semvec/pypi-packages huggingface dataset:
coilspy 0.1.0
A Python package for generating self-referencing Bayesian fields, also known as coils.
License: MIT
Requires Python: >=3.12,<4.0

# Probabilistic Coils: Nonreal Self-Referencing Bayesian Fields
> Python package for generating and experimenting with probabilistic coils

## Table of Contents
* [Installation](#install)
* [General Info](#general-information)
* [Coil Behaviors](#coil-behaviors)
* [Physically-Based Control](#physically-based-control)
* [Pseudo Neural Network Parameterization](#pseudo-neural-network-parameterization)
* [Background](#background)
* [Usage](#usage)
<!-- * [License](#license) -->

## Installation
```

```

## General Information
Probabilistic coils are systems of interacting, conserved, nonreal-valued Bayesian fields where the conditionals
are themselves dependent on all state and conditional probabilities, thus making coils
self-referencing. 

Probabilistic coils are inspired by the need for a mathematical framework to describe
dynamic, interconnected, non-hierarchical systems. By using conserved Bayesian fields,
we can describe the flow of discrete state probabilities. By making these self-referencing,
we can describe interdependent probabilistic flows. The generalization into complex and quaternionic
number systems offers wider extensibility. 

## Coil Behaviors 
Probabilistic coils exhibit a number of interesting behaviors. One key behavior is sustained 
aperiodic oscillation. As a result, many coils exhibit chaos. 

It should be emphasized that coils behave deterministically, thus irregular phenomenon is a result of
interconnectedness. 


## Physically-Based Control
Coils can be constructed with a variety of physically-based constraints. For example, locality
can be enforced, preventing the flow of probability to non-neighboring states. Inertial biases can
be imposed, decreasing the flow of probability out of a state. 

Locality constraints can also be used to sever coils, resulting in separate interacting conserved subcoils.
Parameter symmetry can be used to formulate coils with identical parameterizations.


## Neural Network Parameterization
This package demonstrates how neural networks can be used to parameterize coils to generate dynamic fields with desired behavior.

## Background
The motivation, background, and derivation of probabilistic coils can be found here:

1. [Background]

[Background]: https://docs.google.com/document/d/e/2PACX-1vQaaN5-uBjQy8J5WLnZm3fHybOmhNjezxSUw5pn771v7gWzHI4US4KEtbtfE4dU88CzMnIz2SoLNQo2/pub

## Development Notes

### Development with Poetry
I am using Poetry for package management. This is straightforward, with the exception that if we want to use the GPU version of torch, we must do:

```
poetry source add -p explicit pytorch https://download.pytorch.org/whl/cu121
poetry add --source pytorch torch torchvision
```
deno-vm 0.6.0
A binding to Deno + worker-vm, helps you execute JavaScript safely.
License: MIT
Keywords: deno,js,sandbox,execute,javascript

deno_vm
========

.. image:: https://readthedocs.org/projects/deno-vm/badge/?version=latest
   :target: https://deno-vm.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status
   
.. image:: https://github.com/eight04/deno_vm/actions/workflows/test.yml/badge.svg
   :target: https://github.com/eight04/deno_vm/actions/workflows/test.yml
   :alt: test

A Python 3 to Deno + worker-vm binding, helps you execute JavaScript safely.

How it works
------------

The module launches a Deno REPL server, which can be communicated with JSON. All JavaScript code are encoded in JSON and sent to the server. After the server executing the code in vm, the result is sent back to Python.

Install
-------

Install Deno
^^^^^^^^^^^^

Follow the instruction here:
https://docs.deno.com/runtime/manual/getting_started/installation

Also make sure ``deno`` command works as expected:
https://docs.deno.com/runtime/manual/getting_started/installation#testing-your-installation

Install deno_vm
^^^^^^^^^^^^^^^

.. code-block:: sh

   pip install deno_vm

Usage
-----

Most of the APIs are bound to `worker-vm <https://github.com/eight04/worker-vm>`__.

Simple eval:

.. code-block:: python

   from deno_vm import eval
   
   print(eval("['foo', 'bar'].join()"))
   
Use VM:

.. code-block:: python

   from deno_vm import VM
   
   with VM() as vm:
      vm.run("""
         var sum = 0, i;
         for (i = 0; i < 10; i++) sum += i;
      """)
      print(vm.run("sum"))
      
It is possible to do async task with Promise:

.. code-block:: python

   from datetime import datetime
   from deno_vm import VM

   js = """
   function test() {
      return new Promise(resolve => {
         setTimeout(() => {
            resolve("hello")
         }, 3000);
      });
   };
   """
   with VM() as vm:
      vm.run(js)
      print(datetime.now())
      print(vm.call("test"))
      print(datetime.now())
      
API reference
-------------

http://deno_vm.readthedocs.io/

Changelog
---------

- 0.6.0 (Mar 4, 2024)

  - Change: use --unstable-worker-options instead of --unstable.
  - Change: vendor deno dependencies. Now deno_vm doesn't require network and filesystem write access.
  - Fix: suppres cleanup error.
  - Fix: improve uninitialized error message.

-  0.5.1 (Oct 10, 2023)

   -  Fix: unable to pass initial code to ``VM()``.

-  0.5.0 (Oct 10, 2023)

   -  Switch to deno_vm.