spacekit 1.0.0

Astronomical Data Science and Machine Learning Toolkit

spacekit

Astronomical Data Science and Machine Learning Toolkit

Setup

Install with pip

# install full deps for using analysis / data viz / non-pipeline tools
$ pip install spacekit[x]

# for minimal STScI pipeline operations (skopes module) install:
$ pip install spacekit

Install from source

$ git clone https://github.com/spacetelescope/spacekit
$ cd spacekit
$ pip install -e .[x]

Testing

See tox.ini for a list of test suite markers.

# run all tests
$ pytest

# some tests, like the `scan` module rely on the test `env` option 
$ pytest --env svm -m scan
$ pytest --env cal -m scan

Pre-Trained Neural Nets

JWST Calibration Pipeline Resource Prediction (JWST)

JWST CAL Docs

• Inference spacekit.skopes.jwst.cal.predict

Single Visit Mosaic Alignment (HST)

SVM Docs

• Preprocessing: spacekit.skopes.hst.svm.prep
• Predict Image Alignments: spacekit.skopes.hst.svm.predict
• Train Ensemble Classifier: spacekit.skopes.hst.svm.train
• Generate synthetic misalignments†: spacekit.skopes.hst.svm.corrupt

† requires Drizzlepac

HST Calibration Pipeline Resource Prediction (HST)

HST CAL Docs

• Training spacekit.skopes.hst.cal.train
• Inference spacekit.skopes.hst.cal.predict

Exoplanet Detection with time-series photometry (K2, TESS)

K2 Docs

• spacekit.skopes.kepler.light_curves

Customizable Model Building Classes

Build, train and experiment with multiple model iterations using the builder.architect.Builder classes

Example: Build and train an MLP and 3D CNN ensemble network

• continuous/encoded data for the multi-layer perceptron
• 3 RGB image "frames" per image input for the CNN
• Stack mixed inputs and use the outputs of MLP and CNN as inputs for the final ensemble model

ens = BuilderEnsemble(XTR, YTR, XTS, YTS, name="svm_ensemble")
ens.build()
ens.batch_fit()

# Save Training Metrics
outputs = f"data/{date_timestamp}"
com = ComputeBinary(builder=ens, res_path=f"{outputs}/results/test")
com.calculate_results()

Load and plot metrics to evaluate and compare model performance

Analyze and compare results across iterations from metrics saved using analyze.compute.Computer class objects. Almost all plots are made using plotly and are dynamic/interactive.

# Load data and metrics
from spacekit.analyzer.scan import MegaScanner
res = MegaScanner(perimeter="data/2022-*-*-*")
res._scan_results()

Preprocessing and Analysis Tools for Space Telescope Instrument Data

from spacekit.analyzer.explore import HstCalPlots
res.load_dataframe()
hst = HstCalPlots(res.df, group="instr")
hst.scatter

spacekit
└── spacekit
    └── analyzer
        └── compute.py
        └── explore.py
        └── scan.py
        └── track.py
    └── builder
        └── architect.py
        └── blueprints.py
    └── dashboard
    └── datasets
    └── extractor
        └── load.py
        └── radio.py
        └── scrape.py
    └── generator
        └── augment.py
        └── draw.py
    └── preprocessor
        └── encode.py
        └── scrub.py
        └── transform.py
    └── skopes
        └── hst
            └── cal
            └── svm
                └── corrupt.py
                └── predict.py
                └── prep.py
                └── train.py
        └── kepler
        └── trained_networks
└── setup.py
└── tests
└── docker
└── LICENSE
└── README.md

                       
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