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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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Add _type to Uncertain Data #191

Closed teubert closed 2 years ago

teubert commented 2 years ago

Add _type parameter to uncertain data.

Now state estimators will return uncertaindata that will return containers on call to .mean or .median

Should fix issue with LinearModels and State Estimators.

codecov-commenter commented 2 years ago

Codecov Report

Merging #191 (6d4598b) into dev (f489a10) will increase coverage by 0.14%. The diff coverage is 100.00%.

@@            Coverage Diff             @@
##              dev     #191      +/-   ##
==========================================
+ Coverage   90.53%   90.68%   +0.14%     
==========================================
  Files          52       52              
  Lines        3266     3317      +51     
==========================================
+ Hits         2957     3008      +51     
  Misses        309      309
Impacted Files Coverage Δ
src/prog_algs/predictors/monte_carlo.py 94.25% <100.00%> (ø)
src/prog_algs/predictors/unscented_transform.py 96.06% <100.00%> (ø)
src/prog_algs/state_estimators/kalman_filter.py 95.16% <100.00%> (ø)
src/prog_algs/state_estimators/particle_filter.py 95.29% <100.00%> (ø)
...g_algs/state_estimators/unscented_kalman_filter.py 98.36% <100.00%> (ø)
...og_algs/uncertain_data/multivariate_normal_dist.py 96.42% <100.00%> (+0.20%) :arrow_up:
src/prog_algs/uncertain_data/scalar_data.py 96.55% <100.00%> (+0.18%) :arrow_up:
src/prog_algs/uncertain_data/uncertain_data.py 100.00% <100.00%> (ø)
src/prog_algs/uncertain_data/unweighted_samples.py 91.45% <100.00%> (+0.22%) :arrow_up:
tests/test_state_estimators.py 94.70% <100.00%> (+0.75%) :arrow_up:

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