semvec/pypi-packages huggingface dataset:
lightspeed-oracle 0.0.1
Lightweight, lightning-speed library for seamless retrieval of analysis-ready Binance spot market data
Requires Python: >=3.9

# lightspeed

[![PyPI - Version](https://img.shields.io/pypi/v/lightspeed.svg)](https://pypi.org/project/lightspeed)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/lightspeed.svg)](https://pypi.org/project/lightspeed)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install lightspeed
```

## License

`lightspeed` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
tomo-detect 0.1.7
A CLI tool for detecting flagellar motor coordinates in tomography data
Requires Python: >=3.8
Keywords: Bacterial Flagellar Motor, tomography, motor detection, deep learning, CNN

# Tomo-Detect

A command-line tool for detecting motor coordinates in tomography data using deep learning models.

## Installation

```bash
pip install tomo-detect
```

## Usage

Basic usage:

```bash
tomo-detect input_path_A
tomo-detect input_path_A input_path_B
```

The input path can be either:

- A .npy file containing tomography data
- A zip file containing multiple tomography files
- A directory containing .npy or .mrc files

### Options

```bash
tomo-detect --help                        # Show help message and usage information
tomo-detect input.npy --debug             # Enable debug logging
tomo-detect input.zip --test              # Run test mode with additional validations
tomo-detect input.npy --output path       # Specify custom output directory
tomo-detect input.npy --detailed          # Outputs visualizations for the inputs
tomo-detect input.npy --batch-size 4      # Set custom batch size for inference
tomo-detect input.npy --device cpu/gpu    # Force CPU inference

```

### Output Files

The tool generates several output files:

1. `motor_detections_submission.csv` - Contains motor coordinates (primary output)
2. `motor_detections_detailed.csv` - Includes additional detection information
3. `predictions.npy` - Raw probability maps
4. `masks.npy` - Binary masks derived from predictions
5. `summary.json` - Detection summary and statistics

### Example

```bash
# Process a single .npy file
tomo-detect sample.npy

# Process multiple files in a zip
tomo-detect samples.zip

# Enable debug mode for detailed logging
tomo-detect sample.npy --debug
```

## Tips and Tricks

1. **Input Preparation**:

   - Ensure input files are properly formatted numpy arrays
   - For .mrc files, they will be automatically converted
   - Zip files should contain only supported file types
   - Supports multiple inputs and enbales parallel processing based on the system specs

2. **Performance Optimization**:

   - Use GPU acceleration when available
   - Adjust batch size based on your memory capacity
   - For large datasets, consider processing in chunks
   - It is advised to provide unzipped files so as to not bottleneck the cpu performance
   - Run it using --test to confirm the working of the model and save time

3. **Troubleshooting**:
   - Enable --debug mode for detailed logging
   - Check system requirements before running
   - Verify input file formats and dimensions
   - --detailed consumes lot of cpu memory

## System Requirements

- Python 3.8 or higher
- CUDA-capable GPU (optional but recommended)
- Minimum 8GB RAM
- 2GB disk space for models

## License

MIT License
liminality 0.1.2
Lightweight, lightning-speed library for seamless retrieval of analysis-ready Binance spot market data
Requires Python: >=3.8

# liminality

[![PyPI - Version](https://img.shields.io/pypi/v/liminality.svg)](https://pypi.org/project/liminality)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/liminality.svg)](https://pypi.org/project/liminality)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install liminality
```

## License

`liminality` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
parsegument 0.3.1
Parsegument is a python package for creating CLIs and adding text-based function execution for other applications!.
Requires Python: >=3.11
Keywords: parsegument, parser, argument parse, parse, CLI, command line interface

# Parsegument
[![Downloads](https://img.shields.io/pypi/dm/parsegument)](https://pypi.org/project/parsegument/)
[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)

Parsegument is a python package for creating CLIs and adding text-based function execution for other applications!.

Read The Docs: https://ryanstudio.dev/docs/parsegument

## Installation
```commandline
pip install parsegument
```

## Quick Start
```python
import parsegument as pg
parser = pg.Parsegumenter() # Create Parsegumenter

group1 = pg.CommandGroup("group1") # Define a Group
parser.add_child(group1) # Add the Group to the main Parsegumenter

@group1.command() # Add decorator to create command
def foo(bar:str):
    print(bar)

parser.execute("group1 foo bar_string") # Execute string
```
```
>>> bar_string
```