semvec/pypi-packages huggingface dataset:
cells2table 0.2.1
Table image parsing with cell detection models
Requires Python: >=3.12
Keywords: docling, plugin

# cells2table

Parsing tables in document images with cell detection models

## Implemented pipelines

### PaddlePaddle models

- Classification model (wired / wireless)
- Cell detection model with different weights for each class

Using [ONNX weights](https://huggingface.co/jspast/paddlepaddle-table-models-onnx) (downloaded automatically on first use with `huggingface_hub`)

## Instalation

With [uv](https://docs.astral.sh/uv/), add to your project with:

```sh
uv add cells2table
```

ONNX models need a [ONNX Runtime](https://onnxruntime.ai/getting-started) installed to run. You can install one on your own or use one of the optionals already configured.

| Optional   | Description             |
| ---------- | ----------------------- |
| `cuda`     | For NVIDIA GPUs         |
| `openvino` | For Intel GPUs and CPUs |
| `cpu`      | Default CPU runtime     |
| `docling`  | For docling usage       |

## Usage

cells2table only extract structural information from the tables. Another library is needed to extract content from the cells.

### Docling

A [docling plugin](https://docling-project.github.io/docling/concepts/plugins/) is provided to allow integrating cells2table in a complete pipeline.

Usage example:

```python
from cells2table.docling import CustomDoclingTableStructureOptions

pipeline_options = PdfPipelineOptions(
    allow_external_plugins=True,
    table_structure_options=CustomDoclingTableStructureOptions(),
)

converter = DocumentConverter(
    format_options={
        InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options),
        InputFormat.IMAGE: PdfFormatOption(pipeline_options=pipeline_options),
    }
)

result = converter.convert("path/to/document.pdf")
print(result.document.export_to_markdown())
```
isbtchot 1.1.5
TBD

# isbtchot: BTC Hotness Index Dashboard

The `isbtchot` package provides a minimalist terminal dashboard for visualizing Bitcoin's (BTC) hotness index. It utilizes historical BTC data to generate visualizations of BTC's price alongside the Hotness Index, directly in your terminal.

## Features:

- Visualizes BTC candlestick plots with log scale options.
- Displays the Hotness Index based on proprietary indicators with buy/sell signals.
- Supports different time frames and custom period back settings to tailor the data.
- Offers multiple dashboard views including standard hotness index and power-law delta.
- Data is cached locally to expedite subsequent visualizations.

## Installation

```bash
pip install isbtchot
```

## Usage

You can run the main dashboard with flexible command-line options:

```bash
isbtchot [--periods_back P] [--time T] [--dashboard D]
```

- `--periods_back`, `-p`: Specifies the number of periods back to process for generating the visualization. Defaults to 85.
- `--time`, `-t`: Specifies the candlestick time frame (e.g., Weekly, Monthly). Defaults to Weekly.
- `--dashboard`, `-d`: Choose the type of dashboard to display: `isbtchot` for the standard hotness index or `power_law` for the power-law distribution analysis. Defaults to `isbtchot`.

### Command Line Arguments

| Argument       | Short Form | Description                                                                     | Default   |
|----------------|------------|---------------------------------------------------------------------------------|-----------|
| `--periods_back` | `-p`       | Number of periods to be processed for the data visualization.                    | 85        |
| `--time`        | `-t`       | Candlestick time to use (e.g., M for Monthly, W for Weekly).                   | W         |
| `--dashboard`   | `-d`       | Dashboard type to display: `isbtchot` or `power_law`.                           | isbtchot  |

## Demo

### Hotness Index Dashboard
Historically very reliable for bull market tops:
![BTC Hotness Index Dashboard Demo](media/demo.png)

### Power Law Delta Dashboard
Historically very reliable for bear market bottoms:
![BTC Power Law Delta Dashboard Demo](media/demo2.png)

## Notes

- For optimal visualization, maximize your terminal window.
- Data is fetched from CryptoCompare API and cached to enhance performance and responsiveness.

## Contributions

Contributions are welcome! Please feel free to submit issues, enhancements, or pull requests. If you encounter a bug or have a feature suggestion, please open an issue on GitHub.
pybricksmenu 0.0.6
Main menu for pybricks
Requires Python: >=3.9

# pybricks menu

Main menu for pybricks

Copyright (C) 2025 Joey Parrish

The pybricks firmware doesn't come with a menu or support for multiple programs
like the default Spike firmware.  This is a simple menu system and basic
startup checks to make it easier for kids to get started coding missions.

The startup checks ensure they don't start runs while plugged in, and show
battery status while charging.  If the battery level is too low on startup, a
warning animation will be shown.  Please charge to avoid inaccuracies caused by
low torque.

## Sample usage

```py
from pybricks.hubs import PrimeHub
from pybricks.parameters import Direction, Port
from pybricks.pupdevices import Motor
from pybricks.robotics import DriveBase

from pybricksmenu import main_menu, startup_checks


hub = PrimeHub()
left_motor = Motor(Port.A, Direction.COUNTERCLOCKWISE)
right_motor = Motor(Port.B, Direction.CLOCKWISE)
wheel_diameter = 175
wheel_separation = 200
drive = DriveBase(left_motor, right_motor, wheel_diameter, wheel_separation)
run = 1


def run1(drive: DriveBase) -> None:
    drive.straight(1000)


def run2(drive: DriveBase) -> None:
    drive.turn(720)


startup_checks(hub)

while True:
    run = main_menu(hub, num_items=9, item=run)
    if run == 1:
        run1(drive)
    elif run == 2:
        run2(drive)
```