semvec/pypi-packages huggingface dataset:
TUMMET 0.0.12
A packages to evaluate material test data
Requires Python: >=3.9



# TUMMET: Analysis and Evaluation for experimental data.
[![PyPI Latest Release](https://img.shields.io/pypi/v/TUMMET?color=blue&style=for-the-badge)](https://pypi.org/project/TUMMET/)

## What is it?

**TUMMET** is a Python package that provides a multitude of functions to quickly analyse, evaluate and visualize experimental material data, while also being easy to use and highly adjustable.

Originally built for the Chair of Metal Structures at the Technical University of Munich [[TUM].](https://www.cee.ed.tum.de/en/metallbau/welcome-page/)

Created and maintained by Michael Winkler -  michael.b.winkler@tum.de

## Current List of Features
Includes: 
- Strain-controlled fatigue test:
  - Reading and evaluating raw data
  - interactive stable zone determination
  - Fitting the Ramberg-Osgood relationship to individual fatigue tests
  - Plotting Hystereses
  - Plotting Cycles-Stress 

- Stress-controlled fatigue test:
    - In Development ... 

- Tensile test:
    - In Development ...

- ...


 



## Where to get it
The source code is currently hosted on GitHub at:
https://github.com/mbwinkler/TUMMET

The latest released version is available through the [Python
Package Index (PyPI)](https://pypi.org/project/TUMMET/).


```sh
pip install TUMMET
```

## Dependencies
- [Pandas](https://pandas.pydata.org/)

- [NumPy](https://www.numpy.org)

- [Matplotlib](https://matplotlib.org/)

- [Seaborn](https://seaborn.pydata.org/)

- [SciPy](https://scipy.org/)

- [tqdm](https://tqdm.github.io/)




## License
[BSD 3](LICENSE)

## Getting Help

For usage questions for a specific function, run the following statement to print the documentation: 
```
help(function_name)
```
warmup-scheduler-pytorch 0.1.2
A Warmup Scheduler for Pytorch
Requires Python: >=3.6

# Warmup Scheduler Pytorch

[![pypi version](https://img.shields.io/pypi/v/warmup-scheduler-pytorch.svg)](https://pypi.org/project/warmup-scheduler-pytorch/)
[![pypi downloads](https://static.pepy.tech/personalized-badge/warmup-scheduler-pytorch?period=total&units=international_system&left_color=grey&right_color=green&left_text=downloads)](https://pypi.org/project/warmup-scheduler-pytorch/)
[![pypi pyversions](https://img.shields.io/pypi/pyversions/warmup-scheduler-pytorch.svg)](https://pypi.python.org/pypi/warmup-scheduler-pytorch/)

[![github license](https://img.shields.io/github/license/LEFTeyex/warmup)](https://github.com/LEFTeyex/warmup/blob/master/LICENSE)
[![github repository size](https://img.shields.io/github/repo-size/LEFTeyex/warmup)](https://github.com/LEFTeyex/warmup)
[![github tests](https://github.com/LEFTeyex/warmup/actions/workflows/tests.yaml/badge.svg)](https://github.com/LEFTeyex/warmup/actions/workflows/tests.yaml)
[![codecov coverage](https://codecov.io/gh/LEFTeyex/warmup/branch/master/graph/badge.svg?token=E90TZPO40B)](https://codecov.io/gh/LEFTeyex/warmup)

## Description

A Warmup Scheduler in Pytorch to make the learning rate change at the beginning of training for warmup.

## Install

Notice: need to install pytorch>=1.1.0 manually. \
The official website is [PyTorch](https://pytorch.org/)

Then install as follows:

```bash
pip install warmup_scheduler_pytorch
```

## Usage

Detail to see [GitHub example.py](https://github.com/LEFTeyex/warmup/blob/master/example.py) file.

```python
import torch

from torch.optim import SGD  # example
from torch.optim.lr_scheduler import CosineAnnealingLR  # example

from warmup_scheduler_pytorch import WarmUpScheduler

model = Model()
optimizer = SGD(model.parameters(), lr=0.1)
lr_scheduler = CosineAnnealingLR(optimizer, T_max=100, eta_min=0.01)
data_loader = torch.utils.data.DataLoader(...)
warmup_scheduler = WarmUpScheduler(optimizer, lr_scheduler,
                                   len_loader=len(data_loader),
                                   warmup_steps=100,
                                   warmup_start_lr=0.01,
                                   warmup_mode='linear')
epochs = 100
for epoch in range(epochs):
    for batch_data in data_loader:
        output = model(...)
        # loss = loss_fn(output, ...)
        # loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        warmup_scheduler.step()

    # lr_scheduler.step() is no longer needed
```
farmyard 0.0.1.dev3
The Minimal Reinforcement Learning Framework
Requires Python: >=3.8


<p align="center">
    <h1 align="center">🌾 Farmyard 🐄</h1>
    <p align="center">
        <i>The Minimal Reinforcement Learning Framework</i>
    </p>
</p>

<p align="center">
    <a href="https://choosealicense.com/licenses/mit/" target="_blank">
        <img alt="license" src="https://img.shields.io/github/license/nmichlo/farmyard?style=flat-square&color=lightgrey"/>
    </a>
    <a href="https://pypi.org/project/farmyard" target="_blank">
        <img alt="python versions" src="https://img.shields.io/pypi/pyversions/farmyard?style=flat-square"/>
    </a>
    <a href="https://pypi.org/project/farmyard" target="_blank">
        <img alt="pypi version" src="https://img.shields.io/pypi/v/farmyard?style=flat-square&color=blue"/>
    </a>
    <a href="https://github.com/nmichlo/farmyard/actions?query=workflow%3Atest">
        <img alt="tests status" src="https://img.shields.io/github/workflow/status/nmichlo/farmyard/test?label=tests&style=flat-square"/>
    </a>
    <a href="https://codecov.io/gh/nmichlo/farmyard/">
        <img alt="code coverage" src="https://img.shields.io/codecov/c/gh/nmichlo/farmyard?token=86IZK3J038&style=flat-square"/>
    </a>
</p>

<p align="center">
    <p align="center">
        <a href="https://github.com/nmichlo/farmyard/issues/new/choose">Contributions</a> are welcome!
    </p>
</p>

----------------------

## Table Of Contents

- [Overview](#overview)
- [Roadmap](#roadmap)

----------------------

## Overview

The minimal reinforcement learning framework.

Get started with `farmyard` by installing it with $`pip install farmyard` or cloning this repository.

## Roadmap

This project is being built while I learn about reinforcement
learning, the roadmap for the project includes:
- Common RL Algorithms (Easily Comparable)
- Common RL Performance Tricks (Separate from Algorithms)
- Common RL Models
- Flexible/Modular Components
- Multiprocessing
- Distributed Computing
vimura-server 1.3
Alternative server for Emacs pdf-tools
Requires Python: >=3.6

# Vimura server

This is an alternative server for Emacs
[pdf-tools](https://github.com/vedang/pdf-tools).

The motivation for creating this alternative extra server, is that it lowers the
barrier for adding extra features (like arrow and free text annotations) into
the server. The [PyMuPDF](https://pypi.org/project/PyMuPDF/) is a great and
powerful library, which uses python as a wrapper language for using the Poppler
library. It is still very fast as it just provides a convenient scripting
language for using the underlying C code. No advanced C knowledge is required
because these 'hard parts' have been taken care of by its maintainers.

Many thanks to Jorj X. McKie, for creating the PyMuPDF library and providing
assistance.
AttrLinker 0.0.6
A package for assisting linking attributes.
Requires Python: >=3.6

# AttrLinker
## What it is
It is an Attribute Linker for class instances. It links a certain attribute name for the class instances to a certain other attribute name before being returned as a value for given attribute name.

## How does it work?
It works using property. To be more specific, it links an attribute name on the class with property, to its target attribute, which is passed through a converter and then returned as the attribute value.

## What is Presets
To help ease linking stuff, because preparing converters is often tedious.

## What is this for?
IMHO, The best use case for this is in dynamically allocated data, periodically updated data, an example is for an app that periodically fetches data from some server about something, and on it's response, there's a lot of entries, which is needed to be updated on its attributes. You could use property for this, but over a certain point, it will become to repetitive. This is the problem this module strives to solve.

### Reminders
 - **Please note that an implementation of the module may or may not work across different versions.**

## Examples
### 1. Dynamically Allocated Data
```py
from attrLinker.linkMethod import MultiDictionary, FormattedText
from attrLinker.preparedLink import PreparedLink
from attrLinker import LinkedClass

@LinkedClass
class User:
    # linkMap pair => {destination_attribute: source_key, ...}
    # enableSetter set to True to be able to assign value back to it.
    __LINKS__ = [PreparedLink(MultiDictionary, 'userData', linkMap={'ID':'id', 'name':'name', 'onlineTime':'online_time', 'status':'status'}, enableSetter=True),
                 PreparedLink(FormattedText, 'userData', 'nameTag', formattableText="{Name}#{id}")]
    def __init__(self):
        self.userData = {}

    def fetchData(self):
        if len(self.userData):
            self.userData = {'id':1, 'name':'Foo', 'online_time':0, 'status': 'idle'}
        else:
            self.userData['status']='online'
            self.userData['online_time']+=1

>>> testuser = User()
>>> testuser.fetchData()
>>> testuser.ID
1
>>> testuser.name
'Foo'
>>> testuser.onlineTime
0
>>> testuser.status
'idle'
>>> testuser.nameTag
'Foo#1'
>>> testuser.userData # Checking the data directly
{'id': 1, 'name': 'Foo', 'online_time': 0, 'status': 'idle'}
>>> testuser.onlineTime+=10 # Change it remotely from the assigned attribute (only if enableSetter is True)
>>> testuser.userData
{'id': 1, 'name': 'Foo', 'online_time': 10, 'status': 'idle'}
>>> testuser.fetchData() # Simulating new data fetched from a server on the internet
>>> testuser.onlineTime
11
>>> testuser.status
'online'
>>> testuser.userData # Checking the data directly
{'id': 1, 'name': 'Foo', 'online_time': 11, 'status': 'online'}
```
programmershousewrapper 2.2.9
ProgrammersHouse Api official wrapper
Requires Python: >=3.6
Keywords: discord,discordpy,programmershouse,alets_ua

# programmershousewrapper
Programmers House api official wrapper: https://www.programmershouse-api.ga
--
Install:
```py
pip install programmershousewrapper
```


# Example of using:
```py
import programmershousewrapper

joke = programmershousewrapper.joke()
print(joke)
​​​​
```


 




# NSFW endpoints using example
```py
import programmershousewrapper

const nsfw = programmershousewrapper.hentai("api key from our discord server: https://discord.gg/gqKbGBWmRz")
```
# NEW endpoints
- alerts - endpoint for air alert notification in Ukraine
```py
import programmershousewrapper

const alerts = programmershousewrapper.alerts()
```
# All endpoints
- joke
- eightball
- cats
- ukraine
- wanted
- wasted
- reddit
- ukrmemes
- reversetext
- emojify
- shotoniphone
- numberfact
- yearsfact
- cosmosfacts
- qrcode
- randomtext
- currentday
- minecraftserver
- aboutinvite
- mars
- geometrydash
- subreddit
- randomanime
- hug
- cry
- dance
- laugh
- kiss
- pat
- wink
- punch
- hentai
hopybox 0.0.2
This is an open source, practical and exquisite command box
Requires Python: >=3.8

# HOPYBOX
This is an open source, practical and exquisite command box
So I named it 'hopybox'
Hopybox contains a large number of practical functions
It can help your use the device easily
All you need to do is type in a concise line of commands,It can take care of everything

# Installation method
pip install hopybox
pip install -U hopybox

# Import method
import 

# usage method
See help built in the program