create_stac.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58b2f6bb-fd12-4b6e-9963-6311ec1f25e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import rioxarray\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pathlib\n",
    "import json\n",
    "import boto3\n",
    "import s3fs\n",
    "import shutil\n",
    "from typing import Any, Dict, Generic, Iterable, List, Optional, TypeVar, Union, cast\n",
    "from datetime import datetime\n",
    "from pprint import pprint\n",
    "\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import box, mapping, Polygon, LinearRing\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pystac\n",
    "from pystac import Catalog, get_stac_version, Collection, Item, STACObject, CatalogType, Link\n",
    "from pystac.stac_io import DefaultStacIO, StacIO\n",
    "from pystac.extensions.projection import AssetProjectionExtension, ProjectionExtension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b68f5126-fda7-4dc2-92a7-19edb9277146",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete this block once we are in GDAL >= 3.5\n",
    "\n",
    "awsdir = pathlib.Path(\"~/.aws\").expanduser()\n",
    "awsdir.mkdir(exist_ok=True)\n",
    "awscred = awsdir / \"credentials\"\n",
    "\n",
    "session = boto3.session.Session()\n",
    "credentials = session.get_credentials()\n",
    "credentials = credentials.get_frozen_credentials()\n",
    "\n",
    "#Credentials are refreshable, so accessing your access key / secret key\n",
    "#separately can lead to a race condition. Use this to get an actual matched\n",
    "#set. \n",
    "\n",
    "outstring = f\"\"\"\n",
    "[default]\n",
    "aws_access_key_id={credentials.access_key}\n",
    "aws_secret_access_key={credentials.secret_key}\n",
    "aws_session_token={credentials.token}\n",
    "\"\"\"\n",
    "with awscred.open(mode='w') as f:\n",
    "    f.write(outstring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0e47e4e-3d72-4c89-bbff-d88150bfd40c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Bucket = \"dh-shift-curated\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f95c67d2-7038-4624-bf9c-4744dc16f786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.0'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pystac.get_stac_version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7b2aabfc-cf98-4b52-b351-44f9bf63019b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create root STAC catalog\n",
    "catalog_url = \"efs/SBG-SHIFT-STAC/catalog.json\"\n",
    "\n",
    "catalog = pystac.Catalog(id=\"SBG-SHIFT-STAC\",\n",
    "                         description=\"STAC catalog for the SBG-SHIFT AVIRIS-NG campaign\",\n",
    "                         title=\"SBG SHIFT STAC Catalog\",\n",
    "                         href = catalog_url,\n",
    "                         catalog_type = CatalogType.SELF_CONTAINED\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d765fea9-c111-473c-bd5d-60e596dce44c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create collection for AVIRIS-NG\n",
    "# should include AVIRIS-NG-”raw”, (Eventually) AVIRIS-NG-mosaic, Distance to roads, NAIP imagery, DEM\n",
    "dt1 = datetime(2022, 2, 28, 0) # first flight date\n",
    "dt2 = datetime(2022, 5, 31, 0) # last day of study\n",
    "collection_interval = sorted([dt1, dt2])\n",
    "\n",
    "temporal_extent = pystac.TemporalExtent(intervals=[collection_interval])\n",
    "spatial_extent = pystac.SpatialExtent([[705900,  3802100, 825900, 3868100]]) # covers all SHIFT fields\n",
    "extent = pystac.Extent(spatial_extent, temporal_extent)\n",
    "\n",
    "collection_url = \"/efs/SBG-SHIFT-STAC/collection.json\"\n",
    "\n",
    "collection = pystac.Collection(id='AVIRIS-NG',\n",
    "                               description='SBG-SHIFT AVIRIS_NG data',\n",
    "                               href=collection_url,\n",
    "                               license = 'MIT',\n",
    "                               extent=extent,\n",
    "                               stac_extensions=['https://stac-extensions.github.io/projection/v1.0.0/schema.json']\n",
    "                              )\n",
    "\n",
    "collection.providers = [\n",
    "    pystac.Provider(name=\"AVIRIS-NG\", roles=[pystac.ProviderRole.PRODUCER], url=\"https://avirisng.jpl.nasa.gov/aviris-ng.html\"),\n",
    "    pystac.Provider(name=\"SBG\", roles=[pystac.ProviderRole.HOST], url=\"https://sbg.jpl.nasa.gov\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90189472-56b4-4f7a-80b6-1021b119a453",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data from S3 bucket\n",
    "def get_zarrs(Bucket, dataset_date):\n",
    "    s3 = boto3.client('s3')\n",
    "    Prefix = f'aviris/{dataset_date}'\n",
    "    kwargs = {'Bucket': Bucket, 'Prefix': Prefix}\n",
    "    substring = '100-100-100.zarr'\n",
    "    links = []\n",
    "    while True:\n",
    "        objects = s3.list_objects_v2(**kwargs)\n",
    "        for obj in objects['Contents']:\n",
    "            if substring in obj['Key']:\n",
    "                key = obj['Key']\n",
    "                url = key[:key.index(substring)+ len(substring)]\n",
    "                zarr = url.replace(f\"aviris/{dataset_date}/\", \"\")\n",
    "                links.append(str(zarr))\n",
    "            \n",
    "        try:\n",
    "            kwargs['ContinuationToken'] = objects['NextContinuationToken']\n",
    "        except KeyError:\n",
    "            break\n",
    "     \n",
    "    data_set = set(links)\n",
    "    data = list(data_set)\n",
    "    #print(data) # see results\n",
    "    print(\"Data retrieved\")\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "77bfeda6-118d-4d85-9572-2d822e0866bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_items_add_assets(Bucket, dataset_date, zarr):\n",
    "    Prefix = f\"aviris/{dataset_date}/{zarr}\"\n",
    "    s3_key = os.path.join(Bucket, Prefix)\n",
    "    \n",
    "    # Open flight path zarr\n",
    "    s3 = s3fs.S3FileSystem(anon=False, client_kwargs=dict(region_name='us-west-2'))\n",
    "    s3_path = f\"s3://{s3_key}\"\n",
    "    store = s3fs.S3Map(root=s3_path, s3=s3, check=False)\n",
    "    ds = xr.open_zarr(store=store, decode_coords=\"all\", consolidated=True)\n",
    "    print(\"Zarr read done!\")\n",
    "    \n",
    "    # Set dimensions and coordinates\n",
    "    ds = ds.rio.set_spatial_dims(x_dim='x', y_dim='y')\n",
    "    ds = ds.set_coords(('Easting', 'Northing'))\n",
    "\n",
    "     # Calculate extent, bbox, and footprint using Easting & Northing\n",
    "    ul = ds.isel(x=0, y=0)  # Upper left corner\n",
    "    ur = ds.isel(x=-1, y=0) # Upper right corner\n",
    "    ll = ds.isel(x=0, y=-1) # Lower left corner\n",
    "    lr = ds.isel(x=-1, y=-1) # Lower right corner\n",
    "\n",
    "    ul2 = (np.min(ul.Easting.values), np.max(ul.Northing.values))\n",
    "    ur2 = (np.max(ur.Easting.values), np.max(ur.Northing.values))\n",
    "    ll2 = (np.min(ll.Easting.values), np.min(ll.Northing.values))\n",
    "    lr2 = (np.max(lr.Easting.values), np.min(lr.Northing.values))\n",
    "    \n",
    "    extent = Polygon([ul2, ll2, lr2, ur2])\n",
    "    footprint = mapping(extent)\n",
    "\n",
    "    # save flight outline as GeoJSON and upload to s3\n",
    "    substring = '_100-100-100.zarr'\n",
    "    item_name = zarr[:zarr.index(substring)]\n",
    "\n",
    "    gpd.GeoDataFrame({\"geometry\": extent}, index=[0]).to_file(f\"{item_name}_flight_outline.geojson\", driver='GeoJSON')\n",
    "\n",
    "    s3 = boto3.client('s3')\n",
    "    kwargs = {'Bucket': Bucket, 'Key': f\"aviris/{dataset_date}/{item_name}_flight_outline.geojson\"}\n",
    "    s3.put_object(**kwargs)\n",
    "    \n",
    "    bbox = extent.bounds\n",
    "    \n",
    "    # create datetime object from date string\n",
    "    dt = datetime.strptime(dataset_date, f\"%Y%m%d\")\n",
    "    \n",
    "    item_url = \"/efs/SBG-SHIFT-STAC/AVIRIS-NG/{item_name}.json\"\n",
    "\n",
    "    # item should be flight line\n",
    "    item = pystac.Item(id=f\"{item_name}\",\n",
    "                    geometry=footprint, \n",
    "                    bbox=bbox, \n",
    "                    datetime=dt,\n",
    "                    href=item_url,\n",
    "                    stac_extensions=['https://stac-extensions.github.io/projection/v1.0.0/schema.json'],\n",
    "                    properties={},\n",
    "                    collection = 'AVIRIS-NG'\n",
    "                      )\n",
    "    # add instrument metadata\n",
    "    item.common_metadata.instruments = ['AVIRIS-NG']\n",
    "\n",
    "    # add projection extension to item\n",
    "    proj_ext = ProjectionExtension.ext(item, add_if_missing = True)\n",
    "    proj_ext.epsg = 32610\n",
    "    print(\"Item created!\")\n",
    "    \n",
    "    # Add dataset asset\n",
    "    print(\"Adding assets\")\n",
    "    item.add_asset(\n",
    "        key=\"dataset\",\n",
    "        asset=pystac.Asset(\n",
    "            href= f\"https://dh-shift-curated.s3.us-west-2.amazonaws.com/aviris/{dataset_date}/{zarr}\"\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # extend the asset with projection extension\n",
    "    asset_ext = AssetProjectionExtension.ext(item.assets[\"dataset\"])\n",
    "    asset_ext.epsg = 32610\n",
    "    asset_ext.bbox = bbox\n",
    "    asset_ext.geometry = footprint\n",
    "\n",
    "    # Add flight outline GeoJSON asset\n",
    "    item.add_asset(\n",
    "        key=\"Flight Outline\",\n",
    "        asset=pystac.Asset(\n",
    "            href= f\"https://dh-shift-curated.s3.us-west-2.amazonaws.com/aviris/{dataset_date}/{item_name}_flight_outline.geojson\",\n",
    "            media_type=pystac.MediaType.GEOJSON\n",
    "            )\n",
    "    )\n",
    "    print(\"Assets added!\")\n",
    "    zarrs_to_items[item_name] = item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4c3dc6dc-4934-4b7f-8146-cda38a6a0d41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Starting ang20220405t185108\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t194236\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t202743\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t211706\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t210448\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t192935\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t190223\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t204007\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t201359\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t193603\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t215533\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t205228\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t214144\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220405t195821\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Data retrieved\n",
      "Starting ang20220412t195932\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t205405\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t193109\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t215642\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t202627\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t185410\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t210701\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t203940\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t190510\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t201217\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t212151\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t194550\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n",
      "Starting ang20220412t192034\n",
      "Zarr read done!\n",
      "Item created!\n",
      "Adding assets\n",
      "Assets added!\n"
     ]
    }
   ],
   "source": [
    "# additional dates after 20220412 to be added later\n",
    "dates = ['20220224', '20220228', '20220308', '20220316', \n",
    "        '20220322', '20220405', '20220412'#, '20220420', '20220429', '20220503'\n",
    "        ]\n",
    "\n",
    "zarrs_to_items = {}\n",
    "\n",
    "for dataset_date in dates:\n",
    "    data = get_zarrs(Bucket, dataset_date)\n",
    "    \n",
    "    for zarr in data:\n",
    "        substring = '_100-100-100.zarr'\n",
    "        item_name = zarr[:zarr.index(substring)]\n",
    "        print(f\"Starting {item_name}\")\n",
    "        \n",
    "        # create STAC item and add assets, including Zarr dataset\n",
    "        create_items_add_assets(Bucket, dataset_date, zarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a63d5bd-ce74-4160-942c-da2dbbe7c062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Items added to collection!\n",
      "* <Collection id=AVIRIS-NG>\n",
      "  * <Item id=ang20220224t200332>\n",
      "  * <Item id=ang20220224t214423>\n",
      "  * <Item id=ang20220224t213004>\n",
      "  * <Item id=ang20220224t223027>\n",
      "  * <Item id=ang20220224t215759>\n",
      "  * <Item id=ang20220224t195402>\n",
      "  * <Item id=ang20220224t221356>\n",
      "  * <Item id=ang20220224t211618>\n",
      "  * <Item id=ang20220224t204803>\n",
      "  * <Item id=ang20220224t203333>\n",
      "  * <Item id=ang20220224t210144>\n",
      "  * <Item id=ang20220224t201928>\n",
      "  * <Item id=ang20220228t195958>\n",
      "  * <Item id=ang20220228t183924>\n",
      "  * <Item id=ang20220228t185150>\n",
      "  * <Item id=ang20220228t204228>\n",
      "  * <Item id=ang20220228t193333>\n",
      "  * <Item id=ang20220228t202944>\n",
      "  * <Item id=ang20220228t205624>\n",
      "  * <Item id=ang20220228t185720>\n",
      "  * <Item id=ang20220228t190702>\n",
      "  * <Item id=ang20220228t210940>\n",
      "  * <Item id=ang20220228t192104>\n",
      "  * <Item id=ang20220228t201833>\n",
      "  * <Item id=ang20220228t194708>\n",
      "  * <Item id=ang20220308t201508>\n",
      "  * <Item id=ang20220308t185140>\n",
      "  * <Item id=ang20220308t192816>\n",
      "  * <Item id=ang20220308t195648>\n",
      "  * <Item id=ang20220308t191151>\n",
      "  * <Item id=ang20220308t214629>\n",
      "  * <Item id=ang20220308t204043>\n",
      "  * <Item id=ang20220308t190523>\n",
      "  * <Item id=ang20220308t194253>\n",
      "  * <Item id=ang20220308t202617>\n",
      "  * <Item id=ang20220308t211733>\n",
      "  * <Item id=ang20220308t183206>\n",
      "  * <Item id=ang20220308t205512>\n",
      "  * <Item id=ang20220308t213310>\n",
      "  * <Item id=ang20220308t184127>\n",
      "  * <Item id=ang20220316t211819>\n",
      "  * <Item id=ang20220316t183443>\n",
      "  * <Item id=ang20220316t194717>\n",
      "  * <Item id=ang20220316t185139>\n",
      "  * <Item id=ang20220316t200240>\n",
      "  * <Item id=ang20220316t190239>\n",
      "  * <Item id=ang20220316t193240>\n",
      "  * <Item id=ang20220316t203318>\n",
      "  * <Item id=ang20220316t184402>\n",
      "  * <Item id=ang20220316t191705>\n",
      "  * <Item id=ang20220316t204811>\n",
      "  * <Item id=ang20220316t202123>\n",
      "  * <Item id=ang20220316t210303>\n",
      "  * <Item id=ang20220322t203801>\n",
      "  * <Item id=ang20220322t221256>\n",
      "  * <Item id=ang20220322t194447>\n",
      "  * <Item id=ang20220322t201228>\n",
      "  * <Item id=ang20220322t220619>\n",
      "  * <Item id=ang20220322t193854>\n",
      "  * <Item id=ang20220322t192924>\n",
      "  * <Item id=ang20220322t205950>\n",
      "  * <Item id=ang20220322t215548>\n",
      "  * <Item id=ang20220322t204749>\n",
      "  * <Item id=ang20220322t195241>\n",
      "  * <Item id=ang20220322t212712>\n",
      "  * <Item id=ang20220322t210856>\n",
      "  * <Item id=ang20220322t202643>\n",
      "  * <Item id=ang20220322t200335>\n",
      "  * <Item id=ang20220405t191148>\n",
      "  * <Item id=ang20220405t185108>\n",
      "  * <Item id=ang20220405t194236>\n",
      "  * <Item id=ang20220405t202743>\n",
      "  * <Item id=ang20220405t211706>\n",
      "  * <Item id=ang20220405t210448>\n",
      "  * <Item id=ang20220405t192935>\n",
      "  * <Item id=ang20220405t190223>\n",
      "  * <Item id=ang20220405t204007>\n",
      "  * <Item id=ang20220405t201359>\n",
      "  * <Item id=ang20220405t193603>\n",
      "  * <Item id=ang20220405t215533>\n",
      "  * <Item id=ang20220405t205228>\n",
      "  * <Item id=ang20220405t214144>\n",
      "  * <Item id=ang20220405t195821>\n",
      "  * <Item id=ang20220412t195932>\n",
      "  * <Item id=ang20220412t205405>\n",
      "  * <Item id=ang20220412t193109>\n",
      "  * <Item id=ang20220412t215642>\n",
      "  * <Item id=ang20220412t202627>\n",
      "  * <Item id=ang20220412t185410>\n",
      "  * <Item id=ang20220412t210701>\n",
      "  * <Item id=ang20220412t203940>\n",
      "  * <Item id=ang20220412t190510>\n",
      "  * <Item id=ang20220412t201217>\n",
      "  * <Item id=ang20220412t212151>\n",
      "  * <Item id=ang20220412t194550>\n",
      "  * <Item id=ang20220412t192034>\n",
      "  * <Item id=ang20220224t200332>\n",
      "  * <Item id=ang20220224t214423>\n",
      "  * <Item id=ang20220224t213004>\n",
      "  * <Item id=ang20220224t223027>\n",
      "  * <Item id=ang20220224t215759>\n",
      "  * <Item id=ang20220224t195402>\n",
      "  * <Item id=ang20220224t221356>\n",
      "  * <Item id=ang20220224t211618>\n",
      "  * <Item id=ang20220224t204803>\n",
      "  * <Item id=ang20220224t203333>\n",
      "  * <Item id=ang20220224t210144>\n",
      "  * <Item id=ang20220224t201928>\n",
      "  * <Item id=ang20220228t195958>\n",
      "  * <Item id=ang20220228t183924>\n",
      "  * <Item id=ang20220228t185150>\n",
      "  * <Item id=ang20220228t204228>\n",
      "  * <Item id=ang20220228t193333>\n",
      "  * <Item id=ang20220228t202944>\n",
      "  * <Item id=ang20220228t205624>\n",
      "  * <Item id=ang20220228t185720>\n",
      "  * <Item id=ang20220228t190702>\n",
      "  * <Item id=ang20220228t210940>\n",
      "  * <Item id=ang20220228t192104>\n",
      "  * <Item id=ang20220228t201833>\n",
      "  * <Item id=ang20220228t194708>\n",
      "  * <Item id=ang20220308t201508>\n",
      "  * <Item id=ang20220308t185140>\n",
      "  * <Item id=ang20220308t192816>\n",
      "  * <Item id=ang20220308t195648>\n",
      "  * <Item id=ang20220308t191151>\n",
      "  * <Item id=ang20220308t214629>\n",
      "  * <Item id=ang20220308t204043>\n",
      "  * <Item id=ang20220308t190523>\n",
      "  * <Item id=ang20220308t194253>\n",
      "  * <Item id=ang20220308t202617>\n",
      "  * <Item id=ang20220308t211733>\n",
      "  * <Item id=ang20220308t183206>\n",
      "  * <Item id=ang20220308t205512>\n",
      "  * <Item id=ang20220308t213310>\n",
      "  * <Item id=ang20220308t184127>\n",
      "  * <Item id=ang20220316t211819>\n",
      "  * <Item id=ang20220316t183443>\n",
      "  * <Item id=ang20220316t194717>\n",
      "  * <Item id=ang20220316t185139>\n",
      "  * <Item id=ang20220316t200240>\n",
      "  * <Item id=ang20220316t190239>\n",
      "  * <Item id=ang20220316t193240>\n",
      "  * <Item id=ang20220316t203318>\n",
      "  * <Item id=ang20220316t184402>\n",
      "  * <Item id=ang20220316t191705>\n",
      "  * <Item id=ang20220316t204811>\n",
      "  * <Item id=ang20220316t202123>\n",
      "  * <Item id=ang20220316t210303>\n",
      "  * <Item id=ang20220322t203801>\n",
      "  * <Item id=ang20220322t221256>\n",
      "  * <Item id=ang20220322t194447>\n",
      "  * <Item id=ang20220322t201228>\n",
      "  * <Item id=ang20220322t220619>\n",
      "  * <Item id=ang20220322t193854>\n",
      "  * <Item id=ang20220322t192924>\n",
      "  * <Item id=ang20220322t205950>\n",
      "  * <Item id=ang20220322t215548>\n",
      "  * <Item id=ang20220322t204749>\n",
      "  * <Item id=ang20220322t195241>\n",
      "  * <Item id=ang20220322t212712>\n",
      "  * <Item id=ang20220322t210856>\n",
      "  * <Item id=ang20220322t202643>\n",
      "  * <Item id=ang20220322t200335>\n",
      "  * <Item id=ang20220405t191148>\n",
      "  * <Item id=ang20220405t185108>\n",
      "  * <Item id=ang20220405t194236>\n",
      "  * <Item id=ang20220405t202743>\n",
      "  * <Item id=ang20220405t211706>\n",
      "  * <Item id=ang20220405t210448>\n",
      "  * <Item id=ang20220405t192935>\n",
      "  * <Item id=ang20220405t190223>\n",
      "  * <Item id=ang20220405t204007>\n",
      "  * <Item id=ang20220405t201359>\n",
      "  * <Item id=ang20220405t193603>\n",
      "  * <Item id=ang20220405t215533>\n",
      "  * <Item id=ang20220405t205228>\n",
      "  * <Item id=ang20220405t214144>\n",
      "  * <Item id=ang20220405t195821>\n",
      "  * <Item id=ang20220412t195932>\n",
      "  * <Item id=ang20220412t205405>\n",
      "  * <Item id=ang20220412t193109>\n",
      "  * <Item id=ang20220412t215642>\n",
      "  * <Item id=ang20220412t202627>\n",
      "  * <Item id=ang20220412t185410>\n",
      "  * <Item id=ang20220412t210701>\n",
      "  * <Item id=ang20220412t203940>\n",
      "  * <Item id=ang20220412t190510>\n",
      "  * <Item id=ang20220412t201217>\n",
      "  * <Item id=ang20220412t212151>\n",
      "  * <Item id=ang20220412t194550>\n",
      "  * <Item id=ang20220412t192034>\n"
     ]
    }
   ],
   "source": [
    "# add items to collection\n",
    "collection.add_items(zarrs_to_items.values())\n",
    "collection.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "967434a4-d93c-4708-958c-4cb9db9dacd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* <Catalog id=SBG-SHIFT-STAC>\n",
      "    * <Collection id=AVIRIS-NG>\n",
      "      * <Item id=ang20220224t200332>\n",
      "      * <Item id=ang20220224t214423>\n",
      "      * <Item id=ang20220224t213004>\n",
      "      * <Item id=ang20220224t223027>\n",
      "      * <Item id=ang20220224t215759>\n",
      "      * <Item id=ang20220224t195402>\n",
      "      * <Item id=ang20220224t221356>\n",
      "      * <Item id=ang20220224t211618>\n",
      "      * <Item id=ang20220224t204803>\n",
      "      * <Item id=ang20220224t203333>\n",
      "      * <Item id=ang20220224t210144>\n",
      "      * <Item id=ang20220224t201928>\n",
      "      * <Item id=ang20220228t195958>\n",
      "      * <Item id=ang20220228t183924>\n",
      "      * <Item id=ang20220228t185150>\n",
      "      * <Item id=ang20220228t204228>\n",
      "      * <Item id=ang20220228t193333>\n",
      "      * <Item id=ang20220228t202944>\n",
      "      * <Item id=ang20220228t205624>\n",
      "      * <Item id=ang20220228t185720>\n",
      "      * <Item id=ang20220228t190702>\n",
      "      * <Item id=ang20220228t210940>\n",
      "      * <Item id=ang20220228t192104>\n",
      "      * <Item id=ang20220228t201833>\n",
      "      * <Item id=ang20220228t194708>\n",
      "      * <Item id=ang20220308t201508>\n",
      "      * <Item id=ang20220308t185140>\n",
      "      * <Item id=ang20220308t192816>\n",
      "      * <Item id=ang20220308t195648>\n",
      "      * <Item id=ang20220308t191151>\n",
      "      * <Item id=ang20220308t214629>\n",
      "      * <Item id=ang20220308t204043>\n",
      "      * <Item id=ang20220308t190523>\n",
      "      * <Item id=ang20220308t194253>\n",
      "      * <Item id=ang20220308t202617>\n",
      "      * <Item id=ang20220308t211733>\n",
      "      * <Item id=ang20220308t183206>\n",
      "      * <Item id=ang20220308t205512>\n",
      "      * <Item id=ang20220308t213310>\n",
      "      * <Item id=ang20220308t184127>\n",
      "      * <Item id=ang20220316t211819>\n",
      "      * <Item id=ang20220316t183443>\n",
      "      * <Item id=ang20220316t194717>\n",
      "      * <Item id=ang20220316t185139>\n",
      "      * <Item id=ang20220316t200240>\n",
      "      * <Item id=ang20220316t190239>\n",
      "      * <Item id=ang20220316t193240>\n",
      "      * <Item id=ang20220316t203318>\n",
      "      * <Item id=ang20220316t184402>\n",
      "      * <Item id=ang20220316t191705>\n",
      "      * <Item id=ang20220316t204811>\n",
      "      * <Item id=ang20220316t202123>\n",
      "      * <Item id=ang20220316t210303>\n",
      "      * <Item id=ang20220322t203801>\n",
      "      * <Item id=ang20220322t221256>\n",
      "      * <Item id=ang20220322t194447>\n",
      "      * <Item id=ang20220322t201228>\n",
      "      * <Item id=ang20220322t220619>\n",
      "      * <Item id=ang20220322t193854>\n",
      "      * <Item id=ang20220322t192924>\n",
      "      * <Item id=ang20220322t205950>\n",
      "      * <Item id=ang20220322t215548>\n",
      "      * <Item id=ang20220322t204749>\n",
      "      * <Item id=ang20220322t195241>\n",
      "      * <Item id=ang20220322t212712>\n",
      "      * <Item id=ang20220322t210856>\n",
      "      * <Item id=ang20220322t202643>\n",
      "      * <Item id=ang20220322t200335>\n",
      "      * <Item id=ang20220405t191148>\n",
      "      * <Item id=ang20220405t185108>\n",
      "      * <Item id=ang20220405t194236>\n",
      "      * <Item id=ang20220405t202743>\n",
      "      * <Item id=ang20220405t211706>\n",
      "      * <Item id=ang20220405t210448>\n",
      "      * <Item id=ang20220405t192935>\n",
      "      * <Item id=ang20220405t190223>\n",
      "      * <Item id=ang20220405t204007>\n",
      "      * <Item id=ang20220405t201359>\n",
      "      * <Item id=ang20220405t193603>\n",
      "      * <Item id=ang20220405t215533>\n",
      "      * <Item id=ang20220405t205228>\n",
      "      * <Item id=ang20220405t214144>\n",
      "      * <Item id=ang20220405t195821>\n",
      "      * <Item id=ang20220412t195932>\n",
      "      * <Item id=ang20220412t205405>\n",
      "      * <Item id=ang20220412t193109>\n",
      "      * <Item id=ang20220412t215642>\n",
      "      * <Item id=ang20220412t202627>\n",
      "      * <Item id=ang20220412t185410>\n",
      "      * <Item id=ang20220412t210701>\n",
      "      * <Item id=ang20220412t203940>\n",
      "      * <Item id=ang20220412t190510>\n",
      "      * <Item id=ang20220412t201217>\n",
      "      * <Item id=ang20220412t212151>\n",
      "      * <Item id=ang20220412t194550>\n",
      "      * <Item id=ang20220412t192034>\n",
      "      * <Item id=ang20220224t200332>\n",
      "      * <Item id=ang20220224t214423>\n",
      "      * <Item id=ang20220224t213004>\n",
      "      * <Item id=ang20220224t223027>\n",
      "      * <Item id=ang20220224t215759>\n",
      "      * <Item id=ang20220224t195402>\n",
      "      * <Item id=ang20220224t221356>\n",
      "      * <Item id=ang20220224t211618>\n",
      "      * <Item id=ang20220224t204803>\n",
      "      * <Item id=ang20220224t203333>\n",
      "      * <Item id=ang20220224t210144>\n",
      "      * <Item id=ang20220224t201928>\n",
      "      * <Item id=ang20220228t195958>\n",
      "      * <Item id=ang20220228t183924>\n",
      "      * <Item id=ang20220228t185150>\n",
      "      * <Item id=ang20220228t204228>\n",
      "      * <Item id=ang20220228t193333>\n",
      "      * <Item id=ang20220228t202944>\n",
      "      * <Item id=ang20220228t205624>\n",
      "      * <Item id=ang20220228t185720>\n",
      "      * <Item id=ang20220228t190702>\n",
      "      * <Item id=ang20220228t210940>\n",
      "      * <Item id=ang20220228t192104>\n",
      "      * <Item id=ang20220228t201833>\n",
      "      * <Item id=ang20220228t194708>\n",
      "      * <Item id=ang20220308t201508>\n",
      "      * <Item id=ang20220308t185140>\n",
      "      * <Item id=ang20220308t192816>\n",
      "      * <Item id=ang20220308t195648>\n",
      "      * <Item id=ang20220308t191151>\n",
      "      * <Item id=ang20220308t214629>\n",
      "      * <Item id=ang20220308t204043>\n",
      "      * <Item id=ang20220308t190523>\n",
      "      * <Item id=ang20220308t194253>\n",
      "      * <Item id=ang20220308t202617>\n",
      "      * <Item id=ang20220308t211733>\n",
      "      * <Item id=ang20220308t183206>\n",
      "      * <Item id=ang20220308t205512>\n",
      "      * <Item id=ang20220308t213310>\n",
      "      * <Item id=ang20220308t184127>\n",
      "      * <Item id=ang20220316t211819>\n",
      "      * <Item id=ang20220316t183443>\n",
      "      * <Item id=ang20220316t194717>\n",
      "      * <Item id=ang20220316t185139>\n",
      "      * <Item id=ang20220316t200240>\n",
      "      * <Item id=ang20220316t190239>\n",
      "      * <Item id=ang20220316t193240>\n",
      "      * <Item id=ang20220316t203318>\n",
      "      * <Item id=ang20220316t184402>\n",
      "      * <Item id=ang20220316t191705>\n",
      "      * <Item id=ang20220316t204811>\n",
      "      * <Item id=ang20220316t202123>\n",
      "      * <Item id=ang20220316t210303>\n",
      "      * <Item id=ang20220322t203801>\n",
      "      * <Item id=ang20220322t221256>\n",
      "      * <Item id=ang20220322t194447>\n",
      "      * <Item id=ang20220322t201228>\n",
      "      * <Item id=ang20220322t220619>\n",
      "      * <Item id=ang20220322t193854>\n",
      "      * <Item id=ang20220322t192924>\n",
      "      * <Item id=ang20220322t205950>\n",
      "      * <Item id=ang20220322t215548>\n",
      "      * <Item id=ang20220322t204749>\n",
      "      * <Item id=ang20220322t195241>\n",
      "      * <Item id=ang20220322t212712>\n",
      "      * <Item id=ang20220322t210856>\n",
      "      * <Item id=ang20220322t202643>\n",
      "      * <Item id=ang20220322t200335>\n",
      "      * <Item id=ang20220405t191148>\n",
      "      * <Item id=ang20220405t185108>\n",
      "      * <Item id=ang20220405t194236>\n",
      "      * <Item id=ang20220405t202743>\n",
      "      * <Item id=ang20220405t211706>\n",
      "      * <Item id=ang20220405t210448>\n",
      "      * <Item id=ang20220405t192935>\n",
      "      * <Item id=ang20220405t190223>\n",
      "      * <Item id=ang20220405t204007>\n",
      "      * <Item id=ang20220405t201359>\n",
      "      * <Item id=ang20220405t193603>\n",
      "      * <Item id=ang20220405t215533>\n",
      "      * <Item id=ang20220405t205228>\n",
      "      * <Item id=ang20220405t214144>\n",
      "      * <Item id=ang20220405t195821>\n",
      "      * <Item id=ang20220412t195932>\n",
      "      * <Item id=ang20220412t205405>\n",
      "      * <Item id=ang20220412t193109>\n",
      "      * <Item id=ang20220412t215642>\n",
      "      * <Item id=ang20220412t202627>\n",
      "      * <Item id=ang20220412t185410>\n",
      "      * <Item id=ang20220412t210701>\n",
      "      * <Item id=ang20220412t203940>\n",
      "      * <Item id=ang20220412t190510>\n",
      "      * <Item id=ang20220412t201217>\n",
      "      * <Item id=ang20220412t212151>\n",
      "      * <Item id=ang20220412t194550>\n",
      "      * <Item id=ang20220412t192034>\n"
     ]
    }
   ],
   "source": [
    "# Add STAC Collection to STAC catalog\n",
    "catalog.add_child(collection)\n",
    "catalog.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f7f706d5-4d1a-46f3-a3a5-5e86d1e3ce8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://schemas.stacspec.org/v1.0.0/collection-spec/json-schema/collection.json',\n",
       " 'https://stac-extensions.github.io/projection/v1.0.0/schema.json']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection.validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "81071476-51a6-418f-b8da-f3695dd17ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://schemas.stacspec.org/v1.0.0/catalog-spec/json-schema/catalog.json']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog.validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2bf6bb03-afcd-4d0b-ae90-6899d0b7e192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Save STAC Catalog to efs\n",
    "catalog.normalize_hrefs(catalog_url)\n",
    "catalog.save(catalog_type=CatalogType.SELF_CONTAINED)\n",
    "print('Done!')"
   ]
  }
 ],
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