semvec/pypi-packages huggingface dataset:
chineserand 0.0.4
随机生成中文字符工具包
Requires Python: >=3.0

# 引用
本词表来自BCC语料库汉语部分的报刊（news）、科技（technology）、博客（blog）、微博（weibo）和文学（literature）几个频道。Global为此五个频道合并后的全局词表。

词表剪枝频次为10。未去除字母词。存在部分数量词残留。

本词表仅供学术使用。引用请注明“北京语言大学BCC语料库http://bcc.blcu.edu.cn”和文献（以bcc网站的最新引用说明为准）。最终解释权归北语大数据与教育技术研究所所有。


北京语言大学·信息科学学院·大数据与教育技术研究所
2015年10月

联系人：荀恩东，edxun@126.com

# 概述
最近大模型兴起，基于中文文字、字体、图片相关的中文模型较少，训练中文模型需要，现开发相关中文文字、字体、图片中文的基础工具，开源给大家使用，欢迎大家star，谢谢。


# 随机生成中文字符
本工具包根据BCC语料库汉语词表词频获取中文字符，随机生成的，已标明数据来源，如有侵权，请通知下线谢谢。

## 使用方法

### 安装
```commandline
pip install chineserand
```

### 生成随机文字
```python
from chineserand import raw
chinese_words = raw(10)
print(chinese_words) 
```

### 生成句子
```python
from chineserand import sentences
many_sentences = sentences(many=4)
print(many_sentences)
```

如果有任何问题，欢迎提issue。

# 后期规划

1. 随机生成中文词语、语句、段落；
2. 根据操作系统字符集，生成所有中文字体对应的图片（用于字体模型训练）；


------

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官网地址：[www.lezhifu.cc](https://lezhifu.cc)

公众号：
<div style="text-align: center;">  
    <img src="images/image.png" alt="乐知付" style="width: 50%;">  
</div>
nieto-curcio-example 0.2.0
Add your description here
Requires Python: >=3.9

uv run -- python -m src.nieto_curcio_example.main

uv run -- python -m nieto_curcio_example.main

uv run nieto_curcio_example

source .venv/bin/activate
python -m nieto_curcio_example.main

## Building

uv build

This command creates the `dist/` folder:
```sh
dist
├── nieto_curcio_example-0.1.0-py3-none-any.whl
└── nieto_curcio_example-0.1.0.tar.gz
```

## Publishing

uv publish

https://docs.astral.sh/uv/guides/publish/#preparing-your-project-for-packaging:

"""
If you have internal packages that you do not want to be published, you can mark them as private:


```toml
[project]
classifiers = ["Private :: Do Not Upload"]
This setting makes PyPI reject your uploaded package from publishing. It does not affect security or privacy settings on alternative registries.
```

We also recommend only generating per-project tokens: Without a PyPI token matching the project, it can't be accidentally published.
"""


Set a PyPI token with --token or UV_PUBLISH_TOKEN, or set a username with --username or UV_PUBLISH_USERNAME and password with --password or UV_PUBLISH_PASSWORD.

export $(cat .env | xargs)
echo $UV_PUBLISH_PASSWORD
cpix 1.4.1
CPIX

# cpix
Python library for working with CPIX 2.2 (DASH-IF Content Protection Information Exchange) documents

For more details on CPIX see:

* https://dashif.org/guidelines/
* https://dash-industry-forum.github.io/docs/CPIX2.2/Cpix.html

## Supported features

* Creation of CPIX documents
* Content keys
* Usage rules
* DRM systems
* Parsing of CPIX documents
* Validation against CPIX XSD

## Not yet implemented

* Validation of document correctness (e.g. kid referenced by usage rule matches a content key)

## Not supported

* Encryption, decryption and signing are not supported.

## Installation

Install using [pip](https://pip.pypa.io/):

```
pip install cpix
```

## Examples

### Scripts

Example scripts which can be used with Widevine and Playready test servers to
get or create keys and produce CPIX documents are available in `example`.

### Simple CPIX

To create a simple CPIX document with a single key:

```python
import cpix

full_cpix = cpix.CPIX(
    content_keys=cpix.ContentKeyList(
        cpix.ContentKey(
            kid="0DC3EC4F-7683-548B-81E7-3C64E582E136",
            cek="WADwG2qCqkq5TVml+U5PXw=="
        )
    ),
    drm_systems=cpix.DRMSystemList(
        cpix.DRMSystem(
            kid="0DC3EC4F-7683-548B-81E7-3C64E582E136",
            system_id="EDEF8BA9-79D6-4ACE-A3C8-27DCD51D21ED",
            pssh=("AAAAxnBzc2gBAAAA7e+LqXnWSs6jyCfc1R0h7QAAAAINw+xPdoNUi4HnPGT"
                  "lguE2FEe37S9mVyu9EwbOfPNhDQAAAIISEBRHt+0vZlcrvRMGznzzYQ0SEF"
                  "rGoR6qL17Vv2aMQByBNMoSEG7hNRbI51h7rp9+zT6Zom4SEPnsEqYaJl1Hj"
                  "4MzTjp40scSEA3D7E92g1SLgec8ZOWC4TYaDXdpZGV2aW5lX3Rlc3QiEXVu"
                  "aWZpZWQtc3RyZWFtaW5nSOPclZsG")
        )
    )
)
```

This can then be printed as a formatted XML document:

```python
print(str(full_cpix.pretty_print(xml_declaration=True), "utf-8'"))
```

```xml
<?xml version='1.0' encoding='utf-8'?>
<CPIX xmlns:pskc="urn:ietf:params:xml:ns:keyprov:pskc" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="urn:dashif:org:cpix" xsi:schemaLocation="urn:dashif:org:cpix cpix.xsd">
  <ContentKeyList>
    <ContentKey kid="0dc3ec4f-7683-548b-81e7-3c64e582e136">
      <Data>
        <pskc:Secret>
          <pskc:PlainValue>WADwG2qCqkq5TVml+U5PXw==</pskc:PlainValue>
        </pskc:Secret>
      </Data>
    </ContentKey>
  </ContentKeyList>
  <DRMSystemList>
    <DRMSystem kid="0dc3ec4f-7683-548b-81e7-3c64e582e136" systemId="edef8ba9-79d6-4ace-a3c8-27dcd51d21ed">
      <PSSH>AAAAxnBzc2gBAAAA7e+LqXnWSs6jyCfc1R0h7QAAAAINw+xPdoNUi4HnPGTlguE2FEe37S9mVyu9EwbOfPNhDQAAAIISEBRHt+0vZlcrvRMGznzzYQ0SEFrGoR6qL17Vv2aMQByBNMoSEG7hNRbI51h7rp9+zT6Zom4SEPnsEqYaJl1Hj4MzTjp40scSEA3D7E92g1SLgec8ZOWC4TYaDXdpZGV2aW5lX3Rlc3QiEXVuaWZpZWQtc3RyZWFtaW5nSOPclZsG</PSSH>
    </DRMSystem>
  </DRMSystemList>
</CPIX>
```
tenzro 0.1.0
Official Python SDK for Tenzro API
Requires Python: >=3.7

# Tenzro Python SDK

Official Python SDK for interacting with the Tenzro API.

## Installation

```bash
pip install tenzro
```

## Usage

```python
from tenzro import Client

async with Client(api_key="your-api-key", nid="node-id") as client:
    # Create a training job
    job = await client.jobs.create(
        type="training",
        model="custom-model",
        config={
            "epochs": 100,
            "batch_size": 32
        }
    )
    print(f"Created job: {job.id}")

    # Get job status
    job_status = await client.jobs.get(job.id)
    print(f"Job status: {job_status.status}")

    # List jobs
    jobs = await client.jobs.list(limit=10, offset=0)
    print(f"Total jobs: {len(jobs)}")

    # Cancel job
    cancelled_job = await client.jobs.cancel(job.id)
    print(f"Cancelled job: {cancelled_job.id}")
```

## API Reference

### Client

```python
Client(api_key: str, nid: str)
```

### Jobs

- `create(type: str, model: str, config: Dict[str, Any]) -> Job`
- `get(job_id: str) -> Job`
- `list(limit: int = 10, offset: int = 0) -> Dict[str, Any]`
- `cancel(job_id: str) -> Job`

## License

MIT License
neuralcore 1.0.0
A Python client for NeuralCore AI API
Requires Python: >=3.6

# NeuralCore Python Client

Official Python client for the NeuralCore AI API platform.

## Installation


## Features

- Chat completions with NeuralCore models
- Vision analysis capabilities
- Configurable parameters (temperature, max tokens)
- Simple and intuitive API

## Configuration

The client can be configured with the following parameters:

- `api_key` (required): Your NeuralCore API key
- `base_url` (optional): Custom API endpoint
- `default_temperature` (optional): Default temperature for generations
- `default_tokens` (optional): Default maximum tokens for responses

## Methods

### Chat

### Vision

## Quick Start


python
from neuralcore import NeuralCore
Initialize the client
client = NeuralCore(api_key="your-api-key")
Chat completion
response = client.chat("Hello, how are you?")
print(response)
Vision analysis
vision_response = client.vision(
image_url="https://example.com/image.jpg",
prompt="Describe this image"
)
print(vision_response)


python
client.vision(
image_url="https://example.com/image.jpg",
prompt="Describe this image",
model="neura-vision-3.5",
temperature=0.7,
tokens=200  
)

python
client.chat(
messages="Your message",
model="neura-3.5-aala",
temperature=0.7,
tokens=200
)

## Requirements

- Python 3.6+
- requests>=2.25.1

## License

This project is licensed under the MIT License.
streamlit-big-text-tabs 0.0.4
Streamlit component that allows you to build big text tabs
Requires Python: >=3.7

# streamlit-big-text-tabs 

Streamlit component that allows you to do big text tabs

## Installation instructions

```sh
pip install streamlit-big-text-tabs 
```

## Usage instructions

```python
import streamlit as st
from big_text_tabs import big_text_tabs


styles_ =  """
        .tab-container { }
        .tab-text-container { }
        .tab-text { }
        .tab-text.clicked-tab { }
    """

tabs_data = [
    { "index": 0, "title": "Tab 1", "value": "tab_one" },
    { "index": 1, "title": "Tab 2", "value": "tab_two" },
    { "index": 2, "title": "Tab 3", "value": "tab_three" }
  ]

tab_selected = big_text_tabs(tab_data=tabs_data, styles=styles_)

st.write(tab_selected)

# returns { "index":0, "value":"tab_one" }
```
CPTtool 2.2.2
CPT tool for the OURS model

OURS CPT tool
-------------

This is python code for the CPT tool of [OURS](https://github.com/rivm-syso/OURS).

The OURS CPT tool reads the information from the
[BRO CPT dataset](https://www.pdok.nl/introductie/-/article/bro-geotechnisch-sondeeronderzoek-cpt-),
the [geomorphological map](https://basisregistratieondergrond.nl/inhoud-bro/registratieobjecten/modellen/geomorfologische-kaart-gmm/)
and the [NHI data](https://data.nhi.nu/) to create subsurface schematisations for the OURS model.

The methodology for subsoil scenario schematisation is illustrated in the figure below.

![Schematic methodology](./documentation/images/method.png)


More details about the OURS CPT tool can be found in this [publication](https://www.researchgate.net/publication/347297118_SUBSOIL_MODELLING_FOR_RAILWAY_INDUCED_VIBRATIONS).