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nasa / prog_algs

The Prognostic Algorithm Package is a python framework for model-based prognostics (computation of remaining useful life) of engineering systems, and provides a set of algorithms for state estimation and prediction, including uncertainty propagation. The algorithms take as inputs prognostic models (from NASA's Prognostics Model Package), and perform estimation and prediction functions. The library allows the rapid development of prognostics solutions for given models of components and systems. Different algorithms can be easily swapped to do comparative studies and evaluations of different algorithms to select the best for the application at hand.
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Implement monotonicity for ToEPredictionProfile #184

Closed lawrence-hwang closed 2 years ago

codecov-commenter commented 2 years ago

Codecov Report

Merging #184 (2307074) into dev (4a6f70f) will increase coverage by 0.14%. The diff coverage is 100.00%.

@@            Coverage Diff             @@
##              dev     #184      +/-   ##
==========================================
+ Coverage   90.47%   90.61%   +0.14%     
==========================================
  Files          52       52              
  Lines        3211     3260      +49     
==========================================
+ Hits         2905     2954      +49     
  Misses        306      306
Impacted Files Coverage Δ
src/prog_algs/predictors/prediction.py 92.30% <ø> (ø)
src/prog_algs/metrics/__init__.py 100.00% <100.00%> (ø)
src/prog_algs/metrics/toe_profile_metrics.py 92.85% <100.00%> (+2.15%) :arrow_up:
src/prog_algs/predictors/toe_prediction_profile.py 92.59% <100.00%> (+0.43%) :arrow_up:
...og_algs/uncertain_data/multivariate_normal_dist.py 96.22% <100.00%> (ø)
src/prog_algs/uncertain_data/scalar_data.py 96.36% <100.00%> (ø)
src/prog_algs/uncertain_data/unweighted_samples.py 91.22% <100.00%> (ø)
tests/test_metrics.py 91.93% <100.00%> (+0.84%) :arrow_up:

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