{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "geographic-department",
   "metadata": {},
   "source": [
    "# Diabetes related genes expressed in pancreas\n",
    "\n",
    "This notebook shows how to integrate genomic and image data resources.\n",
    "This notebook looks at the question **Which diabetes related genes are expressed in the pancreas?**\n",
    "Tissue and disease can be modified.\n",
    "\n",
    "\n",
    "Steps:\n",
    "\n",
    "* Query [humanmine.org](https://www.humanmine.org/humanmine), an integrated database of *Homo sapiens* genomic data using the intermine API to find the genes.\n",
    "* Using the list of found genes, search in the Image Data Resource (IDR) for images linked to the genes, tissue and disease.\n",
    "\n",
    " \n",
    "We use the intermine API and the IDR API. This notebook is inspired by [Workshop_Pax6Workflow](https://github.com/intermine/intermine-ws-python-docs/blob/master/Workshop_Pax6Workflow.ipynb).\n",
    "\n",
    "## Summary:\n",
    "![Overview](./includes/HumanMineIDR.png)\n",
    "\n",
    "## Settings:\n",
    "\n",
    "\n",
    "### Auxiliary libraries used\n",
    "* [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels): Enables a Jupyter Notebook or JupyterLab application in one conda environment to access kernels for Python, R, and other languages found in other environments.\n",
    "* [jupyter_contrib_nbextensions](https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/index.html): Package containing a collection of community-contributed unofficial extensions that add functionality to the Jupyter notebook.\n",
    "\n",
    "## Launch\n",
    "\n",
    "### binder\n",
    "\n",
    "If not already running, you can launch by clicking on the logo [![Binder <](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/IDR/idr-notebooks/master?urlpath=notebooks%2Fhumanmine.ipynb)\n",
    "\n",
    "### run locally using Docker and repo2docker\n",
    "\n",
    "With ``jupyter-repo2docker`` installed, run:\n",
    "\n",
    "```\n",
    "git clone https://github.com/IDR/idr-notebooks.git\n",
    "cd idr-notebooks\n",
    "repo2docker .\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conscious-metabolism",
   "metadata": {},
   "source": [
    "### Install dependencies if required\n",
    "The cell below will install dependencies if you choose to run the notebook in [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb#recent=true)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "hazardous-complement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: intermine in /Users/jmarie/opt/anaconda3/envs/stardist-1/lib/python3.9/site-packages (1.13.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install intermine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "external-swaziland",
   "metadata": {},
   "source": [
    "### Import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "collect-sensitivity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries to interact with intermine\n",
    "from intermine.webservice import Service\n",
    "\n",
    "# libraries to interact with IDR\n",
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-transsexual",
   "metadata": {},
   "source": [
    "## Search for genes in HumanMine\n",
    "\n",
    "We first define the output columns, then add the constraints i.e. specify the tissue and the disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "interracial-petite",
   "metadata": {},
   "outputs": [],
   "source": [
    "service = Service(\"https://www.humanmine.org/humanmine/service\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "empty-yeast",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = service.new_query(\"Gene\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "miniature-sailing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<intermine.query.Query at 0x111b1b220>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query.add_view(\n",
    "    \"primaryIdentifier\", \"symbol\", \"proteinAtlasExpression.cellType\",\n",
    "    \"proteinAtlasExpression.level\", \"proteinAtlasExpression.reliability\",\n",
    "    \"proteinAtlasExpression.tissue.name\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ordered-director",
   "metadata": {},
   "source": [
    "We look for those genes in the specified tissue and that are also associated with the specified disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "684b4167",
   "metadata": {},
   "outputs": [],
   "source": [
    "TISSUE = \"Pancreas\"\n",
    "DISEASE = \"diabetes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "prospective-stable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BinaryConstraint: Gene.diseases.name CONTAINS diabetes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query.add_constraint(\"proteinAtlasExpression.tissue.name\", \"=\", TISSUE)\n",
    "query.add_constraint(\"proteinAtlasExpression.level\", \"ONE OF\", [\"Medium\", \"High\"])\n",
    "query.add_constraint(\"organism.name\", \"=\", \"Homo sapiens\")\n",
    "query.add_constraint(\"diseases.name\", \"CONTAINS\", DISEASE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proved-bronze",
   "metadata": {},
   "source": [
    "Collect the genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adopted-treaty",
   "metadata": {},
   "outputs": [],
   "source": [
    "upin_tissue = set()\n",
    "for row in query.rows():\n",
    "    upin_tissue.add(row[\"symbol\"])\n",
    "genes = sorted(upin_tissue, reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selective-pathology",
   "metadata": {},
   "source": [
    "Print out the list of genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "secret-labor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YIPF5 WFS1 VEGFA TCF7L2 TBC1D4 SOD2 SLC30A8 PTPN22 \n",
      "PDX1 MIA3 KCNJ11 IRS2 IRS1 INSR INS IGF2BP2 \n",
      "IER3IP1 HNF4A HNF1B HMGA1 HFE GPD2 GCK ENPP1 \n",
      "EIF2AK3 DNAJC3 CEL CAPN10 APPL1 AKT2 ABCC8 "
     ]
    }
   ],
   "source": [
    "for i, a in enumerate(genes):\n",
    "    print(a, end=' ')\n",
    "    if i % 8 == 7: \n",
    "        print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pending-fusion",
   "metadata": {},
   "source": [
    "## Search for images in IDR associated to the genes found in HumanMine\n",
    "\n",
    "From the list of genes found using the intermine API, we are now looking in [Image Data Resource](https://idr.openmicroscopy.org/) for studies linked to those genes and with **TISSUE**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "round-dancing",
   "metadata": {},
   "source": [
    "### Set up where to query and session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "supported-jaguar",
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "INDEX_PAGE = \"https://idr.openmicroscopy.org/webclient/?experimenter=-1\"\n",
    "\n",
    "# create http session\n",
    "with requests.Session() as session:\n",
    "    request = requests.Request('GET', INDEX_PAGE)\n",
    "    prepped = session.prepare_request(request)\n",
    "    response = session.send(prepped)\n",
    "    if response.status_code != 200:\n",
    "        response.raise_for_status()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elect-batch",
   "metadata": {},
   "source": [
    "### Search studies\n",
    "Search the studies related to the list of genes found in the HumanMine resource."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e91eab83",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEARCH_URL = \"https://idr.openmicroscopy.org/searchengine/api/v1/resources/{type}/search/\"\n",
    "KEY_VALUE_SEARCH = SEARCH_URL + \"?key={key}&value={value}\"\n",
    "KEY = \"Gene Symbol\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "perfect-throw",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.02 s, sys: 335 ms, total: 1.36 s\n",
      "Wall time: 10.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import collections\n",
    "from collections import defaultdict\n",
    "\n",
    "results = {}\n",
    "for gene in genes:\n",
    "    qs1 = {'type': 'image', 'key': KEY, 'value': gene}\n",
    "    url = KEY_VALUE_SEARCH.format(**qs1)\n",
    "    json = session.get(url).json()\n",
    "    images = json['results']['results']\n",
    "    results[gene] = images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "returning-retreat",
   "metadata": {},
   "source": [
    "### First we filter images with the development stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aec7ada7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotation key in IDR to find and filter by.\n",
    "EXPRESSION_KEY = \"Expression Pattern Description\"\n",
    "EXPRESSION = \"Islets\"\n",
    "STAGE = \"Developmental Stage\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ad0e81b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "development_stage = {}\n",
    "for k in results:\n",
    "    images = results[k]\n",
    "    result_images = defaultdict(list)\n",
    "    for image in images:\n",
    "        values = image[\"key_values\"]\n",
    "        stage = \"\"\n",
    "        for v in values:\n",
    "            name = v[\"name\"]\n",
    "            value = v['value']\n",
    "            if name == STAGE:\n",
    "                stage = value\n",
    "            if name == EXPRESSION_KEY and EXPRESSION in value:\n",
    "                result_images[stage].append(image[\"id\"])\n",
    "    development_stage[k] = result_images.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "adapted-ethics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'YIPF5': dict_items([]), 'WFS1': dict_items([]), 'VEGFA': dict_items([]), 'TCF7L2': dict_items([]), 'TBC1D4': dict_items([]), 'SOD2': dict_items([]), 'SLC30A8': dict_items([]), 'PTPN22': dict_items([]), 'PDX1': dict_items([('15PCW', [9841210, 9841211]), ('9PCW', [9841212, 9841213, 9841218, 9841219]), ('CS21', [9841214, 9841215]), ('CS16', [9841216, 9841217])]), 'MIA3': dict_items([]), 'KCNJ11': dict_items([]), 'IRS2': dict_items([]), 'IRS1': dict_items([]), 'INSR': dict_items([]), 'INS': dict_items([('15PCW', [9839153, 9839154])]), 'IGF2BP2': dict_items([]), 'IER3IP1': dict_items([]), 'HNF4A': dict_items([]), 'HNF1B': dict_items([]), 'HMGA1': dict_items([]), 'HFE': dict_items([]), 'GPD2': dict_items([]), 'GCK': dict_items([]), 'ENPP1': dict_items([]), 'EIF2AK3': dict_items([]), 'DNAJC3': dict_items([]), 'CEL': dict_items([]), 'CAPN10': dict_items([]), 'APPL1': dict_items([]), 'AKT2': dict_items([]), 'ABCC8': dict_items([])}\n"
     ]
    }
   ],
   "source": [
    "print(development_stage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fallen-girlfriend",
   "metadata": {},
   "source": [
    "## Display the images\n",
    "Display the images associated to the genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "sunset-playlist",
   "metadata": {},
   "outputs": [],
   "source": [
    "# URLs to retrieve the thumbnails and link to the images in IDR\n",
    "BASE_URL = \"https://idr.openmicroscopy.org/webclient\"\n",
    "IMAGE_DATA_URL = BASE_URL + \"/render_thumbnail/{id}\"\n",
    "LINK_URL = BASE_URL + \"/?show=image-{id}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subjective-radius",
   "metadata": {},
   "source": [
    "## Display the images with development stage\n",
    "Click on the thumbnail to open the image in IDR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "detailed-oakland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5c1c2a4649f4f5781841abe8671fea4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "AppLayout(children=(HTML(value='<table><tr><td><h2>Gene: PDX1</h2></td></tr><tr><tr><td><h4>Developmental stag…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the images\n",
    "from ipywidgets import AppLayout, widgets\n",
    "\n",
    "table_widget = widgets.HTML(\"\")\n",
    "\n",
    "html = \"<table>\"\n",
    "for gene in development_stage:\n",
    "    images = development_stage[gene]\n",
    "    if len(images) > 0:\n",
    "        html += '<tr><td><h2>Gene: '+gene+'</h2></td></tr><tr>'\n",
    "        for k, v in images:\n",
    "            html += '<tr><td><h4>Developmental stage: '+k+'</h4></td></tr><tr>'\n",
    "            for i in v:\n",
    "                qs = {'id': i}\n",
    "                url = IMAGE_DATA_URL.format(**qs)\n",
    "                url_link = LINK_URL.format(**qs)\n",
    "                html += '<td><a href=\"'+url_link+'\" target=\"_blank\"><img src=\"'+url+'\"/></a></td>'\n",
    "            html += \"</tr>\"\n",
    "        html += \"</tr>\"\n",
    "html += \"</table>\"\n",
    "\n",
    "table_widget.value = html\n",
    "AppLayout(header=None,\n",
    "          left_sidebar=None,\n",
    "          center=table_widget,\n",
    "          right_sidebar=None,\n",
    "          footer=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d0b34e",
   "metadata": {},
   "source": [
    "## Find the image with the following key(s) and value(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1385e2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "PART_KEY = \"Organism Part\"\n",
    "PATHOLOGY_KEY = \"Pathology\"\n",
    "PATHOLOGY_NORMAL_VALUE = \"Normal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "55f1cf9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pathology_images = {}\n",
    "for k in results:\n",
    "    images = results[k]\n",
    "    result_images = defaultdict(list)\n",
    "    for image in images:\n",
    "        values = image[\"key_values\"]\n",
    "        part = None\n",
    "        for v in values:\n",
    "            name = v[\"name\"]\n",
    "            value = v['value']\n",
    "            if PART_KEY in name and (TISSUE or EXPRESSION in value):\n",
    "                part = value\n",
    "        for v in values:\n",
    "            name = v[\"name\"]\n",
    "            value = v['value']\n",
    "            if part is not None and name == PATHOLOGY_KEY:\n",
    "                if PATHOLOGY_NORMAL_VALUE in value:\n",
    "                    result_images[PATHOLOGY_NORMAL_VALUE].append(image[\"id\"])\n",
    "                else:\n",
    "                    result_images[value].append(image[\"id\"])\n",
    "    pathology_images[k] = result_images.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d107a03",
   "metadata": {},
   "source": [
    "**Plot the disease vs number of images found**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367f36ee",
   "metadata": {},
   "source": [
    "## Filter images with a given Organism Part value\n",
    "\n",
    "We explore the images associated to the gene **PDX1** with abnormal pathology status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7fe56469",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "disease_map = {}\n",
    "gene = \"PDX1\"\n",
    "images = pathology_images[gene]\n",
    "if len(images) == 0:\n",
    "    print(\"No images found\")\n",
    "else:\n",
    "    for k, v in images:\n",
    "        if k != PATHOLOGY_NORMAL_VALUE:\n",
    "            disease_map[k] = len(v)\n",
    "\n",
    "    disease_ordered = collections.OrderedDict(sorted(disease_map.items()))\n",
    "    df = pd.DataFrame({'disease':disease_ordered.items(),\n",
    "                       'number of images':disease_ordered.values()})\n",
    "    df.plot(kind='barh', x='disease', y='number of images', figsize=(10,10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae84af6c",
   "metadata": {},
   "source": [
    "## Select the disease and display the associated images\n",
    "Click on the thumbnail to open the image in IDR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b16ac025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c532ecf4735743f8ae436c7e8ee752c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "GridspecLayout(children=(HTML(value='Gene: <b>PDX1</b>', layout=Layout(grid_area='widget001')), Dropdown(descr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import GridspecLayout, widgets\n",
    "\n",
    "increase = 8\n",
    "max_value = increase\n",
    "min_value = 0\n",
    "\n",
    "disease = \"\"\n",
    "\n",
    "def display_images(images, min, max):\n",
    "    html = \"<table>\"\n",
    "    html += '<tr>'\n",
    "    if min < 0:\n",
    "        min = 0\n",
    "    if max >= len(images):\n",
    "        max = len(images)\n",
    "\n",
    "    \n",
    "    for i in images[min:max]:\n",
    "        qs = {'id': i}\n",
    "        url = IMAGE_DATA_URL.format(**qs)\n",
    "        url_link = LINK_URL.format(**qs)\n",
    "        html += '<td><a href=\"'+url_link+'\" target=\"_blank\"><img src=\"'+url+'\"/></a>&nbsp;</td>'\n",
    "    html += \"</tr>\"\n",
    "    html += \"</table>\"\n",
    "    html_widget.value = html\n",
    "    \n",
    "    # Set the number of images found\n",
    "    count_widget.value = \"<b>Number of images found: \" + str(len(images)) + \"</b>\"\n",
    "    \n",
    "def on_selection_change(change):\n",
    "    global disease\n",
    "    if change['name'] == 'value':\n",
    "        values = get_images(change['new']) \n",
    "        if values is None:\n",
    "            return\n",
    "        disease = change['new']\n",
    "        min_value = 0\n",
    "        max_value = increase \n",
    "        display_images(values, min_value, max_value)\n",
    "                \n",
    "def get_images(disease):\n",
    "    for k, v in images:\n",
    "        if k == disease:\n",
    "            return v\n",
    "    return None\n",
    "    \n",
    "def on_click_next(b):\n",
    "    global min_value\n",
    "    global max_value\n",
    "    max_value = max_value + increase\n",
    "    min_value = min_value + increase\n",
    "    values = get_images(disease)\n",
    "    button_previous.disabled = False\n",
    "    if values is None:\n",
    "        return\n",
    "    if max_value > len(values):\n",
    "        button_next.disabled = True\n",
    "        \n",
    "    display_images(values, min_value, max_value)\n",
    "\n",
    "def on_click_previous(b):\n",
    "    global min_value\n",
    "    global max_value\n",
    "    max_value = max_value - increase\n",
    "    min_value = min_value - increase\n",
    "    button_next.disabled = False\n",
    "    if min_value <= 0:  # reset \n",
    "        min_value = 0\n",
    "        max_value = increase\n",
    "        button_previous.disabled = True\n",
    "    values = get_images(disease)\n",
    "    if values is not None:\n",
    "        display_images(values, min_value, max_value)\n",
    "            \n",
    "def dropdown_widget(disease_list,\n",
    "                    dropdown_widget_name,\n",
    "                    displaywidget=False):\n",
    "\n",
    "    selection = widgets.Dropdown(\n",
    "        options=disease_list,\n",
    "        value=disease_list[0],\n",
    "        description=dropdown_widget_name,\n",
    "        disabled=False,\n",
    "    )\n",
    "    selection.observe(on_selection_change)\n",
    "    display_images(get_images(selection.value), min_value, max_value)\n",
    "    return selection\n",
    "\n",
    "disease_list = list(disease_ordered.keys())\n",
    "disease = disease_list[0]\n",
    "gene_widget = widgets.HTML(\"\")\n",
    "count_widget = widgets.HTML(\"\")\n",
    "html_widget = widgets.HTML(\"\")\n",
    "disease_box = dropdown_widget(\n",
    "    disease_list,\n",
    "    'Disease: ', True\n",
    ")\n",
    "\n",
    "button_next = widgets.Button(description=\"Next>>\")\n",
    "button_next.on_click(on_click_next)\n",
    "\n",
    "button_previous = widgets.Button(description=\"<<Previous\", disabled=True)\n",
    "button_previous.on_click(on_click_previous)\n",
    "\n",
    "gene_widget.value = \"Gene: <b>\" + gene + \"</b>\"\n",
    "\n",
    "grid = GridspecLayout(3, 3)\n",
    "grid[0, 0] = gene_widget\n",
    "grid[0, 1] = disease_box\n",
    "grid[0, 2] = count_widget\n",
    "grid[2, 0] = button_previous\n",
    "grid[1, :] = html_widget\n",
    "grid[2, 2] = button_next\n",
    "grid\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sought-subsection",
   "metadata": {},
   "source": [
    "### License (BSD 2-Clause)¶\n",
    "\n",
    "Copyright (C) 2021-2022 University of Dundee. All Rights Reserved.\n",
    "\n",
    "Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n",
    "\n",
    "Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31f1dbaa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "stardist-1",
   "language": "python",
   "name": "stardist-1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}