本文整理汇总了Python中data_store.Relax_data_store.models方法的典型用法代码示例。如果您正苦于以下问题：Python Relax_data_store.models方法的具体用法？Python Relax_data_store.models怎么用？Python Relax_data_store.models使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类data_store.Relax_data_store的用法示例。
在下文中一共展示了Relax_data_store.models方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: not
# 需要导入模块: from data_store import Relax_data_store [as 别名]
# 或者: from data_store.Relax_data_store import models [as 别名]
# 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'):示例2: Relax_data_store
# 需要导入模块: from data_store import Relax_data_store [as 别名]
# 或者: from data_store.Relax_data_store import models [as 别名]
# 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.
#######################
示例3: Relax_data_store
# 需要导入模块: from data_store import Relax_data_store [as 别名]
# 或者: from data_store.Relax_data_store import models [as 别名]
# 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
示例4: Relax_data_store
# 需要导入模块: from data_store import Relax_data_store [as 别名]
# 或者: from data_store.Relax_data_store import models [as 别名]
# 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'