add tutorial about using __geo_interface__ objects for search intersects

CHANGELOG.md

@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - lru_cache to several methods [#167](https://github.com/stac-utils/pystac-client/pull/167)
 - Direct item GET via ogcapi-features, if conformant [#166](https://github.com/stac-utils/pystac-client/pull/166)
 - `py.typed` for downstream type checking [#163](https://github.com/stac-utils/pystac-client/pull/163)
+- Added tutorial for using various geometry objects (e.g., shapely, geojson) as an Item Search intersects argument [#232](https://github.com/stac-utils/pystac-client/pull/232)
 
 ### Changed
 

docs/tutorials.rst

@@ -28,6 +28,16 @@ CQL2 Filtering
 This tutorial gives an introduction to using CQL2-JSON filtering in searches to
 search by arbitrary STAC Item properties.
 
+Item Search with Intersects
+-----------------------------------------------------
+
+- :tutorial:`GitHub version <item-search-intersects.ipynb>`
+- :ref:`Docs version </tutorials/item-search-intersects.ipynb>`
+
+This tutorial demonstrates the use of different Python library
+object types that can be used for the `intersects`
+search parameter.
+
 Calculating Coverage Percentage of the AOI by an Item
 -----------------------------------------------------
 

docs/tutorials/item-search-intersects.ipynb

@@ -0,0 +1,257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e06a27bf",
+   "metadata": {},
+   "source": [
+    "# Item Search with Intersects\n",
+    "\n",
+    "This notebook shows the use of pystac-client to perform item search with the `intersects` parameter, to restrict the results to an Area of Interest (AOI)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e16077c",
+   "metadata": {},
+   "source": [
+    "# Client\n",
+    "\n",
+    "We first connect to an API by retrieving the root catalog, or landing page, of the API with the `Client.open` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98942e75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pystac_client import Client\n",
+    "from typing import Any, Dict\n",
+    "import json\n",
+    "\n",
+    "# STAC API root URL\n",
+    "URL = 'https://planetarycomputer.microsoft.com/api/stac/v1'\n",
+    "\n",
+    "client = Client.open(URL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62e26114",
+   "metadata": {},
+   "source": [
+    "# Item Search\n",
+    "\n",
+    "When the Catalog is a STAC API, we have the ability to search for items based on spatio-temporal properties.\n",
+    "\n",
+    "The STAC API endpoint `/search` accepts a parameter `intersects` that is a GeoJSON Geometry representing the AOI of the search.\n",
+    "\n",
+    "The `search` method of the pystac_client `Client` class accepts several different types of objects:\n",
+    "\n",
+    "1. a string representing a GeoJSON geometry\n",
+    "2. a dictionary representing a GeoJSON geometry\n",
+    "3. any object that implements a ``__geo_interface__`` property, [an informal specification](https://gist.github.com/sgillies/2217756)    \n",
+    "   supported by several libraries for generating a GeoJSON representation from an object. Several prominent libraries support this\n",
+    "   protocol for their objects that represent geometries, including [Shapely](https://shapely.readthedocs.io), [ArcPy](https://pro.arcgis.com/en/pro-app/2.8/arcpy/get-started/what-is-arcpy-.htm), [PySAL](https://pysal.org/), [geojson](https://github.com/jazzband/geojson), [pyshp](https://pypi.org/project/pyshp/), [descartes](https://docs.descarteslabs.com/), and [pygeoif](https://github.com/cleder/pygeoif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d27fabf",
+   "metadata": {},
+   "source": [
+    "## Item Search with AOI as a Dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8af6334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# AOI around Delfzijl, in northern Netherlands\n",
+    "aoi_as_dict: Dict[str, Any] = {\n",
+    "    \"type\": \"Polygon\",\n",
+    "    \"coordinates\": [\n",
+    "      [\n",
+    "        [\n",
+    "          6,\n",
+    "          53\n",
+    "        ],\n",
+    "        [\n",
+    "          7,\n",
+    "          53\n",
+    "        ],\n",
+    "        [\n",
+    "          7,\n",
+    "          54\n",
+    "        ],\n",
+    "        [\n",
+    "          6,\n",
+    "          54\n",
+    "        ],\n",
+    "        [\n",
+    "          6,\n",
+    "          53\n",
+    "        ]\n",
+    "      ]\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "search = client.search(\n",
+    "    max_items = 25,\n",
+    "    collections = \"aster-l1t\",\n",
+    "    intersects = aoi_as_dict,\n",
+    ")\n",
+    "\n",
+    "print(f\"AOI as dictionary, found {len(list(search.items()))} items\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da4956b",
+   "metadata": {},
+   "source": [
+    "## Item Search with AOI as a String"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a7c9336",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aoi_as_str: str  = json.dumps(aoi_as_dict)\n",
+    "\n",
+    "search = client.search(\n",
+    "    max_items = 25,\n",
+    "    collections = \"aster-l1t\",\n",
+    "    intersects = aoi_as_str,\n",
+    ")\n",
+    "\n",
+    "print(f\"AOI as string, found {len(list(search.items()))} items\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da4956b",
+   "metadata": {},
+   "source": [
+    "## Item Search with AOI as a Shapely Geometry Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a7c9336",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shapely.geometry\n",
+    "\n",
+    "aoi_as_shapely_shape = shapely.geometry.shape(aoi_as_dict)\n",
+    "\n",
+    "search = client.search(\n",
+    "    max_items = 25,\n",
+    "    collections = \"aster-l1t\",\n",
+    "    intersects = aoi_as_shapely_shape,\n",
+    ")\n",
+    "\n",
+    "print(f\"AOI as Shapely Geometry object from shape(), found {len(list(search.items()))} items\")\n",
+    "\n",
+    "aoi_as_shapely_polygon = shapely.geometry.Polygon(aoi_as_dict[\"coordinates\"][0])\n",
+    "\n",
+    "search = client.search(\n",
+    "    max_items = 25,\n",
+    "    collections = \"aster-l1t\",\n",
+    "    intersects = aoi_as_shapely_polygon,\n",
+    ")\n",
+    "\n",
+    "print(f\"AOI as Shapely Geometry object with Polygon, found {len(list(search.items()))} items\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da4956b",
+   "metadata": {},
+   "source": [
+    "## Item Search with AOI as a \"geojson\" library object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a7c9336",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geojson\n",
+    "\n",
+    "aoi_as_geojson_polygon = geojson.Polygon(aoi_as_dict[\"coordinates\"])\n",
+    "\n",
+    "search = client.search(\n",
+    "    max_items = 25,\n",
+    "    collections = \"aster-l1t\",\n",
+    "    intersects = aoi_as_geojson_polygon,\n",
+    ")\n",
+    "\n",
+    "print(f\"AOI as geojson Polygon, found {len(list(search.items()))} items\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da4956b",
+   "metadata": {},
+   "source": [
+    "## Item Search with AOI as a pygeoif Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a27dcb72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pygeoif\n",
+    "aoi_as_pygeoif_polygon = pygeoif.geometry.Polygon(aoi_as_dict[\"coordinates\"])\n",
+    "\n",
+    "search = client.search(\n",
+    "    max_items = 25,\n",
+    "    collections = \"aster-l1t\",\n",
+    "    intersects = aoi_as_pygeoif_polygon,\n",
+    ")\n",
+    "\n",
+    "print(f\"AOI as pygeoif Polygon, found {len(list(search.items()))} items\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.11 ('.venv': venv)",
+   "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": "3.9.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pystac_client/collection_client.py

@@ -30,8 +30,8 @@ def get_items(self) -> Iterable["Item_Type"]:
         root = self.get_root()
         if link is not None and root is not None:
             search = ItemSearch(
-                url=link.href, method="GET", stac_io=root._stac_io
-            )  # type: ignore
+                url=link.href, method="GET", stac_io=root._stac_io  # type: ignore
+            )
             yield from search.items()
         else:
             yield from super().get_items()

requirements-docs.txt

@@ -7,4 +7,6 @@ nbsphinx~=0.8
 jinja2<4.0
 geopandas~=0.10.2
 hvplot~=0.8.0
-matplotlib~=3.5.2
+matplotlib~=3.5.2
+geojson~=2.5.0
+pygeoif~=0.7