semvec/pypi-packages huggingface dataset:
SpeedsUtilities 0.0.3
A few utlitites
License: MIT
Keywords: button

This is probably very bad and slow

Change log
==========

0.0.3 (20/6/2022) (DD/MM/YYYY)
------------------------------
- import function not working trying to fix that

0.0.2 (19/6/2022) (DD/MM/YYYY)
------------------------------
- Made __init__.py blank
- Added a help command

0.0.1 (18/6/2022) (DD/MM/YYYY)
------------------------------
- First Release
litehash 0.2.2
Fast coarse hashes from files
License: MIT
Keywords: hash,file,fibonacci,crc32,md5,sha256

# # Install

``` bash
$ pip3 install litehash
```

## Use

### Fibonacci

Calculate the checksum based on the bytes located at an increasing distance from
each other, and the file size. We will read more bytes from the
file header than from the body.

``` python3
from pathlib import Path
from litehash import file_to_hash_fibonacci

print(file_to_hash_fibonacci(Path('/path/to/file.dat')))
```

### Equidistant

Calculate the checksum based on ten equidistant bytes, and the file size. 
The very first and the very last byte of the file will be among the ten read.

``` python3
from pathlib import Path
from litehash import file_to_hash_equidistant 

print(file_to_hash_equidistant(Path('/path/to/file.dat'), n=10))
```


### HashAlgo

The optional `HashAlgo` argument allows you to select a hashing algorithm.

By default, MD5 is used as a compromise between speed and size.


| Algorithm         | Digest Size (bytes) |
|-------------------|---------------------|
| `HashAlgo.crc32`  | 4                   |
| `HashAlgo.md5`    | 24                  |
| `HashAlgo.sha256` | 32                  |

```python3
from pathlib import Path
from litehash import file_to_hash_fibonacci, HashAlgo

print(file_to_hash_fibonacci(Path('/path/to/file.dat'), algo=HashAlgo.crc32))
```
typtrain 1.0.0
practice typing
License: MIT

# typtrain - practice typing
単純なtuiタイピング練習ツール

## インストール

```
pip install typtrain
```

Windowsは以下も必要:

```
pip install windows-curses
```

## 使い方
コマンドラインで以下を実行:

```
typtrain
```

制限時間60秒の中で、できるだけ多くの単語を打ち込んでください。  
連続して成功すればスコアが加速度的に増えます。

また、単語のリストを自分で用意する場合は、[dic.csv](typtrain/assets/dic.csv)を参考にcsv形式で用意し、コマンドの引数にファイル名を指定してください。

## お願い
結果をSNS等にシェアするときは、「#typtrain」をつけてください。作者が喜びます。

## ライセンス
このプログラムはMIT-Licenceのもとにあります。  
[LICENCE](LICENCE)も参照してください。

## 作者
Laddge
tatsusi23 0.0.1
An async python api wrapper for the Tatsu's API.
License: MIT
Requires Python: >=3.6
Keywords: tatsu,discord,api

# tatsupy
An async python api wrapper for the Tatsu's (discord bot) API.

## Installation
```
# Linux/macOS
python3 -m pip install -U tatsu-py

# Windows
py -3 -m pip install -U tatsu-py
```
To install the development version, do the following:
```
$ git clone https://github.com/si23/tatsu-py
```


## Quick Examples
#### Gets `member's points` & `member's score`
```py
import tatsu

client = tatsu.Client(key = "YOUR API KEY")

async def get_member_points() -> int:
	result = await client.get_member_points(guild_id = 1234, user_id = 5678)
	return result.points


async def get_member_score() -> int:
	result = await client.get_member_score(guild_id = 1234, user_id = 5678)
	return result.score
```



#### Edit `member's points` & `member's score`
- You must have the `Manage Guild` permission.

```py
import tatsu 
from tatsu import ActionType

client = tatsu.Client(key =  "YOUR API KEY")  

async def remove_member_score() -> int: 
	result = await client.edit_member_score(guild_id = 1234, user_id = 5678, action = ActionType.Remove, amount = 100)
	return result.score # New score (-100)
	

async def add_member_points() -> int: 
	result =  await client.edit_member_points(guild_id = 1234, user_id = 5678, action = ActionType.Add, amount = 100)
	return result.points # New points (+100)
```



####  Gets `member's raking` & `guild's rankings`
```py
import tatsu 

client = tatsu.Client(key =  "YOUR API KEY")  

async def get_member_ranking() -> int: 
	result = await client.get_member_ranking(guild_id = 1234, user_id = 5678)  
	return result.rank
	

async def get_guild_rankings() -> int: 
	result =  await client.get_guild_rankings(guild_id = 1234)
	return result.rankings
```
##### Using 'page' argument:
```py
async def get_guild_rankings() -> int: 
	result =  await client.get_guild_rankings(guild_id = 1234, page = 2)
	return result.rakings[0].rank  # return 101
```



## Links
- [Tatsu API](https://dev.tatsu.gg)
pyhelp 0.4.0
A Python library for the assessment of spatially distributed groundwater recharge and hydrological components with HELP.
License: MIT

PyHELP is an object oriented Python library providing a set of tools to estimate spatially distributed groundwater recharge and other hydrological components (runoff and evapotranspiration) using the HELP ([Hydrologic Evaluation of Landfill Performance](https://www.epa.gov/land-research/hydrologic-evaluation-landfill-performance-help-model)) model.PyHELP integrates weather data (from grids or stations), land conditions defined by a series of GIS maps as well as soil and geological material properties into HELP input files. PyHELP also processes HELP simulation results and outputs them as maps and graphs, including comparisons of simulation results with stream hydrographs. PyHELP thus accompanies users through the entire workflow from input file assembly to model calibration and to the documentation of results. This workflow is based on the method originally developed by [Croteau et al. (2011)](https://www.tandfonline.com/doi/abs/10.4296/cwrj3504451) to assess spatially distributed groundwater recharge at the regional scale.