ToolVIPER

Tools and utilities for optimized radio astronomy processing using the VIPER framework.

ToolVIPER provides a suite of high-level tools designed to simplify Dask cluster management, logging, profiling, and data distribution for radio astronomy applications. It is part of the VIPER (Very Large Interferometry Post-processing and Exploratory Research) ecosystem.

Features

• Dask Cluster Management: Easily create and manage local or SLURM-based Dask clusters with optimized configurations.
• Unified Logging: Structured, colorized, and worker-aware logging system.
• Resource Profiling: Built-in decorators to monitor CPU and memory usage of functions.
• Interactive Utilities: Enhanced data display for Jupyter notebooks, including interactive JSON and HTML views for complex dictionaries.
• Data Management: Simple interface for downloading and managing external datasets from Cloudflare.
• Parameter Validation: Decorator-based system for validating function arguments using JSON schemas.
• Graph Distribution: Tools to distribute functions across datasets along specific axes using Dask.

Installation

ToolVIPER requires Python 3.11, 3.12, or 3.13.

Basic Installation

pip install toolviper

With Optional Dependencies

ToolVIPER offers several optional dependency sets:

• test: For running tests (pytest, black, etc.)
• interactive: For Jupyter/IPython support (jupyterlab, matplotlib, ipympl, etc.)
• docs: For building documentation (sphinx, nbsphinx, etc.)
• all: Installs all optional dependencies.

Example:

pip install "toolviper[interactive]"

Quick Start

1. Dask Client Management

Example Notebook

ToolVIPER simplifies setting up a Dask environment.

from toolviper.dask.client import local_client

# Start a local Dask client with 4 workers
client = local_client(cores=4, memory_limit="8GB")
print(client.dashboard_link)

For SLURM clusters:

from toolviper.dask.client import slurm_cluster_client

client = slurm_cluster_client(
    workers_per_node=1,
    cores_per_node=4,
    memory_per_node="16GB",
    number_of_nodes=2,
    queue="normal",
    interface="ib0",
    python_env_dir="/path/to/env",
    dask_local_dir="/tmp/dask",
    dask_log_dir="dask_logs"
)

2. Logging

Example Notebook

ToolVIPER provides a pre-configured logger that can be used throughout your application.

from toolviper.utils import logger

logger.info("Initializing process...")
logger.warning("Low memory detected", verbose=True)

3. Profiling

Monitor your functions' resource consumption using decorators.

from toolviper.utils.profile import cpu_usage

@cpu_usage(filename="my_profile.csv")
def heavy_computation():
    # Your code here
    pass

heavy_computation()

4. Interactive Data Exploration

In a Jupyter notebook, you can use DataDict for better visualization of nested dictionaries.

from toolviper.utils.display import DataDict

my_data = {"level1": {"level2": {"key": "value"}}}
dd = DataDict(my_data)
dd.display()  # Renders an interactive JSON tree in Notebooks

5. Data Management

Access and download external datasets easily.

import toolviper

# List available files
toolviper.utils.data.list_files()

# Get a python list available files
toolviper.utils.data.get_files()

# Download a specific dataset
toolviper.utils.data.download("example_dataset.zip", folder="./data")

6. Parameter Validation

A detailed write-up can be found here: Parameter Validation.

Ensure your functions receive correct arguments using the `@validate` decorator.

from toolviper.utils.parameter import validate

@validate()
def my_function(param1: int, param2: str):
    # This function will automatically validate arguments 
    # against a corresponding .param.json schema.
    pass

Development and Testing

To contribute or run tests locally:

1. Clone the repository:

   git clone https://github.com/casangi/toolviper.git
   cd toolviper

2. Install in editable mode with test dependencies:

   pip install -e ".[test]"

3. Run tests:

   pytest tests

License

ToolVIPER is licensed under the terms of the LICENSE file included in the root directory.