semvec/pypi-packages huggingface dataset:
cipdb 0.1.0
Conditional iPDB - Simple conditional debugging for Python
License: MIT
Requires Python: >=3.6
Keywords: debugging, ipdb, pdb, conditional, breakpoint

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!-- Timestamp: 2025-08-29 04:53:00
!-- Author: ywatanabe
!-- File: /home/ywatanabe/proj/cipdb/README.md
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# cipdb - Conditional iPDB

Simple conditional debugging for Python with ID-based breakpoint control.

[![PyPI version](https://badge.fury.io/py/cipdb.svg)](https://badge.fury.io/py/cipdb)
[![Python 3.6+](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Installation

```bash
pip install cipdb
```

## Quick Start

### Control by Global State Call in Python

```python
# Disable all debugging programmatically
cipdb.disable()
```

# Disable all debugging by Environmental Variable

``` python
CIPDB_ID=false python your_script.py
```

### Control by Boolean

```python
import cipdb

cipdb.set_trace()       # Identical with ipdb.set_trace()
cipdb.set_trace(False)  # Identical with pass
cipdb.set_trace(os.getenv("DEBUG")) # Development vs Production
cipdb.set_trace(os.getenv("AGENT_ID")=="DebuggingAgent_01") # Runner Specific for debugging by multiple agent
```

### Control by Breakpoint IDs
``` python
# your_script.py
def process_user(user):
    cipdb.set_trace(id="validate")
    validate(user)
    
    cipdb.set_trace(id="save")
    save_user(user)
    
    cipdb.set_trace(id="notify")
    send_notification(user)
```

``` bash
# Development mode: All ID breakpoints work (no env vars needed)
python your_script.py                            # All ID breakpoints trigger

# Production mode: Control with environment variables
CIPDB_IDS=validate,save python your_script.py   # Only stops at validate and save
CIPDB_IDS=save python your_script.py            # Only stops at save
CIPDB_ID=save python your_script.py             # Only stops at save (Equivalent to CIPDB_IDS=save)
```

## Priority Logic

Global Control > `CIPDB=false` Environmental Variable > ID Matching > Boolean Conditioning

## License

MIT

<!-- EOF -->
GPmix 0.1.6
GPmix is an ensemble clustering algorithm for functional data via random projections.
License: MIT
Requires Python: >=3.9

## About

GPmix is a clustering algorithm for functional data generated from Gaussian process mixtures. While designed specifically for Gaussian process mixtures, GPmix has been shown to perform well on functional data beyond this setting.

The main steps of the algorithm are:

1. **Smoothing:** Apply smoothing techniques to raw data to obtain continuous functions.  
2. **Projection:** Project the functional data onto a set of randomly generated functions.  
3. **Learning GMMs:** Fit univariate Gaussian mixture models to the projection coefficients for each projection function.  
4. **Ensemble:** Combine the multiple GMMs to extract a consensus clustering.

## Links

- [Documentation](https://gpmix.readthedocs.io/en/latest/)  
- [Source code](https://github.com/EAkeweje/GPmix)  
- [Bug reports](https://github.com/EAkeweje/GPmix/issues)

## Contributing

This project is under active development. If you encounter any bugs or have suggestions for improvements, please let us know.

Pull requests are welcome. For major changes, please open an issue first to discuss your proposed modifications. Remember to update tests as appropriate.