setting parameters directly

examples/01_Simulation.ipynb

@@ -1200,11 +1200,11 @@
  "results1 = m.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
  "fig = results1.outputs.plot(title='default')\n",
  "\n",
- "m.parameters['throwing_speed'] = 10\n",
+ "m['throwing_speed'] = 10\n",
  "results2 = m.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
  "fig = results2.outputs.plot(title='slow')\n",
  "\n",
- "m.parameters['throwing_speed'] = 80\n",
+ "m['throwing_speed'] = 80\n",
  "results3 = m.simulate_to_threshold(threshold_keys='impact', dt=0.1, save_freq=0.1)\n",
  "fig = results3.outputs.plot(title='fast')"
  ]

examples/02_param_est.ipynb

@@ -137,7 +137,7 @@
  "# Printing state before\n",
  "print('Model configuration before')\n",
  "for key in keys:\n",
- " print(\"-\", key, m.parameters[key])\n",
+ " print(\"-\", key, m[key])\n",
  "print(' Error: ', m.calc_error(times, inputs, outputs, dt=0.1))"
  ]
  },
@@ -176,7 +176,7 @@
  "source": [
  "print('\\nOptimized configuration')\n",
  "for key in keys:\n",
- " print(\"-\", key, m.parameters[key])\n",
+ " print(\"-\", key, m[key])\n",
  "print(' Error: ', m.calc_error(times, inputs, outputs, dt=0.1))"
  ]
  },
@@ -223,7 +223,7 @@
  "m.estimate_params(times = times, inputs = inputs, outputs = outputs, keys = keys, dt=0.1, tol=1e-6)\n",
  "print('\\nOptimized configuration')\n",
  "for key in keys:\n",
- " print(\"-\", key, m.parameters[key])\n",
+ " print(\"-\", key, m[key])\n",
  "print(' Error: ', m.calc_error(times, inputs, outputs, dt=0.1))"
  ]
  },
@@ -260,14 +260,14 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "m.parameters['thrower_height'] = 3.1\n",
- "m.parameters['throwing_speed'] = 29\n",
+ "m['thrower_height'] = 3.1\n",
+ "m['throwing_speed'] = 29\n",
  "\n",
  "# Using MAE, or Mean Absolute Error instead of the default Mean Squared Error.\n",
  "m.estimate_params(times = times, inputs = inputs, outputs = outputs, keys = keys, dt=0.1, tol=1e-9, error_method='MAX_E')\n",
  "print('\\nOptimized configuration')\n",
  "for key in keys:\n",
- " print(\"-\", key, m.parameters[key])\n",
+ " print(\"-\", key, m[key])\n",
  "print(' Error: ', m.calc_error(times, inputs, outputs, dt=0.1, method='MAX_E'))"
  ]
  },
@@ -309,9 +309,9 @@
  "results = m.simulate_to_threshold(save_freq=0.5, dt=('auto', 0.1))\n",
  "\n",
  "# Resetting parameters to their incorrectly set values.\n",
- "m.parameters['thrower_height'] = 20\n",
- "m.parameters['throwing_speed'] = 3.1\n",
- "m.parameters['g'] = 15\n",
+ "m['thrower_height'] = 20\n",
+ "m['throwing_speed'] = 3.1\n",
+ "m['g'] = 15\n",
  "keys = ['thrower_height', 'throwing_speed', 'g']"
  ]
  },
@@ -324,7 +324,7 @@
  "m.estimate_params(times = results.times, inputs = results.inputs, outputs = results.outputs, keys = keys)\n",
  "print('\\nOptimized configuration')\n",
  "for key in keys:\n",
- " print(\"-\", key, m.parameters[key])\n",
+ " print(\"-\", key, m[key])\n",
  "print(' Error: ', m.calc_error(results.times, results.inputs, results.outputs))"
  ]
  },
@@ -353,7 +353,7 @@
  "def AME(m, keys):\n",
  " error = 0\n",
  " for key in keys:\n",
- " error += abs(m.parameters[key] - true_Values.parameters[key])\n",
+ " error += abs(m[key] - true_Values.parameters[key])\n",
  " return error"
  ]
  },
@@ -393,9 +393,9 @@
  " results = m.simulate_to_threshold(save_freq=0.5, dt=('auto', 0.1))\n",
  " \n",
  " # Resetting parameters to their originally incorrectly set values.\n",
- " m.parameters['thrower_height'] = 20\n",
- " m.parameters['throwing_speed'] = 3.1\n",
- " m.parameters['g'] = 15\n",
+ " m['thrower_height'] = 20\n",
+ " m['throwing_speed'] = 3.1\n",
+ " m['g'] = 15\n",
  "\n",
  " m.estimate_params(times = results.times, inputs = results.inputs, outputs = results.outputs, keys = keys, dt=0.1)\n",
  " error = AME(m, ['thrower_height', 'throwing_speed', 'g'])\n",
@@ -439,9 +439,9 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "m.parameters['thrower_height'] = 20\n",
- "m.parameters['throwing_speed'] = 3.1\n",
- "m.parameters['g'] = 15"
+ "m['thrower_height'] = 20\n",
+ "m['throwing_speed'] = 3.1\n",
+ "m['g'] = 15"
  ]
  },
  {
@@ -461,7 +461,7 @@
  "m.estimate_params(times=times, inputs=inputs, outputs=outputs, keys=keys, dt=0.1)\n",
  "print('\\nOptimized configuration')\n",
  "for key in keys:\n",
- " print(\"-\", key, m.parameters[key])\n",
+ " print(\"-\", key, m[key])\n",
  "error = AME(m, ['thrower_height', 'throwing_speed', 'g'])\n",
  "print('AME Error: ', error)"
  ]

examples/04_New Models.ipynb

@@ -192,8 +192,8 @@
  "class ThrownObject(ThrownObject):\n",
  " def initialize(self, u=None, z=None):\n",
  " return self.StateContainer({\n",
- " 'x': self.parameters['thrower_height'],\n",
- " 'v': self.parameters['throwing_speed']\n",
+ " 'x': self['thrower_height'],\n",
+ " 'v': self['throwing_speed']\n",
  " })"
  ]
  },
@@ -237,7 +237,7 @@
  " def event_state(self, x): \n",
  " x_max = x['x'] + np.square(x['v'])/(9.81*2)\n",
  " return {\n",
- " 'falling': np.maximum(x['v']/self.parameters['throwing_speed'],0),\n",
+ " 'falling': np.maximum(x['v']/self['throwing_speed'],0),\n",
  " 'impact': np.maximum(x['x']/x_max,0) if x['v'] < 0 else 1\n",
  " }"
  ]
@@ -415,8 +415,8 @@
  "\n",
  " def initialize(self, u=None, z=None):\n",
  " return self.StateContainer({\n",
- " 'x': self.parameters['thrower_height'], # Thrown, so initial altitude is height of thrower\n",
- " 'v': self.parameters['throwing_speed'] # Velocity at which the ball is thrown - this guy is a professional baseball pitcher\n",
+ " 'x': self['thrower_height'], # Thrown, so initial altitude is height of thrower\n",
+ " 'v': self['throwing_speed'] # Velocity at which the ball is thrown - this guy is a professional baseball pitcher\n",
  " })"
  ]
  },
@@ -440,7 +440,7 @@
  " def dx(self, x, u):\n",
  " return self.StateContainer({\n",
  " 'x': x['v'], \n",
- " 'v': self.parameters['g']}) # Acceleration of gravity"
+ " 'v': self['g']}) # Acceleration of gravity"
  ]
  },
  {
@@ -480,11 +480,11 @@
  " \n",
  " def event_state(self, x): \n",
  " # Use speed and position to estimate maximum height\n",
- " x_max = x['x'] + np.square(x['v'])/(-self.parameters['g']*2)\n",
+ " x_max = x['x'] + np.square(x['v'])/(-self['g']*2)\n",
  " # 1 until falling begins\n",
  " x_max = np.where(x['v'] > 0, x['x'], x_max)\n",
  " return {\n",
- " 'falling': max(x['v']/self.parameters['throwing_speed'],0), # Throwing speed is max speed\n",
+ " 'falling': max(x['v']/self['throwing_speed'],0), # Throwing speed is max speed\n",
  " 'impact': max(x['x']/x_max,0) # 1 until falling begins, then it's fraction of height\n",
  " }"
  ]
@@ -654,7 +654,7 @@
  "outputs": [],
  "source": [
  "obj = ThrownObject_ST()\n",
- "print(\"Default Settings:\\n\\tthrower_height: {}\\n\\tthrowing_speed: {}\".format(obj.parameters['thrower_height'], obj.parameters['throwing_speed']))"
+ "print(\"Default Settings:\\n\\tthrower_height: {}\\n\\tthrowing_speed: {}\".format(obj['thrower_height'], obj['throwing_speed']))"
  ]
  },
  {
@@ -670,8 +670,8 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "obj.parameters['thrower_height'] = 1.75 # Our thrower is 1.75 m tall\n",
- "print(\"\\nUpdated Settings:\\n\\tthrower_height: {}\\n\\tthowing_speed: {}\".format(obj.parameters['thrower_height'], obj.parameters['throwing_speed']))"
+ "obj['thrower_height'] = 1.75 # Our thrower is 1.75 m tall\n",
+ "print(\"\\nUpdated Settings:\\n\\tthrower_height: {}\\n\\tthowing_speed: {}\".format(obj['thrower_height'], obj['throwing_speed']))"
  ]
  },
  {
@@ -743,7 +743,7 @@
  " def time_of_event(self, x, *args, **kwargs):\n",
  " # calculate time when object hits ground given x['x'] and x['v']\n",
  " # 0 = x0 + v0*t - 0.5*g*t^2\n",
- " g = self.parameters['g']\n",
+ " g = self['g']\n",
  " t_impact = -(x['v'] + np.sqrt(x['v']*x['v'] - 2*g*x['x']))/g\n",
  "\n",
  " # 0 = v0 - g*t\n",
@@ -890,7 +890,7 @@
  " def next_state(self, x, u, dt):\n",
  "\n",
  " A = np.array([[0, 1], [0, 0]]) # State transition matrix\n",
- " B = np.array([[0], [self.parameters['g']]]) # Acceleration due to gravity\n",
+ " B = np.array([[0], [self['g']]]) # Acceleration due to gravity\n",
  " x.matrix += (np.matmul(A, x.matrix) + B) * dt\n",
  "\n",
  " return x"

examples/06_Combining_Models.ipynb

@@ -468,7 +468,7 @@
  "outputs": [],
  "source": [
  "import numpy as np\n",
- "m_ensemble.parameters['aggregation_method'] = np.median\n",
+ "m_ensemble['aggregation_method'] = np.median\n",
  "\n",
  "results_ensemble_median = m_ensemble.simulate_to(t_end, future_loading)\n",
  "fig = plt.plot(results_ensemble_median.times, [z['v'] for z in results_ensemble_median.outputs], color='orange', label='ensemble -median')\n",

src/progpy/prognostics_model.py

@@ -202,6 +202,12 @@ def __init__(self, data):
 
  self.parameters = PrognosticsModelParameters(self, params, self.param_callbacks)
 
+ def __getitem__(self, arg):
+ return self.parameters[arg]
+
+ def __setitem__(self, key, value):
+ self.parameters[key] = value
+
  def initialize(self, u=None, z=None):
  """
  Calculate initial state given inputs and outputs. If not defined for a model, it will return parameters['x0']