semvec/pypi-packages huggingface dataset:
cfmaker 0.2.0
A lightweight compilation tool for Fortran and C projects, primarily targeting the Intel oneAPI compilers, ifort and ifx.
Requires Python: >=3.8
Keywords: oneapi, fortran, project, c, compiler

# fmaker

简易的用于intel oneapi fortran与c项目的编译，适用于小项目
若oneapi又发疯改一堆编译选项，可以自定义设置编译和链接等选项

## Getting Started

1. write 'maker.py'

```python
from cfmaker import compile_manager, ProjectConfig

config = ProjectConfig(
  f_srcs=['src/**'],
  c_srcs=['src/**'],
  project_build_dir='./build',
  f_exe_name='example.exe',
  c_lib_name='example.lib',
  static_libraries=['example.lib']
)

config.auto_add_intel_mpi()
config.auto_set_cc_options()
config.auto_set_fc_options()

compile_manager(config).make()
```

2. execute 'maker.py'

```python
python maker.py #默认以debug模式编译
python maker.py debug #以debug模式编译
python maker.py release #以release模式编译
python maker.py debug update #更新源文件列表后进行debug模式编译
python maker.py release update #更新源文件列表后进行release模式编译
python maker.py clear #删除中间文件
python maker.py clear rmexe #删除中间文件和exe
```

## Advanced

```python
from cfmaker import inner_flag_opt
config.fc_options_debug = '-g -O0'  #设置fortran debug编译选项

#修改输出exe的选项
config.f_flag_exe = inner_flag_opt('/Fe', '', ispath=True, isList=False )
#修改输出include的选项
config.f_flag_include = inner_flag_opt('-I', '', ispath=True, isList=True )

```

## Contributing

```
pip install build installer
python -m build
```
pokemon-pinyin 0.0.2
Learn pinyin with pokemon
Requires Python: >=3.10

## 生成宝可梦的拼音、文字、图片，用于学习拼音和汉字

![image](/example.png)

## 安装
```shell
pip install pokemon_pinyin
```

## 使用
运行会生成满足条件的pdf文档

```shell
# 选择1-10号（包含10）
ppinyin run 1..10

# 选择1 3 5 7号
ppinyin run 1,3,5,7

# 选择1-100号，过滤某个字只包含l,v
ppinyin run 1..100 -fb m,a 
# 选择编号为1..100的宝可梦
# 共搜索到符合条件的宝可梦3只
# --- 文件已保存到`output_a4.pdf` ---
```

## 字体

默认自带simhei字体

也可以使用你自己喜欢的字体,只需要在命令行里面修改对应的参数即可


## package

Publish package
```
python -m build
```

Development mode
```
pip install --editable .
```
py-jaxtyping 0.1.5
Pydantic support for Jaxtyping array annotations
Requires Python: >=3.10

# Py Jaxtyping

> Pydantic support for Jaxtyping array annotations

## Usage
  
Instead of `Int[np.ndarray, 'B 256 64 3']`, do `PyArray[Int, int, 'B 256 64 3']`

You can use it with `jaxtyping` as normal, but also it will:
- Serialize to nested lists
- Validate the correct shape and datatypes from serialized lists

### Example
```
from pydantic import BaseModel, ConfigDict
from py_jaxtyping import PyArray
from jaxtyping import Int
import numpy as np

class Sample(BaseModel):
  model_config = ConfigDict(arbitrary_types_allowed=True)
  img: PyArray[Int, int, "W H 3"]
  label: str

Sample.model_validate({
  'img': np.ones((256, 64, 3)),
  'label': 'car'
})
# checks out!


Sample.model_validate({
  'img': np.ones((256, 64, 1)),
  'label': 'car'
})
# fails: invalid dims :/
```
KrxRank 1.0.1
KRX 정보데이터시스템 순위 통계 데이터 얻어오기
Requires Python: >=3.8

KRX 정보데이터시스템 순위 통계 데이터 스크래핑 

1. 주가등락률 상하위 50종목
- price_fluctuation_high
- price_fluctuation_low
2. 거래량 / 거래대금 상하위 50종목
- trading_volume_high
- trading_volume_low
- trading_value_high
- trading_value_low
3. 시가총액 상하위 50종목
- market_cap_high
- market_cap_low
4. 환산주가 상위 50종목
- convert_price_high
5. 회전율 상위 50종목
- turnover_high
6. 외국인 지분율 상위 50종목
- foreigner_share_high
7. 외국인 한도소진율 상위 50종목
- foreigner_limit_high
8. 상한가 / 하한가 종목
- upper_limit
- lower_limit
9. ETF / ETN 주가등락률 상하위 50종목
- etf_price_fluctuation_high
- etf_price_fluctuation_low
- etn_price_fluctuation_high
- etn_price_fluctuation_low
10. ETF / ETN 거래량 / 거래대금 상하위 50종목
- etf_trading_volume_high
- etf_trading_volume_low
- etn_trading_volume_high
- etn_trading_volume_low
- etf_trading_value_high
- etf_trading_value_low
- etn_trading_value_high
- etn_trading_value_low
11. ETF / ETN 가치총액 상하위 50종목
- etf_net_asset_value_high
- etf_net_asset_value_low
- etn_net_asset_value_high
- etn_net_asset_value_low
orchard-basket 0.1.1
Basket is the essential toolkit for seamless MaaS integration.
Requires Python: >=3.6

# Basket

## Introduction

Basket is the essential toolkit for seamless MaaS integration.

It provides a unified interface for interacting with different MaaS services, allowing developers to easily switch between different services without changing their code. It is the first free toolkit that allows developers to use LLM services without registering or paying for them. Know more about [FreeModel](https://github.com/OrchardUniverse/FreeModel).

- Fast and free to access Model as a Service
- Unified interface for interacting with different MaaS services
- Easy to use and integrate with existing projects
- Manage API KEY for MaaS and easy to switch
- Extensible and customizable for local and remote services
- One step to chat with LLM service without coding and configurating

Refer to more [documentation](https://orchardai.github.io/basket/).


## Install

```
pip install orchard-basket
```

## Usage

1. Chat with FreeModel.

```
basket chat "Who are you"
```

2. List all available MaaS.

```
basket maas list
```

3. Choose the MaaS to use.

```
basket maas use siliconflow
```

4. List all available models.

```
basket model list
```

5. Choose the model to use.

```
basket model use qwen/qwen-7b-chat
```

6. Chat with the model.

```
basket chat "What is the meaning of life?"
```

8. Show current configuration.

```
basket config
```

## Contribution

Please help us to integrate with more MaaS.

- [x] [FreeModel](https://github.com/OrchardUniverse/FreeModel)
- [x] [OpenAI](https://platform.openai.com/)
- [x] [SiliconFlow](https://siliconflow.cn/siliconcloud)
- [x] [DashScope](https://dashscope.aliyun.com/)
- [x] [OpenRouter](https://openrouter.ai/)
- [x] [DeepSeek](https://platform.deepseek.com/)
- [x] [MoonShot](https://platform.moonshot.cn/)
- [x] [ZhiPu](https://maas.aminer.cn/)
- [x] [Lime](https://github.com/OrchardUniverse/lime)
pyDRTtools 0.2
pyDRTtools: A Python-based DRTtools to Deconvolve the Distribution of Relaxation Times from Electrochemical Impedance Spectroscopy Data
Requires Python: >=3

# pyDRTtools

We are pleased to introduce the pyDRTtools, the python version of DRTtools for computing distribution relaxation times (DRT) from electrochemical impedance spectroscopy (EIS) data. 

**What is the pyDRTtools? Why would I want it?**

pyDRTtools is a Python GUI that analyzes EIS data via the DRT model. pyDRTtools includes:

- an intuitive GUI for computing DRT based on Tikhonov regularization

- several options for optimizing the estimation of the DRT

- a sampler that allows you to determine the credible intervals of your DRT
  
- an optimal selection of the regularization parameter

- Hilbert-transform subroutines that allow you to assess and score the quality of your data

Hopefully, by now you are inclined to think that this toolbox may be useful to the interpretation of your EIS data. If you are interested, you will find an explanation of the toolbox's capabilities it in the user's guide as well as in the references below.

## Distribution and Release Information

pyDRTtools is freely available under the MIT license from this site.

**System requirements**

To install and run pyDRTtools, you need: Python >= 3

**Installation details**

For details about the installation procedures, you can consult the user manual [manual](manual)

**Run the following on anaconda prompt:**
```
conda create --name DRT pip ipython pandas matplotlib scikit-learn spyder
conda activate DRT
pip install cvxopt PyQt5
pip install pyDRTtools
```
**ipython**
```
!launchGUI
```
**How to cite this work?**

Just write to francesco.ciucci@ust.hk or francesco.ciucci@uni-bayreuth.de

**How to get support?**

Just write to francesco.ciucci@ust.hk or francesco.ciucci@uni-bayreuth.de