Metadata-Version: 2.1
Name: opvious
Version: 0.5.5
Summary: Opvious Python SDK
Home-page: https://github.com/opvious/sdk.py
Author: Opvious Engineering
Author-email: oss@opvious.io
Requires-Python: >=3.8,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Provides-Extra: aio
Requires-Dist: Brotli (>=1.0.9,<2.0.0); extra == "aio"
Requires-Dist: aiohttp (>=3.8,<4.0); extra == "aio"
Requires-Dist: backoff (>=2.2,<3.0)
Requires-Dist: humanize (>=4.4.0,<5.0.0)
Requires-Dist: pandas (>=1.4,<2.0)
Project-URL: Repository, https://github.com/opvious/sdk.py
Description-Content-Type: text/markdown

# Opvious Python SDK  [![CI](https://github.com/opvious/sdk.py/actions/workflows/ci.yml/badge.svg)](https://github.com/opvious/sdk.py/actions/workflows/ci.yml) [![Pypi badge](https://badge.fury.io/py/opvious.svg)](https://pypi.python.org/pypi/opvious/)

This package provides a lightweight SDK for solving optimization models with the
[Opvious API][api]. Its main features are:

+ Seamless data import/export via native support for [`pandas`][pandas]
+ Powerful built-in debugging capabilities: automatic infeasibility relaxation,
  variable pinning, and more
+ Non-blocking APIs for performant parallel calls


## Quickstart

First, install this package and have an API access token handy (these can be
generated [here][token]).

```sh
pip install opvious[aio] # aio is recommended for improved performance
```

With these steps out of the way, you are ready to solve any formulation:

```python
import opvious

# Instantiate an API client from an API token
client = opvious.Client.from_token(TOKEN)

# Assemble and validate inputs for a registered formulation
inputs = await client.assemble_inputs(
    formulation_name='my-formulation',
    parameters={
        # Formulation parameters...
    }
)

# Start an attempt and wait for it to complete
attempt = await client.start_attempt(inputs)

# Wait for the attempt to complete
outcome = await client.wait_for_outcome(attempt)
```


## Environments

Clients are compatible with Pyodide environments, for example [JupyterLite][]
kernels. Simply install the package as usual in a notebook, omitting the `aio`
optional dependencies:

```python
import piplite
await piplite.install('opvious')
```

In other environments, prefer using the `aiohttp`-powered clients as they are
more performant (this is the default if the `aio` dependencies were specified).


## Next steps

This SDK is focused on solving optimization models. For convenient access to the
rest of Opvious API's functionality, consider using the [TypeScript SDK and
CLI][cli].


[api]: https://www.opvious.io
[cli]: https://www.opvious.io/sdk.ts
[JupyterLite]: https://jupyterlite.readthedocs.io/
[token]: https://hub.beta.opvious.io/authorizations
[pandas]: https://pandas.pydata.org

