Skip to main content

Python bindings for the Ophidia Data Analytics Platform

Project description

PyOphidia is a GPLv3-licensed Python package for interacting to the Ophidia platform.

It is an alternative to Oph_Term, the no-GUI interpreter component bundled with Ophidia, and a convenient way to submit SOAP HTTPS requests to an Ophidia server or to develop your own client using Python.

It runs on Python 2.6, 2.7, and 3.4, has no dependencies and is pure-Python.

It provides 2 main modules:

  • generic low level class to submit any type of requests (simple tasks and workflows), using SSL and SOAP with the client;

  • high level cube-oriented class to interact directly with cubes, with several methods wrapping some of the most useful operators.


Import PyOphidia

From the PyOphidia package import the client module:

from PyOphidia import client

Instantiate a client

Create a new Client() using the login parameters username,*password*,*host* and port. It will also try to resume the last session the user was connected to, as well as the last working directory and the last produced cube.

ophclient = client.Client("oph-user","oph-passwd","","11732")

Client attributes

  • username: Ophidia username

  • password: Ophidia password

  • server: Ophidia server address

  • port: Ophidia server port (default is 11732)

  • session: ID of the current session

  • cwd: Current Working Directory

  • cube: Last produced cube PID

  • exec_mode: Execution mode, ‘sync’ for synchronous mode (default),’async’ for asynchronous mode

  • ncores: Number of cores for each operation (default is 1)

  • last_request: Last submitted query

  • last_response: Last response received from the server (JSON string)

  • last_jobid: Job ID associated to the last request

Client methods

  • submit(query) -> self: Submit a query like ‘operator=myoperator;param1=value1;’ or ‘myoperator param1=value1;’ to the Ophidia server according to all login parameters of the Client and its state.

  • deserialize_response() -> dict: Return the last_response JSON string attribute as a Python dictionary.

  • resume_session() -> self: Resume the last session the user was connected to.

  • resume_cwd() -> self: Resume the last cwd (current working directory) the user was located into.

  • resume_cube() -> self: Resume the last cube produced by the user.

  • wsubmit(workflow,*params) -> self: Submit an entire workflow passing a JSON string or the path of a JSON file and an optional series of parameters that will replace $1, $2 etc. in the workflow. The workflow will be validated against the Ophidia Workflow JSON Schema.

  • wisvalid(workflow) -> bool: Return True if the workflow (a JSON string or a Python dict) is valid against the Ophidia Workflow JSON Schema or False.

Submit a request

Execute the request oph_list level=2:

ophclient.submit("oph_list level=2")

View the result

View the JobID of the last request and the returned JSON response:

print("Last JobID: " + ophclient.last_jobid)
print("Last response: " + ophclient.last_response)

Set a Client for the Cube class

Instantiate a new Client common to all Cube instances:

from PyOphidia import cube

Create a new container

Create a new container to contain our cubes called test, with 3 double dimensions (lat,*lon* and time):


Import a new cube

Import the variable T2M from the NetCDF file /path/to/ into a new cube inside the test container. Use lat and lon as explicit dimensions and time as implicit dimension expressed in days. Use the host partition testpartition and distribute the cube across 1 host, 1 DBMS instance, 2 databases and 16 fragments (8 fragments per database):

mycube = cube.Cube(container='test',exp_dim='lat|lon',host_partition='testpartition',imp_dim='time',measure='T2M',src_path='/path/to/',exp_concept_level='c|c',imp_concept_level='d',ndb=2,ndbms=1,nfrag=8,nhost=1)

Create a Cube object with an existing cube

Instantiate a new Cube using the PID of an existing cube:

mycube2 = cube.Cube(pid='')

Pretty print information on a Cube

Print in a structured way the main information regarding a Cube object:


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PyOphidia-1.2.1.tar.gz (32.9 kB view hashes)

Uploaded source

Built Distribution

PyOphidia-1.2.1-py2.py3-none-any.whl (23.0 kB view hashes)

Uploaded 2 7

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page