semvec/pypi-packages huggingface dataset:
easy-lottery-zs 1.0.0
this package can use lottery
Requires Python: >=3.6

# easy_lottery

此抽奖程序只需要配置result.csv
格式就是一行，一个人名+编号，

### 控制台版本用法如下：

1. 首先在洛谷下载排名信息的csv文件
2. 在控制台（请不要用ide运行）输入：

    ```sh
    python main_cli.py
    ```

3. 首先程序会自动读取并打印成绩信息，成绩为权重，然后等待键盘输入

命令介绍：

- `info`：输出当前成员信息；
- `del user_id`：手动删除成员；
- `roll prize num`：开始抽奖，抽`num`次，默认以0.05秒的时间间隔滚动，滚动期间按下Ctrl+C停止滚动，抽下一位。每抽出一个用户会自动从抽奖列表中剔除该用户；
- `pub`：打印获奖信息。

### 图形化版本用法如下：

使用 Python 3.10 及以上版本并安装 PyQt6 库，双击 `main_gui.pyw` 即可。
webthing_client 0.2.0
Client for the Dynamic Dashboard Webthings
Requires Python: >=3.8,<4.0

# Webthing Client Python

A package for performing data operations on remote Webthing and listening to Observations.

## Installation

Install the package `webthing-client` using your Python package manager of choice.
You can import the client class itself as:
```python
from webthing_client.client import WebthingClient
```
sdypy-excitation 0.1.1
Excitation signals as used in structural dynamics and vibration fatigue.
Requires Python: >=3.10
Keywords: excitation signals,structural dynamics,vibration fatigue

sdypy-excitation
================

Frequency response function as used in structural dynamics.
-----------------------------------------------------------

The ``sdypy-excitation`` is a namespace project of the ``sdypy`` framework and is a direct link
to the ``pyExSi`` package (developed at `pyExSi <https://github.com/ladisk/pyExSi>`_).

Use the ``sdypy`` package to conveniently access the functionality of the
`pyExSi <https://github.com/ladisk/pyExSi>`_ package through its namespace (see example below). 

Other functionalities of the ``sdypy`` framework include:

- `sdypy-EMA <https://github.com/sdypy/sdypy-EMA>`_: Experimental Modal Analysis
- `sdypy-io <https://github.com/sdypy/sdypy-io>`_: Input/Output operations (LVM files, UFF files)
- `sdypy-FRF <https://github.com/sdypy/sdypy-FRF>`_: Frequency Response Function estimation


For more information check out the showcase examples and see documentation_.

Basic ``sdypy-excitation`` usage:
---------------------------------

Import the module:
~~~~~~~~~~~~~~~~~~

.. code:: python

    import numpy as np

    # import the sdypy module
    import sdypy as sd

    N = 2**16 # number of data points of time signal
    fs = 1024 # sampling frequency [Hz]
    t = np.arange(0,N)/fs # time vector

    # define frequency vector and one-sided flat-shaped PSD
    M = N//2 + 1 # number of data points of frequency vector
    freq = np.arange(0, M, 1) * fs / N # frequency vector
    freq_lower = 50 # PSD lower frequency limit  [Hz]
    freq_upper = 100 # PSD upper frequency limit [Hz]
    PSD = sd.excitation.get_psd(freq, freq_lower, freq_upper) # one-sided flat-shaped PSD

    #get gaussian stationary signal
    gausian_signal = sd.excitation.random_gaussian((N, PSD, fs))

    #get non-gaussian non-stationary signal, with kurtosis k_u=10
    #amplitude modulation, modulating signal defined by PSD
    PSD_modulating = sd.excitation.get_psd(freq, freq_lower=1, freq_upper=10)
    #define array of parameters delta_m and p
    delta_m_list = np.arange(.1,2.1,.5)
    p_list = np.arange(.1,2.1,.5)
    #get signal
    nongaussian_nonstationary_signal = sd.excitation.nonstationary_signal(N,PSD,fs,k_u=5,modulating_signal=('PSD', PSD_modulating),param1_list=p_list,param2_list=delta_m_list)


.. _documentation: https://sdypy-excitation.readthedocs.io/en/latest/
Overseerr-API 1.0.0
Overseerr API
Keywords: OpenAPI,OpenAPI-Generator,Overseerr API

    This is the documentation for the Overseerr API backend.  Two primary authentication methods are supported:  - **Cookie Authentication**: A valid sign-in to the &#x60;/auth/plex&#x60; or &#x60;/auth/local&#x60; will generate a valid authentication cookie. - **API Key Authentication**: Sign-in is also possible by passing an &#x60;X-Api-Key&#x60; header along with a valid API Key generated by Overseerr.
python-can-csscan-txt 2.4.0
Extract bus logging data from TXT files generated by CLX000 from CSS Electronics
Requires Python: >=3.7

# python-can-csscan-txt

The **python-can-csscan-txt** package allows for opening TXT CAN bus logging files in the **python-can** framework.

For usage guidelines, consult the manual or the documentation for the [**python-can**](https://python-can.readthedocs.io/en/stable/) package.
python-can-csscan-mf4 2.4.0
Extract bus logging data from MDF4 files generated by CANedge from CSS Electronics
Requires Python: >=3.7

# python-can-csscan-mf4

The **python-can-csscan-mf4** package allows for opening MF4 CAN bus logging files in the **python-can** framework.

For usage guidelines, consult the manual or the documentation for the [**python-can**](https://python-can.readthedocs.io/en/stable/) package.
GPS-mapping 1.60
Genetics-informed pathogenic spatial mapping
Requires Python: >=3.8

# GPS: Genetics-informed Pathogenic Spatial Mapping Program

## Overview

GPS (Genetics-informed Pathogenic Spatial Mapping) is Python command-line tool designed for ....

## Features

....

## Installation

install use pip:

```bash
pip install GPS-mapping
```

install from source:

```bash
git clone
cd GPS-mapping
pip install -e .
```

## Usage

To use GPS, navigate to the command line and enter `GPS` followed by the subcommand that corresponds to the desired operation. Each subcommand may require specific arguments to run.

### Basic Command Structure

```bash
GPS subcommand [arguments...]
```

- `subcommand`: The specific operation you wish to perform.
- `arguments`: The arguments and options required for the subcommand.

### Available Subcommands

(Provide a list and brief description of each available subcommand. For example:)

- `run_find_latent_representations`: Finds latent representations using a GNN-VAE model.
- `run_latent_to_gene`: Maps latent representations to gene markers.
- `run_generate_ldscore`: Generates LD scores for genomic spots.
- `run_spatial_ldsc`: Conducts spatial LDSC analysis.
- `run_cauchy_combination`: Performs Cauchy combination tests for annotations.
- `run_all_mode`: Executes a comprehensive pipeline covering all steps.

### Examples

To run a specific functionality, you need to provide the appropriate subcommand and arguments. For example:
### Running Requirement


```bash
GPS run_find_latent_representations --input_hdf5_path <path> --output_hdf5_path <path> --sample_name <name>
```

This command initiates the process of finding latent representations based on the given HDF5 input and output paths and sample name.

## Contributing

...

## License

....

---