semvec/pypi-packages huggingface dataset:
nonebot-railway-tools 1.0.0
这是一个火车迷也许觉得很好用的铁路机器人工具箱
Requires Python: <4.0,>=3.9

# nonebot-plugin-railwaytools🚅🚞🛤️

### 这是一个火车迷也许觉得很好用的铁路工具箱，具有多种功能。

## Required 🔨
- Python >= 3.8

- 需提前安装以下依赖库：
```sh
pip install requests
pip install datetime
```

## Getting Started 🚀
将本repo中的nonebot-plugin-railwaytools文件夹clone到机器人的src\plugins目录中，即可使用。

```sh
git clone https://github.com/leaf2006/nonebot-plugin-railwaytools.git
```

## Features ✨

- 通过车次查询担当的动车组车组号：/车号 或 /ch（例如：/车号 D3211）
```
example：
🤵：/车号 D3211
🤖：
D3211次列车近8次担当的车组号为：
2025-11-28 12:59：CRH2A2158
2025-11-27 12:59：CRH2A2190
2025-11-26 12:59：CRH2A2158
2025-11-25 12:59：CRH2A2030
2025-11-24 12:59：CRH2B2115
2025-11-23 12:59：CRH2B2116
2025-11-22 12:59：CRH2B2115
2025-11-21 12:59：CRH2A2006
```
- 通过动车组车组号查询担当车次：/车次 或 /cc （例如：/车次 CRH2A-2001）
```
example：
🤵：/车次 CRH2A-2001
🤖：
CRH2A-2001近8次担当的车次为：
2025-11-28 20:14：D2212
2025-11-27 22:46：D3062
2025-11-27 15:05：D3060
2025-11-26 20:22：C3793
2025-11-26 13:57：D5666
2025-11-26 10:09：D5665
2025-11-25 17:29：C3896
2025-11-25 12:36：C3897
```
- 通过车号查询下关站收录的机车户口照：/下关站 或 /xgz （例如：/下关站 DF7C-5030）
```
example:
🤵：/下关站 DF7C-5058
🤖：正在加载图片，时间可能略久...
🤖：[DF7C-5058的机车户口照]
```
- 通过列车车次查询该车次的始发终到、担当客运段、车型信息以及配属，同时支持动车组与普速列车：/查询 或 /cx （例如：/查询 Z99）
```
example:
🤵：/查询 Z99
🤖：
车次：Z99（上海——广州白云) 
担当客运段：上海客运段
车型信息：25T
配属：上海机辆段
 
----------停站信息----------
1.上海：17:19到,17:19发，停车0分
2.杭州南：19:26到,19:29发，停车3分
3.金华：20:40到,20:46发，停车6分
4.广州白云：09:45到,09:45发，停车0分
------------------------------
```
- 帮助：/帮助 或 /help
```
example:
🤵：/help
🤖：
这是一个火车迷也许觉得很好用的铁路工具箱，具有多种功能 
 
----------使用方法----------
① 通过车次查询担当的动车组车组号：/车号 或 /ch （例如：/车号 D3211） 
 
② 通过动车组车组号查询担当车次：/车次 或 /cc （例如：/车次 CRH2A-2001） 
 
③ 通过车号查询下关站机车户口照：/下关站 或 /xgz （例如：/下关站 DF7C-5030） 
 
④ 通过列车车次查询该车次的始发终到、担当客运段、车型信息以及配属，同时支持动车组与普速列车：/查询 或 /cx （例如：/查询 Z99）
 
⑤ 帮助：/帮助 或 /help 
 
更多功能正在开发中，尽情期待！ 
------------------------------ 
 
Powered by Nonebot2 and Onebot v11
Copyright © Leaf developer 2023-2025
```

## Alert ⚠️
由于下关站的性能较差，在查询机车户口照时，返回的图片可能会加载较慢。

## TODO 🔜

- 计划加入12306车站大屏功能 **（作为日后开发重点）**
- ~~正在计划加入12306查询列车始发终到、中途停站的功能~~ **（已实现）**
- 优化列车时刻表的显示，比如说始发站不要显示到站时间，很怪异。目前可以看作是一个已知的bug
- 如用户的输入内容非法，错误值仅停留在后台而无法通过机器人输出给用户
- 由于下关站的性能问题导致的加载图片较慢，可以通过切换为小图模式解决。但是小图比较模糊，只能说在加载速度快和图片质量高这两者之间只能二选一。本人意向添加切换大/小图的设置选项，但是目前还未开发
- 更多功能正在思考/开发中

## 数据来源 
- 动车组交路数据来源于：https://rail.re
- 机车户口照图片来源于下关站：http://www.xiaguanzhan.com/
- 列车查询数据来源于12306


Copyright © Leaf developer 2023-2025，遵循MIT开源协议
python-shamsi 1.0.0
A Python library for Julian Day and Solar Date conversions.
Requires Python: >=3.7

# 🌞 Shamsi Calendar

[![Python Version](https://img.shields.io/badge/python-3.6%2B-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)

**Shamsi Calendar** is a powerful and easy-to-use Python library designed for seamless conversion between **Gregorian dates**, **Julian Day Numbers**, and a custom **Solar Calendar**.

Whether you are building an astronomical application, a historical date converter, or a specialized scheduling tool, Shamsi Calendar provides the precision and flexibility you need.

---

## ✨ Features

- 📅 **Gregorian to Solar Conversion**: Instantly convert standard dates to the custom Solar Calendar.
- 🔢 **Julian Day Support**: Robust handling of Julian Day Numbers for astronomical calculations.
- 🗓️ **Day & Month Names**: Built-in support for retrieving localized Solar day and month names.
- 🚀 **Lightweight & Fast**: Optimized for performance with minimal dependencies.

## 📦 Installation

Install the package easily via pip:

```bash
pip install python-shamsi
```

## 🛠️ Usage

Here is a quick example to get you started:

```python
from shamsi import JulianDay, SOLAR_DAYS_NAME, SOLAR_MONTHS_NAME

# 1. Convert Gregorian to Solar Date
# Input: Year, Month, Day
solar_date = JulianDay.to_solar(2025, 12, 1)
print(f"🌞 Solar Date: {solar_date}")  # Output: [day, month_index, year]

# 2. Get the Solar Month Name
month_name = SOLAR_MONTHS_NAME[solar_date[1]]
print(f"📅 Month: {month_name}")

# 3. Get the Day of the Week
# Calculate the index and fetch the name
day_index = JulianDay.get_day_of_week_index(solar_date[0], solar_date[1], solar_date[2])
day_name = SOLAR_DAYS_NAME[day_index]

print(f"🗓️ Day: {day_name}")
```

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## 👨‍💻 Author

**Muhammad Sajid**
- 📧 Email: [dev.sajid09@gmail.com](mailto:dev.sajid09@gmail.com)

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
gsnetact 0.0.20
The GSNetAct Python Package.

# GSNetAct

Please cite this repository if you use this code in your work.

## Prerequisites

- Tested only on python 3.12, should work python 3.7+
- Numpy
- Scanpy
- Dataclasses
- Json
- Scikit-learn (Should come with scanpy but still)
- Requests

## Installing

#### Directly install latest version with pip

```
pip install gsnetact
```

#### Or clone the repository from this source.
```
git clone https://github.com/BMGLab/GSNetAct
```

- Then inside the project directory, do 
```
pip install .
```
The package should be installed. You can use the **-e** flag for automatic updates.

## Usage

You can use every part of this package to develop a further approach to this algorithm.
But for simple analysis, a basic use case below would be : 

```
#!/usr/bin/env python

from gsnetact import runGSNA

import scanpy as sc
import pandas as pd

adata_ = sc.read_h5ad("./test_data/pbmc3k.h5ad")
# Read the anndata object.

jsonFile = pjson("./test_data/deneme.json")
# Parse the json file into a pjson() object.

gsnaObject = runGSNA(adata_, jsonFile, normalized=True)
# Call the createObject function from the package.

df = pd.DataFrame(gsnaObject.X)
# Create a pandas dataframe from the AnnData object's X layer.

df.columns = gsnaObject.var
# Set the column names to the geneset names that are located in the var layer.

df.to_csv("output.csv", sep="\t")
# Create an output file, named  as output.csv.

```

This code firstly imports the **createObject** from the package, which is used for creating an AnnData object
using the analysis results. Then, we specify our file paths. The pathwayScoring package needs two file inputs,
one gene expression data file, and one JSON file that contains genesets and their relations. We then call the 
createObject function to get our files, analyzes them using the pathwayScoring's respected algorihm and creates an AnnData object using scanpy. We can use this object any way we want, for here it is used for creating a csv file 
that contains the scorings. 

For further explanation, check the test files in test/ folder.

## File Format for Genesets 

The file that contains genesets and their relations has to be like this : 
```
{
	"GeneSet1": {
		"Gene1": {
			"Gene2": 0.35,
			"Gene3": 0.77,
			"Gene4": 0.16
		},
		"Gene2": {
			"Gene1": 0.35,
			"Gene3": 0.51
		},
		"Gene3": {
			"Gene1": 0.77,
			"Gene2": 0.51,
			"Gene4": 0.40
		},
		"Gene4": {
			"Gene1": 0.16,
			"Gene3": 0.40
		}
	},
	"GeneSet2": {
		"Gene1": {
			"Gene2": 0.99
		},
		"Gene2": {
			"Gene1": 0.99
		}
	}
}
```
Here, GeneSet1 looks like this: 

![Graph for GeneSet1](/genesets.png)

You can create this format easily though, check below.

## JSON Creator Tool 

You can create the needed JSON file containing genesets with the data from msigdb databases. All you need to do is : 

```
#!/usr/bin/env python

from gsnetact import makeJson

makeJson("Path to your msigdb JSON file.","Name of the JSON file containing genesets, name them whatever you want.")

```
The makeJson function takes your geneset names from msigdb data and finds the relations between individual genes 
using the STRING database.
fluffyrocket 0.1.0
PyTorch implementation of MiniRocket with soft PPV
Requires Python: >=3.10

# FluffyRocket - Soft and Mini Rocket

Neural-network based MiniRocket with soft PPV.

## Related projects

- [Rocket](https://github.com/angus924/rocket) : Previous version of MiniRocket.
- [MiniRocket](https://github.com/angus924/minirocket) : Original MiniRocket, developed by the author of Rocket with intention to replace it.
- [sktime](https://github.com/sktime/sktime), [aeon](https://github.com/aeon-toolkit/aeon): Scikit-Learn compatible toolkits which support MiniRocket.
- [TorchRocket](https://github.com/alexander-milne/gpu_rocket) : Neural-network based Rocket with soft PPV.
- [tsai](https://github.com/timeseriesAI/tsai) : Provides neural-network based MiniRocket, but without soft PPV.