semvec/pypi-packages huggingface dataset:
pycequeau 0.0.3
A library that obtains the necessary files to build and run the CEQUEAU hydrological-water temperature model
Requires Python: >=3.10

pycequeau
=======================================

Pycequeau is a library that obtains the necessary files to build and run the CEQUEAU hydrological-water temperature model.

To use this library it is highly recommended to clone the repository and create a fresh conda environment to have all the needed dependencies. To avoid compatibility issues, the new conda enviroment must contain a specific version of the GDAL library.

.. code-block:: bash
    
    conda create -n pycequeau python=3.10 gdal=3.11.0

Then, the package can be installed using the following command:

.. code-block:: bash

    pip install pycequeau

Alternatively, you can build and install the package from the source code by copying the git repository into your local machine and running the following command:

.. code-block:: bash

    git clone https://github.com/erinconv/pycequeau.git
    cd pycequeau
    pip install .

The tutorials and examples on how to use the library can be found in: https://pycequeau.readthedocs.io/en/latest/. The documentation is still under development, so any feedback/suggestions or contributions are welcome.
quickstock 1.0.3
QuickStock SDK - 现代化的金融数据获取SDK，提供统一的股票、基金、指数数据访问接口
Requires Python: >=3.7
Keywords: stock, finance, data, sdk, baostock, eastmoney, tonghuashun, 股票, 金融数据

# QuickStock SDK

QuickStock SDK是一个现代化的金融数据获取SDK，提供统一的股票、基金、指数数据访问接口。

## 特性

- 🚀 支持多数据源集成（东方财富、同花顺、Baostock）
- 💾 多级缓存系统（内存缓存 + SQLite持久化）
- 🔄 异步数据获取支持
- 📊 标准化数据格式
- 🛡️ 完善的错误处理和重试机制
- ⚙️ 灵活的配置管理系统
- 📈 性能优化和内存管理

## 安装

```bash
pip install quickstock
```

可选依赖：

```bash
# 安装Baostock支持
pip install quickstock[baostock]

# 安装开发依赖
pip install quickstock[dev]
```

## 快速开始

```python
from quickstock import QuickStockClient

# 创建客户端
client = QuickStockClient()

# 获取股票基础信息
stocks = client.stock_basic()

# 获取股票日线数据
daily_data = client.stock_daily('000001.SZ')

# 获取分钟级数据
minute_data = client.stock_minute('000001.SZ', freq='1min')

# 获取指数数据
indices = client.index_basic()
index_data = client.index_daily('000001.SH')

# 获取基金数据
funds = client.fund_basic()
nav_data = client.fund_nav('000001.OF')

# 交易日历
trade_cal = client.trade_cal()
is_trade = client.is_trade_date('20240115')
```

## 系统要求

- Python >= 3.7
- pandas >= 1.3.0
- numpy >= 1.20.0
- requests >= 2.25.0
- aiohttp >= 3.8.0
- pyyaml >= 5.4.0

## 许可证

MIT License
noos-tf 0.1.0
HashiCorp Terraform Cloud API client
Requires Python: <3.13,>=3.12.0

[![CircleCI](https://circleci.com/gh/noosenergy/noos-terraform.svg?style=svg&circle-token=eec3000121ab0ce0659e9c0b2c09347a6e3cd000)](https://circleci.com/gh/noosenergy/noos-terraform)

# Noos Energy Terraform Client

A Python client wrapping up HashiCorp's Terraform Cloud API.

## Installation

Package available from the [PyPi repository](https://pypi.org/project/noos-tf/):

    $ pip install noos-tf

## Usage as a library

Import the namespace within your project,

```python
import os
import noos_tf

# Instantiate client
tf_client = noos_tf.TerraformClient()

# Authenticate client
tf_client.set_auth_header(token=os.getenv("TERRAFORM_TOKEN"))
```

Then query directly from the Terraform Cloud API ; as a example:

* a workspace ID:
```python
tf_client.get_workspace_id("myOrganisation", "myWorkspace")
```

* the variable IDs from a given workspace:
```python
tf_client.get_variable_ids("myOrganisation", "myWorkspace")
```

The library offers as well some helper functions for more involved workflows, such as:

* updating a variable value:
```python
noos_tf.update_workspace_variable(
    "myOrganisation",
    "myWorkspace",
    os.getenv("TERRAFORM_TOKEN"),
    "myVariable",
    "new_value",
)
```

* running and applying a plan:
```python
noos_tf.run_workspace_plan(
    "myOrganisation",
    "myWorkspace",
    os.getenv("TERRAFORM_TOKEN"),
    "Test run",
)
```

## Usage as a command line tool

The above helper functions could be accessed directly from the command line.

```bash
$ noostf

Usage: noostf [--core-opts] <subcommand> [--subcommand-opts] ...

Subcommands:

  run      Run a plan in Terraform cloud.
  update   Update variable in Terraform cloud.
```

### Development

Make sure [uv](https://docs.astral.sh/uv/) has been installed and pre-configured,

This project is shipped with a Makefile, which is ready to do basic common tasks.

```bash
$ make

help                           Display this auto-generated help message
update                         Lock and install build dependencies
clean                          Clean project from temp files / dirs
format                         Run auto-formatting linters
install                        Install build dependencies from lock file
lint                           Run python linters
test                           Run pytest with all tests
package                        Build project wheel distribution
release                        Publish wheel distribution to PyPi
```
lactopy 0.2.1
Add your description here
Requires Python: >=3.10

# Lactopy

A python package to analyse lactate based tresholds. Strongly inspired by [lacteter](https://github.com/fmmattioni/lactater)

docs can be found: https://dierickxsimon.github.io/lactopy/


`lactopy` can be installed with `pip`:

```bash
pip install lactopy
```
pyipses 0.0.5
PYIPSES library.
Requires Python: >=3.8

# PYIPSES

Python library for [IPSES](https://www.ipses-ri.it/index.php/it)

PYIPSES provides an API to connect with IPSES (e.g., to retrieve the user's workspace data).

The main Target of the API is the [Jupyter](https://jupyter.org) notebooks.

## Build from sources

Create a virtual environment and install PYIPSES in *editable* mode with the optional development dependencies:

```sh
python -m venv .venv
source .venv/bin/activate

pip install --upgrade setuptools pip
pip install -r requirements.txt

pip install -e .
```

Please note that pip only supports editable installs (enabled with the option -e) from a pyproject.toml files since v21.3.

Make sure you have an up-to-date version of pip

```sh
pip install --upgrade pip
```

To run unit tests:

```bash
pytest tests
```


To run linting inspection with `pylint`:
```bash
pylint pyipses
```


## Packaging for distribution

To package PYIPSES in a .whl file, for distribution:

```bash
python -m build --wheel
```

The 'created .whl file' will be available in the 'dist' directory. To install the .whl file:

```bash
pip install <'path to the created .whl file'>
```


## Packaging for distribution using docker compose

To package PYIPSES in a .whl file, for distribution, using docker:

```bash
docker compose -f builder-docker-compose.yml run --rm build_wheel
```
refig 0.1.0
Minimally-invasive reproducibility helpers for matplotlib figures.
Requires Python: >=3.12

<p align=center>
<img width="303" height="192" alt="Image" src="https://github.com/user-attachments/assets/c52f23a7-f106-4dc0-b715-5a1ee211cc78" />
</p>

refig is a Python library for minimally-invasive reproducibility in Jupyter notebooks, with a focus on figures and plots.

## Installation

You can install refig using pip:

```bash
pip install refig
uv add refig
```

## Usage

To use refig, simply import it in your Jupyter notebook:

```python
from refig import refig
```

Then use `refig.savefig(NAME)` instead of `matplotlib.pyplot.savefig(PATH)` to save your figures with embedded metadata. `NAME` can end in `.png` or `.svg`.

When you save a figure, refig will automatically save the figure along with a steganographic pointer to the code that generated it, including (all optionally),

-   The notebook / file that generated the figure
-   The cell number (if applicable / available)
-   The date and time the figure was generated
-   The git commit hash (if applicable / available)
-   The installed `refig` package version

You can view these metadata by loading the figure with refig:

```python
uv run refig meta figure.png
uv run refig meta figure.svg
```

### Figure Saving Paths

By default, refig saves figures in a `figures/` directory. There are several subdirectories:

-   `figures/latest/`: Contains the most recently generated version of each figure.
-   `figures/history/`: Contains all historical versions of generated figures, sorted by timestamp and git commit hash.

For example,

```
figures/
    latest/
        figure1.png
        figure2.png
    history/
        figure1/
            _20250101_abcdef.png
            _20250201_bcdefa.png
        figure2/
            _20250115_cdefab.png
```
bank131-sdk 0.1.0
Python SDK for Bank131 API
Requires Python: >=3.8

# Bank131 Python SDK

Официальный Python SDK для работы с API [Bank131](https://developer.131.ru/).

## Установка
```bash
pip install bank131-python-sdk.zip
```

## Использование
```python
from bank131 import Bank131Client

client = Bank131Client(
    project_id="your_project_id",
    private_key_path="private.pem",
    base_url="https://demo.bank131.ru/api/v1"
)

session = client.create_session(1000, "RUB", "Test payment")
print(session)
```
astk 0.1.4
A comprehensive toolkit for alternative splicing analysis

What is ASTK?
===============

**ASTK** is a command-line software for comprehensive alternative splicing analysis including:
- characterizing distribution and expression patterns of alternative splicing events
- systematically analyzing functional effects of AS events at the gene- and exon-level respectively
- dissecting out splicing-associated sequence features and epigenetic marks
- inferring the splicing regulatory code for each type of AS. 

<img src='https://huang-sh.github.io/astk-doc/static/img/workflow.png' alt="workflow.png"></img>

Documentation
===============
The documentation for ***ASTK*** is available on [astk-doc](https://huang-sh.github.io/astk-doc/).

How to cite
===============
Huang, S., He, J., Yu, L., Guo, J., Jiang, S., Sun, Z., Cheng, L., Chen, X., Ji, X. and Zhang, Y. (2024), ASTK: A Machine Learning-Based Integrative Software for Alternative Splicing Analysis. Adv. Intell. Syst. 2300594. https://doi.org/10.1002/aisy.202300594


How to contact
===============

- Shenghui Huang (hsh-me@outlook.com)
bank131 0.1.0
Python SDK for Bank131 API
Requires Python: >=3.8

# Bank131 Python SDK

Официальный Python SDK для работы с API [Bank131](https://developer.131.ru/).

## Установка
```bash
pip install bank131-python-sdk.zip
```

## Использование
```python
from bank131 import Bank131Client

client = Bank131Client(
    project_id="your_project_id",
    private_key_path="private.pem",
    base_url="https://demo.bank131.ru/api/v1"
)

session = client.create_session(1000, "RUB", "Test payment")
print(session)
```
reicore-sdk 0.2.1
A Python SDK for interacting with the Reigent API.
Requires Python: >=3.6

# Rei Core SDK (Python)

A Python SDK for interacting with the Reigent, providing a simple interface for chat completions and agent management.

## Features

- Chat completion functionality
- Agent management

## Installation

Install the package using pip:

```bash
pip install reicore_sdk
```

## Quick Start

```python
from reicore_sdk import ReiCoreSdk

# Initialize the SDK with your Reigent Secret key
rei_agent = ReiCoreSdk("your-reiagent-secret-key")

# Get agent details
agent = rei_agent.get_agent()
print("Agent Details:", agent)

# Send a chat completion request
# Type: Text
message = {
    "messages": [
        {
            "role": "user",
            "content": "Hello world"
        }
    ],
    "tools": []
}

# Type: Image_url
message = {
    "messages": [
        {
            "role": "user",
            "content": [
                { "type": "text", "text": "What is in this image" },
                { "type": "image_url",
                    "image_url": {
                        "url": "https://test.png"
                    }
                }
            ]
        }
    ],
    "tools": []
}
response = rei_agent.chat.completion(message)
print("Chat Completion:", response)
```

## Error Handling

The SDK includes built-in error handling for API requests. All methods will raise exceptions with descriptive messages if there are any issues with the API calls.

## Requirements

- Python >= 3.6
- requests >= 2.25.1

## License

MIT License

## Support

For support, please contact rei.analoglabs@proton.me
celeries 0.1.5
Celestial mechanics series
Requires Python: >=3.8

celeries: Celestial mechanics series
====================================

Read the documentation at https://obswww.unige.ch/~delisle/celeries/doc/.

Contribute
----------

Everyone is welcome to open issues and/or contribute code via pull-requests.
A SWITCH edu-ID account is necessary to sign in to https://gitlab.unige.ch.
If you don't have an account, you can easily create one at https://eduid.ch.
Then you can sign in to https://gitlab.unige.ch by selecting "SWITCH edu-ID" as your organisation.