Merge pull request #233 from nasa/release/v1.5

release/v1.5

.github/workflows/python-package.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.7']
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
@@ -36,7 +36,7 @@ jobs:
   #   runs-on: ubuntu-latest
   #   strategy:
   #     matrix:
-  #       python-version: ['3.9']
+  #       python-version: ['3.7']
   #   steps:
   #   - uses: actions/checkout@v2
   #   - name: Set up Python ${{ matrix.python-version }}
@@ -55,7 +55,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9']
+        python-version: ['3.10']
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
@@ -69,7 +69,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.9]
+        python-version: ['3.10']
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}

.github/workflows/release-tests.yml

@@ -0,0 +1,54 @@
+name: Python package
+
+on:
+  push:
+    branches:
+      - 'release/**'
+  pull_request:
+    branches:
+      - 'release/**'
+    paths:
+      - prog_algs
+
+jobs:
+  test-prog_models-dev:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['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 prog_models
+      run: |
+        python -m pip install git+https://github.com/nasa/prog_models.git@dev
+    - 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: ['3.8', '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

.gitignore

@@ -102,3 +102,5 @@ cython_debug/
 .DS_Store
 data_test.pkl
 predictor_test.pkl
+
+.VSCodeCounter/

README.md

@@ -17,7 +17,6 @@ See documentation [here](https://nasa.github.io/progpy/prog_algs_guide.html)
 ## Repository Directory Structure 
 
 `src/prog_algs/` - The prognostics algorithm python package<br />
-`docs/` - Project documentation (see also [github.io](https://nasa.github.io/prog_algs/))<br />
 `examples/` - Example Python scripts using prog_algs<br />
 `tests/` - Tests for prog_models<br />
 `README.md` - The readme (this file)<br />
@@ -26,23 +25,23 @@ See documentation [here](https://nasa.github.io/progpy/prog_algs_guide.html)
 Use the following to cite this repository:
 
 ```
-@misc{2022_nasa_prog_algs,
+@misc{2023_nasa_prog_algs,
     author    = {Christopher Teubert and Matteo Corbetta and Chetan Kulkarni},
     title     = {Prognostics Algorithm Python Package},
-    month     = Dec,
-    year      = 2022,
-    version   = {1.4.3},
+    month     = May,
+    year      = 2023,
+    version   = {1.5},
     url       = {https://github.com/nasa/prog\_algs}
     }
 ```
 
 The corresponding reference should look like this:
 
-C. Teubert, M. Corbetta, C. Kulkarni, Prognostics Algorithm Python Package, v1.4.3, Dec 2022. URL https://github.com/nasa/prog_algs.
+C. Teubert, M. Corbetta, C. Kulkarni, Prognostics Algorithm Python Package, v1.5, May 2023. 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, December 2022. URL https://nasa.github.io/progpy.
+C. Teubert, C. Kulkarni, M. Corbetta, K. Jarvis, M. Daigle, ProgPy Prognostics Python Packages, v1.5, May 2023. 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

predictor_template.py

@@ -13,7 +13,7 @@ class TemplatePredictor(Predictor):
     """
 
     # REPLACE THE FOLLOWING LIST WITH CONFIGURED PARAMETERS
-    default_parameters = { # Default Parameters, used as config for UKF
+    default_parameters = {  # Default Parameters, used as config for UKF
         'Example Parameter': 0.0
     } 
 
@@ -76,10 +76,10 @@ def predict(self, state, future_loading_eqn, **kwargs):
         # time_of_event.final_state = {'event1': ScalarData({'state1': 10, 'state2': 20}), 'event2': ScalarData({'state1': 12, 'state2': 18})}
 
         return PredictionResults(
-            times, 
-            inputs, 
-            states, 
-            outputs, 
-            event_states, 
+            times,
+            inputs,
+            states,
+            outputs,
+            event_states,
             time_of_event
         )

setup.py

@@ -10,7 +10,7 @@
 
 setup(
     name = 'prog_algs',
-    version = '1.4.3',
+    version = '1.5.0.pre',
     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',
@@ -36,13 +36,13 @@
     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',
+    python_requires='>=3.7, <3.12',
     install_requires = [
         'numpy',
         'scipy',
         'filterpy',
         'matplotlib',
-        'prog_models>=1.4.0'
+        'prog_models>=1.5.0.pre'
     ],
     license = 'NOSA',
     project_urls={  # Optional

src/prog_algs/__init__.py

@@ -5,7 +5,7 @@
 
 import warnings
 
-__version__ = '1.4.3'
+__version__ = '1.5.0.pre'
 
 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)

src/prog_algs/metrics/samples.py

@@ -8,7 +8,7 @@
 from .toe_metrics import prob_success
 from .toe_profile_metrics import alpha_lambda
 
-def mean_square_error(values : list, ground_truth : float) -> float:
+def mean_square_error(values: list, ground_truth: float) -> float:
     """Mean Square Error
     Args:
         values (List[float]): Times of Event (ToE) for a single event, output from predictor
@@ -28,7 +28,7 @@ def root_mean_square_error(values, ground_truth):
     """
     return sqrt(sum([(mean(x) - ground_truth)**2 for x in values])/len(values))
 
-def percentage_in_bounds(toe : list, bounds : tuple) -> float:
+def percentage_in_bounds(toe: list, bounds: tuple) -> float:
     """Calculate percentage of ToE dist is within specified bounds
 
     Args:

src/prog_algs/metrics/toe_metrics.py

@@ -8,7 +8,7 @@
 
 from ..uncertain_data import UncertainData, UnweightedSamples
 
-def prob_success(toe : UncertainData, time : float, **kwargs) -> float:
+def prob_success(toe: UncertainData, time: float, **kwargs) -> float:
     """Calculate probability of success - i.e., probability that event will not occur within a given time (i.e., success)
 
     Args:

src/prog_algs/metrics/toe_profile_metrics.py

@@ -9,7 +9,7 @@
 
 from ..predictors import ToEPredictionProfile
 
-def alpha_lambda(toe_profile : ToEPredictionProfile, ground_truth : dict, lambda_value : float, alpha : float, beta : float, **kwargs) -> dict: 
+def alpha_lambda(toe_profile: ToEPredictionProfile, ground_truth: dict, lambda_value: float, alpha: float, beta: float, **kwargs) -> dict: 
     """
     Compute alpha lambda metric, a common metric in prognostics. Alpha-Lambda is met if alpha % of the Time to Event (TtE) distribution is within beta % of the ground truth at prediction time lambda.
 
@@ -45,7 +45,7 @@ def alpha_lambda(toe_profile : ToEPredictionProfile, ground_truth : dict, lambda
                     print('\tBounds: [{} - {}]({}%)'.format(bounds[key][0], bounds[key][1], pib[key]))
             return result
 
-def prognostic_horizon(toe_profile : ToEPredictionProfile, criteria_eqn : Callable, ground_truth : dict, **kwargs) -> dict:
+def prognostic_horizon(toe_profile: ToEPredictionProfile, criteria_eqn: Callable, ground_truth: dict, **kwargs) -> dict:
     """
     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
@@ -69,7 +69,7 @@ def prognostic_horizon(toe_profile : ToEPredictionProfile, criteria_eqn : Callab
     }
     params.update(kwargs)
 
-    ph_result = {k:None for k in ground_truth.keys()} # False means not yet met; will be either a numerical value or None if met
+    ph_result = {k:None for k in ground_truth.keys()}  # False means not yet met; will be either a numerical value or None if met
     for (t_prediction, toe) in toe_profile.items():
         # Convert to TtE for toe and ground_truth
         tte = toe - t_prediction
@@ -81,14 +81,14 @@ def prognostic_horizon(toe_profile : ToEPredictionProfile, criteria_eqn : Callab
             if v and (ph_result[k] == None):
                 ph_calc = ground_truth[k] - t_prediction
                 if ph_calc > 0:
-                    ph_result[k] = ph_calc # PH = EOL - ti # ground truth is a dictionary {'EOD': 3005.2} should be ph_result[k] = g_truth[key] - t_prediction
+                    ph_result[k] = ph_calc  # PH = EOL - ti # ground truth is a dictionary {'EOD': 3005.2} should be ph_result[k] = g_truth[key] - t_prediction
                 if (all(v != None for v in ph_result.values())):
                     # Return PH once all criteria are met
                     return ph_result
     # Return PH when criteria not met for at least one event key
     return ph_result
 
-def cumulative_relative_accuracy(toe_profile : ToEPredictionProfile, ground_truth : dict, **kwargs) -> Dict[str, float]:
+def cumulative_relative_accuracy(toe_profile: ToEPredictionProfile, ground_truth: dict, **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.
     
@@ -108,7 +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, float]:
         """Calculate monotonicty for a prediction profile. 
         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.

src/prog_algs/metrics/uncertain_data_metrics.py

@@ -10,7 +10,7 @@
 
 from ..uncertain_data import UncertainData, UnweightedSamples
 
-def calc_metrics(data : UncertainData, ground_truth : Union[float, dict] = None, **kwargs) -> dict:
+def calc_metrics(data: UncertainData, ground_truth: Union[float, dict] = None, **kwargs) -> dict:
     """Calculate all time of event metrics
 
     Args:

src/prog_algs/predictors/monte_carlo.py

@@ -39,7 +39,7 @@ class MonteCarlo(Predictor):
         'n_samples': 100  # Default number of samples to use, if none specified
     }
 
-    def predict(self, state : UncertainData, future_loading_eqn : Callable, **kwargs) -> PredictionResults:
+    def predict(self, state: UncertainData, future_loading_eqn: Callable, **kwargs) -> PredictionResults:
         if isinstance(state, dict) or isinstance(state, self.model.StateContainer):
             from prog_algs.uncertain_data import ScalarData
             state = ScalarData(state, _type = self.model.StateContainer)

src/prog_algs/predictors/prediction.py

@@ -25,7 +25,7 @@ def __init__(self, times : list, data : list):
         self.times = times
         self.data = data
 
-    def __eq__(self, other : "Prediction") -> bool:
+    def __eq__(self, other: "Prediction") -> bool:
         """Compare 2 Predictions
 
         Args:
@@ -36,7 +36,7 @@ def __eq__(self, other : "Prediction") -> bool:
         """
         return self.times == other.times and self.data == other.data
 
-    def snapshot(self, time_index : int) -> UncertainData:
+    def snapshot(self, time_index: int) -> UncertainData:
         """Get all samples from a specific timestep
 
         Args:
@@ -63,7 +63,7 @@ def mean(self) -> List[dict]:
         """
         return [dist.mean for dist in self.data]
 
-    def time(self, index : int):
+    def time(self, index: int):
         warn("Deprecated. Please use prediction.times[index] instead.")
         return self.times[index]
 
@@ -110,7 +110,7 @@ class UnweightedSamplesPrediction(Prediction, UserList):
             Data points where data[n] is a SimResult for sample n
     """
 
-    def __init__(self, times : list, data : list):
+    def __init__(self, times: list, data: list):
         super(UnweightedSamplesPrediction, self).__init__(times, data)
         self.__transformed = False  # If transform has been calculated
 
@@ -133,11 +133,11 @@ def mean(self) -> list:
             self.__calculate_tranform()
         return [dist.mean for dist in self.__transform]
 
-    def sample(self, sample_id : int):
+    def sample(self, sample_id: int):
         warn("Deprecated. Please use prediction[sample_id] instead.")
         return self[sample_id]
 
-    def snapshot(self, time_index : int) -> UnweightedSamples:
+    def snapshot(self, time_index: int) -> UnweightedSamples:
         """Get all samples from a specific timestep
 
         Args: