update key

examples/01_Simulation.ipynb

@@ -237,7 +237,7 @@
    "source": [
     "By default, `simulate_to_threshold` simulates until the first event occurs. In this case, that's `falling` (i.e., when the object begins falling). For this model `falling` will always occur before `impact`, but for many models you won't have such a strict ordering of events. \n",
     "\n",
-    "For users interested in when a specific event is reached, you can indicate which event(s) you'd like to simulate to using the `threshold_keys` argument. For example,"
+    "For users interested in when a specific event is reached, you can indicate which event(s) you'd like to simulate to using the `events` argument. For example,"
    ]
   },
   {
@@ -246,7 +246,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = m.simulate_to_threshold(save_freq=0.5, threshold_keys='impact')\n",
+    "results = m.simulate_to_threshold(save_freq=0.5, events='impact')\n",
     "fig = results.outputs.plot(ylabel='Position (m)')\n",
     "fig = results.event_states.plot()"
    ]
@@ -255,7 +255,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now the model simulated past the `falling` event untill the `impact` event occured. `threshold_keys` accepts a single event, or a list of events, so for models with many events you can specify a list of events where any will stop simulation."
+    "Now the model simulated past the `falling` event untill the `impact` event occured. `events` accepts a single event, or a list of events, so for models with many events you can specify a list of events where any will stop simulation."
    ]
   },
   {
@@ -273,7 +273,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = m.simulate_to_threshold(save_freq=0.5, threshold_keys='impact', horizon=7)\n",
+    "results = m.simulate_to_threshold(save_freq=0.5, events='impact', horizon=7)\n",
     "fig = results.outputs.plot(ylabel='Position (m)')\n",
     "fig = results.event_states.plot()"
    ]
@@ -291,7 +291,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = m.simulate_to_threshold(save_freq=0.5, threshold_keys='impact', horizon=10)\n",
+    "results = m.simulate_to_threshold(save_freq=0.5, events='impact', horizon=10)\n",
     "fig = results.outputs.plot(ylabel='Position (m)')\n",
     "fig = results.event_states.plot()"
    ]
@@ -318,7 +318,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = m.simulate_to_threshold(save_freq=0.5, threshold_keys='impact', print=True, progress=True)"
+    "results = m.simulate_to_threshold(save_freq=0.5, events='impact', print=True, progress=True)"
    ]
   },
   {
@@ -970,7 +970,7 @@
    "outputs": [],
    "source": [
     "results = m.simulate_to_threshold(\n",
-    "    threshold_keys='impact',\n",
+    "    events='impact',\n",
     "    dt=2.5,\n",
     "    save_freq=2.5)\n",
     "fig = results.outputs.plot(ylabel='Position (m)')"
@@ -992,7 +992,7 @@
    "outputs": [],
    "source": [
     "results = m.simulate_to_threshold(\n",
-    "    threshold_keys='impact',\n",
+    "    events='impact',\n",
     "    dt=0.25,\n",
     "    save_freq=0.25)\n",
     "fig = results.outputs.plot(ylabel='Position (m)')"
@@ -1016,7 +1016,7 @@
    "outputs": [],
    "source": [
     "results = m.simulate_to_threshold(\n",
-    "    threshold_keys='impact',\n",
+    "    events='impact',\n",
     "    dt=0.05,\n",
     "    save_freq=0.05)\n",
     "fig = results.outputs.plot(ylabel='Position (m)')"
@@ -1052,7 +1052,7 @@
    "outputs": [],
    "source": [
     "results = m.simulate_to_threshold(\n",
-    "    threshold_keys='impact',\n",
+    "    events='impact',\n",
     "    dt=1,\n",
     "    save_freq=1.5)\n",
     "print('Times saved: ', results.times)"
@@ -1074,7 +1074,7 @@
    "outputs": [],
    "source": [
     "results = m.simulate_to_threshold(\n",
-    "    threshold_keys='impact',\n",
+    "    events='impact',\n",
     "    dt=('auto', 1),\n",
     "    save_freq=1.5)\n",
     "print('Times saved: ', results.times)"
@@ -1118,7 +1118,7 @@
     "        return 0.25\n",
     "    return 1\n",
     "\n",
-    "results=m.simulate_to_threshold(dt=next_time, save_freq= 0.25, threshold_keys='impact')\n",
+    "results=m.simulate_to_threshold(dt=next_time, save_freq= 0.25, events='impact')\n",
     "\n",
     "print(results.times)"
    ]
@@ -1197,15 +1197,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results1 = m.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
+    "results1 = m.simulate_to_threshold(events='impact', dt=0.1, save_freq=0.1)\n",
     "fig = results1.outputs.plot(title='default')\n",
     "\n",
     "m['throwing_speed'] = 10\n",
-    "results2 = m.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
+    "results2 = m.simulate_to_threshold(events='impact', dt=0.1, save_freq=0.1)\n",
     "fig = results2.outputs.plot(title='slow')\n",
     "\n",
     "m['throwing_speed'] = 80\n",
-    "results3 = m.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
+    "results3 = m.simulate_to_threshold(events='impact', dt=0.1, save_freq=0.1)\n",
     "fig = results3.outputs.plot(title='fast')"
    ]
   },
@@ -1223,11 +1223,11 @@
    "outputs": [],
    "source": [
     "m_e = ThrownObject(g=-9.81)  # Earth gravity\n",
-    "results_earth = m_e.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
+    "results_earth = m_e.simulate_to_threshold(events='impact', dt=0.1, save_freq=0.1)\n",
     "fig = results_earth.outputs.plot(title='Earth')\n",
     "\n",
     "m_j = ThrownObject(g=-24.79)  # Jupiter gravity\n",
-    "results_jupiter = m_j.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
+    "results_jupiter = m_j.simulate_to_threshold(events='impact', dt=0.1, save_freq=0.1)\n",
     "fig = results_jupiter.outputs.plot(title='Jupiter')"
    ]
   },
@@ -1293,7 +1293,7 @@
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+    "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
    }
   }
  },

examples/04_New Models.ipynb

@@ -256,7 +256,7 @@
    "outputs": [],
    "source": [
     "m = ThrownObject()\n",
-    "save = m.simulate_to_threshold(print=True, save_freq=1, threshold_keys='impact', dt=0.1)"
+    "save = m.simulate_to_threshold(print=True, save_freq=1, events='impact', dt=0.1)"
    ]
   },
   {
@@ -547,7 +547,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll start by simulating to impact. We'll specify the `threshold_keys` to specifically indicate we are interested in impact. For more information on simulation, see 1. Simulation. "
+    "We'll start by simulating to impact. We'll specify the `events` to specifically indicate we are interested in impact. For more information on simulation, see 1. Simulation. "
    ]
   },
   {
@@ -558,7 +558,7 @@
    "source": [
     "# Simulate to impact\n",
     "event = 'impact'\n",
-    "simulated_results = m_st.simulate_to_threshold(threshold_keys=[event], dt=0.005, save_freq=1, print = True)\n",
+    "simulated_results = m_st.simulate_to_threshold(events=event, dt=0.005, save_freq=1, print = True)\n",
     "\n",
     "# Print result: \n",
     "print('The object hit the ground in {} seconds'.format(round(simulated_results.times[-1],2)))"
@@ -938,7 +938,7 @@
     "# Simulate to threshold \n",
     "m_matrix.simulate_to_threshold(\n",
     "        print = True, \n",
-    "        threshold_keys = 'impact', \n",
+    "        events = 'impact', \n",
     "        dt = 0.1, \n",
     "        save_freq = 1)\n",
     "toc_matrix = time.perf_counter()"
@@ -963,7 +963,7 @@
     "tic_st = time.perf_counter()\n",
     "m_st.simulate_to_threshold(\n",
     "        print = True, \n",
-    "        threshold_keys = 'impact', \n",
+    "        events = 'impact', \n",
     "        dt = 0.1, \n",
     "        save_freq = 1)\n",
     "toc_st = time.perf_counter()\n",
@@ -1030,7 +1030,7 @@
    "outputs": [],
    "source": [
     "event = 'impact'\n",
-    "simulated_results = m_limits.simulate_to_threshold(threshold_keys=[event], dt=0.005, save_freq=1)\n",
+    "simulated_results = m_limits.simulate_to_threshold(events=event, dt=0.005, save_freq=1)\n",
     "\n",
     "print('Example: No State Limits')\n",
     "for i, state in enumerate(simulated_results.states):\n",
@@ -1086,7 +1086,7 @@
    "outputs": [],
    "source": [
     "event = 'impact'\n",
-    "simulated_results = m_limits.simulate_to_threshold(threshold_keys=[event], dt=0.005, save_freq=1)\n",
+    "simulated_results = m_limits.simulate_to_threshold(events=event, dt=0.005, save_freq=1)\n",
     "\n",
     "print('Example: With State Limits')\n",
     "for i, state in enumerate(simulated_results.states):\n",
@@ -1117,7 +1117,7 @@
     "x0 = m_limits.initialize(u = {}, z = {})\n",
     "x0['x'] = -1 # Initial position value set to an unrealistic value of -1\n",
     "\n",
-    "simulated_results = m_limits.simulate_to_threshold(threshold_keys=[event], dt=0.005, save_freq=1, x = x0)\n",
+    "simulated_results = m_limits.simulate_to_threshold(events=event, dt=0.005, save_freq=1, x = x0)\n",
     "\n",
     "# Print states\n",
     "print('Example 2: With -1 as initial x value')\n",
@@ -1373,7 +1373,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "simulated_results = m.simulate_to_threshold(future_loading, threshold_keys=['EOD'], print = True)\n",
+    "simulated_results = m.simulate_to_threshold(future_loading, events='EOD', print = True)\n",
     "\n",
     "simulated_results.event_states.plot()\n",
     "plt.show()"

examples/full_lstm_model.py

@@ -30,11 +30,11 @@ def future_loading(t, x=None):
     # Step 1: Generate additional data
     # We will use data generated above, but we also want data at additional timesteps 
     print('Generating data...')
-    data = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP, dt=TIMESTEP)
-    data_half = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP/2, dt=TIMESTEP/2)
-    data_quarter = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP/4, dt=TIMESTEP/4)
-    data_twice = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP*2, dt=TIMESTEP*2)
-    data_four = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP*4, dt=TIMESTEP*4)
+    data = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP, dt=TIMESTEP)
+    data_half = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP/2, dt=TIMESTEP/2)
+    data_quarter = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP/4, dt=TIMESTEP/4)
+    data_twice = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP*2, dt=TIMESTEP*2)
+    data_four = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP*4, dt=TIMESTEP*4)
 
     # Step 2: Data Prep
     # We need to add the timestep as a input
@@ -109,8 +109,8 @@ def future_loading3(t, x = None):
     # Use new dt, not used in training
     # Using a dt not used in training will demonstrate the model's 
     # ability to handle different timesteps not part of training set
-    data = m.simulate_to_threshold(future_loading, threshold_keys='impact', dt=TIMESTEP*3, save_freq=TIMESTEP*3)
-    results3 = m2.simulate_to_threshold(future_loading3, threshold_keys='impact', dt=TIMESTEP*3, save_freq=TIMESTEP*3)
+    data = m.simulate_to_threshold(future_loading, events='impact', dt=TIMESTEP*3, save_freq=TIMESTEP*3)
+    results3 = m2.simulate_to_threshold(future_loading3, events='impact', dt=TIMESTEP*3, save_freq=TIMESTEP*3)
 
     # Step 6: Compare Results
     print('Comparing results...')

examples/lstm_model.py

@@ -36,7 +36,7 @@ def run_example():
     def future_loading(t, x=None):
         return m.InputContainer({})  # No input for thrown object 
 
-    data = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP, dt=TIMESTEP)
+    data = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP, dt=TIMESTEP)
 
     # Step 2: Generate model
     # We'll use the LSTMStateTransitionModel class to generate a model
@@ -91,10 +91,10 @@ def future_loading2(t, x=None):
     # We will use data generated above, but we also want data at additional timesteps 
     print('\n------------------------------------------\nExample 2...')
     print('Generating additional data...')
-    data_half = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP/2, dt=TIMESTEP/2)
-    data_quarter = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP/4, dt=TIMESTEP/4)
-    data_twice = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP*2, dt=TIMESTEP*2)
-    data_four = m.simulate_to_threshold(future_loading, threshold_keys='impact', save_freq=TIMESTEP*4, dt=TIMESTEP*4)
+    data_half = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP/2, dt=TIMESTEP/2)
+    data_quarter = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP/4, dt=TIMESTEP/4)
+    data_twice = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP*2, dt=TIMESTEP*2)
+    data_four = m.simulate_to_threshold(future_loading, events='impact', save_freq=TIMESTEP*4, dt=TIMESTEP*4)
 
     # Step 2: Data Prep
     # We need to add the timestep as a input

examples/new_model.py

@@ -75,7 +75,7 @@ def future_load(t, x=None):
 
     # Step 3: Simulate to impact
     event = 'impact'
-    simulated_results = m.simulate_to_threshold(future_load, threshold_keys=[event], dt=0.005, save_freq=1, print = True)
+    simulated_results = m.simulate_to_threshold(future_load, events=event, dt=0.005, save_freq=1, print = True)
 
     # Print flight time
     print('The object hit the ground in {} seconds'.format(round(simulated_results.times[-1],2)))
@@ -86,24 +86,24 @@ def future_load(t, x=None):
 
     # The first way to change the configuration is to pass in your desired config into construction of the model
     m = ThrownObject(g = grav_moon)
-    simulated_moon_results = m.simulate_to_threshold(future_load, threshold_keys=[event], dt=0.005, save_freq=1)
+    simulated_moon_results = m.simulate_to_threshold(future_load, events=event, dt=0.005, save_freq=1)
 
     grav_mars = -3.711
     # You can also update the parameters after it's constructed
     m.parameters['g'] = grav_mars
-    simulated_mars_results = m.simulate_to_threshold(future_load, threshold_keys=[event], dt=0.005, save_freq=1)
+    simulated_mars_results = m.simulate_to_threshold(future_load, events=event, dt=0.005, save_freq=1)
 
     grav_venus = -8.87
     m.parameters['g'] = grav_venus
-    simulated_venus_results = m.simulate_to_threshold(future_load, threshold_keys=[event], dt=0.005, save_freq=1)
+    simulated_venus_results = m.simulate_to_threshold(future_load, events=event, dt=0.005, save_freq=1)
 
     print('Time to hit the ground: ')
     print('\tvenus: {}s'.format(round(simulated_venus_results.times[-1],2)))
     print('\tearth: {}s'.format(round(simulated_results.times[-1],2)))
     print('\tmars: {}s'.format(round(simulated_mars_results.times[-1],2)))
     print('\tmoon: {}s'.format(round(simulated_moon_results.times[-1],2)))
 
-    # We can also simulate until any event is met by neglecting the threshold_keys argument
+    # We can also simulate until any event is met by neglecting the events argument
     simulated_results = m.simulate_to_threshold(future_load, dt=0.005, save_freq=1)
     threshs_met = m.threshold_met(simulated_results.states[-1])
     for (key, met) in threshs_met.items():

examples/noise.py

@@ -17,7 +17,7 @@ def future_load(t=None, x=None):
 
     # Define configuration for simulation
     config = {
-        'threshold_keys': 'impact', # Simulate until the thrown object has impacted the ground
+        'events': 'impact', # Simulate until the thrown object has impacted the ground
         'dt': 0.005, # Time step (s)
         'save_freq': 0.5, # Frequency at which results are saved (s)
     }

examples/sensitivity.py

@@ -22,7 +22,7 @@ def run_example():
     eods = np.empty(len(thrower_height_range))
     for (i, thrower_height) in zip(range(len(thrower_height_range)), thrower_height_range):
         m.parameters['thrower_height'] = thrower_height
-        simulated_results = m.simulate_to_threshold(threshold_keys=[event], dt =1e-3, save_freq =10)
+        simulated_results = m.simulate_to_threshold(events=event, dt =1e-3, save_freq =10)
         eods[i] = simulated_results.times[-1]
 
     # Step 4: Analysis
@@ -36,7 +36,7 @@ def run_example():
     eods = np.empty(len(throw_speed_range))
     for (i, throw_speed) in zip(range(len(throw_speed_range)), throw_speed_range):
         m.parameters['throwing_speed'] = throw_speed
-        simulated_results = m.simulate_to_threshold(threshold_keys=[event], options={'dt':1e-3, 'save_freq':10})
+        simulated_results = m.simulate_to_threshold(events=event, options={'dt':1e-3, 'save_freq':10})
         eods[i] = simulated_results.times[-1]
 
     print('\nFor a reasonable range of throwing speeds, impact time is between {} and {}'.format(round(eods[0],3), round(eods[-1],3)))

examples/sim_battery_eol.py

@@ -37,7 +37,7 @@ def future_loading(t, x=None):
     options = {
         'save_freq': 1000,  # Frequency at which results are saved
         'dt': 2,  # Timestep
-        'threshold_keys': ['InsufficientCapacity'],  # Simulate to InsufficientCapacity
+        'events': 'InsufficientCapacity',  # Simulate to InsufficientCapacity
         'print': True
     }
     simulated_results = batt.simulate_to_threshold(future_loading, **options)

examples/vectorized.py

@@ -23,7 +23,7 @@ def future_load(t, x=None):
 
     # Step 3: Simulate to threshold
     # Here we are simulating till impact using the first state defined above
-    (times, inputs, states, outputs, event_states) = m.simulate_to_threshold(future_load, x = first_state, threshold_keys=['impact'], print = True, dt=0.1, save_freq=2)
+    (times, inputs, states, outputs, event_states) = m.simulate_to_threshold(future_load, x = first_state, events='impact', print = True, dt=0.1, save_freq=2)
 
     # Now lets do the same thing but only stop when all hit the ground
     def thresholds_met_eqn(thresholds_met):

src/progpy/data_models/data_model.py

@@ -120,7 +120,7 @@ def from_model(cls, m: PrognosticsModel, load_functions: list, **kwargs) -> "Dat
             # Only one function
             load_functions = [load_functions]
 
-        sim_cfg_params = ['dt', 't0', 'integration_method', 'save_freq', 'save_pts', 'horizon', 'first_output', 'threshold_keys', 'x', 'thresholds_met_eqn']
+        sim_cfg_params = ['dt', 't0', 'integration_method', 'save_freq', 'save_pts', 'horizon', 'first_output', 'events', 'x', 'thresholds_met_eqn']
 
         # Check format of cfg item and split into cfg for each sim if scalar
         for cfg in sim_cfg_params:

src/progpy/data_models/dmd.py

@@ -416,14 +416,14 @@ def next_state(self, x, u, _):
 
         return x   
 
-    def simulate_to_threshold(self, future_loading_eqn, first_output=None, threshold_keys=None, **kwargs):
+    def simulate_to_threshold(self, future_loading_eqn, first_output=None, events=None, **kwargs):
         # Save keyword arguments same as DMD training for approximation
         kwargs_sim = kwargs.copy()
         kwargs_sim['save_freq'] = self.dt
         kwargs_sim['dt'] = self.dt
 
         # Simulate to threshold at DMD time step
-        results = super().simulate_to_threshold(future_loading_eqn, first_output, threshold_keys, **kwargs_sim)
+        results = super().simulate_to_threshold(future_loading_eqn, first_output, events, **kwargs_sim)
 
         # Interpolate results to be at user-desired time step
         if 'dt' in kwargs: