semvec/pypi-packages huggingface dataset:
gie-py 0.4.6
A python API wrapper for alsi.gie.eu and agsi.gie.eu
License: MIT
Keywords: GIE data api energy gas

# gie-py
Python client for the API endpoints of gie.eu. Both ASGI for gas storage and ALSI for LNG terminals and lso's are supported

Using it requires an api key which you can get by registering an account [at the alsi website](https://alsi.gie.eu/#/api).
API keys are valid for both ASGI and ALSI if you set "access to" to "Both ALSI and AGSI+" when registering or later in account settings.

## Installation
`python3 -m pip install gie-py`

## Usage
The package comes with 2 clients:
- [`GieRawClient`](#GieRawClient): Returns data in its raw format direct from api, a list of dictionaries 
- [`GiePandasClient`](#GiePandasClient): Returns data parsed as a Pandas DataFrame

### Supported methods:
The same for both clients. Each method has same setup for arguments, a string to denominate the target 
and a start and end parameter which is either a pandas timestamp or a string
* ```query_gas_storage```
* ```query_gas_company```
* ```query_gas_country```
* ```query_lng_terminal```
* ```query_lng_lso```
* ```query_lng_country```

### Example
```python
from gie import GiePandasClient

client = GiePandasClient(api_key=<YOUR API KEY>)
df_terminal=client.query_lng_terminal('zeebrugge', start='2020-01-01', end='2022-07-10')
df_lso=client.query_lng_lso('fluxys_lng', start='2020-01-01', end='2022-07-10')
```

## meaning of dataframe columns
For the meaning of the columns in the resulting dataframes please consult the official [documentation](https://alsi.gie.eu/GIE_API_documentation_v007.pdf) chapter 2 page 5 and 6
bitquery-trading-indicators-stream 0.1.3
Real-time trading indicators (RSI, VWAP) streamed from Bitquery GraphQL
License: MIT
Requires Python: >=3.7

# Streaming Crypto Trading Indicators in Real-time

Real-time Token Volume,SMA,RSI and VWAP streaming from Bitquery.

## Installation

```bash
pip install bitquery-trading-indicators-stream
```

## Configuration

You need to generate an authorization token on your Bitquery account. Generate one [here](https://account.bitquery.io/user/api_v2/access_tokens)

Provide your Bitquery token via one of:

- Environment variable: `BITQUERY_TOKEN` (or `BITQUERY_OAUTH_TOKEN`)
- Config file JSON with key `oauth_token` or `token` at one of:
  - `./config.json`
  - `~/.config/trading-indicators/config.json`
  - `~/.trading-indicators.json`
- CLI flag: `--token` (highest precedence)

Example `config.json`:

```json
{ "oauth_token": "YOUR_TOKEN_HERE" }
```

## Usage

Run via CLI:

```bash
bitquery-trading-indicators-stream --duration 120 --rsi-period 14 --vwap-period 20
```

Or import as a library:

```python
from trading_indicators import load_token, run_stream

token = load_token()
run_stream(token, rsi_period=14, vwap_period=20, duration_seconds=120)
```

Source Price Feed is [here](https://docs.bitquery.io/docs/trading/crypto-price-api/introduction/)

Tutorial is [here](https://docs.bitquery.io/docs/usecases/trading-indicators/)
fuck-reborn 0.1.4
AI-powered command corrector (like thefuck, but with Gemini)
License: MIT

# fuck-reborn 🚀

**AI-powered command fixer**, inspired by [`thefuck`](https://github.com/nvbn/thefuck), but powered by **Google Gemini AI** for smarter, context-aware corrections.

---

## ✨ Features
- 🧠 Uses **Gemini AI** to understand and correct failed commands
- 🖥️ Detects command errors automatically
- 🤝 Interactive mode: approve or reject suggested fixes
- 🔑 `.env` support for storing your **Google API Key**
- 📦 Lightweight & cross-platform

---

## 🔧 Installation

### From PyPI (Recommended)
```bash
pip install fuck-reborn
```

### From Source (Development)
```bash
git clone https://github.com/blackXploit-404/fuck-reborn.git
cd fuck-reborn
python -m venv venv
source venv/bin/activate   # Linux/macOS
venv\Scripts\activate      # Windows
pip install -e .
```

---

## ⚙️ Configuration

fuck-reborn requires a **Google Gemini API Key**.
You can set it in two ways:

1. **`.env` file** (recommended):
   Create a `.env` file in your home or project directory:
   ```env
   GEMINI_API_KEY=your_api_key_here
   ```

2. **Environment variable**:
   ```bash
   export GEMINI_API_KEY="your_api_key_here"
   ```

> 🔑 Get your API key from [Google AI Studio](https://makersuite.google.com/).

---

## 🚀 Usage

Just run `fuck-reborn` after a failed command:
```bash
$ gti status
/bin/sh: gti: command not found
$ fuck-reborn gti status
Gemini suggests: git status
Run it? (y/n): y
```

---

## 🛠 Development

1. Clone the repo and install dependencies:
   ```bash
   git clone https://github.com/blackXploit-404/fuck-reborn.git
   cd fuck-reborn
   python -m venv venv
   source venv/bin/activate
   pip install -e ".[dev]"
   ```

2. Run tests:
   ```bash
   pytest
   ```

---

## 🐛 Troubleshooting

### **1. Missing API Key**
- **Error**: `GEMINI_API_KEY not found`
- **Fix**: Ensure the key is set in `.env` or exported in your shell.

### **2. Command Not Found**
- **Error**: `fuck-reborn: command not found`
- **Fix**: Ensure the package is installed and your `PATH` includes the Python `bin` directory.

### **3. PyPI Version Issues**
- **Error**: `ERROR: Could not find a version that satisfies the requirement`
- **Fix**: Upgrade pip and try again:
  ```bash
  pip install --upgrade pip
  pip install fuck-reborn
  ```

---


## 📄 License
[MIT License](LICENSE)

---

## 🙏 Credits
- Inspired by [`thefuck`](https://github.com/nvbn/thefuck)
- Powered by **Google Gemini AI**
```
saxsmart 1.0.0
SAXSMART: SAXS modeling and refinement toolkit (Dash app)
License: MIT
Requires Python: >=3.9
Keywords: SAXS, PEPSI-SAXS, Dash, bioinformatics, scattering

# SAXSMART: SAXS Modelling and Refinement Toolkit

## Overview

SAXSMART is an interactive web application for the analysis and refinement of Small-Angle X-ray Scattering (SAXS) data. It provides a user-friendly interface to perform common SAXS analyses and refine structural models against experimental data using PEPSI-SAXS.

## Features

- **Data Upload:** Upload SAXS curves (.dat), protein sequences (.fasta), and structures (.pdb/.cif).
- **Database Fetch:** Fetch structural models directly from the PDB or AlphaFold Database.
- **Automated Analysis:** Automatic calculation of Guinier fit, P(r) distribution, and Kratky plots.
- **Interactive Refinement:** Integrated PEPSI-SAXS for rigid-body and flexible model refinement.
- **Rich Visualization:** Interactive plots and 3D molecular viewers.

---

## Installation

### From Pypi

```bash
# Create and activate a virtual environment (or use conda)
python3 -m venv .venv
source .venv/bin/activate

# Install
pip install -e saxsmart
```

### From source

```bash
# Clone your repository
git clone https://gitlab.esrf.fr/hayden5a/saxsmart.git
cd saxsmart

# Create and activate a virtual environment (or use conda)
python3 -m venv .venv
source .venv/bin/activate

# Install
python -m pip install -U pip
pip install -e .
```

> **Note:** Dependencies are declared in `pyproject.toml`.

---

## Running

After installation, use the console script:

```bash
saxsmart
```

Alternative entry points:

```bash
python -m saxsmart
```

- Dash automatically serves files in the packaged `assets/` directory.
- Example data is bundled under `saxsmart/example_data/`.

---

## Configuration

You can configure paths via environment variables:

- `PEPSI_SAXS_BIN` — Full path to the `pepsi-SAXS` executable (if not on `PATH`).
  ```bash
  export PEPSI_SAXS_BIN=/opt/pepsi/pepsi-SAXS
  ```
- `SAXSMART_CACHE_DIR` — Override the directory for long-callback caching.
  ```bash
  export SAXSMART_CACHE_DIR=/scratch/$USER/saxsmart-cache
  ```
- `SAXSMART_TEMP` — Override the temporary working directory.
  ```bash
  export SAXSMART_TEMP=/scratch/$USER/saxsmart-tmp
  ```

> If `PEPSI-SAXS` is not required for your workflow, related actions will be disabled or produce a friendly error message.

---

## License

MIT (see `LICENSE`).
tools-pydantic 0.2.0
Pydantic-powered tools for building reliable, type-safe AI agents.
License: MIT
Requires Python: >=3.13

# PyTools

A collection of Pydantic-powered tools for building AI agents.  
Includes utilities such as file readers, file listers, private network readers, crawlers, and more — all designed to integrate smoothly with agent frameworks.

---

## 🚀 Features

- **Typed Tools** — Strict input/output validation with Pydantic models.  
- **Reusable Components** — Common utilities like file reading, listing, and crawling.  
- **Agent-Friendly** — Easily pluggable into LLM agents or tool-based frameworks.  
- **Modern Packaging** — Built and published using [`uv`](https://github.com/astral-sh/uv).  

---

## 📦 Installation

```bash
pip install tools-pydantic
```

Or, if you’re using uv
```bash
uv pip install tools-pydantic
```

## 🛠️ Usage

```python
from ToolsPydantic import FileReaderPyTools, ListProjectFilesPyTools, ReadPrivateNetworkPyTools
from pydantic_ai import Agent

# Example usage
tool = FileReaderPyTools().get_tool()
Agent(
    name="Example Agent",
    # ...
    tools=[
        FileReaderPyTools().get_tool(),
        ListProjectFilesPyTools().get_tool(),
        ReadPrivateNetworkPyTools("https://example.private.tech").get_tool()
    ]
)
```

## 🧪 Running Tests

```bash
uv run pytest
```

This project uses pytest and coverage.

To check coverage:

```bash
uv run coverage run -m pytest
uv run coverage report -m
```
python-deepcoin 0.2.1
Unofficial Python wrapper for the DeepCoin API (REST + WebSocket)
License: MIT
Requires Python: >=3.13

# Welcome to python-deepcoin v0.2.0

This is an unofficial Python wrapper for the [DeepCoin API](https://www.deepcoin.com/docs/authentication)
This package provides an easy-to-use, Pythonic interface for interacting with DeepCoin's REST and WebSocket APIs, supporting both spot and derivatives trading.

## Features
- **Account Endpoints**
  - `get_balances()` – Get Account Balance
  - `get_bills()` – Get Bills
  - `get_positions()` – Get Positions
  - `set_leverage()` – Set Leverage

- **Market Endpoints**
  - `get_order_book()` – Get Order Book
  - `get_candles()` – Get K-line Data
  - `get_instruments()` – Get Product Info
  - `get_tickers()` – Get Market Tickers

- **Trade Endpoints**
  - `place_order()` – Place Order
  - `replace_order()` – Amend Order
  - `cancel_order()` - Cancel Order
  - `batch_cancel_order()` – Batch Cancel Orders
  - `cancel_all_swap_orders()` – Cancel All Orders
  - `get_fills()` – Get Trade Details
  - `get_order_by_id()` - Get Order by ID
  - `get_finished_order_by_id()` – Get Historical Order by ID
  - `get_orders_history()` – Order History
  - `get_orders_pending()` – Get All Pending Orders
  - `get_funding_rate_cycle()` – Get Funding Rate
  - `get_current_funding_rate()` - Get Current Funding Rate 
  - `get_funding_rate_history()` - Get Funding Rate History
  - `replace_order_sltp()` - Modify Take Profit and Stop Loss for Open Limit Orders

- **WebSocket**
  - Public
  - Private

## Quick Start

### Installation

Install via pip:

```bash
pip install python-deepcoin
```

### Authentication

Most endpoints require authentication. To begin, initialize the client with your API key and secret:

```python
from deepcoin import DeepcoinClient

client = DeepcoinClient(
    api_key='your_api_key',
    api_secret='your_api_secret',
    passphrase='your_api_passphrase'
)
```

### Example Usage

Get Account Balance

```python
balance = client.get_balances()
print(balance)
```

More Examples

You can find more usage examples in the `examples/` folder.
wagtail-admin-login-url 0.1.0
Reusable Wagtail Admin URL package to manage wagtail admin login url.
License: MIT
Requires Python: >=3.11
Keywords: wagtail, headers, footers, django, malvin ndip

# Wagtail Admin Login URL

A **Wagtail** package to enhance the security of the Wagtail admin login page by allowing administrators to configure a **custom login URL**, apply **IP whitelisting/blacklisting**, enable **optional throttling**, and view **detailed login logs** directly from the admin dashboard.

## ✨ Features

- 🔒 **Custom Admin Login URL**
  Hide the default `/admin/login/` endpoint by specifying your own secure path.
  Example: `/secret-portal/`

- ⚙️ **Enable/Disable from Dashboard**
  Easily toggle the custom login URL setting without touching code.

- 🛡️ **IP Whitelisting & Blacklisting**
  Restrict access to the admin login page by allowing or blocking specific IP ranges.

- ⏳ **Optional Login Throttling**
  Protect against brute force attacks by limiting repeated login attempts.

- 📑 **Login Logs**
  Track login activity, including failed attempts, directly from Wagtail’s **Reports** menu.

## 📦 Installation

```bash
pip install wagtail-admin-login-url
```

Add to your `INSTALLED_APPS` in **settings.py**:

```python
INSTALLED_APPS = [
    ...
    'wagtail_admin_login_url',
    ...
]
```

## ⚙️ Configuration

### Middleware

Add the middleware to your **MIDDLEWARE** list:

```python
MIDDLEWARE = [
    ...
    "wagtail_admin_login_url.middleware.AdminLoginURLMiddleware",
]
```

### Migrate Wagtail Admin URL

```bash
python manage.py migrate wagtail_admin_login_url
```

## ⚙️ Usage

### Settings

In **Wagtail Admin > Settings > Admin Login URL**, you can configure:

- **Enable Custom URL**: Toggle to enable or disable custom login URL.
- **Custom Admin Path**: Set your custom path (e.g. `/my-secret-admin/`).
- **IP Whitelist**: Allow access only from specified IPs (optional).
- **IP Blacklist**: Block access from specified IPs (optional).
- **Throttling**: Limit login attempts per IP (optional).
- **Logging**: Enable or disable login attempt logging.

![Wagtail Admin URL Settings Screenshot](https://raw.githubusercontent.com/dazzymlv/wagtail-admin-login-url/main/docs/static/screenshot-settings-0.1.0-dev.1.png)

### Reports

- Login activity is available under **Reports > Admin Login Logs**.
- Includes details such as:

  - IP Address
  - Timestamp
  - Username (if provided)
  - Success/Failure status
  - Reason (blocked, throttled, etc.)

![Wagtail Admin Screenshot](https://raw.githubusercontent.com/dazzymlv/wagtail-admin-login-url/main/docs/static/screenshot-reports-0.1.0-dev.1.png)

## 🔒 Security Notes

- Use this package as an additional **security-through-obscurity** layer.
- Always combine with:

  - Strong passwords
  - Multi-Factor Authentication (MFA)
  - Proper server-level hardening

## ⚙️ Testing

```bash
python -m unittest discover tests
```

## 📜 License

This project is licensed under the **MIT License**.

## 🙌 Contributing

Contributions are welcome!
Please open issues and pull requests on the [GitHub repository](https://github.com/dazzymlv/wagtail-admin-login-url).
aistats 0.0.1
A Python package for descriptive and advanced statistical analysis
License: MIT
Requires Python: >=3.6

Got it 👍 You’re building a **Python package called `aistats`** (AI + Statistics).
Here’s a clean, professional **README.md** draft you can directly use in your project.

---

````markdown
# aistats

**aistats** is a Python package that bridges the gap between **Artificial Intelligence (AI)** and **Statistics**, providing tools that range from **basic descriptive statistics** to **advanced machine learning and AI-driven statistical analysis**.  

It is designed for students, researchers, and professionals who want a single package to handle everything from **basic stats** to **AI-powered insights**.

---

## ✨ Features

- 📊 **Descriptive Statistics**  
  - Mean, Median, Mode, Variance, Standard Deviation, Skewness, Kurtosis  

- 📈 **Inferential Statistics**  
  - Hypothesis Testing (t-test, chi-square, ANOVA, etc.)  
  - Confidence Intervals  
  - Correlation & Regression  

- 🧠 **AI + Machine Learning**  
  - Linear & Logistic Regression  
  - Decision Trees, Random Forest  
  - Neural Network-based estimations  
  - Information Equivalent Design for Regression Models  

- 📚 **Educational Support**  
  - Step-by-step explanations of results  
  - Examples from real-life domains (Agriculture, Education, Sports, Engineering, etc.)  

---

## 📦 Installation

You can install the package (once uploaded to PyPI) using:

pip install aistats

```
aistats/
│── __init__.py
│── descriptive.py      # Basic statistics
│── inferential.py      # Hypothesis testing, probability
│── ml.py               # AI + Machine Learning models
│── design.py           # Information Equivalent Design algorithms
│── utils.py            # Helper functions
```
## 🤝 Contribution

Contributions are welcome! Please fork the repo and submit a pull request.

---

## 📜 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

## 🙌 Acknowledgments

This package is inspired by the need to **combine traditional statistical methods with modern AI approaches** to make statistical analysis smarter, faster, and more interpretable.

```
## 👨‍💻 Author

**Name:** Rohit Kumar Behera  
**Profession:** Mathematics Teacher, Researcher in Statistics & AI  
**Location:** Odisha, India  

- 📧 Email: rohitsmartbehera@gmail.com  
- 🌐 GitHub: [github.com/muinrohit]
- 📝 Research Interests: Statistics, Machine Learning, AI Applications in Real-life Problems  
- 📊 Projects: `aistats`, and educational tools for applied statistics