REST API for OpenDRR data
- Docker engine installed and running: https://docs.docker.com/get-docker/
- Download or clone this repository to your local development environment
Make a copy of the sample.env file and rename it to .env. Make changes if required otherwise leave the default settings.
The settings below can be found in the .env file and control which data sets are loaded into ElasticSearch and Kibana:
loadDsraScenario=true loadPsraModels=true loadHazardThreat=false loadPhysicalExposure=true loadRiskDynamics=true loadSocialFabric=true
Make a copy of python/sample_config.ini and rename it config.ini. Open this file in an editor, add the required github_token (see https://docs.github.com/en/github/authenticating-to-github/creating-a-personal-access-token) and set the remaining parameters as follows:
[auth]
# Github Token for Private Repo Accesss
github_token = a00049ba79152d03380c34652f2cb612
[rds]
# PostGIS Connection Details
postgres_host = db-opendrr
postgres_port = 5432
postgres_un = postgres
postgres_pw = password
postgres_db = opendrr
postgres_address = db-opendrr:5432/opendrr
[es]
# Elasticsearch Connection Details
es_un = elastic
es_pw = changeme
es_endpoint = elasticsearch-opendrr:9200
kibana_endpoint = localhost:5601
docker-compose up --build
NOTE: you will see errors thrown by the opendrr-api_pygeoapi-opendrr_1 container as the stack builds. These can be ignored.
Once the stack is built (~20min) you will need to verify that everything is working.
NOTE: you can stop the stack whenever you like with
Ctrl-Cordocker-compose stop. See below on how you can bring the stack back up without re-building.
Check Elasticsearch to ensure that the indexes were created:
http://localhost:9200/_cat/indices?v&pretty
You should see something similar to:
health status index ...
green open afm7p2_lrdmf_scenario_shakemap_intensity_building
green open .apm-custom-link
green open afm7p2_lrdmf_damage_state_building
green open .kibana_task_manager_1
green open afm7p2_lrdmf_social_disruption_building
green open .apm-agent-configuration
green open afm7p2_lrdmf_recovery_time_building
green open afm7p2_lrdmf_scenario_shakemap_intensity_building
green open afm7p2_lrdmf_casualties_building
green open .kibana_1
You can explore the indexes in Elasticsearch using Kibana
Check pygeoapi to make sure the collections can be accessed
http://localhost:5001/collections
Feature collections can be accessed as follows or by clicking on the links provided on the collections page
You should see something similar to:
{
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [
-117.58484079,
49.58943143
]
},
"properties": {
"AssetID": "59002092-RES3A-W1-PC",
"Sauid": "59002092",
"sL_Asset_b0": 27.602575,
"sL_Bldg_b0": 27.602575,
"sLr_Bldg_b0": 1,
"sLr2_BCR_b0": 0.000819,
"SLr2_RoI": 0.001359,
"sL_Str_b0": 27.602575,
"sLsd_Str_b0": 23.4638,
"sL_NStr_b0": 0,
"sLsd_NStr_b0": 0,
"sL_Cont_b0": 0,
"sLsd_Cont_b0": 0,
"sL_Asset_r2": 0.311704,
"sL_Bldg_r2": 0.311704,
"sLr_Bldg_r2": 1,
"sL_Str_r2": 0.311704,
"sLsd_Str_r2": 0.264966,
"sL_NStr_r2": 0,
"sLsd_NStr_r2": 0,
"sL_Cont_r2": 0,
"sLsd_Cont_r2": 0,
"geom_point": "0101000020E6100000AD9A10086E655DC0D18A357D72CB4840"
},
"id": "59002092"
}
],
"numberMatched": 173630,
"numberReturned": 1,
"links": [
{
"type": "application/geo+json",
"rel": "self",
"title": "This document as GeoJSON",
"href": "http://localhost:5001/collections/afm7p2_lrdmf_scenario_shakemap_intensity_building/items?f=json&limit=1"
},
{
"rel": "alternate",
"type": "application/ld+json",
"title": "This document as RDF (JSON-LD)",
"href": "http://localhost:5001/collections/afm7p2_lrdmf_scenario_shakemap_intensity_building/items?f=jsonld&limit=1"
},
{
"type": "text/html",
"rel": "alternate",
"title": "This document as HTML",
"href": "http://localhost:5001/collections/afm7p2_lrdmf_scenario_shakemap_intensity_building/items?f=html&limit=1"
},
{
"type": "application/geo+json",
"rel": "next",
"title": "items (next)",
"href": "http://localhost:5001/collections/afm7p2_lrdmf_scenario_shakemap_intensity_building/items?startindex=1&limit=1"
},
{
"type": "application/json",
"title": "Economic loss buildings",
"rel": "collection",
"href": "http://localhost:5001/collections/afm7p2_lrdmf_scenario_shakemap_intensity_building"
}
],
"timeStamp": "2020-08-18T22:46:10.513010Z"
}
Refer to the pygeoapi documentation for general guidance:
http://localhost:5001/openapi?f=html
`NOTE: querying is currently limited to spatial extent and exact value queries. For more complex querying use Elasticsearch (see below).
http://localhost:5001/collections/afm7p2_lrdmf_scenario_shakemap_intensity_building/items?sH_Mag=7.2
OR using curl:
curl -XGET "http://localhost:9200/afm7p2_lrdmf_scenario_shakemap_intensity_building/_search" -H 'Content-Type:
application/json' -d'
{
"query": {
"range": {
"properties.sH_PGA": {
"gte": 0.047580,
"lte": 0.047584
}
}
}
}'
OR using curl:
curl -XGET "http://localhost:9200/afm7p2_lrdmf_scenario_shakemap_intensity_building/_search" -H 'Content-Type:
application/json' -d'
{
"query": {
"match": {
"properties.sH_PGA" : 0.047584
}
}
}'
curl -XGET "http://localhost:9200/afm7p2_lrdmf_scenario_shakemap_intensity_building/_search" -H 'Content-Type:
application/json' -d'
{
"query": {
"bool": {
"filter": [
{
"geo_shape": {
"geometry": {
"shape": {
"type": "envelope",
"coordinates": [ [ -118.7, 50 ], [ -118.4, 49.9 ] ]
},
"relation": "intersects"
}
}
}
]
}
}
}'
curl -XGET "http://localhost:9200/nhsl_hazard_threat_all_indicators_s/_search" -H 'Content-Type:
application/json' -d'
{
"query": {
"geo_shape": {
"geometry": {
"shape": {
"type": "circle",
"radius": "20km",
"coordinates": [ -118, 49 ]
}
}
}
}
}'
The spatial database is implemented using PostGIS. You can connect to PostGIS using pgAdmin (https://www.pgadmin.org/) with the connection parameters in your .env file. For example:
POSTGRES_USER: postgres
POSTGRES_PASSWORD: password
POSTGRES_PORT: 5432
DB_NAME: opendrr
You have two options:
- Add a "New Connection" by right clicking on the "PostGIS" data type in the browser
- Enter a name for your connection (i.e. "OpenDRR")
- Add the credentials as per your
.envfile (see above) - Click the "OK" button
- Add a "New Connection" by right clicking on the "WFS / OGC API -Features" data type in the browser
- Enter a name for your connection (i.e. "OpenDRR")
- Enter
http://localhost:5001in the URL field - Select "OGC API - Features" in the "Version" dropdown
- Click the "OK" button
Once the stack is built you only need to re-build when there is new data. The docker-compose-run.yml script is an override that you can use to run the built stack - it doesn't create the python container that pulls the latest code and data from GitHub to populate the stack.
To start the stack:
docker-compose -f docker-compose-run.yml start
To stop the stack:
docker-compose -f docker-compose-run.yml stop
Take the stack down and remove the volumes:
docker-compose down -v
Rebuild the stack:
docker-compose up --build