semvec/pypi-packages huggingface dataset:
objspct 1.1.0
Introspect any Python object
License: MIT
Requires Python: >=3.9

# ObjSpct

objspct is a Python introspection tool that lets you explore any Python object. Whether you’re debugging, learning a library, or documenting your code, objspct gives you a rich, structured overview of an object’s attributes, methods, properties, and more — all in a beautifully formatted tree using Rich.

## Features

Deep object introspection: Inspect types, classes, methods, properties, and attributes.
Classify members intelligently: Automatically separates magic methods, internal/external methods, and attributes.
Property-aware: Detects and lists properties separately.
Pretty-print with Rich: Generates a tree view that’s easy to read and understand.
Flexible filtering: Skip empty fields or select which categories to display.

## Installation

```bash
pip install objspct
```

## Usage

```python
from objspct import inspect_object

class MyClass:
    def __init__(self):
        self.public_attr = 42
        self._internal_attr = "secret"

    @property
    def computed(self):
        return self.public_attr * 2

    def public_method(self):
        return "Hello"

    def _internal_method(self):
        return "Internal"

obj = MyClass()
inspect_object(obj)
```

This will output a rich, hierarchical tree showing all categorized members of the object.
___

### Filtering

```python
# Only display methods and properties
inspect_object(obj, categories=["methods_external", "properties"])
```
___

### Skip Empty Attributes
```python
# Skip empty attributes in the tree
inspect_object(obj, skip_empty=True)
```
___

## Why Use objspct?

- Learn unfamiliar objects and libraries quickly.
- Debug complex classes or instances without manually exploring dir() or inspect.
- Document object structures visually.
- Perfect for teaching or self-learning Python OOP patterns.
ipgs 1.2.6
Interactive ANIMATED progress bar for Jupyter notebook projects
License: MIT
Requires Python: >=3.8
Keywords: progressbar, jupyter, notebook, animated, ipython

# iPgs – Interactive Animated Progress Bar for Jupyter

**iPgs** provides an interactive and animated progress bar designed for Jupyter Notebook and Google Colab environments. It enables clear visualization of training or processing progress, including nested loops such as epochs and batches, with automatic computation of estimated completion time (ETA).

This package is **framework-agnostic** and **dependency-free**, making it lightweight and easy to integrate into any Python workflow.

---

## Key Highlights

- **Interactive and Animated**: Real-time visual feedback enhances comprehension of long-running tasks, making iterative processes like model training more intuitive.  
- **Nested Progress Tracking**: Simultaneously displays epoch-level (outer) and batch-level (inner) progress, providing a granular view of the workflow.  
- **Automatic ETA Calculation**: Continuously estimates remaining time for both epochs and batches, improving planning and monitoring.  
- **Framework-Agnostic**: Fully compatible with PyTorch, TensorFlow, Keras, NumPy arrays, Python lists, or any iterable—no modifications required.  
- **Dependency-Free**: Relies only on built-in Python libraries and IPython display functionality, ensuring easy installation and minimal overhead.  
- **Flexible Usage**: Works seamlessly with both DataLoader-style iterables and standard Python loops, supporting diverse workflows.  

---

## Installation

```bash
pip install ipgs
```

## Usage Example
```
from ipgs import iPgs

for epoch_idx, batch_idx, batch in iPgs(loader, num_epochs=5, desc="Training"):
    bx, by = batch
    # training step
```
fourmat 2.0.0
A library for batteries-included linting and autoformatting
License: MIT
Requires Python: >=3.10
Keywords: lint autoformat black flake8 isort

# Fourmat

A library for batteries-included linting and autoformatting.

## Installation and Usage

Install fourmat through pip:

```sh
$ pip install fourmat
```

Create a `.fourmat` file to specify the directories and files to check.

```sh
$ fourmat check

$ fourmat fix
```

## Adding Fourmat as a pre-commit hook

Install the pre-commit package:

```sh
$ pip install pre-commit
```

Create a `.pre-commit-config.yaml` containing:

```yaml
repos:
- repo: https://github.com/4Catalyzer/fourmat
  rev: master  # or specify a version
  hooks:
    - id: fourmat
```

then install the hook:

```
$ pre-commit install
```
easy-middleware 0.1.1
A collection of useful FastAPI middleware (correlation ID, security headers, request timing)
License: MIT
Requires Python: >=3.8
Keywords: fastapi, middleware, observability, security, timing

# easy_middleware

A collection of lightweight and useful **FastAPI middleware** for production-ready apps.  

✅ Features:
- 🔗 **Correlation ID Middleware** – adds request IDs for logging & tracing  
- 🛡 **Security Headers Middleware** – injects standard security headers  
- ⏱ **Request Timing Middleware** – adds `X-Process-Time-ms` header  

---

## 📦 Installation

```bash
pip install easy_middleware