diff --git a/workshop/content/docs/assets/images/features-hyderabad.png b/workshop/content/docs/assets/images/features-hyderabad.png index 1b10d72..816be13 100644 Binary files a/workshop/content/docs/assets/images/features-hyderabad.png and b/workshop/content/docs/assets/images/features-hyderabad.png differ diff --git a/workshop/content/docs/assets/images/jupyter1.png b/workshop/content/docs/assets/images/jupyter1.png new file mode 100644 index 0000000..1980e07 Binary files /dev/null and b/workshop/content/docs/assets/images/jupyter1.png differ diff --git a/workshop/content/docs/publishing/ogcapi-features.md b/workshop/content/docs/publishing/ogcapi-features.md index 2064799..91125b4 100644 --- a/workshop/content/docs/publishing/ogcapi-features.md +++ b/workshop/content/docs/publishing/ogcapi-features.md @@ -8,10 +8,10 @@ title: Exercise 2 - Vector data via OGC API - Features data (geometries and their attributes). While the core specification covers basic data access and query, additional related standards and extensions are in development for the following capabilities: -- [OGC API - Features - Part 1: Core](https://docs.opengeospatial.org/is/17-069r4/17-069r4.html) provides basic access and query capabilities -- [OGC API - Features - Part 2: Coordinate Reference Systems by Reference](https://docs.opengeospatial.org/is/18-058r1/18-058r1.html) enables the import and export of any data according to dedicated projections -- [OGC API - Features - Part 3: Filtering](https://docs.ogc.org/DRAFTS/19-079r1.html) (**draft**) adds the ability for complex queries using Common Query Language (CQL) -- [OGC API - Features - Part 4: Create, Replace, Update and Delete](https://docs.ogc.org/DRAFTS/20-002.html) (**draft**) adds transactional capabilities +- [OGC API - Features - Part 1: Core](https://docs.ogc.org/is/17-069r4/17-069r4.html) provides basic access and query capabilities +- [OGC API - Features - Part 2: Coordinate Reference Systems by Reference](https://docs.ogc.org/is/18-058r1/18-058r1.html) enables the import and export of any data according to dedicated projections +- [OGC API - Features - Part 3: Filtering](https://docs.ogc.org/is/19-079r2/19-079r2.html) adds the ability for complex queries using [Common Query Language (CQL2)](https://docs.ogc.org/is/21-065r2/21-065r2.html) +- [OGC API - Features - Part 4: Create, Replace, Update and Delete](https://docs.ogc.org/DRAFTS/20-002r1.html) (**draft**) adds transactional capabilities ## pygeoapi support @@ -38,7 +38,7 @@ vector data source. It may be helpful to open the dataset in [QGIS](https://qgis.org) while adding and updating your pygeoapi server to easily evaluate table attributes, names, spatial properties and CRS. -Let's add the file `workshop/exercises/data/cp-tartu2.gpkg.zip`: +Let's add the file `workshop/exercises/data/tartu/cp-tartu2.gpkg.zip`: !!! question "Update the pygeoapi configuration" @@ -53,7 +53,7 @@ Let's add the file `workshop/exercises/data/cp-tartu2.gpkg.zip`: cp-tartu: type: collection title: Tartu Cadastral Parcels - description: Cadasral parcels in downtown Tartu + description: Cadastral parcels in downtown Tartu keywords: - Cadastral parcels - Tartu @@ -113,15 +113,15 @@ docker compose up If you experience startup problems, consult the [README file](https://github.com/geopython/pygeoapi-examples/blob/main/docker/elastic/README.md). You may need to adapt your local host system's virtual memory setting. -First we will load `bathingwater-estonia.geojson` into the Elasticsearch server. +First we will load `greater_hyderabad_municipal_corporation_ward_Boundaries.geojson` into the Elasticsearch server. Edit the `add-data.sh` script within the `ES` folder, adding these two lines before the end: ``` {.bash linenums="1"} - curl -o /tmp/bathingwater-estonia.geojson https://raw.githubusercontent.com/geopython/diving-into-pygeoapi/main/workshop/exercises/data/tartu/bathingwater-estonia.geojson - python3 /load_es_data.py /tmp/bathingwater-estonia.geojson id +curl -o /tmp/hyderabad.geojson https://raw.githubusercontent.com/geopython/diving-into-pygeoapi/refs/heads/main/workshop/exercises/data/hyderabad/greater_hyderabad_municipal_corporation_ward_Boundaries.geojson +python3 /load_es_data.py /tmp/hyderabad.geojson objectid ``` -Through these changes the file `bathingwater-estonia.geojson` is downloaded inside the Elasticsearch Docker container and then loaded into Elasticsearch. +Through these changes the file `greater_hyderabad_municipal_corporation_ward_Boundaries.geojson` is downloaded inside the Elasticsearch Docker container and then loaded into Elasticsearch. After this we need to rebuild the docker image: @@ -136,7 +136,7 @@ This effectively enables publishing the file `greater_hyderabad_municipal_corpor using the Elasticsearch backend provider. ``` {.yaml linenums="1"} - greater_hyderabad_municipal_corporation_ward_boundaries: + hyderabad: type: collection title: Greater Hyderabad Municipal Corporation ward boundaries description: The city ward boundaries represent the administrative and electoral boundary areas of the city. It plays a great role in planning of the city, for each council of the municipal corporation. @@ -160,27 +160,27 @@ using the Elasticsearch backend provider. providers: - type: feature name: Elasticsearch - # note: elastic_search is the Docker container name as defined in `docker-compose.yml` - data: http://elastic_search:9200/greater_hyderabad_municipal_corporation_ward_boundaries + #Note elastic_search is the docker container of ES the name is defined in the docker-compose.yml + data: http://elastic_search:9200/hyderabad id_field: objectid ``` -On startup the pygeaoapi container will wait until the data has been ingested and the Elasticsearch index has been built. +On startup (e.g.: docker compose up -d) the pygeaoapi container will wait until the data has been ingested and the Elasticsearch index has been built. You can check the logs using:
```bash -docker compose logs --follow pygeoapi +docker compose logs --follow ```
After the server has started you can access the collection page here: - + And the feature items here: - + ![](../assets/images/features-hyderabad.png){ width=100% } @@ -245,6 +245,14 @@ QGIS is one of the first GIS Desktop clients which added support for OGC API - F ``` + Check summary information about the layer with: + +
+ ```bash + ogrinfo OAPIF:https://demo.pygeoapi.io/master/collections/obs obs -so + ``` +
+ Now, let's convert the observations into a shapefile
@@ -255,7 +263,7 @@ QGIS is one of the first GIS Desktop clients which added support for OGC API - F !!! Note - You can even use OGR to append new features to an OGC API - Features collection which supports transactions (pygeoapi transaction support is planned for future implementation) + You can even use OGR to append new features to an OGC API - Features collection which supports transactions. Read more [here](https://docs.pygeoapi.io/en/latest/transactions.html) about support for transactions in pygeoapi. !!! tip "Use GDAL from the commandline with Docker" @@ -312,6 +320,56 @@ QGIS is one of the first GIS Desktop clients which added support for OGC API - F [OWSLib](https://owslib.readthedocs.io) is a Python library to interact with OGC Web Services and supports a number of OGC APIs including OGC API - Features. +This exercise will be done using a jupyter notebook. If you prefer, you can do it using python from the command line (see bellow). + +Before continuing, make sure you are in the `workshop/exercises` folder. You will need that, to be able to use the jupyter notebook. + +=== "Linux/Mac" + +
+ ```bash + pwd + ``` +
+ +=== "Windows" + +
+ ```bash + cd + ``` +
+ +Then run a container to start a jupyter notebook, mounting the local folder: + +=== "Linux/Mac" + +
+ ```bash + docker run -p 8888:8888 -v $(pwd):/home/jovyan/work jupyter/base-notebook + ``` +
+ +=== "Windows" + +
+ ```bash + docker run -p 8888:8888 -v ${pwd}:/home/jovyan/work jupyter/base-notebook + ``` +
+ +Enter the url stated on the command line, `http://127.0.0.1:8888/lab` followed by a token. Enter the `work` folder and open the `features-owslib.ipynb`. + +![jupyter notebook](../assets/images/jupyter1.png) + +Run through the notebook, to explore an OGC API - Features server, using owslib. + +!!! note + + You can run the same instructions using your local pygeoapi server, instead of the demo pygeoapi instance. + +#### Using python from the command line + !!! question "Interact with OGC API - Features via OWSLib" If you do not have Python installed, consider running this exercise in a Docker container. See the [Setup Chapter](../setup.md#using-docker-for-python-clients). diff --git a/workshop/exercises/features-owslib.ipynb b/workshop/exercises/features-owslib.ipynb new file mode 100644 index 0000000..348ee1b --- /dev/null +++ b/workshop/exercises/features-owslib.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1e8fc759-a2cb-4c9e-9884-6355c72b4dbb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting owslib\n", + " Downloading OWSLib-0.31.0-py2.py3-none-any.whl.metadata (6.7 kB)\n", + "Collecting lxml (from owslib)\n", + " Downloading lxml-5.3.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (3.8 kB)\n", + "Requirement already satisfied: python-dateutil>=1.5 in /opt/conda/lib/python3.11/site-packages (from owslib) (2.8.2)\n", + "Requirement already satisfied: pytz in /opt/conda/lib/python3.11/site-packages (from owslib) (2023.3.post1)\n", + "Requirement already satisfied: 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"metadata": {}, + "outputs": [], + "source": [ + "w = Features('https://demo.pygeoapi.io/master')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7b600f06-06bc-4289-8435-24f92e398b10", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://demo.pygeoapi.io/master/'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w.url" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "41327b78-03ea-4976-b578-3e85cbe02f40", + "metadata": {}, + "outputs": [], + "source": [ + "conformance = w.conformance()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ca16ceed-6c14-4132-8dca-945f642544ae", + "metadata": {}, + "outputs": [], + "source": [ + "api = w.api() # OpenAPI document" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad2bc865-b84d-44ac-88d9-7be45513394b", + "metadata": {}, + "outputs": [], + "source": [ + "collections = w.collections()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7156ee4f-8743-466d-8181-2c2b6d49e2db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "17" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(collections['collections'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b8aeba52-296f-4fd3-acfe-cf67742c9840", + "metadata": {}, + "outputs": [], + "source": [ + "feature_collections = w.feature_collections()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "797bd30a-c30a-4e1c-bd32-daa34e529aa6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(feature_collections)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "19e706bc-1b4e-4663-ae2c-6ac6eb4dd816", + "metadata": {}, + "outputs": [], + "source": [ + "lakes = w.collection('lakes')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3dc97850-989a-4381-b395-5247dad8a643", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'lakes'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lakes['id']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8e85d304-160c-4c23-8f91-33b21accb79d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Large Lakes'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lakes['title']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "be654238-32f5-456a-8968-eeb357e7556a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'lakes of the world, public domain'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lakes['description']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f742a28f-ef7f-4b53-bf04-87168160fab7", + "metadata": {}, + "outputs": [], + "source": [ + "lakes_queryables = w.collection_queryables('lakes')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d49cbbec-30f7-4d2a-8e56-c31498da2898", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(lakes_queryables['properties'])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e687bf9e-3d81-4520-b795-5c4c085907f4", + "metadata": {}, + "outputs": [], + "source": [ + "lakes_query = w.collection_items('lakes')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f3dbaebe-79d6-4ed5-92ee-80585ee4685a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 0,\n", + " 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pandas, contourpy, matplotlib, geopandas\n", + "Successfully installed contourpy-1.3.0 cycler-0.12.1 fonttools-4.54.1 geopandas-1.0.1 kiwisolver-1.4.7 matplotlib-3.9.2 numpy-2.1.2 pandas-2.2.3 pillow-11.0.0 pyogrio-0.10.0 pyparsing-3.2.0 pyproj-3.7.0 shapely-2.0.6 tzdata-2024.2\n" + ] + } + ], + "source": [ + "!pip install geopandas matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "dfec0969-8a6a-4093-9ffe-f19b28d5ce63", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "570f937f-cef3-4e4f-a25a-dd72c7df1670", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7c4de3dc-19c3-4fa7-889e-8f3619c1f038", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams['figure.figsize'] = (20, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "52551b37-0841-4055-88eb-642e949a978c", + "metadata": {}, + "outputs": [], + "source": [ + "df_lakes = gpd.read_file(\"https://demo.pygeoapi.io/master/collections/lakes/items\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "72db9e92-968d-4fd1-a458-f823506455f6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = df_lakes.plot(color='blue')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workshop/exercises/pygeoapi.config.yml b/workshop/exercises/pygeoapi.config.yml index 5bcee3f..7cd1a0e 100644 --- a/workshop/exercises/pygeoapi.config.yml +++ b/workshop/exercises/pygeoapi.config.yml @@ -230,7 +230,7 @@ resources: # cp-tartu: # type: collection # title: Tartu Cadastral Parcels -# description: Cadasral parcels in downtown Tartu +# description: Cadastral parcels in downtown Tartu # keywords: # - Cadastral parcels # - Tartu