semvec/pypi-packages huggingface dataset:
exosuit-python 0.0.2
IMU sensor codes in Python for the exosuit
Requires Python: <3.14,>=3.11

# Python Exosuit
[![Coverage Status](https://coveralls.io/repos/github/TUM-Aries-Lab/exosuit-python/badge.svg?branch=main)](https://coveralls.io/github/TUM-Aries-Lab/exosuit-python?branch=main)
![Docker Image CI](https://github.com/TUM-Aries-Lab/exosuit-python/actions/workflows/ci.yml/badge.svg)

This repo is the main codebase to run the lower-limb exosuit on a single board computer like the Jetson Nano or Raspberry Pi.

## Install
To install the library run:

```bash
uv pip install exosuit-python==latest
```
OR
```bash
uv add git+https://github.com/TUM-Aries-Lab/exosuit-python.git@<specific-tag>  # need credentials
```

## Development
0. Install [**uv**](https://docs.astral.sh/uv/getting-started/installation/) from Astral.
1. `git clone git@github.com:TUM-Aries-Lab/exosuit-python.git`
2. Install the dependencies to use Makefiles.
3. `make init` to create the virtual environment and install dependencies
3. `make format` to format the code and check for errors
4. `make test` to run the test suite
5. `make clean` to delete the temporary files and directories

## Publishing
It's super easy to publish your own packages on PyPI. To build and publish this package run:

```bash
uv build
uv publish  # make sure your version in pyproject.toml is updated
```
The package can then be found at: **https://pypi.org/project/exosuit-python**

## Module Usage
```python
"""Basic docstring for the exosuit module."""

def main() -> None:
    """Run a simple demonstration."""
    logger.info("Hello World!")

if __name__ == "__main__":
    main()
```

## Program Usage
```bash
uv run python -m exosuit_python
```

## Structure
Run `make tree` to update the repository tree scene below.
<!-- TREE-START -->
```
├── src
│   └── exosuit_python
│       ├── __init__.py
│       ├── __main__.py
│       ├── definitions.py
│       ├── exosuit.py
│       └── utils.py
├── tests
│   ├── __init__.py
│   ├── conftest.py
│   ├── exosuit_test.py
│   └── utils_test.py
├── .dockerignore
├── .gitignore
├── .pre-commit-config.yaml
├── .python-version
├── CONTRIBUTING.md
├── Dockerfile
├── LICENSE
├── Makefile
├── README.md
├── pyproject.toml
├── repo_tree.py
└── uv.lock
```
<!-- TREE-END -->
binarychain 0.9.2
A simple but flexible binary container format, primarily meant for sending blocks of binary data over streaming interfaces like sockets. Similar to NetStrings, but for binary data.
Requires Python: >=3.8

# Binary Chains

Binary Chains are a simple but flexible binary container format, primarily meant for sending blocks of binary data over
streaming interfaces like sockets. Similar to NetStrings, but for binary data.


## Binary Chain Format

A binary chain consists of a prefix, which is an ascii string (strictly bytes of value 0x00 to 0x7F) plus zero or more
binary parts.

Each binary part starts with a SOP byte (0x80 to 0x88), the binary part length (big endian encoded), and then the actual
data.

The number of bytes in the binary part length is indicated by the SOP byte.
 * 0x80 indicates 0 bytes - implying that the length of the binary part is 0. In this case there are no binary part
   length bytes, and the data for that part of the chain is of zero length.
 * 0x81 indicates a 1 byte part length - implying that the length of the binary part is between 1 and 255 bytes.
 * 0x82 indicates a 2 byte part length - implying that the length of the binary part is between 256 (0x0100) and
   65535 (0xFFFF) bytes.
 * And so on...

The end of the chain is indicated by the EOC marker (0xFF).

So every part (prefix or binary part) is always terminated by a SOP byte or a EOC byte.

The prefix may be empty, and there may be no parts, in which case the serialisation is just the EOC (0xFF) byte.
subinsoman-eda-tool 0.1.3
A tool for Exploratory Data Analysis and Excel report generation
Requires Python: >=3.8

# EDA Tool

A powerful CLI tool for Exploratory Data Analysis (EDA) and automated Excel report generation.

## Features

- **Automated Metrics Calculation**: Calculates a wide range of statistical metrics for numerical, categorical, and date columns.
- **Excel Report Generation**: Generates a comprehensive Excel report with:
    - Summary statistics.
    - Detailed column metrics.
    - Individual column analysis.
    - Dynamic charts.
    - Data definitions.
- **Performance**: Uses Dask for efficient processing of large datasets.
- **CLI Interface**: Easy-to-use command-line interface.

## Installation

You can install the package using pip:

```bash
pip install subinsoman-eda-tool
```

## Usage

Run the tool from the command line:

```bash
eda-tool input.csv --output report.xlsx
```

### Arguments

- `input_file`: Path to the input CSV file (required).
- `--output`, `-o`: Path to the output Excel file (optional, defaults to `[input_stem]_metrics_report_[timestamp].xlsx`).
- `--intermediate`, `-i`: Save intermediate metrics to `metrics.json` (optional).

### Example

```bash
eda-tool data.csv -o my_report.xlsx -i
```

## Requirements

- Python 3.8+
- dask[complete]
- numpy
- openpyxl
- scipy
- tqdm
cvo251202app 0.1.0
Development flow as Paleofuturistic Python
Requires Python: >=3.12

# cvo251202app

Development flow as Paleofuturistic Python

## Usage

Legacy: `pip install cvo251202app`

Preferred: `uv add cvo251202app`

## Developing further

> Development flow as [Paleofuturistic Python](https://github.com/schubergphilis/paleofuturistic_python)

Prerequisite: [uv](https://docs.astral.sh/uv/)

### Setup

- Clone this repository.
- Download additional dependencies: `uv sync --all-extras --dev`
- Optional: validate the setup with `uv run python -m unittest`

### Workflow

- Download dependencies (if you need any): `uv add some_lib_you_need`
- Develop (optional, tinker: `uvx --with-editable . ptpython`)
- QA:
    - Format: `uv run ruff format`
    - Lint: `uv run ruff check`
    - Type check: `uv run mypy`
    - Test: `uv run python -m unittest`
- Build (to validate it works): `uv build`
- Review documentation updates: `uv run mkdocs serve`
- Make a pull request.