Convert FITS to SGI

Scientific image format developed by NASA and the International Astronomical Union FITS Working Group (IAUFWG), first defined in 1981. Supports 8/16/32/64-bit integer and 32/64-bit floating-point pixel data with multi-extension architecture for storing multiple images and tables per file. Includes WCS (World Coordinate System) metadata for celestial coordinate mapping. The standard data format for astronomical observatories worldwide.

Raster image format developed by Silicon Graphics Inc. for IRIX workstations. Supports 8-bit and 16-bit per channel with optional RLE compression. Historically important in professional 3D graphics, scientific visualization, and film production.

• Data Types: Integer (8-64 bit) and floating-point (32-64 bit)
• Multi-Extension: Multiple images and binary tables per file
• WCS Metadata: World Coordinate System for celestial mapping
• Header Keywords: Extensive ASCII keyword-value metadata
• Dynamic Range: Full floating-point for scientific flux data
• Coordinate Systems: Equatorial, galactic, ecliptic reference frames

• 8-bit and 16-bit per channel depth
• RLE compression option
• 1-4 channel support (grayscale to RGBA)
• Big-endian byte order
• Simple flat file structure
• Historical SGI workstation standard

FITS data handling with astropy and Python:

from astropy.io import fits
import numpy as np

# Open FITS file with full header access
hdul = fits.open('observation.fits')
header = hdul[0].header  # WCS, telescope info
data = hdul[0].data       # Pixel array

# Access multi-extension data
for ext in hdul:
    print(ext.name, ext.data.shape if ext.data is not None else 'No data')

from astropy.io import fits
from PIL import Image
import numpy as np

hdul = fits.open('supernova.fits')
data = np.clip(hdul[0].data, 0, 255).astype('uint8')
img = Image.fromarray(data).convert('RGB')
img.save('supernova.sgi')

• Full floating-point dynamic range for scientific data
• Multi-extension architecture for complex datasets
• WCS metadata preserves celestial coordinate information
• Extensive header keywords for observation metadata
• Universal standard across all astronomical observatories
• Supported by every major astronomical software package

• 16-bit per channel depth
• Lossless RLE compression
• Simple and fast to process
• Historical scientific computing format
• Pillow native read/write
• Multi-channel support

• Not viewable in standard image viewers or browsers
• Requires specialized astronomical software
• Large file sizes for high-resolution observations
• Big-endian byte order can cause processing overhead
• Complex multi-extension structure

• Niche format with limited modern use
• Big-endian byte order can cause issues
• No animation or transparency standards
• Largely obsolete outside legacy workflows
• Limited modern tool support

• Space telescope observations (Hubble, JWST, Chandra)
• Ground observatory data (VLT, Keck, Gemini)
• Sky survey archives (SDSS, 2MASS, Gaia)
• Solar observation data (SDO, SOHO)
• Radio astronomy imaging (ALMA, VLA)

• Legacy SGI IRIX workstation compatibility
• Scientific visualization archives
• Film production legacy workflows
• 3D graphics texture storage
• Historical data preservation

• Scientific astronomical observations with precise flux data
• Multi-band imaging campaigns requiring coordinated datasets
• Archival storage with full observation metadata
• Pipeline processing requiring WCS coordinate transforms

• Legacy SGI workstation astronomy software compatibility
• Scientific visualization archives from observatories
• 16-bit depth preservation for astronomical data
• Historical astronomical data format migration