reflspeckit 0.1.6

Utility package for working with spectral data cubes.

🛰️ reflspeckit

⚙️ A modern toolkit for working with any and all flavors of spectral data with a focus on applications for reflectance/emittance imaging spectroscopy

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🧠 What is reflspeckit?

reflspeckit is a lightweight, modular Python package designed to make analysis of spectral data cubes simple, flexible, and fun. Whether you're exploring planetary hyperspectral data, performing band analysis, or building your own spectral pipelines — this toolkit’s got you covered.

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⚙️ Package Structure

reflspeckit provides two primary classes for the analysis of spectral data plus a third class specialized for large datasets:

• 📄Spec1D - handles 1-dimensional, single spectrum data
• 📒Spec3D - handles 3-dimensional spectral image cubes
• 🗄️StreamingSpec3D - handles large image cubes using a streaming approach

Each class has equivalent methods, which are listed below:

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🧰 Available Methods

Method	Description
🚩outlier_removal	Removes anomalous data in the spectral domain
🔊noise_reduction	Provides filtering methods to smooth data in the spectral domain
📈polyfit	Performs a least squares polynomial fit over a specified spectral region

💡 Spectral Utilities

Various spectral utilities are available through the reflspeckit.utils subpackge.

Module	Description
get_nonzero	If you have an empty 3D image array with the first two dimensions being pixels and the third dimension of size N, and each pixel is filled in to a certain depth, M <= N, this function returns a 2D image array that picks out all the pixel values at position M.

More utilities coming soon! As a work through my Ph.D., I will add all the various utility functions I write for spectral data processing here!

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🚀 Quick Start

pip install reflspeckit

import reflspeckit as rsk

# Loading in a single spectrum
my_spectrum = rsk.Spec1d(spectrum_array, wavelength_array)
my_spectrum.remove_outliers()
myspectrum.noise_reduction(method="box_filter", filter_width=5)
print(myspectrum.filtered)  # Contains filtered spectrum

# Loading in a spectral image cube
my_cube = rsk.Spec1d(cube_array, wavelength_array)
my_cube.remove_outliers()
my_cube.noise_reduction(method="box_filter", filter_width=5)
print(myspectrum.cube)  # Sequentially replaces myspectrum.cube to save memory.

🔗 Links

• GitHub: https://github.com/z-vig/reflspeckit.git
• Docs: (coming soon!)