helper for running different scipy 2D interpolations
Project description
SciPolate
=========
Scipolate offers a small helper class that can be used to perform
2D interpolation tasks using scipy. It is meant to be used as a common
interface to run and validate the task automated in the same way.
Installation
============
Install Scipolate using pip:
```bash
pip install scipolate
```
Note
====
Scipolate was originally a part of a interpolation web-app used in one of my
lectures. That means it was used in an API. Hence, the parameters are set in
one single JSON-like dictionary, which is un-pythonic.
For the same reason, the class does provide an output *Report* including the
result as a base64 encoded image. Nevertheless, the class can be used outside
of a web-application context. Mind that performance was not important during
development. In case you need a fast algorithm, use scipy directly, or
something like the [interpolation](https://pypi.org/project/interpolation/)
library.
With the new version the Interpolation itself is outsourced into a class on
its own. All the image processing and transformation used for the reporting
tools in my web based applications, a class called `WebInterface` is implemented.
Usage
=====
There are two main interfaces that can be used:
* The *Interpolator* class, which is the core class performing the
interpolation.
* The *WebInterface* class which is meant to be used in a API, as it takes the
arguments as JSON and returns JSON along with base64 encoded images.
Example
-------
An Example will follow.
=========
Scipolate offers a small helper class that can be used to perform
2D interpolation tasks using scipy. It is meant to be used as a common
interface to run and validate the task automated in the same way.
Installation
============
Install Scipolate using pip:
```bash
pip install scipolate
```
Note
====
Scipolate was originally a part of a interpolation web-app used in one of my
lectures. That means it was used in an API. Hence, the parameters are set in
one single JSON-like dictionary, which is un-pythonic.
For the same reason, the class does provide an output *Report* including the
result as a base64 encoded image. Nevertheless, the class can be used outside
of a web-application context. Mind that performance was not important during
development. In case you need a fast algorithm, use scipy directly, or
something like the [interpolation](https://pypi.org/project/interpolation/)
library.
With the new version the Interpolation itself is outsourced into a class on
its own. All the image processing and transformation used for the reporting
tools in my web based applications, a class called `WebInterface` is implemented.
Usage
=====
There are two main interfaces that can be used:
* The *Interpolator* class, which is the core class performing the
interpolation.
* The *WebInterface* class which is meant to be used in a API, as it takes the
arguments as JSON and returns JSON along with base64 encoded images.
Example
-------
An Example will follow.
Project details
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