semvec/pypi-packages huggingface dataset:
debt 1.0.1
memory storage structure for directories


******

Bravo!  You have received a Medical Diploma in "debt" from   
the Orbital Convergence University International Air and Water Embassy of the Tangerine Planet.  

You are now officially certified to include "debt" in your practice.

******

# 🪙 debt

## 💳 obtain
`(ZSH) pip install debt`

## 📈 tutorial
`(ZSH) debt shares`
pywintun-pmd3 0.0.4
wintun (WireGuard) wrapper for Python

`Wintun <https://github.com/WireGuard/wintun/tree/0.14.1>`__ wrapper for Python 3
=================================================================================

Example
=======

.. code:: python

   import pywintun_pmd3

   def log(level: int, timestamp: Int64, message: str):
     pass

   pywintun_pmd3.set_logger(log)
   pywintun_pmd3.install_wetest_driver()

   tun_dev = pywintun_pmd3.TunTapDevice()
   # Avaliable constructor include
   # or TunTapDevice(name='XX')
   # or TunTapDevice(name='XX', type='xxx')
   # or TunTapDevice(name='XX', type='xxx', guid='xxxs')
   # or TunTapDevice(name='XX', type='xxx', proto_aware=True)
   # tundev.name, readonly property
   tundev.ring_capacity = 8*1024*1024
   tun_dev.mtu4 = 1460             # set ipv4 subinterface mtu
   tun_dev.mtu = 1452              # set ipv6 subinterface mtu
   tundev.addr4 = '10.2.3.4'       # set ipv4 subinterface address
   tundev.addr = 'ffee:aadf:8877:2'# set ipv6 subinterface mtu
   tundev.up()

   packet = tundev.read() # receive a packet
   tundev.wait_read_event()

   tundev.write(b'\x00') # send a packet..

   tundev.down()

   ...
   tundev.close()
cdopt 0.5.5
A Python package for optimization on closed Riemannian manifolds
Requires Python: >=3.6
Keywords: Optimization,Riemannian optimization

# Welcome to CDOpt

**A Python toolbox for optimization on closed Riemannian manifolds with support for automatic differentiation**



Riemannian optimization is a powerful framework to tackle nonlinear optimization problems with structural equality constraints. By transforming these Riemannian optimization problems into the minimization of constraint dissolving functions, CDOpt allows for elegant and direct implementation various unconstrained optimization approaches for Riemannian optimization problems. CDOpt also provides user-friendly frameworks for training manifold constrained neural networks by PyTorch and Flax.



The constraint dissolving approaches have the following advantages:

* **Direct optimization & high efficiency:** CDOpt is developed from the *[constraint dissolving approaches](https://arxiv.org/abs/2203.10319)*, which transforms Riemannian optimization problems to unconstrained ones. Therefore, we can utilize various highly efficient solvers for unconstrained optimization, and directly apply them to solve Riemannian optimization problems. Benefited from the rich expertise gained over the decades for unconstrained optimization, CDOpt is very efficient and naturally avoids the difficulties in developing specialized solvers for Riemannian optimization.
* **Plug-in neural layers:** CDOpt provides various plug-in neural layers for PyTorch and \Pkg{Flax} packages. With only minor changes in the original codes, users can easily build and train the neural network while constrain the weights over various manifolds.
* **High efficiency:** CDOpt has high compatibility with various numerical backends, including NumPy, SciPy, PyTorch, JAX, Flax, etc . Users can directly apply the advanced features of these packages to accelerate optimization, including the automatic differentiation, GPU/TPU supports, distributed optimization frameworks, just-in-time (JIT) compilation, etc.
* **Customized constraints:** The manifold classes in CDOpt can be constructed only from the expressions of constraints. Users can easily and directly describe Riemannian optimization problems in CDOpt without any geometrical materials of the Riemannian manifold (e.g., retractions and their inverse, vector-transports, etc.).



```{tableofcontents}
```
hashr 1.0.0
Custom hash function for Python
Requires Python: >=3.6

# hashr

Simply a custom hashing package with optional salting, able to be both run from the command line and imported as a package.

## Installation

To install, just run either of the below commands.
```bash
pip install hashr
```
```bash
pip3 install hashr
```

## Usage

#### Command Line

To use hashr from the command line, run:
```bash
hashr [input]
```
Make sure to surround an input with double quotes if it contains spaces.  
There is also the option to add a salt, which causes duplicate string to have unique hashes. To use this, add the ```-s``` flag to the command.

#### Import Package

To import hashr as a package, add ```from hashr import [function]``` to the top of your Python file.
The available functions are as follows:
 - hashr: main hashing algorithm, turns any string into a string hash
 - saltr: generates a seven digit salt, either numerical or alphabetical
 - number_to_letters: converts a number to letters based on each digit