tutorials/General.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1fb578b1-473f-42aa-8e7f-d6baf1291d57",
   "metadata": {},
   "source": [
    "# General\n",
    "This tutorial guides you through the general concepts of the htc framework and how it can be used to work with HSI images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ece75385-2876-4936-b2dd-c00847cd6885",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "\n",
    "from htc import Config, DataPath, DatasetImage, LabelMapping, settings, settings_atlas, tivita_wavelengths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597c1c87-7d58-461e-b130-9dade2fc3e9d",
   "metadata": {},
   "source": [
    "## The `DataPath` class\n",
    "The general interface for operating with the HSI images is the `DataPath` class. You can use it to read data cubes, annotations or meta data. But first, you need a `DataPath` object and there are three ways to create it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82b2178f-c888-4cf5-a657-2d589ed81675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/mnt/nvme_4tb1/HeiPorSPECTRAL/data/subjects/P086/2021_04_15_09_22_02"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Via the unique name of an image\n",
    "path = DataPath.from_image_name(\"P086#2021_04_15_09_22_02\")\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "54d099b6-53a6-43fe-b39f-a45290cdaee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2021_04_15_09_22_02',\n",
       " '2021_04_15_09_22_20',\n",
       " '2021_04_15_09_22_37',\n",
       " '2021_04_15_09_23_19',\n",
       " '2021_04_15_09_23_36',\n",
       " '2021_04_15_09_23_54',\n",
       " '2021_04_15_09_24_41',\n",
       " '2021_04_15_09_24_59',\n",
       " '2021_04_15_09_25_15',\n",
       " '2021_04_15_09_26_38']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Iterate over the complete dataset\n",
    "paths = list(DataPath.iterate(settings.data_dirs[\"HeiPorSPECTRAL\"]))\n",
    "[p.timestamp for p in paths[:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "927a61b9-d24a-4fb5-8d13-f93b2275044c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/mnt/nvme_4tb1/HeiPorSPECTRAL/data/subjects/P086/2021_04_15_09_22_02"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Directly based on its path\n",
    "# This is only for images from unstructured datasets (HeiPorSPECTRAL is a structured dataset)\n",
    "path = DataPath(settings.data_dirs[\"HeiPorSPECTRAL\"] / \"subjects/P086/2021_04_15_09_22_02\")\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19937ca7-8e7d-4f13-96f3-355fd414feda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0202-00118#2021_04_15_09_22_02'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the direct path is not the same as from_image()\n",
    "# The direct path is more general and works for every Tivita image but at the same time knows less about the structure of the dataset\n",
    "# For example, it knows nothing about the subject name which also results in a different image name\n",
    "path.image_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dab77cb8-e999-4d39-8ea5-127ce6cfb080",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject_name =  P086\n",
      "image_name =  P086#2021_04_15_09_22_02\n"
     ]
    }
   ],
   "source": [
    "# This data path, however, does know the subject name which is also reflected in the image name\n",
    "# Note: there may be more than one image name for the same data path but it is always unique\n",
    "path = DataPath.from_image_name(\"P086#2021_04_15_09_22_02\")\n",
    "print(\"subject_name = \", path.subject_name)\n",
    "print(\"image_name = \", path.image_name())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f3d2c92-5c69-43f0-be2c-6395dfe980fb",
   "metadata": {},
   "source": [
    "Once you have a `DataPath` object, you can do many cool things with it. Feel free to explore the methods yourself or use your favorite IDE to list them :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1a68165-44a9-48be-b97b-ce46d58c7172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640, 100)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data cube\n",
    "path.read_cube().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54ec9632-1d22-4ae0-8549-c7480c0c8270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handy normalization option included\n",
    "# Note: It is recommended to always work with normalized data except when you have a very good reson not to do so.\n",
    "#       This will reduce e.g. multiplicative changes which can arise from different lightning conditions.\n",
    "np.sum(path.read_cube(normalization=1)[0, 0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8614c6a-7289-4ab7-a688-25c044f02f4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the RGB image\n",
    "path.read_rgb_reconstructed().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "811949e2-9f21-4c0c-ab33-447f69ef4334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parameter images\n",
    "path.compute_sto2().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25172fe2-d3e2-4aee-899d-5a3b8e5e1489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'polygon#annotator1': array([[255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
       " 'polygon#annotator2': array([[255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
       " 'polygon#annotator3': array([[255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255],\n",
       "        [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the corresponding segmentation masks\n",
    "# annotation_name can also be set to load only the annotations from one annotator (e.g. polygon#annotator1)\n",
    "path.read_segmentation(annotation_name=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ea4d0da-b064-448d-a680-df824ecdf802",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timestamp =  2021_04_15_09_22_02\n",
      "subject_name =  P086\n",
      "image_name =  P086#2021_04_15_09_22_02\n"
     ]
    }
   ],
   "source": [
    "# Access image information\n",
    "print(\"timestamp = \", path.timestamp)\n",
    "print(\"subject_name = \", path.subject_name)\n",
    "print(\"image_name = \", path.image_name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "716b8130-822c-4c2f-a3f6-831c439aefeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0202-00118'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access meta information\n",
    "# This contains information from the *_meta.log file and you can access the other attributes in a similar way\n",
    "path.meta(\"Camera_CamID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5f74dcb8-1e52-4a80-9ee4-5aeb4a12625b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subject_name': 'P086',\n",
       " 'timestamp': '2021_04_15_09_22_02',\n",
       " 'Camera_CamID': '0202-00118',\n",
       " 'Camera_Exposure': 75.0,\n",
       " 'Camera_analoger Gain': 4.0,\n",
       " 'Camera_digitaler Gain': 32.0,\n",
       " 'Camera_Speed': 950.0,\n",
       " 'SW_Name': 'TIVITA® Suite',\n",
       " 'SW_Version': '1.6.0.1',\n",
       " 'Fremdlichterkennung_Fremdlicht erkannt?': False,\n",
       " 'Fremdlichterkennung_PixelmitFremdlicht': 0.0,\n",
       " 'Fremdlichterkennung_Breite LED Rot': 50.0,\n",
       " 'Fremdlichterkennung_Breite LED Gruen': 50.0,\n",
       " 'Fremdlichterkennung_Grenzwert Pixelanzahl': 100.0,\n",
       " 'Fremdlichterkennung_Intensity Grenzwert': 7.0,\n",
       " 'Aufnahme_Aufnahmemodus': 'Reflektanz',\n",
       " 'camera_name': '0202-00118_correct-1',\n",
       " 'path': 'subjects/P086/2021_04_15_09_22_02',\n",
       " 'annotation_name': array(['polygon#annotator1', 'polygon#annotator2', 'polygon#annotator3'],\n",
       "       dtype=object),\n",
       " 'dsettings': Settings for the dataset data\n",
       " The following settings are available:\n",
       " ['dataset_name', 'data_path_class', 'shape', 'shape_names', 'label_mapping', 'last_valid_label_index', 'label_ordering', 'annotator_mapping', 'subject_mapping', 'spatial_shape'],\n",
       " 'dataset_env_name': 'PATH_Tivita_HeiPorSPECTRAL',\n",
       " 'data_dir': PosixPath('/mnt/nvme_4tb1/HeiPorSPECTRAL/data'),\n",
       " 'intermediates_dir': PosixPath('/mnt/nvme_4tb1/HeiPorSPECTRAL/intermediates'),\n",
       " 'label_meta': {'spleen': {'situs': 1, 'angle': 0, 'repetition': 1}}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Omit the key to get all available meta information\n",
    "path.meta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ccf62f43-248d-4ccc-99a3-7e53d20cb36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nested meta information can be accessed in a similar way as the config files (see below)\n",
    "path.meta(\"label_meta/spleen/situs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "617f9846-9d4b-443f-9f0e-41bd48872bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640, 100)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each path also holds a reference to the global dataset settings\n",
    "path.dataset_settings[\"shape\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e2f07e1-9cd7-49ce-b2c4-e440d7809791",
   "metadata": {},
   "source": [
    "## Global `settings`\n",
    "You may already noticed the usage of the `settings` object above. This can be used to access data directories, logging and defines other global attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fbfd5304-f5e1-47d5-9a2a-34c782acf08c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<htc.utils.Datasets.DatasetAccessor at 0x7fb5ea8b6090>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Direct access to the registered datasets (depending on the set environment variables)\n",
    "settings.data_dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "033a2c6d-d4f7-4b28-bfa7-c3d66bcd5c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class: MultiPath\n",
       "Root location: /mnt/nvme_4tb1/HeiPorSPECTRAL/intermediates (exists=True)\n",
       "Best location: /mnt/nvme_4tb1/HeiPorSPECTRAL/intermediates (exists=True)\n",
       "All locations:\n",
       "/mnt/nvme_4tb1/HeiPorSPECTRAL/intermediates (exists=True)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Directory with the preprocessed data\n",
    "# Note In case you have more than one dataset, this path can be used to access files from all datasets (with unique names)\n",
    "settings.intermediates_dir_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbae10da-3b0d-40d8-b930-6c77d1c0256b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class: MultiPath\n",
       "Root location: /mnt/ssd_8tb/htc/results_test (exists=True)\n",
       "Best location (considering needle /mnt/ssd_8tb/htc/results_test): /mnt/ssd_8tb/htc/results_test (exists=True)\n",
       "All locations:\n",
       "/mnt/ssd_8tb/htc/results_test (exists=True)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Directory for the results\n",
    "settings.results_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "04115c1f-bd61-42ec-831d-26fb47a1503c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span><span style=\"color: #008700; text-decoration-color: #008700\">INFO</span><span style=\"font-weight: bold\">][</span><span style=\"font-style: italic\">htc</span><span style=\"font-weight: bold\">]</span> my log message                                                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">134398714.py:2</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\u001b[38;5;28mINFO\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[3mhtc\u001b[0m\u001b[1m]\u001b[0m my log message                                                                                \u001b[2m134398714.py:2\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logging\n",
    "settings.log.info(\"my log message\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc8078f-11fc-44ec-a751-89d49fd031f8",
   "metadata": {},
   "source": [
    "## Advanced data loading\n",
    "The `DataPath` reads the original data cubes which is fine for simple tasks but in more advanced settings (e.g. training a neural network), you may want a more flexible solution which also make use of the preprocessed L1 normalized files in the intermediates directory. For this, dataset classes can be used and configured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b9a9eebd-8028-4430-a959-452a72fb8849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['labels', 'valid_pixels', 'features', 'image_name', 'image_index'])\n"
     ]
    }
   ],
   "source": [
    "# The config object can be used to specify the data loading (and more)\n",
    "# It is also used to specify all the training settings and every trained model also stores its used config so that you can always read it later\n",
    "# Here, we want to read L1 normalized HSI images (for label_mapping see below)\n",
    "config = Config({\n",
    "    \"input/n_channels\": 100,\n",
    "    \"input/preprocessing\": \"L1\",\n",
    "    \"input/annotation_name\": \"polygon#annotator1\",  # Only annotations from one annotator in this example\n",
    "    \"label_mapping\": settings_atlas.label_mapping,  # This changes the index values of the original segmentations\n",
    "})\n",
    "# Note: The file htc/utils/config.schema contains descriptions of common configuration options\n",
    "\n",
    "# A dataset can be used to iterate over images or select certain images\n",
    "# Here we use all the paths which we selected above\n",
    "dataset = DatasetImage(paths, train=False, config=config)\n",
    "for sample in dataset:\n",
    "    # The sample is just a dictionary with everything related to the image file, e.g. label or features\n",
    "    print(sample.keys())\n",
    "    break\n",
    "\n",
    "sample = dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "103a9e3f-9c85-475f-87f0-ab7f631daeb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([480, 640])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The sample contains the image data as torch tensors\n",
    "# (height, width)\n",
    "sample[\"labels\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da90a4bf-1d63-4af4-9455-7b408d6c74e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([480, 640, 100])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (height, width, channels)\n",
    "# Note: the features are the L1 normalized HSI cube from the blosc compressed files in the intermediates folder\n",
    "sample[\"features\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "71c2b8cc-c394-48fc-a858-5203779cd5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[False, False, False,  ..., False, False, False],\n",
       "        [False, False, False,  ..., False, False, False],\n",
       "        [False, False, False,  ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False,  ..., False, False, False],\n",
       "        [False, False, False,  ..., False, False, False],\n",
       "        [False, False, False,  ..., False, False, False]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This mask indicates the pixels with valid annotations (not every pixel is annotated!)\n",
    "sample[\"valid_pixels\"]"
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   "execution_count": 25,
   "id": "d44c118d-f720-4d8f-9414-022a946552e5",
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    {
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     "text": [
      "19: spleen\n",
      "100: unlabeled\n"
     ]
    }
   ],
   "source": [
    "# The labels are stored as index with values corresponding to the set label mapping in the config (there is a separate mapping for each task)\n",
    "# There is a extra class to convert these indices back to names\n",
    "mapping = LabelMapping.from_config(config)\n",
    "for label_index in sample[\"labels\"].unique():\n",
    "    print(f\"{label_index.item()}: {mapping.index_to_name(label_index)}\")"
   ]
  },
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   "execution_count": 26,
   "id": "2bbdfc90-467a-4035-b148-aa294d1f9b3e",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
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   ],
   "source": [
    "# The label mapping class also has other usueful features like the number of classes used during training\n",
    "# Here: 20 organ classes\n",
    "len(mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a771d8e-b2b2-4a13-92e2-d33c004a7e71",
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   "source": [
    "Really advanced data loading: if you need high efficient data loading for the GPU, then have a look at the example in [`DatasetImageStream`](../htc/models/image/DatasetImageStream.py) which implements a circular memory buffer in pinned memory to optimize loading performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4808178-1b87-472c-9708-9ab683f4d152",
   "metadata": {},
   "source": [
    "# Example: calculate the median spectra for spleen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "88d25e30-5d73-4630-98f3-c917e53e6ec5",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([14370, 100])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all colon spectra\n",
    "colon_spectra = sample[\"features\"][sample[\"labels\"] == mapping.name_to_index(\"spleen\")]\n",
    "colon_spectra.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c1708964-ac92-4b09-ab5b-447d0eb0f359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Calculate the median on a per-channel basis\n",
    "colon_median_spectrum = colon_spectra.float().quantile(q=0.5, dim=0)\n",
    "colon_median_spectrum.shape"
   ]
  },
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   "execution_count": 29,
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          "autotypenumbers": "strict",
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          "colorway": [
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           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
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            "linecolor": "white",
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           },
           "bgcolor": "#E5ECF6",
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            "linecolor": "white",
            "ticks": ""
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          },
          "scene": {
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            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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           },
           "yaxis": {
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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            "zerolinecolor": "white"
           },
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
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            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
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            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
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           "gridcolor": "white",
           "linecolor": "white",
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           "zerolinewidth": 2
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         }
        },
        "xaxis": {
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         "autorange": true,
         "domain": [
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         ],
         "range": [
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         ],
         "title": {
          "text": "wavelength [nm]"
         },
         "type": "linear"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
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         ],
         "range": [
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         ],
         "title": {
          "text": "L1 normalized reflectance"
         },
         "type": "linear"
        }
       }
      },
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",
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   "source": [
    "# Plot the result\n",
    "fig = px.line(x=tivita_wavelengths(), y=colon_median_spectrum)\n",
    "fig.update_layout(xaxis_title=\"wavelength [nm]\", yaxis_title=\"L1 normalized reflectance\")"
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