semvec/pypi-packages huggingface dataset:
jrnl2tumblr 0.1.3
Publish your jrnl entries on your Tumblr.
Requires Python: >=3.6

# jrnl2tumblr

![PyPI - Version](https://img.shields.io/pypi/v/jrnl2tumblr)
[![PyPI Supported Python Versions](https://img.shields.io/pypi/pyversions/jrnl2tumblr.svg)](https://pypi.python.org/pypi/jrnl2tumblr)
[![python-package](https://github.com/eigenric/jrnl2tumblr/actions/workflows/python-package.yml/badge.svg)](https://github.com/eigenric/jrnl2tumblr/actions/workflows/python-package.yml)


## Description

`jrnl2tumblr` is a tool that allows you to import journal entries from [jrnl](https://jrnl.sh/) to your Tumblr blog.

## Features
- Imports journal entries from a JSON file exported by jrnl.
- Posts entries to a specified Tumblr blog.
- Ensures privacy and security by requiring OAuth authentication.

## Installation

```bash
$ pip install jrnl2tumblr
```

## Usage
1. Export your journal entries from jrnl to a JSON file:

   ```bash
   jrnl --export json > journal.json
   ```

3. Create a new Tumblr app at [https://www.tumblr.com/oauth/apps](https://www.tumblr.com/oauth/apps) and obtain your OAuth credentials.

4. Run `jrnl2tumblr` and provide the path to the JSON file and your Tumblr blog name.

   ```bash
   jrnl2tumblr journal.json my-tumblr-blog
   ```

5. Follow the prompts to enter your Tumblr OAuth credentials.

6. Your journal entries will be imported and posted to your Tumblr blog.

## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue if you encounter any bugs or have suggestions for improvements.

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
UnitPie 1.0.0.2
Tired of these testing libraries such as pytest and unittest? UnitPie is here to change that!
Requires Python: >=3.9

# UnitPie
Tired of these testing libraries such as `pytest` and `unittest`? `UnitPie` is here to change that!
It allows you to group and categorize tests into different TestCases and run all tests by calling a single function!
No command-line trickery needed!

## Command Line Syntax
* `-v`, `-version`: This will print out the version of `UnitPie` you currently have installed
sudokuwizard 0.0.1
Simple program to solve Sudoku puzzles only with the source image.
Requires Python: >=3.6

# Sudoku Wizard 💻🪄

Sudoku Wizard is a Python project that digitizes and solves Sudoku puzzles from images using advanced image processing techniques and OCR (Optical Character Recognition).

![Screenshot from 2024-06-18 14-46-34](https://github.com/INIGO-7/SudokuSolver/assets/58185185/b4ae52d5-a8a4-4fe2-9d82-e30dd35ab044)

## Features

- **Image Processing**: Detects and extracts Sudoku grids from images.
- **OCR and Template Matching**: Recognizes digits within the Sudoku grid using either OCR or template matching.
- **Sudoku Solving**: Solves the extracted Sudoku puzzle using a DFS algorithm.
- **Visualization**: Overlays the solution onto the original image.

## Installation

To install Sudoku Wizard, follow these steps:

### Prerequisites

- Python 3.8 or later
- Git

### Clone the Repository

```bash
git clone https://github.com/yourusername/SudokuWizard.git
cd SudokuWizard
```

### Using `requirements.txt`

To install the dependencies using `pip`, run:

```bash
pip install -r requirements.txt
```

### Using `environment.yml`

To create a conda environment and install the dependencies, run:

```bash
conda env create -f environment.yml
conda activate sudoku-wizard
```

## Usage

Here's a quick example of how to use Sudoku Wizard:

```python
import cv2 as cv
from sudoku_wizard import SudokuWizard

# Create a SudokuWizard instance
sw = SudokuWizard()

# Load your sudoku image (works with .jpg, .jpeg, .png and .webp; support for other extensions is unknown)
sw.load_image('image.jpg')

# Run the Sudoku digitalization and resolution process
sw.run()
```

## Project Structure

- `sudoku_wizard.py`: Main implementation of the Sudoku Wizard class.
- `sudoku_algorithms.py`: Contains various algorithms for solving Sudoku puzzles.
- `exceptions.py`: Custom exceptions for error handling.
- `res/photos/`: Contains template images for number recognition.
- `requirements.txt`: Python dependencies.
- `environment.yml`: Conda environment configuration.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## License

This project is licensed under the MIT License.

---

Feel free to reach out if you have any questions or need further assistance!

---

Enjoy solving Sudoku puzzles with Sudoku Wizard! 🧩✨
gegravity 0.3
General equilibrium (GE) gravity modeling tools for international trade
Requires Python: >=3.7

# gegravity: General Equilibrium Gravity Modeling in Python
--------------------
gegravity is a Python package containing tools used to estimate general equilibrium (GE) structural gravity models and simulate counterfactual experiments. The package is based on the well established version of the gravity model described by Yotov, Piermartini, Monteiro, and Larch (2016) *An Advanced Guide to Trade  Policy  Analysis:  The  Structural  Gravity  Model*. It implements the structural GE gravity model in a general, robust, and easy to use manner in an effort to make GE gravity modeling more accessible for researchers and policy analysts.

The package provides several useful tools for structural gravity modeling.

1. It computes theory consistent estimates of the structural multilateral resistance terms of Anderson and van Wincoop (2003) "Gravity with Gravitas" from standard econometric gravity results.

2. It simulates GE effects from counterfactual experiments such as new trade agreements or changes to other trade costs. The model can be flexibly used to study aggregate or sector level trade as well as many different types of trade costs and policies.

3. It conducts Monte Carlo simulations that provide a means to compute standard errors and other measures of statistical precision for the GE model results.

For more information about the GE gravity model, its implementation, and the various components of the package, see the companion paper ["gegravity: General Equilibrium Gravity Modeling in Python"](https://usitc.gov/sites/default/files/publications/332/working_papers/herman_2021_gegravity_modeling_in_python.pdf) as well as the [technical documentation](https://peter-herman.github.io/gegravity/).


## Citation and license
The package is publicly available and free to use under the MIT license. Users are asked to please cite the following document,

Herman, Peter (2021) "gegravity: General Equilibrium Gravity Modeling in Python." USITC Economics Working Paper 2021-04-B.
gegravity-peter-herman 0.3
General equilibrium (GE) gravity modeling tools for international trade
Requires Python: >=3.7

# gegravity: General Equilibrium Gravity Modeling in Python
--------------------
gegravity is a Python package containing tools used to estimate general equilibrium (GE) structural gravity models and simulate counterfactual experiments. The package is based on the well established version of the gravity model described by Yotov, Piermartini, Monteiro, and Larch (2016) *An Advanced Guide to Trade  Policy  Analysis:  The  Structural  Gravity  Model*. It implements the structural GE gravity model in a general, robust, and easy to use manner in an effort to make GE gravity modeling more accessible for researchers and policy analysts.

The package provides several useful tools for structural gravity modeling.

1. It computes theory consistent estimates of the structural multilateral resistance terms of Anderson and van Wincoop (2003) "Gravity with Gravitas" from standard econometric gravity results.

2. It simulates GE effects from counterfactual experiments such as new trade agreements or changes to other trade costs. The model can be flexibly used to study aggregate or sector level trade as well as many different types of trade costs and policies.

3. It conducts Monte Carlo simulations that provide a means to compute standard errors and other measures of statistical precision for the GE model results.

For more information about the GE gravity model, its implementation, and the various components of the package, see the companion paper ["gegravity: General Equilibrium Gravity Modeling in Python"](https://usitc.gov/sites/default/files/publications/332/working_papers/herman_2021_gegravity_modeling_in_python.pdf) as well as the [technical documentation](https://peter-herman.github.io/gegravity/).


## Citation and license
The package is publicly available and free to use under the MIT license. Users are asked to please cite the following document,

Herman, Peter (2021) "gegravity: General Equilibrium Gravity Modeling in Python." USITC Economics Working Paper 2021-04-B.