semvec/pypi-packages huggingface dataset:
aiopvapi 3.3.0
Powerview blinds API wrapper
License: MIT
Requires Python: >=3.5.0


AioPvApi
========

A python asyncio API for PowerView blinds.
Written for Home-Assistant. Adding features as I go...

Have a look at the examples folder for some guidance how to use it.

Links
-----
- https://home-assistant.io/
- https://www.hunterdouglas.com/operating-systems/powerview-motorization

Changelog
---------

**v3.3.0**
- Limit battery max to 100%
- Move co-ordinator of shades from HA to API Core
- Remove shade limiter causing issues with tilt shades

**v3.2.1**
- Add type 57 (Carole Roman Shades)

**v3.2.0**
- Bug fix for gen1 shades with bad data
- Fix bad close position on tilt shades
- Add hub registration capability
- Revert gen 1/2 shades to previous power type mapping
- Add retry logic to battery status requests

**v3.1.2**
- Add type 72 (Silhoutte)

**v3.1.1**
- Fix missed timeout blocks and handle in websession

**v3.1.0**
- General docstring updates
- Handle kwargs in websessions for management of timeout internally
- Update error handling in tools
- Handle empty values and zeros better
- Add type 53 (Sonnette) and yype 95 (Aura Illuminated, Roller). Note: *Type 95 do not support light control*
- Handle PowerType 11 + 12. Both are fixed and cannot be edited

**v3.0.2**

- Add type 19 (Provenance Woven Wood)
- Fix Positioning for ShadeVerticalTiltAnywhere + ShadeTiltOnly (Mid only)  
- Fix logging regression on initial setup
- Fixes for ShadeVerticalTiltAnywhere + ShadeTiltOnly
- Fix tests
- Remove unneeded declerations
- Fix shade position reporting for v2 shades
- Handle empty hub data being returned

**v3.0.1**

- Raw hub data updates made via defined function (`request_raw_data`, `request_home_data`, `request_raw_firware`, `detect_api_version`)
- Parse Gen 3 hub name based on serial + mac
- Find API version based on firmware revision
- Remove async_timeout and move to asyncio

**v3.0.0**

- Major overhaul to incorporate gateway version 3 API.  Version can be automatically detected or manually specified.
- UserData class is deprecated and replaced with Hub.
- ShadePosition class now replaces the raw json management of shades in support of cross generational management.
- Schedules / Automations are now supported by the API
- New get_*objecttype* methods available to returned structured data objects for consistent management

**v2.0.4**

- Add Type 10 - SkyLift
- Handle calls to update shade position during maintenance
- Raise error directly on hub calls instead of logger

**v2.0.3**

- Add Type 26, 27 & 28 - Skyline Panels
- Force capability 1 for Type 44 - Twist
- Align class name standard

**v2.0.2**

- Bug Fix to handle shades with unexpected json responses

**v2.0.1**

- Invert type 3 & 4 to match api documentation from hunter douglas
- Add type 10

**v2.0.0**

- Add support for all known shade types
- Fallback to shade recognition based on capability
- Clamping to prevent MIN_POSITION or MAX_POSITION being exceeded
- Code refactoring

**v1.6.19**

- Add endpoints and handle 423 response
- Remove loop as argument
imgalz 0.0.9.0
A library providing lightweight tools for basic image processing and quick construction of detection-tracking pipelines.
License: MIT
Requires Python: >=3.8
Keywords: image-processing, image-filtering, onnx, YOLO

# imgalz: A Modular Library for Simple Image Analysis

![img](https://cdn.jsdelivr.net/gh/pleb631/ImgManager@main/img/2024-01-24-14-31-32.png)

## Usage

See below for quickstart installation and usage examples.
All guidance can refer to full [imgalz docs](https://pleb631.github.io/imgalz)

### Installation

```bash
pip install .[all]
# or
pip install imgalz[all]
```

### util example

```python
# Filter and retain images based on hash similarity, then copy selected images to a target directory.
from imgalz import ImageFilter, save_pkl

f = ImageFilter(hash='ahash')

# Parameters:
#   "/src/to/"       : Source directory containing images
#   threshold=5      : Hash similarity threshold (5 means similar but not identical)
#   recursive=True   : Whether to process images in subdirectories
#   bucket_bit='auto': Hash bucket grouping strategy (auto selects optimal)
#   n_tables=2       : Number of hash tables to use for filtering
# Returns 'keep', a list of image paths to retain
keep = f.run("/src/to/", threshold=5, recursive=True, bucket_bit='auto',n_tables=2)

# Save the retained image paths to a pickle file
save_pkl("keep.pkl", keep)

# Copy the retained images to the target directory
ImageFilter.copy_files(keep, "/src/to/", "/save/to", True)

```

### model example

```python
import cv2
import numpy as np

import imgalz
from imgalz.models.detector import YOLOv5

# Use local path if available, otherwise download from Hugging Face
model = YOLOv5(model_path = "yolov5n.onnx")
# model = YOLOv5("yolov6n.onnx")
im = imgalz.imread("resources/bus.jpg",1)
bboxes = model.detect(im, aug=True)
# plot box on img
for box in bboxes:
    cv2.rectangle(
        im, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2
    )

imgalz.cv_imshow("yolov5-det", im)

```

You can refer to the specific usage by [demo](https://github.com/pleb631/imgalz/tree/main/demo)

## Optional Models

### Detector

- YOLOv5/6
- YOLOv8/11
- YOLOv8pose
- YOLOv8seg

### Tracker

- ByteTrack
- Motpy
- NorFair
- OCSort

### Pose

- ViT-Pose

## todo

- add more tool for image processing

## Weights

The ONNX model in the example is exported directly from the official code and can be obtained from the [huggingface](https://huggingface.co/pleb631/onnxmodel).
bioseq-toolkit 0.1.1
Enhanced Sequence Analyzer (FASTA/ORF/protein analysis)
License: MIT
Requires Python: >=3.10

[![CI](https://github.com/Dheeraj-espada/bioseq-toolkit/actions/workflows/ci.yml/badge.svg)](https://github.com/Dheeraj-espada/bioseq-toolkit/actions/workflows/ci.yml)

# bioseq-toolkit

**Enhanced Sequence Analyzer** — a Python/BioPython tool for FASTA parsing, ORF finding, translation, and protein analysis.  
Includes unit tests, reproducible conda environment, and CI integration.

---

## 🔍 Features
- Parse FASTA and compute GC%, length, and base composition  
- Find the **longest ORF** (ATG..stop) on both strands  
- Allow **partial ORFs** (ATG..end)  
- Compute protein molecular weight, isoelectric point, and composition  
- Export results to CSV  
- Optionally write ORF nucleotide and amino-acid FASTA files  
- Includes **unit tests** (pytest) and **GitHub Actions CI**

---

## 🚀 Quick Start

```bash
# Clone this repository
git clone https://github.com/Dheeraj-espada/bioseq-toolkit.git
cd bioseq-toolkit

# Create the environment
conda env create -f environment.yml
conda activate bioinfo

# Run analysis
python3 enhanced_seq_analyzer_cli.py input.fasta \
  --orf --both-strands --allow-partial --write-orf-fasta -o results.csv

# Run tests
pytest -q

📂 Repository structure
bioseq-toolkit/
│
├── enhanced_seq_analyzer_cli.py    # Main CLI tool
├── tests/                          # Unit tests
│   └── test_orf_finder.py
├── environment.yml                 # Conda environment
├── .github/workflows/ci.yml        # Continuous Integration workflow
├── .gitignore
└── README.md

📜 License

This project is licensed under the MIT License.

👤 Author

Dheeraj Babu — aspiring Bioinformatician

GitHub: https://github.com/Dheeraj-espada

LinkedIn: https://www.linkedin.com/in/dheeraj-babu-m-74b787219/

## Output directories

- Plots are written under: \$BIOSEQ_OUTPUT_DIR/example_summary_plots (defaults to ./results/example_summary_plots)
- You can override with BIOSEQ_OUTPUT_DIR or pass absolute paths to save_plot functions.
dircol 0.1.0
Pretty column-formatted and color-coded dir() output using Rich.
License: MIT
Requires Python: >=3.7

# dircol

`dircol` provides two small helpers, `dirc` and `dirfc`, that display the output of Python's built-in `dir()` function in a cleaner, more readable layout.

This is useful when exploring unfamiliar objects or browsing large libraries in the Python REPL.

---

## Features

| Function | Output | Color Coding | Purpose |
|---------|--------|--------------|---------|
| `dirc(obj)` | Columns | No | Simple, compact directory listing |
| `dirfc(obj)` | Columns | Yes | Highlights whether attributes are modules, classes, methods, functions, or built-ins |

`dirfc` uses **Rich** to color-code attribute names according to their type:
- **Blue** = Module  
- **Green** = Class  
- **Yellow** = Method  
- **Magenta** = Function  
- **Gray** = Built-in  
- **White** = Everything else  

---

## Example

```python
from dircol import dirc, dirfc

dirc(str)   # simple column view
dirfc(str)  # fancy, color-coded view
```

---

## Install

```bash 
pip install dircol
```