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First of all we have to know which routes we need to use. For this it is recommended to take a look at the query builder or swagger ui (http://localhost:8002/ui/). Under the Edit tab there are all routes which can be used to insert or update data of the property service.

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For this tutorial we are using just two of them. One to add new provenances datasets and one to add new regions to the property service.

• /provenancedataset/bulk
• /region/{provenancedataset}

Requests

These two routes are both POST routes which requires them to be called with a POST request. To create simple post requests we recommend the python package requests.

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# define data to store
ml_provenancedatasets = '[
  	{
   	 	"name": "ml-algorithms'
",
    	"responsible": "John Doe",
    	"type": "algorithm",
    	"description": "no comment!"
  	}
]
ml_result = {
     'time_end': '2016-01-22T17:27:59.001Z',
    'lat_hg': '2016-01-22T17:27:59.001Z',
    'long_hg': '2016-01-22T17:27:59.001Z',
    'algorithm_name': 'LassoCV',
    'algorithm_parameters': {
        'store': 'what you want',
        'also here': [1, 2, 3, 4]
    }
}

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• Every region needs at least a time_start, lat_hg and long_hg attribute.
• Every specific data has to be added to the data attribute and has to be a key-value pair.

This would then look like this:

# define data to store
post_data = {
    'time_start': '2016-01-21T17:27:59.001Z',
    'datalat_hg': ml_result
}12,
    'long_hg': 4.2,
    'data': {
	    "LassoCV": {
			'store': 'what you want',
	        'also here': [1, 2, 3, 4]
		}
    }
}

Ingest

Now the ingest of this post_data is very simple. We first have to add the new provenance dataset to the property service and then add a new region.

# add provenance
print('creating provenance...')
requests.post("http://localhost:8002/provenancedataset/bulk", json=ml_provenancedatasets)

# add region
print('storing data...')
requests.post("http://localhost:8002/region/%s" % ml_provenancedatasets[0]['name'], json=post_data)

The addresses of the routes are the same we looked up before.

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Now to check if everything worked we should retrieve all the properties regions which are stored under this provenancedataset. This can be done with a GET request.

# retrieving data
print('downloading all properties...')
ml_propertiesregions = requests.get("http://localhost:8002/propertyregion/%s/list" % ml_provenancedatasets[0]['name']).json()

print(ml_propertiesregions)

Source Code

Here you can download the full python source code.

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