semvec/pypi-packages huggingface dataset:
python-jsonrpc-server 0.4.0
JSON RPC 2.0 server library

Python JSON RPC Server
======================

.. image:: https://circleci.com/gh/palantir/python-jsonrpc-server/tree/develop.svg?style=shield
    :target: https://circleci.com/gh/palantir/python-jsonrpc-server/tree/develop

.. image:: https://ci.appveyor.com/api/projects/status/r0jlmvkqwneieeh6/branch/develop?svg=true
    :target: https://ci.appveyor.com/project/gatesn/python-jsonrpc-server

.. image:: https://img.shields.io/github/license/palantir/python-jsonrpc-server.svg
     :target: https://github.com/palantir/python-jsonrpc-server/blob/develop/LICENSE

A Python 2.7 and 3.4+ server implementation of the `JSON RPC 2.0`_ protocol. This library has been pulled
out of the `Python Language Server`_ project.

Asynchronous request handling is supported using Python 3's ``concurrent.futures`` module and the Python 2 `concurrent.futures backport`_.

Installation
------------

``pip install -U python-jsonrpc-server``

Examples
--------

The examples directory contains two examples of running language servers over websockets. ``examples/langserver.py`` shows how to run a language server in-memory. ``examples/langserver_ext.py`` shows how to run a subprocess language server, in this case the `Python Language Server`_.

Start by installing `tornado` and `python-language-server`

``pip install python-language-server[all] tornado``

Then running `python examples/langserver.py` or `python examples/langserver_ext.py` will host a websocket on ``ws://localhost:3000/python``.

To setup a client, you can use the examples from `Monaco Language Client`_.

Development
-----------

To run the test suite:

``pip install .[test] && tox``

License
-------

This project is made available under the MIT License.

.. _JSON RPC 2.0: http://www.jsonrpc.org/specification
.. _Python Language Server: https://github.com/palantir/python-language-server
.. _concurrent.futures backport: https://github.com/agronholm/pythonfutures
.. _Monaco Language Client: https://github.com/TypeFox/monaco-languageclient
torchph 0.1.1
torchph
Requires Python: >=3.6
Keywords: Persistent Homology

# torchph

This repository contains [PyTorch](http://pytorch.org) extensions to **compute**
persistent homology and to **differentiate** through the persistent homology computation.
The packaging structure is similar to PyTorch's structure to facilitate usage for people 
familiar with PyTorch. 

## Documentation

[Read the docs!](https://c-hofer.github.io/torchph/)

The folder *tutorials* (within `docs`) contains some (more or less) minimalistic examples in form of Jupyter notebooks
to demonstrate how to use the `PyTorch` extensions. 

## Associated publications

If you use any of these extensions, please cite the following works (depending on which functionality you use, obviously :)

```bash
@inproceedings{Hofer17a,
  author    = {C.~Hofer, R.~Kwitt, M.~Niethammer and A.~Uhl},
  title     = {Deep Learning with Topological Signatures},
  booktitle = {NIPS},
  year      = {2017}}

@inproceedings{Hofer19a,
  author    = {C.~Hofer, R.~Kwitt, M.~Dixit and M.~Niethammer},
  title     = {Connectivity-Optimized Representation Learning via Persistent Homology},
  booktitle = {ICML},
  year      = {2019}}

@article{Hofer19b,
  author    = {C.~Hofer, R.~Kwitt, and M.~Niethammer},
  title     = {Learning Representations of Persistence Barcodes},
  booktitle = {JMLR},
  year      = {2019}}
  
@inproceedings{Hofer20a},
  author    = {C.~Hofer, F.~Graf, R.~Kwitt, B.~Rieck and M.~Niethammer},
  title     = {Graph Filtration Learning},
  booktitle = {arXiv},
  year      = {2020}}
  
@inproceedings{Hofer20a,     
  author    = {C.~Hofer, F.~Graf, M.~Niethammer and R.~Kwitt},     
  title     = {Topologically Densified Distributions},     
  booktitle = {arXiv},    
  year      = {2020}} 
```