Dear Edward,
I'm trying to run a global fit on some 15N CPMG relaxation dispersion
data. I have already run the residues with individual fits and am using
a script (copied below), based on one in the relax example scripts
folder. At the moment the script fails due to the size of the grid
search. I thought that by loading existing single-residue fits and using
the relax_disp.parameter_copy function, this should reduce the size of
the grid search. Could you provide any suggestions?
Many thanks,
Mark
---------------------------------------------------------------------------
# Python module imports.
from os import getcwd, sep
# 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, MODEL_CR72,
MODEL_CR72_FULL, MODEL_TSMFK01, MODEL_IT99, MODEL_B14_FULL,
MODEL_NS_CPMG_2SITE_EXPANDED, MODEL_NS_CPMG_2SITE_3D_FULL,
MODEL_NS_CPMG_2SITE_STAR_FULL
from pipe_control.mol_res_spin import spin_loop
#########################################
#### Setup
# 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.
print(hasattr(ds,'models'))
if not hasattr(ds, 'models'):
if 0:
ds.models = [MODEL_R2EFF, MODEL_CR72, MODEL_CR72_FULL]
else:
ds.models = [MODEL_CR72]
print(ds.models)
# 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 = 11
# 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 = './fit_CR72_final/' + sep + 'R2eff'
print(ds.pre_run_dir)
# The result directory.
if not hasattr(ds, 'results_dir'):
ds.results_dir = './fit_clustered_CR72/'
## 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
#Relax_disp.opt_func_tol = ds.opt_func_tol
#if not hasattr(ds, 'opt_max_iterations'):
# ds.opt_max_iterations = int(1e7)
#Relax_disp.opt_max_iterations = ds.opt_max_iteration
#########################################
# Create the data pipe.
ini_pipe_name = '%s - %s' % (ds.models[0], ds.pipe_bundle)
pipe.create(pipe_name=ini_pipe_name, bundle=ds.pipe_bundle,
pipe_type=ds.pipe_type)
# Load the previous results into the base pipe.
results.read(file='results', dir=ds.pre_run_dir)
# Create a new pipe, where the clustering analysis will happen.
# We will copy the pipe to get all information.
pipe.copy(pipe_from=ini_pipe_name, pipe_to=ds.pipe_name,
bundle_to=ds.pipe_bundle_cluster)
pipe.switch(ds.pipe_name)
#pipe.display()
#Spin clusters
N15_cluster_remove = [":2@N",":3@N",":4@N"]
for spin_id in N15_cluster_remove:
relax_disp.cluster('N15_cluster', spin_id)
# See the clustering in the current data pipe "cdp".
for key in cdp.clustering:
print(key, cdp.clustering[key])
# Print parameter kex before copying.
#for cur_spin, mol_name, resi, resn, spin_id in
spin_loop(full_info=True, return_id=True, skip_desel=True):
# print(cur_spin.kex)
## Make advanced parameter copy.
# It is more advanced than the value.copy user function, in that
clustering is taken into account.
# When the destination data pipe has spin clusters defined, then the new
parameter values, when required, will be taken as the median value.
relax_disp.parameter_copy(pipe_from=ini_pipe_name, pipe_to=ds.pipe_name)
pipe.display()
# Run the analysis.
Relax_disp(pipe_name=ds.pipe_name, pipe_bundle=ds.pipe_bundle_cluster,
results_dir=ds.results_dir, models=ds.models, grid_inc=ds.grid_inc,
mc_sim_num=ds.mc_sim_num, modsel=ds.modsel)
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