Metadata-Version: 2.1
Name: corebridge
Version: 0.2.11
Summary: Bridge for Stactics AICore
Home-page: https://github.com/fenke/corebridge
Author: Fenke Meijer
Author-email: fenkemeijer@gmail.com
License: Apache Software License 2.0
Keywords: python aicore stactics whysor
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: python-dateutil
Requires-Dist: pytz
Requires-Dist: numpy
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Provides-Extra: dev
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# 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 custom `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.

## Development

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 .\[dev\]

### nbdev cycle

- edit
- nbdev_export
- nbdev_test
- nbdev_clean
- nbdev_readme
- nbdev_prepare
- git add .
- 
