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https://github.com/Orange-OpenSource/python-ngsild-client
ngsildclient is a Python library dedicated to NGSI-LD. It's both a NGSI-LD API client and a toolbox to create and manipulate NGSI-LD entities effortlessly.
https://github.com/Orange-OpenSource/python-ngsild-client
fiware ngsi-ld ngsi-ld-client python-library python-wrapper
Last synced: about 1 month ago
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ngsildclient is a Python library dedicated to NGSI-LD. It's both a NGSI-LD API client and a toolbox to create and manipulate NGSI-LD entities effortlessly.
- Host: GitHub
- URL: https://github.com/Orange-OpenSource/python-ngsild-client
- Owner: Orange-OpenSource
- License: apache-2.0
- Created: 2021-05-07T13:57:47.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2023-08-14T23:10:18.000Z (11 months ago)
- Last Synced: 2024-05-09T23:45:56.841Z (about 2 months ago)
- Topics: fiware, ngsi-ld, ngsi-ld-client, python-library, python-wrapper
- Language: Python
- Homepage:
- Size: 4.24 MB
- Stars: 13
- Watchers: 18
- Forks: 7
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Lists
- awesome - Python ngsildclient - A Python library that helps building NGSI-LD compliant entities and allows to interact with a NGSI-LD Context Broker. (Open-Source FIWARE from third parties / Processing)
README
# The ngsildclient library
[](https://www.etsi.org/deliver/etsi_gs/CIM/001_099/009/01.02.01_60/gs_CIM009v010201p.pdf)
[](http://stackoverflow.com/questions/tagged/fiware)
[](https://opensource.org/licenses/Apache-2.0)
[](https://ngsildclient.readthedocs.io/en/latest/index.html)
[](https://github.com/Orange-OpenSource/python-ngsild-client/actions)
[](https://pypi.org/project/ngsildclient/)
[](https://pypi.org/project/ngsildclient/)
## Overview
**ngsildclient** is a Python library dedicated to NGSI-LD.
It combines :
- a toolbox to create and modify NGSI-LD entities effortlessly
- a NGSI-LD API client to interact with a Context Broker
## Features
### Build NGSI-LD entities
ngsildclient aims at :
- programmatically generate NGSI-LD entities
- load entities from JSON-LD payloads
Four primitives are provided `prop()`, `gprop()`, `tprop()`, `rel()` to build respectively a Property, GeoProperty, TemporalProperty and Relationship.
An Entity is backed by a Python dictionary that stores the JSON-LD payload.
The library operates the mapping between the Entity's attributes and their JSON-LD counterpart, allowing to easily manipulate NGSI-LD value and metadata directly in Python.
### Features list
- primitives to build properties and relationships (chainable)
- benefit from uri naming convention, omit scheme and entity's type, e.g. `parking = Entity("OffStreetParking", "Downtown1")`
- support dot-notation facility, e.g. `reliability = parking["availableSpotNumber.reliability"]`
- easily manipulate a property's value, e.g. `reliability.value = 0.8`
- easily manipulate a property's metadata, e.g. `reliability.datasetid = "dataset1"`
- support nesting
- support multi-attribute
- load/save to file
- load from HTTP
- load well-known sample entities, e.g. `parking = Entity.load(SmartDataModels.SmartCities.Parking.OffStreetParking)`
- provide helpers to ease building some structures, e.g. PostalAddress
- pretty-print entity and properties
### Interact with the Context Broker
Two clients are provided, `Client` and `AsyncClient` respectively for synchronous and asynchronous modes.
Prefer the synchronous one when working in interactive mode, for example to explore and visualize context data in a Jupyter notebook.
Prefer the async one if you're looking for performance, for example to develop a real-time NGSI-LD Agent with a high data-acquisition frequency rate.
### Features list
- synchronous and asynchronous clients
- support batch operations
- support pagination : transparently handle pagination (sending as many requests as needed under the hood)
- support auto-batch : transparently divide into many batch requests if needed
- support queries and alternate (POST) queries
- support temporal queries
- support pandas dataframe as a temporal query result
- support subscriptions
- find subscription conflicts
- SubscriptionBuilder to help build subscriptions
- auto-detect broker vendor and version
- support follow relationships (chainable), e.g. `camera = parking.follow("availableSpotNumber.providedBy")`
## Getting started
### Create our first parking Entity
The following code snippet builds the `OffstreetParking` sample entity from the ETSI documentation.
```python
from datetime import datetime
from ngsildclient import Entity
PARKING_CONTEXT = "https://raw.githubusercontent.com/smart-data-models/dataModel.Parking/master/context.jsonld"
e = Entity("OffStreetParking", "Downtown1")
e.ctx.append(PARKING_CONTEXT)
e.prop("name", "Downtown One")
e.prop("availableSpotNumber", 121, observedat=datetime(2022, 10, 25, 8)).anchor()
e.prop("reliability", 0.7).rel("providedBy", "Camera:C1").unanchor()
e.prop("totalSpotNumber", 200).loc(41.2, -8.5)
```
Let's print the JSON-LD payload.
```python
e.pprint()
```
The result is available [here](https://github.com/Orange-OpenSource/python-ngsild-client/blob/master/parking_sample.jsonld).
### Persist our parking in the Context Broker
The following example assumes that an Orion-LD context broker is running on localhost.
A docker-compose config [file](https://raw.githubusercontent.com/Orange-OpenSource/python-ngsild-client/master/brokers/orionld/docker-compose-troe.yml) file is provided for that purpose.
```python
from ngsildclient import Client
client = Client(port=8026, port_temporal=8027)
client.create(e)
```
### Increase our parking occupancy as the day goes on
Each hour ten more parkings spots are occupied, until 8 p.m.
```python
from datetime import timedelta
prop = e["availableSpotNumber"]
for _ in range(12):
prop.observedat += timedelta(hours=1)
prop.value -= 10
client.update(e)
```
### Retrieve our parking
Get back our parking from the broker and display its `availableSpotNumber` property.
```python
parking = client.get("OffStreetParking:Downtown1", ctx=PARKING_CONTEXT)
parking["availableSpotNumber"].pprint()
```
Only one available parking spot remains at 8 p.m.
```json
{
"type": "Property",
"value": 1,
"observedAt": "2022-10-25T20:00:00Z",
"reliability": {
"type": "Property",
"value": 0.7
},
"providedBy": {
"type": "Relationship",
"object": "urn:ngsi-ld:Camera:C1"
}
}
```
### Request the Temporal Representation of our parking
For convenience we retrieve it as a pandas dataframe.
*If you don't have pandas installed, just omit the `as_dataframe` argument and get JSON instead.*
```python
df = client.temporal.get(e, ctx=PARKING_CONTEXT, as_dataframe=True)
```
Let's display the three last rows.
```python
df.tail(3)
```
| | OffStreetParking | observed | availableSpotNumber |
|---:|:-------------------|:--------------------------|----------------------:|
| 10 | Downtown1 | 2022-10-25 18:00:00+00:00 | 21 |
| 11 | Downtown1 | 2022-10-25 19:00:00+00:00 | 11 |
| 12 | Downtown1 | 2022-10-25 20:00:00+00:00 | 1 |
### Let's throw in a more realistic parking management system
Let us move from our first example to the more realistic parking example provided by the Smart Data Models Program.
```python
from ngsildclient import SmartDataModels
parking = Entity.load(SmartDataModels.SmartCities.Parking.OffStreetParking)
```
Once loaded we can manipulate our new parking the same way we've done until now.
Let's see how it is occupied.
```python
n_total = parking["totalSpotNumber"].value
n_occupied = parking["occupiedSpotNumber"].value
n_avail= parking["availableSpotNumber"].value
print(n_total, n_occupied, n_avail)
```
This parking has 414 parking slots. 282 are occupied. 132 are available.
In order to complete our parking system we would like to add 414 spots to our datamodel.
Let's create a reference parking spot to be used as a template.
```python
spot = Entity("ParkingSpot", "OffStreetParking:porto-ParkingLot-23889:000")
spot.prop("status", "free")
spot.rel("refParkingSite", parking)
```
Let's clone this spot 414 times, assign a disctinct id to each one and occupy the 282 first spots.
This is a simplistic strategy but enough to keep the parking system consistent.
```python
spots = spot * n_total
for i, spot in enumerate(spots):
spot.id = f"{spot.id[:-3]}{i+1:03}"
if i < n_occupied:
spot["status"].value = "occupied"
```
We now establish the relationship between the parking and its spots by adding a new attribute to the parking.
Having a mutual relationship is not necessarily needed. It depends on how we want to navigate in our datamodel.
Let's do it for the sake of example.
```python
from ngsildclient import MultAttrValue
mrel = MultAttrValue()
for spot in spots:
mrel.add(spot, datasetid=f"Dataset:{spot.id[-26:]}")
parking.rel("refParkingSpot", mrel)
```
To sum up we have obtained 415 entities : 1 parking and 414 spots.
Make a single list of these parts and save it into a file.
```python
datamodel = sum(([parking], spots), []) # flatten lists
Entity.save_batch(datamodel, "parking_system.jsonld")
```
The result is available [here](https://github.com/Orange-OpenSource/python-ngsild-client/blob/master/parking_system.jsonld).
Time now to populate our parking system in the broker.
```python
client.upsert(datamodel)
```
Check everything is fine by asking the broker for the number of occupied spots.
Eventually close the client.
```python
client.count("ParkingSpot", q='refParkingSite=="urn:ngsi-ld:OffStreetParking:porto-ParkingLot-23889";status=="occupied"') # 282
client.close()
```
### Let's go further
1. Develop a NGSI-LD Agent
- Collect incoming data from parking IoT *(ground sensors, cameras)* and the parking system API
- Clean data, process data and convert to NGSI-LD entities
- Create and update entities into the NGSI-LD broker *in real-time*
2. Subscribe to events
- Create a subscription to be informed when parking occupation exceeds 90%
- The software that listens to these highly-occupied parking entities can also be a NGSI-LD Agent
Example : programmatically subscribe to events
```python
from ngsildclient import SubscriptionBuilder
subscr = SubscriptionBuilder("https://parkingsystem.example.com:8000/subscription/high-occupancy")
.description("Notify me of high occupancy on parking porto-23889")
.select_type("OffStreetParking")
.watch(["occupancy"])
.query('occupancy>0.9;controlledAsset=="urn:ngsi-ld:OffStreetParking:porto-ParkingLot-23889"')
.build()
client.subscriptions.create(subscr)
```
## Where to get it
The source code is currently hosted on GitHub at :
https://github.com/Orange-OpenSource/python-ngsild-client
Binary installer for the latest released version is available at the [Python
package index](https://pypi.org/project/ngsildclient).
## Installation
**ngsildclient** requires Python 3.9+.
```sh
pip install ngsildclient
```
## Documentation
User guide is available on [Read the Docs](https://ngsildclient.readthedocs.io/en/latest/index.html).
Refer to the [Cookbook](https://ngsildclient.readthedocs.io/en/latest/cookbook.html) chapter that provides many HOWTOs to :
- develop various NGSI-LD Agents collecting data from heterogeneous datasources
- forge NGSI-LD sample entities from the Smart Data Models initiative
## License
[Apache 2.0](LICENSE)