semvec/pypi-packages huggingface dataset:
AiSnake 0.1
A beginner-friendly programming language focused on AI.

# Ai Snake Programming Language

Ai Snake is a beginner-friendly programming language for creating AI models easily.

## Installation

To install Ai Snake, simply run:

```bash
pip install AiSnake
```

## Usage

After installation, you can run Ai Snake scripts or start an interactive REPL:

### Create and train an AI model:

```plaintext
ai_model m1 linear_regression
ai_train m1 [1, 2, 3, 4, 5] [2, 4, 6, 8, 10]
ai_predict m1 [6]
```

### Start REPL (Interactive Mode):

```bash
python -m ai_snake.interpreter
```

### Save and Load Models:

```plaintext
ai_save m1 "model1.joblib"
ai_load m2 "model1.joblib"
```

## Features
- Simple print statement support
- Easy project structure to extend further

## Project Structure

```
AiSnakeLanguage/
â”œâ”€â”€ ai_snake/
â”œâ”€â”€ examples/
â””â”€â”€ tests/
```

## Run Example

```bash
python -m ai_snake.interpreter examples/hello_world.aisnake
```

## Output

```
Hello, Ai Snake!
```

## License

Ai Snake is open-source, released under the MIT license.
ASReview-Dory 1.0rc1
ASReview New Exciting Models
Requires Python: >=3.10
Keywords: systematic review

# ASReview Dory 🐟

> ❣️❗ ASReview Dory is part of the upcoming ASReview LAB version 2 release ❗❣️ 

ASReview Dory is an extension to the [ASReview
software](https://github.com/asreview/asreview), providing new models for
classification and feature extraction. The extension is maintained by the
ASReview LAB team.

## Installation

You can install ASReview Dory via PyPI using the following command:

```bash
pip install asreview-dory
```

> ⚠️ **XGBoost on MacOS**
> If you are using macOS and plan to use XGBoost, you should first install OpenMP (`brew install libomp`)

## Model components

Feature Extractors:

    GTR T5
    LaBSE
    MPNet
    Multilingual E5
    MXBAI

Classifiers:

    AdaBoost
    Neural Network - 2-Layer
    Neural Network - Dynamic
    Neural Network - Warm Start
    XGBoost

Explore the performance of these models in our [Simulation
Gallery](https://jteijema.github.io/synergy-simulations-website/models.html)!
Look for the 🐟 icon to spot the Dory models.

## Usage

Once installed, the plugins will be available in the front-end of ASReview, as
well as being accessible via the command-line interface.

You can check all available models using:
```console
asreview algorithms
```

### Caching Models

You can pre-load models to avoid downloading them during runtime by using the
`cache` command. To `cache` specific models, such as `xgboost` and `sbert`, run:

```console
asreview dory cache nb xgboost sbert
```

To cache all available models at once, use:

```console
asreview dory cache-all
```

## Compatibility

This plugin is compatible with ASReview version 2 or later. Ensure that your ASReview
installation is up-to-date to avoid compatibility issues.

The development of this plugin is done in parallel with the development of the
ASReview software. We aim to maintain compatibility with the latest version of
ASReview, but please report any issues you encounter.

## Contributing

We welcome contributions from the community. To contribute, please follow these
steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Implement your changes.
4. Commit your changes with a clear message.
5. Push your changes to your fork.
6. Open a pull request to the main repository.

Please ensure your code adheres to the existing style and includes relevant
tests.

For any questions or further assistance, feel free to contact the ASReview Lab
Developers.

---

Enjoy using ASReview Dory! We hope these new models enhance your systematic
review processes.
finplot 1.9.7
Finance plotting

# Finance Plot

Finance Plotter, or finplot, is a performant library with a clean api to help you with your backtesting. It's
optionated with good defaults, so you can start doing your work without having to setup plots, colors, scales,
autoscaling, keybindings, handle panning+vertical zooming (which all non-finance libraries have problems with).
And best of all: it can show hundreds of thousands of datapoints without batting an eye.

<img src="https://badge.fury.io/py/finplot.svg"/> <img src="https://pepy.tech/badge/finplot/month"/> <img src="https://img.shields.io/badge/License-MIT-yellow.svg"/>


## Features

* Great performance compared to mpl_finance, plotly and Bokeh
* Clean api
* Works with both stocks as well as cryptocurrencies on any time resolution
* Show as many charts as you want on the same time axis, zoom on all of them at once
* Auto-reload position where you were looking last run
* Overlays, fill between, value bands, symbols, labels, legend, volume profile, heatmaps, etc.
* Can show real-time updates, including orderbook. Save screenshot.
* Comes with a [dozen](https://github.com/highfestiva/finplot/blob/master/finplot/examples) great examples.

![feature1](https://raw.githubusercontent.com/highfestiva/finplot/master/feature1.png)

![feature2](https://raw.githubusercontent.com/highfestiva/finplot/master/feature2.jpg)

![feature3](https://raw.githubusercontent.com/highfestiva/finplot/master/feature3.jpg)

![feature3](https://raw.githubusercontent.com/highfestiva/finplot/master/feature-nuts.jpg)


## What it is not

finplot *is not a web app*. It does not help you create an homebrew exchange. It does not work with Jupyter Labs.

It is only intended for you to do backtesting in. That is not to say that you can't create a ticker or a trade
widget yourself. The library is based on the eminent pyqtgraph, which is fast and flexible, so feel free to hack
away if that's what you want.


## Easy installation

```bash
$ pip install finplot
```


## Example

It's straight-forward to start using. This shows every daily candle of Apple since the 80'ies:

```python
import finplot as fplt
import yfinance

df = yfinance.download('AAPL')
fplt.candlestick_ochl(df[['Open', 'Close', 'High', 'Low']])
fplt.show()
```

For more examples and a bunch of snippets, see the [examples](https://github.com/highfestiva/finplot/blob/master/finplot/examples/)
directory or the [wiki](https://github.com/highfestiva/finplot/wiki). There you'll find how to plot MACD, Parabolic SAR, RSI,
volume profile and much more.


## Coffee

For future support and features, consider a small donation.

BTC: bc1qk8m8yh86l2pz4eypflchr0tkn5aeud6cmt426m

ETH: 0x684d7d4C52ed428AE9a36B2407ba909D896cDB67