influenza tutorial

docs/influenza_1718.md

@@ -301,11 +301,11 @@ if __name__ == '__main__':
     data=[df_influenza[start_date:end_calibration], ]
     states = ["Im_inc",]
     log_likelihood_fnc = [ll_negative_binomial,]
-    log_likelihood_fnc_args = [5*[0.03,],]
+    log_likelihood_fnc_args = [4*[0.03,],]
 
     # Calibated parameters and bounds
-    pars = ['beta', 'f_a']
-    labels = ['$\\beta$', '$f_a$']
+    pars = ['beta', 'f_ud']
+    labels = ['$\\beta$', '$f_{ud}$']
     bounds = [(1e-6,0.06), (0,0.99)]
 
     # Setup objective function (no priors --> uniform priors based on bounds)

tutorials/influenza_1718/calibration.py

@@ -160,7 +160,7 @@
     # Assign results to model
     model.parameters = assign_theta(model.parameters, pars, theta)
     # Simulate model
-    out = model.sim([start_calibration, end_visualisation], samples={}, N=n, tau=tau, output_timestep=1)
+    out = model.sim([start_calibration, end_visualisation], N=n, tau=tau)
     # Add poisson obervational noise
     out = add_negative_binomial_noise(out, alpha)
     # Visualize
@@ -223,7 +223,8 @@ def draw_fcn(parameters, samples):
         parameters['f_ud'] = np.array([slice[idx] for slice in samples['f_ud']])
         return parameters
     # Simulate model
-    out = model.sim([start_visualisation, end_visualisation], N=n, tau=tau, output_timestep=1, samples=samples_dict, draw_function=draw_fcn, processes=processes)
+    out = model.sim([start_visualisation, end_visualisation], N=n, tau=tau, output_timestep=1,
+                    draw_function=draw_fcn, draw_function_kwargs={'samples': samples_dict}, processes=processes)
     # Add negative binomial observation noise
     out_noise = add_negative_binomial_noise(out, alpha)