semvec/pypi-packages huggingface dataset:
arma-server-tools 0.1.2
Tools to manage content for arma3Server
Requires Python: >=3.8,<4.0

# arma server tools

Tools to manage running content on arma3server

## reference material

### commands

    poetry run server --help
    poetry run pull --help

### Arma server stuff

- <https://community.bistudio.com/wiki/Arma_Dedicated_Server>
- <https://community.bistudio.com/wiki/server.cfg>
- <https://developer.valvesoftware.com/wiki/Arma_3_Dedicated_Server>

### python libraries

- [poetry](https://python-poetry.org)
- [click](https://click.palletsprojects.com/en/7.x/)
- [click_log](https://click-log.readthedocs.io)
- [rich](https://rich.readthedocs.io/en/stable/introduction.html)
- [PyYAML](https://pyyaml.org/wiki/PyYAMLDocumentation)
- [pytest](https://docs.pytest.org/en/stable/)


### articles references

- <https://docs.python.org/3/library/subprocess.html>
- <https://docs.python.org/3/library/shutil.html>
- <https://docs.python.org/3/library/os.html?highlight=os%20symlink#os.symlink>

## yaml files

### yaml config in home dir

~~~
--- # ~/arma_server.yaml
username: steam_username
password: steam_password
workshop: "/home/steam/.steam/steamapps/workshop/content/107410"
arma_home: "/home/steam/.steam/steamcmd/arma3"
arma_configs: "/home/steam/arma_configs"
~~~

### yaml config for specific server

- name: name of the server
- config: relative path to the arma cfg file, starting from the arma_configs folder
- port: port the server is on, defaults to 2302 if nothing is set
- mods: list of mods to load

without mods

~~~
--- # example without mods
name: direct_action_altis
config: direct_action/direct_action_altis.cfg
port: 2302
~~~

with mods

~~~
--- # example with some mods 
name: survival_altis
config: survival/survival_altis.cfg
mods: 
  - cba_a3 
  - niarms_all
~~~
chippermillet 0.0.1
Cryptography from madhava
Requires Python: >=3.6

Cryptography


## Installation

```bash
$ python3 -m pip install chippermillet
```

##import:

```python
    >>> from chippermillet import encrypt
    >>> from chippermillet import decrypt
```

##encrypt:


```python
    >>> e = encrypt(b'data',  'secret', 60000)
```

##decrypt:


```python
    >>> decrypt(e, 'secret', 60000)
    'data'
```
impulseest 1.0
non-parametric impulse response estimation with input-output data
Requires Python: >=3

# impulseest() is a non-parametric impulse response estimation function with input-output data

As the variance increases linearly with the finite impulse response (FIR) model order, it is important for higher order FIR models to counteract this situation by regularizing the estimative. In impulseest(), this is done as proposed in T. Chen et al [2012] using the Empirical Bayes method (Carlin and Louis [1996]).

The six arguments in this function are: <br />
    - u [NumPy array]: input signal (size N x 1); <br />
    - y [NumPy array]: output signal (size N x 1); <br />
    - n [integer]: number of impulse response estimates (default is n = 100); <br />
    - RegularizationKernel [string]: regularization method - 'none', 'DC', 'DI', 'TC' (default is 'none'); <br />
    - MinimizationMethod [string]: bound-constrained optimization method used to minimize the cost function - 'L-BFGS-B', 'Powell', 'TNC' (default is 'L-BFGS-B').

The impulseest function returns a NumPy array of size n x 1 containing all the n impulse response estimates.

## Importing

```Python
from impulseest import impulseest
```

## Example

For a detailed example, please check the example folder in the package's homepage at GitHub. Basic usage:

```Python
ir_est = impulseest(u,y,n=100,RegularizationKernel='DC')
```

## Contributor

Luan Vinícius Fiorio - vfluan@gmail.com
handmademl 1.1
'Personal machine/deep learning implementation inspired by sklearn and keras'
Requires Python: >=3.6

# handmadeML
This package is created to merge to sub-projects : handmade-neural-network and handmade-machine-learning
The components and logics from both projects should converge

# Objective
This is a personal challenge : re-code all the components of machine learning and deep learning algos

## The code is inspired by the logic of sklaern and keras, but is much simpler:
  - common terminology and workflow (model instanciation, model.fit, model.predict etc..) but simplified (no .compile method for instance)
  - less features implemented
  - less checks, exceptions, tricky cases allowed, etc.
  - probably much less computionaly efficient

## Implemented so far:
### linear classifier :
  - loss options : hinge, squared_hinge (for SVC) and logit (for Logistic regression)
  - penalty options : l1,l2
  - kernel : only linear

### decision trees
  - decion tree classifier

### neuralnets
  - dense networks only
  - 5 activation functions:
    - relu, tanh (mostly for hidden layers)
    - linear (for regression output)
    - sigmoid, softmax (for classifiction outputs)
  - 4 loss functions:
    - mse, mae (for regression tasks)
    - binary crossentropy (for binary classification tasks)
    - multiclass crossentropy (for multiclassification)
  - gradient descent with back-propagation
  - simple GrandientDescent optimizer only
    - stochastic or mini-batch
    - adjustable learning rate
    - without momentum
  - metric computation at the end of each batch/epoch for monitoring during training
    - only loss functions
  - weights and bias initializers : zeros, ones and glorot_uniform only

  - a pedagogical notebook in the notebooks folder contains a guided implementation, step by step, of a simple neural network, equivalent to the Tensorflow Playgroung

## To be coded later :
### linear classifier :
  - kernel trick with simple kernels : polynomials, rbf
  - create a pedagogical tutorial notebook about linear SVC

### decision trees :
  - regression trees

### ensemble methods and other optmization methods :
  - bagging
  - boosting
  - cross validation
  - grid search
  - randomized search

### neuralnets :
  - regularization:
     - l1, l2, elasticnet
     - on kernels (weights)

  - early stopping on validation data
  - training history tracking
  - momentum for SGD optimizer
  - other metrics (accuracy, roc_auc, etc.)
  - other optimizers
    - adam

## To be coded much later :
### linear models :
  - more for svm :
    - Linear SV regressors
    - more hinge options
    - more kernels
 - regressors with ridge/lasso/elasticnet

### neuralnets :
  - dropout
  - regularization on biais and activity of the neurons
  - other optimizers
    - rmsprop

### usupervised models :
  - k-mean
  - pca


## To be probably never coded :
### neuralnets :
  - padding
  - CNN specifics:
    - conv2D layers
    - kernels
    - max pooling layers
    - flatting layer
  - RNN specifics:
    - simple RNN layer
    - masking layer
    - LSTM layer
flow-network 0.1.13
Flow Network Python Library

# Flow Network

网络流的工业级应用

![Release Drafter](https://github.com/LucienShui/flow-network/workflows/Release%20Drafter/badge.svg)
![Upload Python Package](https://github.com/LucienShui/flow-network/workflows/Upload%20Python%20Package/badge.svg)

使用 `Dinic` 和朴素费用流，算法来自 [DuckKnowNothing - 网络流](https://github.com/UPCACM/DuckKnowNothing/tree/master/src/GraphAlgorithm/%E7%BD%91%E7%BB%9C%E6%B5%81) 

## 支持平台

> 在尝试了各种方法之后，GitHub Actions 在 Windows 平台下始终无法正确编译 C++，所以放弃支持 Windows 平台

+ Linux
+ macOS

## 安装

```bash
pip install flow-network
```

## 样例代码

```python
from flow_network import MaximumFlow, MinimumCostFlow

mf = MaximumFlow(2)  # 创建一个包含 2 个点的网络流对象，下标从 0 开始
mf.add_edge(0, 1, 3)  # 添加一条从 0 指向 1 的边，容量为 3
result = mf.run(0, 1)  # 指定源点为 0，汇点为 1，跑最大流 & 最小割
print(result)  # 3

for edge in mf.edges:  # 遍历每条边
    print(edge.u, edge.v, edge.flow, edge.capacity)  # 边的起点、终点、流过的流量、最大容量

mcf = MinimumCostFlow(2)  # 创建一个包含 2 个点的费用流对象，下标从 0 开始
mcf.add_edge(0, 1, 3, 2)  # 添加一条从 0 指向 1 的边，容量为 3，单位流量的费用为 2
flow, cost = mcf.run(0, 1)  # 指定源点为 0，汇点为 1，跑最大流 & 最小费
print(flow, cost)  # 3 6

for edge in mcf.edges:
    print(edge.u, edge.v, edge.flow, edge.capacity, edge.cost)  # 边的起点、终点、流过的流量、最大容量、单位流量的费用
```

### 测试代码

[tests.py](./flow_network/tests.py)

## Reference

+ [How to use C++ classes with ctypes?](https://stackoverflow.com/questions/1615813/how-to-use-c-classes-with-ctypes)
+ [Calling C/C++ from Python?](https://stackoverflow.com/questions/145270/calling-c-c-from-python)
+ [C Class Instance from Void Pointer using Ctypes](https://stackoverflow.com/questions/19389124/c-class-instance-from-void-pointer-using-ctypes)
+ [Python 库打包分发、setup.py 编写、混合 C 扩展打包的简易指南（转载）](https://www.cnblogs.com/xueweihan/p/12030457.html)
+ [Binary wheel can't be uploaded on pypi using twine](https://stackoverflow.com/questions/59451069/binary-wheel-cant-be-uploaded-on-pypi-using-twine)
+ [Manylinux wheel builder](https://github.com/Niraj-Kamdar/manylinux-wheel-builder)
redditPosts 0.0.3
Get funny and exciting reddit posts into your discord server!
Requires Python: >=3.7

# RedditPosts
RedditPosts was built so you can have memes and all sort of reddit posts into your discord server with only a few lines of code!

For more information, or if you have any concerns, please email nooby xviii. His email: ```xviii2008@gmail.com```

## Features
  • Modern discord.py module using ```asyncio``` and ```aiohttp```
  
## Installation
You can install RedditPosts from [PyPi](https://pypi.org/project/redditPosts/):
  ```pip install redditPosts```
The module is available for python versions 3.7+

## How to use
Inorder to use RedditPosts for your discord bot you must import the module into your code:
  ```import redditPosts```

## Usage
Inorder to get memes you must write this phrase:
  ```await redditPosts.reddit(subreddit, channel)```
As you can see you will need to put a subreddit thats from reddit on the "subreddit" part and must put a channel ID or identifier on the "channel" part. If you need any help there is an example below, and for any additional questions please email me at ```xviii2008@gmail.com```

## Examples
Meme Command:

```python
import discord
from discord.ext import commands
import redditPosts

client = commands.Bot(command_prefix="!")

@client.command()
async def meme(ctx):
  await redditPosts.reddit("memes", ctx.channel)
  ```
instVirt 1.1a8
instVirt, este projeto constroi um instrutor virtual para auxílio em

**============
Instrutor virtual
============**
Aplicativo para auxílio 
*****************
em sala de aula.
Este se aplica ao curso de capacitação em ciência da computação.
**Academia do software livre**
Até o momento temos dez etapas para o curso, divididas em quatro disciplinas.



Autor(Leonardo de Araújo Lima).

(leonardo@asl-sl.com.br).
lib-ddos-simulator 0.1.2
DDOS simulator
Keywords: Furuness,Gorbenko,DDOS,DOS,Simulation,Sieve,Protag,KPO,DOSE,Distributed Denial of Service,Denial of Service

Docs [here](https://github.com/jfuruness/lib_ddos_simulator/wiki)

One of the many algorithms this package can run! Click the video to see!
[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/J0TexyxPCog/0.jpg)](https://www.youtube.com/watch?v=J0TexyxPCog)
reachy-2019 1.4.0
Open source interactive robot to explore real-world applications!

# Reachy 2019: Open source interactive robot to explore real-world applications!

**:warning::warning: This documentation is meant for Reachy 2019. If you have a newer version (Reachy 2021) please refer to the corresponding documentation! :warning::warning:** 

Reachy is a unique open prototyping platform. It makes AI & robotics accessible to researchers, innovation professionals and creatives. 

[![Hello I'm Reachy, presentation video](https://img.youtube.com/vi/iSL39WFxCLE/0.jpg)](https://www.youtube.com/watch?v=iSL39WFxCLE)

In this repository, you will find:
* the software to control and program your Reachy 2019,
* the 3d models,
* documentation and examples to get you started.

The software is open-sourced and released under an [Apache License v2.0](./software/LICENSE).

Visit [pollen-robotics.com](https://pollen-robotics.com) to learn more or visit [our forum](https://forum.pollen-robotics.com).

Follow [@PollenRobotics](https://twitter.com/pollenrobotics) on Twitter for important announcements.

This project adheres to the Contributor [code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. Please report unacceptable behavior to [contact@pollen-robotics.com](mailto:contact@pollen-robotics.com).

## Documentation (WIP)

* The full documentation is available at: [https://pollen-robotics.github.io/reachy-docs/](https://pollen-robotics.github.io/reachy-docs/)
* The APIs can be found at: [https://pollen-robotics.github.io/reachy/](https://pollen-robotics.github.io/reachy/)

## Installation

* From Pypi: ```pip install reachy-2019```
* Or from the source: ```pip install -e ./software/```