Astronomical Data Science and Machine Learning Toolkit
Project description
spacekit
Astronomical Data Science and Machine Learning Toolkit
Setup
Install with pip
# install extra deps for all non-pipeline tools (analysis, training, data viz)
$ pip install spacekit[x]
# for bare-minimum dependencies (STScI/SDP pipeline operations):
$ 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
# specify the `env` option to limit tests to a specific 'skope'
# env options: "svm", "hstcal", "jwstcal"
$ pytest --env svm -m svm
$ pytest --env hstcal -m cal
$ pytest --env jwstcal -m jwst
Pre-Trained Neural Nets
JWST Calibration Pipeline Resource Prediction (JWST)
- Inference
spacekit.skopes.jwst.cal.predict
From the command line:
$ python -m spacekit.skopes.jwst.cal.predict /path/to/inputs
# optionally specify a Program ID
$ python -m spacekit.skopes.jwst.cal.predict /path/to/inputs --pid 1076
From python:
> from spacekit.skopes.jwst.cal.predict import JwstCalPredict
> input_path = "/path/to/level1/exposures"
# optionally specify a Program ID `pid` (default is None)
> jcal = JwstCalPredict(input_path, pid=1076)
> jcal.run_inference()
# estimations for L3 product memory footprints (GB) are stored in a dict under the `predictions` attribute. Ground truth values (latest actual footprints recorded) are shown as inline comments.
> jcal.predictions
{
'jw01076-o101-t1_nircam_clear-f212n': {'gbSize': 10.02}, # actual: 10.553384
'jw01076-o101-t1_nircam_clear-f210m': {'gbSize': 8.72}, # actual: 11.196752
'jw01076-o101-t1_nircam_clear-f356w': {'gbSize': 7.38}, # actual: 6.905737
}
# NOTE: the target number "t1" is not intended to match actual target IDs used by the pipeline.
Single Visit Mosaic Alignment (HST)
- 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)
- Training
spacekit.skopes.hst.cal.train
- Inference
spacekit.skopes.hst.cal.predict
Exoplanet Detection with time-series photometry (K2, TESS)
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
└── trained_networks
└── dashboard
└── cal
└── svm
└── datasets
└── _base.py
└── beam.py
└── meta.py
└── extractor
└── load.py
└── radio.py
└── scrape.py
└── generator
└── augment.py
└── draw.py
└── logger
└── log.py
└── preprocessor
└── encode.py
└── ingest.py
└── prep.py
└── scrub.py
└── transform.py
└── skopes
└── hst
└── cal
└── config.py
└── predict.py
└── train.py
└── validate.py
└── svm
└── corrupt.py
└── predict.py
└── prep.py
└── train.py
└── jwst
└── cal
└── config.py
└── predict.py
└── kepler
└── light_curves.py
└── pyproject.toml
└── setup.cfg
└── tox.ini
└── tests
└── docker
└── docs
└── scripts
└── LICENSE
└── README.md
└── CONTRIBUTING.md
└── CODE_OF_CONDUCT.md
└── MANIFEST.in
└── bandit.yml
└── readthedocs.yaml
└── conftest.py
└── CHANGES.rst
/\ _ _ _ *
/\_/\_____/ \__| |_____| |_________________________| |___________________*___
[===] / /\ \ | | _ | _ | _ \/ __/ -__| \| \_ _/ _ \ \_/ | * _/| | |
\./ /_/ \_\|_| ___|_| |_|__/\_\ \ \____|_|\__| \__/__/\_\___/|_|\_\|_|_|
| / |___/
|/
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