Add parameters class

examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb

@@ -1467,7 +1467,7 @@
    "id": "bf63b4f9-de38-4e70-9472-1de4973a0954",
    "metadata": {},
    "source": [
-    "In this example, we are going to try to fit all five parameters at once. To do this, we define a `pybop.parameter` for each fitting parameter as,"
+    "In this example, we are going to try to fit all five parameters at once. To do this, we define a `pybop.Parameter` for each fitting parameter and compile them in pybop.Parameters,"
    ]
   },
   {
@@ -1484,7 +1484,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"R0 [Ohm]\",\n",
     "        prior=pybop.Gaussian(0.005, 0.0001),\n",
@@ -1510,7 +1510,7 @@
     "        prior=pybop.Gaussian(3000, 2500),\n",
     "        bounds=[0.5, 1e4],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/equivalent_circuit_identification.ipynb

@@ -239,7 +239,7 @@
    "id": "bf63b4f9-de38-4e70-9472-1de4973a0954",
    "metadata": {},
    "source": [
-    "In this example, we are going to try to fit all five parameters at once. This isn't recommend for real-life application as identifiablity is challenging to guarantee with this large a parameter space. To do this, we define the `pybop.parameters` as,"
+    "In this example, we are going to try to fit all five parameters at once. This isn't recommend for real-life application as identifiablity is challenging to guarantee with this large a parameter space. To do this, we define the `pybop.Parameters` as,"
    ]
   },
   {
@@ -256,7 +256,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"R0 [Ohm]\",\n",
     "        prior=pybop.Gaussian(0.0002, 0.0001),\n",
@@ -278,11 +278,11 @@
     "        bounds=[2500, 5e4],\n",
     "    ),\n",
     "    pybop.Parameter(\n",
-    "        \"C1 [F]\",\n",
+    "        \"C2 [F]\",\n",
     "        prior=pybop.Gaussian(10000, 2500),\n",
     "        bounds=[2500, 5e4],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/multi_model_identification.ipynb

@@ -3729,7 +3729,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Positive electrode thickness [m]\",\n",
     "        prior=pybop.Gaussian(7.56e-05, 0.05e-05),\n",
@@ -3742,7 +3742,7 @@
     "        bounds=[65e-06, 90e-06],\n",
     "        true_value=parameter_set[\"Negative electrode thickness [m]\"],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/multi_optimiser_identification.ipynb

@@ -259,7 +259,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Negative electrode active material volume fraction\",\n",
     "        prior=pybop.Gaussian(0.6, 0.02),\n",
@@ -270,7 +270,7 @@
     "        prior=pybop.Gaussian(0.48, 0.02),\n",
     "        bounds=[0.4, 0.7],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/optimiser_calibration.ipynb

@@ -232,7 +232,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Negative electrode active material volume fraction\",\n",
     "        prior=pybop.Gaussian(0.7, 0.025),\n",
@@ -243,7 +243,7 @@
     "        prior=pybop.Gaussian(0.6, 0.025),\n",
     "        bounds=[0.5, 0.8],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/optimiser_interface.ipynb

@@ -100,13 +100,11 @@
     ")\n",
     "\n",
     "# Define the parameters\n",
-    "parameters = [\n",
-    "    pybop.Parameter(\n",
-    "        \"R0 [Ohm]\",\n",
-    "        prior=pybop.Gaussian(0.0002, 0.0001),\n",
-    "        bounds=[1e-4, 1e-2],\n",
-    "    )\n",
-    "]\n",
+    "parameters = pybop.Parameter(\n",
+    "    \"R0 [Ohm]\",\n",
+    "    prior=pybop.Gaussian(0.0002, 0.0001),\n",
+    "    bounds=[1e-4, 1e-2],\n",
+    ")\n",
     "\n",
     "# Generate synthetic data\n",
     "t_eval = np.arange(0, 900, 2)\n",

examples/notebooks/pouch_cell_identification.ipynb

@@ -323,7 +323,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Negative electrode active material volume fraction\",\n",
     "        prior=pybop.Gaussian(0.7, 0.05),\n",
@@ -334,7 +334,7 @@
     "        prior=pybop.Gaussian(0.58, 0.05),\n",
     "        bounds=[0.5, 0.8],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/notebooks/spm_electrode_design.ipynb

@@ -143,7 +143,7 @@
    },
    "outputs": [],
    "source": [
-    "parameters = [\n",
+    "parameters = pybop.Parameters(\n",
     "    pybop.Parameter(\n",
     "        \"Positive electrode thickness [m]\",\n",
     "        prior=pybop.Gaussian(7.56e-05, 0.05e-05),\n",
@@ -154,7 +154,7 @@
     "        prior=pybop.Gaussian(5.22e-06, 0.05e-06),\n",
     "        bounds=[2e-06, 9e-06],\n",
     "    ),\n",
-    "]"
+    ")"
    ]
   },
   {

examples/scripts/BPX_spm.py

@@ -10,7 +10,7 @@
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "Negative particle radius [m]",
         prior=pybop.Gaussian(6e-06, 0.1e-6),
@@ -23,7 +23,7 @@
         bounds=[1e-7, 9e-7],
         true_value=parameter_set["Positive particle radius [m]"],
     ),
-]
+)
 
 # Generate data
 sigma = 0.001
@@ -47,13 +47,7 @@
 
 # Run the optimisation
 x, final_cost = optim.run()
-print(
-    "True parameters:",
-    [
-        parameters[0].true_value,
-        parameters[1].true_value,
-    ],
-)
+print("True parameters:", parameters.true_value())
 print("Estimated parameters:", x)
 
 # Plot the timeseries output

examples/scripts/ecm_CMAES.py

@@ -45,7 +45,7 @@
 )
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "R0 [Ohm]",
         prior=pybop.Gaussian(0.0002, 0.0001),
@@ -56,7 +56,7 @@
         prior=pybop.Gaussian(0.0001, 0.0001),
         bounds=[1e-5, 1e-2],
     ),
-]
+)
 
 sigma = 0.001
 t_eval = np.arange(0, 900, 3)

examples/scripts/exp_UKF.py

@@ -7,42 +7,41 @@
 # Parameter set and model definition
 parameter_set = pybamm.ParameterValues({"k": "[input]", "y0": "[input]"})
 model = ExponentialDecay(parameter_set=parameter_set, n_states=1)
-x0 = np.array([0.1, 1.0])
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "k",
         prior=pybop.Gaussian(0.1, 0.05),
         bounds=[0, 1],
+        true_value=0.1,
     ),
     pybop.Parameter(
         "y0",
         prior=pybop.Gaussian(1, 0.05),
         bounds=[0, 3],
+        true_value=1.0,
     ),
-]
-
-# Verification: save fixed inputs for testing
-inputs = dict()
-for i, param in enumerate(parameters):
-    inputs[param.name] = x0[i]
+)
 
 # Make a prediction with measurement noise
 sigma = 1e-2
 t_eval = np.linspace(0, 20, 10)
-values = model.predict(t_eval=t_eval, inputs=inputs)
+model.parameters = parameters
+values = model.predict(t_eval=t_eval, inputs=parameters.true_value())
 values = values["2y"].data
 corrupt_values = values + np.random.normal(0, sigma, len(t_eval))
 
 # Verification step: compute the analytical solution for 2y
-expected_values = 2 * inputs["y0"] * np.exp(-inputs["k"] * t_eval)
+expected_values = (
+    2 * parameters["y0"].true_value * np.exp(-parameters["k"].true_value * t_eval)
+)
 
 # Verification step: make another prediction using the Observer class
 model.build(parameters=parameters)
-simulator = pybop.Observer(parameters, model, signal=["2y"], x0=x0)
+simulator = pybop.Observer(parameters, model, signal=["2y"])
 simulator._time_data = t_eval
-measurements = simulator.evaluate(x0)
+measurements = simulator.evaluate(parameters.true_value())
 
 # Verification step: Compare by plotting
 go = pybop.PlotlyManager().go
@@ -82,11 +81,10 @@
     measurement_noise,
     dataset,
     signal=signal,
-    x0=x0,
 )
 
 # Verification step: Find the maximum likelihood estimate given the true parameters
-estimation = observer.evaluate(x0)
+estimation = observer.evaluate(parameters.true_value())
 
 # Verification step: Add the estimate to the plot
 line4 = go.Scatter(

examples/scripts/gitt.py

@@ -35,14 +35,12 @@
 # Define the cost to optimise
 model = pybop.lithium_ion.WeppnerHuggins(parameter_set=parameter_set)
 
-parameters = [
-    pybop.Parameter(
-        "Positive electrode diffusivity [m2.s-1]",
-        prior=pybop.Gaussian(5e-14, 1e-13),
-        bounds=[1e-16, 1e-11],
-        true_value=parameter_set["Positive electrode diffusivity [m2.s-1]"],
-    ),
-]
+parameters = pybop.Parameter(
+    "Positive electrode diffusivity [m2.s-1]",
+    prior=pybop.Gaussian(5e-14, 1e-13),
+    bounds=[1e-16, 1e-11],
+    true_value=parameter_set["Positive electrode diffusivity [m2.s-1]"],
+)
 
 problem = pybop.FittingProblem(
     model,

examples/scripts/spm_CMAES.py

@@ -7,7 +7,7 @@
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "Negative particle radius [m]",
         prior=pybop.Gaussian(6e-06, 0.1e-6),
@@ -20,7 +20,7 @@
         bounds=[1e-6, 9e-6],
         true_value=parameter_set["Positive particle radius [m]"],
     ),
-]
+)
 
 # Generate data
 sigma = 0.001
@@ -46,13 +46,7 @@
 
 # Run the optimisation
 x, final_cost = optim.run()
-print(
-    "True parameters:",
-    [
-        parameters[0].true_value,
-        parameters[1].true_value,
-    ],
-)
+print("True parameters:", parameters.true_value())
 print("Estimated parameters:", x)
 
 # Plot the time series

examples/scripts/spm_IRPropMin.py

@@ -7,7 +7,7 @@
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "Negative electrode active material volume fraction",
         prior=pybop.Gaussian(0.6, 0.05),
@@ -16,7 +16,7 @@
         "Positive electrode active material volume fraction",
         prior=pybop.Gaussian(0.48, 0.05),
     ),
-]
+)
 
 # Generate data
 sigma = 0.001

examples/scripts/spm_MAP.py

@@ -7,7 +7,7 @@
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "Negative electrode active material volume fraction",
         prior=pybop.Gaussian(0.6, 0.05),
@@ -18,7 +18,7 @@
         prior=pybop.Gaussian(0.48, 0.05),
         bounds=[0.4, 0.7],
     ),
-]
+)
 
 # Set initial parameter values
 parameter_set.update(

examples/scripts/spm_MLE.py

@@ -7,7 +7,7 @@
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
 # Fitting parameters
-parameters = [
+parameters = pybop.Parameters(
     pybop.Parameter(
         "Negative electrode active material volume fraction",
         prior=pybop.Gaussian(0.6, 0.05),
@@ -18,7 +18,7 @@
         prior=pybop.Gaussian(0.48, 0.05),
         bounds=[0.4, 0.7],
     ),
-]
+)
 
 # Set initial parameter values
 parameter_set.update(