pyCallisto

What's New in Version 1.2.3

New Features

• GOES X-ray Flux Overlay: New spectrogramWithGOES() function to overlay GOES satellite X-ray data on spectrograms
• Enhanced Spectrogram Control: Added blevel (background level) and vmax (maximum value) parameters for better visualization control

Improvements

• Added beautifulsoup4 and scipy to core dependencies for better out-of-the-box functionality
• Optional dependencies for GOES integration
• Improved Windows compatibility

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pyCallisto is an image processing and data analysis Python library developed for e-CALLISTO data (http://www.e-callisto.org/). This library can be used to process data obtained using various solar radio spectrometers available around the globe.

Installation

Basic Installation

For core functionality (spectrogram visualization, time/frequency slicing, light curves):

pip install pyCallisto

Installation with GOES Support

For GOES X-ray flux overlay capabilities:

pip install pyCallisto[goes]

Full Installation

For all features:

pip install pyCallisto[all]

⚠️ Important for Windows Users:

• Python 3.9-3.11 recommended for best compatibility
• Python 3.12+: Some dependencies may fail to compile. Use the basic installation and install optional dependencies individually if needed
• If you encounter compilation errors, consider using a conda environment or installing Visual Studio Build Tools

Alternative: Manual Dependency Installation

If you encounter issues with optional dependencies:

pip install pyCallisto
# Then install only what you need:
pip install sunpy  # For GOES features

Prerequisites and Dependencies

Core Dependencies (automatically installed)

• Python 3.7 or higher
• numpy
• matplotlib
• astropy
• beautifulsoup4
• scipy

Optional Dependencies (for GOES integration)

Install with pip install pyCallisto[goes]:

• sunpy
• drms
• zeep
• lxml
• h5netcdf
• h5py
• cdflib
• reproject
• mpl-animators

Features

• Spectrogram Visualization: Generate spectrograms from FITS files with customizable colormaps and parameters
• Time and Frequency Slicing: Extract specific time ranges and frequency bands from observations
• Light Curve Analysis: Create mean light curves by collapsing data along time axis
• Spectrum Analysis: Generate frequency spectra for specific time instances
• GOES Integration: Overlay GOES X-ray flux data on spectrograms for comprehensive solar event analysis
• Background Subtraction: Automated background estimation and removal
• Universal Plot: Combined visualization of spectrogram, light curve, and spectrum
• File Concatenation: Merge multiple FITS files along time axis for extended observations

Troubleshooting

Module Not Found Error

If you get ModuleNotFoundError: No module named 'pyCallisto':

1. Ensure you're using the correct Python environment
2. Reinstall: pip uninstall pyCallisto && pip install pyCallisto

Import Errors for GOES Features

If you get import errors when using spectrogramWithGOES():

pip install pyCallisto[goes]

Compilation Errors on Windows (Python 3.12+)

1. Use Python 3.9-3.11 instead, or
2. Install basic version and skip optional dependencies:

   pip install pyCallisto
   

Visual Studio Build Tools Error

If you see "Microsoft Visual C++ 14.0 or greater is required":

1. Download Build Tools for Visual Studio
2. Install "Desktop development with C++"
3. Retry installation

For more help, please open an issue on GitHub.

Quick Start

GOES Spectrogram Overlay

Overlay GOES X-ray flux data on your spectrogram:

import matplotlib.pyplot as plt

# After loading and processing your data use this function at the end

joined_bg_subtracted.spectrogramWithGOES(    #joined_bg_subtracted is just a variable name
    tstart="2024-07-16 13:05",
    tend="2024-07-16 14:34",
    satellite_number=16,    
    xtick=5,
    save_path='spectrogram_with_goes_overlay_2024-07-16.png'
)
plt.show()

Adjusting Spectrogram Parameters

Control dynamic range and background levels:

# Use blevel to adjust background level
# Use vmax to set maximum value for color scaling
obj.spectrogram(blevel=10, vmax=20) #Example

Contributors

Mr. Ravindra Pawase
Data Scientist
Cummins Inc.
Pune-411 004, India

Dr. K. Sasikumar Raja
Assistant Professor
Indian Institute of Astrophysics
II Block, Koramangala, Bengaluru-560 034, India

Mr. Mrutyunjaya Muduli
B.E. Computer Science and Engineering
Department of Computer Science and Engineering
HKBK College of Engineering
22/1, Opposite Manyata Tech Park, Nagavara, Bengaluru-560 045, India

Feedback

For feedback, queries, or feature requests, please contact:

• ravi.pawase@gmail.com
• sasikumarraja@gmail.com
• mudulimrutyunjaya42@gmail.com

Citation

If you find pyCallisto useful in your work, we appreciate acknowledgment. We recommend using the following citation:

"This work makes use of the pyCallisto library, which is available at https://github.com/ravipawase/pyCallisto"