Prog models features/sim result with pandas

.github/workflows/pr-messages.yml

@@ -18,10 +18,12 @@ jobs:
           pullRequestOpened: >
             Thank you for opening this PR. Each PR into dev requires a code review. For the code review, look at the following:
 
-            - [ ] Reviewer (someone other than author) should look for bugs, efficiency, readability, testing, and coverage in examples (if relevant).
+            - [ ] 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

.github/workflows/python-package.yml

@@ -110,42 +110,31 @@ jobs:
     - name: Run copyright check
       run: |
         python scripts/test_copyright.py
-  coverage:
-    timeout-minutes: 30
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ['3.10']
-    steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install -e .
-        pip install coverage
-        pip install notebook
-        pip install testbook
-        pip install requests
-        pip install importlib_metadata
-    - name: Run coverage
-      run: |
-        coverage run -m tests.test_base_models
-        coverage run -a -m tests.test_battery
-        coverage run -a -m tests.test_centrifugal_pump
-        coverage run -a -m tests.test_composite
-        coverage run -a -m tests.test_datasets
-        coverage run -a -m tests.test_dict_like_matrix_wrapper
-        coverage run -a -m tests.test_direct
-        coverage run -a -m tests.test_ensemble
-        coverage run -a -m tests.test_linear_model
-        coverage run -a -m tests.test_pneumatic_valve
-        coverage run -a -m tests.test_powertrain
-        coverage run -a -m tests.test_serialization
-        coverage run -a -m tests.test_sim_result
-        coverage xml -i
-    - name: "Upload coverage to Codecov"
-      uses: codecov/codecov-action@v3
+  # coverage:
+  #   timeout-minutes: 30
+  #   runs-on: ubuntu-latest
+  #   strategy:
+  #     matrix:
+  #       python-version: ['3.9']
+  #   steps:
+  #   - uses: actions/checkout@v3
+  #   - name: Set up Python ${{ matrix.python-version }}
+  #     uses: actions/setup-python@v4
+  #     with:
+  #       python-version: ${{ matrix.python-version }}
+  #   - name: Install dependencies
+  #     run: |
+  #       python -m pip install --upgrade pip
+  #       python -m pip install -e .
+  #       pip install coverage
+  #       pip install notebook
+  #       pip install testbook
+  #       pip install requests
+  #   - name: Run coverage
+  #     run: |
+  #       coverage run -m tests
+  #       coverage xml
+  #   - name: "Upload coverage to Codecov"
+  #     uses: codecov/codecov-action@v3
+  #     with:
+  #       fail_ci_if_error: true

examples/param_est.py

@@ -48,11 +48,5 @@ def run_example():
 
     # Sure enough- parameter estimation determined that the thrower's height wasn't 20 m, instead was closer to 1.9m, a much more reasonable height!
 
-    # Note: You can also adjust the metric that is used to estimate parameters.
-    # This is done by setting the "error_method" argument. 
-    # e.g., m.estimate_params([(times, inputs, outputs)], keys, dt=0.01, error_method='MAX_E')
-    # Default is Mean Squared Error (MSE)
-    # See calc_error method for list of options.
-
 if __name__=='__main__':
     run_example()

src/prog_models/__init__.py

@@ -2,11 +2,10 @@
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
 # PrognosticsModel must be first, since the others build on this
-from prog_models.prognostics_model import PrognosticsModel
-from prog_models.ensemble_model import EnsembleModel
-from prog_models.composite_model import CompositeModel
-from prog_models.linear_model import LinearModel
-from prog_models.models.thrown_object import LinearThrownObject
-from prog_models.exceptions import ProgModelException, ProgModelInputException, ProgModelTypeError
+from .prognostics_model import PrognosticsModel
+from .ensemble_model import EnsembleModel
+from .composite_model import CompositeModel
+from .linear_model import LinearModel
+from .exceptions import ProgModelException, ProgModelInputException, ProgModelTypeError
 
 __version__ = '1.5.0.pre'

src/prog_models/composite_model.py

@@ -3,7 +3,7 @@
 
 from collections.abc import Iterable
 
-from prog_models import PrognosticsModel
+from . import PrognosticsModel
 
 DIVIDER = '.'
 
@@ -32,7 +32,8 @@ class CompositeModel(PrognosticsModel):
         outputs (list[str]):
             Model outputs in format "model_name.output_name". Must be subset of all outputs from models. If not provided, all outputs will be included. 
     """
-    def __init__(self, models, connections = [], **kwargs):
+
+    def __init__(self, models, connections=[], **kwargs):
         # General Input Validation
         if not isinstance(models, Iterable):
             raise ValueError('The models argument must be a list')
@@ -54,8 +55,9 @@ def __init__(self, models, connections = [], **kwargs):
         # Handle models
         for m in models:
             if isinstance(m, Iterable):
-                if len(m) != 2: 
-                    raise ValueError('Each model tuple must be of the form (name: str, model). For example ("Batt1", BatteryElectroChem())')
+                if len(m) != 2:
+                    raise ValueError(
+                        'Each model tuple must be of the form (name: str, model). For example ("Batt1", BatteryElectroChem())')
                 if not isinstance(m[0], str):
                     raise ValueError('The first element of each model tuple must be a string')
                 if not isinstance(m[1], PrognosticsModel):
@@ -73,15 +75,16 @@ def __init__(self, models, connections = [], **kwargs):
                 self.model_names.add(m[0])
                 kwargs['models'].append(m)
             else:
-                raise ValueError(f'Each model must be a PrognosticsModel or tuple (name: str, PrognosticsModel), was {type(m)}')
+                raise ValueError(
+                    f'Each model must be a PrognosticsModel or tuple (name: str, PrognosticsModel), was {type(m)}')
 
         for (name, m) in kwargs['models']:
             self.inputs |= set([name + DIVIDER + u for u in m.inputs])
             self.states |= set([name + DIVIDER + x for x in m.states])
             self.outputs |= set([name + DIVIDER + z for z in m.outputs])
             self.events |= set([name + DIVIDER + e for e in m.events])
             self.performance_metric_keys |= set([name + DIVIDER + p for p in m.performance_metric_keys])
-        
+
         # Handle outputs
         if 'outputs' in kwargs:
             if isinstance(kwargs['outputs'], str):
@@ -91,11 +94,11 @@ def __init__(self, models, connections = [], **kwargs):
             if not set(kwargs['outputs']).issubset(self.outputs):
                 raise ValueError('The outputs of the composite model must be a subset of the outputs of the models')
             self.outputs = kwargs['outputs']
-        
+
         # Handle Connections
         kwargs['connections'] = []
-        self.__to_input_connections = {m_name : [] for m_name in self.model_names}
-        self.__to_state_connections = {m_name : [] for m_name in self.model_names}
+        self.__to_input_connections = {m_name: [] for m_name in self.model_names}
+        self.__to_state_connections = {m_name: [] for m_name in self.model_names}
 
         for connection in connections:
             # Input validation
@@ -107,13 +110,14 @@ def __init__(self, models, connections = [], **kwargs):
             in_key, out_key = connection
             # Validation
             if out_key not in self.inputs:
-                raise ValueError(f'The output key, {out_key}, must be an input to one of the composite models. Options include {self.inputs}')
+                raise ValueError(
+                    f'The output key, {out_key}, must be an input to one of the composite models. Options include {self.inputs}')
 
             # Remove the out_key from inputs
             # These no longer are an input to the composite model
             # as they are now satisfied internally
             self.inputs.remove(out_key)
-                
+
             # Split the keys into parts (model, key_part)
             (in_model, in_key_part) = in_key.split('.')
             (out_model, out_key_part) = out_key.split('.')
@@ -125,7 +129,7 @@ def __init__(self, models, connections = [], **kwargs):
                 raise ValueError('The input model must be one of the models in the composite model')
             if out_model not in self.model_names:
                 raise ValueError('The output model must be one of the models in the composite model')
-            
+
             # Add to connections
             if in_key in self.states:
                 self.__to_input_connections[out_model].append((in_key, out_key_part))
@@ -138,11 +142,11 @@ def __init__(self, models, connections = [], **kwargs):
                 self.states.add(in_key)
             else:
                 raise ValueError('The input key must be an output or state of one of the composite models')
-        
+
         # Finish initialization
         super().__init__(**kwargs)
 
-    def initialize(self, u = {}, z = {}):
+    def initialize(self, u={}, z={}):
         if u is None:
             u = {}
         if z is None:
@@ -156,7 +160,7 @@ def initialize(self, u = {}, z = {}):
             x_i = m.initialize(u_i, z_i)
             for key, value in x_i.items():
                 x_0[name + '.' + key] = value
-        
+
             # Process connections
             # This initializes the states that are connected to outputs
             for (in_key_part, in_key) in self.__to_state_connections[name]:
@@ -165,7 +169,7 @@ def initialize(self, u = {}, z = {}):
                 else:  # Missing from z, so estimate using initial state
                     z_ii = m.output(x_i)
                     x_0[in_key] = z_ii.get(in_key_part, None)
-                
+
         return self.StateContainer(x_0)
 
     def next_state(self, x, u, dt):
@@ -177,17 +181,17 @@ def next_state(self, x, u, dt):
             for (in_key, out_key_part) in self.__to_input_connections[name]:
                 u_i[out_key_part] = x[in_key]
             u_i = m.InputContainer(u_i)
-            
+
             # Prepare state
             x_i = m.StateContainer({key: x[name + '.' + key] for key in m.states})
 
-            # Propogate state
+            # Propagate state
             x_next_i = m.next_state(x_i, u_i, dt)
 
             # Save to super state
             for key, value in x_next_i.items():
                 x[name + '.' + key] = value
-            
+
             # Process connections
             # This updates the states that are connected to outputs
             if len(self.__to_state_connections[name]) > 0:

src/prog_models/ensemble_model.py

@@ -1,10 +1,9 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
-from collections.abc import Sequence
 import numpy as np
 
-from prog_models import PrognosticsModel
+from . import PrognosticsModel
 
 
 class EnsembleModel(PrognosticsModel):
@@ -20,7 +19,7 @@ class EnsembleModel(PrognosticsModel):
     See example :download:`examples.ensemble <../../../../prog_models/examples/ensemble.py>`
 
     Args:
-        models (list[PrognosticsModel]): List of at least 2 models that form the ensemble
+        models (list): List of models that form the ensemble
 
     Keyword Arguments:
         aggregation_method (function): Function that aggregates the outputs of the models in the ensemble. Default is np.mean
@@ -31,14 +30,6 @@ class EnsembleModel(PrognosticsModel):
     }
 
     def __init__(self, models, **kwargs):
-        if not isinstance(models, Sequence):
-            raise TypeError(f'EnsembleModel must be initialized with a list of models, got {type(models)}')
-        if len(models) < 2:
-            raise ValueError('EnsembleModel requires at least two models')
-        for i, m in enumerate(models):
-            if not isinstance(m, PrognosticsModel):
-                raise TypeError(f'EnsembleModel requires all models to be PrognosticsModel instances. models[{i}] was {type(m)}')
-
         inputs = set()
         states = set()
         outputs = set()
@@ -56,12 +47,8 @@ def __init__(self, models, **kwargs):
         super().__init__(**kwargs)
         self.parameters['models'] = models
 
-    def initialize(self, u=None, z=None):
-        xs = [
-            m.initialize(
-                m.InputContainer(u) if u is not None else None, 
-                m.OutputContainer(z) if z is not None else None
-            ) for m in self.parameters['models']]
+    def initialize(self, u, z=None):
+        xs = [m.initialize(m.InputContainer(u), m.OutputContainer(z) if z is not None else None) for m in self.parameters['models']]
         x0 = {}
         for x in xs:
             for key in x.keys():