Decouple Thrown Object from Examples

examples/derived_params.py

@@ -5,7 +5,7 @@
 Example demonstrating ways to use the derived parameters feature for model building. 
 """
 
-from .new_model import ThrownObject
+from prog_models.models.thrown_object import ThrownObject
 
 def run_example():
     # For this example we will use the ThrownObject model from the new_model example.

examples/dynamic_step_size.py

@@ -6,7 +6,7 @@
 """
 
 import prog_models
-from .new_model import ThrownObject
+from prog_models.models.thrown_object import ThrownObject
 
 def run_example():
     print("EXAMPLE 1: dt of 1 until 8 sec, then 0.5\n\nSetting up...\n")

examples/future_loading.py

@@ -30,8 +30,8 @@ def future_loading(t, x=None):
         return {'i': i}
     # Simulate to threshold
     options = {
-        'save_freq': 100, # Frequency at which results are saved
-        'dt': 2 # Timestep
+        'save_freq': 100,  # Frequency at which results are saved
+        'dt': 2  # Timestep
     }
     (times, inputs, states, outputs, event_states) = m.simulate_to_threshold(future_loading, {'t': 18.95, 'v': 4.183}, **options)
 
@@ -53,7 +53,7 @@ def moving_avg(i):
         for key in m.inputs:
             moving_avg.loads[key].append(i[key])
             if len(moving_avg.loads[key]) > window:
-                del moving_avg.loads[key][0] # Remove first item
+                del moving_avg.loads[key][0]  # Remove first item
 
         # Update future loading eqn
         future_loading.load = {key : mean(moving_avg.loads[key]) for key in m.inputs} 
@@ -152,11 +152,11 @@ def moving_avg(i):
     def future_loading(t, x=None):
         if x is not None:
             event_state = future_loading.event_state(x)
-            return {'i': future_loading.start + (1-event_state['EOD']) * future_loading.slope} # default
+            return {'i': future_loading.start + (1-event_state['EOD']) * future_loading.slope}  # default
         return {'i': future_loading.start}
     future_loading.t = 0
     future_loading.event_state = m.event_state
-    future_loading.slope = 2 # difference between input with EOD = 1 and 0. 
+    future_loading.slope = 2  # difference between input with EOD = 1 and 0. 
     future_loading.start = 0.5
 
     # Simulate to threshold

examples/new_model.py

@@ -7,7 +7,7 @@
 
 from prog_models import PrognosticsModel
 
-# Model used in example
+
 class ThrownObject(PrognosticsModel):
     """
     Model that similates an object thrown into the air without air resistance
@@ -28,22 +28,22 @@ class ThrownObject(PrognosticsModel):
 
     # The Default parameters. Overwritten by passing parameters dictionary into constructor
     default_parameters = {
-        'thrower_height': 1.83, # m
-        'throwing_speed': 40, # m/s
-        'g': -9.81, # Acceleration due to gravity in m/s^2
-        'process_noise': 0.0 # amount of noise in each step
+        'thrower_height': 1.83,  # m
+        'throwing_speed': 40,  # m/s
+        'g': -9.81,  # Acceleration due to gravity in m/s^2
+        'process_noise': 0.0  # amount of noise in each step
     }
 
     def initialize(self, u, z):
         self.max_x = 0.0
         return {
-            '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
             }
 
     def dx(self, x, u):
         return {'x': x['v'],
-                'v': self.parameters['g']} # Acceleration of gravity
+                'v': self.parameters['g']}  # Acceleration of gravity
 
     def output(self, x):
         return {'x': x['x']}
@@ -57,10 +57,10 @@ def threshold_met(self, x):
         }
 
     def event_state(self, x): 
-        self.max_x = max(self.max_x, x['x']) # Maximum altitude
+        self.max_x = max(self.max_x, x['x'])  # Maximum altitude
         return {
-            'falling': max(x['v']/self.parameters['throwing_speed'],0), # Throwing speed is max speed
-            'impact': max(x['x']/self.max_x,0) # 1 until falling begins, then it's fraction of height
+            'falling': max(x['v']/self.parameters['throwing_speed'],0),  # Throwing speed is max speed
+            'impact': max(x['x']/self.max_x,0)  # 1 until falling begins, then it's fraction of height
         }
 
 def run_example():

examples/noise.py

@@ -6,7 +6,7 @@
 """
 
 # Deriv prog model was selected because the model can be described as x' = x + dx*dt
-from .new_model import ThrownObject
+from prog_models.models.thrown_object import ThrownObject
 
 def run_example():
     def future_load(t, x=None):
@@ -60,7 +60,7 @@ def future_load(t, x=None):
     # Ex6: Measurement noise
     # Everything we've done with process noise, we can also do with measurement noise.
     # Just use 'measurement_noise' and 'measurement_noise_dist' 
-    measurement_noise = {'x': 0.25} # For each output
+    measurement_noise = {'x': 0.25}  # For each output
     measurement_noise_dist = 'uniform'
     model_config = {'measurement_noise_dist': measurement_noise_dist, 'measurement_noise': measurement_noise}
     m = ThrownObject(**model_config) 
@@ -75,7 +75,7 @@ def future_load(t, x=None):
     # This can be used to do custom or more complex noise distributions
     def apply_proportional_process_noise(self, x, dt = 1):
         return {
-            'x': x['x'], # No noise on state
+            'x': x['x'],  # No noise on state
             'v': x['v'] - dt*0.5*x['v']
         }
     model_config = {'process_noise': apply_proportional_process_noise}

examples/param_est.py

@@ -5,7 +5,7 @@
 Example of the model parameter estimation feature. Run using the command `python -m examples.param_est'
 """
 
-from .new_model import ThrownObject
+from prog_models.models.thrown_object import ThrownObject
 
 def run_example():
     # Step 1: Build the model with your best guess in parameters

examples/sensitivity.py

@@ -6,7 +6,7 @@
 """
 
 # Deriv prog model was selected because the model can be described as x' = x + dx*dt
-from .new_model import ThrownObject
+from prog_models.models.thrown_object import ThrownObject
 import numpy as np
 
 def run_example():

examples/state_limits.py

@@ -5,7 +5,7 @@
 Example demonstrating ways to use state limits. Run using the command `python -m examples.state_limits`
 """
 
-from .new_model import ThrownObject
+from prog_models.models.thrown_object import ThrownObject
 from math import inf
 
 def run_example():

examples/visualize.py

@@ -8,7 +8,7 @@
 import matplotlib.pyplot as plt
 from prog_models.visualize import plot_timeseries
 
-from .new_model import ThrownObject
+from prog_models.models.thrown_object import ThrownObject
 
 def run_example():
     print('Visualize Module Example')

src/prog_models/models/thrown_object.py

@@ -0,0 +1,60 @@
+# Copyright © 2021 United States Government as represented by the Administrator of the
+# National Aeronautics and Space Administration.  All Rights Reserved.
+
+from .. import PrognosticsModel
+
+
+class ThrownObject(PrognosticsModel):
+    """
+    Model that similates an object thrown into the air without air resistance
+    """
+
+    inputs = []  # no inputs, no way to control
+    states = [
+        'x',  # Position (m) 
+        'v'  # Velocity (m/s)
+        ]
+    outputs = [
+        'x'  # Position (m)
+    ]
+    events = [
+        'falling',  # Event- object is falling
+        'impact'  # Event- object has impacted ground
+    ]
+
+    # The Default parameters. Overwritten by passing parameters dictionary into constructor
+    default_parameters = {
+        'thrower_height': 1.83,  # m
+        'throwing_speed': 40,  # m/s
+        'g': -9.81,  # Acceleration due to gravity in m/s^2
+        'process_noise': 0.0  # amount of noise in each step
+    }
+
+    def initialize(self, u, z):
+        self.max_x = 0.0
+        return {
+            '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
+            }
+
+    def dx(self, x, u):
+        return {'x': x['v'],
+                'v': self.parameters['g']}  # Acceleration of gravity
+
+    def output(self, x):
+        return {'x': x['x']}
+
+    # 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
+    def threshold_met(self, x):
+        return {
+            'falling': x['v'] < 0,
+            'impact': x['x'] <= 0
+        }
+
+    def event_state(self, x): 
+        self.max_x = max(self.max_x, x['x'])  # Maximum altitude
+        return {
+            'falling': max(x['v']/self.parameters['throwing_speed'],0),  # Throwing speed is max speed
+            'impact': max(x['x']/self.max_x,0)  # 1 until falling begins, then it's fraction of height
+        }