From 536d4620f9531776ee0967337b04afc062411fc8 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Tue, 10 May 2022 19:32:11 -0700 Subject: [PATCH 01/64] Function declaration for monotonicity --- src/prog_algs/metrics/toe_profile_metrics.py | 17 +++++++++++++++ src/prog_algs/predictors/prediction.py | 2 +- .../predictors/toe_prediction_profile.py | 21 ++++++++++++++++++- 3 files changed, 38 insertions(+), 2 deletions(-) diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index 794c9401..1ed4edbf 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -107,4 +107,21 @@ def cumulative_relative_accuracy(toe_profile : ToEPredictionProfile, ground_trut ra_sums[event] += value return {event:ra_sum/len(toe_profile) for event, ra_sum in ra_sums.items()} +def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, float]: + """Calculate monotonicty for a prediction profile. + Given a prediction profile, for each prediction: go through all predicted states and compare those to the next one. + Calculates monotonicity for each prediction key using its associated mean value in UncertainData. + + monotonoicity = |Σsign(i+1 - i) / N-1| + Where N is number of measurements and sign indicates sign of calculation. + Coble, J., et. al. (2021). Identifying Optimal Prognostic Parameters from Data: A Genetic Algorithms Approach. Annual Conference of the PHM Society. + http://www.papers.phmsociety.org/index.php/phmconf/article/view/1404 + Baptistia, M., et. al. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Aritifical Intelligence, Volume 306. + https://www.sciencedirect.com/science/article/pii/S0004370222000078 + Args: + toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions + Returns: + float: Value between [0, 1] indicating monotonicity of a given event for the Prediction. + """ + pass diff --git a/src/prog_algs/predictors/prediction.py b/src/prog_algs/predictors/prediction.py index ff683393..45f47442 100644 --- a/src/prog_algs/predictors/prediction.py +++ b/src/prog_algs/predictors/prediction.py @@ -82,7 +82,7 @@ def monotonicity(self) -> Dict[str, float]: Args: None Returns: - float: Value between [0, 1] indicating monotonicity of a given event for the Prediction. + dict (str, float): Value between [0, 1] indicating monotonicity of a given event for the Prediction. """ # Collect and organize mean values for each event by_event = defaultdict(list) diff --git a/src/prog_algs/predictors/toe_prediction_profile.py b/src/prog_algs/predictors/toe_prediction_profile.py index 95b034e4..d358d1cc 100644 --- a/src/prog_algs/predictors/toe_prediction_profile.py +++ b/src/prog_algs/predictors/toe_prediction_profile.py @@ -86,7 +86,6 @@ def prognostic_horizon(self, criteria_eqn, ground_truth, **kwargs) -> Dict[str, from ..metrics import prognostic_horizon return prognostic_horizon(self, criteria_eqn, ground_truth, **kwargs) - def cumulative_relative_accuracy(self, ground_truth, **kwargs) -> Dict[str, float]: """ Compute cumulative relative accuracy for a given profile, defined as the normalized sum of relative prediction accuracies at specific time instances. @@ -102,6 +101,26 @@ def cumulative_relative_accuracy(self, ground_truth, **kwargs) -> Dict[str, floa """ from ..metrics import cumulative_relative_accuracy return cumulative_relative_accuracy(self, ground_truth, **kwargs) + + def monotonicity(self, **kwargs) -> Dict[str, float]: + """Calculate monotonicty for a prediction profile. + Given a prediction profile, for each prediction: go through all predicted states and compare those to the next one. + Calculates monotonicity for each prediction key using its associated mean value in UncertainData. + + monotonoicity = |Σsign(i+1 - i) / N-1| + Where N is number of measurements and sign indicates sign of calculation. + Coble, J., et. al. (2021). Identifying Optimal Prognostic Parameters from Data: A Genetic Algorithms Approach. Annual Conference of the PHM Society. + http://www.papers.phmsociety.org/index.php/phmconf/article/view/1404 + Baptistia, M., et. al. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Aritifical Intelligence, Volume 306. + https://www.sciencedirect.com/science/article/pii/S0004370222000078 + + Args: + toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions + Returns: + float: Value between [0, 1] indicating monotonicity of a given event for the Prediction. + """ + from ..metrics import monotonicity + return monotonicity(self, **kwargs) def plot(self, ground_truth : dict = None , alpha : float = None, show : bool = True) -> dict: # use ground truth, alpha if given, """Produce an alpha-beta plot depicting the TtE distribution by time of prediction. From 202b2a4fa2a4fa78b2a09fc7577c17383d34a754 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Tue, 10 May 2022 19:39:25 -0700 Subject: [PATCH 02/64] Add testing structure + init metrics import --- src/prog_algs/metrics/__init__.py | 4 ++-- src/prog_algs/metrics/toe_profile_metrics.py | 4 +++- tests/test_metrics.py | 13 ++++++++++++- 3 files changed, 17 insertions(+), 4 deletions(-) diff --git a/src/prog_algs/metrics/__init__.py b/src/prog_algs/metrics/__init__.py index eb52dd7a..4c39fe28 100644 --- a/src/prog_algs/metrics/__init__.py +++ b/src/prog_algs/metrics/__init__.py @@ -3,6 +3,6 @@ from . import samples from .toe_metrics import prob_success from .uncertain_data_metrics import calc_metrics -from .toe_profile_metrics import alpha_lambda, prognostic_horizon, cumulative_relative_accuracy +from .toe_profile_metrics import alpha_lambda, prognostic_horizon, cumulative_relative_accuracy, monotonicity -__all__ = ['alpha_lambda', 'calc_metrics', 'prob_success', 'prognostic_horizon', 'cumulative_relative_accuracy'] +__all__ = ['alpha_lambda', 'calc_metrics', 'prob_success', 'prognostic_horizon', 'cumulative_relative_accuracy', 'monotonicity'] diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index 1ed4edbf..9d8b2fe8 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -124,4 +124,6 @@ def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, floa Returns: float: Value between [0, 1] indicating monotonicity of a given event for the Prediction. """ - pass + for k,v in toe_profile.items(): + print(k,v) + diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 2b87cc93..3406e0a1 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -466,7 +466,18 @@ def test_toe_profile_cumulative_relative_accuracy(self): with self.assertRaises(TypeError): GROUND_TRUTH = [] raise_error = profile.cumulative_relative_accuracy(GROUND_TRUTH) - + + def test_toe_profile_monotonicity(self): + from prog_algs.predictors import ToEPredictionProfile + profile = ToEPredictionProfile() # Empty profile + # Loading profile + for i in range(10): + data = [{'a': j, 'b': j -1 , 'c': (j-4.5) * 2 + 4.5} for j in range(i, i+20)] + profile.add_prediction( + 10-i, # Time (reverse so data is decreasing) + UnweightedSamples(data) # ToE Prediction + ) + profile.monotonicity() # This allows the module to be executed directly From 802a0669cf6101aa8ccf71b8b3d0a10f85a8a640 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Tue, 10 May 2022 20:18:24 -0700 Subject: [PATCH 03/64] Update docs + implementation --- src/prog_algs/metrics/toe_profile_metrics.py | 24 +++++++++++++++---- .../predictors/toe_prediction_profile.py | 2 +- tests/test_metrics.py | 2 +- 3 files changed, 21 insertions(+), 7 deletions(-) diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index 9d8b2fe8..ee890596 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -3,6 +3,7 @@ """ This file includes functions for calculating metrics given a Time of Event (ToE) profile (i.e., ToE's calculated at different times of prediction resulting from running prognostics multiple times, e.g., on playback data). The metrics calculated here are specific to multiple ToE estimates (e.g. alpha-lambda metric) """ +from numpy import sign from collections import defaultdict from typing import Callable from ..predictors import ToEPredictionProfile @@ -107,7 +108,7 @@ def cumulative_relative_accuracy(toe_profile : ToEPredictionProfile, ground_trut ra_sums[event] += value return {event:ra_sum/len(toe_profile) for event, ra_sum in ra_sums.items()} -def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, float]: +def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, dict]: """Calculate monotonicty for a prediction profile. Given a prediction profile, for each prediction: go through all predicted states and compare those to the next one. Calculates monotonicity for each prediction key using its associated mean value in UncertainData. @@ -122,8 +123,21 @@ def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, floa Args: toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions Returns: - float: Value between [0, 1] indicating monotonicity of a given event for the Prediction. + dict (str, dict): Dictionary where keys represent a profile and dict is a subdictionary representing an event and its respective monotonicitiy value between [0, 1]. """ - for k,v in toe_profile.items(): - print(k,v) - + result = dict() + for k,v in toe_profile.items(): # str, UnweightedSamples + # Collect and organize mean values for each event in the individual prediction v + by_event = defaultdict(list) + for subdict in v: + for event,value in subdict.items(): + by_event[event].append(value) + # For each event of this prediction v, calculate monotonicity using formula + sub_result = {} + for key,l in by_event.items(): + mono_sum = [] + for i in range(len(l)-1): + mono_sum.append(sign(l[i+1] - l[i])) + sub_result[key] = abs(sum(mono_sum) / (len(l)-1)) + result[k] = sub_result + return result diff --git a/src/prog_algs/predictors/toe_prediction_profile.py b/src/prog_algs/predictors/toe_prediction_profile.py index d358d1cc..3bbc0826 100644 --- a/src/prog_algs/predictors/toe_prediction_profile.py +++ b/src/prog_algs/predictors/toe_prediction_profile.py @@ -117,7 +117,7 @@ def monotonicity(self, **kwargs) -> Dict[str, float]: Args: toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions Returns: - float: Value between [0, 1] indicating monotonicity of a given event for the Prediction. + dict (str, dict): Dictionary where keys represent a profile and dict is a subdictionary representing an event and its respective monotonicitiy value between [0, 1]. """ from ..metrics import monotonicity return monotonicity(self, **kwargs) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 3406e0a1..8c665251 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -477,7 +477,7 @@ def test_toe_profile_monotonicity(self): 10-i, # Time (reverse so data is decreasing) UnweightedSamples(data) # ToE Prediction ) - profile.monotonicity() + print(profile.monotonicity()) # This allows the module to be executed directly From e0d6ed216c545d2055bbffbfdd82b572a6ea16bc Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Tue, 10 May 2022 20:35:03 -0700 Subject: [PATCH 04/64] Testing for UnweightedSamples Ask about data type, currently iterating because testing with UnweightedSamples --- tests/test_metrics.py | 47 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 44 insertions(+), 3 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 8c665251..45b65527 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -469,15 +469,56 @@ def test_toe_profile_cumulative_relative_accuracy(self): def test_toe_profile_monotonicity(self): from prog_algs.predictors import ToEPredictionProfile + + # Test monotonically increasing and decreasing profile = ToEPredictionProfile() # Empty profile - # Loading profile for i in range(10): - data = [{'a': j, 'b': j -1 , 'c': (j-4.5) * 2 + 4.5} for j in range(i, i+20)] + data = [{'a': 1+i/10, 'b': 2-i/5} for i in range(10)] + profile.add_prediction( + 10-i, # Time (reverse so data is decreasing) + UnweightedSamples(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 1.0}, 2: {'a': 1.0, 'b': 1.0}, 3: {'a': 1.0, 'b': 1.0}, 4: {'a': 1.0, 'b': 1.0}, 5: {'a': 1.0, 'b': 1.0}, 6: {'a': 1.0, 'b': 1.0}, 7: {'a': 1.0, 'b': 1.0}, 8: {'a': 1.0, 'b': 1.0}, 9: {'a': 1.0, 'b': 1.0}, 10: {'a': 1.0, 'b': 1.0}}) + + # Test no monotonicity + profile = ToEPredictionProfile() # Empty profile + for i in range(10): + data = [{'a': i*(i%2-1), 'b': i*(i%2-1)} for i in range(10)] + profile.add_prediction( + 10-i, # Time (reverse so data is decreasing) + UnweightedSamples(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.0, 'b': 0.0}, 2: {'a': 0.0, 'b': 0.0}, 3: {'a': 0.0, 'b': 0.0}, 4: {'a': 0.0, 'b': 0.0}, 5: {'a': 0.0, 'b': 0.0}, 6: {'a': 0.0, 'b': 0.0}, 7: {'a': 0.0, 'b': 0.0}, 8: {'a': 0.0, 'b': 0.0}, 9: {'a': 0.0, 'b': 0.0}, 10: {'a': 0.0, 'b': 0.0}}) + + # Test monotonicity between range [0,1] + profile = ToEPredictionProfile() # Empty profile + for i in range(10): + data = [{'a': i*(i%3-1), 'b': i*(i%3-1)} for i in range(10)] + profile.add_prediction( + 10-i, # Time (reverse so data is decreasing) + UnweightedSamples(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 2: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 3: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 4: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 5: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 6: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 7: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 8: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 9: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 10: {'a': 0.2222222222222222, 'b': 0.2222222222222222}}) + + # Test mixed + profile = ToEPredictionProfile() # Empty profile + for i in range(10): + data = [{'a': i, 'b': i*(i%3+5)} for i in range(10)] profile.add_prediction( 10-i, # Time (reverse so data is decreasing) UnweightedSamples(data) # ToE Prediction ) - print(profile.monotonicity()) + self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 0.5555555555555556}, 2: {'a': 1.0, 'b': 0.5555555555555556}, 3: {'a': 1.0, 'b': 0.5555555555555556}, 4: {'a': 1.0, 'b': 0.5555555555555556}, 5: {'a': 1.0, 'b': 0.5555555555555556}, 6: {'a': 1.0, 'b': 0.5555555555555556}, 7: {'a': 1.0, 'b': 0.5555555555555556}, 8: {'a': 1.0, 'b': 0.5555555555555556}, 9: {'a': 1.0, 'b': 0.5555555555555556}, 10: {'a': 1.0, 'b': 0.5555555555555556}}) + + # # Test ScalarData + # profile = ToEPredictionProfile() # Empty profile + # for i in range(10): + # data = [{'a': i, 'b': i*(i%3+5)} for i in range(10)] + # profile.add_prediction( + # 10-i, # Time (reverse so data is decreasing) + # ScalarData(data) # ToE Prediction + # ) + # self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 0.5555555555555556}, 2: {'a': 1.0, 'b': 0.5555555555555556}, 3: {'a': 1.0, 'b': 0.5555555555555556}, 4: {'a': 1.0, 'b': 0.5555555555555556}, 5: {'a': 1.0, 'b': 0.5555555555555556}, 6: {'a': 1.0, 'b': 0.5555555555555556}, 7: {'a': 1.0, 'b': 0.5555555555555556}, 8: {'a': 1.0, 'b': 0.5555555555555556}, 9: {'a': 1.0, 'b': 0.5555555555555556}, 10: {'a': 1.0, 'b': 0.5555555555555556}}) # This allows the module to be executed directly From f07e4c904dfdbb6000aba1352d71c0b6a57c4987 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Wed, 11 May 2022 10:57:28 -0700 Subject: [PATCH 05/64] Reworked implementation + broken tests --- src/prog_algs/metrics/toe_profile_metrics.py | 19 +++++------ tests/test_metrics.py | 36 ++++++++++---------- 2 files changed, 26 insertions(+), 29 deletions(-) diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index ee890596..427c51a7 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -126,18 +126,15 @@ def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, dict dict (str, dict): Dictionary where keys represent a profile and dict is a subdictionary representing an event and its respective monotonicitiy value between [0, 1]. """ result = dict() + by_event = defaultdict(list) for k,v in toe_profile.items(): # str, UnweightedSamples # Collect and organize mean values for each event in the individual prediction v - by_event = defaultdict(list) - for subdict in v: - for event,value in subdict.items(): - by_event[event].append(value) + for event,value in v.mean.items(): + by_event[event].append(value - k) # For each event of this prediction v, calculate monotonicity using formula - sub_result = {} - for key,l in by_event.items(): - mono_sum = [] - for i in range(len(l)-1): - mono_sum.append(sign(l[i+1] - l[i])) - sub_result[key] = abs(sum(mono_sum) / (len(l)-1)) - result[k] = sub_result + for key,l in by_event.items(): + mono_sum = [] + for i in range(len(l)-1): + mono_sum.append(sign(l[i+1] - l[i])) + result[key] = abs(sum(mono_sum) / (len(l)-1)) return result diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 45b65527..9a0b2271 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -490,25 +490,25 @@ def test_toe_profile_monotonicity(self): ) self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.0, 'b': 0.0}, 2: {'a': 0.0, 'b': 0.0}, 3: {'a': 0.0, 'b': 0.0}, 4: {'a': 0.0, 'b': 0.0}, 5: {'a': 0.0, 'b': 0.0}, 6: {'a': 0.0, 'b': 0.0}, 7: {'a': 0.0, 'b': 0.0}, 8: {'a': 0.0, 'b': 0.0}, 9: {'a': 0.0, 'b': 0.0}, 10: {'a': 0.0, 'b': 0.0}}) - # Test monotonicity between range [0,1] - profile = ToEPredictionProfile() # Empty profile - for i in range(10): - data = [{'a': i*(i%3-1), 'b': i*(i%3-1)} for i in range(10)] - profile.add_prediction( - 10-i, # Time (reverse so data is decreasing) - UnweightedSamples(data) # ToE Prediction - ) - self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 2: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 3: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 4: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 5: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 6: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 7: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 8: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 9: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 10: {'a': 0.2222222222222222, 'b': 0.2222222222222222}}) + # # Test monotonicity between range [0,1] + # profile = ToEPredictionProfile() # Empty profile + # for i in range(10): + # data = [{'a': i*(i%3-1), 'b': i*(i%3-1)} for i in range(10)] + # profile.add_prediction( + # 10-i, # Time (reverse so data is decreasing) + # UnweightedSamples(data) # ToE Prediction + # ) + # self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 2: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 3: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 4: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 5: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 6: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 7: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 8: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 9: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 10: {'a': 0.2222222222222222, 'b': 0.2222222222222222}}) - # Test mixed - profile = ToEPredictionProfile() # Empty profile - for i in range(10): - data = [{'a': i, 'b': i*(i%3+5)} for i in range(10)] - profile.add_prediction( - 10-i, # Time (reverse so data is decreasing) - UnweightedSamples(data) # ToE Prediction - ) - self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 0.5555555555555556}, 2: {'a': 1.0, 'b': 0.5555555555555556}, 3: {'a': 1.0, 'b': 0.5555555555555556}, 4: {'a': 1.0, 'b': 0.5555555555555556}, 5: {'a': 1.0, 'b': 0.5555555555555556}, 6: {'a': 1.0, 'b': 0.5555555555555556}, 7: {'a': 1.0, 'b': 0.5555555555555556}, 8: {'a': 1.0, 'b': 0.5555555555555556}, 9: {'a': 1.0, 'b': 0.5555555555555556}, 10: {'a': 1.0, 'b': 0.5555555555555556}}) + # # Test mixed + # profile = ToEPredictionProfile() # Empty profile + # for i in range(10): + # data = [{'a': i, 'b': i*(i%3+5)} for i in range(10)] + # profile.add_prediction( + # 10-i, # Time (reverse so data is decreasing) + # UnweightedSamples(data) # ToE Prediction + # ) + # self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 0.5555555555555556}, 2: {'a': 1.0, 'b': 0.5555555555555556}, 3: {'a': 1.0, 'b': 0.5555555555555556}, 4: {'a': 1.0, 'b': 0.5555555555555556}, 5: {'a': 1.0, 'b': 0.5555555555555556}, 6: {'a': 1.0, 'b': 0.5555555555555556}, 7: {'a': 1.0, 'b': 0.5555555555555556}, 8: {'a': 1.0, 'b': 0.5555555555555556}, 9: {'a': 1.0, 'b': 0.5555555555555556}, 10: {'a': 1.0, 'b': 0.5555555555555556}}) # # Test ScalarData # profile = ToEPredictionProfile() # Empty profile From fe91c6e8441da7ad8ffb92c5022e8221d6e0f585 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Wed, 11 May 2022 11:05:00 -0700 Subject: [PATCH 06/64] Update docs and first test --- src/prog_algs/metrics/toe_profile_metrics.py | 10 +++++----- tests/test_metrics.py | 18 +++++++++--------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index 427c51a7..ac931d15 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -108,9 +108,9 @@ def cumulative_relative_accuracy(toe_profile : ToEPredictionProfile, ground_trut ra_sums[event] += value return {event:ra_sum/len(toe_profile) for event, ra_sum in ra_sums.items()} -def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, dict]: +def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, float]: """Calculate monotonicty for a prediction profile. - Given a prediction profile, for each prediction: go through all predicted states and compare those to the next one. + Given a prediction profile, for each prediction: go through all predicted events and compare those to the next one. Calculates monotonicity for each prediction key using its associated mean value in UncertainData. monotonoicity = |Σsign(i+1 - i) / N-1| @@ -123,15 +123,15 @@ def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, dict Args: toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions Returns: - dict (str, dict): Dictionary where keys represent a profile and dict is a subdictionary representing an event and its respective monotonicitiy value between [0, 1]. + dict (str, float): Dictionary where keys represent an event and values are float representing its respective monotonicitiy value between [0, 1]. """ result = dict() by_event = defaultdict(list) - for k,v in toe_profile.items(): # str, UnweightedSamples + for k,v in toe_profile.items(): # Collect and organize mean values for each event in the individual prediction v for event,value in v.mean.items(): by_event[event].append(value - k) - # For each event of this prediction v, calculate monotonicity using formula + # For each event of this prediction v, calculate monotonicity using formula for key,l in by_event.items(): mono_sum = [] for i in range(len(l)-1): diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 9a0b2271..e8ac1cde 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -478,17 +478,17 @@ def test_toe_profile_monotonicity(self): 10-i, # Time (reverse so data is decreasing) UnweightedSamples(data) # ToE Prediction ) - self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 1.0}, 2: {'a': 1.0, 'b': 1.0}, 3: {'a': 1.0, 'b': 1.0}, 4: {'a': 1.0, 'b': 1.0}, 5: {'a': 1.0, 'b': 1.0}, 6: {'a': 1.0, 'b': 1.0}, 7: {'a': 1.0, 'b': 1.0}, 8: {'a': 1.0, 'b': 1.0}, 9: {'a': 1.0, 'b': 1.0}, 10: {'a': 1.0, 'b': 1.0}}) + self.assertDictEqual(profile.monotonicity(), {'a': 1.0, 'b': 1.0}) # Test no monotonicity - profile = ToEPredictionProfile() # Empty profile - for i in range(10): - data = [{'a': i*(i%2-1), 'b': i*(i%2-1)} for i in range(10)] - profile.add_prediction( - 10-i, # Time (reverse so data is decreasing) - UnweightedSamples(data) # ToE Prediction - ) - self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.0, 'b': 0.0}, 2: {'a': 0.0, 'b': 0.0}, 3: {'a': 0.0, 'b': 0.0}, 4: {'a': 0.0, 'b': 0.0}, 5: {'a': 0.0, 'b': 0.0}, 6: {'a': 0.0, 'b': 0.0}, 7: {'a': 0.0, 'b': 0.0}, 8: {'a': 0.0, 'b': 0.0}, 9: {'a': 0.0, 'b': 0.0}, 10: {'a': 0.0, 'b': 0.0}}) + # profile = ToEPredictionProfile() # Empty profile + # for i in range(10): + # data = [{'a': i*(i%2-1), 'b': i*(i%2-1)} for i in range(10)] + # profile.add_prediction( + # 10-i, # Time (reverse so data is decreasing) + # UnweightedSamples(data) # ToE Prediction + # ) + # self.assertDictEqual(profile.monotonicity(), {'a': 1.0, 'b': 1.0}) # # Test monotonicity between range [0,1] # profile = ToEPredictionProfile() # Empty profile From a4abf070d4a1707742f630a46cf09ed531d7fc1e Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Wed, 11 May 2022 11:11:49 -0700 Subject: [PATCH 07/64] Failing zero mono - review implementation --- tests/test_metrics.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index e8ac1cde..683a2ca5 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -481,14 +481,14 @@ def test_toe_profile_monotonicity(self): self.assertDictEqual(profile.monotonicity(), {'a': 1.0, 'b': 1.0}) # Test no monotonicity - # profile = ToEPredictionProfile() # Empty profile - # for i in range(10): - # data = [{'a': i*(i%2-1), 'b': i*(i%2-1)} for i in range(10)] - # profile.add_prediction( - # 10-i, # Time (reverse so data is decreasing) - # UnweightedSamples(data) # ToE Prediction - # ) - # self.assertDictEqual(profile.monotonicity(), {'a': 1.0, 'b': 1.0}) + profile = ToEPredictionProfile() # Empty profile + for i in range(10): + data = [{'a': i*(i%2-1), 'b': i*(i%2-1)} for i in range(10)] + profile.add_prediction( + 10-i, # Time (reverse so data is decreasing) + UnweightedSamples(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {'a': 0.0, 'b': 0.0}) # # Test monotonicity between range [0,1] # profile = ToEPredictionProfile() # Empty profile From dbef96c7473dbcb11713bc1f2112e9eba0e0b386 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Wed, 11 May 2022 11:32:03 -0700 Subject: [PATCH 08/64] Variable renaming for clarity --- src/prog_algs/metrics/toe_profile_metrics.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index ac931d15..35a97700 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -127,10 +127,10 @@ def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, floa """ result = dict() by_event = defaultdict(list) - for k,v in toe_profile.items(): + for time,unvertaindata in toe_profile.items(): # Collect and organize mean values for each event in the individual prediction v - for event,value in v.mean.items(): - by_event[event].append(value - k) + for event,value in unvertaindata.mean.items(): + by_event[event].append(value - time) # For each event of this prediction v, calculate monotonicity using formula for key,l in by_event.items(): mono_sum = [] From e8d13ceaa0e61e5afb03b81a597fff46b5b03a8c Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 11 May 2022 13:44:23 -0700 Subject: [PATCH 09/64] Updated test --- tests/test_metrics.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 683a2ca5..a68b21b8 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -454,10 +454,10 @@ def test_toe_profile_cumulative_relative_accuracy(self): ) # Test positive floats ground truth GROUND_TRUTH = {'a': 9.0, 'b': 8.0, 'c': 18.0} - self.assertEquals(profile.cumulative_relative_accuracy(GROUND_TRUTH), {'a': 0.4444444444444445, 'b': 0.375, 'c': 0.6388888888888888}) + self.assertEqual(profile.cumulative_relative_accuracy(GROUND_TRUTH), {'a': 0.4444444444444445, 'b': 0.375, 'c': 0.6388888888888888}) # Test negative floats ground truth GROUND_TRUTH = {'a': -9.0, 'b': -8.0, 'c': -18.0} - self.assertEquals(profile.cumulative_relative_accuracy(GROUND_TRUTH), {'a': 3.555555555555556, 'b': 3.625, 'c': 3.305555555555556}) + self.assertEqual(profile.cumulative_relative_accuracy(GROUND_TRUTH), {'a': 3.555555555555556, 'b': 3.625, 'c': 3.305555555555556}) # Test ground truth values of 0; already caught by relative_accuracy with self.assertRaises(ZeroDivisionError): GROUND_TRUTH = {'a': 0, 'b': 0, 'c': 0} @@ -482,10 +482,10 @@ def test_toe_profile_monotonicity(self): # Test no monotonicity profile = ToEPredictionProfile() # Empty profile - for i in range(10): - data = [{'a': i*(i%2-1), 'b': i*(i%2-1)} for i in range(10)] + for i in range(0,11): + data = [{'a': i + i*(i%2-0.5), 'b': i + i*(i%2-0.5)}]*10 profile.add_prediction( - 10-i, # Time (reverse so data is decreasing) + i, # Time (reverse so data is decreasing) UnweightedSamples(data) # ToE Prediction ) self.assertDictEqual(profile.monotonicity(), {'a': 0.0, 'b': 0.0}) From ae5b5352d542279c6b432cdbed3f63bbc6275c78 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Thu, 12 May 2022 10:12:05 -0700 Subject: [PATCH 10/64] Quick test suggestions --- tests/test_metrics.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index a68b21b8..c41878d2 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -475,7 +475,7 @@ def test_toe_profile_monotonicity(self): for i in range(10): data = [{'a': 1+i/10, 'b': 2-i/5} for i in range(10)] profile.add_prediction( - 10-i, # Time (reverse so data is decreasing) + i, # Time (reverse so data is decreasing) UnweightedSamples(data) # ToE Prediction ) self.assertDictEqual(profile.monotonicity(), {'a': 1.0, 'b': 1.0}) @@ -490,15 +490,15 @@ def test_toe_profile_monotonicity(self): ) self.assertDictEqual(profile.monotonicity(), {'a': 0.0, 'b': 0.0}) - # # Test monotonicity between range [0,1] - # profile = ToEPredictionProfile() # Empty profile - # for i in range(10): - # data = [{'a': i*(i%3-1), 'b': i*(i%3-1)} for i in range(10)] - # profile.add_prediction( - # 10-i, # Time (reverse so data is decreasing) - # UnweightedSamples(data) # ToE Prediction - # ) - # self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 2: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 3: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 4: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 5: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 6: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 7: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 8: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 9: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 10: {'a': 0.2222222222222222, 'b': 0.2222222222222222}}) + # Test monotonicity between range [0,1] + profile = ToEPredictionProfile() # Empty profile + for i in range(10): + data = [{'a': i*(i%3), 'b': i*(i%3)} for i in range(10)] + profile.add_prediction( + i, # Time (reverse so data is decreasing) + UnweightedSamples(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 2: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 3: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 4: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 5: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 6: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 7: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 8: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 9: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 10: {'a': 0.2222222222222222, 'b': 0.2222222222222222}}) # # Test mixed # profile = ToEPredictionProfile() # Empty profile From d9c43562ee189bd3e99584186bd1827f33cac6d6 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Thu, 12 May 2022 10:14:03 -0700 Subject: [PATCH 11/64] Updating tests --- tests/test_metrics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index c41878d2..2e2cc204 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -493,12 +493,12 @@ def test_toe_profile_monotonicity(self): # Test monotonicity between range [0,1] profile = ToEPredictionProfile() # Empty profile for i in range(10): - data = [{'a': i*(i%3), 'b': i*(i%3)} for i in range(10)] + data = [{'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)} for i in range(10)] profile.add_prediction( i, # Time (reverse so data is decreasing) UnweightedSamples(data) # ToE Prediction ) - self.assertDictEqual(profile.monotonicity(), {1: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 2: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 3: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 4: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 5: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 6: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 7: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 8: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 9: {'a': 0.2222222222222222, 'b': 0.2222222222222222}, 10: {'a': 0.2222222222222222, 'b': 0.2222222222222222}}) + self.assertDictEqual(profile.monotonicity(), {'a': 1.0, 'b': 1.0}) # # Test mixed # profile = ToEPredictionProfile() # Empty profile From 268d8d3ce8ae6445e6f9961df63105e7ce4eab67 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 12 May 2022 10:22:08 -0700 Subject: [PATCH 12/64] Fixed monotonic test issue --- tests/test_metrics.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 2e2cc204..28a6ec3c 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -492,13 +492,13 @@ def test_toe_profile_monotonicity(self): # Test monotonicity between range [0,1] profile = ToEPredictionProfile() # Empty profile - for i in range(10): - data = [{'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)} for i in range(10)] + for i in range(11): + data = [{'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)}] * 10 profile.add_prediction( i, # Time (reverse so data is decreasing) UnweightedSamples(data) # ToE Prediction ) - self.assertDictEqual(profile.monotonicity(), {'a': 1.0, 'b': 1.0}) + self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.4}) # # Test mixed # profile = ToEPredictionProfile() # Empty profile From fbbcf9a60a0a637f3baf8014176a35f663a50b8c Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Thu, 12 May 2022 11:31:03 -0700 Subject: [PATCH 13/64] Test mixed monotonicity --- tests/test_metrics.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 28a6ec3c..b527a9f2 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -500,15 +500,15 @@ def test_toe_profile_monotonicity(self): ) self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.4}) - # # Test mixed - # profile = ToEPredictionProfile() # Empty profile - # for i in range(10): - # data = [{'a': i, 'b': i*(i%3+5)} for i in range(10)] - # profile.add_prediction( - # 10-i, # Time (reverse so data is decreasing) - # UnweightedSamples(data) # ToE Prediction - # ) - # self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 0.5555555555555556}, 2: {'a': 1.0, 'b': 0.5555555555555556}, 3: {'a': 1.0, 'b': 0.5555555555555556}, 4: {'a': 1.0, 'b': 0.5555555555555556}, 5: {'a': 1.0, 'b': 0.5555555555555556}, 6: {'a': 1.0, 'b': 0.5555555555555556}, 7: {'a': 1.0, 'b': 0.5555555555555556}, 8: {'a': 1.0, 'b': 0.5555555555555556}, 9: {'a': 1.0, 'b': 0.5555555555555556}, 10: {'a': 1.0, 'b': 0.5555555555555556}}) + # Test mixed + profile = ToEPredictionProfile() # Empty profile + for i in range(11): + data = [{'a': i+i*(i%3-0.5), 'b': i + i*(i%2-0.5), 'c': 1+i/10}] * 10 + profile.add_prediction( + i, # Time (reverse so data is decreasing) + UnweightedSamples(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.0, 'c': 1.0}) # # Test ScalarData # profile = ToEPredictionProfile() # Empty profile From 8b3f041839038b1eb74e02140d0379fb46d05218 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Thu, 12 May 2022 11:41:51 -0700 Subject: [PATCH 14/64] Typo fix + testing scalar - FAIL --- src/prog_algs/metrics/toe_profile_metrics.py | 4 ++-- tests/test_metrics.py | 18 +++++++++--------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index 35a97700..fc87547e 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -127,9 +127,9 @@ def monotonicity(toe_profile : ToEPredictionProfile, **kwargs) -> Dict[str, floa """ result = dict() by_event = defaultdict(list) - for time,unvertaindata in toe_profile.items(): + for time,uncertaindata in toe_profile.items(): # Collect and organize mean values for each event in the individual prediction v - for event,value in unvertaindata.mean.items(): + for event,value in uncertaindata.mean.items(): by_event[event].append(value - time) # For each event of this prediction v, calculate monotonicity using formula for key,l in by_event.items(): diff --git a/tests/test_metrics.py b/tests/test_metrics.py index b527a9f2..a56c0494 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -510,15 +510,15 @@ def test_toe_profile_monotonicity(self): ) self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.0, 'c': 1.0}) - # # Test ScalarData - # profile = ToEPredictionProfile() # Empty profile - # for i in range(10): - # data = [{'a': i, 'b': i*(i%3+5)} for i in range(10)] - # profile.add_prediction( - # 10-i, # Time (reverse so data is decreasing) - # ScalarData(data) # ToE Prediction - # ) - # self.assertDictEqual(profile.monotonicity(), {1: {'a': 1.0, 'b': 0.5555555555555556}, 2: {'a': 1.0, 'b': 0.5555555555555556}, 3: {'a': 1.0, 'b': 0.5555555555555556}, 4: {'a': 1.0, 'b': 0.5555555555555556}, 5: {'a': 1.0, 'b': 0.5555555555555556}, 6: {'a': 1.0, 'b': 0.5555555555555556}, 7: {'a': 1.0, 'b': 0.5555555555555556}, 8: {'a': 1.0, 'b': 0.5555555555555556}, 9: {'a': 1.0, 'b': 0.5555555555555556}, 10: {'a': 1.0, 'b': 0.5555555555555556}}) + # Test ScalarData + profile = ToEPredictionProfile() # Empty profile + for i in range(11): + data = [{'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)}] * 10 + profile.add_prediction( + i, # Time (reverse so data is decreasing) + ScalarData(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.4}) # This allows the module to be executed directly From c9f7e58ea28d027a8eb29c596c156a4a00d54a6e Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Thu, 12 May 2022 12:03:41 -0700 Subject: [PATCH 15/64] MVND profile test - PASS --- .../uncertain_data/unweighted_samples.py | 2 +- tests/test_metrics.py | 19 +++++++++++++++---- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/src/prog_algs/uncertain_data/unweighted_samples.py b/src/prog_algs/uncertain_data/unweighted_samples.py index 5ee02895..bd93a9c1 100644 --- a/src/prog_algs/uncertain_data/unweighted_samples.py +++ b/src/prog_algs/uncertain_data/unweighted_samples.py @@ -119,7 +119,7 @@ def median(self) -> array: return self[min_index] @property - def mean(self) -> array: + def mean(self) -> dict: mean = {} for key in self.keys(): values = array([x[key] for x in self.data if x is not None and x[key] is not None]) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index a56c0494..b469c3e1 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -510,15 +510,26 @@ def test_toe_profile_monotonicity(self): ) self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.0, 'c': 1.0}) - # Test ScalarData + # Test MultivariateNormalDist profile = ToEPredictionProfile() # Empty profile + covar = [[0.1, 0.01], [0.01, 0.1]] for i in range(11): - data = [{'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)}] * 10 + data = [{'a': i, 'b': i*(i%3+5)}] * 11 profile.add_prediction( i, # Time (reverse so data is decreasing) - ScalarData(data) # ToE Prediction + MultivariateNormalDist(data[i].keys(), data[i].values(), covar) # ToE Prediction ) - self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.4}) + self.assertDictEqual(profile.monotonicity(), {'a': 0.0, 'b': 0.5}) + + # # Test ScalarData + # profile = ToEPredictionProfile() # Empty profile + # for i in range(11): + # data = [{'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)}] * 10 + # profile.add_prediction( + # i, # Time (reverse so data is decreasing) + # ScalarData(data) # ToE Prediction + # ) + # self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.4}) # This allows the module to be executed directly From 2307074fe250e51f2a19a873ff91d6112c7b4453 Mon Sep 17 00:00:00 2001 From: Lawrence Hwang Date: Thu, 12 May 2022 12:55:44 -0700 Subject: [PATCH 16/64] ScalarData profile test - PASS --- .../uncertain_data/multivariate_normal_dist.py | 2 +- src/prog_algs/uncertain_data/scalar_data.py | 2 +- tests/test_metrics.py | 18 +++++++++--------- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/prog_algs/uncertain_data/multivariate_normal_dist.py b/src/prog_algs/uncertain_data/multivariate_normal_dist.py index b1216fb8..9aa03eff 100644 --- a/src/prog_algs/uncertain_data/multivariate_normal_dist.py +++ b/src/prog_algs/uncertain_data/multivariate_normal_dist.py @@ -69,7 +69,7 @@ def median(self) -> float: return self.mean @property - def mean(self) -> array: + def mean(self) -> dict: return {key: value for (key, value) in zip(self.__labels, self.__mean)} def __str__(self) -> str: diff --git a/src/prog_algs/uncertain_data/scalar_data.py b/src/prog_algs/uncertain_data/scalar_data.py index 57a5480b..e6c15d1e 100644 --- a/src/prog_algs/uncertain_data/scalar_data.py +++ b/src/prog_algs/uncertain_data/scalar_data.py @@ -55,7 +55,7 @@ def median(self) -> array: return self.mean @property - def mean(self) -> array: + def mean(self) -> dict: return self.__state @property diff --git a/tests/test_metrics.py b/tests/test_metrics.py index b469c3e1..560576a6 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -521,15 +521,15 @@ def test_toe_profile_monotonicity(self): ) self.assertDictEqual(profile.monotonicity(), {'a': 0.0, 'b': 0.5}) - # # Test ScalarData - # profile = ToEPredictionProfile() # Empty profile - # for i in range(11): - # data = [{'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)}] * 10 - # profile.add_prediction( - # i, # Time (reverse so data is decreasing) - # ScalarData(data) # ToE Prediction - # ) - # self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.4}) + # Test ScalarData + profile = ToEPredictionProfile() # Empty profile + for i in range(11): + data = {'a': i+i*(i%3-0.5), 'b': i+i*(i%3-0.5)} + profile.add_prediction( + i, # Time (reverse so data is decreasing) + ScalarData(data) # ToE Prediction + ) + self.assertDictEqual(profile.monotonicity(), {'a': 0.4, 'b': 0.4}) # This allows the module to be executed directly From f489a105c7c80a3af123f43eba3e79d387011329 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 16 May 2022 16:00:19 -0700 Subject: [PATCH 17/64] Fix bibtex --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9c7a14be..c39cca93 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ Use the following to cite this repository: month = May, year = 2022, version = {1.3.0}, - url = {https://github.com/nasa/prog_algs} + url = {https://github.com/nasa/prog\_algs} } ``` From aeb4931fe93053ab30fca21698ab4120accc3a5d Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 17 May 2022 20:29:30 -0700 Subject: [PATCH 18/64] fix type hint --- src/prog_algs/uncertain_data/scalar_data.py | 2 +- src/prog_algs/uncertain_data/uncertain_data.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/prog_algs/uncertain_data/scalar_data.py b/src/prog_algs/uncertain_data/scalar_data.py index e6edb863..a0cbd379 100644 --- a/src/prog_algs/uncertain_data/scalar_data.py +++ b/src/prog_algs/uncertain_data/scalar_data.py @@ -51,7 +51,7 @@ def __isub__(self, other : int) -> "UncertainData": return self @property - def median(self) -> array: + def median(self) -> dict: return self.mean @property diff --git a/src/prog_algs/uncertain_data/uncertain_data.py b/src/prog_algs/uncertain_data/uncertain_data.py index 78d140e0..522af6eb 100644 --- a/src/prog_algs/uncertain_data/uncertain_data.py +++ b/src/prog_algs/uncertain_data/uncertain_data.py @@ -30,7 +30,7 @@ def sample(self, nSamples : int = 1): @property @abstractproperty - def median(self) -> array: + def median(self) -> dict: """The median of the UncertainData distribution or samples Returns: @@ -42,7 +42,7 @@ def median(self) -> array: @property @abstractproperty - def mean(self) -> array: + def mean(self) -> dict: """The mean of the UncertainData distribution or samples Returns: From 3a0abdd3bad82f35f89c003fc1ba8b34b319584c Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 11 Jul 2022 14:54:19 -0700 Subject: [PATCH 19/64] More optimizations --- src/prog_algs/predictors/monte_carlo.py | 137 +++++++++--------- .../state_estimators/particle_filter.py | 3 +- tests/benchmark.py | 126 ++++++++++++++++ 3 files changed, 199 insertions(+), 67 deletions(-) create mode 100644 tests/benchmark.py diff --git a/src/prog_algs/predictors/monte_carlo.py b/src/prog_algs/predictors/monte_carlo.py index a18d77c7..d8996f51 100644 --- a/src/prog_algs/predictors/monte_carlo.py +++ b/src/prog_algs/predictors/monte_carlo.py @@ -49,11 +49,13 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs params = deepcopy(self.parameters) # copy parameters params.update(kwargs) # update for specific run + params['print'] = False + params['progress'] = False # Sample from state if n_samples specified or state is not UnweightedSamples - state = state.sample(params['n_samples']) + if not isinstance(state, UnweightedSamples) or len(state) != params['n_samples']: + state = state.sample(params['n_samples']) - ouput_eqn = self.model.output es_eqn = self.model.event_state tm_eqn = self.model.threshold_met simulate_to_threshold = self.model.simulate_to_threshold @@ -68,94 +70,97 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs # Perform prediction for x in state: - events_remaining = params['events'].copy() - first_output = ouput_eqn(x) + first_output = self.model.output(x) time_of_event = {} last_state = {} - times = [] - inputs = SimResult() - states = SimResult() - outputs = LazySimResult(fcn = ouput_eqn) - event_states = LazySimResult(fcn = es_eqn) t0 = params.get('t0', 0) if 'save_freq' in params and not isinstance(params['save_freq'], tuple): params['save_freq'] = (t0, params['save_freq']) - - # Non-vectorized prediction - while len(events_remaining) > 0: # Still events to predict - (t, u, xi, z, es) = simulate_to_threshold(future_loading_eqn, first_output, - **{**params, 'threshold_keys': events_remaining, 'print': False, 'progress': False, 't0': t0, 'x': x} # Merge then separate + if len(params['events']) == 0: # Predict to time + (times, inputs, states, outputs, event_states) = simulate_to_threshold(future_loading_eqn, + first_output, + threshold_keys = [], + t0 = t0, + x = x, + **params ) - if len(times) != 0: - # is not the first iteration, therefore the first saved state is when the last event - t.pop(0) - u.pop(0) - xi.pop(0) - z.pop(0) - es.pop(0) - - # Add results - times.extend(t) - inputs.extend(u) - states.extend(xi) - outputs.extend(z) - event_states.extend(es) - - # Get which event occurs - t_met = tm_eqn(states[-1]) - t_met = {key: t_met[key] for key in events_remaining} # Only look at remaining keys - try: - event = list(t_met.keys())[list(t_met.values()).index(True)] - except ValueError: - # no event has occured - for event in events_remaining: - time_of_event[event] = None - last_state[event] = None - break - - # An event has occured - time_of_event[event] = times[-1] - events_remaining.remove(event) # No longer an event to predect to - - # Remove last state (event) - t0 = times.pop() - inputs.pop() - x = states.pop() - last_state[event] = x.copy() - outputs.pop() - event_states.pop() + else: + events_remaining = params['events'].copy() + + times = [] + inputs = SimResult(_copy = False) + states = SimResult(_copy = False) + outputs = LazySimResult(fcn = self.model.output, _copy = False) + event_states = LazySimResult(fcn = es_eqn, _copy = False) + + # Non-vectorized prediction + while len(events_remaining) > 0: # Still events to predict + (t, u, xi, z, es) = simulate_to_threshold(future_loading_eqn, + first_output, + threshold_keys = events_remaining, + t0 = t0, + x = x, + **params + ) + + # Add results + times.extend(t) + inputs.extend(u) + states.extend(xi) + outputs.extend(z, _copy = False) + event_states.extend(es, _copy = False) + + # Get which event occurs + t_met = tm_eqn(states[-1]) + t_met = {key: t_met[key] for key in events_remaining} # Only look at remaining keys + + try: + event = list(t_met.keys())[list(t_met.values()).index(True)] + except ValueError: + # no event has occured - hit horizon + for event in events_remaining: + time_of_event[event] = None + last_state[event] = None + break + + # An event has occured + time_of_event[event] = times[-1] + events_remaining.remove(event) # No longer an event to predect to + + # Remove last state (event) + t0 = times.pop() + inputs.pop() + x = states.pop() + last_state[event] = x.copy() + outputs.pop() + event_states.pop() # Add to "all" structures - times_all.append(times) + if len(times) > len(times_all): # Keep longest + times_all = times inputs_all.append(inputs) states_all.append(states) outputs_all.append(outputs) event_states_all.append(event_states) time_of_event_all.append(time_of_event) last_states.append(last_state) - - # Return longest time array - times_length = [len(t) for t in times_all] - times_max_len = max(times_length) - times = times_all[times_length.index(times_max_len)] - - inputs_all = UnweightedSamplesPrediction(times, inputs_all) - states_all = UnweightedSamplesPrediction(times, states_all) - outputs_all = UnweightedSamplesPrediction(times, outputs_all) - event_states_all = UnweightedSamplesPrediction(times, event_states_all) + + inputs_all = UnweightedSamplesPrediction(times_all, inputs_all) + states_all = UnweightedSamplesPrediction(times_all, states_all) + outputs_all = UnweightedSamplesPrediction(times_all, outputs_all) + event_states_all = UnweightedSamplesPrediction(times_all, event_states_all) time_of_event = UnweightedSamples(time_of_event_all) # Transform final states: - last_states = { + time_of_event.final_state = { key: UnweightedSamples([sample[key] for sample in last_states], _type = self.model.StateContainer) for key in time_of_event.keys() } - time_of_event.final_state = last_states return PredictionResults( - times, + times_all, inputs_all, states_all, outputs_all, diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index b218c76d..429b3dc3 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -82,7 +82,8 @@ def __measure(x): samples = [random.normal(x[i], sd[i], self.parameters['num_particles']) for i in range(len(x))] else: raise ProgAlgTypeError("ProgAlgTypeError: x0 must be of type UncertainData or x0_uncertainty must be of type [dict, Number].") - self.particles = dict(zip(x0.keys(), samples)) + + self.particles = model.StateContainer(array(samples)) if 'R' in self.parameters: diff --git a/tests/benchmark.py b/tests/benchmark.py new file mode 100644 index 00000000..a9778489 --- /dev/null +++ b/tests/benchmark.py @@ -0,0 +1,126 @@ +# Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. + +import time +from io import StringIO +import sys + +FORMAT_STR = '{:40s}' +CLOCK = time.process_time + +if __name__ == "__main__": + print(FORMAT_STR.format('import main'), end='') + t = CLOCK() + from prog_algs import * + t2 = CLOCK() + print(t2-t) + + from prog_models.models import BatteryElectroChemEOD as Battery + def future_loading(t, x = None): + # Variable (piece-wise) future loading scheme + if (t < 600): + i = 2 + elif (t < 900): + i = 1 + elif (t < 1800): + i = 4 + elif (t < 3000): + i = 2 + else: + i = 3 + return {'i': i} + R_vars = { + 't': 2, + 'v': 0.02 + } + batt = Battery(measurement_noise = R_vars) + initial_state = batt.parameters['x0'] + + print('PF') + print(FORMAT_STR.format(' Initialize'), end='') + temp_out = StringIO() + sys.stdout = temp_out + sys.stderr = temp_out + t = CLOCK() + filt = state_estimators.ParticleFilter(batt, initial_state) + t2 = CLOCK() + sys.stdout = sys.__stdout__ + sys.stderr = sys.__stderr__ + print(t2-t) + + print(FORMAT_STR.format(' Step'), end='') + example_measurements = {'t': 32.2, 'v': 3.915} + t = 0.1 + t = CLOCK() + filt.estimate(t, future_loading(t), example_measurements) + t2 = CLOCK() + print(t2-t) + + print('UKF') + print(FORMAT_STR.format(' Initialize '), end='') + temp_out = StringIO() + sys.stdout = temp_out + sys.stderr = temp_out + t = CLOCK() + filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) + t2 = CLOCK() + sys.stdout = sys.__stdout__ + sys.stderr = sys.__stderr__ + print(t2-t) + + # print(FORMAT_STR.format(' Step'), end='') + # example_measurements = {'t': 32.2, 'v': 3.915} + # t = 0.1 + # temp_out = StringIO() + # sys.stdout = temp_out + # sys.stderr = temp_out + # t = CLOCK() + # filt.estimate(t, future_loading(t), example_measurements) + # t2 = CLOCK() + # sys.stdout = sys.__stdout__ + # sys.stderr = sys.__stderr__ + # print(t2-t) + + print(FORMAT_STR.format('Plot Results'), end='') + t = CLOCK() + filt.x.plot_scatter(label='prior') + t2 = CLOCK() + print(t2-t) + + print('MC') + print(FORMAT_STR.format(' Initialize '), end='') + t = CLOCK() + mc = predictors.MonteCarlo(batt) + t2 = CLOCK() + print(t2-t) + + print(FORMAT_STR.format(' Prediction'), end='') + NUM_SAMPLES = 5 + STEP_SIZE = 0.1 + t = CLOCK() + mc_results = mc.predict(batt.initialize(), future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE) + t2 = CLOCK() + print(t2-t) + + print(FORMAT_STR.format('Metrics'), end='') + t = CLOCK() + mc_results.time_of_event.percentage_in_bounds([3005.2, 3005.6]) + mc_results.time_of_event.metrics(ground_truth=3005.25) + t2 = CLOCK() + print(t2-t) + + print(FORMAT_STR.format('Plot Scatter'), end='') + t = CLOCK() + fig = mc_results.states.snapshot(0).plot_scatter(label = "t={} s".format(int(mc_results.times[0]))) # 0 + quarter_index = int(len(mc_results.times)/4) + mc_results.states.snapshot(quarter_index).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index]))) # 25% + mc_results.states.snapshot(quarter_index*2).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index*2]))) # 50% + mc_results.states.snapshot(quarter_index*3).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index*3]))) # 75% + mc_results.states.snapshot(-1).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[-1]))) # 100% + t2 = CLOCK() + print(t2-t) + + print(FORMAT_STR.format('Plot Hist'), end='') + t = CLOCK() + mc_results.time_of_event.plot_hist() + t2 = CLOCK() + print(t2-t) From 9433475f639cfcf05d408050ba6fc9967d225298 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 11 Jul 2022 16:11:53 -0700 Subject: [PATCH 20/64] Fix unweighted_samples --- src/prog_algs/uncertain_data/unweighted_samples.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/prog_algs/uncertain_data/unweighted_samples.py b/src/prog_algs/uncertain_data/unweighted_samples.py index 032deb4b..300649ac 100644 --- a/src/prog_algs/uncertain_data/unweighted_samples.py +++ b/src/prog_algs/uncertain_data/unweighted_samples.py @@ -6,6 +6,8 @@ from numpy import array, cov, random from warnings import warn +from prog_models.utils.containers import DictLikeMatrixWrapper + class UnweightedSamples(UncertainData, UserList): """ @@ -19,7 +21,7 @@ class UnweightedSamples(UncertainData, UserList): """ def __init__(self, samples : list = [], _type = dict): super().__init__(_type) - if isinstance(samples, dict): + if isinstance(samples, dict) or isinstance(samples, DictLikeMatrixWrapper): # Is in form of {key: [value, ...], ...} # Convert to array of samples if len(samples.keys()) == 0: From 4176182e71e6a11f24265c39942efc22390897e7 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 11 Jul 2022 16:12:12 -0700 Subject: [PATCH 21/64] fix test --- tests/test_state_estimators.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_state_estimators.py b/tests/test_state_estimators.py index 37d8aad9..c78eb288 100644 --- a/tests/test_state_estimators.py +++ b/tests/test_state_estimators.py @@ -234,10 +234,10 @@ def test_UKF_incorrect_input(self): self.__incorrect_input_tests(UnscentedKalmanFilter) def test_PF(self): - m = ThrownObject(process_noise={'x': 1, 'v': 3}, measurement_noise=1, num_particles = 1000) + m = ThrownObject(process_noise={'x': 0.25, 'v': 1}, measurement_noise=1.5) x_guess = {'x': 1.75, 'v': 38.5} # Guess of initial state, actual is {'x': 1.83, 'v': 40} - filt = ParticleFilter(m, x_guess) + filt = ParticleFilter(m, x_guess, num_particles = 1000) self.__test_state_est(filt, m) # Test ParticleFilter ScalarData From 94d56687dc4fd6e5ea2347c4407644224060cf4b Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 10:25:09 -0700 Subject: [PATCH 22/64] Use of containers in examples --- examples/basic_example.py | 21 +++++++++++---------- examples/benchmarking_example.py | 5 ++--- examples/horizon.py | 3 ++- examples/kalman_filter.py | 3 ++- examples/measurement_eqn_example.py | 4 ++-- examples/particle_filter_battery_example.py | 6 +++--- examples/predict_specific_event.py | 7 +++---- examples/thrown_object_example.py | 11 +++++++---- examples/utpredictor.py | 5 ++--- 9 files changed, 34 insertions(+), 31 deletions(-) diff --git a/examples/basic_example.py b/examples/basic_example.py index 0cbe2fcd..4b365e5c 100644 --- a/examples/basic_example.py +++ b/examples/basic_example.py @@ -22,6 +22,12 @@ def run_example(): # Step 1: Setup model & future loading + # Measurement noise + R_vars = { + 't': 2, + 'v': 0.02 + } + batt = Battery(measurement_noise = R_vars) def future_loading(t, x = None): # Variable (piece-wise) future loading scheme if (t < 600): @@ -34,14 +40,8 @@ def future_loading(t, x = None): i = 2 else: i = 3 - return {'i': i} - # Measurement noise - R_vars = { - 't': 2, - 'v': 0.02 - } - batt = Battery(measurement_noise = R_vars) - initial_state = batt.parameters['x0'] + return batt.InputContainer({'i': i}) + initial_state = batt.initialize() # Step 2: Demonstrating state estimator print("\nPerforming State Estimation Step") @@ -57,9 +57,10 @@ def future_loading(t, x = None): fig = filt.x.plot_scatter(label='prior') # Step 2c: Perform state estimation step - example_measurements = {'t': 32.2, 'v': 3.915} + example_measurements = batt.OutputContainer({'t': 32.2, 'v': 3.915}) t = 0.1 - filt.estimate(t, future_loading(t), example_measurements) + u = future_loading(t) + filt.estimate(t, u, example_measurements) # Step 2d: Print & Plot Resulting Posterior State print("\nPosterior State:", filt.x.mean) diff --git a/examples/benchmarking_example.py b/examples/benchmarking_example.py index 1f1679fb..b1b5e36d 100644 --- a/examples/benchmarking_example.py +++ b/examples/benchmarking_example.py @@ -21,6 +21,7 @@ def run_example(): # Step 1: Setup Model and Future Loading + batt = Battery() def future_loading(t, x={}): # Variable (piece-wise) future loading scheme if (t < 600): @@ -33,9 +34,7 @@ def future_loading(t, x={}): i = 2 else: i = 3 - return {'i': i} - - batt = Battery() + return batt.InputContainer({'i': i}) # Step 2: Setup Predictor pred = predictors.MonteCarlo(batt, dt= 0.05) diff --git a/examples/horizon.py b/examples/horizon.py index 1442a22c..39048d32 100644 --- a/examples/horizon.py +++ b/examples/horizon.py @@ -36,7 +36,8 @@ def future_loading(t, x = None): # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) # Step 2b: Print & Plot Prior State - filt.estimate(0.1, {}, m.output(initial_state)) + u = m.InputContainer({}) # No input for ThrownObject + filt.estimate(0.1, u, m.output(initial_state)) # Note: in a prognostic application the above state estimation step would be repeated each time # there is new data. Here we're doing one step to demonstrate how the state estimator is used diff --git a/examples/kalman_filter.py b/examples/kalman_filter.py index eab72e2d..2cbc7de9 100644 --- a/examples/kalman_filter.py +++ b/examples/kalman_filter.py @@ -102,7 +102,8 @@ def run_example(): # Step 2: Instantiate the Kalman Filter State Estimator # Define the initial state to be slightly off of actual - x_guess = {'x': 1.75, 'v': 35} # Guess of initial state, actual is {'x': 1.83, 'v': 40} + x_guess = m.StateContainer({'x': 1.75, 'v': 35}) # Guess of initial state + # Note: actual is {'x': 1.83, 'v': 40} kf = KalmanFilter(m, x_guess) # Step 3: Run the Kalman Filter State Estimator diff --git a/examples/measurement_eqn_example.py b/examples/measurement_eqn_example.py index cd2fdca4..fae5d17f 100644 --- a/examples/measurement_eqn_example.py +++ b/examples/measurement_eqn_example.py @@ -26,6 +26,7 @@ class MyBattery(Battery): outputs = ['v'] # Step 2: Setup model & future loading + batt = MyBattery() def future_loading(t, x={}): # Variable (piece-wise) future loading scheme if (t < 600): @@ -38,9 +39,8 @@ def future_loading(t, x={}): i = 2 else: i = 3 - return {'i': i} + return batt.InputContainer({'i': i}) - batt = MyBattery() x0 = batt.parameters['x0'] # Step 3: Use the updated model diff --git a/examples/particle_filter_battery_example.py b/examples/particle_filter_battery_example.py index 4b9a9856..782508a0 100644 --- a/examples/particle_filter_battery_example.py +++ b/examples/particle_filter_battery_example.py @@ -11,9 +11,6 @@ def run_example(): ## Setup - def future_loading(t, x=None): - load = 1 - return {"i": load} # Save battery model # Time increment dt = 1 @@ -36,6 +33,9 @@ def future_loading(t, x=None): battery = BatteryElectroChemEOD(process_noise= Q_vars, measurement_noise = R_vars, dt = dt) + def future_loading(t, x=None): + load = 1 + return battery.InputContainer({"i": load}) # Simulate data until EOD start_u = future_loading(0) diff --git a/examples/predict_specific_event.py b/examples/predict_specific_event.py index 053a179f..f7f1359c 100644 --- a/examples/predict_specific_event.py +++ b/examples/predict_specific_event.py @@ -9,11 +9,10 @@ def run_example(): ## Setup - def future_loading(t, x = None): - return {} - m = ThrownObject() - initial_state = m.initialize({}, {}) + initial_state = m.initialize() + def future_loading(t, x = None): + return m.InputContainer({}) ## State Estimation - perform a single ukf state estimate step filt = state_estimators.UnscentedKalmanFilter(m, initial_state) diff --git a/examples/thrown_object_example.py b/examples/thrown_object_example.py index 54c8ac22..1be59d44 100644 --- a/examples/thrown_object_example.py +++ b/examples/thrown_object_example.py @@ -21,10 +21,10 @@ def run_example(): # Step 1: Setup model & future loading - def future_loading(t, x = None): - return {} m = ThrownObject(process_noise = 0.25, measurement_noise = 0.2) - initial_state = m.initialize({}, {}) + initial_state = m.initialize() + def future_loading(t, x = None): + return m.InputContainer({}) # Step 2: Demonstrating state estimator print("\nPerforming State Estimation Step...") @@ -36,7 +36,10 @@ def future_loading(t, x = None): # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) # Step 2b: Print & Plot Prior State - filt.estimate(0.1, {}, m.output(initial_state)) + u = m.InputContainer({}) # No input for ThrownObject + z = m.output(initial_state) # Measured output (here as an example we use the model output instead) + # Note: In practice, replace this with actual measured data + filt.estimate(0.1, u, z) # Note: in a prognostic application the above state estimation step would be repeated each time # there is new data. Here we're doing one step to demonstrate how the state estimator is used diff --git a/examples/utpredictor.py b/examples/utpredictor.py index 3ec1544a..ef0253c0 100644 --- a/examples/utpredictor.py +++ b/examples/utpredictor.py @@ -11,6 +11,7 @@ def run_example(): ## Setup + batt = BatteryCircuit() def future_loading(t, x = None): # Variable (piece-wise) future loading scheme if (t < 600): @@ -23,9 +24,7 @@ def future_loading(t, x = None): i = 2 else: i = 3 - return {'i': i} - - batt = BatteryCircuit() + return batt.InputContainer({'i': i}) ## State Estimation - perform a single ukf state estimate step filt = state_estimators.UnscentedKalmanFilter(batt, batt.parameters['x0']) From b20163f7bc62a71a4c1322c0eefb3dcaceab2b3b Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 10:25:23 -0700 Subject: [PATCH 23/64] Tune test --- tests/test_state_estimators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_state_estimators.py b/tests/test_state_estimators.py index c78eb288..5f8b91ed 100644 --- a/tests/test_state_estimators.py +++ b/tests/test_state_estimators.py @@ -234,7 +234,7 @@ def test_UKF_incorrect_input(self): self.__incorrect_input_tests(UnscentedKalmanFilter) def test_PF(self): - m = ThrownObject(process_noise={'x': 0.25, 'v': 1}, measurement_noise=1.5) + m = ThrownObject(process_noise={'x': 0.25, 'v': 0.5}, measurement_noise=1.5) x_guess = {'x': 1.75, 'v': 38.5} # Guess of initial state, actual is {'x': 1.83, 'v': 40} filt = ParticleFilter(m, x_guess, num_particles = 1000) From e5e8cd50a5469eafcdf8c0d4e993df08239182c5 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 10:34:08 -0700 Subject: [PATCH 24/64] Fix PF type --- src/prog_algs/state_estimators/particle_filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index 429b3dc3..ecd872e7 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -163,7 +163,7 @@ def estimate(self, t : float, u, z): for state in self.particles.keys()] # Particles as a dictionary - self.particles = dict(zip(self.particles.keys(), samples)) + self.particles = self.model.StateContainer(array(samples)) @property def x(self) -> UnweightedSamples: From 262c59554834fc421d034aa9789440a5715281c4 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 11:02:30 -0700 Subject: [PATCH 25/64] remove measurement eqn tests --- tests/test_state_estimators.py | 50 +--------------------------------- 1 file changed, 1 insertion(+), 49 deletions(-) diff --git a/tests/test_state_estimators.py b/tests/test_state_estimators.py index 5f8b91ed..ea3df0f9 100644 --- a/tests/test_state_estimators.py +++ b/tests/test_state_estimators.py @@ -234,7 +234,7 @@ def test_UKF_incorrect_input(self): self.__incorrect_input_tests(UnscentedKalmanFilter) def test_PF(self): - m = ThrownObject(process_noise={'x': 0.25, 'v': 0.5}, measurement_noise=1.5) + m = ThrownObject(process_noise={'x': 0.25, 'v': 0.25}, measurement_noise=1) x_guess = {'x': 1.75, 'v': 38.5} # Guess of initial state, actual is {'x': 1.83, 'v': 40} filt = ParticleFilter(m, x_guess, num_particles = 1000) @@ -288,54 +288,6 @@ def test_PF(self): # Case 2: Raise ProgAlgTypeError if x0 not UncertainData or x0_uncertainty not of type {{dict, Number}}. with self.assertRaises(ProgAlgTypeError): filt_scalar = ParticleFilter(m, {'x': 1.75, 'v': 38.5}, num_particles = 20, x0_uncertainty = []) - - def test_measurement_eq_UKF(self): - m = MockProgModel2() - x0 = m.initialize() - - # Setup - filt = UnscentedKalmanFilter(m, x0) - - # Try using - filt.estimate(0.2, {'i1': 1, 'i2': 2}, {'o1': -2.0, 'o2': 7}) - - # Add Measurement eqn - def measurement_eqn(x): - z = m.output(x) - del z['o2'] - return z - filt = UnscentedKalmanFilter(m, x0, measurement_eqn=measurement_eqn) - filt.estimate(0.1, {'i1': 1, 'i2': 2}, {'o1': -2.0}) - - # New Measurement eqn method - class MyModel(MockProgModel2): - outputs = ['o1'] - filt = UnscentedKalmanFilter(MyModel(), x0) - filt.estimate(0.1, {'i1': 1, 'i2': 2}, {'o1': -2.0}) - - def test_measurement_eq_PF(self): - m = MockProgModel2() - x0 = m.initialize() - - # Setup - filt = ParticleFilter(m, x0) - - # This one should work - filt.estimate(0.2, {'i1': 1, 'i2': 2}, {'o1': -2.0, 'o2': 7}) - - # Add Measurement eqn - def measurement_eqn(x): - z = m.output(x) - del z['o2'] - return z - filt = ParticleFilter(m, x0, measurement_eqn=measurement_eqn) - filt.estimate(0.1, {'i1': 1, 'i2': 2}, {'o1': -2.0}) - - # New Measurement eqn method - class MyModel(MockProgModel2): - outputs = ['o1'] - filt = ParticleFilter(MyModel(), x0) - filt.estimate(0.1, {'i1': 1, 'i2': 2}, {'o1': -2.0}) def test_PF_incorrect_input(self): self.__incorrect_input_tests(ParticleFilter) From 550c255b7f0a4e18db704f52ed785bac97dcef73 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 11:02:45 -0700 Subject: [PATCH 26/64] PF input conversion --- src/prog_algs/state_estimators/particle_filter.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index ecd872e7..6b7f28a3 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -101,6 +101,12 @@ def estimate(self, t : float, u, z): dt = t - self.t self.t = t + # Check Types + if isinstance(u, dict): + u = self.model.InputContainer(u) + if isinstance(z, dict): + z = self.model.OutputContainer(z) + # Optimization particles = self.particles next_state = self.model.next_state From f5c5b48f3167afad48def8d41d81145d3edf92a9 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 11:03:08 -0700 Subject: [PATCH 27/64] Example containers --- examples/benchmarking_example.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/benchmarking_example.py b/examples/benchmarking_example.py index b1b5e36d..8aecc0f5 100644 --- a/examples/benchmarking_example.py +++ b/examples/benchmarking_example.py @@ -43,8 +43,10 @@ def future_loading(t, x={}): x0 = batt.initialize() state_estimator = state_estimators.ParticleFilter(batt, x0) # Send in some data to estimate state - state_estimator.estimate(0.1, future_loading(0.1), {'t': 32.2, 'v': 3.915}) - state_estimator.estimate(0.2, future_loading(0.2), {'t': 32.3, 'v': 3.91}) + z1 = batt.OutputContainer({'t': 32.2, 'v': 3.915}) + z2 = batt.OutputContainer({'t': 32.3, 'v': 3.91}) + state_estimator.estimate(0.1, future_loading(0.1), z1) + state_estimator.estimate(0.2, future_loading(0.2), z2) # Step 4: Benchmark Predictions # Here we're comparing the results given different numbers of samples From 34f6990fd63576489b4a5ea2cc0bab37c99b9583 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 11:11:09 -0700 Subject: [PATCH 28/64] Fix tutorial --- tutorial.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index 1eab11d1..4267c25c 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -253,8 +253,8 @@ "fig = est.x.plot_scatter(label='prior')\n", "\n", "t = 0.1\n", - "u = {'i': 2}\n", - "example_measurements = {'t': 32.2, 'v': 3.915}\n", + "u = m.InputContainer({'i': 2})\n", + "example_measurements = m.OutputContainer({'t': 32.2, 'v': 3.915})\n", "est.estimate(t, u, example_measurements)\n", "\n", "print(\"Posterior State:\", est.x.mean)\n", @@ -539,8 +539,8 @@ "se = BlindlyStumbleEstimator(m, x0)\n", "\n", "for i in range(25):\n", - " u = {'i': 0}\n", - " z = {'t': 18.95, 'v': 4.183}\n", + " u = m.InputContainer({'i': 0})\n", + " z = m.OutputContainer({'t': 18.95, 'v': 4.183})\n", " se.estimate(i, u, z)\n", " print(se.x.mean)\n", " print(\"\\tcorrect: {'tb': 18.95, 'qb': 7856.3254, 'qcp': 0, 'qcs': 0}\")" From 759ffa8005c8313e0e1c4526001438daa7d07a17 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 12 Jul 2022 11:46:38 -0700 Subject: [PATCH 29/64] Remove release tests --- .github/workflows/python-package.yml | 38 ++++++++++++++-------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index f26b70e8..c334224c 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -32,25 +32,25 @@ jobs: python -m pip install testbook - name: Run tests run: python -m tests - test-prog_models-released: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ['3.7', '3.8', '3.9', '3.10'] - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install - run: | - python -m pip install --upgrade pip - python -m pip install -e . - python -m pip install notebook - python -m pip install testbook - - name: Run tests - run: python -m tests + # test-prog_models-released: + # runs-on: ubuntu-latest + # strategy: + # matrix: + # python-version: ['3.7', '3.8', '3.9', '3.10'] + # steps: + # - uses: actions/checkout@v2 + # - name: Set up Python ${{ matrix.python-version }} + # uses: actions/setup-python@v2 + # with: + # python-version: ${{ matrix.python-version }} + # - name: Install + # run: | + # python -m pip install --upgrade pip + # python -m pip install -e . + # python -m pip install notebook + # python -m pip install testbook + # - name: Run tests + # run: python -m tests copyright: runs-on: ubuntu-latest strategy: From 1711a74821af1779032d249988a7292fed245d14 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 14 Jul 2022 16:15:30 -0700 Subject: [PATCH 30/64] Fix UT Horizon Bug --- src/prog_algs/predictors/unscented_transform.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index ab73fedf..8ea500ef 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -155,9 +155,11 @@ def predict(self, state, future_loading_eqn : Callable, **kwargs) -> PredictionR event_states: [[dict]] Estimated event state (e.g., SOH), between 1-0 where 0 is event occurance, for each sample and time in times where event_states[sample_id][index] corresponds to time times[sample_id][index] - toe: UncertainData + time_of_event: UncertainData Estimated time where a predicted event will occur for each sample. Note: Mean and Covariance Matrix will both be nan if every sigma point doesnt reach threshold within horizon + Also, includes member final_state (time_of_event.final_state) which is the state at the last time step. + time_of_event.final_state is a dict of the form {'state_name': state_value}, is equal to None if event does not occur within horizon """ if isinstance(state, dict) or isinstance(state, self.model.StateContainer) or isinstance(state, ScalarData): raise TypeError("state must be a distribution (e.g., MultivariateNormalDist, UnweightedSamples), not scalar") @@ -249,6 +251,10 @@ def update_all(): # Transform final state into {event_name: MultivariateNormalDist} final_state = {} for event_key in last_state.keys(): + if any([isnan(last_state_i) for last_state_i in last_state[event_key]]): + # If any sigma point has not met the event threshold + final_state[event_key] = None + continue last_state_pts = array([[last_state_i[state_key] for state_key in state_keys] for last_state_i in last_state[event_key]]) # last_state_pts = transpose(last_state_pts) last_state_mean, last_state_cov = kalman.unscented_transform(last_state_pts, sigma_points.Wm, sigma_points.Wc) From 785daf7bfaf3d997c18b708199845f765aafd433 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 14 Jul 2022 16:23:52 -0700 Subject: [PATCH 31/64] Fix original example --- src/prog_algs/predictors/unscented_transform.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index 8ea500ef..cffec0e7 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -190,7 +190,7 @@ def predict(self, state, future_loading_eqn : Callable, **kwargs) -> PredictionR t = params['t0'] save_pt_index = 0 ToE = {key: [float('nan') for i in range(n_points)] for key in events_to_predict} # Keep track of final ToE values - last_state = {key: [float('nan') for i in range(n_points)] for key in events_to_predict} # Keep track of final state values + last_state = {key: [None for i in range(n_points)] for key in events_to_predict} # Keep track of final state values times = [] inputs = [] @@ -251,7 +251,7 @@ def update_all(): # Transform final state into {event_name: MultivariateNormalDist} final_state = {} for event_key in last_state.keys(): - if any([isnan(last_state_i) for last_state_i in last_state[event_key]]): + if any([last_state_i is None for last_state_i in last_state[event_key]]): # If any sigma point has not met the event threshold final_state[event_key] = None continue From bb4db9b17a40c4720d5985dd7a2b2e1078338b15 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 14 Jul 2022 16:57:59 -0700 Subject: [PATCH 32/64] add check for horizon --- src/prog_algs/predictors/monte_carlo.py | 3 +++ src/prog_algs/predictors/unscented_transform.py | 5 ++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/src/prog_algs/predictors/monte_carlo.py b/src/prog_algs/predictors/monte_carlo.py index d8996f51..635743ac 100644 --- a/src/prog_algs/predictors/monte_carlo.py +++ b/src/prog_algs/predictors/monte_carlo.py @@ -52,6 +52,9 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs params['print'] = False params['progress'] = False + if len(kwargs['events']) == 0 and 'horizon' not in kwargs: + raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon") + # Sample from state if n_samples specified or state is not UnweightedSamples if not isinstance(state, UnweightedSamples) or len(state) != params['n_samples']: state = state.sample(params['n_samples']) diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index cffec0e7..2d52ae96 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -170,9 +170,12 @@ def predict(self, state, future_loading_eqn : Callable, **kwargs) -> PredictionR params = deepcopy(self.parameters) # copy parameters params.update(kwargs) # update for specific run - events_to_predict = params['events'] + + if len(kwargs['events']) == 0 and 'horizon' not in kwargs: + raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon") # Optimizations + events_to_predict = params['events'] dt = params['dt'] model = self.model filt = self.filter From 9cf2b384e384a84928f6d0b4f759b09986f29edd Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 20 Jul 2022 15:48:31 -0700 Subject: [PATCH 33/64] Fix mc --- src/prog_algs/predictors/monte_carlo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/prog_algs/predictors/monte_carlo.py b/src/prog_algs/predictors/monte_carlo.py index 635743ac..38b2fd4b 100644 --- a/src/prog_algs/predictors/monte_carlo.py +++ b/src/prog_algs/predictors/monte_carlo.py @@ -52,7 +52,7 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs params['print'] = False params['progress'] = False - if len(kwargs['events']) == 0 and 'horizon' not in kwargs: + if len(params['events']) == 0 and 'horizon' not in params: raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon") # Sample from state if n_samples specified or state is not UnweightedSamples From e0e3a29f59b701c616dd17e6ef78499753bb5e6b Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 20 Jul 2022 15:58:31 -0700 Subject: [PATCH 34/64] fix utp --- src/prog_algs/predictors/unscented_transform.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index 2d52ae96..a6af7c0d 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -171,7 +171,7 @@ def predict(self, state, future_loading_eqn : Callable, **kwargs) -> PredictionR params = deepcopy(self.parameters) # copy parameters params.update(kwargs) # update for specific run - if len(kwargs['events']) == 0 and 'horizon' not in kwargs: + if len(params['events']) == 0 and 'horizon' not in params: raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon") # Optimizations From c47038bb76febb82499c0441c1446ec035b5274e Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 17 Aug 2022 15:52:11 -0700 Subject: [PATCH 35/64] Doc updates --- examples/eol_event.py | 2 ++ src/prog_algs/predictors/predictor.py | 26 +++++++++++--------------- 2 files changed, 13 insertions(+), 15 deletions(-) diff --git a/examples/eol_event.py b/examples/eol_event.py index 82b1eaf6..f01c9f4a 100644 --- a/examples/eol_event.py +++ b/examples/eol_event.py @@ -3,7 +3,9 @@ This example demonstrates a use case where someone wants to predict the first event (i.e., End Of Life (EOL)) of a system. Many system models have multiple events that can occur. In some prognostics applications, users are not interested in predicting a specific event, and are instead interested in when the first event occurs, regardless of the event. This example demonstrates how to predict the first event of a system. Method: An instance of ThrownObject is used for this example. In this case it is trivial because the event 'falling' will always occur before 'impact', but for some other models that might not be true. The ThrownObject class is subclassed to add a new event 'EOL' which occurs if any other event occurs. The new model is then instantiated and used for prognostics like in basic_example. Prediction specifically specifies EOL as the event to be predicted. + Results: + i) Predicted future values (inputs, states, outputs, event_states) with uncertainty from prediction ii) Time the event 'EOL' is predicted to occur (with uncertainty) iii) Histogram of the event 'EOL' diff --git a/src/prog_algs/predictors/predictor.py b/src/prog_algs/predictors/predictor.py index 53219818..754c3358 100644 --- a/src/prog_algs/predictors/predictor.py +++ b/src/prog_algs/predictors/predictor.py @@ -13,12 +13,12 @@ class Predictor(ABC): """ Interface class for predictors - Abstract base class for creating predictors that perform prediction. Predictor subclasses must implement this interface. Equivilant to "Observers" in NASA's Matlab Prognostics Algorithm Library + Abstract base class for creating predictors that perform prediction. Predictor subclasses must implement this interface. Equivilant to "Predictors" in NASA's Matlab Prognostics Algorithm Library Parameters ---------- model : PrognosticsModel - See: Prognostics Model Package\n + See: :py:mod:`prog_models` package\n A prognostics model to be used in prediction kwargs : optional, keyword arguments """ @@ -62,18 +62,14 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs * save_freq (float): Frequency at which results are saved (s) * save_pts (list[float]): Any additional savepoints (s) e.g., [10.1, 22.5] - Return PredictionResults namedtuple + Return ---------- - times : List[float] - Times for each savepoint such that inputs.snapshot(i), states.snapshot(i), outputs.snapshot(i), and event_states.snapshot(i) are all at times[i] - inputs : Prediction - Inputs at each savepoint such that inputs.snapshot(i) is the input distribution (type UncertainData) at times[i] - states : Prediction - States at each savepoint such that states.snapshot(i) is the state distribution (type UncertainData) at times[i] - outputs : Prediction - Outputs at each savepoint such that outputs.snapshot(i) is the output distribution (type UncertainData) at times[i] - event_states : Prediction - Event states at each savepoint such that event_states.snapshot(i) is the event state distribution (type UncertainData) at times[i] - time_of_event : UncertainData - Distribution of predicted Time of Event (ToE) for each predicted event, represented by some subclass of UncertaintData (e.g., MultivariateNormalDist) + result esults from prediction, including: NameTuple + * times (List[float]): Times for each savepoint such that inputs.snapshot(i), states.snapshot(i), outputs.snapshot(i), and event_states.snapshot(i) are all at times[i] + * inputs (Prediction): Inputs at each savepoint such that inputs.snapshot(i) is the input distribution (type UncertainData) at times[i] + * states (Prediction): States at each savepoint such that states.snapshot(i) is the state distribution (type UncertainData) at times[i] + * outputs (Prediction): Outputs at each savepoint such that outputs.snapshot(i) is the output distribution (type UncertainData) at times[i] + * event_states (Prediction): Event states at each savepoint such that event_states.snapshot(i) is the event state distribution (type UncertainData) at times[i] + * time_of_event (UncertainData): Distribution of predicted Time of Event (ToE) for each predicted event, represented by some subclass of UncertaintData (e.g., MultivariateNormalDist) """ + pass From bca04651a9026965fd1faa3ca40b756b86327eda Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 24 Aug 2022 13:11:30 -0700 Subject: [PATCH 36/64] Add additional steps to workflow --- .github/workflows/pr-messages.yml | 34 ++++++++++++++++ .github/workflows/python-package.yml | 2 +- .github/workflows/release-messages.yml | 56 ++++++++++++++++++++++++++ 3 files changed, 91 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/pr-messages.yml create mode 100644 .github/workflows/release-messages.yml diff --git a/.github/workflows/pr-messages.yml b/.github/workflows/pr-messages.yml new file mode 100644 index 00000000..3a839832 --- /dev/null +++ b/.github/workflows/pr-messages.yml @@ -0,0 +1,34 @@ +name: Print PR Message - Non Release + +on: + pull_request: + branches: + - 'dev' + types: [opened] + +jobs: + benchmark_branch: + runs-on: ubuntu-latest + steps: + - name: Auto Comment + uses: wow-actions/auto-comment@v1 + with: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + pullRequestOpened: > + Thank you for opening this PR. Each PR into dev requires a code review. For the code review, look at the following: + + - [ ] Reviewer should look for bugs, efficiency, readability, testing, and coverage in examples (if relevant). + + - [ ] Ensure that each PR adding a new feature should include a test verifying that feature. + + - [ ] All tests must be passing. + + - [ ] All errors from static analysis must be resolved. + + - [ ] Review the test coverage reports (if there is a change) - will be added as comment on PR if there is a change + + - [ ] Review the software benchmarking results (if there is a change) - will be added as comment on PR + + - [ ] Any added dependencies are included in requirements.txt, setup.py, and dev_guide.rst (this document) + + - [ ] All warnings from static analysis must be reviewed and resolved - if deemed appropriate. diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index c334224c..79136947 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -36,7 +36,7 @@ jobs: # runs-on: ubuntu-latest # strategy: # matrix: - # python-version: ['3.7', '3.8', '3.9', '3.10'] + # python-version: ['3.9'] # steps: # - uses: actions/checkout@v2 # - name: Set up Python ${{ matrix.python-version }} diff --git a/.github/workflows/release-messages.yml b/.github/workflows/release-messages.yml new file mode 100644 index 00000000..2842dc5a --- /dev/null +++ b/.github/workflows/release-messages.yml @@ -0,0 +1,56 @@ +name: Print PR Message - Release + +on: + pull_request: + branches: + - 'release/**' + types: [opened] + +jobs: + benchmark_branch: + runs-on: ubuntu-latest + steps: + - name: Auto Comment + uses: wow-actions/auto-comment@v1 + with: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + pullRequestOpened: > + Thank you for opening this PR. Since this is a release branch, the PR must complete the release checklist, below: + + - [ ] Check that each new feature has corresponding tests + + - [ ] Confirm all dependencies are in the following: requirements.txt, setup.py, the bottom of dev_guide.rst + + - [ ] Confirm that all issues associated with the release have been closed (i.e., requirements have been met) or assigned to another release + + - [ ] Run unit tests `python -m tests` + + - [ ] If present, run manual tests `python -m tests.test_manual` + + - [ ] Review the template(s) + + - [ ] Review static-analysis/linter results + + - [ ] Review the tutorial + + - [ ] Run and review the examples + + - [ ] Check that all examples are tested + + - [ ] Check new files in PR for any accidentally added + + - [ ] Check documents + + - [ ] Check that all desired examples are in docs + + - [ ] General review: see if any updates are required + + - [ ] Rebuild sphinx documents: `sphinx-build sphinx_config/ docs/` + + - [ ] Write release notes + + - [ ] Update version number in src/\*/__init__.py and setup.py + + - [ ] For releases adding new features- ensure that NASA release process has been followed. + + - [ ] Confirm that on GitHub Releases page, the next release has been started and that a schedule is present including at least Release Date, Release Review Date, and Release Branch Opening Date.` From b2a59e7ea50c14a4c5fc1e3e62009c43d0d92656 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 26 Sep 2022 11:05:12 -0700 Subject: [PATCH 37/64] Remove dict translation --- src/prog_algs/predictors/unscented_transform.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index a6af7c0d..916461ea 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -108,12 +108,12 @@ def __init__(self, model, **kwargs): self.parameters['Q'] = diag([1.0e-1 for i in range(num_states)]) def measure(x): - x = model.StateContainer({key: value for (key, value) in zip(self.__state_keys, x)}) + x = model.StateContainer(x) z = model.output(x) - return model.OutputContainer({array(list(z.values()))}) + return model.OutputContainer(z) def state_transition(x, dt): - x = model.StateContainer({key: value for (key, value) in zip(self.__state_keys, x)}) + x = model.StateContainer(x) x = model.next_state(x, self.__input, dt) x = model.apply_limits(x) return array(list(x.values())) @@ -230,7 +230,8 @@ def update_all(): points = sigma_points.sigma_points(filt.x, filt.P) all_failed = True for i, point in zip(range(n_points), points): - x = StateContainer({key: x for (key, x) in zip(state_keys, point)}) + # x = StateContainer({key: x for (key, x) in zip(state_keys, point)}) + x = StateContainer(point) t_met = threshold_met(x) # Check Thresholds From f0b63730d5270458672194be060eb37e400585d7 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 11:09:04 -0700 Subject: [PATCH 38/64] Update basic example --- examples/basic_example.py | 74 ++++++++---------- examples/basic_example_battery.py | 124 ++++++++++++++++++++++++++++++ 2 files changed, 158 insertions(+), 40 deletions(-) create mode 100644 examples/basic_example_battery.py diff --git a/examples/basic_example.py b/examples/basic_example.py index 4b365e5c..c86b431f 100644 --- a/examples/basic_example.py +++ b/examples/basic_example.py @@ -3,7 +3,7 @@ """ This example performs a state estimation and prediction with uncertainty given a Prognostics Model. -Method: An instance of the BatteryCircuit model in prog_models is created, and the prediction process is achieved in three steps: +Method: An instance of the ThrownObject model in prog_models is created, and the prediction process is achieved in three steps: 1) State estimation of the current state is performed using a chosen state_estimator, and samples are drawn from this estimate 2) Prediction of future states (with uncertainty) and the times at which the event threshold will be reached 3) Metrics tools are used to further investigate the results of prediction @@ -14,73 +14,61 @@ iv) Figures illustrating results """ -from prog_models.models import BatteryCircuit as Battery -# VVV Uncomment this to use Electro Chemistry Model VVV -from prog_models.models import BatteryElectroChemEOD as Battery +from prog_models.models import ThrownObject from prog_algs import * def run_example(): # Step 1: Setup model & future loading - # Measurement noise - R_vars = { - 't': 2, - 'v': 0.02 - } - batt = Battery(measurement_noise = R_vars) + m = ThrownObject() def future_loading(t, x = None): - # Variable (piece-wise) future loading scheme - if (t < 600): - i = 2 - elif (t < 900): - i = 1 - elif (t < 1800): - i = 4 - elif (t < 3000): - i = 2 - else: - i = 3 - return batt.InputContainer({'i': i}) - initial_state = batt.initialize() + # No load for a thrown object + return m.InputContainer({}) + initial_state = m.initialize() # Step 2: Demonstrating state estimator + # The state estimator is used to estimate the system state given sensor data. print("\nPerforming State Estimation Step") # Step 2a: Setup - filt = state_estimators.ParticleFilter(batt, initial_state) + filt = state_estimators.ParticleFilter(m, initial_state) # VVV Uncomment this to use UKF State Estimator VVV # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) # Step 2b: Print & Plot Prior State print("Prior State:", filt.x.mean) - print('\tSOC: ', batt.event_state(filt.x.mean)['EOD']) + print('\nevent state: ', m.event_state(filt.x.mean)) fig = filt.x.plot_scatter(label='prior') - # Step 2c: Perform state estimation step - example_measurements = batt.OutputContainer({'t': 32.2, 'v': 3.915}) + # Step 2c: Perform state estimation step, given some measurement, above what's expected + example_measurements = m.OutputContainer({'x': 7.5}) t = 0.1 u = future_loading(t) - filt.estimate(t, u, example_measurements) + filt.estimate(t, u, example_measurements) # Update state, given (example) sensor data # Step 2d: Print & Plot Resulting Posterior State + # Note the posterior state is greater than the predicted state of 5.95 + # This is because of the high measurement print("\nPosterior State:", filt.x.mean) - print('\tSOC: ', batt.event_state(filt.x.mean)['EOD']) + # Event state for 'falling' is less, because velocity has decreased + print('\nEvent State: ', m.event_state(filt.x.mean)) filt.x.plot_scatter(fig=fig, label='posterior') # Add posterior state to figure from prior state # Note: in a prognostic application the above state estimation step would be repeated each time # there is new data. Here we're doing one step to demonstrate how the state estimator is used - # Step 3: Demonstrating Predictor + # Step 3: Demonstrating Prediction print("\n\nPerforming Prediction Step") # Step 3a: Setup Predictor - mc = predictors.MonteCarlo(batt) + mc = predictors.MonteCarlo(m) # Step 3b: Perform a prediction - NUM_SAMPLES = 5 - STEP_SIZE = 0.1 - mc_results = mc.predict(filt.x, future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE) - print('ToE', mc_results.time_of_event.mean) + NUM_SAMPLES = 10 + STEP_SIZE = 0.01 + mc_results = mc.predict(filt.x, future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE, save_freq=STEP_SIZE) + print('Predicted time of event (ToE): ', mc_results.time_of_event.mean) + # Here there are 2 events predicted, when the object starts falling, and when it impacts the ground. # Step 3c: Analyze the results @@ -93,19 +81,21 @@ def future_loading(t, x = None): # now you have all the samples corresponding to times[1] # You can also access the final state (of type UncertainData), like so: + # Note: to get a more accurate final state, you can decrease the step size. final_state = mc_results.time_of_event.final_state - print('Final state @EOD: ', final_state['EOD'].mean) + print('State when object starts falling: ', final_state['falling'].mean) # You can also use the metrics package to generate some useful metrics on the result of a prediction print("\nEOD Prediction Metrics") from prog_algs.metrics import prob_success - print('\tPortion between 3005.2 and 3005.6: ', mc_results.time_of_event.percentage_in_bounds([3005.2, 3005.6])) - print('\tAssuming ground truth 3002.25: ', mc_results.time_of_event.metrics(ground_truth=3005.25)) - print('\tP(Success) if mission ends at 3002.25: ', prob_success(mc_results.time_of_event, 3005.25)) + print('\tPortion between 3.65 and 3.8: ', mc_results.time_of_event.percentage_in_bounds([3.65, 3.8], keys='falling')) + print('\tAssuming ground truth 3.7: ', mc_results.time_of_event.metrics(ground_truth=3.7, keys='falling')) + print('\tP(Success) if mission ends at 7.6: ', prob_success(mc_results.time_of_event, 7.6, keys='impact')) # Plot state transition # Here we will plot the states at t0, 25% to ToE, 50% to ToE, 75% to ToE, and ToE + # You should see the states move together (i.e., velocity is lowest and highest when closest to the ground (before impact, and at beginning, respectively)) fig = mc_results.states.snapshot(0).plot_scatter(label = "t={} s".format(int(mc_results.times[0]))) # 0 quarter_index = int(len(mc_results.times)/4) mc_results.states.snapshot(quarter_index).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index]))) # 25% @@ -113,7 +103,11 @@ def future_loading(t, x = None): mc_results.states.snapshot(quarter_index*3).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index*3]))) # 75% mc_results.states.snapshot(-1).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[-1]))) # 100% - mc_results.time_of_event.plot_hist() + # Plot time of event for each event + # If you dont see many bins here, this is because there is not much variety in the estimate. + # You can increase the number of bins, decrease step size, or increase the number of samples to see more of a distribution + mc_results.time_of_event.plot_hist(keys='impact') + mc_results.time_of_event.plot_hist(keys='falling') # Step 4: Show all plots import matplotlib.pyplot as plt # For plotting diff --git a/examples/basic_example_battery.py b/examples/basic_example_battery.py new file mode 100644 index 00000000..4b365e5c --- /dev/null +++ b/examples/basic_example_battery.py @@ -0,0 +1,124 @@ +# Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. + +""" +This example performs a state estimation and prediction with uncertainty given a Prognostics Model. + +Method: An instance of the BatteryCircuit model in prog_models is created, and the prediction process is achieved in three steps: + 1) State estimation of the current state is performed using a chosen state_estimator, and samples are drawn from this estimate + 2) Prediction of future states (with uncertainty) and the times at which the event threshold will be reached + 3) Metrics tools are used to further investigate the results of prediction +Results: + i) Predicted future values (inputs, states, outputs, event_states) with uncertainty from prediction + ii) Time event is predicted to occur (with uncertainty) + iii) Various prediction metrics + iv) Figures illustrating results +""" + +from prog_models.models import BatteryCircuit as Battery +# VVV Uncomment this to use Electro Chemistry Model VVV +from prog_models.models import BatteryElectroChemEOD as Battery + +from prog_algs import * + +def run_example(): + # Step 1: Setup model & future loading + # Measurement noise + R_vars = { + 't': 2, + 'v': 0.02 + } + batt = Battery(measurement_noise = R_vars) + def future_loading(t, x = None): + # Variable (piece-wise) future loading scheme + if (t < 600): + i = 2 + elif (t < 900): + i = 1 + elif (t < 1800): + i = 4 + elif (t < 3000): + i = 2 + else: + i = 3 + return batt.InputContainer({'i': i}) + initial_state = batt.initialize() + + # Step 2: Demonstrating state estimator + print("\nPerforming State Estimation Step") + + # Step 2a: Setup + filt = state_estimators.ParticleFilter(batt, initial_state) + # VVV Uncomment this to use UKF State Estimator VVV + # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) + + # Step 2b: Print & Plot Prior State + print("Prior State:", filt.x.mean) + print('\tSOC: ', batt.event_state(filt.x.mean)['EOD']) + fig = filt.x.plot_scatter(label='prior') + + # Step 2c: Perform state estimation step + example_measurements = batt.OutputContainer({'t': 32.2, 'v': 3.915}) + t = 0.1 + u = future_loading(t) + filt.estimate(t, u, example_measurements) + + # Step 2d: Print & Plot Resulting Posterior State + print("\nPosterior State:", filt.x.mean) + print('\tSOC: ', batt.event_state(filt.x.mean)['EOD']) + filt.x.plot_scatter(fig=fig, label='posterior') # Add posterior state to figure from prior state + + # Note: in a prognostic application the above state estimation step would be repeated each time + # there is new data. Here we're doing one step to demonstrate how the state estimator is used + + # Step 3: Demonstrating Predictor + print("\n\nPerforming Prediction Step") + + # Step 3a: Setup Predictor + mc = predictors.MonteCarlo(batt) + + # Step 3b: Perform a prediction + NUM_SAMPLES = 5 + STEP_SIZE = 0.1 + mc_results = mc.predict(filt.x, future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE) + print('ToE', mc_results.time_of_event.mean) + + # Step 3c: Analyze the results + + # Note: The results of a sample-based prediction can be accessed by sample, e.g., + states_sample_1 = mc_results.states[1] + # now states_sample_1[n] corresponds to times[n] for the first sample + + # You can also access a state distribution at a specific time using the .snapshot function + states_time_1 = mc_results.states.snapshot(1) + # now you have all the samples corresponding to times[1] + + # You can also access the final state (of type UncertainData), like so: + final_state = mc_results.time_of_event.final_state + print('Final state @EOD: ', final_state['EOD'].mean) + + # You can also use the metrics package to generate some useful metrics on the result of a prediction + print("\nEOD Prediction Metrics") + + from prog_algs.metrics import prob_success + print('\tPortion between 3005.2 and 3005.6: ', mc_results.time_of_event.percentage_in_bounds([3005.2, 3005.6])) + print('\tAssuming ground truth 3002.25: ', mc_results.time_of_event.metrics(ground_truth=3005.25)) + print('\tP(Success) if mission ends at 3002.25: ', prob_success(mc_results.time_of_event, 3005.25)) + + # Plot state transition + # Here we will plot the states at t0, 25% to ToE, 50% to ToE, 75% to ToE, and ToE + fig = mc_results.states.snapshot(0).plot_scatter(label = "t={} s".format(int(mc_results.times[0]))) # 0 + quarter_index = int(len(mc_results.times)/4) + mc_results.states.snapshot(quarter_index).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index]))) # 25% + mc_results.states.snapshot(quarter_index*2).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index*2]))) # 50% + mc_results.states.snapshot(quarter_index*3).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[quarter_index*3]))) # 75% + mc_results.states.snapshot(-1).plot_scatter(fig = fig, label = "t={} s".format(int(mc_results.times[-1]))) # 100% + + mc_results.time_of_event.plot_hist() + + # Step 4: Show all plots + import matplotlib.pyplot as plt # For plotting + plt.show() + +# This allows the module to be executed directly +if __name__ == '__main__': + run_example() From bbd85feeef53e01729979663aab0ab11a433fa86 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 11:09:20 -0700 Subject: [PATCH 39/64] Support for single keys --- src/prog_algs/metrics/toe_metrics.py | 2 ++ src/prog_algs/metrics/uncertain_data_metrics.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/src/prog_algs/metrics/toe_metrics.py b/src/prog_algs/metrics/toe_metrics.py index c9a36b82..c4be4528 100644 --- a/src/prog_algs/metrics/toe_metrics.py +++ b/src/prog_algs/metrics/toe_metrics.py @@ -29,6 +29,8 @@ def prob_success(toe : UncertainData, time : float, **kwargs) -> float: if isinstance(toe, UncertainData): # Default to all keys keys = params.setdefault('keys', toe.keys()) + if isinstance(keys, str): + keys = [keys] if isinstance(toe, UnweightedSamples): samples = toe diff --git a/src/prog_algs/metrics/uncertain_data_metrics.py b/src/prog_algs/metrics/uncertain_data_metrics.py index acfaeb0d..63fcb0f4 100644 --- a/src/prog_algs/metrics/uncertain_data_metrics.py +++ b/src/prog_algs/metrics/uncertain_data_metrics.py @@ -30,6 +30,8 @@ def calc_metrics(data : UncertainData, ground_truth : Union[float, dict] = None, if isinstance(data, UncertainData): # Default to all keys keys = params.setdefault('keys', data.keys()) + if isinstance(keys, str): + keys = [keys] if ground_truth and isscalar(ground_truth): # If ground truth is scalar, create dict (expected below) From e6dd726f40b0ce2fbca8c8b3d95e9010c91b92b9 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 11:35:36 -0700 Subject: [PATCH 40/64] Remove examples --- examples/basic_example_battery.py | 36 +++++++++++----- examples/thrown_object_example.py | 68 ------------------------------- examples/utpredictor.py | 66 ------------------------------ 3 files changed, 26 insertions(+), 144 deletions(-) delete mode 100644 examples/thrown_object_example.py delete mode 100644 examples/utpredictor.py diff --git a/examples/basic_example_battery.py b/examples/basic_example_battery.py index 4b365e5c..6b2032c9 100644 --- a/examples/basic_example_battery.py +++ b/examples/basic_example_battery.py @@ -1,7 +1,7 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. """ -This example performs a state estimation and prediction with uncertainty given a Prognostics Model. +This example extends the "basic example" to perform a state estimation and prediction with uncertainty given a more complicated. Models, state estimators, and predictors can be switched out. See documentation nasa.github.io/progpy for description of options Method: An instance of the BatteryCircuit model in prog_models is created, and the prediction process is achieved in three steps: 1) State estimation of the current state is performed using a chosen state_estimator, and samples are drawn from this estimate @@ -16,9 +16,15 @@ from prog_models.models import BatteryCircuit as Battery # VVV Uncomment this to use Electro Chemistry Model VVV -from prog_models.models import BatteryElectroChemEOD as Battery +# from prog_models.models import BatteryElectroChemEOD as Battery -from prog_algs import * +from prog_algs.state_estimators import ParticleFilter as StateEstimator +# VVV Uncomment this to use UKF State Estimator VVV +# from prog_algs.state_estimators import UnscentedKalmanFilter as StateEstimator + +from prog_algs.predictors import MonteCarlo as Predictor +# VVV Uncomment this to use UnscentedTransform Predictor VVV +# from prog_algs.predictors import UnscentedTransformPredictor as Predictor def run_example(): # Step 1: Setup model & future loading @@ -47,10 +53,8 @@ def future_loading(t, x = None): print("\nPerforming State Estimation Step") # Step 2a: Setup - filt = state_estimators.ParticleFilter(batt, initial_state) - # VVV Uncomment this to use UKF State Estimator VVV - # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) - + filt = StateEstimator(batt, initial_state) + # Step 2b: Print & Plot Prior State print("Prior State:", filt.x.mean) print('\tSOC: ', batt.event_state(filt.x.mean)['EOD']) @@ -74,24 +78,36 @@ def future_loading(t, x = None): print("\n\nPerforming Prediction Step") # Step 3a: Setup Predictor - mc = predictors.MonteCarlo(batt) + mc = Predictor(batt) # Step 3b: Perform a prediction NUM_SAMPLES = 5 STEP_SIZE = 0.1 - mc_results = mc.predict(filt.x, future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE) + SAVE_FREQ = 100 # How often to save results + mc_results = mc.predict(filt.x, future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE, save_freq = SAVE_FREQ) print('ToE', mc_results.time_of_event.mean) # Step 3c: Analyze the results # Note: The results of a sample-based prediction can be accessed by sample, e.g., - states_sample_1 = mc_results.states[1] + from prog_algs.predictors import UnweightedSamplesPrediction + if isinstance(mc_results, UnweightedSamplesPrediction): + states_sample_1 = mc_results.states[1] # now states_sample_1[n] corresponds to times[n] for the first sample # You can also access a state distribution at a specific time using the .snapshot function states_time_1 = mc_results.states.snapshot(1) # now you have all the samples corresponding to times[1] + # Print Results + print('Results: ') + for i, time in enumerate(mc_results.times): + print('\nt = {}'.format(time)) + print('\tu = {}'.format(mc_results.inputs.snapshot(i).mean)) + print('\tx = {}'.format(mc_results.states.snapshot(i).mean)) + print('\tz = {}'.format(mc_results.outputs.snapshot(i).mean)) + print('\tevent state = {}'.format(mc_results.event_states.snapshot(i).mean)) + # You can also access the final state (of type UncertainData), like so: final_state = mc_results.time_of_event.final_state print('Final state @EOD: ', final_state['EOD'].mean) diff --git a/examples/thrown_object_example.py b/examples/thrown_object_example.py deleted file mode 100644 index 1be59d44..00000000 --- a/examples/thrown_object_example.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. - -""" -This example performs a state estimation and prediction with uncertainty given a Prognostics Model. Unlike basic_example, this example uses a model with multiple events (ThrownObject). Prediction only ends when all events are met - -Method: An instance of the Thrown Object model in prog_models is created, and the prediction process is achieved in three steps: - 1) State estimation of the current state is performed using a chosen state_estimator, and samples are drawn from this estimate - 2) Prediction of future states (with uncertainty) and the times at which the event thresholds will be reached -Results: - i) Predicted future values (inputs, states, outputs, event_states) with uncertainty from prediction - ii) Time event is predicted to occur (with uncertainty) -""" - -from prog_models.models.thrown_object import ThrownObject -# VVV Uncomment this to use Electro Chemistry Model VVV -# from prog_models.models import BatteryElectroChem as Battery - -from prog_algs import * - -from pprint import pprint - -def run_example(): - # Step 1: Setup model & future loading - m = ThrownObject(process_noise = 0.25, measurement_noise = 0.2) - initial_state = m.initialize() - def future_loading(t, x = None): - return m.InputContainer({}) - - # Step 2: Demonstrating state estimator - print("\nPerforming State Estimation Step...") - - # Step 2a: Setup - NUM_SAMPLES = 1000 - filt = state_estimators.ParticleFilter(m, initial_state, num_particles = NUM_SAMPLES) - # VVV Uncomment this to use UKF State Estimator VVV - # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) - - # Step 2b: Print & Plot Prior State - u = m.InputContainer({}) # No input for ThrownObject - z = m.output(initial_state) # Measured output (here as an example we use the model output instead) - # Note: In practice, replace this with actual measured data - filt.estimate(0.1, u, z) - - # Note: in a prognostic application the above state estimation step would be repeated each time - # there is new data. Here we're doing one step to demonstrate how the state estimator is used - - # Step 3: Demonstrating Predictor - print("\nPerforming Prediction Step...") - - # Step 3a: Setup Predictor - mc = predictors.MonteCarlo(m) - - # Step 3b: Perform a prediction - samples = filt.x # Since we're using a particle filter, which is also sample-based, we can directly use the samples, without changes - STEP_SIZE = 0.01 - mc_results = mc.predict(samples, future_loading, dt=STEP_SIZE, horizon=8) - print("\nPredicted Time of Event:") - pprint(mc_results.time_of_event.metrics()) # Note this takes some time - mc_results.time_of_event.plot_hist(keys = 'impact') - mc_results.time_of_event.plot_hist(keys = 'falling') - - # Step 4: Show all plots - import matplotlib.pyplot as plt # For plotting - plt.show() - -# This allows the module to be executed directly -if __name__ == '__main__': - run_example() diff --git a/examples/utpredictor.py b/examples/utpredictor.py deleted file mode 100644 index ef0253c0..00000000 --- a/examples/utpredictor.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. - -""" -An example using the UnscentedTransformPredictor class to predict the degredation of a battery. -""" - -from prog_models.models import BatteryCircuit -from prog_algs import * -# from prog_algs.visualize import plot_hist -# import matplotlib.pyplot as plt - -def run_example(): - ## Setup - batt = BatteryCircuit() - def future_loading(t, x = None): - # Variable (piece-wise) future loading scheme - if (t < 600): - i = 2 - elif (t < 900): - i = 1 - elif (t < 1800): - i = 4 - elif (t < 3000): - i = 2 - else: - i = 3 - return batt.InputContainer({'i': i}) - - ## State Estimation - perform a single ukf state estimate step - filt = state_estimators.UnscentedKalmanFilter(batt, batt.parameters['x0']) - - import matplotlib.pyplot as plt # For plotting - print("Prior State:", filt.x.mean) - print('\tSOC: ', batt.event_state(filt.x.mean)['EOD']) - example_measurements = {'t': 32.2, 'v': 3.915} - t = 0.1 - filt.estimate(t, future_loading(t), example_measurements) - print("Posterior State:", filt.x.mean) - print('\tSOC: ', batt.event_state(filt.x.mean)['EOD']) - - ## Prediction - Predict EOD given current state - # Setup prediction - mc = predictors.UnscentedTransformPredictor(batt) - - # Predict with a step size of 0.1 - mc_results = mc.predict(filt.x, future_loading, dt=0.1, save_freq= 100) - - # Print Results - for i, time in enumerate(mc_results.times): - print('\nt = {}'.format(time)) - print('\tu = {}'.format(mc_results.inputs.snapshot(i).mean)) - print('\tx = {}'.format(mc_results.states.snapshot(i).mean)) - print('\tz = {}'.format(mc_results.outputs.snapshot(i).mean)) - print('\tevent state = {}'.format(mc_results.event_states.snapshot(i).mean)) - - print('\nToE:', mc_results.time_of_event.mean) - - # You can also access the final state (of type UncertainData), like so: - final_state = mc_results.time_of_event.final_state - print('Final state @EOD: ', final_state['EOD'].mean) - # toe.plot_hist() - # plt.show() - -# This allows the module to be executed directly -if __name__ == '__main__': - run_example() From 5b80a06485fae8800f6fc9d0b9c86fa01ba0b17b Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 11:35:46 -0700 Subject: [PATCH 41/64] Reduce noise --- src/prog_algs/predictors/unscented_transform.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index 916461ea..f5326161 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -105,7 +105,7 @@ def __init__(self, model, **kwargs): if 'Q' not in self.parameters: # Default - self.parameters['Q'] = diag([1.0e-1 for i in range(num_states)]) + self.parameters['Q'] = diag([1.0e-8 for _ in range(num_states)]) def measure(x): x = model.StateContainer(x) From 8173c075675d6af30774e41160ca8d3dea26404d Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 11:59:29 -0700 Subject: [PATCH 42/64] Update examples --- examples/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/__init__.py b/examples/__init__.py index 2bd4c7c0..010b98c3 100644 --- a/examples/__init__.py +++ b/examples/__init__.py @@ -1,3 +1,3 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. -__all__ = ['basic_example', 'benchmarking_example', 'eol_event', 'horizon', 'kalman_filter', 'utpredictor', 'new_state_estimator_example', 'measurement_eqn_example', 'playback', 'predict_specific_event', 'thrown_object_example'] +__all__ = ['basic_example', 'basic_example_battery', 'benchmarking_example', 'eol_event', 'horizon', 'kalman_filter', 'new_state_estimator_example', 'measurement_eqn_example', 'playback', 'predict_specific_event'] From d9ad694a78ecaaf411ee8272ecda71820da7fdb0 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 11:59:59 -0700 Subject: [PATCH 43/64] Remove line --- src/prog_algs/state_estimators/particle_filter.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index 6b7f28a3..01e98437 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -85,7 +85,6 @@ def __measure(x): self.particles = model.StateContainer(array(samples)) - if 'R' in self.parameters: # For backwards compatibility warn("'R' is deprecated. Use 'measurement_noise' instead.", DeprecationWarning) From d0723c57d79e4b40254464bc248e0d1a839c00b2 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 12:02:21 -0700 Subject: [PATCH 44/64] Fix percentage in bounds --- src/prog_algs/uncertain_data/unweighted_samples.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/prog_algs/uncertain_data/unweighted_samples.py b/src/prog_algs/uncertain_data/unweighted_samples.py index 300649ac..a7854623 100644 --- a/src/prog_algs/uncertain_data/unweighted_samples.py +++ b/src/prog_algs/uncertain_data/unweighted_samples.py @@ -165,6 +165,8 @@ def size(self) -> int: def percentage_in_bounds(self, bounds, keys : list = None) -> dict: if not keys: keys = self.keys() + if isinstance(keys, str): + keys = [keys] if isinstance(bounds, list): bounds = {key: bounds for key in self.keys()} if not isinstance(bounds, dict) or all([isinstance(b, list) and len(b) == 2 for b in bounds]): From 205b11f0cf6c1fcaf03c73805889c77791e01062 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 16:07:34 -0700 Subject: [PATCH 45/64] Fix UTP tests --- tests/test_predictors.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_predictors.py b/tests/test_predictors.py index 998ff8d3..34a1d1eb 100644 --- a/tests/test_predictors.py +++ b/tests/test_predictors.py @@ -83,9 +83,9 @@ def future_loading(t, x={}): return {} mc_results = pred.predict(samples, future_loading, dt=0.01, events=['falling'], save_freq=1) - self.assertAlmostEqual(mc_results.time_of_event.mean['falling'], 4.15, 0) + self.assertAlmostEqual(mc_results.time_of_event.mean['falling'], 3.8, 0) self.assertTrue('impact' not in mc_results.time_of_event.mean) - self.assertAlmostEqual(mc_results.times[-1], 4, 1) # Saving every second, last time should be around the nearest 1s before falling event + self.assertAlmostEqual(mc_results.times[-1], 3, 1) # Saving every second, last time should be around the nearest 1s before falling event def test_UKP_Battery(self): def future_loading(t, x = None): From de524eedfaed95542efa55c94927044ef8ca1968 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 16:08:42 -0700 Subject: [PATCH 46/64] Remove PF Initialization method --- .../state_estimators/particle_filter.py | 41 +++++++------------ 1 file changed, 15 insertions(+), 26 deletions(-) diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index 01e98437..5b6e4556 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -1,16 +1,17 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. -from prog_algs.uncertain_data.uncertain_data import UncertainData -from typing import Callable -from . import state_estimator -from numpy import array, empty, random, take, exp, max, take from filterpy.monte_carlo import residual_resample -from numbers import Number +from numpy import array, empty, take, exp, max, take from scipy.stats import norm -from ..uncertain_data import UnweightedSamples -from ..exceptions import ProgAlgTypeError +from typing import Callable from warnings import warn +from prog_models.utils.containers import DictLikeMatrixWrapper + +from . import state_estimator +from ..uncertain_data import UnweightedSamples, ScalarData, UncertainData +from ..exceptions import ProgAlgTypeError + class ParticleFilter(state_estimator.StateEstimator): """ @@ -42,7 +43,6 @@ class ParticleFilter(state_estimator.StateEstimator): 't0': -1e-99, # practically 0, but allowing for a 0 first estimate 'num_particles': 20, 'resample_fcn': residual_resample, - 'x0_uncertainty': 0.5 } def __init__(self, model, x0, measurement_eqn : Callable = None, **kwargs): @@ -51,7 +51,6 @@ def __init__(self, model, x0, measurement_eqn : Callable = None, **kwargs): if measurement_eqn: warn("Warning: measurement_eqn depreciated as of v1.3.1, will be removed in v1.4. Use Model subclassing instead. See examples.measurement_eqn_example") # update output_container - from prog_models.utils.containers import DictLikeMatrixWrapper z0 = measurement_eqn(x0) class MeasureContainer(DictLikeMatrixWrapper): def __init__(self, z): @@ -64,24 +63,14 @@ def __measure(x): else: self._measure = model.output - # Caching for optimization - paramters_x0_exist = 'x0_uncertainty' in self.parameters - if paramters_x0_exist: # Only create these optimizations if x0_uncertainty exists as key in self.parameters - parameters_x0_dict, parameters_x0_num = isinstance(self.parameters['x0_uncertainty'], dict), isinstance(self.parameters['x0_uncertainty'], Number) # Build array inplace - if isinstance(x0, UncertainData): - sample_gen = x0.sample(self.parameters['num_particles']) - samples = [array(sample_gen.key(k)) for k in x0.keys()] - elif paramters_x0_exist and (parameters_x0_dict or parameters_x0_num): - warn("Warning: x0_uncertainty depreciated as of v1.3, will be removed in v1.4. Use UncertainData type if estimating filtering with uncertain data.") - x = array(list(x0.values())) - if parameters_x0_dict: - sd = array([self.parameters['x0_uncertainty'][key] for key in x0.keys()]) - elif parameters_x0_num: - sd = array([self.parameters['x0_uncertainty']] * len(x0)) - samples = [random.normal(x[i], sd[i], self.parameters['num_particles']) for i in range(len(x))] - else: - raise ProgAlgTypeError("ProgAlgTypeError: x0 must be of type UncertainData or x0_uncertainty must be of type [dict, Number].") + if isinstance(x0, DictLikeMatrixWrapper) or isinstance(x0, dict): + x0 = ScalarData(x0) + elif not isinstance(x0, UncertainData): + raise ProgAlgTypeError(f"ProgAlgTypeError: x0 must be of type UncertainData or StateContainer, was {type(x0)}.") + + sample_gen = x0.sample(self.parameters['num_particles']) + samples = [array(sample_gen.key(k)) for k in x0.keys()] self.particles = model.StateContainer(array(samples)) From 5eb49fcdba4ae83e85383a4a146ef2ae4175a437 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 4 Oct 2022 16:16:59 -0700 Subject: [PATCH 47/64] Fix test --- tests/test_state_estimators.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/tests/test_state_estimators.py b/tests/test_state_estimators.py index ea3df0f9..950165d9 100644 --- a/tests/test_state_estimators.py +++ b/tests/test_state_estimators.py @@ -234,7 +234,7 @@ def test_UKF_incorrect_input(self): self.__incorrect_input_tests(UnscentedKalmanFilter) def test_PF(self): - m = ThrownObject(process_noise={'x': 0.25, 'v': 0.25}, measurement_noise=1) + m = ThrownObject(process_noise={'x': 0.75, 'v': 0.75}, measurement_noise=1) x_guess = {'x': 1.75, 'v': 38.5} # Guess of initial state, actual is {'x': 1.83, 'v': 40} filt = ParticleFilter(m, x_guess, num_particles = 1000) @@ -282,12 +282,6 @@ def test_PF(self): for k in mean1.keys(): self.assertEqual(mean1[k], mean2[k]) self.assertTrue((filt_scalar.x.cov == x_scalar.cov).all()) - # Case 1: Only x0_uncertainty provided; expect a warning issued - with self.assertWarns(Warning): - filt_scalar = ParticleFilter(m, {'x': 1.75, 'v': 38.5}, x0_uncertainty = 0.5) - # Case 2: Raise ProgAlgTypeError if x0 not UncertainData or x0_uncertainty not of type {{dict, Number}}. - with self.assertRaises(ProgAlgTypeError): - filt_scalar = ParticleFilter(m, {'x': 1.75, 'v': 38.5}, num_particles = 20, x0_uncertainty = []) def test_PF_incorrect_input(self): self.__incorrect_input_tests(ParticleFilter) From 8552cec1e38f021458e5dcbb5b477a2927a15e28 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 17 Oct 2022 15:08:19 -0700 Subject: [PATCH 48/64] Remove x0 uncert from test --- tests/test_state_estimators.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_state_estimators.py b/tests/test_state_estimators.py index 950165d9..49c37524 100644 --- a/tests/test_state_estimators.py +++ b/tests/test_state_estimators.py @@ -273,9 +273,9 @@ def test_PF(self): self.assertAlmostEqual(filt_us.x.cov[i][j], x_us.cov[i][j], delta=0.1) # Test x0 if-else Control - # Case 0: Both isinstance(x0, UncertainData) and x0_uncertainty parameter provided; expect x0_uncertainty to be skipped + # Case 0: isinstance(x0, UncertainData) x_scalar = ScalarData({'x': 1.75, 'v': 38.5}) # Testing with ScalarData - filt_scalar = ParticleFilter(m, x_scalar, num_particles = 20, x0_uncertainty = 0.5) # Sample count does not affect ScalarData testing + filt_scalar = ParticleFilter(m, x_scalar, num_particles = 20) # Sample count does not affect ScalarData testing mean1 = filt_scalar.x.mean mean2 = x_scalar.mean self.assertSetEqual(set(mean1.keys()), set(mean2.keys())) From 022bb63a0c0815f19d8a4f04f0443d4db1c2c524 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 17 Oct 2022 15:07:53 -0700 Subject: [PATCH 49/64] Remove from template --- state_estimator_template.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/state_estimator_template.py b/state_estimator_template.py index 9a6ef7d1..9aec63c6 100644 --- a/state_estimator_template.py +++ b/state_estimator_template.py @@ -15,11 +15,11 @@ class TemplateStateEstimator(state_estimators.StateEstimator): 't0' : 0.0 # Initial timestamp } - def __init__(self, model, x0, measurement_eqn=None, **kwargs): + def __init__(self, model, x0, **kwargs): """ Constructor (optional) """ - super().__init__(model, x0, measurement_eqn, **kwargs) + super().__init__(model, x0, **kwargs) # ADD PARAMETER CHECKS HERE # e.g., self.parameters['some_value'] < 0 From 07821b408747ac6d833cb1323d3d388263a107d7 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Mon, 17 Oct 2022 15:09:50 -0700 Subject: [PATCH 50/64] Remove measurement eqn --- .../state_estimators/kalman_filter.py | 4 +-- .../state_estimators/particle_filter.py | 19 +++----------- .../state_estimators/state_estimator.py | 9 +------ .../unscented_kalman_filter.py | 25 +++++++------------ 4 files changed, 15 insertions(+), 42 deletions(-) diff --git a/src/prog_algs/state_estimators/kalman_filter.py b/src/prog_algs/state_estimators/kalman_filter.py index 9b95661a..3076c670 100644 --- a/src/prog_algs/state_estimators/kalman_filter.py +++ b/src/prog_algs/state_estimators/kalman_filter.py @@ -46,12 +46,12 @@ class KalmanFilter(state_estimator.StateEstimator): 'dt': 1 } - def __init__(self, model, x0, measurement_eqn : Callable = None, **kwargs): + def __init__(self, model, x0, **kwargs): # Note: Measurement equation kept in constructor to keep it consistent with other state estimators. This way measurement equation can be provided as an ordered argument, and will just be ignored here if not isinstance(model, LinearModel): raise Exception('Kalman Filter only supports Linear Models (i.e., models derived from prog_models.LinearModel)') - super().__init__(model, x0, None, **kwargs) + super().__init__(model, x0, **kwargs) self.x0 = x0 diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index 5b6e4556..f4903734 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -45,23 +45,10 @@ class ParticleFilter(state_estimator.StateEstimator): 'resample_fcn': residual_resample, } - def __init__(self, model, x0, measurement_eqn : Callable = None, **kwargs): - super().__init__(model, x0, measurement_eqn = measurement_eqn, **kwargs) + def __init__(self, model, x0, **kwargs): + super().__init__(model, x0, **kwargs) - if measurement_eqn: - warn("Warning: measurement_eqn depreciated as of v1.3.1, will be removed in v1.4. Use Model subclassing instead. See examples.measurement_eqn_example") - # update output_container - z0 = measurement_eqn(x0) - class MeasureContainer(DictLikeMatrixWrapper): - def __init__(self, z): - super().__init__(list(z0.keys()), z) - - def __measure(x): - return MeasureContainer(measurement_eqn(x)) - - self._measure = __measure - else: - self._measure = model.output + self._measure = model.output # Build array inplace if isinstance(x0, DictLikeMatrixWrapper) or isinstance(x0, dict): diff --git a/src/prog_algs/state_estimators/state_estimator.py b/src/prog_algs/state_estimators/state_estimator.py index cb2f8d9c..18314ebf 100644 --- a/src/prog_algs/state_estimators/state_estimator.py +++ b/src/prog_algs/state_estimators/state_estimator.py @@ -30,7 +30,7 @@ class StateEstimator(ABC): 't0': 0 } - def __init__(self, model, x0, measurement_eqn : Callable = None, **kwargs): + def __init__(self, model, x0, **kwargs): # Check model if not hasattr(model, 'output'): raise ProgAlgTypeError("model must have `output` method") @@ -42,18 +42,11 @@ def __init__(self, model, x0, measurement_eqn : Callable = None, **kwargs): raise ProgAlgTypeError("model must have `states` property") self.model = model - if measurement_eqn is not None: - warn('Measurement_eqn was deprecated in v1.3 in favor of model subclassing. I will remove this in v1.4. See measurement_equation example for more information', DeprecationWarning) - # Check x0 for key in model.states: if key not in x0: raise ProgAlgTypeError("x0 missing state `{}`".format(key)) - # Check measurement equation - if measurement_eqn and not callable(measurement_eqn): - raise ProgAlgTypeError("measurement_eqn must be callable") - # Process kwargs (configuration) self.parameters = deepcopy(self.default_parameters) self.parameters.update(kwargs) diff --git a/src/prog_algs/state_estimators/unscented_kalman_filter.py b/src/prog_algs/state_estimators/unscented_kalman_filter.py index 442e9740..580b8a96 100644 --- a/src/prog_algs/state_estimators/unscented_kalman_filter.py +++ b/src/prog_algs/state_estimators/unscented_kalman_filter.py @@ -43,27 +43,20 @@ class UnscentedKalmanFilter(state_estimator.StateEstimator): 'dt': 1 } - def __init__(self, model, x0, measurement_eqn : Callable = None, **kwargs): - super().__init__(model, x0, measurement_eqn, **kwargs) + def __init__(self, model, x0, **kwargs): + super().__init__(model, x0, **kwargs) self.__input = None self.x0 = x0 # Saving for reduce pickling - if measurement_eqn is None: - def measure(x): - x = model.StateContainer({key: value for (key, value) in zip(x0.keys(), x)}) - R_err = model.parameters['measurement_noise'].copy() - model.parameters['measurement_noise'] = dict.fromkeys(R_err, 0) - z = model.output(x) - model.parameters['measurement_noise'] = R_err - return array(list(z.values())).ravel() - else: - warn("Warning: measurement_eqn depreciated as of v1.3.1, will be removed in v1.4. Use Model subclassing instead. See examples.measurement_eqn_example") - def measure(x): - x = model.StateContainer({key: value for (key, value) in zip(x0.keys(), x)}) - z = measurement_eqn(x) - return array(list(z.values())).ravel() + def measure(x): + x = model.StateContainer({key: value for (key, value) in zip(x0.keys(), x)}) + R_err = model.parameters['measurement_noise'].copy() + model.parameters['measurement_noise'] = dict.fromkeys(R_err, 0) + z = model.output(x) + model.parameters['measurement_noise'] = R_err + return array(list(z.values())).ravel() if 'Q' not in self.parameters: self.parameters['Q'] = diag([1.0e-3 for i in x0.keys()]) From 2db99571b6245a1eea1270721de0e363247eddc5 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 19 Oct 2022 08:54:44 -0700 Subject: [PATCH 51/64] Remove imports --- src/prog_algs/state_estimators/kalman_filter.py | 1 - src/prog_algs/state_estimators/particle_filter.py | 1 - src/prog_algs/state_estimators/state_estimator.py | 2 -- src/prog_algs/state_estimators/unscented_kalman_filter.py | 3 +-- 4 files changed, 1 insertion(+), 6 deletions(-) diff --git a/src/prog_algs/state_estimators/kalman_filter.py b/src/prog_algs/state_estimators/kalman_filter.py index 3076c670..61e89a78 100644 --- a/src/prog_algs/state_estimators/kalman_filter.py +++ b/src/prog_algs/state_estimators/kalman_filter.py @@ -1,7 +1,6 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. from copy import deepcopy -from typing import Callable import numpy as np from warnings import warn from filterpy import kalman diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index f4903734..581496e5 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -3,7 +3,6 @@ from filterpy.monte_carlo import residual_resample from numpy import array, empty, take, exp, max, take from scipy.stats import norm -from typing import Callable from warnings import warn from prog_models.utils.containers import DictLikeMatrixWrapper diff --git a/src/prog_algs/state_estimators/state_estimator.py b/src/prog_algs/state_estimators/state_estimator.py index 18314ebf..eea270de 100644 --- a/src/prog_algs/state_estimators/state_estimator.py +++ b/src/prog_algs/state_estimators/state_estimator.py @@ -1,12 +1,10 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. from abc import ABC, abstractmethod, abstractproperty -from typing import Callable from ..uncertain_data import UncertainData from ..exceptions import ProgAlgTypeError from copy import deepcopy -from warnings import warn class StateEstimator(ABC): """ diff --git a/src/prog_algs/state_estimators/unscented_kalman_filter.py b/src/prog_algs/state_estimators/unscented_kalman_filter.py index 580b8a96..5500b01b 100644 --- a/src/prog_algs/state_estimators/unscented_kalman_filter.py +++ b/src/prog_algs/state_estimators/unscented_kalman_filter.py @@ -1,7 +1,6 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. from warnings import warn -from typing import Callable from . import state_estimator from filterpy import kalman from numpy import diag, array @@ -74,7 +73,7 @@ def state_transition(x, dt): self.filter = kalman.UnscentedKalmanFilter(num_states, num_measurements, self.parameters['dt'], measure, state_transition, points) if isinstance(x0, dict) or isinstance(x0, model.StateContainer): - warn("Warning: x0_uncertainty depreciated as of v1.3, will be removed in v1.4. Use UncertainData type if estimating filtering with uncertain data.") + warn("Use UncertainData type if estimating filtering with uncertain data.") self.filter.x = array(list(x0.values())) self.filter.P = self.parameters['Q'] / 10 elif isinstance(x0, UncertainData): From d3a6043b0c8c7c5d1a9d3764bc759a9fdf397625 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 19 Oct 2022 14:07:33 -0700 Subject: [PATCH 52/64] Update version # --- README.md | 17 +++++++---------- setup.py | 2 +- src/prog_algs/__init__.py | 2 +- 3 files changed, 9 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index c39cca93..7217ac12 100644 --- a/README.md +++ b/README.md @@ -17,13 +17,7 @@ See documentation [here](https://nasa.github.io/prog_algs/) ## Repository Directory Structure `src/prog_algs/` - The prognostics algorithm python package
-   |- `metrics/` - Tools for analyzing the results of state estimation and prognostics -   |- `predictors/` - Algorithms for performing the prediction step of model-based prognostics
-   |- `state_estimators/` - Algorithms for performing the state estimation step of model-based prognostics
-   |- `uncertain_data/` - Tools for storing and manipulating data with uncertainty
-   |- `visualize/` - Tools for visualizing the results of state estimation and prognostics
`docs/` - Project documentation (see also [github.io](https://nasa.github.io/prog_algs/))
-`sphinx_config/` - Configuration for automatic documentation generation
`examples/` - Example Python scripts using prog_algs
`tests/` - Tests for prog_models
`README.md` - The readme (this file)
@@ -35,16 +29,20 @@ Use the following to cite this repository: @misc{2022_nasa_prog_algs, author = {Christopher Teubert and Matteo Corbetta and Chetan Kulkarni}, title = {Prognostics Algorithm Python Package}, - month = May, + month = Oct, year = 2022, - version = {1.3.0}, + version = {1.4.0}, url = {https://github.com/nasa/prog\_algs} } ``` The corresponding reference should look like this: -C. Teubert, M. Corbetta, C. Kulkarni, Prognostics Algorithm Python Package, v1.3.0, May 2022. URL https://github.com/nasa/prog_algs. +C. Teubert, M. Corbetta, C. Kulkarni, Prognostics Algorithm Python Package, v1.4.0, Oct 2022. URL https://github.com/nasa/prog_algs. + +Alternatively, if using both prog_models and prog_algs, you can cite the combined package as + +C. Teubert, C. Kulkarni, M. Corbetta, K. Jarvis, M. Daigle, ProgPy Prognostics Python Packages, v1.4, October 2022. URL https://github.com/nasa/prog_models. ## Acknowledgements The structure and algorithms of this package are strongly inspired by the [MATLAB Prognostics Algorithm Library](https://github.com/nasa/PrognosticsAlgorithmLibrary) and the [MATLAB Prognostics Metrics Library](https://github.com/nasa/PrognosticsMetricsLibrary). We would like to recognize Matthew Daigle, Shankar Sankararaman and the rest of the team that contributed to the Prognostics Model Library for the contributions their work on the MATLAB library made to the design of prog_algs @@ -53,7 +51,6 @@ The structure and algorithms of this package are strongly inspired by the [MATLA Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. ## Disclaimers - No Warranty: THE SUBJECT SOFTWARE IS PROVIDED "AS IS" WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR FREEDOM FROM INFRINGEMENT, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL BE ERROR FREE, OR ANY WARRANTY THAT DOCUMENTATION, IF PROVIDED, WILL CONFORM TO THE SUBJECT SOFTWARE. THIS AGREEMENT DOES NOT, IN ANY MANNER, CONSTITUTE AN ENDORSEMENT BY GOVERNMENT AGENCY OR ANY PRIOR RECIPIENT OF ANY RESULTS, RESULTING DESIGNS, HARDWARE, SOFTWARE PRODUCTS OR ANY OTHER APPLICATIONS RESULTING FROM USE OF THE SUBJECT SOFTWARE. FURTHER, GOVERNMENT AGENCY DISCLAIMS ALL WARRANTIES AND LIABILITIES REGARDING THIRD-PARTY SOFTWARE, IF PRESENT IN THE ORIGINAL SOFTWARE, AND DISTRIBUTES IT "AS IS." Waiver and Indemnity: RECIPIENT AGREES TO WAIVE ANY AND ALL CLAIMS AGAINST THE UNITED STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS, AS WELL AS ANY PRIOR RECIPIENT. IF RECIPIENT'S USE OF THE SUBJECT SOFTWARE RESULTS IN ANY LIABILITIES, DEMANDS, DAMAGES, EXPENSES OR LOSSES ARISING FROM SUCH USE, INCLUDING ANY DAMAGES FROM PRODUCTS BASED ON, OR RESULTING FROM, RECIPIENT'S USE OF THE SUBJECT SOFTWARE, RECIPIENT SHALL INDEMNIFY AND HOLD HARMLESS THE UNITED STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS, AS WELL AS ANY PRIOR RECIPIENT, TO THE EXTENT PERMITTED BY LAW. RECIPIENT'S SOLE REMEDY FOR ANY SUCH MATTER SHALL BE THE IMMEDIATE, UNILATERAL TERMINATION OF THIS AGREEMENT. diff --git a/setup.py b/setup.py index 0b9c063f..1479cfc9 100644 --- a/setup.py +++ b/setup.py @@ -10,7 +10,7 @@ setup( name = 'prog_algs', - version = '1.3.1', + version = '1.4.0', description = "The NASA Prognostics Algorithm Package is a framework for model-based prognostics (computation of remaining useful life) of engineering systems. It includes algorithms for state estimation and prediction, including uncertainty propagation. The algorithms use prognostic models (see prog_models) to perform estimation and prediction. The package enables rapid development of prognostics solutions for given models of components and systems. Algorithms can be swapped for comparative studies and evaluations", long_description=long_description, long_description_content_type='text/markdown', diff --git a/src/prog_algs/__init__.py b/src/prog_algs/__init__.py index 657faf71..0b6e1e61 100644 --- a/src/prog_algs/__init__.py +++ b/src/prog_algs/__init__.py @@ -5,7 +5,7 @@ import warnings -__version__ = '1.3.1' +__version__ = '1.4.0' def run_prog_playback(obs, pred, future_loading, output_measurements, **kwargs): warnings.warn("Depreciated in 1.2.0, will be removed in a future release.", DeprecationWarning) From 45fa7e2ea1eec0e5c803cc30842e77e0575b1406 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 19 Oct 2022 14:07:40 -0700 Subject: [PATCH 53/64] Remove sphinx config --- sphinx_config/_templates/layout.html | 6 -- sphinx_config/conf.py | 65 ------------------- sphinx_config/getting_started.rst | 4 -- sphinx_config/images/alpha_lambda_plt_ex.png | Bin 319394 -> 0 bytes sphinx_config/images/package_structure.png | Bin 360789 -> 0 bytes sphinx_config/images/scatter_hist_ex.png | Bin 67744 -> 0 bytes sphinx_config/images/scatter_plt_ex.png | Bin 395520 -> 0 bytes sphinx_config/index.rst | 4 -- sphinx_config/metrics.rst | 4 -- sphinx_config/prediction.rst | 4 -- sphinx_config/predictors.rst | 4 -- sphinx_config/release.rst | 4 -- sphinx_config/state_estimators.rst | 4 -- sphinx_config/uncertain_data.rst | 4 -- 14 files changed, 103 deletions(-) delete mode 100644 sphinx_config/_templates/layout.html delete mode 100644 sphinx_config/conf.py delete mode 100644 sphinx_config/getting_started.rst delete mode 100644 sphinx_config/images/alpha_lambda_plt_ex.png delete mode 100644 sphinx_config/images/package_structure.png delete mode 100644 sphinx_config/images/scatter_hist_ex.png delete mode 100644 sphinx_config/images/scatter_plt_ex.png delete mode 100644 sphinx_config/index.rst delete mode 100644 sphinx_config/metrics.rst delete mode 100644 sphinx_config/prediction.rst delete mode 100644 sphinx_config/predictors.rst delete mode 100644 sphinx_config/release.rst delete mode 100644 sphinx_config/state_estimators.rst delete mode 100644 sphinx_config/uncertain_data.rst diff --git a/sphinx_config/_templates/layout.html b/sphinx_config/_templates/layout.html deleted file mode 100644 index 8c1a51a3..00000000 --- a/sphinx_config/_templates/layout.html +++ /dev/null @@ -1,6 +0,0 @@ -{% extends '!layout.html' %} - -{% block footer %} - -Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. -{% endblock %} diff --git a/sphinx_config/conf.py b/sphinx_config/conf.py deleted file mode 100644 index e4cf31c2..00000000 --- a/sphinx_config/conf.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright © 2021 United States Government as represented by the Administrator of the -# National Aeronautics and Space Administration. All Rights Reserved. - -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. - -import os -import sys -sys.path.insert(0, os.path.abspath('../src')) -sys.path.insert(0, os.path.abspath('..')) -sys.path.insert(0, os.path.abspath('.')) - - -# -- Project information ----------------------------------------------------- - -project = 'Prognostics Algorithms Python Package' -copyright = '2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved.' -author = 'Chris Teubert, Matteo Corbetta, and Chetan Kulkarni' - -# The full version, including alpha/beta/rc tags -from prog_algs import __version__ as version -release = version - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.coverage', "sphinx.ext.githubpages", 'sphinx.ext.napoleon', 'sphinx_reredirects'] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] - -autodoc_mock_imports = ['numpy', 'scipy', 'filterpy', 'prog_models'] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'alabaster' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -redirects = { - "*": "https://nasa.github.io/progpy" -} diff --git a/sphinx_config/getting_started.rst b/sphinx_config/getting_started.rst deleted file mode 100644 index 0a95e38b..00000000 --- a/sphinx_config/getting_started.rst +++ /dev/null @@ -1,4 +0,0 @@ -Getting Started -=============== - -Documentation moved to https://nasa.github.io/progpy diff --git a/sphinx_config/images/alpha_lambda_plt_ex.png b/sphinx_config/images/alpha_lambda_plt_ex.png deleted file mode 100644 index c489c48f7824ad8ede872ed5de02dd3950664ca8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 319394 zcmeFZby!tf7e0!8ED%L$Ft7-bk_Ht75d@SHB$N;-k&aE7G$;s2ZB$CSJ5?Il8|g+t zO3F<)cg$@a&iQ`#-v93N+~@dw-w}|t)|_LGc*pyWwcX`qCHL;4*+oJ^vRCS|_;nJJ z-MJ(ryS)C|4nMi@mpTHVX0tx$I|AuxgN>Y+m`o@O)ZTLv=3YBnOhi`-s3vM 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zf{V=OyUpjL^enh8x&O-IfKwE^5DjciO48osg$qY>7|i$Jgk8g>dJsx1q{4dS(S8V>s`#+W ztam3>N^Bar#$uq~tSJKr8LOl%EiEt$#Z6*|2v)Px5pM+;1`dp#J;h*v1m^uXP1H8` zpc_rD95;8R=avtAo?cwg*S2FVsU0y2dP@lgH$(HD4S=usHA+)b6*IDX6h7UkSaNt% zkB_6tWiLHP;U^W2u=^d*MpH8l#C}xzZ5+7#T{K*CKg8Ef1Xq3i2gx5<1Q`1a%i0Xx z)VE|S=6^3pK1KYdQAa(!z!#b9jk-&`k_L9$mw4mWOIhAPPr-Gz76w$UBl$oXfc2QT zxP0uo0ge4}+vvY8TH}8PRHAq zjcwij_xXNXND^lH7i9CB!gi?tI}sC!0%5lHXgqG^m1M} g?&T`@KUoEHLo^OL5AUmXbU&Vd*}0|o!=R)81!ywYm;e9( diff --git a/sphinx_config/index.rst b/sphinx_config/index.rst deleted file mode 100644 index e2b86f10..00000000 --- a/sphinx_config/index.rst +++ /dev/null @@ -1,4 +0,0 @@ -Prognostics Algorithms Python Package -============================================================= - -Documentation moved to https://nasa.github.io/progpy diff --git a/sphinx_config/metrics.rst b/sphinx_config/metrics.rst deleted file mode 100644 index d884a27e..00000000 --- a/sphinx_config/metrics.rst +++ /dev/null @@ -1,4 +0,0 @@ -Metrics and Figures -==================== - -Documentation moved to https://nasa.github.io/progpy diff --git a/sphinx_config/prediction.rst b/sphinx_config/prediction.rst deleted file mode 100644 index 22209253..00000000 --- a/sphinx_config/prediction.rst +++ /dev/null @@ -1,4 +0,0 @@ -Prediction -======================= - -Documentation moved to https://nasa.github.io/progpy diff --git a/sphinx_config/predictors.rst b/sphinx_config/predictors.rst deleted file mode 100644 index c61b4be0..00000000 --- a/sphinx_config/predictors.rst +++ /dev/null @@ -1,4 +0,0 @@ -Predictors -======================= - -Documentation moved to https://nasa.github.io/progpy diff --git a/sphinx_config/release.rst b/sphinx_config/release.rst deleted file mode 100644 index d0980713..00000000 --- a/sphinx_config/release.rst +++ /dev/null @@ -1,4 +0,0 @@ -Release Notes -======================= - -Documentation moved to https://nasa.github.io/progpy diff --git a/sphinx_config/state_estimators.rst b/sphinx_config/state_estimators.rst deleted file mode 100644 index 2a7c2bc1..00000000 --- a/sphinx_config/state_estimators.rst +++ /dev/null @@ -1,4 +0,0 @@ -State Estimators -======================= - -Documentation moved to https://nasa.github.io/progpy diff --git a/sphinx_config/uncertain_data.rst b/sphinx_config/uncertain_data.rst deleted file mode 100644 index 6f960fa0..00000000 --- a/sphinx_config/uncertain_data.rst +++ /dev/null @@ -1,4 +0,0 @@ -Uncertain Data -======================= - -Documentation moved to https://nasa.github.io/progpy From 5f1a7023c6d3fe821bdbaee477a08552d17490c2 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 19 Oct 2022 14:08:22 -0700 Subject: [PATCH 54/64] Reorder imports --- examples/basic_example.py | 1 - examples/horizon.py | 4 ++-- examples/new_state_estimator_example.py | 4 +++- examples/particle_filter_battery_example.py | 5 +++-- examples/playback.py | 7 +++---- predictor_template.py | 1 + src/prog_algs/metrics/samples.py | 6 +++--- src/prog_algs/metrics/toe_metrics.py | 2 +- src/prog_algs/metrics/toe_profile_metrics.py | 4 ++-- src/prog_algs/metrics/uncertain_data_metrics.py | 1 + src/prog_algs/predictors/monte_carlo.py | 5 +++-- src/prog_algs/predictors/unscented_transform.py | 7 ++++--- src/prog_algs/state_estimators/kalman_filter.py | 4 +++- src/prog_algs/state_estimators/state_estimator.py | 3 ++- src/prog_algs/state_estimators/unscented_kalman_filter.py | 5 +++-- src/prog_algs/uncertain_data/multivariate_normal_dist.py | 3 ++- src/prog_algs/uncertain_data/scalar_data.py | 1 + src/prog_algs/uncertain_data/unweighted_samples.py | 3 ++- src/prog_algs/visualize/plot_hist.py | 1 - src/prog_algs/visualize/plot_scatter.py | 2 -- 20 files changed, 39 insertions(+), 30 deletions(-) diff --git a/examples/basic_example.py b/examples/basic_example.py index c86b431f..f6e1f492 100644 --- a/examples/basic_example.py +++ b/examples/basic_example.py @@ -15,7 +15,6 @@ """ from prog_models.models import ThrownObject - from prog_algs import * def run_example(): diff --git a/examples/horizon.py b/examples/horizon.py index 39048d32..4c754ec4 100644 --- a/examples/horizon.py +++ b/examples/horizon.py @@ -11,14 +11,14 @@ ii) Time event is predicted to occur (with uncertainty) """ +from pprint import pprint + from prog_models.models.thrown_object import ThrownObject # VVV Uncomment this to use Electro Chemistry Model VVV # from prog_models.models import BatteryElectroChem as Battery from prog_algs import * -from pprint import pprint - def run_example(): # Step 1: Setup model & future loading def future_loading(t, x = None): diff --git a/examples/new_state_estimator_example.py b/examples/new_state_estimator_example.py index cc409ce0..6e87dc8c 100644 --- a/examples/new_state_estimator_example.py +++ b/examples/new_state_estimator_example.py @@ -6,9 +6,11 @@ In this example a basic state estimator is constructed by subclassing the StateEstimator class. This StateEstimator is then demonstrated with a ThrownObject model """ +import random + from prog_algs.state_estimators import StateEstimator from prog_algs.uncertain_data import ScalarData -import random + class BlindlyStumbleEstimator(StateEstimator): """ diff --git a/examples/particle_filter_battery_example.py b/examples/particle_filter_battery_example.py index 782508a0..8fd4374f 100644 --- a/examples/particle_filter_battery_example.py +++ b/examples/particle_filter_battery_example.py @@ -4,11 +4,12 @@ In this example the BatteryElectroChemEOD model is used with a particle filter to estimate the state of the battery """ -from prog_models.models import BatteryElectroChemEOD -from prog_algs import * import numpy as np import matplotlib.pyplot as plt +from prog_models.models import BatteryElectroChemEOD +from prog_algs import * + def run_example(): ## Setup # Save battery model diff --git a/examples/playback.py b/examples/playback.py index bc982487..f1e2d90c 100644 --- a/examples/playback.py +++ b/examples/playback.py @@ -16,6 +16,9 @@ iv) Figures illustrating results """ +import csv +import numpy as np + from prog_models.models import BatteryCircuit as Battery # VVV Uncomment this to use Electro Chemistry Model VVV # from prog_models.models import BatteryElectroChem as Battery @@ -32,10 +35,6 @@ from prog_algs.uncertain_data.multivariate_normal_dist import MultivariateNormalDist -import csv -import matplotlib.pyplot as plt -import numpy as np - # Constants NUM_SAMPLES = 20 NUM_PARTICLES = 1000 # For state estimator (if using ParticleFilter) diff --git a/predictor_template.py b/predictor_template.py index 33cc3452..595b3b6b 100644 --- a/predictor_template.py +++ b/predictor_template.py @@ -1,6 +1,7 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. from copy import deepcopy + from prog_algs.predictors import Predictor, Prediction, PredictionResults # Replace the following with whatever form of UncertainData you would like to use to represent ToE from prog_algs.uncertain_data import ScalarData diff --git a/src/prog_algs/metrics/samples.py b/src/prog_algs/metrics/samples.py index 6dcb5f60..d72d3e1d 100644 --- a/src/prog_algs/metrics/samples.py +++ b/src/prog_algs/metrics/samples.py @@ -1,13 +1,13 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. # This file is kept for backwards compatability +from numpy import mean, sqrt +from warnings import warn + from .uncertain_data_metrics import calc_metrics as eol_metrics from .toe_metrics import prob_success from .toe_profile_metrics import alpha_lambda -from numpy import mean, sqrt -from warnings import warn - def mean_square_error(values : list, ground_truth : float) -> float: """Mean Square Error Args: diff --git a/src/prog_algs/metrics/toe_metrics.py b/src/prog_algs/metrics/toe_metrics.py index 0fc934ce..ec0e9603 100644 --- a/src/prog_algs/metrics/toe_metrics.py +++ b/src/prog_algs/metrics/toe_metrics.py @@ -3,9 +3,9 @@ """ This file includes functions for calculating metrics specific to Time of Event (ToE) from a single event or multiple events given the same time of prediction """ - from typing import Iterable from numpy import isscalar + from ..uncertain_data import UncertainData, UnweightedSamples def prob_success(toe : UncertainData, time : float, **kwargs) -> float: diff --git a/src/prog_algs/metrics/toe_profile_metrics.py b/src/prog_algs/metrics/toe_profile_metrics.py index fc87547e..3f61ee28 100644 --- a/src/prog_algs/metrics/toe_profile_metrics.py +++ b/src/prog_algs/metrics/toe_profile_metrics.py @@ -5,9 +5,9 @@ """ from numpy import sign from collections import defaultdict -from typing import Callable +from typing import Callable, Dict + from ..predictors import ToEPredictionProfile -from typing import Dict def alpha_lambda(toe_profile : ToEPredictionProfile, ground_truth : dict, lambda_value : float, alpha : float, beta : float, **kwargs) -> dict: """ diff --git a/src/prog_algs/metrics/uncertain_data_metrics.py b/src/prog_algs/metrics/uncertain_data_metrics.py index 63fcb0f4..948b854c 100644 --- a/src/prog_algs/metrics/uncertain_data_metrics.py +++ b/src/prog_algs/metrics/uncertain_data_metrics.py @@ -7,6 +7,7 @@ from numpy import isscalar, mean, std, array from scipy import stats from warnings import warn + from ..uncertain_data import UncertainData, UnweightedSamples def calc_metrics(data : UncertainData, ground_truth : Union[float, dict] = None, **kwargs) -> dict: diff --git a/src/prog_algs/predictors/monte_carlo.py b/src/prog_algs/predictors/monte_carlo.py index 38b2fd4b..916434d7 100644 --- a/src/prog_algs/predictors/monte_carlo.py +++ b/src/prog_algs/predictors/monte_carlo.py @@ -1,12 +1,13 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. -from .prediction import UnweightedSamplesPrediction, PredictionResults -from .predictor import Predictor from copy import deepcopy from typing import Callable from prog_models.sim_result import SimResult, LazySimResult from prog_algs.uncertain_data import UnweightedSamples, UncertainData +from .prediction import UnweightedSamplesPrediction, PredictionResults +from .predictor import Predictor + class MonteCarlo(Predictor): """ diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index f5326161..7ea416ae 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -1,11 +1,12 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. +from copy import deepcopy +from filterpy import kalman +from numpy import diag, array, transpose, isnan from typing import Callable + from .prediction import Prediction, UnweightedSamplesPrediction, PredictionResults from .predictor import Predictor -from numpy import diag, array, transpose, isnan -from copy import deepcopy -from filterpy import kalman from prog_algs.uncertain_data import MultivariateNormalDist, UncertainData, ScalarData diff --git a/src/prog_algs/state_estimators/kalman_filter.py b/src/prog_algs/state_estimators/kalman_filter.py index 61e89a78..1c76a353 100644 --- a/src/prog_algs/state_estimators/kalman_filter.py +++ b/src/prog_algs/state_estimators/kalman_filter.py @@ -1,10 +1,12 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. from copy import deepcopy +from filterpy import kalman import numpy as np from warnings import warn -from filterpy import kalman + from prog_models import LinearModel + from . import state_estimator from ..uncertain_data import MultivariateNormalDist, UncertainData diff --git a/src/prog_algs/state_estimators/state_estimator.py b/src/prog_algs/state_estimators/state_estimator.py index eea270de..e13e248f 100644 --- a/src/prog_algs/state_estimators/state_estimator.py +++ b/src/prog_algs/state_estimators/state_estimator.py @@ -1,10 +1,11 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. from abc import ABC, abstractmethod, abstractproperty +from copy import deepcopy from ..uncertain_data import UncertainData from ..exceptions import ProgAlgTypeError -from copy import deepcopy + class StateEstimator(ABC): """ diff --git a/src/prog_algs/state_estimators/unscented_kalman_filter.py b/src/prog_algs/state_estimators/unscented_kalman_filter.py index 5500b01b..179ed722 100644 --- a/src/prog_algs/state_estimators/unscented_kalman_filter.py +++ b/src/prog_algs/state_estimators/unscented_kalman_filter.py @@ -1,9 +1,10 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. -from warnings import warn -from . import state_estimator from filterpy import kalman from numpy import diag, array +from warnings import warn + +from . import state_estimator from ..uncertain_data import MultivariateNormalDist, UncertainData class UnscentedKalmanFilter(state_estimator.StateEstimator): diff --git a/src/prog_algs/uncertain_data/multivariate_normal_dist.py b/src/prog_algs/uncertain_data/multivariate_normal_dist.py index 1010ceb4..2f8b40c7 100644 --- a/src/prog_algs/uncertain_data/multivariate_normal_dist.py +++ b/src/prog_algs/uncertain_data/multivariate_normal_dist.py @@ -1,9 +1,10 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. -from . import UncertainData, UnweightedSamples from numpy import array from numpy.random import multivariate_normal +from . import UncertainData, UnweightedSamples + class MultivariateNormalDist(UncertainData): """ diff --git a/src/prog_algs/uncertain_data/scalar_data.py b/src/prog_algs/uncertain_data/scalar_data.py index 325300d2..d559a694 100644 --- a/src/prog_algs/uncertain_data/scalar_data.py +++ b/src/prog_algs/uncertain_data/scalar_data.py @@ -2,6 +2,7 @@ from typing import Union from numpy import array + from . import UncertainData, UnweightedSamples diff --git a/src/prog_algs/uncertain_data/unweighted_samples.py b/src/prog_algs/uncertain_data/unweighted_samples.py index a7854623..da9130f8 100644 --- a/src/prog_algs/uncertain_data/unweighted_samples.py +++ b/src/prog_algs/uncertain_data/unweighted_samples.py @@ -1,6 +1,5 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. -from . import UncertainData from collections import UserList from collections.abc import Iterable from numpy import array, cov, random @@ -8,6 +7,8 @@ from prog_models.utils.containers import DictLikeMatrixWrapper +from . import UncertainData + class UnweightedSamples(UncertainData, UserList): """ diff --git a/src/prog_algs/visualize/plot_hist.py b/src/prog_algs/visualize/plot_hist.py index 428e3fb2..267a5511 100644 --- a/src/prog_algs/visualize/plot_hist.py +++ b/src/prog_algs/visualize/plot_hist.py @@ -3,7 +3,6 @@ from matplotlib.figure import Figure import matplotlib.pyplot as plt from numpy import array - from typing import List def plot_hist(samples : array, fig : Figure = None, keys : List[str] = None, **kwargs) -> Figure: diff --git a/src/prog_algs/visualize/plot_scatter.py b/src/prog_algs/visualize/plot_scatter.py index 38c6a2b6..cab22872 100644 --- a/src/prog_algs/visualize/plot_scatter.py +++ b/src/prog_algs/visualize/plot_scatter.py @@ -3,8 +3,6 @@ from matplotlib.figure import Figure import matplotlib.pyplot as plt from math import sqrt - -from numpy import array from typing import List def plot_scatter(samples : List[dict], fig : Figure = None, keys : List[str] = None, legend : str = 'auto', **kwargs) -> Figure: From 60bb10c4e4d6d648a385e6ad9621c4e66706b987 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 19 Oct 2022 14:12:05 -0700 Subject: [PATCH 55/64] Update examples --- examples/horizon.py | 10 ++++++---- examples/kalman_filter.py | 15 ++++++++++----- 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/examples/horizon.py b/examples/horizon.py index 4c754ec4..2a6d26db 100644 --- a/examples/horizon.py +++ b/examples/horizon.py @@ -6,6 +6,7 @@ Method: An instance of the Thrown Object model in prog_models is created, and the prediction process is achieved in three steps: 1) State estimation of the current state is performed using a chosen state_estimator, and samples are drawn from this estimate 2) Prediction of future states (with uncertainty) and the times at which the event thresholds will be reached, within the prediction horizon. All events outside the horizon come back as None and are ignored in metrics + Results: i) Predicted future values (inputs, states, outputs, event_states) with uncertainty from prediction ii) Time event is predicted to occur (with uncertainty) @@ -24,7 +25,7 @@ def run_example(): def future_loading(t, x = None): return {} m = ThrownObject(process_noise = 0.25, measurement_noise = 0.2) - initial_state = m.initialize({}, {}) + initial_state = m.initialize() # Step 2: Demonstrating state estimator print("\nPerforming State Estimation Step...") @@ -35,12 +36,13 @@ def future_loading(t, x = None): # VVV Uncomment this to use UKF State Estimator VVV # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) - # Step 2b: Print & Plot Prior State + # Step 2b: One step of state estimator u = m.InputContainer({}) # No input for ThrownObject filt.estimate(0.1, u, m.output(initial_state)) - # Note: in a prognostic application the above state estimation step would be repeated each time - # there is new data. Here we're doing one step to demonstrate how the state estimator is used + # Note: in a prognostic application the above state estimation + # step would be repeated each time there is new data. + # Here we're doing one step to demonstrate how the state estimator is used # Step 3: Demonstrating Predictor print("\nPerforming Prediction Step...") diff --git a/examples/kalman_filter.py b/examples/kalman_filter.py index 2cbc7de9..2de5fb97 100644 --- a/examples/kalman_filter.py +++ b/examples/kalman_filter.py @@ -68,7 +68,8 @@ class ThrownObject(LinearModel): C = np.array([[1, 0]]) F = None # Will override method - # The Default parameters. Overwritten by passing parameters dictionary into constructor + # The Default parameters. + # Overwritten by passing parameters dictionary into constructor default_parameters = { 'thrower_height': 1.83, # m 'throwing_speed': 40, # m/s @@ -77,12 +78,15 @@ class ThrownObject(LinearModel): def initialize(self, u=None, z=None): return self.StateContainer({ - 'x': self.parameters['thrower_height'], # Thrown, so initial altitude is height of thrower - 'v': self.parameters['throwing_speed'] # Velocity at which the ball is thrown - this guy is a professional baseball pitcher + 'x': self.parameters['thrower_height'], + # Thrown, so initial altitude is height of thrower + 'v': self.parameters['throwing_speed'] + # Velocity at which the ball is thrown - this guy is a professional baseball pitcher }) # This is actually optional. Leaving thresholds_met empty will use the event state to define thresholds. - # Threshold = Event State == 0. However, this implementation is more efficient, so we included it + # Threshold is met when Event State == 0. + # However, this implementation is more efficient, so we included it def threshold_met(self, x): return { 'falling': x['v'] < 0, @@ -107,7 +111,8 @@ def run_example(): kf = KalmanFilter(m, x_guess) # Step 3: Run the Kalman Filter State Estimator - # Here we're using simulated data from the thrown_object. In a real application you would be using sensor data from the system + # Here we're using simulated data from the thrown_object. + # In a real application you would be using sensor data from the system dt = 0.01 # Time step (s) print_freq = 50 # Print every print_freq'th iteration x = m.initialize() From ccf462bcd77ee96ae6c4baf0d20f181af1e381e9 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 20 Oct 2022 10:53:10 -0700 Subject: [PATCH 56/64] Fix link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7217ac12..e3c8c77e 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ C. Teubert, M. Corbetta, C. Kulkarni, Prognostics Algorithm Python Package, v1.4 Alternatively, if using both prog_models and prog_algs, you can cite the combined package as -C. Teubert, C. Kulkarni, M. Corbetta, K. Jarvis, M. Daigle, ProgPy Prognostics Python Packages, v1.4, October 2022. URL https://github.com/nasa/prog_models. +C. Teubert, C. Kulkarni, M. Corbetta, K. Jarvis, M. Daigle, ProgPy Prognostics Python Packages, v1.4, October 2022. URL https://nasa.github.io/progpy. ## Acknowledgements The structure and algorithms of this package are strongly inspired by the [MATLAB Prognostics Algorithm Library](https://github.com/nasa/PrognosticsAlgorithmLibrary) and the [MATLAB Prognostics Metrics Library](https://github.com/nasa/PrognosticsMetricsLibrary). We would like to recognize Matthew Daigle, Shankar Sankararaman and the rest of the team that contributed to the Prognostics Model Library for the contributions their work on the MATLAB library made to the design of prog_algs From 8fde116f1a04888eaa49c1964aab49c893efa860 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 20 Oct 2022 10:53:40 -0700 Subject: [PATCH 57/64] indent and extra lines --- src/prog_algs/predictors/toe_prediction_profile.py | 1 + src/prog_algs/uncertain_data/scalar_data.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/prog_algs/predictors/toe_prediction_profile.py b/src/prog_algs/predictors/toe_prediction_profile.py index 76435ba9..fc1ae966 100644 --- a/src/prog_algs/predictors/toe_prediction_profile.py +++ b/src/prog_algs/predictors/toe_prediction_profile.py @@ -122,6 +122,7 @@ def monotonicity(self, **kwargs) -> Dict[str, float]: Args: toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions + Returns: dict (str, dict): Dictionary where keys represent a profile and dict is a subdictionary representing an event and its respective monotonicitiy value between [0, 1]. """ diff --git a/src/prog_algs/uncertain_data/scalar_data.py b/src/prog_algs/uncertain_data/scalar_data.py index d559a694..28fe8926 100644 --- a/src/prog_algs/uncertain_data/scalar_data.py +++ b/src/prog_algs/uncertain_data/scalar_data.py @@ -11,7 +11,7 @@ class ScalarData(UncertainData): Data without uncertainty- single value Args: - state (dict or Container): Single state in the form of dict or model.*Container (InputContainer, OutputContainer, Statecontainer) representing states and respective values. + state (dict or Container): Single state in the form of dict or model.*Container (InputContainer, OutputContainer, Statecontainer) representing states and respective values. """ def __init__(self, state, _type = dict): self.__state = state From 082d460b0dd4c5d1209500627d6605820c80e6e5 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 20 Oct 2022 10:54:43 -0700 Subject: [PATCH 58/64] fix bug- duplicate parameters --- src/prog_algs/predictors/monte_carlo.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/src/prog_algs/predictors/monte_carlo.py b/src/prog_algs/predictors/monte_carlo.py index 916434d7..6f644eef 100644 --- a/src/prog_algs/predictors/monte_carlo.py +++ b/src/prog_algs/predictors/monte_carlo.py @@ -83,12 +83,11 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs if 'save_freq' in params and not isinstance(params['save_freq'], tuple): params['save_freq'] = (t0, params['save_freq']) + params['x'] = x if len(params['events']) == 0: # Predict to time (times, inputs, states, outputs, event_states) = simulate_to_threshold(future_loading_eqn, first_output, threshold_keys = [], - t0 = t0, - x = x, **params ) else: @@ -105,8 +104,6 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs (t, u, xi, z, es) = simulate_to_threshold(future_loading_eqn, first_output, threshold_keys = events_remaining, - t0 = t0, - x = x, **params ) @@ -135,10 +132,10 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs events_remaining.remove(event) # No longer an event to predect to # Remove last state (event) - t0 = times.pop() + params['t0'] = times.pop() inputs.pop() - x = states.pop() - last_state[event] = x.copy() + params['x'] = states.pop() + last_state[event] = params['x'].copy() outputs.pop() event_states.pop() From 2f6e71921581b07f8cc39fdadff4de0ce5b0ce16 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 20 Oct 2022 15:33:46 -0700 Subject: [PATCH 59/64] fix setup and readme --- README.md | 2 +- setup.py | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index e3c8c77e..37efef85 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ This is designed to be used with the [Prognostics Models Package](https://github `pip3 install prog_algs` ## Documentation -See documentation [here](https://nasa.github.io/prog_algs/) +See documentation [here](https://nasa.github.io/progpy/prog_algs_guide.html) ## Repository Directory Structure diff --git a/setup.py b/setup.py index 1479cfc9..f0c78a9f 100644 --- a/setup.py +++ b/setup.py @@ -14,7 +14,7 @@ description = "The NASA Prognostics Algorithm Package is a framework for model-based prognostics (computation of remaining useful life) of engineering systems. It includes algorithms for state estimation and prediction, including uncertainty propagation. The algorithms use prognostic models (see prog_models) to perform estimation and prediction. The package enables rapid development of prognostics solutions for given models of components and systems. Algorithms can be swapped for comparative studies and evaluations", long_description=long_description, long_description_content_type='text/markdown', - url = 'https://github.com/nasa/prog_algs', + url = 'https://nasa.github.io/progpy/prog_algs_guide.html', author = 'Christopher Teubert', author_email = 'christopher.a.teubert@nasa.gov', classifiers = [ @@ -33,7 +33,7 @@ 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: 3 :: Only' ], - keywords = ['prognostics', 'diagnostics', 'fault detection', 'fdir', 'prognostics and health management', 'PHM', 'health management'], + keywords = ['prognostics', 'diagnostics', 'fault detection', 'fdir', 'prognostics and health management', 'PHM', 'health management', 'ivhm'], package_dir = {"":"src"}, packages = find_packages(where = 'src'), python_requires='>=3.7, <3.11', @@ -47,7 +47,8 @@ license = 'NOSA', project_urls={ # Optional 'Bug Reports': 'https://github.com/nasa/prog_algs/issues', - 'Organization': 'https://prognostics.nasa.gov/', + 'Docs': 'https://nasa.github.io/progpy/prog_algs_guide.html', + 'Organization': 'https://www.nasa.gov/content/diagnostics-prognostics', 'Source': 'https://github.com/nasa/prog_algs', } ) \ No newline at end of file From c9068cab3aad53d6b503b3d57194ad0bba237865 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Tue, 25 Oct 2022 12:50:37 -0700 Subject: [PATCH 60/64] Update docs --- examples/basic_example.py | 2 +- setup.py | 2 +- src/prog_algs/predictors/prediction.py | 2 +- src/prog_algs/predictors/predictor.py | 2 +- src/prog_algs/predictors/toe_prediction_profile.py | 8 ++++---- src/prog_algs/uncertain_data/uncertain_data.py | 11 ++++++----- 6 files changed, 14 insertions(+), 13 deletions(-) diff --git a/examples/basic_example.py b/examples/basic_example.py index f6e1f492..2ae7fbc3 100644 --- a/examples/basic_example.py +++ b/examples/basic_example.py @@ -32,7 +32,7 @@ def future_loading(t, x = None): # Step 2a: Setup filt = state_estimators.ParticleFilter(m, initial_state) # VVV Uncomment this to use UKF State Estimator VVV - # filt = state_estimators.UnscentedKalmanFilter(batt, initial_state) + # filt = state_estimators.UnscentedKalmanFilter(m, initial_state) # Step 2b: Print & Plot Prior State print("Prior State:", filt.x.mean) diff --git a/setup.py b/setup.py index f0c78a9f..9f247a03 100644 --- a/setup.py +++ b/setup.py @@ -51,4 +51,4 @@ 'Organization': 'https://www.nasa.gov/content/diagnostics-prognostics', 'Source': 'https://github.com/nasa/prog_algs', } -) \ No newline at end of file +) diff --git a/src/prog_algs/predictors/prediction.py b/src/prog_algs/predictors/prediction.py index c3b4efe7..3d9fb79c 100644 --- a/src/prog_algs/predictors/prediction.py +++ b/src/prog_algs/predictors/prediction.py @@ -72,7 +72,7 @@ def monotonicity(self) -> Dict[str, float]: Given a single prediction, for each event: go through all predicted states and compare those to the next one. Calculates monotonicity for each event key using its associated mean value in UncertainData. - monotonoicity = |Σsign(i+1 - i) / N-1| + :math:`monotonoicity = \| \Sigma \dfrac{sign(i+1 - i)}{N-1}\|` Where N is number of measurements and sign indicates sign of calculation. diff --git a/src/prog_algs/predictors/predictor.py b/src/prog_algs/predictors/predictor.py index 754c3358..2d28f3c5 100644 --- a/src/prog_algs/predictors/predictor.py +++ b/src/prog_algs/predictors/predictor.py @@ -64,7 +64,7 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs Return ---------- - result esults from prediction, including: NameTuple + results from prediction as NameTuple, including: * times (List[float]): Times for each savepoint such that inputs.snapshot(i), states.snapshot(i), outputs.snapshot(i), and event_states.snapshot(i) are all at times[i] * inputs (Prediction): Inputs at each savepoint such that inputs.snapshot(i) is the input distribution (type UncertainData) at times[i] * states (Prediction): States at each savepoint such that states.snapshot(i) is the state distribution (type UncertainData) at times[i] diff --git a/src/prog_algs/predictors/toe_prediction_profile.py b/src/prog_algs/predictors/toe_prediction_profile.py index fc1ae966..c65228c1 100644 --- a/src/prog_algs/predictors/toe_prediction_profile.py +++ b/src/prog_algs/predictors/toe_prediction_profile.py @@ -68,7 +68,7 @@ def prognostic_horizon(self, criteria_eqn, ground_truth, **kwargs) -> Dict[str, """ Compute prognostic horizon metric, defined as the difference between a time ti, when the predictions meet specified performance criteria, and the time corresponding to the true Time of Event (ToE), for each event. - PH = ToE - ti + :math:`PH = ToE - ti` Args: toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions @@ -92,9 +92,9 @@ def cumulative_relative_accuracy(self, ground_truth, **kwargs) -> Dict[str, floa """ Compute cumulative relative accuracy for a given profile, defined as the normalized sum of relative prediction accuracies at specific time instances. - CRA = Σ(RA)/N for each event + :math:`CRA = \Sigma( \dfrac{RA}{N} )` for each event - Where Σ is summation of all relative accuracies for a given profile and N is the total count of profiles (Journal Prognostics Health Management, Saxena et al.) + Where :math:`\Sigma` is summation of all relative accuracies for a given profile and N is the total count of profiles (Journal Prognostics Health Management, Saxena et al.) Args: ground_truth (dict): Dictionary containing ground truth; specified as key, value pairs for event and its value. E.g, {'event1': 47.3, 'event2': 52.1, 'event3': 46.1} @@ -110,7 +110,7 @@ def monotonicity(self, **kwargs) -> Dict[str, float]: Given a prediction profile, for each prediction: go through all predicted states and compare those to the next one. Calculates monotonicity for each prediction key using its associated mean value in UncertainData. - monotonoicity = |Σsign(i+1 - i) / N-1| + :math:`monotonoicity = \|\Sigma \dfrac{sign(i+1 - i)}{N-1}\|` Where N is number of measurements and sign indicates sign of calculation. diff --git a/src/prog_algs/uncertain_data/uncertain_data.py b/src/prog_algs/uncertain_data/uncertain_data.py index c5f8777e..617719ea 100644 --- a/src/prog_algs/uncertain_data/uncertain_data.py +++ b/src/prog_algs/uncertain_data/uncertain_data.py @@ -65,16 +65,17 @@ def cov(self) -> array: """ def relative_accuracy(self, ground_truth : dict) -> dict: - """The relative accuracy of the mean of the distribution. + """The relative accuracy is how close the mean of the distribution is to the ground truth, on relative terms - RA = 1 - |r-p|/r + :math:`RA = 1 - \dfrac{\| r-p \|}{r}` - Where r is ground truth and p is mean of predicted distribution - - Prognostics: The Science of Making Predictions (Goebel et al, 239) + Where r is ground truth and p is mean of predicted distribution [0]_ Returns: dict[str, float]: Relative accuracy for each event where value is relative accuracy between [0,1] + + References: + .. [0] Prognostics: The Science of Making Predictions (Goebel et al, 239) """ # if this check isn't here, goes to divide by zero check and raises AttributeError instead of TypeError. Keep? There are unittests checking for type if not (isinstance(ground_truth, dict) or isinstance(ground_truth, DictLikeMatrixWrapper)): From 0a3a10bec4d1a4edc92a7a1715436efdf706cd9e Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Wed, 26 Oct 2022 10:03:22 -0700 Subject: [PATCH 61/64] fix tutorail --- examples/horizon.py | 2 -- tutorial.ipynb | 24 ++++++++++++------------ 2 files changed, 12 insertions(+), 14 deletions(-) diff --git a/examples/horizon.py b/examples/horizon.py index 2a6d26db..35ae309b 100644 --- a/examples/horizon.py +++ b/examples/horizon.py @@ -15,8 +15,6 @@ from pprint import pprint from prog_models.models.thrown_object import ThrownObject -# VVV Uncomment this to use Electro Chemistry Model VVV -# from prog_models.models import BatteryElectroChem as Battery from prog_algs import * diff --git a/tutorial.ipynb b/tutorial.ipynb index 4267c25c..cb91125b 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -24,7 +24,7 @@ "* `prog_algs.predictors` - Tools for performing prediction\n", "* `prog_algs.uncertain_data` - Tools for representing data with uncertainty\n", "\n", - "In addition to the `prog_algs` package, this repo includes examples showing how to use the package (see `examples/`), a template for implementing a new state estimator (`state_estimator_template`), a template for implementing a new predictor (`predictor_template`), documentation (), and this tutorial (`tutorial.ipynb`).\n", + "In addition to the `prog_algs` package, this repo includes examples showing how to use the package (see `examples/`), a template for implementing a new state estimator (`state_estimator_template`), a template for implementing a new predictor (`predictor_template`), documentation (), and this tutorial (`tutorial.ipynb`).\n", "\n", "Before you start, install `prog_algs` using pip:\n", "\n", @@ -192,7 +192,7 @@ "\n", "In `prog_algs` a State Estimator is used to estimate the system state. \n", "\n", - "To start, import the needed packages. Here we will import the `BatteryCircuit` model and the `UnscentedKalmanFilter` state estimator. See for more details on the available state estimators.\n" + "To start, import the needed packages. Here we will import the `BatteryCircuit` model and the `UnscentedKalmanFilter` state estimator. See for more details on the available state estimators.\n" ] }, { @@ -565,16 +565,14 @@ "metadata": {}, "source": [ "## Conclusion\n", - "This is just the basics, there's much more to learn. Please see the documentation at and the examples in the `examples/` folder for more details on how to use the package, including:\n", + "This is just the basics, there's much more to learn. Please see the documentation at and the examples in the `examples/` folder for more details on how to use the package, including:\n", "* `examples.basic_example` : A basic Example using prog_algs for Prognostics \n", - "* `examples.benchmarking_example` : An example benchmarking the performance of prognostics algorithms\n", + "* `examples.basic_example_battery` : A basic Example using prog_algs for Prognostics, using the more complex battery model\n", "* `examples.eol_event` : An example where a model has multiple events, but the user is only interested in predicting the time when the first event occurs (whatever it is).\n", "* `examples.measurement_eqn_example` : An example where not every output is measured or measurements are not in the same format as outputs, so a measurement equation is defined to translate between outputs and what's measured. \n", "* `examples.new_state_estimator_example` : An example of extending StateEstimator to create a new state estimator class\n", "* `examples.playback` : A full example performing prognostics using playback data.\n", "* `examples.predict_specific_event` : An example where the model has multiple events, but the user is only interested in predicting a specific event (or events).\n", - "* `examples.thrown_object_example` : An example performing prognostics with the simplified ThrownObject model\n", - "* `examples.utpredictor` : An example using the Unscented Transform Predictor for prediction.\n", "\n", "Thank you for trying out this tutorial. Open an issue on github () or email Chris Teubert (christopher.a.teubert@nasa.gov) with any questions or issues." ] @@ -588,11 +586,8 @@ } ], "metadata": { - "interpreter": { - "hash": "ff94885aa2d97705a9dae03869c2058fa855d1acd9df351499300343e2e591a2" - }, "kernelspec": { - "display_name": "Python 3.8.13 64-bit", + "display_name": "Python 3.8.9 64-bit", "language": "python", "name": "python3" }, @@ -606,14 +601,19 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.9" }, "metadata": { "interpreter": { "hash": "ff94885aa2d97705a9dae03869c2058fa855d1acd9df351499300343e2e591a2" } }, - "orig_nbformat": 2 + "orig_nbformat": 2, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } }, "nbformat": 4, "nbformat_minor": 2 From d4d2876a396be0d25429a1a2d51b8240729e311c Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 27 Oct 2022 09:44:49 -0700 Subject: [PATCH 62/64] Doc fixes --- examples/basic_example_battery.py | 3 +- src/prog_algs/predictors/monte_carlo.py | 4 +- src/prog_algs/predictors/prediction.py | 26 +++---- src/prog_algs/predictors/predictor.py | 34 +++++---- .../predictors/toe_prediction_profile.py | 55 +++++++------- .../predictors/unscented_transform.py | 4 +- .../state_estimators/kalman_filter.py | 21 +++--- .../state_estimators/particle_filter.py | 12 ++-- .../state_estimators/state_estimator.py | 17 +++-- .../unscented_kalman_filter.py | 22 +++--- .../uncertain_data/uncertain_data.py | 72 ++++++++++++++----- 11 files changed, 159 insertions(+), 111 deletions(-) diff --git a/examples/basic_example_battery.py b/examples/basic_example_battery.py index 6b2032c9..cd49c134 100644 --- a/examples/basic_example_battery.py +++ b/examples/basic_example_battery.py @@ -1,12 +1,13 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. """ -This example extends the "basic example" to perform a state estimation and prediction with uncertainty given a more complicated. Models, state estimators, and predictors can be switched out. See documentation nasa.github.io/progpy for description of options +This example extends the "basic example" to perform a state estimation and prediction with uncertainty given a more complicated model. Models, state estimators, and predictors can be switched out. See documentation nasa.github.io/progpy for description of options Method: An instance of the BatteryCircuit model in prog_models is created, and the prediction process is achieved in three steps: 1) State estimation of the current state is performed using a chosen state_estimator, and samples are drawn from this estimate 2) Prediction of future states (with uncertainty) and the times at which the event threshold will be reached 3) Metrics tools are used to further investigate the results of prediction + Results: i) Predicted future values (inputs, states, outputs, event_states) with uncertainty from prediction ii) Time event is predicted to occur (with uncertainty) diff --git a/src/prog_algs/predictors/monte_carlo.py b/src/prog_algs/predictors/monte_carlo.py index 6f644eef..bb7a572c 100644 --- a/src/prog_algs/predictors/monte_carlo.py +++ b/src/prog_algs/predictors/monte_carlo.py @@ -23,7 +23,7 @@ class MonteCarlo(Predictor): Initial time at which prediction begins, e.g., 0 dt : float Simulation step size (s), e.g., 0.1 - events : List[string] + events : list[str] Events to predict (subset of model.events) e.g., ['event1', 'event2'] horizon : float Prediction horizon (s) @@ -31,7 +31,7 @@ class MonteCarlo(Predictor): Number of samples to use. If not specified, a default value is used. If state is type UnweightedSamples and n_samples is not provided, the provided unweighted samples will be used directly. save_freq : float Frequency at which results are saved (s) - save_pts : List[float] + save_pts : list[float] Any additional savepoints (s) e.g., [10.1, 22.5] """ diff --git a/src/prog_algs/predictors/prediction.py b/src/prog_algs/predictors/prediction.py index 3d9fb79c..2c124b7b 100644 --- a/src/prog_algs/predictors/prediction.py +++ b/src/prog_algs/predictors/prediction.py @@ -1,7 +1,7 @@ # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved. from collections import UserList, defaultdict, namedtuple -from typing import Dict +from typing import Dict, List from numpy import sign from warnings import warn @@ -49,7 +49,7 @@ def snapshot(self, time_index : int) -> UncertainData: return self.data[time_index] @property - def mean(self) -> list: + def mean(self) -> List[dict]: """Estimate of the mean value of the prediction at each time Returns: @@ -74,19 +74,15 @@ def monotonicity(self) -> Dict[str, float]: :math:`monotonoicity = \| \Sigma \dfrac{sign(i+1 - i)}{N-1}\|` - Where N is number of measurements and sign indicates sign of calculation. - - Coble, J., et. al. (2021). Identifying Optimal Prognostic Parameters from D - ata: A Genetic Algorithms Approach. Annual Conference of the PHM Society. - http://www.papers.phmsociety.org/index.php/phmconf/article/view/1404 - - Baptistia, M., et. al. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Aritifical Intelligence, Volume 306. - https://www.sciencedirect.com/science/article/pii/S0004370222000078 + Where N is number of measurements and sign indicates sign of calculation [0]_ [1]_. - Args: - None Returns: dict (str, float): Value between [0, 1] indicating monotonicity of a given event for the Prediction. + + References: + .. [0] Coble, J., et. al. (2021). Identifying Optimal Prognostic Parameters from Data: A Genetic Algorithms Approach. Annual Conference of the PHM Society. http://www.papers.phmsociety.org/index.php/phmconf/article/view/1404 + .. [1] Baptistia, M., et. al. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Aritifical Intelligence, Volume 306. https://www.sciencedirect.com/science/article/pii/S0004370222000078 + """ # Collect and organize mean values for each event by_event = defaultdict(list) @@ -108,9 +104,9 @@ class UnweightedSamplesPrediction(Prediction, UserList): Immutable data class for the result of a prediction, where the predictions are stored as UnweightedSamples. Is returned from the predict method of a sample based prediction class (e.g., MonteCarlo). Objects of this class can be iterated and accessed like a list (e.g., prediction[0]), where prediction[n] represents a profile for sample n. Args: - times : array(float) - Times for each data point where times[n] corresponds to data[n] - data : array(dict) + times (list[float]): + Times for each data point where times[n] corresponds to data[:][n] + data (list[SimResult]): Data points where data[n] is a SimResult for sample n """ diff --git a/src/prog_algs/predictors/predictor.py b/src/prog_algs/predictors/predictor.py index 2d28f3c5..4696dc81 100644 --- a/src/prog_algs/predictors/predictor.py +++ b/src/prog_algs/predictors/predictor.py @@ -54,22 +54,28 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs Distribution representing current state of the system future_loading_eqn : function (t, x) -> z Function to generate an estimate of loading at future time t, and state x - options : optional, keyword arguments - The following configuration parameters are supported: \n - * dt (float): Simulation step size (s), e.g., 0.1 - * events (list[string]): Events to predict (subset of model.events) e.g., ['event1', 'event2'] - * horizon (float): Prediction horizon (s) - * save_freq (float): Frequency at which results are saved (s) - * save_pts (list[float]): Any additional savepoints (s) e.g., [10.1, 22.5] + + Keyword Arguments + ------------------- + dt : float + Simulation step size (s), e.g., 0.1 + events : list[str] + Events to predict (subset of model.events) e.g., ['event1', 'event2'] + horizon : float + Prediction horizon (s) + save_freq : float + Frequency at which results are saved (s) + save_pts : list[float] + Any additional savepoints (s) e.g., [10.1, 22.5] Return ---------- - results from prediction as NameTuple, including: - * times (List[float]): Times for each savepoint such that inputs.snapshot(i), states.snapshot(i), outputs.snapshot(i), and event_states.snapshot(i) are all at times[i] - * inputs (Prediction): Inputs at each savepoint such that inputs.snapshot(i) is the input distribution (type UncertainData) at times[i] - * states (Prediction): States at each savepoint such that states.snapshot(i) is the state distribution (type UncertainData) at times[i] - * outputs (Prediction): Outputs at each savepoint such that outputs.snapshot(i) is the output distribution (type UncertainData) at times[i] - * event_states (Prediction): Event states at each savepoint such that event_states.snapshot(i) is the event state distribution (type UncertainData) at times[i] - * time_of_event (UncertainData): Distribution of predicted Time of Event (ToE) for each predicted event, represented by some subclass of UncertaintData (e.g., MultivariateNormalDist) + NamedTuple + * times (list[float]): Times for each savepoint such that inputs.snapshot(i), states.snapshot(i), outputs.snapshot(i), and event_states.snapshot(i) are all at times[i] + * inputs (:py:class:`Prediction`): Inputs at each savepoint such that inputs.snapshot(i) is the input distribution (type UncertainData) at times[i] + * states (:py:class:`Prediction`): States at each savepoint such that states.snapshot(i) is the state distribution (type UncertainData) at times[i] + * outputs (:py:class:`Prediction`): Outputs at each savepoint such that outputs.snapshot(i) is the output distribution (type UncertainData) at times[i] + * event_states (:py:class:`Prediction`): Event states at each savepoint such that event_states.snapshot(i) is the event state distribution (type UncertainData) at times[i] + * time_of_event (:py:class:`prog_algs.uncertain_data.UncertainData`): Distribution of predicted Time of Event (ToE) for each predicted event, represented by some subclass of UncertaintData (e.g., :py:class:`MultivariateNormalDist`) """ pass diff --git a/src/prog_algs/predictors/toe_prediction_profile.py b/src/prog_algs/predictors/toe_prediction_profile.py index c65228c1..cfbbb2ea 100644 --- a/src/prog_algs/predictors/toe_prediction_profile.py +++ b/src/prog_algs/predictors/toe_prediction_profile.py @@ -45,7 +45,7 @@ def alpha_lambda(self, ground_truth : Dict[str, float], lambda_value : float, al """Calculate Alpha lambda metric for the prediction profile Args: - ground_truth (Dict[str, float]): + ground_truth (dict[str, float]): Ground Truth time of event for each event (e.g., {'event1': 748, 'event2', 2233, ...}) lambda_value (float): Prediction time at or after which metric is evaluated. Evaluation occurs at this time (if a prediction exists) or the next prediction following. @@ -53,13 +53,15 @@ def alpha_lambda(self, ground_truth : Dict[str, float], lambda_value : float, al percentage bounds around time to event (where 0.2 allows 20% error TtE) beta (float): portion of prediction that must be within those bounds - kwargs (optional, keyword arguments): - configuration arguments. Accepted args include: \n - * keys (list[string]): list of keys to use. If not provided, all keys are used. - * print (bool) : If True, print the results. Default is False. + + Keyword Args: + keys (list[str], optional): + list of keys to use. If not provided, all keys are used. + print (bool, optional) + If True, print the results to the screen. Default is False. Returns: - Dict[str, bool]: If alpha lambda was met for each key (e.g., {'event1': True, 'event2', False, ...}) + dict[str, bool]: If alpha lambda was met for each key (e.g., {'event1': True, 'event2', False, ...}) """ from ..metrics import alpha_lambda return alpha_lambda(self, ground_truth, lambda_value, alpha, beta, **kwargs) @@ -79,8 +81,10 @@ def prognostic_horizon(self, criteria_eqn, ground_truth, **kwargs) -> Dict[str, | Returns: Map of event names to boolean representing if the event has been met. | e.g., {'event1': True, 'event2': False} ground_truth (dict): Dictionary containing ground truth; specified as key, value pairs for event and its value. E.g, {'event1': 47.3, 'event2': 52.1, 'event3': 46.1} - kwargs (optional): configuration arguments. Accepted args include: - * print (bool): Boolean specifying whether the prognostic horizon metric should be printed. + + Keyword Args: + print (bool): + Boolean specifying whether the prognostic horizon metric should be printed. Returns: dict: Dictionary containing prognostic horizon calculations (value) for each event (key). e.g., {'event1': 12.3, 'event2': 15.1} @@ -89,42 +93,43 @@ def prognostic_horizon(self, criteria_eqn, ground_truth, **kwargs) -> Dict[str, return prognostic_horizon(self, criteria_eqn, ground_truth, **kwargs) def cumulative_relative_accuracy(self, ground_truth, **kwargs) -> Dict[str, float]: - """ + r""" Compute cumulative relative accuracy for a given profile, defined as the normalized sum of relative prediction accuracies at specific time instances. - :math:`CRA = \Sigma( \dfrac{RA}{N} )` for each event + :math:`CRA = \Sigma \left( \dfrac{RA}{N} \right)` for each event - Where :math:`\Sigma` is summation of all relative accuracies for a given profile and N is the total count of profiles (Journal Prognostics Health Management, Saxena et al.) + Where :math:`\Sigma` is summation of all relative accuracies for a given profile and N is the total count of profiles [0]_ Args: ground_truth (dict): Dictionary containing ground truth; specified as key, value pairs for event and its value. E.g, {'event1': 47.3, 'event2': 52.1, 'event3': 46.1} Returns: dict: Dictionary containing cumulative relative accuracy (value) for each event (key). e.g., {'event1': 12.3, 'event2': 15.1} + + References: + .. [0] Journal Prognostics Health Management, Saxena et al. """ from ..metrics import cumulative_relative_accuracy return cumulative_relative_accuracy(self, ground_truth, **kwargs) def monotonicity(self, **kwargs) -> Dict[str, float]: - """Calculate monotonicty for a prediction profile. + r"""Calculate monotonicty for a prediction profile. Given a prediction profile, for each prediction: go through all predicted states and compare those to the next one. - Calculates monotonicity for each prediction key using its associated mean value in UncertainData. + Calculates monotonicity for each prediction key using its associated mean value in :py:class:`prog_algs.uncertain_data.UncertainData`. - :math:`monotonoicity = \|\Sigma \dfrac{sign(i+1 - i)}{N-1}\|` + :math:`monotonoicity = \|\Sigma \left( \dfrac{sign(i+1 - i)}{N-1} \right) \|` - Where N is number of measurements and sign indicates sign of calculation. - - Coble, J., et. al. (2021). Identifying Optimal Prognostic Parameters from Data: A Genetic Algorithms Approach. Annual Conference of the PHM Society. - http://www.papers.phmsociety.org/index.php/phmconf/article/view/1404 - - Baptistia, M., et. al. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Aritifical Intelligence, Volume 306. - https://www.sciencedirect.com/science/article/pii/S0004370222000078 + Where N is number of measurements and sign indicates sign of calculation. [0]_ [1]_ Args: toe_profile (ToEPredictionProfile): A profile of predictions, the combination of multiple predictions Returns: dict (str, dict): Dictionary where keys represent a profile and dict is a subdictionary representing an event and its respective monotonicitiy value between [0, 1]. + + References: + .. [1] Coble, J., et. al. (2021). Identifying Optimal Prognostic Parameters from Data: A Genetic Algorithms Approach. Annual Conference of the PHM Society. http://www.papers.phmsociety.org/index.php/phmconf/article/view/1404 + .. [2] Baptistia, M., et. al. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Aritifical Intelligence, Volume 306. https://www.sciencedirect.com/science/article/pii/S0004370222000078 """ from ..metrics import monotonicity return monotonicity(self, **kwargs) @@ -133,15 +138,15 @@ def plot(self, ground_truth : dict = None , alpha : float = None, show : bool = """Produce an alpha-beta plot depicting the TtE distribution by time of prediction for each event. Args: - ground_truth : dict + ground_truth (dict): Optional dictionary argument containing event and its respective ground truth value; none by default and plotted if specified - alpha : float + alpha (float): Optional alpha value; none by default and plotted if specified - show : bool + show (bool): Optional bool value; specify whether to display generated plots. Default is true Returns: - dict[str, Figure] + dict[str, Figure] : Collection of generated matplotlib figures for each event in profile\n e.g., {'event1': Fig, 'event2': Fig} diff --git a/src/prog_algs/predictors/unscented_transform.py b/src/prog_algs/predictors/unscented_transform.py index 7ea416ae..91116baf 100644 --- a/src/prog_algs/predictors/unscented_transform.py +++ b/src/prog_algs/predictors/unscented_transform.py @@ -70,13 +70,13 @@ class UnscentedTransformPredictor(Predictor): Initial time at which prediction begins, e.g., 0 dt : float Simulation step size (s), e.g., 0.1 - events : List[string] + events : list[str] Events to predict (subset of model.events) e.g., ['event1', 'event2'] horizon : float Prediction horizon (s) save_freq : float Frequency at which results are saved (s) - save_pts : List[float] + save_pts : list[float] Any additional savepoints (s) e.g., [10.1, 22.5] Note diff --git a/src/prog_algs/state_estimators/kalman_filter.py b/src/prog_algs/state_estimators/kalman_filter.py index 1c76a353..5a30889d 100644 --- a/src/prog_algs/state_estimators/kalman_filter.py +++ b/src/prog_algs/state_estimators/kalman_filter.py @@ -19,27 +19,24 @@ class KalmanFilter(state_estimator.StateEstimator): The supported configuration parameters (keyword arguments) for UKF construction are described below: Args: - model : ProgModel + model (PrognosticsModel): A prognostics model to be used in state estimation See: Prognostics Model Package - x0 : UncertainData, model.StateContainer, or dict + x0 (UncertainData, model.StateContainer, or dict): Initial (starting) state, with keys defined by model.states \n e.g., x = ScalarData({'abc': 332.1, 'def': 221.003}) given states = ['abc', 'def'] Keyword Args: - alpha: float + alpha (float, optional): KF Scaling parameter. An alpha > 1 turns this into a fading memory filter. - t0 : float + t0 (float, optional): Starting time (s) - dt : float + dt (float, optional): time step (s) - Q : List[List[float]] - Process Noise Matrix - R : List[List[float]] - Measurement Noise Matrix - - Note: - The Kalman Filter does not support a custom measurement function + Q (list[list[float]], optional): + Kalman Process Noise Matrix + R (list[list[float]], optional): + Kalman Measurement Noise Matrix """ default_parameters = { 'alpha': 1, diff --git a/src/prog_algs/state_estimators/particle_filter.py b/src/prog_algs/state_estimators/particle_filter.py index 581496e5..93dd31d9 100644 --- a/src/prog_algs/state_estimators/particle_filter.py +++ b/src/prog_algs/state_estimators/particle_filter.py @@ -21,21 +21,21 @@ class ParticleFilter(state_estimator.StateEstimator): The supported configuration parameters (keyword arguments) for UKF construction are described below: Args: - model : ProgModel + model (PrognosticsModel): A prognostics model to be used in state estimation See: Prognostics Model Package - x0 : UncertainData, model.StateContainer, or dict + x0 (UncertainData, model.StateContainer, or dict): Initial (starting) state, with keys defined by model.states \n e.g., x = ScalarData({'abc': 332.1, 'def': 221.003}) given states = ['abc', 'def'] Keyword Args: - t0 : float + t0 (float, optional): Starting time (s) - dt : float + dt (float, optional): time step (s) - num_particles : int + num_particles (int, optional): Number of particles in particle filter - resample_fcn : function + resample_fcn (function, optional): Resampling function ([weights]) -> [indexes] e.g., filterpy.monte_carlo.residual_resample """ default_parameters = { diff --git a/src/prog_algs/state_estimators/state_estimator.py b/src/prog_algs/state_estimators/state_estimator.py index e13e248f..fe65bc54 100644 --- a/src/prog_algs/state_estimators/state_estimator.py +++ b/src/prog_algs/state_estimators/state_estimator.py @@ -14,15 +14,14 @@ class StateEstimator(ABC): Abstract base class for creating state estimators that perform state estimation. Subclasses must implement this interface. Equivilant to "Observers" in NASA's Matlab Prognostics Algorithm Library Args: - model : ProgModel + model (PrognosticsModel): A prognostics model to be used in state estimation See: Prognostics Model Package - x0 : UncertainData, model.StateContainer, or dict + x0 (UncertainData, model.StateContainer, or dict): Initial (starting) state, with keys defined by model.states \n e.g., x = ScalarData({'abc': 332.1, 'def': 221.003}) given states = ['abc', 'def'] - options : optional, kwargs - configuration options\n - Dictionary of any additional configuration values. See state-estimator specific documentation + + See state-estimator specific documentation for speicfic keyword arguments. """ default_parameters = { @@ -62,12 +61,12 @@ def estimate(self, t : float, u, z, **kwargs) -> None: t : float Current timestamp in seconds (≥ 0.0) e.g., t = 3.4 - u : dict + u : InputContainer Measured inputs, with keys defined by model.inputs. - e.g., u = {'i':3.2} given inputs = ['i'] - z : dict + e.g., u = m.InputContainer({'i':3.2}) given inputs = ['i'] + z : OutputContainer Measured outputs, with keys defined by model.outputs. - e.g., z = {'t':12.4, 'v':3.3} given inputs = ['t', 'v'] + e.g., z = m.OutputContainer({'t':12.4, 'v':3.3}) given outputs = ['t', 'v'] Note ---- diff --git a/src/prog_algs/state_estimators/unscented_kalman_filter.py b/src/prog_algs/state_estimators/unscented_kalman_filter.py index 179ed722..6118782b 100644 --- a/src/prog_algs/state_estimators/unscented_kalman_filter.py +++ b/src/prog_algs/state_estimators/unscented_kalman_filter.py @@ -16,23 +16,27 @@ class UnscentedKalmanFilter(state_estimator.StateEstimator): The supported configuration parameters (keyword arguments) for UKF construction are described below: Args: - model : ProgModel + model (PrognosticsModel): A prognostics model to be used in state estimation See: Prognostics Model Package - x0 : UncertainData, model.StateContainer, or dict + x0 (UncertainData, model.StateContainer, or dict): Initial (starting) state, with keys defined by model.states \n e.g., x = ScalarData({'abc': 332.1, 'def': 221.003}) given states = ['abc', 'def'] - Keyword Args - alpha, beta, kappa: float - UKF Scaling parameters - t0 : float + Keyword Args: + alpha (float, optional): + UKF Scaling parameter + beta (float, optional): + UKF Scaling parameter + kappa (float, optional): + UKF Scaling parameter + t0 (float, optional): Starting time (s) - dt : float + dt (float, optional): time step (s) - Q : List[List[float]] + Q (list[list[float]], optional): Process Noise Matrix - R : List[List[float]] + R (list[list[float]], optional): Measurement Noise Matrix """ default_parameters = { diff --git a/src/prog_algs/uncertain_data/uncertain_data.py b/src/prog_algs/uncertain_data/uncertain_data.py index 617719ea..7641511a 100644 --- a/src/prog_algs/uncertain_data/uncertain_data.py +++ b/src/prog_algs/uncertain_data/uncertain_data.py @@ -11,12 +11,13 @@ class UncertainData(ABC): - def __init__(self, _type = dict): - self._type = _type - """ Abstract base class for data with uncertainty. Any new uncertainty type must implement this class """ + + def __init__(self, _type = dict): + self._type = _type + @abstractmethod def sample(self, nSamples : int = 1): """Generate samples from data @@ -28,7 +29,9 @@ def sample(self, nSamples : int = 1): samples (UnweightedSamples): Array of nSamples samples Example: - samples = data.samples(100) + :: + + samples = data.samples(100) """ @property @@ -37,10 +40,12 @@ def median(self) -> dict: """The median of the UncertainData distribution or samples Returns: - Dict[str, float]: Median value. e.g., {'key1': 23.2, ...} + dict[str, float]: Median value. e.g., {'key1': 23.2, ...} Example: - median_value = data.median + :: + + median_value = data.median """ @property @@ -49,10 +54,12 @@ def mean(self) -> dict: """The mean of the UncertainData distribution or samples Returns: - Dict[str, float]: Mean value. e.g., {'key1': 23.2, ...} + dict[str, float]: Mean value. e.g., {'key1': 23.2, ...} Example: - mean_value = data.mean + :: + + mean_value = data.mean """ @property @@ -61,7 +68,12 @@ def cov(self) -> array: """The covariance matrix of the UncertiantyData distribution or samples in order of keys (i.e., cov[1][1] is the standard deviation for key keys()[1]) Returns: - array[array[float]]: Covariance matrix + np.array[np.array[float]]: Covariance matrix + + Example: + :: + + covariance_matrix = data.cov """ def relative_accuracy(self, ground_truth : dict) -> dict: @@ -74,6 +86,11 @@ def relative_accuracy(self, ground_truth : dict) -> dict: Returns: dict[str, float]: Relative accuracy for each event where value is relative accuracy between [0,1] + Example: + :: + + ra = data.relative_accuracy({'key1': 22, 'key2': 57}) + References: .. [0] Prognostics: The Science of Making Predictions (Goebel et al, 239) """ @@ -89,7 +106,12 @@ def keys(self): """Get the keys for the property represented Returns: - [string]: keys + list[str]: keys + + Example: + :: + + keys = data.keys() """ def __contains__(self, key): @@ -102,12 +124,18 @@ def percentage_in_bounds(self, bounds : tuple, keys : list = None, n_samples = 1 bounds (tuple[float, float] or dict): Lower and upper bounds. \n if tuple: (lower, upper)\n if dict: {key: (lower, upper), ...} - keys (list of strings, optional): UncertainData keys to consider when calculating. Defaults to all keys. + keys (list[str], optional): UncertainData keys to consider when calculating. Defaults to all keys. n_samples (int, optional): Number of samples to use when calculating Returns: - dict: Percentage within bounds for each key in keys (where 0.5 = 50%).\n - e.g., {'key1': 1, 'key2': 0.75} + dict: Percentage within bounds for each key in keys (where 0.5 = 50%). e.g., {'key1': 1, 'key2': 0.75} + + Example: + :: + + data.percentage_in_bounds((1025, 1075)) + data.percentage_in_bounds({'key1': (1025, 1075), 'key2': (2520, 2675)}) + data.percentage_in_bounds((1025, 1075), keys=['key1', 'key3']) """ return self.sample(n_samples).percentage_in_bounds(bounds, keys=keys) @@ -117,10 +145,17 @@ def metrics(self, **kwargs) -> dict: Keyword Args: ground_truth (int or dict, optional): Ground truth value. Defaults to None. n_samples (int, optional): Number of samples to use for calculating metrics (if not UnweightedSamples) - keys (List[str], optional): Keys to calculate metrics for. Defaults to all keys. + keys (list[str], optional): Keys to calculate metrics for. Defaults to all keys. Returns: dict: Dictionary of metrics + + Example: + :: + + print(data.metrics()) + m = data.metrics(ground_truth={'key1': 200, 'key2': 350}) + m = data.metrics(keys=['key1', 'key3']) """ from ..metrics import calc_metrics return calc_metrics(self, **kwargs) @@ -131,7 +166,7 @@ def plot_scatter(self, fig : Figure = None, keys : list = None, num_samples : in Args: fig (Figure, optional): Existing figure previously used to plot states. If passed a figure argument additional data will be added to the plot. Defaults to creating new figure - keys (list of strings, optional): Keys to plot. Defaults to all keys. + keys (list[str], optional): Keys to plot. Defaults to all keys. num_samples (int, optional): Number of samples to plot. Defaults to 100 **kwargs (optional): Additional keyword arguments passed to scatter function. @@ -160,7 +195,7 @@ def plot_hist(self, fig = None, keys = None, num_samples = 100, **kwargs): Args: fig (MatPlotLib Figure, optional): Existing histogram figure to be overritten. Defaults to create new figure. num_samples (int, optional): Number of samples to plot. Defaults to 100 - keys (List(String), optional): Keys to be plotted. Defaults to None. + keys (list(String), optional): Keys to be plotted. Defaults to None. Example: :: @@ -190,6 +225,11 @@ def describe(self, title : str = "UncertainData Metrics", print : bool = True) - Returns: defaultdict Dictionary of lists used to print metrics. + + Example: + :: + + data.describe() """ recursive_metrics_table = print_table_recursive(self.metrics(), title, print) return recursive_metrics_table From e894ea424ff45bfa68731d0c8cb777f87c1f92f6 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 27 Oct 2022 10:24:41 -0700 Subject: [PATCH 63/64] Fix mc --- src/prog_algs/predictors/monte_carlo.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/prog_algs/predictors/monte_carlo.py b/src/prog_algs/predictors/monte_carlo.py index bb7a572c..8111089f 100644 --- a/src/prog_algs/predictors/monte_carlo.py +++ b/src/prog_algs/predictors/monte_carlo.py @@ -73,17 +73,19 @@ def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs event_states_all = [] # Perform prediction + t0 = params.get('t0', 0) for x in state: first_output = self.model.output(x) time_of_event = {} last_state = {} - t0 = params.get('t0', 0) + params['t0'] = t0 + params['x'] = x + if 'save_freq' in params and not isinstance(params['save_freq'], tuple): - params['save_freq'] = (t0, params['save_freq']) + params['save_freq'] = (params['t0'], params['save_freq']) - params['x'] = x if len(params['events']) == 0: # Predict to time (times, inputs, states, outputs, event_states) = simulate_to_threshold(future_loading_eqn, first_output, From c964abbba0315c646f18160143a3f07090d0d090 Mon Sep 17 00:00:00 2001 From: Christopher Teubert Date: Thu, 27 Oct 2022 12:04:10 -0700 Subject: [PATCH 64/64] Tweak examples --- examples/basic_example.py | 4 +-- examples/basic_example_battery.py | 25 +++++++++++------- examples/benchmarking_example.py | 29 +++++++++++++-------- examples/eol_event.py | 9 ++++--- examples/horizon.py | 4 +-- examples/kalman_filter.py | 2 +- examples/measurement_eqn_example.py | 21 +++++++++------ examples/new_state_estimator_example.py | 3 +-- examples/particle_filter_battery_example.py | 9 +++---- examples/playback.py | 9 +++---- examples/predict_specific_event.py | 5 ++-- 11 files changed, 68 insertions(+), 52 deletions(-) diff --git a/examples/basic_example.py b/examples/basic_example.py index 2ae7fbc3..09a19b9c 100644 --- a/examples/basic_example.py +++ b/examples/basic_example.py @@ -19,7 +19,7 @@ def run_example(): # Step 1: Setup model & future loading - m = ThrownObject() + m = ThrownObject(process_noise = 1) def future_loading(t, x = None): # No load for a thrown object return m.InputContainer({}) @@ -63,7 +63,7 @@ def future_loading(t, x = None): mc = predictors.MonteCarlo(m) # Step 3b: Perform a prediction - NUM_SAMPLES = 10 + NUM_SAMPLES = 50 STEP_SIZE = 0.01 mc_results = mc.predict(filt.x, future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE, save_freq=STEP_SIZE) print('Predicted time of event (ToE): ', mc_results.time_of_event.mean) diff --git a/examples/basic_example_battery.py b/examples/basic_example_battery.py index cd49c134..4599d553 100644 --- a/examples/basic_example_battery.py +++ b/examples/basic_example_battery.py @@ -34,20 +34,27 @@ def run_example(): 't': 2, 'v': 0.02 } - batt = Battery(measurement_noise = R_vars) + batt = Battery(process_noise = 0.25, measurement_noise = R_vars) + # Creating the input containers outside of the function accelerates prediction + loads = [ + batt.InputContainer({'i': 2}), + batt.InputContainer({'i': 1}), + batt.InputContainer({'i': 4}), + batt.InputContainer({'i': 2}), + batt.InputContainer({'i': 3}) + ] def future_loading(t, x = None): # Variable (piece-wise) future loading scheme if (t < 600): - i = 2 + return loads[0] elif (t < 900): - i = 1 + return loads[1] elif (t < 1800): - i = 4 + return loads[2] elif (t < 3000): - i = 2 - else: - i = 3 - return batt.InputContainer({'i': i}) + return loads[3] + return loads[-1] + initial_state = batt.initialize() # Step 2: Demonstrating state estimator @@ -82,7 +89,7 @@ def future_loading(t, x = None): mc = Predictor(batt) # Step 3b: Perform a prediction - NUM_SAMPLES = 5 + NUM_SAMPLES = 25 STEP_SIZE = 0.1 SAVE_FREQ = 100 # How often to save results mc_results = mc.predict(filt.x, future_loading, n_samples = NUM_SAMPLES, dt=STEP_SIZE, save_freq = SAVE_FREQ) diff --git a/examples/benchmarking_example.py b/examples/benchmarking_example.py index 8aecc0f5..9831f931 100644 --- a/examples/benchmarking_example.py +++ b/examples/benchmarking_example.py @@ -17,24 +17,33 @@ # VVV Uncomment this to use Electro Chemistry Model VVV # from prog_models.models import BatteryElectroChem as Battery -from prog_algs import * +from prog_algs import state_estimators, predictors +from prog_algs.metrics import samples as metrics +import time # For timing prediction def run_example(): # Step 1: Setup Model and Future Loading batt = Battery() - def future_loading(t, x={}): + + # Creating the input containers outside of the function accelerates prediction + loads = [ + batt.InputContainer({'i': 2}), + batt.InputContainer({'i': 1}), + batt.InputContainer({'i': 4}), + batt.InputContainer({'i': 2}), + batt.InputContainer({'i': 3}) + ] + def future_loading(t, x = None): # Variable (piece-wise) future loading scheme if (t < 600): - i = 2 + return loads[0] elif (t < 900): - i = 1 + return loads[1] elif (t < 1800): - i = 4 + return loads[2] elif (t < 3000): - i = 2 - else: - i = 3 - return batt.InputContainer({'i': i}) + return loads[3] + return loads[-1] # Step 2: Setup Predictor pred = predictors.MonteCarlo(batt, dt= 0.05) @@ -51,8 +60,6 @@ def future_loading(t, x={}): # Step 4: Benchmark Predictions # Here we're comparing the results given different numbers of samples print('Benchmarking...') - import time # For timing prediction - from prog_algs.metrics import samples as metrics # Perform benchmarking for each number of samples sample_counts = [1, 2, 5, 10] diff --git a/examples/eol_event.py b/examples/eol_event.py index f01c9f4a..0e5c8e39 100644 --- a/examples/eol_event.py +++ b/examples/eol_event.py @@ -11,6 +11,7 @@ iii) Histogram of the event 'EOL' """ +import matplotlib.pyplot as plt from prog_models.models import ThrownObject from prog_algs.predictors import MonteCarlo from prog_algs.uncertain_data import ScalarData @@ -34,7 +35,7 @@ def threshold_met(self, x): return t_met # Step 2: Create instance of subclass - m = ThrownObjectWithEOL(process_noise=0.05) + m = ThrownObjectWithEOL(process_noise=1) # Step 3: Setup for prediction pred = MonteCarlo(m) @@ -43,12 +44,12 @@ def future_loading(t=None, x=None): state = ScalarData(m.initialize()) # Step 4: Predict to EOL event - pred_results = pred.predict(state, future_loading, events=['EOL'], dt=0.025) + pred_results = pred.predict(state, future_loading, events=['EOL'], dt=0.01, n_samples=50) + # In this case EOL is when the object starts falling + # But for some models where events aren't sequential, there might be a mixture of events in the EOL # Step 5: Plot results pred_results.time_of_event.plot_hist() - - import matplotlib.pyplot as plt plt.show() # This allows the module to be executed directly diff --git a/examples/horizon.py b/examples/horizon.py index 35ae309b..bd5e034b 100644 --- a/examples/horizon.py +++ b/examples/horizon.py @@ -12,11 +12,9 @@ ii) Time event is predicted to occur (with uncertainty) """ -from pprint import pprint - from prog_models.models.thrown_object import ThrownObject - from prog_algs import * +from pprint import pprint def run_example(): # Step 1: Setup model & future loading diff --git a/examples/kalman_filter.py b/examples/kalman_filter.py index 2de5fb97..a914f8c2 100644 --- a/examples/kalman_filter.py +++ b/examples/kalman_filter.py @@ -7,10 +7,10 @@ """ import numpy as np - from prog_models import LinearModel from prog_algs.state_estimators import KalmanFilter + # Linear Model for an object thrown into the air class ThrownObject(LinearModel): """ diff --git a/examples/measurement_eqn_example.py b/examples/measurement_eqn_example.py index fae5d17f..6b9674e8 100644 --- a/examples/measurement_eqn_example.py +++ b/examples/measurement_eqn_example.py @@ -27,19 +27,24 @@ class MyBattery(Battery): # Step 2: Setup model & future loading batt = MyBattery() - def future_loading(t, x={}): + loads = [ # Define loads here to accelerate prediction + batt.InputContainer({'i': 2}), + batt.InputContainer({'i': 1}), + batt.InputContainer({'i': 4}), + batt.InputContainer({'i': 2}), + batt.InputContainer({'i': 3}) + ] + def future_loading(t, x = None): # Variable (piece-wise) future loading scheme if (t < 600): - i = 2 + return loads[0] elif (t < 900): - i = 1 + return loads[1] elif (t < 1800): - i = 4 + return loads[2] elif (t < 3000): - i = 2 - else: - i = 3 - return batt.InputContainer({'i': i}) + return loads[3] + return loads[-1] x0 = batt.parameters['x0'] diff --git a/examples/new_state_estimator_example.py b/examples/new_state_estimator_example.py index 6e87dc8c..374d2cd7 100644 --- a/examples/new_state_estimator_example.py +++ b/examples/new_state_estimator_example.py @@ -6,10 +6,9 @@ In this example a basic state estimator is constructed by subclassing the StateEstimator class. This StateEstimator is then demonstrated with a ThrownObject model """ -import random - from prog_algs.state_estimators import StateEstimator from prog_algs.uncertain_data import ScalarData +import random class BlindlyStumbleEstimator(StateEstimator): diff --git a/examples/particle_filter_battery_example.py b/examples/particle_filter_battery_example.py index 8fd4374f..68168e9a 100644 --- a/examples/particle_filter_battery_example.py +++ b/examples/particle_filter_battery_example.py @@ -4,11 +4,10 @@ In this example the BatteryElectroChemEOD model is used with a particle filter to estimate the state of the battery """ -import numpy as np import matplotlib.pyplot as plt - -from prog_models.models import BatteryElectroChemEOD +import numpy as np from prog_algs import * +from prog_models.models import BatteryElectroChemEOD def run_example(): ## Setup @@ -34,9 +33,9 @@ def run_example(): battery = BatteryElectroChemEOD(process_noise= Q_vars, measurement_noise = R_vars, dt = dt) + load = battery.InputContainer({"i": 1}) # Optimization def future_loading(t, x=None): - load = 1 - return battery.InputContainer({"i": load}) + return load # Simulate data until EOD start_u = future_loading(0) diff --git a/examples/playback.py b/examples/playback.py index f1e2d90c..638baf78 100644 --- a/examples/playback.py +++ b/examples/playback.py @@ -18,6 +18,8 @@ import csv import numpy as np +from prog_algs.predictors import ToEPredictionProfile +from prog_algs.uncertain_data.multivariate_normal_dist import MultivariateNormalDist from prog_models.models import BatteryCircuit as Battery # VVV Uncomment this to use Electro Chemistry Model VVV @@ -27,14 +29,10 @@ # VVV Uncomment this to use UnscentedKalmanFilter instead VVV # from prog_algs.state_estimators import ParticleFilter as StateEstimator -from prog_algs.predictors import ToEPredictionProfile - from prog_algs.predictors import UnscentedTransformPredictor as Predictor # VVV Uncomment this to use MonteCarloPredictor instead # from prog_algs.predictors import MonteCarlo as Predictor -from prog_algs.uncertain_data.multivariate_normal_dist import MultivariateNormalDist - # Constants NUM_SAMPLES = 20 NUM_PARTICLES = 1000 # For state estimator (if using ParticleFilter) @@ -64,8 +62,9 @@ def run_example(): filt = StateEstimator(batt, x0, num_particles = NUM_PARTICLES) # Setup Prediction + load = batt.InputContainer({'i': 2.35}) def future_loading(t, x=None): - return {'i': 2.35} + return load Q = np.diag([batt.parameters['process_noise'][key] for key in batt.states]) R = np.diag([batt.parameters['measurement_noise'][key] for key in batt.outputs]) mc = Predictor(batt, Q = Q, R = R) diff --git a/examples/predict_specific_event.py b/examples/predict_specific_event.py index f7f1359c..816a3c67 100644 --- a/examples/predict_specific_event.py +++ b/examples/predict_specific_event.py @@ -4,15 +4,16 @@ In this example we are using the UTPredictor to predict a specific event, in this case impact. This will then ignore the other events which are not of interest. """ +from prog_algs import state_estimators, predictors from prog_models.models.thrown_object import ThrownObject -from prog_algs import * def run_example(): ## Setup m = ThrownObject() initial_state = m.initialize() + load = m.InputContainer({}) # Optimization - create once def future_loading(t, x = None): - return m.InputContainer({}) + return load ## State Estimation - perform a single ukf state estimate step filt = state_estimators.UnscentedKalmanFilter(m, initial_state)