semvec/pypi-packages huggingface dataset:
granturismo-stats 0.3.0
Retrieve statistics for Gran Turismo Sport
Requires Python: >=3.5

# granturismo-stats
Statistics for Gran Turismo online events

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## Purpose
I started this project because I wanted to see how my qualifying times for the 
Gran Turismo Sport daily races compare to other drivers with similar driver and sportsmanship rating.

This is a work in progress. Currently, it is possible to do exactly what my original
goal included: get the complete qualifying leaderboard and compute some basic statistics.  
If you have suggestions what information to add feel free to open an issue and I will
see if it is a) possible and b) I find it interesting as well. ;) 

## Examples
If you would like to see example code, look at the *examples* directory on github.

## Limitations

### Performance
The performance is not the best. While there are probably a few things that can be done
on my side of the code, the main limiting factor when retrieving the qualification 
leaderboards is the Gran Turismo Sport API itself which limits the number of entries that 
can be obtained in a single request and takes quite some time to respond.
With several ten thousand qualification entries for each race it can easily take 10 seconds or 
longer to obtain them all. Just be patient! :) 

### Region
There is currently no option to change the region for which you want to retrieve the results.
The code uses the API URL for Germany and thus the results are all for the EMEA region.
However, this is something I'd like to change in the future (see issue #1).

## Further remarks
Bear in mind that in order to retrieve a full leaderboard somewhat in the region of
50 requests will be sent concurrently to the GT Sport API. 
Use this library responsibly. Querying the leaderboard every now and then should be
totally fine, but doing so over and over again without a pause in an endless loop will 
generate a lot of traffic on the server side.  
I intend to implement a caching mechanism so that executing a script multiple times will 
become faster on the one hand and produce less traffic on the other.
Until then, check out the *get_leaderboard* function of *gtsport_test_drive.py* in the
*examples* directory how to do it yourself.

## Kudos
The unofficial GT Sport API documentation from Alex Persian made the whole process a lot easier:
https://github.com/alexpersian/gt7sp-api
simelixir 0.0.1
A Python package for 1-D blood flow simulation
Requires Python: >=3.6

# Elixir

Modeling blood flow and especially the propagation of the pulse wave in systemic
arteries is a topic that is interesting to the medical society since the shape of the
pressure profiles has diagnostic significance. We build a package that can
simulate blood flow and pressure in large arteries by solving a nonlinear onedimensional 
model based on the incompressible Navier-Stokes equations for a
Newtonian fluid in an elastic tube. Our method, however, does not require the
usage of discretized methods for solving differential equations such as the
popular Lax-Wendroff method (LW) but instead uses automatic differentiation to
achieve a similar result. 

Modeling blood flow and pressure in the systemic arteries has been a topic of
interest both to theoretical and clinical investigators. Thus, research in this area
has a vital interdisciplinary aspect. This project aims to develop a package capable
of performing such simulations using the models we develop to treat
cardiovascular diseases better. This is important since most deaths in developed
countries result from cardiovascular diseases, mostly associated with abnormal
flow in the arteries.

The original project's inspiration arose from previous and present efforts to
develop an anaesthesia simulator based on mathematical models. An important
part of which is having a good model for the cardiovascular system.
However, as stated previously, the traditional focus of such projects generally is
developing a good model.

The choice of the method to be used to solve the associated equations generally
comes from a standard list of such methods.

We present a new method that, unlike its predecessors, offers a continuous
solution, along with other benefits such as GPU support, a massive community,
and such like. 

Watch our video that highlights the fundamental idea of the project. 

[![Project Elixir - Team Disrupt](photo.png)](https://youtu.be/8Q4nvnozVsI "Project Elixir - Team Disrupt")
tpu-star 0.0.1rc10
PyTorch XLA Toolkit for quickly and easy prototyping on TPU (8 cores)

# TPU-Star 
PyTorch XLA Toolkit for quickly and easy prototyping on TPU (8 cores)

[![PyTorch XLA](https://img.shields.io/badge/TPU-PyTorch/XLA-darkgreen.svg)](https://github.com/pytorch/xla)
[![Build Status](https://api.travis-ci.com/shonenkov/TPU-Star.svg)](https://travis-ci.com/shonenkov/TPU-Star)
[![Coverage Status](https://codecov.io/gh/shonenkov/TPU-Star/branch/master/graphs/badge.svg)](https://codecov.io/gh/shonenkov/TPU-Star)
[![Apache license](https://img.shields.io/badge/License-Apache-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)


### Installing
1. Using pip: 
```
pip install tpu-star==0.0.1-rc10
```

### Examples

- tpu_star & MNIST : [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pb465geYicaeCS15nkd4zUOkpo1YPMkx?usp=sharing)
fencicut 1.0.2
一款中文分词工具
Requires Python: ==3.8.*

# fencicut中文分词
## 这是一款分词工具

示例
```Python

from fencicut import fenci

fenci.cut('这里是北京')

```

效果：['这里','是','北京']

## 安装

```

pip install fencicut

```

## 使用说明

fenci.cut有一个参数：要切割的字符串

自定义词典：

示例：

```Python

from fencicut import fenci

a = fenci('xxx','xxx')

a.train('utf-8')

a.cut('我爱中国')

```

其中第一个xxx是指数据文件，编写参考dict.txt,第二个xxx是指模型名

注意：

使用前请下载python3.8和预训练模型

预训练模型下载：https://github.com/Zhou-chengy/fencicut