# The data path if not hasattr(ds, 'data_path'): ds.data_path = getcwd() # The models to analyse. if not hasattr(ds, 'models'): if 0: ds.models = [MODEL_NOREX_R1RHO_FIT_R1, MODEL_DPL94_FIT_R1, MODEL_TP02_FIT_R1, MODEL_TAP03_FIT_R1, MODEL_MP05_FIT_R1] else: ds.models = [MODEL_DPL94_FIT_R1] # The number of increments per parameter, to split up the search interval in grid search. # This is not used, when pointing to a previous result directory. # Then an average of the previous values will be used. if not hasattr(ds, 'grid_inc'): ds.grid_inc = 10 # The number of Monte-Carlo simulations for estimating the error of the parameters of the fitted models. if not hasattr(ds, 'mc_sim_num'): ds.mc_sim_num = 10 # The model selection technique. Either: 'AIC', 'AICc', 'BIC' if not hasattr(ds, 'modsel'): ds.modsel = 'AIC' # The previous result directory with R2eff values. if not hasattr(ds, 'pre_run_dir'): ds.pre_run_dir = getcwd() + sep + 'results_models' + sep + ds.models[0] # The result directory. if not hasattr(ds, 'results_dir'):
if not hasattr(ds, 'data_path'): ds.data_path = getcwd() # The models to analyse. if not hasattr(ds, 'models'): if 0: ds.models = [ MODEL_NOREX_R1RHO_FIT_R1, MODEL_DPL94_FIT_R1, MODEL_TP02_FIT_R1, MODEL_TAP03_FIT_R1, MODEL_MP05_FIT_R1 ] else: ds.models = [MODEL_NOREX_R1RHO_FIT_R1, MODEL_DPL94_FIT_R1] # The number of increments per parameter, to split up the search interval in grid search. if not hasattr(ds, 'grid_inc'): ds.grid_inc = 10 # The number of Monte-Carlo simulations for estimating the error of the parameters of the fitted models. if not hasattr(ds, 'mc_sim_num'): ds.mc_sim_num = 10 # The model selection technique. Either: 'AIC', 'AICc', 'BIC' if not hasattr(ds, 'modsel'): ds.modsel = 'AIC' # The previous result directory with R2eff values. if not hasattr(ds, 'pre_run_dir'): ds.pre_run_dir = getcwd() + sep + 'results_R2eff' + sep + 'R2eff' # The result directory. if not hasattr(ds, 'results_dir'):
ds = Relax_data_store() from lib.dispersion.variables import MODEL_R2EFF ######################################### #### Setup # The data path if not hasattr(ds, 'data_path'): ds.data_path = getcwd() # The models to analyse. if not hasattr(ds, 'models'): ds.models = [MODEL_R2EFF] # The number of increments per parameter, to split up the search interval in grid search. if not hasattr(ds, 'grid_inc'): ds.grid_inc = 21 # The number of Monte-Carlo simulations, for the error analysis in the 'R2eff' model when exponential curves are fitted. # For estimating the error of the fitted R2eff values, # a high number should be provided. Later the high quality R2eff values will be read for subsequent model analyses. if not hasattr(ds, 'exp_mc_sim_num'): ds.exp_mc_sim_num = 2000 # The result directory. if not hasattr(ds, 'results_dir'): ds.results_dir = getcwd() + sep + 'results_R2eff' ## The optimisation function tolerance. ## This is set to the standard value, and should not be changed. #if not hasattr(ds, 'opt_func_tol'): # ds.opt_func_tol = 1e-25
from lib.dispersion.variables import MODEL_R2EFF ######################################### #### Setup # The data path if not hasattr(ds, 'data_path'): ds.data_path = getcwd() # The models to analyse. if not hasattr(ds, 'models'): ds.models = [MODEL_R2EFF] # The number of increments per parameter, to split up the search interval in grid search. if not hasattr(ds, 'grid_inc'): ds.grid_inc = 21 # The number of Monte-Carlo simulations, for the error analysis in the 'R2eff' model when exponential curves are fitted. # For estimating the error of the fitted R2eff values, # a high number should be provided. Later the high quality R2eff values will be read for subsequent model analyses. if not hasattr(ds, 'exp_mc_sim_num'): ds.exp_mc_sim_num = 2000 # The result directory. if not hasattr(ds, 'results_dir'): ds.results_dir = getcwd() + sep + 'results_R2eff' ## The optimisation function tolerance. ## This is set to the standard value, and should not be changed. #if not hasattr(ds, 'opt_func_tol'): # ds.opt_func_tol = 1e-25