本文整理匯總了Python中data_store.Relax_data_store.mc_sim_num方法的典型用法代碼示例。如果您正苦於以下問題：Python Relax_data_store.mc_sim_num方法的具體用法？Python Relax_data_store.mc_sim_num怎麽用？Python Relax_data_store.mc_sim_num使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類data_store.Relax_data_store的用法示例。
在下文中一共展示了Relax_data_store.mc_sim_num方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: hasattr
# 需要導入模塊: from data_store import Relax_data_store [as 別名]
# 或者: from data_store.Relax_data_store import mc_sim_num [as 別名]
# 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'):
ds.results_dir = getcwd() + sep + 'results_clustering'
## The optimisation function tolerance.
## This is set to the standard value, and should not be changed.