semvec/pypi-packages huggingface dataset:
gatecode 0.0.1.dev2
Gate Your Code's Security
Requires Python: >=3.8

# GateCode - Secure Your Python Code 🔒

Python's simplicity and flexibility come with a trade-off: source code is easily exposed when deployed. GateCode provides a secure solution to this long-standing problem by enabling you to encrypt your Python scripts, allowing deployment without revealing the source code.

## Key Features 🔍

- **Secure Code Encryption**: Protect your intellectual property by encrypting your Python scripts.
- **Easy Integration**: Minimal effort required to integrate the encrypted package into your projects.
- **Cross-Platform Deployment**: Deploy your encrypted code to any environment without exposing its contents.

---

## How It Works ⚙️

### 1. Prepare Your Python Script 🔧

Suppose you have a Python file named `my_awesome_code.py` containing sensitive logic:
```python
# secret algorithm with secret numbers
def func(a, b):
    return a * 31 + b * 71
```

### 2. Encrypt Your Script 🛡️

Go to [GateCode](https://www.gatecode.org) and upload your Python script. The system will generate an encrypted package for you, which can be safely deployed.

Once encrypted, you will receive a file, for example: `my_valuable_code`.

### 3. Integrate Encrypted Package 🔐

Use the following script to load and use your encrypted code:
```python
import os
from gatecode import add_dp_package

# Pass the absolute path of the downloaded file to add_dp_package
here = os.path.dirname(os.path.abspath(__file__))
_ = add_dp_package(os.path.join(here, 'my_valuable_code'))

# Import and use `my_awesome_code` (adjust it according to your Python file's name) 
import my_awesome_code

# Example usage
print(my_awesome_code.func(1, 2))
```

---

## Example Use Case 📊

Imagine you’ve developed a proprietary algorithm that you need to deploy to your clients. Using GateCode:
1. Encrypt the Python script containing your algorithm.
2. Provide the encrypted package to your client.
3. Your client integrates the package without accessing the original source code.

This ensures that your intellectual property is secure while maintaining usability.

---

## Why GateCode? 🌎

- **Protect Sensitive Logic**: Prevent unauthorized access to your code.
- **Simple Deployment**: No complicated setup or runtime requirements.
- **Peace of Mind**: Focus on your work without worrying about code theft.

---

### Get Started Now 🏃‍♂️

1. Visit [GateCode](https://www.gatecode.org).
2. Upload your Python script.
3. Download your encrypted package and deploy it securely.

Secure your Python code with GateCode today!
JupyterDebug 0.1.1
A package for debugging Jupyter notebooks using OpenAI.
Requires Python: >=3.6

## Documentation for `JupyterDebug`

### Package Overview
`JupyterDebug` is a Python package designed to help users debug Python code in Jupyter notebooks using OpenAI's language models. It provides two main methods:
1. `init()`: Initializes the package by prompting the user for their OpenAI API key and selecting a model.
2. `debug(max_iter=1)`: Automatically debugs the code in the previous cell by calling the selected language model iteratively to fix errors.

---

### Installation
To install `JupyterDebug`, use pip:
```bash
pip install JupyterDebug
```

---

### Usage

#### 1. `init()`
The `init()` method initializes the package by prompting the user for their OpenAI API key and selecting a model. It must be called before using the `debug()` method.

**Syntax**:
```python
import JupyterDebug as jd
jd.init()
```

**Behavior**:
- Prompts the user to enter their OpenAI API key.
- Asks the user to select a model from the following options:
  - `gpt-4o-mini` (default)
  - `gpt-4o`
  - `o1`
  - `o1-mini`
- Stores the API key and model selection in environment variables for use in the `debug()` method.

---

#### 2. `debug(max_iter=1)`
The `debug()` method automatically debugs the code in the previous cell by calling the selected language model iteratively to fix errors. It should be called in a new cell immediately after the cell that generated the error. 

**Syntax**:
```python
jd.debug(max_iter=1)
```

**Parameters**:
- `max_iter` (optional, default=1): The maximum number of times the language model is called to iteratively debug the code. If the first solution does not fix all errors, the method will retry up to `max_iter` times.

**Behavior**:
- Captures the code from the previous cell and the error trace.
- Calls the selected language model to generate a fix for the code.
- Replaces the code in the previous cell with the fixed version.
- If `max_iter > 1`, the process is repeated until all errors are fixed or the maximum number of iterations is reached.

**Example**:
```python
# Cell 1: Code with an error
x = 10
y = "20"
z = x + y  # This will raise a TypeError
```

```python
# Cell 2: Debug the error
jd.debug()
```

---

### Example Workflow
1. Install the package:
   ```bash
   pip install JupyterDebug
   ```

2. Initialize the package:
   ```python
   import JupyterDebug as jd
   jd.init()
   ```

3. Write and run code in a Jupyter notebook cell:
   ```python
   # Cell 1: Code with an error
   x = 10
   y = "20"
   z = x + y  # This will raise a TypeError
   ```

4. Debug the error in the next cell:
   ```python
   # Cell 2: Debug the error
   jd.debug(max_iter=2)
   ```
dbfread2 0.1.0
Read DBF Files with Python
Requires Python: >=3.12

# 📚 dbfread2

> 🚀 A modern Python library for reading DBF files with elegance and type safety

[![PyPI version](https://badge.fury.io/py/dbfread2.svg)](https://badge.fury.io/py/dbfread2)
[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

`dbfread2` is a modern, type-safe Python library for reading DBF files (dBase, Visual FoxPro, FoxBase+) with zero external dependencies.

## ✨ Highlights

- 🐍 Modern Python 3.12+ implementation
- 🔍 Comprehensive type hints
- 🛠️ Simple yet powerful API
- 📁 Native `pathlib.Path` support
- 🔄 Streaming and memory-loaded modes
- 📝 Support for all major DBF variants
- 📋 18 field types with extensible `FieldParser`
- 📎 Reads `FPT` and `DBT` memo files
- 🔒 Type-safe operations

## 🚀 Quick Start

```python
from dbfread2 import DBF

# Stream records (memory-efficient)
for record in DBF('people.dbf'):
    print(record)
# {'NAME': 'Alice', 'BIRTHDATE': datetime.date(1987, 3, 1)}
# {'NAME': 'Bob', 'BIRTHDATE': datetime.date(1980, 11, 12)}

# Load all records into memory
table = DBF('people.dbf', load=True)
print(table.records[0]['NAME'])  # Returns: 'Alice'
```

## 📦 Installation

```bash
pip install dbfread2
```

## 🔄 Migration from dbfread

### Key Changes

1. **Python Version**

   - ✅ Python 3.12+ required
   - ❌ No support for older versions

2. **Import Updates**

   ```python
   # Before ❌
   from dbfread import DBF
   # After ✅
   from dbfread2 import DBF
   ```

3. **Modern Parameter Names**

   ```python
   # Before ❌
   DBF('file.dbf', recfactory=dict, lowernames=True)
   # After ✅
   DBF('file.dbf', record_factory=dict, lowercase_names=True)
   ```

4. **Path Support**
   ```python
   # ✨ New Feature
   from pathlib import Path
   DBF(Path('data/file.dbf'))
   ```

## 🛠️ Development

### Setup

1. **Install Tools**

   - [mise-en-place](https://mise.jdx.dev/) for environment management
   - [uv](https://github.com/astral-sh/uv) for package management

2. **Clone & Setup**
   ```bash
   git clone https://github.com/wasdee/dbfread2.git
   cd dbfread2
   mise install
   uv pip install -e ".[docs]"
   ```

### 📚 Documentation

```bash
mise run docs:build    # Build docs
mise run docs:serve    # Serve locally
mise run docs:watch    # Watch mode
mise run docs:check    # Check for issues
```

### ✅ Code Quality

- 🧹 [ruff](https://github.com/astral-sh/ruff) - Linting & formatting
- 🔍 [mypy](https://mypy-lang.org/) - Static type checking

## 📄 License

MIT License

## 🙏 Credits

Fork of [dbfread](https://github.com/olemb/dbfread) by Ole Martin Bjørndalen

## 📬 Contact

Nutchanon Ninyawee - me@nutchanon.org

---

⭐️ If this project helps you, consider giving it a star!
carbon-txt-download-disclosures 0.0.1
A carbon.txt plugin to download linked disclosures in a carbon.txt file
Requires Python: >=3.11

# Carbon-txt-download-disclosures

A plugin for the carbon.txt validator that downloads any linked documents in a carbon.txt file to the local file system.


## Installation

Install this plugin in the same environment as the carbon.txt validator.

The validator is listed as dependency, so if you install this package and the validator isn't already installed, you will also download the carbon.txt validator CLI tool as well.

### Using `pip`

Make sure you are in your chosen virtual environment, then run:

```bash
pip install carbon-txt-download-disclosures
```

### Using `uv`

Inside a project with a pyproject.toml file, run the following:

```bash
uv add carbon-txt-download-disclosures
```

# Usage

By default, the `carbon-txt-download-disclosures` plugin is activated whenever you run the carbon.txt validator:
```
carbon-txt validate file https://some-domain.com/carbon.txt
```

You can then run the carbon.txt CLI command

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd carbon-txt-download-disclosures
python -m venv venv
source venv/bin/activate
```

Now install the dependencies and test dependencies:

```bash
pip install -e '.[test]'
```
To run the tests:
```bash
python -m pytest
```
carbon-txt-check-online 0.0.1
A demonstration carbon.txt plugin that checks whether linked documents in a carbon.txt file are still online.
Requires Python: >=3.12

# carbon-txt-check-online

A demo plugin for the carbon.txt validator that checks that files linked in carbon.txt files are still online.


## Installation

Install this plugin in the same environment as the carbon.txt validator.

The validator is listed as dependency, so if you install this package and the validator isn't already installed, you will also download the carbon.txt validator CLI tool as well.

### Using `pip`

Make sure you are in your chosen virtual environment, then run:

```bash
pip install carbon-txt-check-online
```

### Using `uv`

Inside a project with a pyproject.toml file, run the following:

```bash
uv add carbon-txt-check-online
```

# Usage

By default, the `carbon-txt-check-online` plugin is activated whenever you run the carbon.txt validator:
```
carbon-txt validate file https://some-domain.com/carbon.txt
```

You can then run the carbon.txt CLI command

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd carbon-txt-check-online
python -m venv venv
source venv/bin/activate
```

Now install the dependencies and test dependencies:

```bash
pip install -e '.[test]'
```

You can also use `uv`, to avoid faffing around with virtual environments:

```bash

# run uv as above
uv run carbon-txt validate file https://some-domain.com/carbon.txt
```
file-checksum-signer 1.2.0
simple creator for signed checksum files

# file-checksum-signer

This is a simple util that created signed checksum files in the following formats.

A clear-signed Version
```
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256

#aisha-greyrat-sdxl-v10.safetensors 228451292 bytes
SHA256 (aisha-greyrat-sdxl-v10.safetensors) = c809610f75bbc8fef3b0b606951452e3e7b10b3556a57a1b02b0a38c883eb48e
-----BEGIN PGP SIGNATURE-----
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=stl8
-----END PGP SIGNATURE-----
```

A unsigned version
```
#aisha-greyrat-sdxl-v10.safetensors 228451292 bytes
SHA256 (aisha-greyrat-sdxl-v10.safetensors) = c809610f75bbc8fef3b0b606951452e3e7b10b3556a57a1b02b0a38c883eb48e
```


usage for signed checksum files \
`fcsigner [file_to_sign] [PgiGP_ID]`

usage for unsigned checksum files \
`fcsigner --no-sign [file_to_sign]`