Release 1.1.0 · spacetelescope/spacekit

new features

builder.trained_networks jwst_cal.zip includes updated (v2) img3_reg and new spec3_reg predictive models for image and spectroscopic data [#58]
preprocessor.ingest.JwstCalIngest class and cmdline script for automated training data ingest [#57]
extractor.radio.JwstCalRadio subclass for scraping datasets from MAST using ASN metadata [#51]
extractor.scrape.FitsScraper.scrape_dataframe method added for scraping Fits data from dataframe [#52]

skopes.jwst.cal.predict generates predictions for spectrosopic datasets in addition to image data. This update also allows further customization of user arguments: [#58]
- obs to specify selection of a program ID + observation number
- input_path accepts either a directory (default) or a filename. If filename, the script will try to find any input exposures that belong to the same program and observation number as that file.
- sfx attribute is now customizable on instantiation of the class object (default is '_uncal.fits')
architect.builder.Builder.save_model uses preferred keras archive format by default [#50]
preprocessor.transform.SkyTransformer set offsets to 0 for gs/targ fiducial NaN values; custom filename for tx_file [#54]
preprocessor.prep.JwstCalPrep updates in preparation for preprocessing spectroscopic data [#55]
- revise spectroscopic data columns
- save tx_file name with "-{expmode}" to differentiate between image and spec normalization params
- rename target attributes: y_img_train, y_img_test to y_reg_train, y_reg_test
preprocessor.scrub.JwstCalScrubber more sophisticated exposure grouping and L3 product naming [#56]

preprocessor.encode.PairEncoder.handle_unknowns create single new encoding value per unidentified variable [#53]

Full Changelog: 1.0.1...1.1.0