Refactor to include u in the output field

docs/_downloads/05795c561b0fb31137ef5fd753641074/new_state_estimator_example.py

@@ -43,8 +43,8 @@ def estimate(self, t, u, z):
  x2 = {key : float(value) + 10*(random.random()-0.5) for (key,value) in self.state.items()}
 
  # Calculate outputs
- z_est = self.m.output(t, self.state)
- z_est2 = self.m.output(t, x2)
+ z_est = self.m.output(t)
+ z_est2 = self.m.output(t)
 
  # Now score them each by how close they are to the measured z
  z_est_score = sum([abs(z_est[key] - z[key]) for key in self.m.outputs])

docs/_downloads/06717c7dc69742382bea70d35e4e63b5/sim_pump.py

@@ -34,7 +34,7 @@ def future_loading(t, x=None):
  })
 
  # Step 3: Sim
- first_output = pump.output(pump.initialize(future_loading(0),{}))
+ first_output = pump.output(pump.initialize(future_loading(0), {}))
  config = {
  'horizon': 1e5,
  'save_freq': 1e3,

docs/_downloads/07c9eb121dbb5de8d48ddffc704c521d/new_state_estimator_example.py

@@ -43,8 +43,8 @@ def estimate(self, t, u, z):
  x2 = {key : float(value) + 10*(random.random()-0.5) for (key,value) in self.state.items()}
 
  # Calculate outputs
- z_est = self.m.output(t, self.state)
- z_est2 = self.m.output(t, x2)
+ z_est = self.m.output(t)
+ z_est2 = self.m.output(t)
 
  # Now score them each by how close they are to the measured z
  z_est_score = sum([abs(z_est[key] - z[key]) for key in self.m.outputs])

docs/_downloads/0f7c5af7c0ba09c1be3bd3db3e6458c9/prog_model_template.py

@@ -223,7 +223,7 @@ class ProgModelTemplate(PrognosticsModel):
  #
  # return self.StateContainer(next_x)
 
- def output(self, x):
+ def output(self, x, u=None):
  """
  Calculate output, z (i.e., measurable values) given the state x
 
@@ -238,6 +238,7 @@ def output(self, x):
  z : OutputContainer
  Outputs, with keys defined by model.outputs.
  e.g., z = {'t':12.4, 'v':3.3} given inputs = ['t', 'v']
+ :param u:
  """
 
  # REPLACE BELOW WITH LOGIC TO CALCULATE OUTPUTS

docs/_downloads/7c5976947f77971a5ed9939937b15116/measurement_eqn_example.py

@@ -88,7 +88,7 @@ def future_loading(t, x = None):
  class MyBattery(Battery):
  outputs = ['tv'] # output is temperature * voltage (for some reason)
 
- def output(self, x):
+ def output(self, x, u=None):
  parent.parameters = self.parameters # only needed if you expect to change parameters
  z = parent.output(x)
  return self.OutputContainer({'tv': z['v'] * z['t']})

docs/_downloads/b997c950df7e2f695c19319712091ba3/measurement_eqn_example.py

@@ -88,7 +88,7 @@ def future_loading(t, x = None):
  class MyBattery(Battery):
  outputs = ['tv'] # output is temperature * voltage (for some reason)
 
- def output(self, x):
+ def output(self, x, u=None):
  parent.parameters = self.parameters # only needed if you expect to change parameters
  z = parent.output(x)
  return self.OutputContainer({'tv': z['v'] * z['t']})

docs/_downloads/c416c00aa2e02950fa0b0b4547cf49f9/new_model.py

@@ -46,7 +46,7 @@ def dx(self, x, u):
  return self.StateContainer({'x': x['v'],
  'v': self.parameters['g']}) # Acceleration of gravity
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer({'x': x['x']})
 
  # This is actually optional. Leaving thresholds_met empty will use the event state to define thresholds.

docs/_downloads/daafe64ff08a2ab2ed2ab052cf9985bf/sim_pump.py

@@ -35,7 +35,7 @@ def future_loading(t, x=None):
  })
 
  # Step 3: Sim
- first_output = pump.output(pump.initialize(future_loading(0),{}))
+ first_output = pump.output(pump.initialize(future_loading(0), {}))
  config = {
  'horizon': 1e5,
  'save_freq': 1e3,

docs/_downloads/e2fe425b405c324c9ddabb88b4675b84/sim_pump.py

@@ -35,7 +35,7 @@ def future_loading(t, x=None):
  })
 
  # Step 3: Sim
- first_output = pump.output(pump.initialize(future_loading(0),{}))
+ first_output = pump.output(pump.initialize(future_loading(0), {}))
  config = {
  'horizon': 1e5,
  'save_freq': 1e3,

examples/measurement_eqn_example.py

@@ -88,7 +88,7 @@ def future_loading(t, x = None):
  class MyBattery(Battery):
  outputs = ['tv'] # output is temperature * voltage (for some reason)
 
- def output(self, x):
+ def output(self, x, u=None):
  parent.parameters = self.parameters # only needed if you expect to change parameters
  z = parent.output(x)
  return self.OutputContainer({'tv': z['v'] * z['t']})

examples/new_model.py

@@ -46,7 +46,7 @@ def dx(self, x, u):
  return self.StateContainer({'x': x['v'],
  'v': self.parameters['g']}) # Acceleration of gravity
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer({'x': x['x']})
 
  # This is actually optional. Leaving thresholds_met empty will use the event state to define thresholds.

examples/new_state_estimator_example.py

@@ -43,8 +43,8 @@ def estimate(self, t, u, z):
  x2 = {key : float(value) + 10*(random.random()-0.5) for (key,value) in self.state.items()}
 
  # Calculate outputs
- z_est = self.m.output(t, self.state)
- z_est2 = self.m.output(t, x2)
+ z_est = self.m.output(t)
+ z_est2 = self.m.output(t)
 
  # Now score them each by how close they are to the measured z
  z_est_score = sum([abs(z_est[key] - z[key]) for key in self.m.outputs])

examples/sim_pump.py

@@ -35,7 +35,7 @@ def future_loading(t, x=None):
  })
 
  # Step 3: Sim
- first_output = pump.output(pump.initialize(future_loading(0),{}))
+ first_output = pump.output(pump.initialize(future_loading(0), {}))
  config = {
  'horizon': 1e5,
  'save_freq': 1e3,

prog_model_template.py

@@ -223,7 +223,7 @@ class ProgModelTemplate(PrognosticsModel):
  #
  # return self.StateContainer(next_x)
 
- def output(self, x):
+ def output(self, x, u=None):
  """
  Calculate output, z (i.e., measurable values) given the state x
 
@@ -238,6 +238,7 @@ def output(self, x):
  z : OutputContainer
  Outputs, with keys defined by model.outputs.
  e.g., z = {'t':12.4, 'v':3.3} given inputs = ['t', 'v']
+ :param u:
  """
 
  # REPLACE BELOW WITH LOGIC TO CALCULATE OUTPUTS

sphinx-config/auto_examples/sim_pump.py

@@ -34,7 +34,7 @@ def future_loading(t, x=None):
  })
 
  # Step 3: Sim
- first_output = pump.output(pump.initialize(future_loading(0),{}))
+ first_output = pump.output(pump.initialize(future_loading(0), {}))
  config = {
  'horizon': 1e5,
  'save_freq': 1e3,

src/progpy/composite_model.py

@@ -221,7 +221,7 @@ def next_state(self, x, u, dt):
 
  return x
 
- def output(self, x):
+ def output(self, x, u=None):
  z = {}
  for (name, m) in self.parameters['models']:
  # Prepare state

src/progpy/data_models/lstm_model.py

@@ -142,7 +142,7 @@ def next_state(self, x, u, _):
  result = np.array(np.vstack((states, internal_states)), dtype=np.float64)
  return self.StateContainer(result)
 
- def output(self, x):
+ def output(self, x, u=None):
  if x.matrix[0, 0] is None:
  warn(f"Output estimation is not available until at least {1+self.parameters['window']} timesteps have passed.")
  return self.OutputContainer(np.array([[None] for _ in self.outputs]))

src/progpy/ensemble_model.py

@@ -89,7 +89,7 @@ def next_state(self, x, u, dt):
 
  return self.StateContainer(xs_final)
 
- def output(self, x):
+ def output(self, x, u=None):
  zs = [m.output(m.StateContainer(x)) for m in self.parameters['models']]
  zs_final = {}
  for z in zs:

src/progpy/linear_model.py

@@ -222,7 +222,7 @@ def dx(self, x, u):
  dx_array += np.matmul(self.B, u.matrix)
  return self.StateContainer(dx_array)
 
- def output(self, x):
+ def output(self, x, u=None):
  z_array = np.matmul(self.C, x.matrix) + self.D
  return self.OutputContainer(z_array)
 

src/progpy/mixture_of_experts.py

@@ -187,7 +187,7 @@ def best_model(self, x, _excepting=[]):
  best_index = i
  return self.parameters['models'][best_index]
 
- def output(self, x):
+ def output(self, x, u=None):
  excepting = []
  outputs_seen = set()
  z = {}

src/progpy/models/aircraft_model/small_rotorcraft.py

@@ -242,7 +242,7 @@ def event_state(self, x) -> dict:
  # Based on percentage of reference trajectory completed
  return {'TrajectoryComplete': x['mission_complete']}
 
- def output(self, x):
+ def output(self, x, u=None):
  # Output is the same as the state vector, without time and mission_complete
  return self.OutputContainer(x.matrix[0:-2])
 

src/progpy/models/battery_circuit.py

@@ -185,7 +185,7 @@ def event_state(self, x) -> dict:
  'EOD': np.minimum(charge_EOD, voltage_EOD)
  }
 
- def output(self, x):
+ def output(self, x, u=None):
  parameters = self.parameters
  Vcs = x['qcs']/parameters['Cs']
  Vcp = x['qcp']/parameters['Ccp']

src/progpy/models/battery_electrochem.py

@@ -476,7 +476,7 @@ def event_state(self, x) -> dict:
  'EOD': np.clip(min(charge_EOD, voltage_EOD), 0, 1)
  }
 
- def output(self, x):
+ def output(self, x, u=None):
  params = self.parameters
  An = params['An']
  # Negative Surface
@@ -628,7 +628,7 @@ def event_state(self, x) -> dict:
  def threshold_met(self, x) -> dict:
  return {'InsufficientCapacity': x['qMax'] < self.parameters['qMaxThreshold']}
 
- def output(self, _):
+ def output(self, x, u=None):
  return self.OutputContainer(np.array([]))
 
 def merge_dicts(a: dict, b: dict) -> None:
@@ -709,7 +709,7 @@ def dx(self, x, u):
  x_dot.matrix = np.vstack((x_dot.matrix, x_dot2.matrix))
  return x_dot
 
- def output(self, x):
+ def output(self, x, u=None):
  with warnings.catch_warnings():
  warnings.simplefilter("ignore")
  self.parameters['qMobile'] = x['qMax']

src/progpy/models/centrifugal_pump.py

@@ -246,7 +246,7 @@ def next_state(self, x, u, dt: float):
 
  return self.StateContainer(state_array)
 
- def output(self, x):
+ def output(self, x, u=None):
  Qout = np.maximum(0, x['Q']-x['QLeak'])
 
  return self.OutputContainer({

src/progpy/models/dcmotor.py

@@ -196,5 +196,5 @@ def next_state(self, x, u, dt: float):
  x.matrix[4] %= PI2 # Wrap angle
  return x
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer(x.matrix[3:])

src/progpy/models/dcmotor_singlephase.py

@@ -165,7 +165,7 @@ def dx(self, x, u):
  np.atleast_1d(dvrotdt) # rotor speed
  ]))
 
- def output(self, x):
+ def output(self, x, u=None):
  rotor_speed = x['v_rot']
  return self.OutputContainer(np.array([
  np.atleast_1d(rotor_speed),

src/progpy/models/esc.py

@@ -108,5 +108,5 @@ def next_state(self, x, u, dt: float):
  np.atleast_1d(VP[2]),
  np.atleast_1d(x['t'] + dt)]))
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer(x)

src/progpy/models/experimental/paris_law.py

@@ -79,7 +79,7 @@ def dx(self, x, u):
  dxdt = {'c_l': r}
  return self.StateContainer(dxdt)
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer(x)
 
  def event_state(self, x) -> dict:

src/progpy/models/pneumatic_valve.py

@@ -326,7 +326,7 @@ def next_state(self, x, u, dt: float):
  np.atleast_1d(dp) # pL - pR
  ]))
 
- def output(self, x):
+ def output(self, x, u=None):
  params = self.parameters # Optimization
  indicatorTopm = (x['x'] >= params['Ls']-params['indicatorTol'])
  indicatorBotm = (x['x'] <= params['indicatorTol'])

src/progpy/models/powertrain.py

@@ -140,7 +140,7 @@ def next_state(self, x, u, dt: float):
 
  return self.StateContainer(x_esc)
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer(
  {
  'v_rot': x['v_rot'], 

src/progpy/models/propeller_load.py

@@ -60,5 +60,5 @@ def next_state(self, x, u, dt: float):
  return self.StateContainer({
  't_l': self.parameters['C_q']*u['v_rot']**2})
 
- def output(self, x):
+ def output(self, x, u=None):
  return x

src/progpy/models/test_models/other_models.py

@@ -27,7 +27,7 @@ def dx(self, x, u):
  'x0': self.parameters['a'] * u['u0']
  })
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer({
  'x0+b': x['x0'] + self.parameters['b'],
  'x0+c': x['x0'] + self.parameters['c']
@@ -64,7 +64,7 @@ def dx(self, x, u):
  'x0': self.parameters['a'] * u['u0']
  })
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer({
  'x0+d': x['x0'] + self.parameters['d'],
  'x0+c': x['x0'] + self.parameters['c']

src/progpy/models/thrown_object.py

@@ -97,7 +97,7 @@ def next_state(self, x, u, dt: float):
  np.atleast_1d(next_v) # Acceleration of gravity
  ]))
 
- def output(self, x):
+ def output(self, x, u=None):
  return self.OutputContainer(np.array([[x['x']]]))
 
  def threshold_met(self, x) -> dict:

src/progpy/prognostics_model.py

@@ -462,7 +462,7 @@ def performance_metrics(self, x) -> dict:
 
  observables = performance_metrics # For backwards compatibility
 
- def output(self, x):
+ def output(self, x, u=None):
  """
  Calculate :term:`output` given state
 
@@ -471,6 +471,9 @@ def output(self, x):
  x : StateContainer
  state, with keys defined by model.states \n
  e.g., x = m.StateContainer({'abc': 332.1, 'def': 221.003}) given states = ['abc', 'def']
+ u : InputContainer
+ Inputs, with keys defined by model.inputs \n
+ e.g., u = m.InputContainer({'i':3.2}) given inputs = ['i']
 
  Returns
  -------