trying without type specifications
@@ -31,7 +31,7 @@ parallel = False
#%% The core Covasim functions -- compute the infections
-@nb.njit( (nbint, nbfloat[:], nbfloat[:], nbfloat[:], nbfloat, nbfloat, nbfloat), cache=True, parallel=parallel)
+@nb.njit(cache=True, parallel=parallel)
def compute_viral_load(t, time_start, time_recovered, time_dead, frac_time, load_ratio, high_cap):
'''
Calculate relative transmissibility for time t. Includes time varying
@@ -74,7 +74,7 @@ def compute_viral_load(t, time_start, time_recovered, time_dead, frac_time,
return load
-@nb.njit( (nbfloat[:], nbfloat[:], nbbool[:], nbbool[:], nbfloat, nbfloat[:], nbbool[:], nbbool[:], nbbool[:], nbfloat, nbfloat, nbfloat), cache=True, parallel=parallel)
+@nb.njit(cache=True, parallel=parallel)
def compute_trans_sus(rel_trans, rel_sus, inf, sus, beta_layer, viral_load, symp, diag, quar, asymp_factor, iso_factor, quar_factor):
''' Calculate relative transmissibility and susceptibility '''
f_asymp = symp + ~symp * asymp_factor # Asymptomatic factor, changes e.g. [0,1] with a factor of 0.8 to [0.8,1.0]
@@ -85,7 +85,7 @@ def compute_trans_sus(rel_trans, rel_sus, inf, sus, beta_layer,
return rel_trans, rel_sus
-@nb.njit( (nbfloat, nbint[:], nbint[:], nbfloat[:], nbfloat[:], nbfloat[:]), cache=True, parallel=parallel)
+@nb.njit(cache=True, parallel=parallel)
def compute_infections(beta, sources, targets, layer_betas, rel_trans, rel_sus):
''' The heaviest step of the model -- figure out who gets infected on this timestep '''
betas = beta * layer_betas * rel_trans[sources] * rel_sus[targets] # Calculate the raw transmission probabilities
