# 4.1 Specify and model rate of annual layer miscount: BAM (see doctring)
print('Running observation model...')
X = coral
X = X.reshape(len(X), 1)
tp, Xp, tmc = bam_simul_perturb(X,
time,
param=[0.02, 0.02],
name='poisson',
ns=1000,
resize=0)
#======================================================================
# 4.2: Analytical Uncertainty Model:
#4.2.1 Simple Model: just add uncertainty bands based on measurement precision
sigma = 0.1 # permil, measurement precision
coral_upper, coral_lower = analytical_err_simple(X, sigma)
#4.2.2 Gaussian Noise Model for analytical error:
sigma = 0.1
#nsamples = ## enter number of samples here
coral_Xn = analytical_error(X, sigma)
#====================================================================
# Save coral timeseries fields as numpy arrays in current directory.
print('Saving time series...')
outdir = './results/'
np.save(outdir + "simulated_coral_d18O.npy", coral)
np.save(outdir + "coral_age_perturbed.npy", Xp)
#coral_error_bounds=np.save('coral_error.npy',coral_upper, coral_lower)
#====================================================================
predictPositions, topDate) # recast in years CE chronCE = np.fliplr(1950 - chronBP) #========================================================================== # 3.2: Analytical Uncertainty Model: print 'Adding uncertainty...' #Enter final simulated speleothem record (choose from above options) X = d18O_wm1 #X=d18O_wm2 #3.2.1 Simple Model: just add uncertainty bands based on measurement precision sigma = 0.1 # permil, measurement precision speleo_upper, speleo_lower = analytical_err_simple(X, sigma) #3.2.2 Gaussian Noise Model for analytical error: sigma = 0.1 #nsamples = ## enter number of samples here speleo_Xn = analytical_error(X, sigma) #==================================================================== # Save whatever needs to be saved print 'Saving data...' outdir = './results/' np.save(outdir + "speleo_Xn.npy", speleo_Xn) #Whatever else... #==================================================================== ###Move this to a separate plotting script ###
# NOTE: PlACEHOLDER. #dcell = cell_archive() #============================================================== # 5. RUN OBSERVATION MODEL #============================================================== # Call function bam_simul_perturb to compute ensemble of plausible # age-uncertain records and account for analytical uncertainty. # 5.1: Ring Miscount Uncertainty Model: X=dcell t=time X=X.reshape(len(X),1) tp, Xp, tmc = bam_simul_perturb(X,t,param=[0.05,0.05],name='poisson',ns=1000,resize=0) #============================================================== # 5.2: Analytical Uncertainty Model: #5.2.1 Simple Model: just add uncertainty bands based on measurement precision sigma=0.1 # permil, measurement precision dcell_upper, dcell_lower = analytical_err_simple(X,sigma) #5.2.2 Gaussian Noise Model for analytical error: sigma=0.1 #nsamples = ## enter number of samples here dcell_Xn=analytical_error(X,sigma) #==============================================================
X = ice_diffused
X = X.reshape(len(X), 1)
t = time
tp, Xp, tmc = bam_simul_perturb(X,
t,
param=[0.01, 0.01],
name='poisson',
ns=1000,
resize=0)
#======================================================================
# 5.2: Analytical Uncertainty Model:
print 'Adding uncertainty...'
#5.2.1 Simple Model: just add uncertainty bands based on measurement precision
sigma = 0.1 # permil, measurement precision
ice_upper, ice_lower = analytical_err_simple(X, sigma)
#5.2.2 Gaussian Noise Model for analytical error:
sigma = 0.1
#nsamples = ## enter number of samples here
ice_Xn = analytical_error(X, sigma)
#====================================================================
# Save whatever needs to be saved
print 'Saving data...'
outdir = './results/'
np.save(outdir + "ice_Xn.npy", ice_Xn)
np.save(outdir + "ice_diffused.npy", ice_diffused)
np.save(outdir + "ice_depth.npy", z)
np.save(outdir + "ice_time_d.npy", time_d)
#Whatever else...
