# The pipe names. if not (hasattr(ds, 'pipe_name') and hasattr(ds, 'pipe_bundle') and hasattr(ds, 'pipe_type') and hasattr(ds, 'pipe_bundle_cluster')): # Set pipe name, bundle and type. ds.pipe_name = 'base pipe' ds.pipe_bundle = 'relax_disp' ds.pipe_type = 'relax_disp' ds.pipe_bundle_cluster = 'cluster' # 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'):
# relax module imports. from auto_analyses.relax_disp import Relax_disp from data_store import Relax_data_store 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'
# Python module imports. from os import sep # relax module imports. from auto_analyses.relax_disp import Relax_disp from data_store import Relax_data_store; ds = Relax_data_store() from status import Status; status = Status() # Analysis variables. ##################### # The dispersion models. if not hasattr(ds, 'models'): ds.models = ['R2eff', 'TP02'] # The grid search size (the number of increments per dimension). GRID_INC = 4 # The number of Monte Carlo simulations to be used for error analysis at the end of the analysis. MC_NUM = 1 # Set up the data pipe. ####################### # The results directory. if not hasattr(ds, 'tmpdir'): ds.tmpdir = None
# Python module imports. from os import sep # relax module imports. from auto_analyses.relax_disp import Relax_disp from data_store import Relax_data_store; ds = Relax_data_store() from status import Status; status = Status() # Analysis variables. ##################### # The dispersion models. if not hasattr(ds, 'models'): ds.models = ['R2eff', 'No Rex', 'NS MMQ 2-site'] # The grid search size (the number of increments per dimension). GRID_INC = 4 # The number of Monte Carlo simulations to be used for error analysis at the end of the analysis. MC_NUM = 3 # The temporary directory, if needed. if not hasattr(ds, 'tmpdir'): ds.tmpdir = 'temp' # Set up the data pipe. #######################
if not (hasattr(ds, 'pipe_name') and hasattr(ds, 'pipe_bundle') and hasattr(ds, 'pipe_type')): # Set pipe name, bundle and type. ds.pipe_name = 'base pipe' ds.pipe_bundle = 'relax_disp' ds.pipe_type = 'relax_disp' # 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_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'
# Python module imports. from os import sep # relax module imports. from auto_analyses.relax_disp import Relax_disp from data_store import Relax_data_store ds = Relax_data_store() from status import Status status = Status() # Analysis variables. ##################### # The dispersion models. if not hasattr(ds, 'models'): ds.models = ['R2eff', 'TP02'] # The grid search size (the number of increments per dimension). GRID_INC = 4 # The number of Monte Carlo simulations to be used for error analysis at the end of the analysis. MC_NUM = 3 # Set up the data pipe. ####################### # The results directory. if not hasattr(ds, 'tmpdir'): ds.tmpdir = None # Create the data pipe.
# relax module imports. from auto_analyses.relax_disp import Relax_disp from data_store import Relax_data_store; 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'