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Python Adam API

Project description

Installation

Versioning

  1. adamapi==2.2.2.2, This pachage works only with ADAMCORE 2.

Requirements

sudo apt-get install python3-venv python3-gdal gdal-bin

Install with pip

VENVNAME="adamapi"
python3 -m venv "${VENVNAME}"
source "${VENVNAME}/bin/activate";
python3 -m pip install --upgrade pip;
pip install adamapi
ln -s "/usr/lib/python3/dist-packages/osgeo" "${VENVNAME}/lib/python3.8/site-packages/osgeo"

API DEFINITIONS

This document briefly describes the ADMAPI functionalities.
The ADAMAPI library is divided in 4 modules:

  1. Auth --> the authorization module
  2. Datasets --> to get the list of datasets
  3. Search --> to get the lists of products, including associated metadata (e.g. geometry, cloud cover, orbit, tile, ...)
  4. GetData --> to retrieve the product(s). It includes options for subsetting products in space and time, for downloading at native data granularity and with reduced processing capacity

1 - Auth

This module takes care of user authentication and authorization.
Without instancing an object of this module other components don't work.
Auth module is based on the ADAMAPI_KEY, a key that uniquelly identifies the user.

Class contructor and parameters

from adamapi import Auth
a = Auth()

Parameters:

position/keyword mandatory type default description

Public methods and parameters

  • .setKey() --> To setup the ADAMAPI_KEY
    Parameters:
position/keyword mandatory type default description
0 True str The ADAMAPI_KEY
  • .setAdamCore() --> To setup the url of the ADAM-CORE endpoint
    Parameters:
position/keyword mandatory type default description
0 True str The url like https://test.adamplatform.eu
  • .authorize() --> to instanciate an auth object
    Parameters:
position/keyword mandatory type default description
  • .getAuthToken() --> to get the authorization token
    Parameters:
position/keyword mandatory type default description

1.1 - ADAMAPI_KEY retrieval

To get the ADAMAPI_KEY, you need to access your ADAM portal and:

  1. Select the "user icon" on the top right
  2. Expand / click the "USERNAME"
  3. Click on the "Api Key" to display your key

*Command-line ADAMAPI_KEY retrieval TBP*

1.2 - ADAMAPI_KEY setup

There are three methods to setup the ADAMAPI_KEY and the ADAM-CORE instance:

  1. use the method setKey() and setAdamCore()
from adamapi import Auth
a = Auth()
a.setKey('<ADAMAPI_KEY>')
a.setAdamCore('https://test.adamplatform.eu')
  1. Export two envars like
#open a Terminal and type:
export ADAMAPI_KEY='<ADAMAPI_KEY>'
export ADAMAPI_URL='https://test.adamplatform.eu'
  1. create a file called .adamapirc in the user home directory with the following content
key=<ADAMAPI_KEY>
url=https://test.adamplatform.eu

1.3 - Examples

After ADAMAPI_KEY has been set up, an auth instance can be created with:

from adamapi import Auth
a = Auth()
a.authorize()

After authorize method you can retrive your autho token:

from adamapi import Auth
a = Auth()
a.authorize()
a.getAuthToken()

2 - Datasets

This module provides datasets discovery functionality.

Class contructor and parameters

from adamapi import Datasets
datasets = Datasets( a )

Parameters:

position/keyword mandatory type default description
0 True Auth instance The ADAMAPI authorized instance obtained in the previous section

Public methods and parameters

  • .getDatasets() --> To retrieve datasets list
    Parameters:
position/keyword mandatory type default description
0 False str The datasetId.
page False numeric 0 Indicats a specific page
maxRecords False numeric 10 Max number of results in output.

This .getDatasets() function can be used to retrive additional filters which are described in the key filtersEnabled (if exists).

2.1 Examples

This module can be used in 2 different ways.

  1. To list all available datasets:
datasets = Datasets(a)
print(datasets.getDatasets())
  1. To get detailed metadata about a specific dataset
datasets = Datasets(a)
print( datasets.getDatasets( '{{ID:DATASET}}' , page=0 , maxRecords=10 ) )
  1. To get filtersEnabled. To use this additional filters see first example in Search section.
datasets = Datasets(a)
out=datasets.getDatasets("{{ID:DATASET}}")
print(out["filtersEnabled"])

3 - Search

This module provides discovery functionality through the products available on the ADAM instance.

Class contructor and parameters

from adamapi import Search
search = Search( a )

Parameters:

position/keyword mandatory type default description
0 True Auth instance The ADAMAPI authorized instance obtained in section 1-Auth

Public methods and parameters

  • .getProducts() --> To retrieve datasets list and metadata

Parameters:

position/keyword mandatory type default description
0 True str The datasetId.
maxRecords False int 10 number of records
startIndex False int 0 starting record index
startDate False str or datetime the start date
endDate False str or datetime the end date
geometry False str or geojson GeoJson geometry,geojson format appendix

3.1 Examples

  1. Example1:
search=Search(a)
mongo_search=search.getProducts('{{ID:DATASET}}',maxRecords=1,startIndex=0,platform="{{VALUE}}")
  1. Example2:
search=Search(a)
mongo_search=search.getProducts('{{ID:DATASET}}',maxRecords=1,startIndex=0)

4 - GetData

This module provides data access of raster, spatial subset, timeseries in the native data granularity and reduced processing capacity.

Class contructor and parameters

from adamapi import GetData
data=GetData(a)

Parameters:

position/keyword mandatory type default description
0 True Auth Instance The ADAMAPI authorized instance obtained in the section 1-Auth

Public methods and parameters

  • .getData() --> To retrieve a specific product or a dataset in its native granularity, to get a subset of it, to perform a timeseries or to exec simple processing
position/keyword mandatory type default description
0 True str The datasetId
1 True str GetFile request type. available values: GetFile,GetSubset, GetTimeseries and GetProcessing
asynchronous False boolean False rappesents how the request will be performed
compress False boolean False return a zip file
rest False boolean True perform RESTful order ignoring explorer state on the server and equalization configured using the explorer gui
filters True json {} json object with filters parameter. startDate and endDate are required inside it. Geometry is not required for GetFile operation, it is otherwise
options False json {} request option
outputDir False str adamapiresults/ set a different download directory inside adamapiresult/ main directory

4.1 Examples

data=GetData(a)
#to retrive a specific product
image = data.getData('{{ID:DATASET}}',"GetFile",asynchronous=False,compress=False,rest=False,filters={"startDate":'{{STARTDATE}}',"endDate":'{{ENDDATE}}',"productId":'{{PRODUCTID}}'},outputDir='{{OUTPUT_DIR}}')


#to retrieve a dataset in its native granularity
data=GetData(self.a)
image = data.getData('{{ID:DATASET}}',"GetFile",asynchronous=False,compress=False,rest=False,filters={"startDate":'{{STARTDATE}}',"endDate":'{{ENDDATE}}',"geometry":'{{GEOMETRY}}'},outputDir='{{OUTPUT_DIR}}')

For the GetSubset,GetTimeseries and GetProcessing requests you need to add the options parameter with these constraints : output formats and functions(only for processing request)

#subset example
image = data.getData('{{ID:DATASET}}',"GetSubset",asynchronous=False,compress=False,rest=False,filters={"startDate":'{{STARTDATE}}',"endDate":'{{ENDDATE}}',"geometry":'{{GEOMETRY}}'},options={"format":'{{FORMATS}}'},outputDir='{{OUTPUT_DIR}}')

#timeseries example
image = data.getData('{{ID:DATASET}}',"GetTimeseries",asynchronous=False,compress=False,rest=False,filters={"startDate":'{{STARTDATE}}',"endDate":'{{ENDDATE}}',"geometry":'{{GEOMETRY}}'},options={"format":'{{FORMATS}}'},outputDir='{{OUTPUT_DIR}}')

#processing example
image = data.getData('{{ID:DATASET}}',"GetProcessing",asynchronous=False,compress=False,rest=False,filters={"startDate":'{{STARTDATE}}',"endDate":'{{ENDDATE}}',"geometry":'{{GEOMETRY}}'},options={"format":'{{FORMAT}}',"function":'{{FUNCTION}}'},outputDir='{{OUTPUT_DIR}}')

4.3 Asyncronous Example

#1. execute the request
image = data.getData('{{ID:DATASET}}',"GetSubset",asynchronous=False,compress=False,rest=False,filters={"startDate":'{{STARTDATE}}',"endDate":'{{ENDDATE}}',"geometry":'{{GEOMETRY}}'},options={"format":'{{FORMATS}}'},outputDir='{{OUTPUT_DIR}}')


#2. check the status

stat=data.getData(datasetId,"GetSubset",asynchronous=True,id=str(image.pk))
while stat.status != "completed":
    time.sleep(1)
    stat=data.getData(datasetId,"GetSubset",asynchronous=True,id=str(image.pk))

#3. download the zip,unzip it and remove the zip (optional)
for res in stat.list:
    if res["status"] == "failed":
        print(res["exit_code"])
    else:
        r=self.a.client(res["download"]["url"],{},"GET")
        with open(str(res["download"]["url"].split("/")[4])+"_"+str(res["download"]["url"].split("/")[5]), 'wb' ) as f:
            f.write( r.content )

Appendix 1 - Data format

date and date+time

Supported string date/date+time format are:

  • '%Y-%m-%dT%H:%M:%S',
  • '%Y-%m-%dT%H:%M:%SZ',
  • '%Y-%m-%d'

GeoJson

Geometry have to follow the latest geojson standard rfc7946
In particular Polygons and MultiPolygons should follow the right-hand rule

Geometry

#This geometry will return all the results it has intersected within it
geometry = { "type": "Polygon", "coordinates": [ [ [ 43.916666667, 15.716666667 ], [ 43.916666667, 15.416666667 ]    , [ 44.216666667, 15.416666667 ], [ 44.216666667, 15.716666667 ], [ 43.916666667, 15.716666667 ] ] ] }
#This geometry will return all the results it has intersected on its outside
geometry = { "type": "Polygon", "coordinates": [ [ [ 43.84986877441406,15.925676536359038 ], [ 44.6539306640625,15.950766025306109 ],[ 44.681396484375,15.194084972583916 ], [ 43.8189697265625,15.20998780073036 ], [ 43.84986877441406,15.925676536359038 ] ] ] }

Output Formats

request output format
GetFile -
GetSubset tiff,png
GetTimeseries json,csv
GetProcessing experimental tiff,png

Processing Function

type description
average When the GetProcessing retrieves a multi-band product or a set of products it executes the average of their values
overlap When the GetProcessing retrieves a set of products, it executes their overlap without any specific strategy
mosterecent When the GetProcessing retrieves a set of products, it puts on the top the most recent one
leastrecent When the GetProcessing retrieves a set of products, it puts on top the least recent one
minvalue When the GetProcessing retrieves a multi-band product or a set of products for each pixel it puts on top the minimum value of the pixel
maxvalue When the GetProcessing retrieves a multi-band product or a set of products for each pixel it for each pixel, puts on top the maximum value of the pixel

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