LhN2/HjxxUeHp7vdnx9feXr6+va4gFY1a1WSZ1iqmlj6imbr6a8LRbF1auqqKqEGwAly2Nnbrp166Zdu3YpOTnZ+mjTpo0GDhyo5OTkPMFGkmJjY7V69WqbtpUrVyo2NrakygZgx7QBLRVXr6pNW1y9qpo2oKWHKgJwI/PYmZuAgIA8l2/7+/srJCTE2j5o0CDVrFnTOiZnxIgR6ty5s9544w317t1bixcv1rZt2/TOO++UeP0A/hBU0UfzH2mn/acydOB0hiJD/DljA8BjSsV9bvJz6NAheXn9cXKpY8eOWrRokcaNG6exY8cqJiZGn332Gfe4AUqJqKqEGgCeZzGM667fNLn09HQFBQUpLS1NgYGBni4HAAA4wJnPb4/f5wYAAMCVCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUCDcAAMBUPBpuZs6cqWbNmikwMFCBgYGKjY3Vf//733znnzt3riwWi82jQoUKJVgxAAAo7cp5cuO1atXSpEmTFBMTI8MwNG/ePPXt21c7d+5U48aN7S4TGBiolJQU63OLxVJS5QIAgDLAo+HmjjvusHn+6quvaubMmdq8eXO+4cZisSg8PLwkygMAAGVQqRlzk5WVpcWLFysjI0OxsbH5znfhwgXVqVNHERER6tu3r/bs2VPgejMzM5Wenm7zAAAA5uXxcLNr1y5VqlRJvr6+euKJJ7R06VI1atTI7rz169fX+++/r88//1wLFixQdna2OnbsqF9//TXf9SclJSkoKMj6iIiIcNeuAACAUsBiGIbhyQKuXLmiQ4cOKS0tTR9//LH+8Y9/aP369fkGnNyuXr2qhg0basCAAXr55ZftzpOZmanMzEzr8/T0dEVERCgtLU2BgYEu2w8AAOA+6enpCgoKcujz26NjbiSpfPnyqlevniSpdevW2rp1q95++23Nnj270GV9fHzUsmVLpaam5juPr6+vfH19XVYvAAAo3Tz+tdT1srOzbc60FCQrK0u7du1S9erV3VwVAAAoKzx65iYxMVG9evVS7dq1df78eS1atEjr1q3TihUrJEmDBg1SzZo1lZSUJEl66aWX1KFDB9WrV0/nzp3TlClTdPDgQT366KOe3A0AAFCKeDTcnDhxQoMGDdLRo0cVFBSkZs2aacWKFbr11lslSYcOHZKX1x8nl86ePavHHntMx44dU5UqVdS6dWt98803Do3PAQAANwaPDyguac4MSAIAAKWDM5/fpW7MDQAAQHEQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKk4HW4uXbqkixcvWp8fPHhQb731lr788kuXFgYAAFAUToebvn37av78+ZKkc+fOqX379nrjjTfUt29fzZw50+UFAgAAOMPpcLNjxw7dfPPNkqSPP/5YYWFhOnjwoObPn6+pU6c6ta6ZM2eqWbNmCgwMVGBgoGJjY/Xf//63wGWWLFmiBg0aqEKFCmratKm++OILZ3cBAACYmNPh5uLFiwoICJAkffnll7r77rvl5eWlDh066ODBg06tq1atWpo0aZK2b9+ubdu26ZZbblHfvn21Z88eu/N/8803GjBggB555BHt3LlT8fHxio+P1+7du53dDQAAYFIWwzAMZxZo1qyZHn30Ud11111q0qSJlqtgenQAACAASURBVC9frtjYWG3fvl29e/fWsWPHilVQcHCwpkyZokceeSTPtPvvv18ZGRlatmyZta1Dhw5q0aKFZs2a5dD609PTFRQUpLS0NAUGBharVgAAUDKc+fx2+szNCy+8oDFjxigyMlLt27dXbGyspN/P4rRs2bJoFUvKysrS4sWLlZGRYV3n9TZt2qTu3bvbtPXs2VObNm3Kd72ZmZlKT0+3eQAAAPMq5+wC9957r2666SYdPXpUzZs3t7Z369ZNd911l9MF7Nq1S7Gxsbp8+bIqVaqkpUuXqlGjRnbnPXbsmMLCwmzawsLCCjxblJSUpAkTJjhdFwAAKJucOnNz9epVlStXTqdOnVLLli3l5fXH4u3atVODBg2cLqB+/fpKTk7Wli1b9OSTT2rw4MH64YcfnF5PfhITE5WWlmZ9HD582GXrBgAApY9TZ258fHxUu3ZtZWVluayA8uXLq169epKk1q1ba+vWrXr77bc1e/bsPPOGh4fr+PHjNm3Hjx9XeHh4vuv39fWVr6+vy+oFAAClm9Njbp577jmNHTtWZ86ccUc9ys7OVmZmpt1psbGxWr16tU3bypUr8x2jAwAAbjxOj7mZPn26UlNTVaNGDdWpU0f+/v4203fs2OHwuhITE9WrVy/Vrl1b58+f16JFi7Ru3TqtWLFCkjRo0CDVrFlTSUlJkqQRI0aoc+fOeuONN9S7d28tXrxY27Zt0zvvvOPsbgAAAJNyOtzEx8e7bOMnTpzQoEGDdPToUQUFBalZs2ZasWKFbr31VknSoUOHbMb1dOzYUYsWLdK4ceM0duxYxcTE6LPPPlOTJk1cVhMAACjbnL7PTVnHfW4AACh7nPn8dvrMTY7t27dr7969kqTGjRsX6x43AAAAruJ0uDlx4oT69++vdevWqXLlypJ+/wHNrl27avHixapWrZrLiwQAAHCU01dLDRs2TOfPn9eePXt05swZnTlzRrt371Z6erqGDx/ujhoBAAAc5vSYm6CgIK1atUpt27a1af/222/Vo0cPnTt3zqUFuhpjbgAAKHvc+ttS2dnZ8vHxydPu4+Oj7OxsZ1cHAADgUk6Hm1tuuUUjRozQb7/9Zm07cuSIRo0apW7durm0OAAAAGc5HW6mT5+u9PR0RUZGKjo6WtHR0YqKilJ6erqmTZvmjhoBAAAc5vTVUhEREdqxY4dWrVqlH3/8UZLUsGFDde/e3eXFAQAAOIub+AEAgFLP7Tfx27p1q9auXasTJ07kGUT85ptvFmWVAAAALuF0uJk4caLGjRun+vXrKywsTBaLxTot9/8DAAB4gtPh5u2339b777+vIUOGuKEcAACA4nH6aikvLy/FxcW5oxYAAIBiczrcjBo1SjNmzHBHLQAAAMXm9NdSY8aMUe/evRUdHa1GjRrluVvxp59+6rLiAAAAnOV0uBk+fLjWrl2rrl27KiQkhEHEAACgVHE63MybN0+ffPKJevfu7Y56AAAAisXpMTfBwcGKjo52Ry0AAADF5nS4GT9+vF588UVdvHjRHfUAAAAUi9NfS02dOlU///yzwsLCFBkZmWdA8Y4dO1xWHAAAgLOcDjfx8fHuqAMAAMAl+OFMAABQ6jnz+e30mBsAAIDSzOmvpby8vAq8t01WVlaxCgIAACgOp8PN0qVLbZ5fvXpVO3fu1Lx58zRhwgSXFQYAAFAULhtzs2jRIv3zn//U559/7orVuQ1jbgAAKHs8MuamQ4cOWr16tatWBwAAUCQuCTeXLl3S1KlTVbNmTVesDgAAoMicHnNTpUoVmwHFhmHo/PnzqlixohYsWODS4gAAAJzldLj529/+ZhNuvLy8VK1aNbVv315VqlRxaXEAAADOcjrc3HLLLYqIiLB7OfihQ4dUu3ZtlxQGAABQFE6PuYmKitLJkyfztJ8+fVpRUVEuKQoAAKConA43+V05fuHCBVWoUKHYBQEAABSHw19LjR49WpJksVj0wgsvqGLFitZpWVlZ2rJli1q0aOH6CgEAAJzgcLjZuXOnpN/P3OzatUvly5e3TitfvryaN2+uMWPGuL5CAAAAJzgcbtauXStJeuihh/T2229zd18AAFAqOX211Jw5c9xRBwAAgEu47OcXAAAASgPCDQAAMBXCDQAAMBWHwk2rVq109uxZSdJLL72kixcvurUoAACAonIo3Ozdu1cZGRmSpAkTJujChQtuLQoAAKCoHLpaqkWLFnrooYd00003yTAMvf7666pUqZLdeV944QWXFggAAOAMi5Hf7ynkkpKSohdffFE///yzduzYoUaNGqlcuby5yGKxaMeOHW4p1FXS09MVFBSktLQ07tUDAEAZ4cznt0PhJjcvLy8dO3ZMoaGhxSrSUwg3AACUPc58fjt9tVR2drbLgk1SUpLatm2rgIAAhYaGKj4+XikpKQUuM3fuXFksFpsHP9gJAAByOH2HYkn6+eef9dZbb2nv3r2SpEaNGmnEiBGKjo52aj3r169XQkKC2rZtq2vXrmns2LHq0aOHfvjhB/n7++e7XGBgoE0IslgsRdkNAABgQk6HmxUrVujOO+9UixYtFBcXJ0nauHGjGjdurH//+9+69dZbHV7X8uXLbZ7PnTtXoaGh2r59uzp16pTvchaLReHh4c6WDgAAbgBOh5tnn31Wo0aN0qRJk/K0//Wvf3Uq3FwvLS1NkhQcHFzgfBcuXFCdOnWUnZ2tVq1aaeLEiWrcuLHdeTMzM5WZmWl9np6eXuT6AABA6ef0gOIKFSpo165diomJsWn/6aef1KxZM12+fLlIhWRnZ+vOO+/UuXPn9PXXX+c736ZNm7Rv3z41a9ZMaWlpev3117Vhwwbt2bNHtWrVyjP/+PHjNWHChDztDCgGAKDscOuA4mrVqik5OTlPe3JycrEGGickJGj37t1avHhxgfPFxsZq0KBBatGihTp37qxPP/1U1apV0+zZs+3On5iYqLS0NOvj8OHDRa4RAACUfk5/LfXYY4/p//7v//TLL7+oY8eOkn4fc/Paa69p9OjRRSpi6NChWrZsmTZs2GD37EtBfHx81LJlS6Wmptqd7uvrK19f3yLVBQAAyh6nw83zzz+vgIAAvfHGG0pMTJQk1ahRQ+PHj9fw4cOdWpdhGBo2bJiWLl2qdevWKSoqytlylJWVpV27dun22293elkAAGA+To+5ye38+fOSpICAgCIt/9RTT2nRokX6/PPPVb9+fWt7UFCQ/Pz8JEmDBg1SzZo1lZSUJOn3H+7s0KGD6tWrp3PnzmnKlCn67LPPtH37djVq1KjQbXITPwAAyh5nPr+LdJ+bHEUNNTlmzpwpSerSpYtN+5w5czRkyBBJ0qFDh+Tl9cfQoLNnz+qxxx7TsWPHVKVKFbVu3VrffPONQ8EGAACYX7HO3JRFnLkBAKDscevVUgAAAKUZ4QYAAJiKU+Hm6tWr6tatm/bt2+euegAAAIrFqXDj4+Oj77//3l21AAAAFJvTX0s9+OCDeu+999xRCwAAQLE5fSn4tWvX9P7772vVqlVq3bq1/P39baa/+eabLisOAADAWU6Hm927d6tVq1aSfv+xzNwsFotrqgIAACgip8PN2rVr3VEHAACASxT5UvDU1FStWLFCly5dkvT770QBAAB4mtPh5vTp0+rWrZv+9Kc/6fbbb9fRo0clSY888oiefvpplxcIAADgDKfDzahRo+Tj46NDhw6pYsWK1vb7779fy5cvd2lxAAAAznJ6zM2XX36pFStWqFatWjbtMTExOnjwoMsKAwAAKAqnz9xkZGTYnLHJcebMGfn6+rqkKAAAgKJyOtzcfPPNmj9/vvW5xWJRdna2Jk+erK5du7q0OAAAAGc5/bXU5MmT1a1bN23btk1XrlzRX/7yF+3Zs0dnzpzRxo0b3VEjAACAw5w+c9OkSRP99NNPuummm9S3b19lZGTo7rvv1s6dOxUdHe2OGgEAABxmMW6wG9Skp6crKChIaWlpCgwM9HQ5AADAAc58fjv9tZQknT17Vu+995727t0rSWrUqJEeeughBQcHF2V1AAAALuP011IbNmxQZGSkpk6dqrNnz+rs2bOaOnWqoqKitGHDBnfUCAAA4DCnv5Zq2rSpYmNjNXPmTHl7e0uSsrKy9NRTT+mbb77Rrl273FKoq/C1FAAAZY8zn99On7lJTU3V008/bQ02kuTt7a3Ro0crNTXV+WoBAABcyOlw06pVK+tYm9z27t2r5s2bu6QoAACAonJoQPH3339v/f/hw4drxIgRSk1NVYcOHSRJmzdv1owZMzRp0iT3VAkAAOAgh8bceHl5yWKxqLBZLRaLsrKyXFacOzDmBgCAssfll4Lv37/fJYUBAAC4m0Phpk6dOu6uAwAAwCWKdBO/3377TV9//bVOnDih7Oxsm2nDhw93SWEAAABF4XS4mTt3rh5//HGVL19eISEhslgs1mkWi4VwAwAAPMrpm/hFREToiSeeUGJiory8nL6S3OMYUAwAQNnj1pv4Xbx4Uf379y+TwQYAAJif0wnlkUce0ZIlS9xRCwAAQLE5/bVUVlaW+vTpo0uXLqlp06by8fGxmf7mm2+6tEBX42spAADKHpff5ya3pKQkrVixQvXr15ekPAOKAQAAPMnpcPPGG2/o/fff15AhQ9xQDgAAQPE4PebG19dXcXFx7qgFAACg2JwONyNGjNC0adPcUQsAAECxOf211Lfffqs1a9Zo2bJlaty4cZ4BxZ9++qnLigMAAHCW0+GmcuXKuvvuu91RCwAAQLE5HW7mzJnjjjoAAABcgtsMAwAAU3H6zE1UVFSB97P55ZdfilUQAABAcTgdbkaOHGnz/OrVq9q5c6eWL1+uZ555xmWFAQAAFIXT4WbEiBF222fMmKFt27YVuyAAAIDicNmYm169eumTTz5x1eoAAACKxGXh5uOPP1ZwcLCrVgcAAFAkToebli1bqlWrVtZHy5YtVb16dY0dO1Zjx451al1JSUlq27atAgICFBoaqvj4eKWkpBS63JIlS9SgQQNVqFBBTZs21RdffOHsbgAu88vJC1qbckL7T2V4uhS3c9e+3kh9CHPjvVw6OD3mJj4+3ua5l5eXqlWrpi5duqhBgwZOrWv9+vVKSEhQ27Ztde3aNY0dO1Y9evTQDz/8IH9/f7vLfPPNNxowYICSkpLUp08fLVq0SPHx8dqxY4eaNGni7O4ARXbu4hUN/zBZG/adtLZ1iqmmaQNaKqiiTwFLlj3u2tcbqQ9hbryXSxeLYRiGp4vIcfLkSYWGhmr9+vXq1KmT3Xnuv/9+ZWRkaNmyZda2Dh06qEWLFpo1a1ah20hPT1dQUJDS0tIUGBjostpx4xn03rfamHpKWbn+hLwtFsXVq6r5j7TzYGWu5659vZH6EObGe9n9nPn8LlU38UtLS5OkAsfubNq0Sd27d7dp69mzpzZt2mR3/szMTKWnp9s8gOL65eQFbdh30uZAJklZhqEN+06a6pS0u/b1RupDmBvv5dLH4XDj5eUlb2/vAh/lyjn9LZdVdna2Ro4cqbi4uAK/Xjp27JjCwsJs2sLCwnTs2DG78yclJSkoKMj6iIiIKHKNQI6DZy4WOP3AafMczNy1rzdSH8LceC+XPg6nkaVLl+Y7bdOmTZo6daqys7OLXEhCQoJ2796tr7/+usjrsCcxMVGjR4+2Pk9PTyfgoNjqBFcscHpkiP0xY2WRu/b1RupDmBvv5dLH4XDTt2/fPG0pKSl69tln9e9//1sDBw7USy+9VKQihg4dqmXLlmnDhg2qVatWgfOGh4fr+PHjNm3Hjx9XeHi43fl9fX3l6+tbpLqA/NStVkmdYqrl+x17VFXzHMzcta83Uh/C3Hgvlz5FGnPz22+/6bHHHlPTpk117do1JScna968eapTp45T6zEMQ0OHDtXSpUu1Zs0aRUVFFbpMbGysVq9ebdO2cuVKxcbGOrVtoLimDWipuHpVbdri6lXVtAEtPVSR+7hrX2+kPoS58V4uXZy6WiotLU0TJ07UtGnT1KJFC7322mu6+eabi7zxp556SosWLdLnn3+u+vXrW9uDgoLk5+cnSRo0aJBq1qyppKQkSb9fCt65c2dNmjRJvXv31uLFizVx4kSHLwXnaim42v5TGTpwOkORIf6m/xeau/b1RupDmBvvZfdx5vPb4XAzefJkvfbaawoPD9fEiRPtfk3lrPx+XXzOnDkaMmSIJKlLly6KjIzU3LlzrdOXLFmicePG6cCBA4qJidHkyZN1++23O7RNwg0AAGWPW8KNl5eX/Pz81L17d3l7e+c736effupctSWMcAMAQNnjzOe3wwOKBw0alO+ZFgAAgNLC4XCT+2shAACA0qpU3aEYAACguAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVDwabjZs2KA77rhDNWrUkMVi0WeffVbg/OvWrZPFYsnzOHbsWAlVDAAASjuPhpuMjAw1b95cM2bMcGq5lJQUHT161PoIDQ11U4UAAKCsKefJjffq1Uu9evVyernQ0FBVrlzZDRUBAICyrkyOuWnRooWqV6+uW2+9VRs3bixw3szMTKWnp9s8AACAeZWpcFO9enXNmjVLn3zyiT755BNFRESoS5cu2rFjR77LJCUlKSgoyPqIiIgowYoBAEBJsxiGYXi6CEmyWCxaunSp4uPjnVquc+fOql27tj744AO70zMzM5WZmWl9np6eroiICKWlpSkwMLBYNQMAgJKRnp6uoKAghz6/PTrmxhXatWunr7/+Ot/pvr6+8vX1LcGKAACAJ5Wpr6XsSU5OVvXq1T1dBgAAKCU8eubmwoULSk1NtT7fv3+/kpOTFRwcrNq1aysxMVFHjhzR/PnzJUlvvfWWoqKi1LhxY12+fFn/+Mc/tGbNGn355Zee2gUAAFDKeDTcbNu2TV27drU+Hz16tCRp8ODBmjt3ro4ePapDhw5Zp1+5ckVPP/20jhw5oooVK6pZs2ZatWqVzToAAMCNrdQMKC4pzgxIAgAApYMzn99lfswNAABAboQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKuU8ufENGzZoypQp2r59u44ePaqlS5cqPj6+wGXWrVun0aNHa8+ePYqIiNC4ceM0ZMiQkim4EL+cvKAt+09LsqhD3RBFVfW3O8/BMxcVGeJvnW6vzd4y3hYpy5C8LRZlGUae+f+YL+/03NswDMNmfTlty77/TWcyrqpbw1DdHFOt0Lry26+c9dvbvrdFOnLusiyS2tcNsZnXMIxC+y+39SknlPzrObWqXcVarzPLF7butSkn5GWx6E9hAWqfa32u3E6O61/jgvo87/sh/z4tSm3OvO6Ori93fxW3vpLg6j5wdN2/99UZ62vp6DHCFdt2dnphr6M7+7CsKit9Utw6S8N+ejTcZGRkqHnz5nr44Yd19913Fzr//v371bt3bz3xxBNauHChVq9erUcffVTVq1dXz549S6Bi+85dvKKnFu7QNz+ftmmPrRuiWQ+2VlBFH527eEXDP0zWhn0nrdM7RofIMKRNv/yxXKeYapo2oGW+y1yvU0w1vRLfWOM+22N3PnvbKMzcbw6onJdF17INu3Vdv+8F1RhbN0QWi/L0jSNy919uB09nKH7GRp29eNXaVs5Lupbt2PIFOXg6Q3dO/1ppl67lmdYusook6dsDZ4u9nRwF9d/1fe7I+6GwdThbizPL21ufvb+LotZXElzdB46u25ChJxfsyPN32i4yWN5elnyPEa7Ydn7HGmeORc6s60ZUVvqkuHWWpv20GIZhFD6b+1kslkLP3Pz1r3/Vf/7zH+3evdva1r9/f507d07Lly93aDvp6ekKCgpSWlqaAgMDi123JA1679sC/+jnP9JOg977VhtTTymrkO72tlgUV6+qw8t4WywK9Cun9EvXCl13ceSuKzdH96uocvovt5YvfWkTbJxdviDOrLs428lRUP9d3+dF6ev8XjdHa3FmeXvrKyyIFWf97uDqPnB03ZIcDq1Fqaew/SrK9PxqcmcfllVlpU+KW6e799OZz+8yNeZm06ZN6t69u01bz549tWnTpnyXyczMVHp6us3DlX45eaHAg9KGfSe14aeT2rDvpEMfSlmG8b9lTji0TJZh6OzFq24NNrnr2n8qw9qWs+/u3Pb121yfcsKp8HH98gVxdt1F3U6Owvovd58Xta/tvW7O1OLo8vmtz1X1lQRX94Ez63bmbJyz9RS67XyONc4cixxdV2l4nUuaO99XrlTcOkvbfpapcHPs2DGFhYXZtIWFhSk9PV2XLl2yu0xSUpKCgoKsj4iICJfWdPDMxULn2Xn4bKHz5F3mXFHKcbsDp/94gzqy767eZvKvzvdL7uULUpR1F2U7ORztvwOnM4rd14XVVtj63bVvRV2/O7i6D5xZd1E4Wk9h2y7sWOPMsaiweUvD61zS3Pm+cqXi1lna9rNMhZuiSExMVFpamvVx+PBhl66/TnDFQudpGVHF6fW2jKhclHLcLjLkj8Fhjuy7q7fZopbz/ZJ7+YIUZd1F2U4OR/svMsS/2H1dWG2Frd9d+1bU9buDq/vAmXUXhaP1FLbtwo41zhyLCpu3NLzOJc2d7ytXKm6dpW0/y1S4CQ8P1/Hjx23ajh8/rsDAQPn5+dldxtfXV4GBgTYPV6pbrZI6xVTLd3qnmGrq9Kdq6hRTTd4WS6Hr87ZY/rdMqEPLeFssqlLRx6F1F0dOXblHvufsuzu3ff02O9cPVRUnBqZdv3xBnF13UbeTo7D+y93nRe1re6+bM7U4unx+63NVfSXB1X3gzLod6aui1lPotvM51jhzLHJ0XaXhdS5p7nxfuVJx6yxt+1mmwk1sbKxWr15t07Zy5UrFxsZ6qKLfTRvQUh2jQ/K0x9YN0bQBLa3z5AwczNExOkSxdW2Xi6tXtcBlrhdXr6r+lXBTvvPZ24YjynnZvkFz15VbYTXG1g2x2zeOyN1/uf0r4aY8IaScnXdyfssX5F8JNynIz/5FhO0ig61XTBV3OzkK6r/r+9yR90Nh63C2FmeWt7e+wl774qzfHVzdB46ue9qAlnb/TttFBhd4jHDFtos6vajruhGVlT4pbp2laT89erXUhQsXlJqaKklq2bKl3nzzTXXt2lXBwcGqXbu2EhMTdeTIEc2fP1/S75eCN2nSRAkJCXr44Ye1Zs0aDR8+XP/5z38cvhTcHVdL5dh/KkNbfjktQ8r3/if7T2XowOkMm+v/7bXZWybn8uyc/14///Xz5bcNSXnmk6T/fP+bTl24Yr3PTWF15bdfOeu3t/1yXhb9evaS9T4eueeVVGj/5fbVvpPaceis9T43jvS/o77ad1Kr9x6Xt8WimOvuc+PK7eQo6LUrbN6C+rQotTnzuju6vtz9Vdz6SoKr+8DRde8/laHNv5zOc58bV9Xj6LHGkelSwa+jO/uwrCorfVLcOt21n858fns03Kxbt05du3bN0z548GDNnTtXQ4YM0YEDB7Ru3TqbZUaNGqUffvhBtWrV0vPPP+/UTfzcGW4AAIB7lJlw4wmEGwAAyh7T3ucGAACgMIQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKoQbAABgKvZ/IdDEcm7InJ6e7uFKAACAo3I+tx35YYUbLtycP39ekhQREeHhSgAAgLPOnz+voKCgAue54X5bKjs7W7/99psCAgJksVg8XU6xpKenKyIiQocPH+Z3skohXp/Si9emdOP1Kb08+doYhqHz58+rRo0a8vIqeFTNDXfmxsvLS7Vq1fJ0GS4VGBjIAaAU4/UpvXhtSjden9LLU69NYWdscjCgGAAAmArhBgAAmIr3+PHjx3u6CBSdt7e3unTponLlbrhvGMsEXp/Si9emdOP1Kb3Kwmtzww0oBgAA5sbXUgAAwFQINwAAwFQINwAAwFQINwAAwFQIN2VQUlKS2rZtq4CAAIWGhio+Pl4pKSmeLgt2TJo0SRaLRSNHjvR0KfifI0eO6MEHH1RISIj8/PzUtGlTbdu2zdNl3fCysrL0/PPPKyoqSn5+foqOjtbLL7/s0O8IwfU2bNigO+64QzVq1JDFYtFnn31mM90wDL3wwguqXr26/Pz81L17d+3bt89D1eZFuCmD1q9fr4SEBG3evFkrV67U1atX1aNHD2VkZHi6NOSydetWzZ49W82aNfN0Kfifs2fPKi4uTj4+Pvrvf/+rH374QW+88YaqVKni6dJueK+99ppmzpyp6dOna+/evXrttdc0efJkTZs2zdOl3ZAyMjLUvHlzzZgxw+70yZMna+rUqZo1a5a2bNkif39/9ezZU5cvXy7hSu3jUnATOHny2MkkRgAADhlJREFUpEJDQ7V+/Xp16tTJ0+VA0oULF9SqVSv9/e9/1yuvvKIWLVrorbfe8nRZN7xnn31WGzdu1FdffeXpUnCdPn36KCwsTO+995617Z577pGfn58WLFjgwcpgsVi0dOlSxcfHS/r9rE2NGjX09NNPa8yYMZKktLQ0hYWFae7cuerfv78ny5XEmRtTSEtLkyQFBwd7uBLkSEhIUO/evdW9e3dPl4Jc/vWvf6lNmza67777FBoaqpYtW+rdd9/1dFmQ1LFjR61evVo//fSTJOm7777T119/rV69enm4Mlxv//79OnbsmM3xLSgoSO3bt9emTZs8WNkfSu/tBeGQ7OxsjRw5UnFxcWrSpImny4GkxYsXa8eOHdq6daunS8F1fvnlF82cOVOjR4/W2LFjtXXrVg0fPlzly5fX4MGDPV3eDe3ZZ59Venq6GjRoIG9vb2VlZenVV1/VwIEDPV0arnPs2DFJUlhYmE17WFiYdZqnEW7KuISEBO3evVtff/21p0uBpMOHD2vEiBFauXKlKlSo4OlycJ3s7Gy1adNGEydOlCS1bNlSu3fv1qxZswg3HvbRRx9p4cKFWrRokRo3bqzk5GSNHDlSNWrU4LWB0/haqgwbOnSoli1bprVr16pWrVqeLgeStm/frhMnTqhVq1YqV66cypUrp/Xr12vq1KkqV66csrKyPF3iDa169epq1KiRTVvDhg116NAhD1WEHM8884yeffZZ9e/fX02bNtWf//xnjRo1SklJSZ4uDdcJDw+XJB0/ftym/fjx49Zpnka4KYMMw9DQoUO1dOlSrVmzRlFRUZ4uCf/TrVs37dq1S8nJydZHmzZtNHDgQCUnJ8vb29vTJd7Q4uLi8tw24aefflKdOnU8VBFyXLx4UV5eth9J3t7eys7O9lBFyE9UVJTCw8O1evVqa1t6erq2bNmi2NhYD1b2B76WKoMSEhK0aNEiff755woICLB+xxkUFCQ/Pz8PV3djCwgIyDP2yd/fXyEhIYyJKgVGjRqljh07auLEierXr5++/fZbvfPOO3rnnXc8XdoN74477tCrr76q2rVrq3Hjxtq5c6fefPNNPfzww54u7YZ04cIFpaamWp/v379fycnJCg4OVu3atTVy5Ei98soriomJUVRUlJ5//nnVqFHDekWVxxkocyTZfcyZM8fTpcGOzp07GyNGjPB0Gfiff//730aTJk0MX19fo0GDBsY777zj6ZJgGEZ6eroxYsQIo3bt2kaFChWMunXrGs8995yRmZnp6dJuSGvXrrX7OTN48GDDMAwjOzvbeP75542wsDDD19fX6Natm5GSkuLZonPhPjcAAMBUGHMDAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAMD/t3f3MVWXbQDHvwfk7SDkCQwwXiTpBQIRfJkiW6bCgZTMGTBLArQNMTEpatqsyEqImmuVq0UvaAPF0swiguHQFFhahqt0TBnsNHc2LLTBQTvguZ8/nL9xPMATFo95nuuznY1z3ffvvu7f7/zBxX3fZwinIsWNEEIIIZyKFDdCCCGEcCpS3AghxDV0Oh379u0b0xxtbW0EBgbS09MzZjlOnjxJcHAwFotlzHII8W8kxY0QN6mcnBx0Oh2rV692aHviiSfQ6XTk5OSM6RyKi4uZNm3amOa4EcxmM6mpqWOaY+PGjRQUFODj4zNmOaKiopg9ezZbt24dsxxC/BtJcSPETSwkJIRdu3Zx8eJFLXbp0iWqqqoIDQ29gTP7d1BKMTAwMOrrAgMD8fDwGIMZXWEymfjqq6/GvPgEyM3N5d13372u5yDEzUqKGyFuYvHx8YSEhLB3714ttnfvXkJDQ4mLi7Pr+80335CYmMiECRPw8/Nj8eLFtLe3a+07duxg/PjxnD59WoutWbOGe+65h76+PofcFRUVvPTSS5w4cQKdTodOp6OiooLOzk50Oh2tra1a3wsXLqDT6Th48CAABw8eRKfTUVdXR1xcHF5eXsyfP5+uri5qa2uJjIzE19eXRx55xC73n3/+ybp167jtttvw9PQkMTGRY8eOae1Xx62trWX69Ol4eHhw5MgRh7lbrVbWrl1LUFAQnp6ehIWFUVJSorUP3pYqLi7W7m/wq6KiAgCbzUZJSQnh4eF4eXkRGxvLZ599NuLntnv3bmJjY7n99tvt4k1NTcybNw+9Xo/BYMBoNHL+/HkA5s2bR0FBAevXr8dgMBAQEEB5eTkWi4Xc3Fx8fHyIiIigtrbWbsykpCS6u7s5dOjQiHMSwplIcSPETW7lypV8/PHH2vuPPvqI3Nxch34Wi4WnnnqK77//ngMHDuDi4sLSpUux2WwAPPbYYzzwwAM8+uijDAwMUFNTwwcffEBlZSV6vd5hvMzMTJ5++mnuvfdezGYzZrOZzMzMUc29uLiYd955h+bmZn799VcyMjJ48803qaqqoqamhvr6et5++22t/7PPPsuePXvYvn07x48fJyIiAqPRSHd3t924GzZsoLS0lFOnTjF16lSHvG+99Rb79+9n9+7dtLW1UVlZyeTJk4ecY1FRkXZ/ZrOZN954A71ez4wZMwAoKSlhx44dvPfee/zyyy8UFhayYsWKEYuJw4cPa9df1drayoIFC4iKiqKlpYUjR46QlpbG5cuXtT7bt2/H39+fo0ePUlBQQH5+Punp6SQkJHD8+HGSk5PJysqyKwjd3d2ZNm0ahw8fHv6DEMLZ3OD/Si6EuE7Z2dlqyZIlqqurS3l4eKjOzk7V2dmpPD091blz59SSJUtUdnb2sNefO3dOAeqnn37SYt3d3So4OFjl5+ergIAA9eqrr444hxdffFHFxsbaxTo6OhSgfvzxRy12/vx5BajGxkallFKNjY0KUA0NDVqfkpISBaj29nYtlpeXp4xGo1JKqd7eXuXm5qYqKyu1dqvVqiZNmqTKysrsxt23b9+I8y4oKFDz589XNpttyHZAff755w7xlpYW5enpqaqrq5VSSl26dEnp9XrV3Nxs12/VqlVq+fLlw+aPjY1VmzdvtostX75czZ07d9hr7rvvPpWYmKi9HxgYUN7e3iorK0uLmc1mBaiWlha7a5cuXapycnKGHVsIZzPuxpVVQoh/wsSJE1m0aBEVFRUopVi0aBH+/v4O/U6fPs0LL7zAd999x2+//aat2JhMJqKjowEwGAx8+OGHGI1GEhIS2LBhw5jOffCqSkBAAHq9njvuuMMudvToUQDa29vp7+9n7ty5WrubmxuzZs3i1KlTduNeuypyrZycHJKSkrj77rtJSUlh8eLFJCcnj3iNyWTioYceoqioiIyMDADOnDlDX18fSUlJdn2tVqvDtuBgFy9exNPT0y7W2tpKenr6iHMY/LxcXV3x8/MjJiZGiwUEBADQ1dVld52Xl9eQW4tCOCspboRwAitXrmTt2rUAbNu2bcg+aWlphIWFUV5ezqRJk7DZbERHR2O1Wu36ffvtt7i6umI2m7FYLKP+No+Ly5XdbqWUFuvv7x+yr5ubm/azTqeze381drUIGw1vb+8R2+Pj4+no6KC2tpaGhgYyMjJYuHDhsGdlLBYLDz74IHPmzGHz5s1avLe3F4CamhqH8zMjHUj29/fXztJc5eXlNeKcgSGfz7XPEHB4Zt3d3UyZMuW/ji+Es5AzN0I4gZSUFKxWK/39/RiNRof233//nba2NjZt2sSCBQuIjIx0+OUK0NzczGuvvcaXX37J+PHjtYJpOO7u7nZnQuDKShJc+Tr1VYMPF1+vKVOm4O7uTlNTkxbr7+/n2LFjREVFjXo8X19fMjMzKS8vp7q6mj179jic3YErRdqKFSuw2Wx88sknWgEBV75q7eHhgclkIiIiwu4VEhIybO64uDhOnjxpF5s6dSoHDhwY9X38FT///POIK0lCOBtZuRHCCbi6umpbM66urg7tBoMBPz8/3n//fYKCgjCZTA5bTj09PWRlZbFu3TpSU1MJDg5m5syZpKWl8fDDDw+Zd/LkyXR0dNDa2kpwcDA+Pj54eXkxe/ZsSktLCQ8Pp6uri02bNv3te/T29iY/P59nnnmGW2+9ldDQUMrKyujr62PVqlWjGmvr1q0EBQURFxeHi4sLn376KYGBgUyYMMGhb3FxMQ0NDdTX19Pb26ut1txyyy34+PhQVFREYWEhNpuNxMRE/vjjD5qamvD19SU7O3vI/Eajkccff5zLly9rn9fGjRuJiYlhzZo1rF69Gnd3dxobG0lPTx9ym/Gv6uzs5OzZsyxcuPC6xxDiZiMrN0I4CV9fX3x9fYdsc3FxYdeuXfzwww9ER0dTWFjI66+/btfnySefxNvbmy1btgAQExPDli1byMvL4+zZs0OOu2zZMlJSUrj//vuZOHEiO3fuBK58Y2tgYIDp06ezfv16XnnllX/kHktLS1m2bBlZWVnEx8dz5swZ6urqMBgMoxrHx8eHsrIyZsyYwcyZM+ns7OTrr7/WttQGO3ToEL29vSQkJBAUFKS9qqurAXj55Zd5/vnnKSkpITIykpSUFGpqaggPDx82f2pqKuPGjaOhoUGL3XXXXdTX13PixAlmzZrFnDlz+OKLLxg37u/9Dbpz506Sk5MJCwv7W+MIcTPRqcEb40IIIf4ntm3bxv79+6mrqxuzHFarlTvvvJOqqiq7g9hCODvZlhJCiBsgLy+PCxcu0NPTM2b/gsFkMvHcc89JYSP+78jKjRBCCCGcipy5EUIIIYRTkeJGCCGEEE5FihshhBBCOBUpboQQQgjhVKS4EUIIIYRTkeJGCCGEEE5FihshhBBCOBUpboQQQgjhVKS4EUIIIYRT+Q+VdVdwE27ZWQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_subject = pd.read_csv('/home/azureuser/cloudfiles/code/Users/data/msd_quantification/msd_lung_metrics_3D_subject.csv')\n",
        "print(df_subject.columns)\n",
        "# Plot scatter plot of tumor_area vs number_of_tumors where tumor_flag is 1\n",
        "df_subject.plot.scatter(x='max_tumor_size', y='number_tumors')\n",
        "# Axis title\n",
        "plt.xlabel('Max tumor size (cm)')\n",
        "plt.ylabel('Number of tumors')\n",
        "plt.title('Tumor size (cm) vs Number of Tumors for Lungs')\n",
        "plt.ticklabel_format(style='plain')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1697513567220
        }
      },
      "outputs": [],
      "source": [
        "from azure.ai.ml import MLClient\n",
        "from azure.ai.ml.entities import Data\n",
        "from azure.ai.ml.constants import AssetTypes\n",
        "from azure.identity import DefaultAzureCredential\n",
        "from azureml.core import Workspace, Datastore, Dataset\n",
        "\n",
        "# Get the workspace\n",
        "workspace = Workspace.from_config()\n",
        "\n",
        "# Get subscription ID, resource group, and workspace name\n",
        "workspace\n",
        "\n",
        "\n",
        "# # Connect to the AzureML workspace\n",
        "# subscription_id = from azure.ai.ml import MLClient\n",
        "from azure.ai.ml.entities import Data\n",
        "from azure.ai.ml.constants import AssetTypes\n",
        "from azure.identity import DefaultAzureCredential\n",
        "from azureml.core import Workspace, Datastore, Dataset\n",
        "\n",
        "# Get the workspace\n",
        "workspace = Workspace.from_config()\n",
        "\n",
        "# Connect to the AzureML workspace\n",
        "subscription_id = workspace.subscription_id\n",
        "resource_group = workspace.resource_group\n",
        "workspace_name = workspace.name\n",
        "\n",
        "ml_client = MLClient(\n",
        "    DefaultAzureCredential(), subscription_id, resource_group, workspace_name\n",
        ")\n",
        "\n",
        "# Set the version number of the data asset (for example: '1')\n",
        "VERSION = '6'\n",
        "path = '/home/azureuser/cloudfiles/code/Users/data/msd_quantification'\n",
        "\n",
        "# Define the Data asset object\n",
        "my_data = Data(\n",
        "    path=path,\n",
        "    type=AssetTypes.URI_FOLDER,\n",
        "    description=\"Liver, Lung, Colon and Pancreas quantification tumor metrics. Metrics are 2D per organ and 3D per tumor.\",\n",
        "    name=\"mds_quantification\",\n",
        "    version=VERSION,\n",
        ")\n",
        "\n",
        "# Create the data asset in the workspace\n",
        "ml_client.data.create_or_update(my_data)\n"
      ]
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python38-azureml"
    },
    "kernelspec": {
      "display_name": "azureml_py38",
      "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": "3.8.5"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      },
      "ms_spell_check": {
        "ms_spell_check_language": "en"
      }
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}