diff --git a/ENDA.py b/ENDA.py index 4c573296f39f8167cf98000a3fc016642696a97a..5545032abd366f29a47468a48b80ce38a1cf5c8c 100644 --- a/ENDA.py +++ b/ENDA.py @@ -353,7 +353,6 @@ class cycle: if isinstance(nr,CBL): if cbl_settings["simulate_error_growth"]: x0[:-cbl_settings["parameter_number"],:] += RNG.normal(scale=cbl_settings["error_growth_perturbations_amplitude"],size=nens)[None,:] - #x0[:-cbl_settings["parameter_number"],:] += np.random.normal(scale=cbl_settings["error_growth_perturbations_amplitude"],size=nens)[None,:] # Save initial conditions for next cycle da.update(x0) @@ -447,7 +446,6 @@ class experiment: truths[i,j] = observation_operator(variable,state_coordinates,obs_coordinate) observations[i,j] = truths[i,j]+\ RNG.normal(0,self.obs_error_sdev_generate[j]) - #np.random.normal(0,self.obs_error_sdev_generate[j]) # Store truths and observations self.truths = truths diff --git a/models.py b/models.py index e9136eceed8117aadce7689f9cc6b708139f2ae3..0e6abe7e072364325dd75523c91bffbe18fe04fe 100644 --- a/models.py +++ b/models.py @@ -102,12 +102,9 @@ class CBL: if self.perturbations_type == "random" or self.perturbations_type == "uniform": ppt = RNG.standard_normal((randomsize,self.nens)) - #ppt = np.random.randn(randomsize,self.nens) if self.is_bwind: ppu = RNG.standard_normal((self.nens,randomsize)) ppv = RNG.standard_normal((self.nens,randomsize)) - #ppu = np.random.randn(self.nens,randomsize) - #ppv = np.random.randn(self.nens,randomsize) # Smooth perturbations are slightly more complicated if self.perturbations_type == "smooth": @@ -121,7 +118,6 @@ class CBL: randomsize = self.perturbations_smooth_number ppt = np.zeros((self.nz,self.nens))+np.nan pert_t = RNG.standard_normal((randomsize,self.nens)) - #pert_t = np.random.randn(randomsize,self.nens) for n in range(self.nens): f = CubicSpline(ipert,pert_t[:,n]) ppt[:,n] = f(np.arange(self.nz)) @@ -130,8 +126,6 @@ class CBL: ppv = np.zeros((self.nz,self.nens))+np.nan pert_u = RNG.standard_normal((randomsize,self.nens)) pert_v = RNG.standard_normal((randomsize,self.nens)) - #pert_u = np.random.randn(randomsize,self.nens) - #pert_v = np.random.randn(randomsize,self.nens) for n in range(self.nens): f = CubicSpline(ipert,pert_u[:,n]) ppu[:,n] = f(np.arange(self.nz)) @@ -222,7 +216,6 @@ class CBL: for k in range(-self.parameter_number,0): dum = RNG.uniform(self.parameter_ensemble_min[k], self.parameter_ensemble_max[k], size=self.nens) - #dum = np.random.uniform(self.parameter_ensemble_min[k], self.parameter_ensemble_max[k], size=self.nens) pp[k,:] = self.parameter_transform[k](dum , kind='dir') return pp @@ -318,7 +311,6 @@ class CBL: # Add random perturbations to the initial value H0 += RNG.normal(scale=H0_perturbation_ampl_init) - #H0 += np.random.normal(scale=H0_perturbation_ampl_init) # Set the surface momentum flux (ustar) ustar = 0 @@ -369,7 +361,6 @@ class CBL: # Add time-dependent surface perturbations # Then compute sensible heat flux and integrate T equation H[0,j] = H0 + RNG.normal(scale=H0_perturbation_ampl_time) - #H[0,j] = H0 + np.random.normal(scale=H0_perturbation_ampl_time) H[1:-1,j] = -K[1:-1,j]*( (thetap[1:]-thetap[:-1])*rdz - gammac) H[nz,j] = 2*H[nz-1,j]-H[nz-2,j] theta[:,j] = thetap[:]-dt*rdz*(H[1:,j]-H[:-1,j])