corebridge 0.6.6
Corebridge - a bridge between Univia AICore platform and Wodan processor modules
Requires Python: >=3.10

# corebridge


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

This package provides functions and classes to run wodan style
processing functions in the Stactics AICore environment.

## Installation

Use

``` sh
pip install corebridge
```

to install corebrdige.

## How to use

### Introduction

Wodan is a proprietary backend service that applies high performance,
custom analytical processing to timeseries data in the Whysor data and
dashboarding environment.

Each wodan module defines one function that operates as the entry point.
The parameter annotations in this function definition are used to format
data and retrieve parameters from the originating call to the wodan api.
This function is called with data retrieved according to a specification
and with additional parameters as annotated.

A simple function might look like:

``` {python}
import numpy as np

def multiply(data:np.ndarray, multiplier:float=1.0):
    return data * multiplier
    
```

Wodan binds this function to a service endpoint and takes care of
fetching data and parameters and converting the result for the caller.

### AICore modules

For AICore users define a class, always named `Module` with a
constructor `__init__` and a method `infer`.

This package defines a baseclass to quickly construct a `Module` class
that is able to use a wodan processor function inside the AICore system:

``` {python}
import numpy as np
import corebridge

def multiply(data:np.ndarray, multiplier:float=1.0):
    return data * multiplier

class Module(corebridge.aicorebridge.AICoreModule):
    def __init__(self, save_dir, assets_dir, *args, **kwargs):
        super().__init__(multiply, save_dir, assets_dir, *args, **kwargs)
    
```

That’s it. Well, you can add parameters to `__init__` that can be used
as hyperparameters in the web-interface and you could override `infer`
for the same reason. The baseclass takes care of converting call
parameters and data to the function specification and, calls the
function and converts the result for the caller, similar to the original
Wodan service.

You don’t have to use AICoreModule baseclas but can create your own
class and use functionality within CoreBridge to convert call parameters
and data to fit your needs. AICoreModule was developed to make the
deployment of existing Wodan modules within AICore easier. It also helps
developing functions independently of AICore while being able to deploy
these functions as modules within AICore.

## Development

### NBDev

This library is developed with [NBDev](https://nbdev.fast.ai/) - a
literate programming toolkit that supports developing code using jupyter
notebooks and mix code with documentation.

Literate programming is a methodology - introduced in 1984 by Donald
Knuth - that combines a programming language with a documentation
language. In this approach, a program is explained in a human language
(such as English) alongside code snippets. The literate source file is
then processed by a preprocessor to produce both source code and
formatted documentation.

This paradigm enhances program robustness, portability, and
maintainability, making it a valuable tool in scientific computing and
data science[^1]

### Quarto

Documentation is prepared from the notebook with
[Quarto](https://quarto.org/). Quarto too combines code with
documentation but it does not extract source code into modules like
nbdev.

### Installation

#### Quarto

Quarto uses Pandoc and, for pdf format, LaTeX. These must be available
on your system.

Install [Quarto](https://quarto.org/docs/get-started/) as you see fit,
there is a [VSCode
extension](https://marketplace.visualstudio.com/items?itemName=quarto.quarto)
which handles this.

#### NBDev

NBDev is available as PyPi package and is installed with

    pip install nbdev

or if you are using conda

    conda install -c fastai -y nbdev

If so desired you can let NBDev install Quarto with

    nbdev_install_quarto

But this ask for the system admin password.

### Local editing & testing

Setup a virtual environment, activate it and install the development
package and dependencies with, on linux

        pip install -e ‘.[dev]’

or on Windows

        pip install -e .[dev]

#### Jupyter

The above pip install should also install jupyter but to use it the
kernel needs to be installed with:

        python -m ipykernel install --user --name=corebridge.venv

### nbdev cycle

- edit
- nbdev_prepare

The latter performs - nbdev_export - nbdev_test - nbdev_clean -
nbdev_readme

Before committing changes to git run - nbdev_clean

Then commit to git and for a new pip release upload to Pypi with
`nbdev_pypi`

[^1]: [Wikipedia on ‘Literate
    Programming’](https://en.wikipedia.org/wiki/Literate_programming)