spacekit

PyPi Machine Learning Utility Package for Astrophysical Data Science

spacekit
└── spacekit_pkg
    └── __init__.py
    └── analyzer.py
    └── builder.py
    └── computer.py
    └── radio.py
    └── transformer.py
└── setup.py
└── tests
└── LICENSE
└── README.md
  • Radio: downloading data from MAST s3 bucket on AWS

    • mast_aws: downlaods fits files for list of kepler/TESS targets

  • Analyzer: flux-timeseries signal analysis

    • atomic_vector_plotter: Plots scatter and line plots of time series signal values.

    • make_specgram: generate and save spectographs of flux signal frequencies

    • planet_hunter: calculate period, plot folded lightcurve from .fits files

  • Transformer: tools for converting and preprocessing signals as numpy arrays

    • hypersonic_pliers:

    • thermo_fusion_chisel:

    • babel_fish_dispenser: adds a 1D uniform noise filter using timesteps

    • fast_fourier: fast fourier transform utility function

  • Builder: building and fitting convolutional neural networks

    • build_cnn: builds keras 1D CNN architecture

    • fit_cnn: trains keras CNN

  • Computer: gets model predictions and evaluates metrics

    • get_preds

    • fnfp

    • keras_history

    • roc_plots

    • compute

spacekit.Radio()

downloading data from MAST s3 bucket on AWS

mast_aws()

spacekit.Analyzer()

flux-timeseries signal analysis

atomic_vector_plotter

Plots scatter and line plots of time series signal values.

make_specgram

generate and save spectographs of flux signal frequencies

planet_hunter

calculates period and plots folded light curve from single or multiple .fits files

spacekit.Transformer()

tools for converting and preprocessing signals as numpy arrays

hypersonic_pliers

thermo_fusion_chisel

babel_fish_dispenser

fast_fourier

spacekit.Builder()

building and fitting convolutional neural networks

build_cnn

fit_cnn

spacekit.Computer()

gets model predictions and evaluates metrics

get_preds

fnfp

keras_history

fusion_matrix

roc_plots

compute

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