enzyme kinetics tutorial

twallema · Jul 19, 2024 · 5c96143 · 5c96143
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Showing 3 changed files with 5 additions and 5 deletions.
diff --git a/docs/enzyme_kinetics.md b/docs/enzyme_kinetics.md
@@ -265,7 +265,7 @@ if __name__ == '__main__':
         # Update initial condition
         model.initial_states.update(initial_states[i])
         # Simulate model
-        out = model.sim(1600, N=n, draw_function=draw_fcn, samples=samples_dict)
+        out = model.sim(1600, N=n, draw_function=draw_fcn, draw_function_kwargs={'samples': samples_dict})
         # Add 4% observational noise
         out = add_gaussian_noise(out, 0.04, relative=True)
         # Visualize

diff --git a/tutorials/enzyme_kinetics/calibrate_intrinsic_kinetics.py b/tutorials/enzyme_kinetics/calibrate_intrinsic_kinetics.py
@@ -56,7 +56,7 @@
 # Define an initial condition
 init_states = {'S': 46, 'A': 61, 'W': 37, 'Es': 0}
 # Initialize model
-model = PPBB_model(init_states,params)
+model = PPBB_model(states=init_states, parameters=params)
 
 ###############
 ## Load data ##
@@ -153,7 +153,7 @@ def draw_fcn(parameters, samples):
         # Update initial condition
         model.initial_states.update(initial_states[i])
         # Simulate model
-        out = model.sim(1600, N=n, draw_function=draw_fcn, samples=samples_dict)
+        out = model.sim(1600, N=n, draw_function=draw_fcn, draw_function_kwargs={'samples': samples_dict})
         # Add 4% observational noise
         out = add_gaussian_noise(out, 0.04, relative=True)
         # Visualize

diff --git a/tutorials/enzyme_kinetics/simulate_1D_PFR.py b/tutorials/enzyme_kinetics/simulate_1D_PFR.py
@@ -122,7 +122,7 @@ def draw_fcn(parameters, samples):
 ## Concentration profile ##
 ###########################
 
-out = model.sim(end_sim, N=n, draw_function=draw_fcn, samples=samples_dict, processes=processes)
+out = model.sim(end_sim, N=n, draw_function=draw_fcn, draw_function_kwargs={'samples': samples_dict}, processes=processes)
 # Add 4% observational noise
 out = add_gaussian_noise(out, 0.04, relative=True)
 # Visualize 
@@ -178,7 +178,7 @@ def draw_fcn(parameters, samples):
     # Update model parameters
     model.parameters.update({'kL_a': kL*a, 'D_ax': D_ax, 'delta_x': l/nx, 'u': u}) 
     # Simulate
-    out_tmp = model.sim(end_sim, N=n, draw_function=draw_fcn, samples=samples_dict, processes=processes)
+    out_tmp = model.sim(end_sim, N=n, draw_function=draw_fcn, draw_function_kwargs={'samples': samples_dict}, processes=processes)
     # Add 4% observational noise and store
     out.append(add_gaussian_noise(out_tmp, 0.04, relative=True))